Genetics

At the dawn of the genomics era, is the family history still relevant? The answer is a resounding yes.^1,2

The family history is clinically useful because it is a proxy for genetic, environmental, and behavioral risks to health. It can be used to inform risk stratification, allowing for judicious use of screening and opening the door to early and even prophylactic treatment.^3–8 As people live longer, we will need to detect common chronic conditions early in their course so that we can continue to improve health outcomes. Family history can help physicians personalize preventive care for conditions such as diabetes, osteoporosis, and cancers of the breast, colon, and prostate.^2,9–15

However, there is ample evidence that the family history is underused. Most practitioners ask about it infrequently and inconsistently.^16,17 Why is this, and how can we encourage the use of this powerful tool to enhance our daily clinical practice and improve care?

We will discuss here some of the challenges that make it difficult for physicians to collect and use the family history in clinical practice, and review strategies for collecting and using the family history in a more consistent manner. We anticipate that this discussion will be helpful to clinicians, as the family history is an essential input to personalized, preventive care plans.

CHALLENGE 1: ARE PATIENTS’ REPORTS RELIABLE?

A question that often arises when discussing the utility of the family history is the reliability of patients’ reports. Can we trust that patients can accurately report their family history? For many conditions, the answer is yes.^18,19

Ziogas and Anton-Culverl²⁰ asked 1,111 cancer patients whether their relatives had ever had cancer and verified their answers. In more than 95% of cases, if the patient said that a first-degree or second-degree relative did not have cancer of any type, that relative truly did not have cancer. Overall, over-reporting of cancer was rare, occurring in 2.4% of cases.

If the patient said that a relative did have cancer, that statement was usually true as well. The reliability of a report of cancer in first-degree relatives was greater than 75% for most types of cancer (female breast, ovarian, esophageal, colorectal, pancreas, lung, melanoma, brain, thyroid, lymphoma, leukemia). For several of these types of cancer (female breast, colorectal, and brain), the reliability was 90% or higher. For second-degree relatives, the reliability of a reported positive history was moderate (50% to 80%) for the same types of cancer, and for third-degree relatives, the reliability dropped further for all types of cancer except female breast, brain, pancreas, and leukemia, for which the reliability of a positive report remained at 70%.

Wideroff et al²¹ had similar findings in a study of more than 1,000 patients and more than 20,000 of their relatives.

Yoon et al,¹⁸ at the US Centers for Disease Control and Prevention, developed a Web-based risk-assessment tool called Family Healthware, currently undergoing validation trials. They found that patients’ reports were highly reliable for coronary heart disease, stroke, diabetes, and breast, ovarian, and colorectal cancers. They also calculated the degree of risk associated with a positive family history and the prevalence of a family history of each of these diseases.

For the primary care physician, these studies support the reliability of patients’ reports and provide guidance for targeting specific conditions when obtaining a family history.

CHALLENGE 2: NO TIME OR REIMBURSEMENT

Perhaps the most obvious barriers to collecting a family history are lack of time and reimbursement.

Acheson et al,¹⁷ in an observational study of 138 primary care physicians and 4,454 patient visits, found that family history was discussed during 51% of new patient visits and 22% of established patient visits. The rate at which the family history was taken varied from 0% (some physicians never asked) to 81% of all patient visits. On average, physicians spent less than 2.5 minutes collecting the family history.

Not surprisingly, the family history was discussed more often at well-care visits than at illness visits, as the former type of visit tends to be longer and, by definition, to be spent partly on preventive care. A difficulty with this strategy is that, given the shortage of primary care physicians, limited access, and patient preference, most preventive-care visits are combined with problem-focused visits, further decreasing the time available to collect and discuss a family history. While some argue that the family history should routinely be obtained and discussed during preventive-care visits regardless of reimbursement and time, the reality is that it may simply drop on the list of priorities for each visit.

Rich et al³ estimated that taking a family history would increase reimbursement for only one new patient evaluation and management code (99202) and one return-visit code (99213) in Current Procedural Terminology. This action would increase reimbursement enough to support about 10 minutes of physician effort for collecting, documenting, and analyzing the family history. While this is certainly better than the average of less than 2.5 minutes observed by Acheson et al,¹⁷ doctors would probably do it more if they were paid more for it.

Electronic solutions

Given that a lack of time is a barrier, what are some ways to minimize the time it takes to collect a family history?

With more physicians using electronic health records and with increasing use of Internet-based tools in the population at large, information-technology systems have been developed to help obtain the family history. One of the most widely used is the US surgeon general’s My Family Health Portrait, available free at https://familyhistory.hhs.gov. It is one of the broadest electronic family-history collection tools and has been validated for use in risk assessment for diabetes and cancer of the colon, breast, and ovaries.²²

However, electronic solutions have their own challenges. Not all patients have access to the Internet, many need help using these programs, and these tools may not work well with existing electronic medical records systems.²³ Ideally, these programs would also provide built-in decision support for the provider, thereby maximizing data use for final patient risk assessment.²³ Furthermore, electronic solutions are not a one-time-only risk assessment— periodic re-review of family history and reassessment of familial risk are required.²⁴

Does taking a family history improve outcomes? Lessons from breast cancer

One of the reasons physicians don’t get reimbursed for collecting a family history is that it has been difficult to measure any improvement in outcomes associated with risk prediction through family history.

The best examples of improvement in outcomes associated with family history-based risk prediction come from studies of breast cancer. From 5% to 10% of cases of breast cancer are part of hereditary cancer syndromes, many of which have a known genetic cause. The most prevalent of these genetic syndromes is the hereditary breast and ovarian cancer (HBOC) syndrome, caused by mutations in the breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) genes. Clinical testing for BRCA mutations has been available since 1998.²⁵ Women with a BRCA mutation have up to a 65% lifetime risk of developing breast cancer and up to a 40% lifetime risk of developing ovarian cancer.²⁶ Men with a BRCA mutation are at 10 to 100 times the risk of the general population (1% to 10% vs 0.1%) for developing breast cancer, and are also at higher risk of prostate and other cancers.²⁷

People who have a relative who developed breast cancer at a young age are more likely to harbor one of these mutations. For example, based on genetic testing in more than 185,000 people, the prevalence of BRCA mutations among people without cancer, not of Ashkenazi Jewish ancestry (a risk factor for breast cancer), and with no family history of early breast cancer or of ovarian cancer in any relative is 1.5%.²⁸ In contrast, people with no personal history of cancer who have a family history of breast cancer before age 50 have a 5.6% prevalence of BRCA mutation, and if they are of Ashkenazi Jewish ancestry, this number is 16.4%.²⁸

Medical and surgical interventions are available to reduce the risk of cancer in people with hereditary cancer syndromes such as HBOC. Options include screening more often, using advanced screening tests,²⁹ giving preventive drugs such as tamoxifen (Nolvadex), and prophylactic surgery.^30–32 What is the evidence that early screening and intervention in these people improve outcomes?

Domcheck et al³³ prospectively followed more than 2,400 women who had BRCA mutations to assess the effect of prophylactic mastectomy or salpingo-oophorectomy on cancer outcomes. Mastectomy was indeed associated with a lower risk of breast cancer: 0 cases of breast cancer were diagnosed in 3 years of prospective follow-up in the 247 women who elected to undergo mastectomy, compared with 98 cases diagnosed in the 1,372 women who did not elect it over a similar period.

Women who elected to undergo salpingo-oophorectomy had a similarly lower rate of ovarian cancer compared with those who did not elect surgery (1% vs 6%). Additionally, fewer women who elected prophylactic salpingo-oophorectomy died of any cause (3% vs 10%), died of breast cancer (2% vs 6%), or died of ovarian cancer (0.4% vs 3%) compared with women who did not elect surgery.

Taking a family history reduces costs

What is the evidence that appropriate use of the family history decreases health care costs? Let us continue with the example of HBOC syndrome due to BRCA mutations.

Given that germline mutations account for 5% to 10% of cases of breast cancer in the United States and that the women who develop cancer associated with such mutations do so at a relatively young age, these mutations account for a disproportionate share of life-years lost due to cancer.³⁴ Through taking a family history, these women at high risk can be identified and referred for genetic testing. Genetic testing, though costly, is more cost-effective than diagnosing and treating cancer.

Anderson et al,³⁴ in 2006, estimated that cost-effective policies on testing and preventive treatment for persons at high risk of breast cancer could save up to $800 million of the more than $8 billion spent each year on breast cancer diagnosis, prevention, and treatment.

Kwon et al,³⁵ in a simulation model (not a study in real patients), compared four different criteria for BRCA testing in women with ovarian cancer to see which strategy would be most cost-effective in preventing breast and ovarian cancers in their first-degree relatives. The best strategy, according to this analysis, is to test women with ovarian cancer for BRCA mutations if they also have a personal history of breast cancer, have a family history of breast or ovarian cancer, or are of Ashkenazi Jewish ancestry. The estimated cost per life-year gained with this strategy was $32,018, much lower than the widely accepted threshold for cost-effectiveness of $50,000 per life-year gained.

Although many professional organizations, including the US Preventive Services Task Force, have endorsed family-history-based eligibility criteria for genetic counseling and BRCA testing, awareness of the value of genetic testing in people who have been prescreened by family history has been relatively slow in seeping out to insurance carriers, especially Medicaid.^12,36 As evidence continues to accumulate showing that this approach can improve outcomes for at-risk family members, reimbursement and time allotted for obtaining and using the family history should be adjusted.

CHALLENGE 3: A KNOWLEDGE GAP IN CLINICIANS

Another challenge often cited as a cause of the underuse of the family history as a predictor of disease risk is that clinicians may not know enough about the topic. Several studies indicated that even when physicians had obtained some components of the family history, they did not document risk appropriately or recognize the significance of the pattern of inheritance observed.^37–39

In a study comparing primary care physicians and gastroenterologists in their use of the family history to predict the risk of hereditary colon cancer, gastroenterologists were more likely to elicit a family history of colorectal cancer and implement appropriate screening strategies, but overall compliance with screening guidelines was suboptimal in both groups.⁴⁰

A 2011 report by an advisory committee to the secretary of the US Department of Health and Human Services concluded that lack of genetics education in medical school limits the integration of genetics into clinical care.⁴¹

How can we close this knowledge gap?

Recognizing a need, the National Coalition for Health Professional Education in Genetics was established in 1996 by the American Medical Association, the American Nurses Association, and the National Human Genome Research Institute (www.nchpeg.org). Its mission is to promote the education of health professionals and access to information about advances in human genetics to improve the health care of the nation. It offers educational materials, including a newly updated “Core Principles in Family History” program, which can be used to educate medical providers and their patients about various concepts related to genetics and family history.

In addition, physicians can use many risk assessment tools based on family history in patient care. Two of the best known are:

• The Gail breast cancer risk assessment model, hosted by the National Cancer Institute (www.cancer.gov/bcrisktool/)
• The FRAX osteoporosis risk assessment model, developed by the World Health Organization (www.shef.ac.uk/FRAX).

As we continue to educate the medical community about the value of the family history in predicting disease, it will be important to increase efforts in medical schools and residency programs and to find new, more interactive ways of teaching these concepts.

A possible strategy is to highlight the use of pedigree drawing to recognize patterns of inheritance.² In a study of physician attitudes toward using patient-generated pedigrees in practice, such as those produced by the US surgeon general’s My Family Health Portrait, 73% of physicians stated that the patient-generated pedigree would improve their ability to assess the risk of disease, and the majority also agreed that it would not extend the time of the assessment.¹⁶

Is this information clinically useful?

Furthermore, knowing they are at risk might empower people and encourage them to engage with the medical system. For example, counseling people at risk of diabetes as reflected in the family history has been shown to increase their understanding of the risk and of preventive behaviors. Further study is needed to determine if such messages can engender lasting changes in behavior across many diseases.^42–46

TOWARD PERSONALIZED CARE

Especially now that caregivers are striving to provide value-based health care with emphasis on preventive care, the family history remains an important tool for detecting risk of disease. The evidence clearly indicates that medical providers have room for improvement in taking a family history and in using it.

We hope that asking patients about family history and recognizing patterns of disease will help us create personalized preventive-care plans, providing greater opportunity to educate and motivate our patients to work with us towards better health. Future solutions need to focus on time-effective ways to collect and analyze family history and on innovative methods of teaching medical providers at all levels to apply the family history to clinical practice.

At the dawn of the genomics era, is the family history still relevant? The answer is a resounding yes.^1,2

The family history is clinically useful because it is a proxy for genetic, environmental, and behavioral risks to health. It can be used to inform risk stratification, allowing for judicious use of screening and opening the door to early and even prophylactic treatment.^3–8 As people live longer, we will need to detect common chronic conditions early in their course so that we can continue to improve health outcomes. Family history can help physicians personalize preventive care for conditions such as diabetes, osteoporosis, and cancers of the breast, colon, and prostate.^2,9–15

However, there is ample evidence that the family history is underused. Most practitioners ask about it infrequently and inconsistently.^16,17 Why is this, and how can we encourage the use of this powerful tool to enhance our daily clinical practice and improve care?

We will discuss here some of the challenges that make it difficult for physicians to collect and use the family history in clinical practice, and review strategies for collecting and using the family history in a more consistent manner. We anticipate that this discussion will be helpful to clinicians, as the family history is an essential input to personalized, preventive care plans.

CHALLENGE 1: ARE PATIENTS’ REPORTS RELIABLE?

A question that often arises when discussing the utility of the family history is the reliability of patients’ reports. Can we trust that patients can accurately report their family history? For many conditions, the answer is yes.^18,19

Ziogas and Anton-Culverl²⁰ asked 1,111 cancer patients whether their relatives had ever had cancer and verified their answers. In more than 95% of cases, if the patient said that a first-degree or second-degree relative did not have cancer of any type, that relative truly did not have cancer. Overall, over-reporting of cancer was rare, occurring in 2.4% of cases.

If the patient said that a relative did have cancer, that statement was usually true as well. The reliability of a report of cancer in first-degree relatives was greater than 75% for most types of cancer (female breast, ovarian, esophageal, colorectal, pancreas, lung, melanoma, brain, thyroid, lymphoma, leukemia). For several of these types of cancer (female breast, colorectal, and brain), the reliability was 90% or higher. For second-degree relatives, the reliability of a reported positive history was moderate (50% to 80%) for the same types of cancer, and for third-degree relatives, the reliability dropped further for all types of cancer except female breast, brain, pancreas, and leukemia, for which the reliability of a positive report remained at 70%.

Wideroff et al²¹ had similar findings in a study of more than 1,000 patients and more than 20,000 of their relatives.

Yoon et al,¹⁸ at the US Centers for Disease Control and Prevention, developed a Web-based risk-assessment tool called Family Healthware, currently undergoing validation trials. They found that patients’ reports were highly reliable for coronary heart disease, stroke, diabetes, and breast, ovarian, and colorectal cancers. They also calculated the degree of risk associated with a positive family history and the prevalence of a family history of each of these diseases.

For the primary care physician, these studies support the reliability of patients’ reports and provide guidance for targeting specific conditions when obtaining a family history.

CHALLENGE 2: NO TIME OR REIMBURSEMENT

Perhaps the most obvious barriers to collecting a family history are lack of time and reimbursement.

Acheson et al,¹⁷ in an observational study of 138 primary care physicians and 4,454 patient visits, found that family history was discussed during 51% of new patient visits and 22% of established patient visits. The rate at which the family history was taken varied from 0% (some physicians never asked) to 81% of all patient visits. On average, physicians spent less than 2.5 minutes collecting the family history.

Not surprisingly, the family history was discussed more often at well-care visits than at illness visits, as the former type of visit tends to be longer and, by definition, to be spent partly on preventive care. A difficulty with this strategy is that, given the shortage of primary care physicians, limited access, and patient preference, most preventive-care visits are combined with problem-focused visits, further decreasing the time available to collect and discuss a family history. While some argue that the family history should routinely be obtained and discussed during preventive-care visits regardless of reimbursement and time, the reality is that it may simply drop on the list of priorities for each visit.

Rich et al³ estimated that taking a family history would increase reimbursement for only one new patient evaluation and management code (99202) and one return-visit code (99213) in Current Procedural Terminology. This action would increase reimbursement enough to support about 10 minutes of physician effort for collecting, documenting, and analyzing the family history. While this is certainly better than the average of less than 2.5 minutes observed by Acheson et al,¹⁷ doctors would probably do it more if they were paid more for it.

Electronic solutions

Given that a lack of time is a barrier, what are some ways to minimize the time it takes to collect a family history?

With more physicians using electronic health records and with increasing use of Internet-based tools in the population at large, information-technology systems have been developed to help obtain the family history. One of the most widely used is the US surgeon general’s My Family Health Portrait, available free at https://familyhistory.hhs.gov. It is one of the broadest electronic family-history collection tools and has been validated for use in risk assessment for diabetes and cancer of the colon, breast, and ovaries.²²

However, electronic solutions have their own challenges. Not all patients have access to the Internet, many need help using these programs, and these tools may not work well with existing electronic medical records systems.²³ Ideally, these programs would also provide built-in decision support for the provider, thereby maximizing data use for final patient risk assessment.²³ Furthermore, electronic solutions are not a one-time-only risk assessment— periodic re-review of family history and reassessment of familial risk are required.²⁴

Does taking a family history improve outcomes? Lessons from breast cancer

One of the reasons physicians don’t get reimbursed for collecting a family history is that it has been difficult to measure any improvement in outcomes associated with risk prediction through family history.

The best examples of improvement in outcomes associated with family history-based risk prediction come from studies of breast cancer. From 5% to 10% of cases of breast cancer are part of hereditary cancer syndromes, many of which have a known genetic cause. The most prevalent of these genetic syndromes is the hereditary breast and ovarian cancer (HBOC) syndrome, caused by mutations in the breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) genes. Clinical testing for BRCA mutations has been available since 1998.²⁵ Women with a BRCA mutation have up to a 65% lifetime risk of developing breast cancer and up to a 40% lifetime risk of developing ovarian cancer.²⁶ Men with a BRCA mutation are at 10 to 100 times the risk of the general population (1% to 10% vs 0.1%) for developing breast cancer, and are also at higher risk of prostate and other cancers.²⁷

People who have a relative who developed breast cancer at a young age are more likely to harbor one of these mutations. For example, based on genetic testing in more than 185,000 people, the prevalence of BRCA mutations among people without cancer, not of Ashkenazi Jewish ancestry (a risk factor for breast cancer), and with no family history of early breast cancer or of ovarian cancer in any relative is 1.5%.²⁸ In contrast, people with no personal history of cancer who have a family history of breast cancer before age 50 have a 5.6% prevalence of BRCA mutation, and if they are of Ashkenazi Jewish ancestry, this number is 16.4%.²⁸

Medical and surgical interventions are available to reduce the risk of cancer in people with hereditary cancer syndromes such as HBOC. Options include screening more often, using advanced screening tests,²⁹ giving preventive drugs such as tamoxifen (Nolvadex), and prophylactic surgery.^30–32 What is the evidence that early screening and intervention in these people improve outcomes?

Domcheck et al³³ prospectively followed more than 2,400 women who had BRCA mutations to assess the effect of prophylactic mastectomy or salpingo-oophorectomy on cancer outcomes. Mastectomy was indeed associated with a lower risk of breast cancer: 0 cases of breast cancer were diagnosed in 3 years of prospective follow-up in the 247 women who elected to undergo mastectomy, compared with 98 cases diagnosed in the 1,372 women who did not elect it over a similar period.

Women who elected to undergo salpingo-oophorectomy had a similarly lower rate of ovarian cancer compared with those who did not elect surgery (1% vs 6%). Additionally, fewer women who elected prophylactic salpingo-oophorectomy died of any cause (3% vs 10%), died of breast cancer (2% vs 6%), or died of ovarian cancer (0.4% vs 3%) compared with women who did not elect surgery.

Taking a family history reduces costs

What is the evidence that appropriate use of the family history decreases health care costs? Let us continue with the example of HBOC syndrome due to BRCA mutations.

Given that germline mutations account for 5% to 10% of cases of breast cancer in the United States and that the women who develop cancer associated with such mutations do so at a relatively young age, these mutations account for a disproportionate share of life-years lost due to cancer.³⁴ Through taking a family history, these women at high risk can be identified and referred for genetic testing. Genetic testing, though costly, is more cost-effective than diagnosing and treating cancer.

Anderson et al,³⁴ in 2006, estimated that cost-effective policies on testing and preventive treatment for persons at high risk of breast cancer could save up to $800 million of the more than $8 billion spent each year on breast cancer diagnosis, prevention, and treatment.

Kwon et al,³⁵ in a simulation model (not a study in real patients), compared four different criteria for BRCA testing in women with ovarian cancer to see which strategy would be most cost-effective in preventing breast and ovarian cancers in their first-degree relatives. The best strategy, according to this analysis, is to test women with ovarian cancer for BRCA mutations if they also have a personal history of breast cancer, have a family history of breast or ovarian cancer, or are of Ashkenazi Jewish ancestry. The estimated cost per life-year gained with this strategy was $32,018, much lower than the widely accepted threshold for cost-effectiveness of $50,000 per life-year gained.

Although many professional organizations, including the US Preventive Services Task Force, have endorsed family-history-based eligibility criteria for genetic counseling and BRCA testing, awareness of the value of genetic testing in people who have been prescreened by family history has been relatively slow in seeping out to insurance carriers, especially Medicaid.^12,36 As evidence continues to accumulate showing that this approach can improve outcomes for at-risk family members, reimbursement and time allotted for obtaining and using the family history should be adjusted.

CHALLENGE 3: A KNOWLEDGE GAP IN CLINICIANS

Another challenge often cited as a cause of the underuse of the family history as a predictor of disease risk is that clinicians may not know enough about the topic. Several studies indicated that even when physicians had obtained some components of the family history, they did not document risk appropriately or recognize the significance of the pattern of inheritance observed.^37–39

In a study comparing primary care physicians and gastroenterologists in their use of the family history to predict the risk of hereditary colon cancer, gastroenterologists were more likely to elicit a family history of colorectal cancer and implement appropriate screening strategies, but overall compliance with screening guidelines was suboptimal in both groups.⁴⁰

A 2011 report by an advisory committee to the secretary of the US Department of Health and Human Services concluded that lack of genetics education in medical school limits the integration of genetics into clinical care.⁴¹

How can we close this knowledge gap?

Recognizing a need, the National Coalition for Health Professional Education in Genetics was established in 1996 by the American Medical Association, the American Nurses Association, and the National Human Genome Research Institute (www.nchpeg.org). Its mission is to promote the education of health professionals and access to information about advances in human genetics to improve the health care of the nation. It offers educational materials, including a newly updated “Core Principles in Family History” program, which can be used to educate medical providers and their patients about various concepts related to genetics and family history.

In addition, physicians can use many risk assessment tools based on family history in patient care. Two of the best known are:

• The Gail breast cancer risk assessment model, hosted by the National Cancer Institute (www.cancer.gov/bcrisktool/)
• The FRAX osteoporosis risk assessment model, developed by the World Health Organization (www.shef.ac.uk/FRAX).

As we continue to educate the medical community about the value of the family history in predicting disease, it will be important to increase efforts in medical schools and residency programs and to find new, more interactive ways of teaching these concepts.

A possible strategy is to highlight the use of pedigree drawing to recognize patterns of inheritance.² In a study of physician attitudes toward using patient-generated pedigrees in practice, such as those produced by the US surgeon general’s My Family Health Portrait, 73% of physicians stated that the patient-generated pedigree would improve their ability to assess the risk of disease, and the majority also agreed that it would not extend the time of the assessment.¹⁶

Is this information clinically useful?

Furthermore, knowing they are at risk might empower people and encourage them to engage with the medical system. For example, counseling people at risk of diabetes as reflected in the family history has been shown to increase their understanding of the risk and of preventive behaviors. Further study is needed to determine if such messages can engender lasting changes in behavior across many diseases.^42–46

TOWARD PERSONALIZED CARE

Especially now that caregivers are striving to provide value-based health care with emphasis on preventive care, the family history remains an important tool for detecting risk of disease. The evidence clearly indicates that medical providers have room for improvement in taking a family history and in using it.

We hope that asking patients about family history and recognizing patterns of disease will help us create personalized preventive-care plans, providing greater opportunity to educate and motivate our patients to work with us towards better health. Future solutions need to focus on time-effective ways to collect and analyze family history and on innovative methods of teaching medical providers at all levels to apply the family history to clinical practice.

At the dawn of the genomics era, is the family history still relevant? The answer is a resounding yes.^1,2

The family history is clinically useful because it is a proxy for genetic, environmental, and behavioral risks to health. It can be used to inform risk stratification, allowing for judicious use of screening and opening the door to early and even prophylactic treatment.^3–8 As people live longer, we will need to detect common chronic conditions early in their course so that we can continue to improve health outcomes. Family history can help physicians personalize preventive care for conditions such as diabetes, osteoporosis, and cancers of the breast, colon, and prostate.^2,9–15

However, there is ample evidence that the family history is underused. Most practitioners ask about it infrequently and inconsistently.^16,17 Why is this, and how can we encourage the use of this powerful tool to enhance our daily clinical practice and improve care?

We will discuss here some of the challenges that make it difficult for physicians to collect and use the family history in clinical practice, and review strategies for collecting and using the family history in a more consistent manner. We anticipate that this discussion will be helpful to clinicians, as the family history is an essential input to personalized, preventive care plans.

CHALLENGE 1: ARE PATIENTS’ REPORTS RELIABLE?

A question that often arises when discussing the utility of the family history is the reliability of patients’ reports. Can we trust that patients can accurately report their family history? For many conditions, the answer is yes.^18,19

Ziogas and Anton-Culverl²⁰ asked 1,111 cancer patients whether their relatives had ever had cancer and verified their answers. In more than 95% of cases, if the patient said that a first-degree or second-degree relative did not have cancer of any type, that relative truly did not have cancer. Overall, over-reporting of cancer was rare, occurring in 2.4% of cases.

If the patient said that a relative did have cancer, that statement was usually true as well. The reliability of a report of cancer in first-degree relatives was greater than 75% for most types of cancer (female breast, ovarian, esophageal, colorectal, pancreas, lung, melanoma, brain, thyroid, lymphoma, leukemia). For several of these types of cancer (female breast, colorectal, and brain), the reliability was 90% or higher. For second-degree relatives, the reliability of a reported positive history was moderate (50% to 80%) for the same types of cancer, and for third-degree relatives, the reliability dropped further for all types of cancer except female breast, brain, pancreas, and leukemia, for which the reliability of a positive report remained at 70%.

Wideroff et al²¹ had similar findings in a study of more than 1,000 patients and more than 20,000 of their relatives.

Yoon et al,¹⁸ at the US Centers for Disease Control and Prevention, developed a Web-based risk-assessment tool called Family Healthware, currently undergoing validation trials. They found that patients’ reports were highly reliable for coronary heart disease, stroke, diabetes, and breast, ovarian, and colorectal cancers. They also calculated the degree of risk associated with a positive family history and the prevalence of a family history of each of these diseases.

For the primary care physician, these studies support the reliability of patients’ reports and provide guidance for targeting specific conditions when obtaining a family history.

CHALLENGE 2: NO TIME OR REIMBURSEMENT

Perhaps the most obvious barriers to collecting a family history are lack of time and reimbursement.

Acheson et al,¹⁷ in an observational study of 138 primary care physicians and 4,454 patient visits, found that family history was discussed during 51% of new patient visits and 22% of established patient visits. The rate at which the family history was taken varied from 0% (some physicians never asked) to 81% of all patient visits. On average, physicians spent less than 2.5 minutes collecting the family history.

Not surprisingly, the family history was discussed more often at well-care visits than at illness visits, as the former type of visit tends to be longer and, by definition, to be spent partly on preventive care. A difficulty with this strategy is that, given the shortage of primary care physicians, limited access, and patient preference, most preventive-care visits are combined with problem-focused visits, further decreasing the time available to collect and discuss a family history. While some argue that the family history should routinely be obtained and discussed during preventive-care visits regardless of reimbursement and time, the reality is that it may simply drop on the list of priorities for each visit.

Rich et al³ estimated that taking a family history would increase reimbursement for only one new patient evaluation and management code (99202) and one return-visit code (99213) in Current Procedural Terminology. This action would increase reimbursement enough to support about 10 minutes of physician effort for collecting, documenting, and analyzing the family history. While this is certainly better than the average of less than 2.5 minutes observed by Acheson et al,¹⁷ doctors would probably do it more if they were paid more for it.

Electronic solutions

Given that a lack of time is a barrier, what are some ways to minimize the time it takes to collect a family history?

With more physicians using electronic health records and with increasing use of Internet-based tools in the population at large, information-technology systems have been developed to help obtain the family history. One of the most widely used is the US surgeon general’s My Family Health Portrait, available free at https://familyhistory.hhs.gov. It is one of the broadest electronic family-history collection tools and has been validated for use in risk assessment for diabetes and cancer of the colon, breast, and ovaries.²²

However, electronic solutions have their own challenges. Not all patients have access to the Internet, many need help using these programs, and these tools may not work well with existing electronic medical records systems.²³ Ideally, these programs would also provide built-in decision support for the provider, thereby maximizing data use for final patient risk assessment.²³ Furthermore, electronic solutions are not a one-time-only risk assessment— periodic re-review of family history and reassessment of familial risk are required.²⁴

Does taking a family history improve outcomes? Lessons from breast cancer

One of the reasons physicians don’t get reimbursed for collecting a family history is that it has been difficult to measure any improvement in outcomes associated with risk prediction through family history.

The best examples of improvement in outcomes associated with family history-based risk prediction come from studies of breast cancer. From 5% to 10% of cases of breast cancer are part of hereditary cancer syndromes, many of which have a known genetic cause. The most prevalent of these genetic syndromes is the hereditary breast and ovarian cancer (HBOC) syndrome, caused by mutations in the breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) genes. Clinical testing for BRCA mutations has been available since 1998.²⁵ Women with a BRCA mutation have up to a 65% lifetime risk of developing breast cancer and up to a 40% lifetime risk of developing ovarian cancer.²⁶ Men with a BRCA mutation are at 10 to 100 times the risk of the general population (1% to 10% vs 0.1%) for developing breast cancer, and are also at higher risk of prostate and other cancers.²⁷

People who have a relative who developed breast cancer at a young age are more likely to harbor one of these mutations. For example, based on genetic testing in more than 185,000 people, the prevalence of BRCA mutations among people without cancer, not of Ashkenazi Jewish ancestry (a risk factor for breast cancer), and with no family history of early breast cancer or of ovarian cancer in any relative is 1.5%.²⁸ In contrast, people with no personal history of cancer who have a family history of breast cancer before age 50 have a 5.6% prevalence of BRCA mutation, and if they are of Ashkenazi Jewish ancestry, this number is 16.4%.²⁸

Medical and surgical interventions are available to reduce the risk of cancer in people with hereditary cancer syndromes such as HBOC. Options include screening more often, using advanced screening tests,²⁹ giving preventive drugs such as tamoxifen (Nolvadex), and prophylactic surgery.^30–32 What is the evidence that early screening and intervention in these people improve outcomes?

Domcheck et al³³ prospectively followed more than 2,400 women who had BRCA mutations to assess the effect of prophylactic mastectomy or salpingo-oophorectomy on cancer outcomes. Mastectomy was indeed associated with a lower risk of breast cancer: 0 cases of breast cancer were diagnosed in 3 years of prospective follow-up in the 247 women who elected to undergo mastectomy, compared with 98 cases diagnosed in the 1,372 women who did not elect it over a similar period.

Women who elected to undergo salpingo-oophorectomy had a similarly lower rate of ovarian cancer compared with those who did not elect surgery (1% vs 6%). Additionally, fewer women who elected prophylactic salpingo-oophorectomy died of any cause (3% vs 10%), died of breast cancer (2% vs 6%), or died of ovarian cancer (0.4% vs 3%) compared with women who did not elect surgery.

Taking a family history reduces costs

What is the evidence that appropriate use of the family history decreases health care costs? Let us continue with the example of HBOC syndrome due to BRCA mutations.

Given that germline mutations account for 5% to 10% of cases of breast cancer in the United States and that the women who develop cancer associated with such mutations do so at a relatively young age, these mutations account for a disproportionate share of life-years lost due to cancer.³⁴ Through taking a family history, these women at high risk can be identified and referred for genetic testing. Genetic testing, though costly, is more cost-effective than diagnosing and treating cancer.

Anderson et al,³⁴ in 2006, estimated that cost-effective policies on testing and preventive treatment for persons at high risk of breast cancer could save up to $800 million of the more than $8 billion spent each year on breast cancer diagnosis, prevention, and treatment.

Kwon et al,³⁵ in a simulation model (not a study in real patients), compared four different criteria for BRCA testing in women with ovarian cancer to see which strategy would be most cost-effective in preventing breast and ovarian cancers in their first-degree relatives. The best strategy, according to this analysis, is to test women with ovarian cancer for BRCA mutations if they also have a personal history of breast cancer, have a family history of breast or ovarian cancer, or are of Ashkenazi Jewish ancestry. The estimated cost per life-year gained with this strategy was $32,018, much lower than the widely accepted threshold for cost-effectiveness of $50,000 per life-year gained.

Although many professional organizations, including the US Preventive Services Task Force, have endorsed family-history-based eligibility criteria for genetic counseling and BRCA testing, awareness of the value of genetic testing in people who have been prescreened by family history has been relatively slow in seeping out to insurance carriers, especially Medicaid.^12,36 As evidence continues to accumulate showing that this approach can improve outcomes for at-risk family members, reimbursement and time allotted for obtaining and using the family history should be adjusted.

CHALLENGE 3: A KNOWLEDGE GAP IN CLINICIANS

Another challenge often cited as a cause of the underuse of the family history as a predictor of disease risk is that clinicians may not know enough about the topic. Several studies indicated that even when physicians had obtained some components of the family history, they did not document risk appropriately or recognize the significance of the pattern of inheritance observed.^37–39

In a study comparing primary care physicians and gastroenterologists in their use of the family history to predict the risk of hereditary colon cancer, gastroenterologists were more likely to elicit a family history of colorectal cancer and implement appropriate screening strategies, but overall compliance with screening guidelines was suboptimal in both groups.⁴⁰

A 2011 report by an advisory committee to the secretary of the US Department of Health and Human Services concluded that lack of genetics education in medical school limits the integration of genetics into clinical care.⁴¹

How can we close this knowledge gap?

Recognizing a need, the National Coalition for Health Professional Education in Genetics was established in 1996 by the American Medical Association, the American Nurses Association, and the National Human Genome Research Institute (www.nchpeg.org). Its mission is to promote the education of health professionals and access to information about advances in human genetics to improve the health care of the nation. It offers educational materials, including a newly updated “Core Principles in Family History” program, which can be used to educate medical providers and their patients about various concepts related to genetics and family history.

In addition, physicians can use many risk assessment tools based on family history in patient care. Two of the best known are:

• The Gail breast cancer risk assessment model, hosted by the National Cancer Institute (www.cancer.gov/bcrisktool/)
• The FRAX osteoporosis risk assessment model, developed by the World Health Organization (www.shef.ac.uk/FRAX).

As we continue to educate the medical community about the value of the family history in predicting disease, it will be important to increase efforts in medical schools and residency programs and to find new, more interactive ways of teaching these concepts.

A possible strategy is to highlight the use of pedigree drawing to recognize patterns of inheritance.² In a study of physician attitudes toward using patient-generated pedigrees in practice, such as those produced by the US surgeon general’s My Family Health Portrait, 73% of physicians stated that the patient-generated pedigree would improve their ability to assess the risk of disease, and the majority also agreed that it would not extend the time of the assessment.¹⁶

Is this information clinically useful?

Furthermore, knowing they are at risk might empower people and encourage them to engage with the medical system. For example, counseling people at risk of diabetes as reflected in the family history has been shown to increase their understanding of the risk and of preventive behaviors. Further study is needed to determine if such messages can engender lasting changes in behavior across many diseases.^42–46

TOWARD PERSONALIZED CARE

Especially now that caregivers are striving to provide value-based health care with emphasis on preventive care, the family history remains an important tool for detecting risk of disease. The evidence clearly indicates that medical providers have room for improvement in taking a family history and in using it.

We hope that asking patients about family history and recognizing patterns of disease will help us create personalized preventive-care plans, providing greater opportunity to educate and motivate our patients to work with us towards better health. Future solutions need to focus on time-effective ways to collect and analyze family history and on innovative methods of teaching medical providers at all levels to apply the family history to clinical practice.

The concept and promise of personalized health care have been anticipated for decades. Yet in its breadth and in the way we would like it practiced, it is in its infancy.

Personalized health care aims to individualize care by integrating a person’s unique clinical, molecular (ie, genetic, genomic), and environmental information. Applied not only when the patient is sick but also when he or she is well, it builds on and enhances our current standards of care.

As Sir William Osler recognized more than a century ago, “Variability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions which we know as disease.”¹

PREDICTING THE RISK OF DISEASE

For years, we have attempted to predict and stratify the risk of disease, and the Human Genome Project has given us a new set of tools to help understand the complexity of disease and its variability.

We know and have known for decades—and in some cultures, for centuries—that family history is the most clinically validated tool for predicting the risk of disease.

Nevertheless, evidence suggests that we physicians are not collecting adequate family histories, and because of this, we are missing opportunities to intervene and prevent diseases predicted by the family history. The current standard of care is to use family medical histories to hone genetic differential diagnoses, and based on the differential diagnoses, to target specific genes to test in the setting of genetic counseling. Current genetic testing is used for molecular diagnoses and predictive testing so that gene-specific clinical management can be subsequently tailored.

PREDICTING RESPONSE TO TREATMENT

The use of personalized health care to predict response to treatment is a novel and constantly evolving practice.

ABO blood typing is a form of genetics-based personalization of safe transfusion that dates back to World War II.

A prominent, recent success story is in cancer treatment. For example, the American Society of Clinical Oncology now recommends that tumors from patients with node-negative, estrogen-receptor-positive breast cancer be evaluated with the Oncotype DX assay.² This test measures the expression of 21 genes, and the score obtained identifies patients most likely to benefit from adjuvant chemotherapy. A similar 12-gene expression signature has been developed for colon cancer, and others have been developed for hematologic cancers.^2,3 As with other new but apparently valid tests, the risk scores derived are sensitive at the extremes but ambiguous in the mid-ranges. We anticipate many more developments in this field.

In the field of pharmacogenomics, there is evidence to suggest that prior knowledge of CYP2C9 and VKORC1 genotypes enhances outcomes for patients starting treatment with warfarin. The US Food and Drug Administration revised the label on warfarin in February 2010, suggesting that genotypes be taken into consideration when the drug is prescribed.⁴

However, clinicians have been slow to adopt genotype testing when prescribing warfarin. Some cite the paucity of large, randomized, controlled trials demonstrating clinical utility of genotype-informed prescribing. Others cite concern that warfarin will soon become obsolete with the arrival of newer anticoagulants (such as factor X inhibitors) that do not carry warfarin’s adverse effects, and these genotypes will therefore become moot. Perhaps, as we move forward and new drugs are developed, companion genotype tests could be developed at the same time to be used with them.⁵

IMPROVING CARE, SAVING MONEY, AND EMPOWERING PATIENTS

The goal of personalized health care, by customizing treatments (medication types and dosages) and preventive strategies, is to optimize medical care and improve outcomes for each patient. It could improve the quality of care by targeting interventions and reducing adverse events, topics that are important to all of us in the current environment of health care reform.

A personalized approach might also, in the long run, decrease the cost of health care by driving appropriate utilization of resources.

Lastly, the true value of personalized health care may be in its potential to improve patient satisfaction and to empower our patients to work with us towards better health.

WE LAUNCH A NEW SERIES

To keep physicians up-to-date on progress in personalized health care, the Cleveland Clinic Journal of Medicine will present a series of articles on the topic. The series, to run once a quarter, begins in this issue, on page 331, with an article on the importance of the family history as a piece of genetic information that can help to predict the risk of disease and inform preventive care plans. Future topics will include the role of genetics and genomics in personalized care of patients with breast and colorectal cancers; the genetic counselor as a part of the health care team; pharmacogenomics; and ethical, legal, and societal considerations.

Our goal in this series is to provide practical information to help our readers incorporate personalized approaches into daily practice. In addition, as patients become more interested in and informed about personalized health care, we hope this information will help clinicians to effectively coach them about its potential benefits and risks. We also hope this information will enable our readers to ask the right questions so that patient and health care provider can work together to help the patient grow old gracefully.

As the series unfolds, we ask you to send us feedback and to suggest other topics in personalized health care you would like us to cover in this series.

The concept and promise of personalized health care have been anticipated for decades. Yet in its breadth and in the way we would like it practiced, it is in its infancy.

Personalized health care aims to individualize care by integrating a person’s unique clinical, molecular (ie, genetic, genomic), and environmental information. Applied not only when the patient is sick but also when he or she is well, it builds on and enhances our current standards of care.

As Sir William Osler recognized more than a century ago, “Variability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions which we know as disease.”¹

PREDICTING THE RISK OF DISEASE

For years, we have attempted to predict and stratify the risk of disease, and the Human Genome Project has given us a new set of tools to help understand the complexity of disease and its variability.

We know and have known for decades—and in some cultures, for centuries—that family history is the most clinically validated tool for predicting the risk of disease.

Nevertheless, evidence suggests that we physicians are not collecting adequate family histories, and because of this, we are missing opportunities to intervene and prevent diseases predicted by the family history. The current standard of care is to use family medical histories to hone genetic differential diagnoses, and based on the differential diagnoses, to target specific genes to test in the setting of genetic counseling. Current genetic testing is used for molecular diagnoses and predictive testing so that gene-specific clinical management can be subsequently tailored.

PREDICTING RESPONSE TO TREATMENT

The use of personalized health care to predict response to treatment is a novel and constantly evolving practice.

ABO blood typing is a form of genetics-based personalization of safe transfusion that dates back to World War II.

A prominent, recent success story is in cancer treatment. For example, the American Society of Clinical Oncology now recommends that tumors from patients with node-negative, estrogen-receptor-positive breast cancer be evaluated with the Oncotype DX assay.² This test measures the expression of 21 genes, and the score obtained identifies patients most likely to benefit from adjuvant chemotherapy. A similar 12-gene expression signature has been developed for colon cancer, and others have been developed for hematologic cancers.^2,3 As with other new but apparently valid tests, the risk scores derived are sensitive at the extremes but ambiguous in the mid-ranges. We anticipate many more developments in this field.

In the field of pharmacogenomics, there is evidence to suggest that prior knowledge of CYP2C9 and VKORC1 genotypes enhances outcomes for patients starting treatment with warfarin. The US Food and Drug Administration revised the label on warfarin in February 2010, suggesting that genotypes be taken into consideration when the drug is prescribed.⁴

However, clinicians have been slow to adopt genotype testing when prescribing warfarin. Some cite the paucity of large, randomized, controlled trials demonstrating clinical utility of genotype-informed prescribing. Others cite concern that warfarin will soon become obsolete with the arrival of newer anticoagulants (such as factor X inhibitors) that do not carry warfarin’s adverse effects, and these genotypes will therefore become moot. Perhaps, as we move forward and new drugs are developed, companion genotype tests could be developed at the same time to be used with them.⁵

IMPROVING CARE, SAVING MONEY, AND EMPOWERING PATIENTS

The goal of personalized health care, by customizing treatments (medication types and dosages) and preventive strategies, is to optimize medical care and improve outcomes for each patient. It could improve the quality of care by targeting interventions and reducing adverse events, topics that are important to all of us in the current environment of health care reform.

A personalized approach might also, in the long run, decrease the cost of health care by driving appropriate utilization of resources.

Lastly, the true value of personalized health care may be in its potential to improve patient satisfaction and to empower our patients to work with us towards better health.

WE LAUNCH A NEW SERIES

To keep physicians up-to-date on progress in personalized health care, the Cleveland Clinic Journal of Medicine will present a series of articles on the topic. The series, to run once a quarter, begins in this issue, on page 331, with an article on the importance of the family history as a piece of genetic information that can help to predict the risk of disease and inform preventive care plans. Future topics will include the role of genetics and genomics in personalized care of patients with breast and colorectal cancers; the genetic counselor as a part of the health care team; pharmacogenomics; and ethical, legal, and societal considerations.

Our goal in this series is to provide practical information to help our readers incorporate personalized approaches into daily practice. In addition, as patients become more interested in and informed about personalized health care, we hope this information will help clinicians to effectively coach them about its potential benefits and risks. We also hope this information will enable our readers to ask the right questions so that patient and health care provider can work together to help the patient grow old gracefully.

As the series unfolds, we ask you to send us feedback and to suggest other topics in personalized health care you would like us to cover in this series.

The concept and promise of personalized health care have been anticipated for decades. Yet in its breadth and in the way we would like it practiced, it is in its infancy.

Personalized health care aims to individualize care by integrating a person’s unique clinical, molecular (ie, genetic, genomic), and environmental information. Applied not only when the patient is sick but also when he or she is well, it builds on and enhances our current standards of care.

As Sir William Osler recognized more than a century ago, “Variability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions which we know as disease.”¹

PREDICTING THE RISK OF DISEASE

For years, we have attempted to predict and stratify the risk of disease, and the Human Genome Project has given us a new set of tools to help understand the complexity of disease and its variability.

We know and have known for decades—and in some cultures, for centuries—that family history is the most clinically validated tool for predicting the risk of disease.

Nevertheless, evidence suggests that we physicians are not collecting adequate family histories, and because of this, we are missing opportunities to intervene and prevent diseases predicted by the family history. The current standard of care is to use family medical histories to hone genetic differential diagnoses, and based on the differential diagnoses, to target specific genes to test in the setting of genetic counseling. Current genetic testing is used for molecular diagnoses and predictive testing so that gene-specific clinical management can be subsequently tailored.

PREDICTING RESPONSE TO TREATMENT

The use of personalized health care to predict response to treatment is a novel and constantly evolving practice.

ABO blood typing is a form of genetics-based personalization of safe transfusion that dates back to World War II.

A prominent, recent success story is in cancer treatment. For example, the American Society of Clinical Oncology now recommends that tumors from patients with node-negative, estrogen-receptor-positive breast cancer be evaluated with the Oncotype DX assay.² This test measures the expression of 21 genes, and the score obtained identifies patients most likely to benefit from adjuvant chemotherapy. A similar 12-gene expression signature has been developed for colon cancer, and others have been developed for hematologic cancers.^2,3 As with other new but apparently valid tests, the risk scores derived are sensitive at the extremes but ambiguous in the mid-ranges. We anticipate many more developments in this field.

In the field of pharmacogenomics, there is evidence to suggest that prior knowledge of CYP2C9 and VKORC1 genotypes enhances outcomes for patients starting treatment with warfarin. The US Food and Drug Administration revised the label on warfarin in February 2010, suggesting that genotypes be taken into consideration when the drug is prescribed.⁴

However, clinicians have been slow to adopt genotype testing when prescribing warfarin. Some cite the paucity of large, randomized, controlled trials demonstrating clinical utility of genotype-informed prescribing. Others cite concern that warfarin will soon become obsolete with the arrival of newer anticoagulants (such as factor X inhibitors) that do not carry warfarin’s adverse effects, and these genotypes will therefore become moot. Perhaps, as we move forward and new drugs are developed, companion genotype tests could be developed at the same time to be used with them.⁵

IMPROVING CARE, SAVING MONEY, AND EMPOWERING PATIENTS

The goal of personalized health care, by customizing treatments (medication types and dosages) and preventive strategies, is to optimize medical care and improve outcomes for each patient. It could improve the quality of care by targeting interventions and reducing adverse events, topics that are important to all of us in the current environment of health care reform.

A personalized approach might also, in the long run, decrease the cost of health care by driving appropriate utilization of resources.

Lastly, the true value of personalized health care may be in its potential to improve patient satisfaction and to empower our patients to work with us towards better health.

WE LAUNCH A NEW SERIES

To keep physicians up-to-date on progress in personalized health care, the Cleveland Clinic Journal of Medicine will present a series of articles on the topic. The series, to run once a quarter, begins in this issue, on page 331, with an article on the importance of the family history as a piece of genetic information that can help to predict the risk of disease and inform preventive care plans. Future topics will include the role of genetics and genomics in personalized care of patients with breast and colorectal cancers; the genetic counselor as a part of the health care team; pharmacogenomics; and ethical, legal, and societal considerations.

Our goal in this series is to provide practical information to help our readers incorporate personalized approaches into daily practice. In addition, as patients become more interested in and informed about personalized health care, we hope this information will help clinicians to effectively coach them about its potential benefits and risks. We also hope this information will enable our readers to ask the right questions so that patient and health care provider can work together to help the patient grow old gracefully.

As the series unfolds, we ask you to send us feedback and to suggest other topics in personalized health care you would like us to cover in this series.

The ability to scan the entire human genome and to recognize variations in specific nucleotides within recognized genes is more than a technologic feat. It is now possible to assess the risk of some genetic diseases before they are phenotypically expressed. We are increasingly able to predict whether specific drugs will be effective or pose higher risks of adverse effects in individual patients, a field called pharmacogenomics. How much pharmacogenomics can and should be incorporated into our practice as part of personalized medicine remains to be determined,

Genome-wide association studies can answer certain research questions, but also raise additional ones. In some ways, these studies are like molecular epidemiology—they can demonstrate a statistical association between a risk factor and a clinical event such as a heart attack, but just as in traditional epidemiologic studies, association does not always equate with causation.

As discussed by Drs. Manace and Babyatsky in this issue of the Journal, additional techniques can be used to try to sort out the issue of association vs causation—in this case, whether C-reactive protein (CRP) is merely associated with cardiovascular events or is a cause of them. Using the tools of traditional clinical research, it would be ideal to demonstrate that the use of a highly specific inhibitor of the risk factor (CRP) prevents the disease. CRP levels can be lowered with statins, but these drugs also reduce levels of low-density lipoprotein cholesterol, which will lower the risk of cardiac events. Thus, statins do not have the specificity to prove that CRP causes myocardial infarction.

This paper is one of the first in the Journal to discuss advances in genomics that may affect our practice. Beginning in May, the Journal will begin a new series on personalized medicine to highlight the role that genetics and molecular medicine can play in our clinical practice and in our understanding of pathophysiology.

The ability to scan the entire human genome and to recognize variations in specific nucleotides within recognized genes is more than a technologic feat. It is now possible to assess the risk of some genetic diseases before they are phenotypically expressed. We are increasingly able to predict whether specific drugs will be effective or pose higher risks of adverse effects in individual patients, a field called pharmacogenomics. How much pharmacogenomics can and should be incorporated into our practice as part of personalized medicine remains to be determined,

Genome-wide association studies can answer certain research questions, but also raise additional ones. In some ways, these studies are like molecular epidemiology—they can demonstrate a statistical association between a risk factor and a clinical event such as a heart attack, but just as in traditional epidemiologic studies, association does not always equate with causation.

As discussed by Drs. Manace and Babyatsky in this issue of the Journal, additional techniques can be used to try to sort out the issue of association vs causation—in this case, whether C-reactive protein (CRP) is merely associated with cardiovascular events or is a cause of them. Using the tools of traditional clinical research, it would be ideal to demonstrate that the use of a highly specific inhibitor of the risk factor (CRP) prevents the disease. CRP levels can be lowered with statins, but these drugs also reduce levels of low-density lipoprotein cholesterol, which will lower the risk of cardiac events. Thus, statins do not have the specificity to prove that CRP causes myocardial infarction.

This paper is one of the first in the Journal to discuss advances in genomics that may affect our practice. Beginning in May, the Journal will begin a new series on personalized medicine to highlight the role that genetics and molecular medicine can play in our clinical practice and in our understanding of pathophysiology.

The ability to scan the entire human genome and to recognize variations in specific nucleotides within recognized genes is more than a technologic feat. It is now possible to assess the risk of some genetic diseases before they are phenotypically expressed. We are increasingly able to predict whether specific drugs will be effective or pose higher risks of adverse effects in individual patients, a field called pharmacogenomics. How much pharmacogenomics can and should be incorporated into our practice as part of personalized medicine remains to be determined,

Genome-wide association studies can answer certain research questions, but also raise additional ones. In some ways, these studies are like molecular epidemiology—they can demonstrate a statistical association between a risk factor and a clinical event such as a heart attack, but just as in traditional epidemiologic studies, association does not always equate with causation.

As discussed by Drs. Manace and Babyatsky in this issue of the Journal, additional techniques can be used to try to sort out the issue of association vs causation—in this case, whether C-reactive protein (CRP) is merely associated with cardiovascular events or is a cause of them. Using the tools of traditional clinical research, it would be ideal to demonstrate that the use of a highly specific inhibitor of the risk factor (CRP) prevents the disease. CRP levels can be lowered with statins, but these drugs also reduce levels of low-density lipoprotein cholesterol, which will lower the risk of cardiac events. Thus, statins do not have the specificity to prove that CRP causes myocardial infarction.

This paper is one of the first in the Journal to discuss advances in genomics that may affect our practice. Beginning in May, the Journal will begin a new series on personalized medicine to highlight the role that genetics and molecular medicine can play in our clinical practice and in our understanding of pathophysiology.

For some drugs there is a key step in metabolism, often in a rate-limiting pathway, with an enzyme that has known and detectable polymorphisms that differ dramatically in their ability to affect the drug’s degradation. In theory, by determining the patient’s specific genotype ahead of time, the initial dose of the drug can be determined more rationally. In this issue of the Journal, Kitzmiller et al describe several drugs for which this may be true.

However, for this approach to be practical and cost-effective, several conditions should be met. The drug must be one that needs to be dosed to its therapeutic level rapidly: if there is time to titrate slowly, then there is little need for the extra expense associated with genotyping in order to titrate it more rapidly. Also, it should be proven that dosing based on advance knowledge of the genotype of the target actually results in safer or more efficacious dosing.

For carbamazepine (Tegretol, Equetro) and allopurinol (Zyloprim), specific human leukocyte antigen haplotypes are associated with a strikingly increased frequency of serious hypersensitivity reactions. In some patients, these should be checked before giving the drug.

But the concept of pharmacogenomics is broad, and it may yet explain many vagaries of drug-responsiveness in individual patients. Polymorphisms in renal anion transporters may dictate the level of anionic drugs. Drug-receptor polymorphisms may determine the affinity of a drug for its target and, hence, its efficacy. Cell-membrane transporters, which may have functionally different stable alleles or polymorphisms, may regulate intracellular drug levels by pumping the drug into or out of cells with different efficiencies.

As the entire human genome is dissected and analyzed, and as more and more genes (with their polymorphisms) are linked to specific functions readily detectable in specific patients, we will have more opportunities to match the right drug and dose to the right patient. We are not there yet, but that day is coming.

For some drugs there is a key step in metabolism, often in a rate-limiting pathway, with an enzyme that has known and detectable polymorphisms that differ dramatically in their ability to affect the drug’s degradation. In theory, by determining the patient’s specific genotype ahead of time, the initial dose of the drug can be determined more rationally. In this issue of the Journal, Kitzmiller et al describe several drugs for which this may be true.

However, for this approach to be practical and cost-effective, several conditions should be met. The drug must be one that needs to be dosed to its therapeutic level rapidly: if there is time to titrate slowly, then there is little need for the extra expense associated with genotyping in order to titrate it more rapidly. Also, it should be proven that dosing based on advance knowledge of the genotype of the target actually results in safer or more efficacious dosing.

For carbamazepine (Tegretol, Equetro) and allopurinol (Zyloprim), specific human leukocyte antigen haplotypes are associated with a strikingly increased frequency of serious hypersensitivity reactions. In some patients, these should be checked before giving the drug.

But the concept of pharmacogenomics is broad, and it may yet explain many vagaries of drug-responsiveness in individual patients. Polymorphisms in renal anion transporters may dictate the level of anionic drugs. Drug-receptor polymorphisms may determine the affinity of a drug for its target and, hence, its efficacy. Cell-membrane transporters, which may have functionally different stable alleles or polymorphisms, may regulate intracellular drug levels by pumping the drug into or out of cells with different efficiencies.

As the entire human genome is dissected and analyzed, and as more and more genes (with their polymorphisms) are linked to specific functions readily detectable in specific patients, we will have more opportunities to match the right drug and dose to the right patient. We are not there yet, but that day is coming.

For some drugs there is a key step in metabolism, often in a rate-limiting pathway, with an enzyme that has known and detectable polymorphisms that differ dramatically in their ability to affect the drug’s degradation. In theory, by determining the patient’s specific genotype ahead of time, the initial dose of the drug can be determined more rationally. In this issue of the Journal, Kitzmiller et al describe several drugs for which this may be true.

However, for this approach to be practical and cost-effective, several conditions should be met. The drug must be one that needs to be dosed to its therapeutic level rapidly: if there is time to titrate slowly, then there is little need for the extra expense associated with genotyping in order to titrate it more rapidly. Also, it should be proven that dosing based on advance knowledge of the genotype of the target actually results in safer or more efficacious dosing.

For carbamazepine (Tegretol, Equetro) and allopurinol (Zyloprim), specific human leukocyte antigen haplotypes are associated with a strikingly increased frequency of serious hypersensitivity reactions. In some patients, these should be checked before giving the drug.

But the concept of pharmacogenomics is broad, and it may yet explain many vagaries of drug-responsiveness in individual patients. Polymorphisms in renal anion transporters may dictate the level of anionic drugs. Drug-receptor polymorphisms may determine the affinity of a drug for its target and, hence, its efficacy. Cell-membrane transporters, which may have functionally different stable alleles or polymorphisms, may regulate intracellular drug levels by pumping the drug into or out of cells with different efficiencies.

As the entire human genome is dissected and analyzed, and as more and more genes (with their polymorphisms) are linked to specific functions readily detectable in specific patients, we will have more opportunities to match the right drug and dose to the right patient. We are not there yet, but that day is coming.

Since the human genome was sequenced in 2000, the American public has continued to hold hope that our growing understanding of genetics will revolutionize the practice of medicine.

See related article

One way genetics promises to improve the quality and value of health care is in personalized medicine, by helping us tailor treatment to a person’s individual genetic makeup. One such approach is called pharmacogenomics.

Pharmacogenomics uses knowledge of a person’s genetics to understand how a particular drug will work, or not work, in his or her body. For instance, some people might carry genes that make them more sensitive than average to a drug, and therefore they would require a lower dose. Others might have genes that make them resistant to the drug, meaning the drug is ineffective unless they receive a higher dose.

Adverse drug reactions are a leading cause of death in hospitalized patients in the United States and are responsible for billions of dollars in health care costs.^1,2 Our current practice of prescribing for adult patients is largely trial-and-error, with the same dose given to all patients, in many cases with little regard even to sex, height, or weight.

Pharmacogenomics promises to change this way of prescribing to a customized approach that uses genetic information to predict an individual’s response to medications. It is one piece of an overall initiative to personalize patient care based on the patient’s individual characteristics and preferences.

OVERCOMING BARRIERS TO USING PHARMACOGENOMICS IN PRACTICE

If personalized medicine has promised to improve the quality and value of health care for our patients, why have we been so slow to adopt this information in clinical practice?

The usual barriers to clinical adoption certainly exist. We need further studies to determine whether genetic-based prescribing is truly valid, and for which patient populations. We need to determine whether this approach is cost-effective and better than the current standard of care. We need to work on payment options.

However, one of the largest barriers for busy primary care physicians is the lack of time to keep up with new information. Many practicing physicians were taught little about formal genetics in medical school. The body of scientific literature on pharmacogenomics is fragmented, and it crosses disease states and specialties, making it difficult to unite. Given the breadth of diseases treated and drugs prescribed by primary care physicians, it is unrealistic for most to keep track of the vast body of literature of pharmacogenomic testing and to decipher how to apply this to clinical practice.

In this issue of the Journal, Kitzmiller et al³ provide one solution to this problem, giving an overview of pharmacogenomic applications that might be pertinent to practicing physicians. However, as we try to make pharmacogenomics accessible to busy physicians, we need other solutions to integrate pharmacogenomic information efficiently into the clinical work flow. One approach might be to build pharmacogenomics into the electronic medical record. We can also store the integrated information in research databases and provide clinical recommendations on Internet sites such as www.pharmgkb.org, and we can develop applications to run on cell phones and iPads.

QUESTIONS REMAIN

Kitzmiller et al discuss an important step in this process, highlighting several key questions:

Should we seek genetics-based information to personalize drug selection? Based on the information presented in the literature and in the Kitzmiller paper, there may be circumstances when it is appropriate to consider doing so. While the evidence is not yet compelling to order these tests on a regular basis in clinical practice, this information might be helpful in some situations, such as for patients who have had adverse effects from minimal doses of antidepressants.

For now, clinicians should not abandon their current practice of personalizing patient care on the basis of personal, cultural, and economic preferences. Rather, they should consider pharmacogenomic information an additional piece of information when selecting drug therapy. We should also encourage health care systems and interested providers to be early adopters and to study how their outcomes compare with the standard of care.

Once we have this information, what is our obligation to use it? An increasing number of patients already have genetic information in their health record, either ordered by or provided to their physicians. However, there is little in the scientific literature to guide us in this arena.

Yet most of us would agree that if we have information (genetic or otherwise) that can help to select a drug type or dose or reduce adverse events or costs, we should consider this information in our decision-making. Several circumstances are documented in this paper and in the literature in which prior knowledge about drug metabolism can help in selecting a dose of medication. One example would be the 50% recommended reduction in tricyclic antidepressant dose if the patient is a CYP2D6 poor metabolizer.⁴

MOVING FORWARD AS A TEAM

In summary, Kitzmiller et al bring to light the promise and the uncertainties that currently exist in the field of pharmacogenomics. While it is unclear if we should incorporate pharmacogenomic tests into standard medical practice at this time, it is clear that this information is becoming more readily available, whether or not we have requested it. Some would argue that, once we have the information, we have an obligation to use it, just as we use other information in our clinical decision-making. This means we need to develop tools and resources to help practitioners evaluate pharmacogenomic data and incorporate it into clinical care in an efficient manner.

The authors also highlight the need for more education about drug metabolism in general, and they cite several instances in which knowledge of drug interactions and metabolism can clearly influence decision-making. An example is paroxetine (Paxil) inhibition of tamoxifen (Nolvadex).⁵

Lastly, regardless of our personal feelings about the clinical usefulness of genetic testing in large populations, we need to work together to determine clinical utility and validity and to develop efficient ways to put into practice findings that could affect patient care. As we move forward, we need to work as a team, utilizing our clinical partners—pharmacists, pharmacologists, metabolism and health information technology experts, and medical geneticists. Working as a team, pooling our resources and tools, we move closer to providing world-class personalized health care.

Since the human genome was sequenced in 2000, the American public has continued to hold hope that our growing understanding of genetics will revolutionize the practice of medicine.

See related article

One way genetics promises to improve the quality and value of health care is in personalized medicine, by helping us tailor treatment to a person’s individual genetic makeup. One such approach is called pharmacogenomics.

Pharmacogenomics uses knowledge of a person’s genetics to understand how a particular drug will work, or not work, in his or her body. For instance, some people might carry genes that make them more sensitive than average to a drug, and therefore they would require a lower dose. Others might have genes that make them resistant to the drug, meaning the drug is ineffective unless they receive a higher dose.

Adverse drug reactions are a leading cause of death in hospitalized patients in the United States and are responsible for billions of dollars in health care costs.^1,2 Our current practice of prescribing for adult patients is largely trial-and-error, with the same dose given to all patients, in many cases with little regard even to sex, height, or weight.

Pharmacogenomics promises to change this way of prescribing to a customized approach that uses genetic information to predict an individual’s response to medications. It is one piece of an overall initiative to personalize patient care based on the patient’s individual characteristics and preferences.

OVERCOMING BARRIERS TO USING PHARMACOGENOMICS IN PRACTICE

If personalized medicine has promised to improve the quality and value of health care for our patients, why have we been so slow to adopt this information in clinical practice?

The usual barriers to clinical adoption certainly exist. We need further studies to determine whether genetic-based prescribing is truly valid, and for which patient populations. We need to determine whether this approach is cost-effective and better than the current standard of care. We need to work on payment options.

However, one of the largest barriers for busy primary care physicians is the lack of time to keep up with new information. Many practicing physicians were taught little about formal genetics in medical school. The body of scientific literature on pharmacogenomics is fragmented, and it crosses disease states and specialties, making it difficult to unite. Given the breadth of diseases treated and drugs prescribed by primary care physicians, it is unrealistic for most to keep track of the vast body of literature of pharmacogenomic testing and to decipher how to apply this to clinical practice.

In this issue of the Journal, Kitzmiller et al³ provide one solution to this problem, giving an overview of pharmacogenomic applications that might be pertinent to practicing physicians. However, as we try to make pharmacogenomics accessible to busy physicians, we need other solutions to integrate pharmacogenomic information efficiently into the clinical work flow. One approach might be to build pharmacogenomics into the electronic medical record. We can also store the integrated information in research databases and provide clinical recommendations on Internet sites such as www.pharmgkb.org, and we can develop applications to run on cell phones and iPads.

QUESTIONS REMAIN

Kitzmiller et al discuss an important step in this process, highlighting several key questions:

Should we seek genetics-based information to personalize drug selection? Based on the information presented in the literature and in the Kitzmiller paper, there may be circumstances when it is appropriate to consider doing so. While the evidence is not yet compelling to order these tests on a regular basis in clinical practice, this information might be helpful in some situations, such as for patients who have had adverse effects from minimal doses of antidepressants.

For now, clinicians should not abandon their current practice of personalizing patient care on the basis of personal, cultural, and economic preferences. Rather, they should consider pharmacogenomic information an additional piece of information when selecting drug therapy. We should also encourage health care systems and interested providers to be early adopters and to study how their outcomes compare with the standard of care.

Once we have this information, what is our obligation to use it? An increasing number of patients already have genetic information in their health record, either ordered by or provided to their physicians. However, there is little in the scientific literature to guide us in this arena.

Yet most of us would agree that if we have information (genetic or otherwise) that can help to select a drug type or dose or reduce adverse events or costs, we should consider this information in our decision-making. Several circumstances are documented in this paper and in the literature in which prior knowledge about drug metabolism can help in selecting a dose of medication. One example would be the 50% recommended reduction in tricyclic antidepressant dose if the patient is a CYP2D6 poor metabolizer.⁴

MOVING FORWARD AS A TEAM

In summary, Kitzmiller et al bring to light the promise and the uncertainties that currently exist in the field of pharmacogenomics. While it is unclear if we should incorporate pharmacogenomic tests into standard medical practice at this time, it is clear that this information is becoming more readily available, whether or not we have requested it. Some would argue that, once we have the information, we have an obligation to use it, just as we use other information in our clinical decision-making. This means we need to develop tools and resources to help practitioners evaluate pharmacogenomic data and incorporate it into clinical care in an efficient manner.

The authors also highlight the need for more education about drug metabolism in general, and they cite several instances in which knowledge of drug interactions and metabolism can clearly influence decision-making. An example is paroxetine (Paxil) inhibition of tamoxifen (Nolvadex).⁵

Lastly, regardless of our personal feelings about the clinical usefulness of genetic testing in large populations, we need to work together to determine clinical utility and validity and to develop efficient ways to put into practice findings that could affect patient care. As we move forward, we need to work as a team, utilizing our clinical partners—pharmacists, pharmacologists, metabolism and health information technology experts, and medical geneticists. Working as a team, pooling our resources and tools, we move closer to providing world-class personalized health care.

Since the human genome was sequenced in 2000, the American public has continued to hold hope that our growing understanding of genetics will revolutionize the practice of medicine.

See related article

One way genetics promises to improve the quality and value of health care is in personalized medicine, by helping us tailor treatment to a person’s individual genetic makeup. One such approach is called pharmacogenomics.

Pharmacogenomics uses knowledge of a person’s genetics to understand how a particular drug will work, or not work, in his or her body. For instance, some people might carry genes that make them more sensitive than average to a drug, and therefore they would require a lower dose. Others might have genes that make them resistant to the drug, meaning the drug is ineffective unless they receive a higher dose.

Adverse drug reactions are a leading cause of death in hospitalized patients in the United States and are responsible for billions of dollars in health care costs.^1,2 Our current practice of prescribing for adult patients is largely trial-and-error, with the same dose given to all patients, in many cases with little regard even to sex, height, or weight.

Pharmacogenomics promises to change this way of prescribing to a customized approach that uses genetic information to predict an individual’s response to medications. It is one piece of an overall initiative to personalize patient care based on the patient’s individual characteristics and preferences.

OVERCOMING BARRIERS TO USING PHARMACOGENOMICS IN PRACTICE

If personalized medicine has promised to improve the quality and value of health care for our patients, why have we been so slow to adopt this information in clinical practice?

The usual barriers to clinical adoption certainly exist. We need further studies to determine whether genetic-based prescribing is truly valid, and for which patient populations. We need to determine whether this approach is cost-effective and better than the current standard of care. We need to work on payment options.

However, one of the largest barriers for busy primary care physicians is the lack of time to keep up with new information. Many practicing physicians were taught little about formal genetics in medical school. The body of scientific literature on pharmacogenomics is fragmented, and it crosses disease states and specialties, making it difficult to unite. Given the breadth of diseases treated and drugs prescribed by primary care physicians, it is unrealistic for most to keep track of the vast body of literature of pharmacogenomic testing and to decipher how to apply this to clinical practice.

In this issue of the Journal, Kitzmiller et al³ provide one solution to this problem, giving an overview of pharmacogenomic applications that might be pertinent to practicing physicians. However, as we try to make pharmacogenomics accessible to busy physicians, we need other solutions to integrate pharmacogenomic information efficiently into the clinical work flow. One approach might be to build pharmacogenomics into the electronic medical record. We can also store the integrated information in research databases and provide clinical recommendations on Internet sites such as www.pharmgkb.org, and we can develop applications to run on cell phones and iPads.

QUESTIONS REMAIN

Kitzmiller et al discuss an important step in this process, highlighting several key questions:

Should we seek genetics-based information to personalize drug selection? Based on the information presented in the literature and in the Kitzmiller paper, there may be circumstances when it is appropriate to consider doing so. While the evidence is not yet compelling to order these tests on a regular basis in clinical practice, this information might be helpful in some situations, such as for patients who have had adverse effects from minimal doses of antidepressants.

For now, clinicians should not abandon their current practice of personalizing patient care on the basis of personal, cultural, and economic preferences. Rather, they should consider pharmacogenomic information an additional piece of information when selecting drug therapy. We should also encourage health care systems and interested providers to be early adopters and to study how their outcomes compare with the standard of care.

Once we have this information, what is our obligation to use it? An increasing number of patients already have genetic information in their health record, either ordered by or provided to their physicians. However, there is little in the scientific literature to guide us in this arena.

Yet most of us would agree that if we have information (genetic or otherwise) that can help to select a drug type or dose or reduce adverse events or costs, we should consider this information in our decision-making. Several circumstances are documented in this paper and in the literature in which prior knowledge about drug metabolism can help in selecting a dose of medication. One example would be the 50% recommended reduction in tricyclic antidepressant dose if the patient is a CYP2D6 poor metabolizer.⁴

MOVING FORWARD AS A TEAM

In summary, Kitzmiller et al bring to light the promise and the uncertainties that currently exist in the field of pharmacogenomics. While it is unclear if we should incorporate pharmacogenomic tests into standard medical practice at this time, it is clear that this information is becoming more readily available, whether or not we have requested it. Some would argue that, once we have the information, we have an obligation to use it, just as we use other information in our clinical decision-making. This means we need to develop tools and resources to help practitioners evaluate pharmacogenomic data and incorporate it into clinical care in an efficient manner.

The authors also highlight the need for more education about drug metabolism in general, and they cite several instances in which knowledge of drug interactions and metabolism can clearly influence decision-making. An example is paroxetine (Paxil) inhibition of tamoxifen (Nolvadex).⁵

Lastly, regardless of our personal feelings about the clinical usefulness of genetic testing in large populations, we need to work together to determine clinical utility and validity and to develop efficient ways to put into practice findings that could affect patient care. As we move forward, we need to work as a team, utilizing our clinical partners—pharmacists, pharmacologists, metabolism and health information technology experts, and medical geneticists. Working as a team, pooling our resources and tools, we move closer to providing world-class personalized health care.

In many patients, certain drugs do not work as well as expected, whereas in other patients they cause toxic effects, even at lower doses. For some patients, the reason may be genetic.

Sizeable minorities of the population carry genetic variants—polymorphisms— that affect their response to various drugs. Thanks to genetic research, our understanding of the variability of drug response has advanced markedly in the last decade. Many relevant polymorphisms have been identified, and tests for some of them are available.

See related editorial

Armed with the knowledge of their patients’ genetic status, physicians could predict their response to certain drugs, leading to better efficacy, fewer adverse drug reactions, and a better cost-benefit ratio.

The possible impact is substantial, since more than half of the drugs most commonly involved in adverse drug reactions are metabolized by polymorphic enzymes.¹ Adverse drug reactions remain a significant detriment to public health, having a substantial impact on rates of morbidity and death and on healthcare costs.^2–8 In the United States, adverse drug reactions are a leading cause of death in hospitalized patients⁴ and are annually responsible for hundreds of thousands of deaths and hundreds of billions of dollars in added costs.^2,4,6–8

In the meantime, physicians can educate their patients and promote efforts to incorporate genomic information into standard clinical decision-making.

This article offers an overview of pharmacogenomic testing, discussing implications and limitations of a few validated tests. Specifically, we will discuss testing that is relevant when using warfarin (Coumadin), clopidogrel (Plavix), statins, tamoxifen (Nolvadex), codeine, and psychotropic medications, as well as the future role of pharmacogenomic testing in medicine.

WHAT IS PHARMACOGENOMICS?

Pharmacogenomics is the study of how genetic factors relate to interindividual variability of drug response.

Many clinicians may not be familiar with the background and terminology used in the pharmacogenomic literature. Below, a brief review of the terminology is followed by a schematic describing the various stages of research involved in pharmacogenomics and the advancement of a test into standard practice.

The review and schematic may be helpful for evaluating the clinical significance of pharmacogenomics-related articles.

From genotype to phenotype

Genotype refers to the coding sequence of DNA base pairs for a particular gene, and phenotype (eg, disease or drug response) refers to a trait resulting from the protein product encoded by the gene. The name of a gene often refers to its protein product and is italicized (eg, the CYP3A4 gene encodes for the CYP3A4 enzyme).

Two alleles per autosomal gene (one paternal and one maternal) form the genotype. Heterozygotes possess two different alleles, and homozygotes possess two of the same alleles. The most common allele in a population is referred to as the wild type, and allele frequencies can vary greatly in different populations.⁹

Most sequence variations are single nucleotide polymorphisms (SNPs, pronounced “snips”), a single DNA base pair substitution that may result in a different gene product. SNPs can be classified as structural RNA polymorphisms (srSNPs), regulatory polymorphisms (rSNPs), or polymorphisms in coding regions (cSNPs)¹⁰: srSNPs alter mRNA processing and translation, rSNPs alter transcription, and cSNPs alter protein sequence and function.

Recently, genetic associations with a phenotype have been done on a large scale, with millions of SNPs measured in each of many subjects. This approach, called a genomewide association study or GWAS, has revealed countless candidate genes for clinical traits, but only a few have resulted in a practical clinical application. SNPs may by themselves exert a pharmacokinetic effect (ie, how the body processes the drug), a pharmacodynamic effect (ie, how the drug affects the body), or both, or they may act in concert with other genetic factors. Pharmacodynamic effects can result from a pharmacokinetic effect or can result from variations in a pharmacologic target.

Establishing a genotype-phenotype association can involve clinical studies, animal transgenic studies, or molecular and cellular functional assays.

Clinical applications are emerging

WARFARIN: IMPORTANCE OF CYP2C9, VKORC1

Warfarin is used for the long-term treatment and prevention of thromboembolic events.

This drug has a narrow therapeutic window and shows substantial interpatient dose variability. The start of warfarin therapy is associated with one of the highest rates of adverse events and emergency room visits of any single drug.¹² More than 2 million patients start warfarin each year in the United States alone,¹³ and about 20% of them are hospitalized within the first 6 months because of bleeding due to overanticoagulation.¹⁴

The findings from a recent study suggest that pharmacogenomic testing may eventually allow more patients to safely benefit from warfarin therapy. In this large, nationwide, prospective study, hospitalization rates were 30% lower when pharmacogenomic testing was used.¹⁴ However, no reduction was seen in the time needed to reach the target international normalized ratio (INR) or in the need for INR checks at 6 months. Furthermore, this study used historical control data, and some or all of the reduction in hospitalization rates may be attributed to more frequent INR checks in the patients who underwent testing than in the historical control group.

A relationship between warfarin dose requirements and the genetic status of CYP2C9, which encodes a major drug-metabolizing enzyme, has been demonstrated in retrospective and prospective studies.^15–17

S-warfarin is metabolized by CYP2C9, which is polymorphic

Warfarin contains equal amounts of two isomers, designated S and R. S-warfarin, which is more potent, is metabolized principally by CYP2C9, while R-warfarin is metabolized by CYP1A2, CYP2C19, and CYP3A4.

People who possess two copies of the wild type CYP2C9 gene CYP2C9*1 metabolize warfarin very well and so are called “extensive warfarin metabolizers.” Carriers of the allelic variants CYP2C9*2 and CYP2C9*3 (which have point mutations in exons 3 and 7 of CYP2C9, respectively), have less capacity. Compared with those who are homozygous for the wild-type gene, homozygous carriers of CYP2C9* 3 clear S-warfarin at a rate that is 90% lower, and those with the CYP2C9*1/*3, CYP2C9* 1/*2, CYP2C9*2/*2, or CYP2C9*2/*3 genotypes clear it at a rate 50% to 75% lower. A meta-analysis of 12 studies found that the CYP2C9 genotype accounted for 12% of the interindividual variability of warfarin dose requirements.¹⁸

About 8% of whites carry at least one copy of CYP2C9*2, as do 1% of African Americans; the allele is rare in Asian populations. The frequency of CYP2C9*3 is 6% in whites, 1% in African Americans, and 3% in Asians.^19,20 People with CYP2C9*4 or CYP2C9*5 have a diminished capacity to clear warfarin; however, these variants occur so infrequently that their clinical relevance may be minimal.

Warfarin’s target, VKOR, is also polymorphic

Genetic variation in warfarin’s pharmacologic target, vitamin K 2,3-epoxide reductase (VKOR), also influences dose requirements. Warfarin decreases the synthesis of vitamin-K-dependent clotting factors by inhibiting VKOR. This inhibition depends on the patient’s C1 subunit gene, VKORC1. Patients with a guanine-to-adenine SNP 1,639 bases upstream of VKORC1 (−1639G>A) need lower warfarin doses—an average of 25% lower in those with the GA genotype (ie, one allele has guanine in the −1639 position and the other allele has adenine in that position) and 50% lower in those with the AA genotype compared with the wild-type genotype GG.²¹ This promoter SNP, positioned upstream (ie, before the gene-coding region), greatly influences VKORC1 expression.

A meta-analysis of 10 studies found that the VKORC1 polymorphism accounts for 25% of the interindividual variation in warfarin dose.¹⁸ In one study, the frequency of the AA genotype in a white population was 14%, AG 47%, and GG 39%; in a Chinese population the frequency of AA was 82%, AG 18%, and GG 0.35%.²²

CYP4F2 and GGCX also affect warfarin’s dose requirements

Genetic variations in the enzymes CYP4F2 and gamma-glutamyl carboxylase (GGCX) also influence warfarin dose requirements. Although the data are limited and the effects are smaller than those of CYP2C9 and VKORC1, people with a SNP in CYP4F2 need 8% higher doses of warfarin, while those with a SNP in GGCX need 6% lower doses.²³

CYP2C9 and VKORC1 testing is available

Currently, the clinical pharmacogenetic tests relevant for warfarin use are for CYP2C9 and VKORC1.¹⁰

The FDA has approved four warfarin pharmacogenetic test kits, but most third-party payers are reluctant to reimburse for testing because it is not currently considered a standard of care. Testing typically costs a few hundred dollars, but it should become less expensive as it becomes more commonplace. The current FDA-approved product label for warfarin does not recommend routine pharmacogenomic testing for determining initial or maintenance doses, but it does acknowledge that dose requirements are influenced by CYP2C9 and VKORC1 and states that genotype information, when available, can assist in selecting the starting dose.²⁴

Clinical trials of warfarin pharmacogenomic testing are under way

Although genetic status can greatly influence an individual patient’s warfarin dosing requirement, routine prospective pharmacogenomic testing is not endorsed by the FDA or by other expert panels²⁶ because there is currently insufficient evidence to recommend for or against it.

Several large prospective trials are under way. For example, the National Heart, Lung, and Blood Institute began a prospective trial in about 1,200 patients to evaluate the use of clinical plus genetic information to guide the initiation of warfarin therapy and to improve anticoagulation control for patients.²⁷ The results, expected in September 2011, and those of other large prospective trials should provide adequate evidence for making recommendations about the clinical utility of routine pharmacogenetic testing for guiding warfarin therapy.

Several recent cost-utility and cost-effectiveness studies have attempted to quantify the value of pharmacogenomic testing for warfarin therapy,^28–30 but their analyses are largely limited because the benefit (clinical utility) is yet to be sufficiently characterized.

The relevance of such analyses may soon be drastically diminished, as several non-vitamin-K-dependent blood thinners such as rivaroxaban (Xarelto), dabigatran (Pradaxa), and apixaban are poised to enter clinical practice.³¹

CLOPIDOGREL IS ACTIVATED BY CYP2C19

Clopidogrel, taken by about 40 million patients worldwide, is used to prevent atherothrombotic events and cardiac stent thrombosis when given along with aspirin.

Studies of clopidogrel pharmacogenomics

A recent genome-wide association study conducted in a cohort of 429 healthy Amish persons revealed a SNP in CYP2C19 to be associated with a diminished response to clopidogrel and to account for 12% of the variation in drug response.³³ Traditional factors (the patient’s age, body-mass index, and cholesterol level) combined accounted for less than 10% of the variation.

Findings were similar in a subsequent investigation in 227 cardiac patients receiving clopidogrel: 21% of those with the variant had a cardiovascular ischemic event or died during a 1-year follow-up period compared with 10% of those without the variant (hazard ratio 2.42, P = .02).³³

A 12-year prospective study investigating clopidogrel efficacy in 300 cardiac patients under the age of 45 used cardiovascular death, nonfatal myocardial infarction, and urgent coronary revascularization as end points. It concluded that the only independent predictor of these events was the patient’s CYP2C19 status.³⁴

A study in 2,200 patients with recent myocardial infarction examined whether any of the known allelic variations that modulate clopidogrel’s absorption (ABCB1), metabolic activation (CYP3A4/5 and CYP2C19), or biologic activity (P2RY12 and ITGB3) was associated with a higher rate of the combined end point of all-cause mortality, nonfatal myocardial infarction, or stroke. None of the SNPs in CYP3A4/5, P2RY12, or ITGB3 that were evaluated was associated with a higher risk at 1 year. However, the allelic variations modulating clopidogrel’s absorption (ABCB1) and metabolism (CYP2C19) were associated with higher event rates. Patients with two variant ABCB1 alleles had a higher adjusted hazard ratio (95% confidence interval [CI] 1.2–2.47) than those with the wild-type allele. Patients who had one or two CYP2C19 loss-of-function alleles had a higher event rate than those with two wild-type alleles (95% CI 1.10–3.58 and 1.71–7.51, respectively).³⁵

Conversely, researchers from the Population Health Research Institute found no association between poor-metabolizer status and treatment outcomes when CYP2C19 analysis was retrospectively added to the findings of two large clinical trials (combined N > 5,000). However, patients with acute coronary syndrome benefited more from clopidogrel treatment if they were ultra-rapid metabolizers (possessing the gain-of-function allele CYP2C19*17).³⁶

Current status of clopidogrel testing: Uncertain

A current FDA boxed warning states that poor CYP2C19 metabolizers may not benefit from clopidogrel and recommends that prescribers consider alternative treatment for patients in this category.³⁷ However, routine CYP2C19 testing is not recommended, and no firm recommendations have been established regarding dose adjustments for CYP2C19 status.

In 2010, the American College of Cardiology Foundation Task Force on Clinical Expert Consensus Documents and the American Heart Association collectively pronounced the current evidence base insufficient for recommending routine pharmacogenomic testing.³⁹

Needed are large-scale studies examining the cost-effectiveness and clinical utility of genotype-guided clopidogrel therapy compared with other therapy options such as prasugrel (Effient), an analogue not metabolized by CYP2C19. One such study, sponsored by Medco Health Solutions, plans to enroll 14,600 cardiac patients and has an estimated completion date in June 2011.⁴⁰ The expectation that clopidogrel will be available in generic form in 2012 adds to the uncertainty regarding the cost-effectiveness of CYP2C19 testing for clopidogrel therapy.

STATINS: SLC01B1*5 INCREASES MYOPATHY RISK

Statins lower the concentration of low-density lipoprotein cholesterol (LDL-C), resulting in a relative-risk reduction of about 20% for each 1 mmol/L (39 mg/dL) decrement in LDL-C.⁴¹ They are one of the most commonly prescribed classes of drugs, but their side effects can limit their appeal: statin-induced myopathy occurs in about 1:1,000 to 1:10,000 patients and is difficult to predict.

SLC01B1. The Study of the Effectiveness of Additional Reductions in Cholesterol and Homocysteine (SEARCH), a genome-wide association study, recently found a SNP (SLCO1B1* 5) in the SLC01B1 gene to be associated with a higher risk of statin-induced myopathy in cardiac patients receiving simvastatin (Zocor) 40 or 80 mg daily.⁴² The SLC01B1 gene, located on chromosome 12, influences the extent of the drug’s hepatic uptake and its serum concentration. Only the SLC01B1*5 SNP emerged as a predictor of statin-induced myopathy across the entire genome.⁴²

The authors believe the findings are likely to apply to other statins. The mechanisms leading to statin-induced myopathy and the impact of statin pharmacogenomics are still unclear.⁴³

CYP3A4. Other genetic variants may play a vital role in determining response to statin therapy. Carriers of a newly identified CYP3A4 polymorphism (intron 6 SNP, rs35599367, C>T) required significantly lower statin doses (0.2–0.6 times less) for optimal lipid control. The analyses included atorvastatin (Lipitor), simvastatin, and lovastatin (Mevacor), and the association was robust (P = .019).⁴⁴

Statin pharmacogenomic testing is not routinely recommended

Routine pharmacogenomic testing for statin therapy is not recommended. Additional studies are needed to determine the clinical utility and cost-effectiveness of pharmacogenomic testing (involving a combination of several polymorphisms) in various patient populations delineated by type of statin, dose, and concomitant use of other drugs.

TAMOXIFEN IS ACTIVATED BY CYP2D6

Tamoxifen is prescribed to prevent the recurrence of estrogen-receptor-positive breast cancer, to treat metastatic breast cancer, to prevent cancer in high-risk populations, and to treat ductal carcinoma in situ.

Tamoxifen is metabolized to form endoxifen, which has much higher potency and higher systemic levels than tamoxifen.⁴⁵ Both CYP2D6 and CYP3A4/5 are required to produce endoxifen via two intermediates, but CYP2D6 catalyzes the critical step leading to metabolic activation.

The CYP2D6 gene is highly polymorphic, with more than 75 allelic variants identified. Extensive literature is available describing the influence of CYP2D6 polymorphisms on tamoxifen metabolism and therapy outcomes.^46–52 Several CYP2D6 variants result in reduced or no enzyme activity, and people who have more than two normally functioning alleles have exaggerated enzyme activity (gene amplification).

Classification of CYP2D6 status

Several systems have been developed to categorize the phenotypic activity of CYP2D6 based on genotype.

A genetic basis for the observed diversity in the metabolism of cytochrome P450 substrates was recognized more than 30 years ago. People were categorized as either extensive or poor metabolizers, reflecting normal vs impaired ability to metabolize the CYP2D6 substrates sparteine and debrisoquine. Later work expanded this system to include categories for intermediate (between poor and extensive) and ultra-rapid (better than extensive) metabolizers.

The genetic basis for these categories includes homozygosity for dysfunctional variants (the poor-metabolizer group) or extra copies of normal functioning variants (the ultra-rapid-metabolizer group).

Newer systems have been described for characterizing the CYP2D6 activity phenotype whereby CYP2D6 variants are assigned activity scores.^53–56 The various scoring systems have been reviewed by Kirchheiner.⁵⁷

A recent version of the activity scoring system also takes into consideration the many drugs that inhibit CYP2D6, such as amiodarone (Cordarone) and fluoxetine (Prozac) that can reduce the action of tamoxifen if given with it (Table 4).⁵⁸ For example, the tamoxifen exposure (as predicted by the CYP2D6-activity score) experienced by a CYP2D6 extensive metabolizer taking a CYP2D6-inhibiting drug may be similar to the exposure experienced by a CYP2D6 poor metabolizer receiving the same tamoxifen dose but not taking a CYP2D6-inhibiting drug.

Likewise, the activity score of a CYP2D6 intermediate metabolizer taking a CYP2D6-inducing drug may be similar to that of a CYP2D6 ultra-rapid metabolizer not taking a CYP2D6-inducing drug. Examples of CYP2D6 inducers are dexamethasone, rifampin, and hyperforin (St. John’s wort).

While the newer systems are reported to provide better correlations between genotype and phenotype scores, the older scoring systems and the categorical names are still widely used (eg, in the FDA-approved AmpliChip CYP450 test from Roche,⁵⁹ which includes genotype data for CYP2D6 and CYP2C19).

No firm recommendations for CYP2D6 testing in tamoxifen users

The different genotypes and phenotypes vary in prevalence in different ethnic groups, and significantly different activity levels for endoxifen formation are observed. Tamoxifen lacks efficacy in those who are poor CYP2D6 metabolizers—ie, about 7% of the white population.

However, the FDA has not made firm recommendations about CYP2D6 testing for prescribing tamoxifen because the evidence of benefit, although suggestive, has been considered insufficient.

Clinicians should be aware that tamoxifen’s efficacy is greatly reduced by concomitant therapy with CYP2D6-inhibiting drugs (Table 4).

Other genes affecting tamoxifen: CYP3A4/5, SULT1A1, and UGT2B15

Some investigators propose that polymorphisms in additional genes encoding enzymes in the tamoxifen metabolic and elimination pathways (eg, CYP3A4/5, SULT1A1, and UGT2B15) also need to be considered to account adequately for interindividual variation in drug response.

For example, CYP3A4 and CYP3A5 are also polymorphic, and large interindividual variation exists in their enzyme activities. These enzymes have overlapping substrate specificities, represent the most abundant drug-metabolizing enzymes in the human liver, and are involved in the biotransformation of a broad range of endogenous substrates and most drugs.⁶⁰

Clinical studies evaluating the impact of CYP3A4/5 polymorphisms have been inconsistent in their conclusions, which is generally attributed to the relatively low functional impact or the low prevalence of the SNPs evaluated. Many of the nearly 100 CYP3A4/5 polymorphisms identified have not yet been characterized regarding their functional impact on enzyme expression or activity. CYP-3A4/5 enzyme activity is highly variable between individuals and warrants further study of its role in outcomes of tamoxifen therapy. Ongoing and future prospective clinical trials evaluating CYP2D6, CYP3A4/5, and other relevant polymorphisms are necessary to define their clinical relevance before routine genetic testing for tamoxifen can be justified.

CODEINE IS ALSO ACTIVATED BY CYP2D6

Codeine also depends on the CYP2D6 gene, as it must be activated to its more potent opioid metabolites, including morphine. Poor CYP2D6 metabolizers do not benefit from codeine therapy.

The pharmacogenomics of codeine has become a hot topic, especially regarding breast-feeding mothers. The debate was ignited with the publication in 2006 of a case report of an infant’s death, apparently the result of metabolic polymorphisms.⁶¹ The evolution of this debate and the outcome of the case may be noteworthy to clinicians, as they illustrate the gravity of public and patient interest in pharmacogenomic testing. In this case, the breast-feeding mother had taken codeine regularly for about 14 days when her 13-day-old infant died from toxic levels of morphine. Unknown to her and the prescriber, both the mother and infant were ultra-rapid CYP2D6 metabolizers, resulting in a more rapid and extensive conversion of codeine to morphine.

A logical strategy for preventing similar deaths would be routine CYP2D6 genotyping when prescribing codeine to breast-feeding mothers. However, after several investigations examined the metabolic and excretion pathways of codeine in their entirety, the FDA did not recommend routine CYP2D6 testing when prescribing codeine to breastfeeding mothers because several other factors, including rare genetic variations of other enzymes, proved necessary for reaching the opioid toxicity leading to the infant’s death.⁶²

PHARMACOGENOMICS OF PSYCHOTROPIC DRUGS

Pharmacogenomic testing has clinical utility for some psychotropic drugs.

HLA-B and carbamazepine

Considered a standard of care, HLA-B genotyping is appropriate before prescribing carbamazepine (Tegretol, Equetro) to patients in populations in which HLAB*1502 is likely to be present, such as Asians. Carriers of HLAB* 1502 are at higher risk of life-threatening skin reactions such as Stevens-Johnson syndrome.¹¹

Several other pharmacogenomic applications for psychotropic medications have been suggested, but routine testing has not been recommended by the FDA or endorsed by any expert panel because sufficient clinical utility and cost-effectiveness have not been demonstrated. A brief summary of study findings and a few practical suggestions follow.

Polymorphisms in metabolizing enzymes have been investigated in patients receiving psychotropic drugs.

CYP2D6 and antidepressants

Many antidepressants show significant differences in plasma drug levels with CYP2D6 polymorphisms (in descending order of influence)⁵⁵:

• Imipramine (Tofranil)
• Doxepin (Adapin, Silenor, Sinequan)
• Maprotiline (Deprilept, Ludiomil, Psymion)
• Trimipramine (Surmontil)
• Desipramine (Noraprim)
• Nortriptyline (Aventyl, Pamelor)
• Clomipramine (Anafranil)
• Paroxetine (Paxil)
• Venlafaxine (Effexor)
• Amitriptyline (Elavil)
• Mianserin
• Trazadone (Desyrel)
• Bupropion (Wellbutrin)
• Nefazodone (Serzone)
• Citalopram (Celexa)
• Sertraline (Zoloft).

CYP2D6 and antipsychotics

Several antipsychotics are also influenced by CYP2D6 polymorphisms (also in descending order of influence)⁵⁵:

• Perphenazine (Trilafon)
• Thioridazine (Mellaril)
• Olanzapine (Zyprexa)
• Zuclopenthixol (Cisordinol, Clopixol, Acuphase)
• Aripiprazole (Abilify)
• Flupentixol (Depixol, Fluanxol)
• Haloperidol (Haldol)
• Perazine (Taxilan)
• Risperidone (Risperdal)
• Pimozide (Orap).

CYP2C19 and antidepressants

CYP2C19 polymorphisms are likewise associated with differences in drug metabolism for many antidepressants, such as (in descending order of CYP2C19-mediated influence)⁵⁵:

• Trimipramine
• Doxepin
• Amitriptyline
• Imipramine
• Citalopram (Celexa)
• Clomipramine
• Moclobemide (Aurorix, Manerix)
• Sertraline
• Fluvoxamine (Luvox).

Clinical relevance of CYP2D6 and CYP2C19

Several studies have demonstrated that poor and intermediate CYP2D6 metabolizers have a higher incidence of adverse effects when taking CYP2D6-dependent antidepressants^63–68; however, an almost equal number of studies did not find statistically significant associations.^69–72 Likewise, several studies have found an association between ultra-rapid CYP2D6 metabolizer status and diminished response to antidepressants,^65,73,74 but no association was found in a larger retrospective study.⁷⁵

Routine CYP2D6 and CYP2C19 screening is not recommended when prescribing psychotropic drugs. However, reviews of the pharmacokinetic data have suggested a few practical applications when genetic status is already known. In general, clinicians can consider reducing the dose of tricyclic antidepressants by about 50% when prescribing to CYP2D6-poor-metabolizers.^55,76–78

Genes that affect serotonin metabolism

Several genes in the serotonin pathway have been investigated to determine whether they influence patients’ susceptibility to depression and adverse effects and response to psychotropic medications.

SLC6A4. Polymorphisms in the promoter region of the serotonin transporter gene SLC6A4 appear to influence the treatment response and side-effect profiles of selective serotonin reuptake inhibitors (SSRIs). Carriers of the SLC6A4 5-HTTLPR L alleles have fewer side effects⁷⁹ and better response to SSRI treatment, and carriers of the S allele have a higher incidence of antidepressant-induced mania⁸⁰ and poorer response to SSRI treatment.⁸¹

5-HT. Polymorphisms in serotonin receptors (2A and 2C subtypes) appear to influence SSRI response and side effects. Carriers of 5-HT 2A C alleles had more severe adverse effects from paroxetine,⁷¹ but another 5-HT 2A polymorphism common to Asians is associated with better response to antidepressant therapy.⁸² A 5-HT 2C polymorphism was associated with a lower incidence of antipsychotic-induced weight gain.⁸³

Although the understanding of these relationships is incomplete and routine pharmacogenomic testing is not currently recommended, reviews of the pharmacodynamic data have suggested a few practical applications when a patient’s genetic status is already known. One should consider:

• Selecting treatments other than SSRIs for depressed patients known to possess the SLC6A4 variant
• Selecting citalopram for depressed patients known to carry the 5-HT 2A polymorphism
• Avoiding treatment with antipsychotic drugs for patients known to possess the 5-HT 2C polymorphism.

THE FUTURE OF PHARMACOGENOMIC TESTING

The examples discussed in this article provide some insight about how pharmacogenomic testing is maturing and slowly being integrated into the practice of medicine. They also illustrate the complexity of the multiple stages of research that pharmacogenomic applications must go through in order to be adopted as standard practice.

In the future, pharmacogenomic data will continue to accumulate, and the clinical utility of many other pharmacogenomic tests may be uncovered. The FDA provides information on emerging pharmacogenomic tests at its Web site, www.fda.gov.¹¹ Its up-to-date “Table of Valid Genomic Biomarkers in the Context of Approved Drug Labels” includes boxed warnings, recommendations, research outcomes, and relevant population genetics.

If the FDA continues its current policy, prospective randomized trials that show improvement in patient outcomes will remain the gold standard for determining the clinical significance of a pharmacogenomic test. Furthermore, cost-benefit analyses are likely to continue dictating policy regarding pharmacogenomic testing, and cost-benefit profiles should improve as technology advances and as information gathered from a single test becomes applicable to multiple medications and clinical scenarios.

In the meantime, physicians should become familiar with the terms used in medical genetics and pharmacogenomics and begin to understand genetic contributions to the outcomes of drug therapy. For example, understanding the consequences of metabolizer status and the frequency of variants in a given population can be tremendously helpful when advising our patients about anticipating potential problems when taking specific medications and about making informed decisions about pharmacogenomic testing.

This exchange of information alone may go a long way in improving therapy outcomes even when prospective pharmacogenomic testing is not routinely performed. Furthermore, an increasing number of patients will already have genotyping information available when they come to us, and clinicians need to be aware of the many pharmacogenomic applications recommended by the FDA when genetic status is known.¹⁰

In many patients, certain drugs do not work as well as expected, whereas in other patients they cause toxic effects, even at lower doses. For some patients, the reason may be genetic.

Sizeable minorities of the population carry genetic variants—polymorphisms— that affect their response to various drugs. Thanks to genetic research, our understanding of the variability of drug response has advanced markedly in the last decade. Many relevant polymorphisms have been identified, and tests for some of them are available.

See related editorial

Armed with the knowledge of their patients’ genetic status, physicians could predict their response to certain drugs, leading to better efficacy, fewer adverse drug reactions, and a better cost-benefit ratio.

The possible impact is substantial, since more than half of the drugs most commonly involved in adverse drug reactions are metabolized by polymorphic enzymes.¹ Adverse drug reactions remain a significant detriment to public health, having a substantial impact on rates of morbidity and death and on healthcare costs.^2–8 In the United States, adverse drug reactions are a leading cause of death in hospitalized patients⁴ and are annually responsible for hundreds of thousands of deaths and hundreds of billions of dollars in added costs.^2,4,6–8

In the meantime, physicians can educate their patients and promote efforts to incorporate genomic information into standard clinical decision-making.

This article offers an overview of pharmacogenomic testing, discussing implications and limitations of a few validated tests. Specifically, we will discuss testing that is relevant when using warfarin (Coumadin), clopidogrel (Plavix), statins, tamoxifen (Nolvadex), codeine, and psychotropic medications, as well as the future role of pharmacogenomic testing in medicine.

WHAT IS PHARMACOGENOMICS?

Pharmacogenomics is the study of how genetic factors relate to interindividual variability of drug response.

Many clinicians may not be familiar with the background and terminology used in the pharmacogenomic literature. Below, a brief review of the terminology is followed by a schematic describing the various stages of research involved in pharmacogenomics and the advancement of a test into standard practice.

The review and schematic may be helpful for evaluating the clinical significance of pharmacogenomics-related articles.

From genotype to phenotype

Genotype refers to the coding sequence of DNA base pairs for a particular gene, and phenotype (eg, disease or drug response) refers to a trait resulting from the protein product encoded by the gene. The name of a gene often refers to its protein product and is italicized (eg, the CYP3A4 gene encodes for the CYP3A4 enzyme).

Two alleles per autosomal gene (one paternal and one maternal) form the genotype. Heterozygotes possess two different alleles, and homozygotes possess two of the same alleles. The most common allele in a population is referred to as the wild type, and allele frequencies can vary greatly in different populations.⁹

Most sequence variations are single nucleotide polymorphisms (SNPs, pronounced “snips”), a single DNA base pair substitution that may result in a different gene product. SNPs can be classified as structural RNA polymorphisms (srSNPs), regulatory polymorphisms (rSNPs), or polymorphisms in coding regions (cSNPs)¹⁰: srSNPs alter mRNA processing and translation, rSNPs alter transcription, and cSNPs alter protein sequence and function.

Recently, genetic associations with a phenotype have been done on a large scale, with millions of SNPs measured in each of many subjects. This approach, called a genomewide association study or GWAS, has revealed countless candidate genes for clinical traits, but only a few have resulted in a practical clinical application. SNPs may by themselves exert a pharmacokinetic effect (ie, how the body processes the drug), a pharmacodynamic effect (ie, how the drug affects the body), or both, or they may act in concert with other genetic factors. Pharmacodynamic effects can result from a pharmacokinetic effect or can result from variations in a pharmacologic target.

Establishing a genotype-phenotype association can involve clinical studies, animal transgenic studies, or molecular and cellular functional assays.

Clinical applications are emerging

WARFARIN: IMPORTANCE OF CYP2C9, VKORC1

Warfarin is used for the long-term treatment and prevention of thromboembolic events.

This drug has a narrow therapeutic window and shows substantial interpatient dose variability. The start of warfarin therapy is associated with one of the highest rates of adverse events and emergency room visits of any single drug.¹² More than 2 million patients start warfarin each year in the United States alone,¹³ and about 20% of them are hospitalized within the first 6 months because of bleeding due to overanticoagulation.¹⁴

The findings from a recent study suggest that pharmacogenomic testing may eventually allow more patients to safely benefit from warfarin therapy. In this large, nationwide, prospective study, hospitalization rates were 30% lower when pharmacogenomic testing was used.¹⁴ However, no reduction was seen in the time needed to reach the target international normalized ratio (INR) or in the need for INR checks at 6 months. Furthermore, this study used historical control data, and some or all of the reduction in hospitalization rates may be attributed to more frequent INR checks in the patients who underwent testing than in the historical control group.

A relationship between warfarin dose requirements and the genetic status of CYP2C9, which encodes a major drug-metabolizing enzyme, has been demonstrated in retrospective and prospective studies.^15–17

S-warfarin is metabolized by CYP2C9, which is polymorphic

Warfarin contains equal amounts of two isomers, designated S and R. S-warfarin, which is more potent, is metabolized principally by CYP2C9, while R-warfarin is metabolized by CYP1A2, CYP2C19, and CYP3A4.

People who possess two copies of the wild type CYP2C9 gene CYP2C9*1 metabolize warfarin very well and so are called “extensive warfarin metabolizers.” Carriers of the allelic variants CYP2C9*2 and CYP2C9*3 (which have point mutations in exons 3 and 7 of CYP2C9, respectively), have less capacity. Compared with those who are homozygous for the wild-type gene, homozygous carriers of CYP2C9* 3 clear S-warfarin at a rate that is 90% lower, and those with the CYP2C9*1/*3, CYP2C9* 1/*2, CYP2C9*2/*2, or CYP2C9*2/*3 genotypes clear it at a rate 50% to 75% lower. A meta-analysis of 12 studies found that the CYP2C9 genotype accounted for 12% of the interindividual variability of warfarin dose requirements.¹⁸

About 8% of whites carry at least one copy of CYP2C9*2, as do 1% of African Americans; the allele is rare in Asian populations. The frequency of CYP2C9*3 is 6% in whites, 1% in African Americans, and 3% in Asians.^19,20 People with CYP2C9*4 or CYP2C9*5 have a diminished capacity to clear warfarin; however, these variants occur so infrequently that their clinical relevance may be minimal.

Warfarin’s target, VKOR, is also polymorphic

Genetic variation in warfarin’s pharmacologic target, vitamin K 2,3-epoxide reductase (VKOR), also influences dose requirements. Warfarin decreases the synthesis of vitamin-K-dependent clotting factors by inhibiting VKOR. This inhibition depends on the patient’s C1 subunit gene, VKORC1. Patients with a guanine-to-adenine SNP 1,639 bases upstream of VKORC1 (−1639G>A) need lower warfarin doses—an average of 25% lower in those with the GA genotype (ie, one allele has guanine in the −1639 position and the other allele has adenine in that position) and 50% lower in those with the AA genotype compared with the wild-type genotype GG.²¹ This promoter SNP, positioned upstream (ie, before the gene-coding region), greatly influences VKORC1 expression.

A meta-analysis of 10 studies found that the VKORC1 polymorphism accounts for 25% of the interindividual variation in warfarin dose.¹⁸ In one study, the frequency of the AA genotype in a white population was 14%, AG 47%, and GG 39%; in a Chinese population the frequency of AA was 82%, AG 18%, and GG 0.35%.²²

CYP4F2 and GGCX also affect warfarin’s dose requirements

Genetic variations in the enzymes CYP4F2 and gamma-glutamyl carboxylase (GGCX) also influence warfarin dose requirements. Although the data are limited and the effects are smaller than those of CYP2C9 and VKORC1, people with a SNP in CYP4F2 need 8% higher doses of warfarin, while those with a SNP in GGCX need 6% lower doses.²³

CYP2C9 and VKORC1 testing is available

Currently, the clinical pharmacogenetic tests relevant for warfarin use are for CYP2C9 and VKORC1.¹⁰

The FDA has approved four warfarin pharmacogenetic test kits, but most third-party payers are reluctant to reimburse for testing because it is not currently considered a standard of care. Testing typically costs a few hundred dollars, but it should become less expensive as it becomes more commonplace. The current FDA-approved product label for warfarin does not recommend routine pharmacogenomic testing for determining initial or maintenance doses, but it does acknowledge that dose requirements are influenced by CYP2C9 and VKORC1 and states that genotype information, when available, can assist in selecting the starting dose.²⁴

Clinical trials of warfarin pharmacogenomic testing are under way

Although genetic status can greatly influence an individual patient’s warfarin dosing requirement, routine prospective pharmacogenomic testing is not endorsed by the FDA or by other expert panels²⁶ because there is currently insufficient evidence to recommend for or against it.

Several large prospective trials are under way. For example, the National Heart, Lung, and Blood Institute began a prospective trial in about 1,200 patients to evaluate the use of clinical plus genetic information to guide the initiation of warfarin therapy and to improve anticoagulation control for patients.²⁷ The results, expected in September 2011, and those of other large prospective trials should provide adequate evidence for making recommendations about the clinical utility of routine pharmacogenetic testing for guiding warfarin therapy.

Several recent cost-utility and cost-effectiveness studies have attempted to quantify the value of pharmacogenomic testing for warfarin therapy,^28–30 but their analyses are largely limited because the benefit (clinical utility) is yet to be sufficiently characterized.

The relevance of such analyses may soon be drastically diminished, as several non-vitamin-K-dependent blood thinners such as rivaroxaban (Xarelto), dabigatran (Pradaxa), and apixaban are poised to enter clinical practice.³¹

CLOPIDOGREL IS ACTIVATED BY CYP2C19

Clopidogrel, taken by about 40 million patients worldwide, is used to prevent atherothrombotic events and cardiac stent thrombosis when given along with aspirin.

Studies of clopidogrel pharmacogenomics

A recent genome-wide association study conducted in a cohort of 429 healthy Amish persons revealed a SNP in CYP2C19 to be associated with a diminished response to clopidogrel and to account for 12% of the variation in drug response.³³ Traditional factors (the patient’s age, body-mass index, and cholesterol level) combined accounted for less than 10% of the variation.

Findings were similar in a subsequent investigation in 227 cardiac patients receiving clopidogrel: 21% of those with the variant had a cardiovascular ischemic event or died during a 1-year follow-up period compared with 10% of those without the variant (hazard ratio 2.42, P = .02).³³

A 12-year prospective study investigating clopidogrel efficacy in 300 cardiac patients under the age of 45 used cardiovascular death, nonfatal myocardial infarction, and urgent coronary revascularization as end points. It concluded that the only independent predictor of these events was the patient’s CYP2C19 status.³⁴

A study in 2,200 patients with recent myocardial infarction examined whether any of the known allelic variations that modulate clopidogrel’s absorption (ABCB1), metabolic activation (CYP3A4/5 and CYP2C19), or biologic activity (P2RY12 and ITGB3) was associated with a higher rate of the combined end point of all-cause mortality, nonfatal myocardial infarction, or stroke. None of the SNPs in CYP3A4/5, P2RY12, or ITGB3 that were evaluated was associated with a higher risk at 1 year. However, the allelic variations modulating clopidogrel’s absorption (ABCB1) and metabolism (CYP2C19) were associated with higher event rates. Patients with two variant ABCB1 alleles had a higher adjusted hazard ratio (95% confidence interval [CI] 1.2–2.47) than those with the wild-type allele. Patients who had one or two CYP2C19 loss-of-function alleles had a higher event rate than those with two wild-type alleles (95% CI 1.10–3.58 and 1.71–7.51, respectively).³⁵

Conversely, researchers from the Population Health Research Institute found no association between poor-metabolizer status and treatment outcomes when CYP2C19 analysis was retrospectively added to the findings of two large clinical trials (combined N > 5,000). However, patients with acute coronary syndrome benefited more from clopidogrel treatment if they were ultra-rapid metabolizers (possessing the gain-of-function allele CYP2C19*17).³⁶

Current status of clopidogrel testing: Uncertain

A current FDA boxed warning states that poor CYP2C19 metabolizers may not benefit from clopidogrel and recommends that prescribers consider alternative treatment for patients in this category.³⁷ However, routine CYP2C19 testing is not recommended, and no firm recommendations have been established regarding dose adjustments for CYP2C19 status.

In 2010, the American College of Cardiology Foundation Task Force on Clinical Expert Consensus Documents and the American Heart Association collectively pronounced the current evidence base insufficient for recommending routine pharmacogenomic testing.³⁹

Needed are large-scale studies examining the cost-effectiveness and clinical utility of genotype-guided clopidogrel therapy compared with other therapy options such as prasugrel (Effient), an analogue not metabolized by CYP2C19. One such study, sponsored by Medco Health Solutions, plans to enroll 14,600 cardiac patients and has an estimated completion date in June 2011.⁴⁰ The expectation that clopidogrel will be available in generic form in 2012 adds to the uncertainty regarding the cost-effectiveness of CYP2C19 testing for clopidogrel therapy.

STATINS: SLC01B1*5 INCREASES MYOPATHY RISK

Statins lower the concentration of low-density lipoprotein cholesterol (LDL-C), resulting in a relative-risk reduction of about 20% for each 1 mmol/L (39 mg/dL) decrement in LDL-C.⁴¹ They are one of the most commonly prescribed classes of drugs, but their side effects can limit their appeal: statin-induced myopathy occurs in about 1:1,000 to 1:10,000 patients and is difficult to predict.

SLC01B1. The Study of the Effectiveness of Additional Reductions in Cholesterol and Homocysteine (SEARCH), a genome-wide association study, recently found a SNP (SLCO1B1* 5) in the SLC01B1 gene to be associated with a higher risk of statin-induced myopathy in cardiac patients receiving simvastatin (Zocor) 40 or 80 mg daily.⁴² The SLC01B1 gene, located on chromosome 12, influences the extent of the drug’s hepatic uptake and its serum concentration. Only the SLC01B1*5 SNP emerged as a predictor of statin-induced myopathy across the entire genome.⁴²

The authors believe the findings are likely to apply to other statins. The mechanisms leading to statin-induced myopathy and the impact of statin pharmacogenomics are still unclear.⁴³

CYP3A4. Other genetic variants may play a vital role in determining response to statin therapy. Carriers of a newly identified CYP3A4 polymorphism (intron 6 SNP, rs35599367, C>T) required significantly lower statin doses (0.2–0.6 times less) for optimal lipid control. The analyses included atorvastatin (Lipitor), simvastatin, and lovastatin (Mevacor), and the association was robust (P = .019).⁴⁴

Statin pharmacogenomic testing is not routinely recommended

Routine pharmacogenomic testing for statin therapy is not recommended. Additional studies are needed to determine the clinical utility and cost-effectiveness of pharmacogenomic testing (involving a combination of several polymorphisms) in various patient populations delineated by type of statin, dose, and concomitant use of other drugs.

TAMOXIFEN IS ACTIVATED BY CYP2D6

Tamoxifen is prescribed to prevent the recurrence of estrogen-receptor-positive breast cancer, to treat metastatic breast cancer, to prevent cancer in high-risk populations, and to treat ductal carcinoma in situ.

Tamoxifen is metabolized to form endoxifen, which has much higher potency and higher systemic levels than tamoxifen.⁴⁵ Both CYP2D6 and CYP3A4/5 are required to produce endoxifen via two intermediates, but CYP2D6 catalyzes the critical step leading to metabolic activation.

The CYP2D6 gene is highly polymorphic, with more than 75 allelic variants identified. Extensive literature is available describing the influence of CYP2D6 polymorphisms on tamoxifen metabolism and therapy outcomes.^46–52 Several CYP2D6 variants result in reduced or no enzyme activity, and people who have more than two normally functioning alleles have exaggerated enzyme activity (gene amplification).

Classification of CYP2D6 status

Several systems have been developed to categorize the phenotypic activity of CYP2D6 based on genotype.

A genetic basis for the observed diversity in the metabolism of cytochrome P450 substrates was recognized more than 30 years ago. People were categorized as either extensive or poor metabolizers, reflecting normal vs impaired ability to metabolize the CYP2D6 substrates sparteine and debrisoquine. Later work expanded this system to include categories for intermediate (between poor and extensive) and ultra-rapid (better than extensive) metabolizers.

The genetic basis for these categories includes homozygosity for dysfunctional variants (the poor-metabolizer group) or extra copies of normal functioning variants (the ultra-rapid-metabolizer group).

Newer systems have been described for characterizing the CYP2D6 activity phenotype whereby CYP2D6 variants are assigned activity scores.^53–56 The various scoring systems have been reviewed by Kirchheiner.⁵⁷

A recent version of the activity scoring system also takes into consideration the many drugs that inhibit CYP2D6, such as amiodarone (Cordarone) and fluoxetine (Prozac) that can reduce the action of tamoxifen if given with it (Table 4).⁵⁸ For example, the tamoxifen exposure (as predicted by the CYP2D6-activity score) experienced by a CYP2D6 extensive metabolizer taking a CYP2D6-inhibiting drug may be similar to the exposure experienced by a CYP2D6 poor metabolizer receiving the same tamoxifen dose but not taking a CYP2D6-inhibiting drug.

Likewise, the activity score of a CYP2D6 intermediate metabolizer taking a CYP2D6-inducing drug may be similar to that of a CYP2D6 ultra-rapid metabolizer not taking a CYP2D6-inducing drug. Examples of CYP2D6 inducers are dexamethasone, rifampin, and hyperforin (St. John’s wort).

While the newer systems are reported to provide better correlations between genotype and phenotype scores, the older scoring systems and the categorical names are still widely used (eg, in the FDA-approved AmpliChip CYP450 test from Roche,⁵⁹ which includes genotype data for CYP2D6 and CYP2C19).

No firm recommendations for CYP2D6 testing in tamoxifen users

The different genotypes and phenotypes vary in prevalence in different ethnic groups, and significantly different activity levels for endoxifen formation are observed. Tamoxifen lacks efficacy in those who are poor CYP2D6 metabolizers—ie, about 7% of the white population.

However, the FDA has not made firm recommendations about CYP2D6 testing for prescribing tamoxifen because the evidence of benefit, although suggestive, has been considered insufficient.

Clinicians should be aware that tamoxifen’s efficacy is greatly reduced by concomitant therapy with CYP2D6-inhibiting drugs (Table 4).

Other genes affecting tamoxifen: CYP3A4/5, SULT1A1, and UGT2B15

Some investigators propose that polymorphisms in additional genes encoding enzymes in the tamoxifen metabolic and elimination pathways (eg, CYP3A4/5, SULT1A1, and UGT2B15) also need to be considered to account adequately for interindividual variation in drug response.

For example, CYP3A4 and CYP3A5 are also polymorphic, and large interindividual variation exists in their enzyme activities. These enzymes have overlapping substrate specificities, represent the most abundant drug-metabolizing enzymes in the human liver, and are involved in the biotransformation of a broad range of endogenous substrates and most drugs.⁶⁰

Clinical studies evaluating the impact of CYP3A4/5 polymorphisms have been inconsistent in their conclusions, which is generally attributed to the relatively low functional impact or the low prevalence of the SNPs evaluated. Many of the nearly 100 CYP3A4/5 polymorphisms identified have not yet been characterized regarding their functional impact on enzyme expression or activity. CYP-3A4/5 enzyme activity is highly variable between individuals and warrants further study of its role in outcomes of tamoxifen therapy. Ongoing and future prospective clinical trials evaluating CYP2D6, CYP3A4/5, and other relevant polymorphisms are necessary to define their clinical relevance before routine genetic testing for tamoxifen can be justified.

CODEINE IS ALSO ACTIVATED BY CYP2D6

Codeine also depends on the CYP2D6 gene, as it must be activated to its more potent opioid metabolites, including morphine. Poor CYP2D6 metabolizers do not benefit from codeine therapy.

The pharmacogenomics of codeine has become a hot topic, especially regarding breast-feeding mothers. The debate was ignited with the publication in 2006 of a case report of an infant’s death, apparently the result of metabolic polymorphisms.⁶¹ The evolution of this debate and the outcome of the case may be noteworthy to clinicians, as they illustrate the gravity of public and patient interest in pharmacogenomic testing. In this case, the breast-feeding mother had taken codeine regularly for about 14 days when her 13-day-old infant died from toxic levels of morphine. Unknown to her and the prescriber, both the mother and infant were ultra-rapid CYP2D6 metabolizers, resulting in a more rapid and extensive conversion of codeine to morphine.

A logical strategy for preventing similar deaths would be routine CYP2D6 genotyping when prescribing codeine to breast-feeding mothers. However, after several investigations examined the metabolic and excretion pathways of codeine in their entirety, the FDA did not recommend routine CYP2D6 testing when prescribing codeine to breastfeeding mothers because several other factors, including rare genetic variations of other enzymes, proved necessary for reaching the opioid toxicity leading to the infant’s death.⁶²

PHARMACOGENOMICS OF PSYCHOTROPIC DRUGS

Pharmacogenomic testing has clinical utility for some psychotropic drugs.

HLA-B and carbamazepine

Considered a standard of care, HLA-B genotyping is appropriate before prescribing carbamazepine (Tegretol, Equetro) to patients in populations in which HLAB*1502 is likely to be present, such as Asians. Carriers of HLAB* 1502 are at higher risk of life-threatening skin reactions such as Stevens-Johnson syndrome.¹¹

Several other pharmacogenomic applications for psychotropic medications have been suggested, but routine testing has not been recommended by the FDA or endorsed by any expert panel because sufficient clinical utility and cost-effectiveness have not been demonstrated. A brief summary of study findings and a few practical suggestions follow.

Polymorphisms in metabolizing enzymes have been investigated in patients receiving psychotropic drugs.

CYP2D6 and antidepressants

Many antidepressants show significant differences in plasma drug levels with CYP2D6 polymorphisms (in descending order of influence)⁵⁵:

• Imipramine (Tofranil)
• Doxepin (Adapin, Silenor, Sinequan)
• Maprotiline (Deprilept, Ludiomil, Psymion)
• Trimipramine (Surmontil)
• Desipramine (Noraprim)
• Nortriptyline (Aventyl, Pamelor)
• Clomipramine (Anafranil)
• Paroxetine (Paxil)
• Venlafaxine (Effexor)
• Amitriptyline (Elavil)
• Mianserin
• Trazadone (Desyrel)
• Bupropion (Wellbutrin)
• Nefazodone (Serzone)
• Citalopram (Celexa)
• Sertraline (Zoloft).

CYP2D6 and antipsychotics

Several antipsychotics are also influenced by CYP2D6 polymorphisms (also in descending order of influence)⁵⁵:

• Perphenazine (Trilafon)
• Thioridazine (Mellaril)
• Olanzapine (Zyprexa)
• Zuclopenthixol (Cisordinol, Clopixol, Acuphase)
• Aripiprazole (Abilify)
• Flupentixol (Depixol, Fluanxol)
• Haloperidol (Haldol)
• Perazine (Taxilan)
• Risperidone (Risperdal)
• Pimozide (Orap).

CYP2C19 and antidepressants

CYP2C19 polymorphisms are likewise associated with differences in drug metabolism for many antidepressants, such as (in descending order of CYP2C19-mediated influence)⁵⁵:

• Trimipramine
• Doxepin
• Amitriptyline
• Imipramine
• Citalopram (Celexa)
• Clomipramine
• Moclobemide (Aurorix, Manerix)
• Sertraline
• Fluvoxamine (Luvox).

Clinical relevance of CYP2D6 and CYP2C19

Several studies have demonstrated that poor and intermediate CYP2D6 metabolizers have a higher incidence of adverse effects when taking CYP2D6-dependent antidepressants^63–68; however, an almost equal number of studies did not find statistically significant associations.^69–72 Likewise, several studies have found an association between ultra-rapid CYP2D6 metabolizer status and diminished response to antidepressants,^65,73,74 but no association was found in a larger retrospective study.⁷⁵

Routine CYP2D6 and CYP2C19 screening is not recommended when prescribing psychotropic drugs. However, reviews of the pharmacokinetic data have suggested a few practical applications when genetic status is already known. In general, clinicians can consider reducing the dose of tricyclic antidepressants by about 50% when prescribing to CYP2D6-poor-metabolizers.^55,76–78

Genes that affect serotonin metabolism

Several genes in the serotonin pathway have been investigated to determine whether they influence patients’ susceptibility to depression and adverse effects and response to psychotropic medications.

SLC6A4. Polymorphisms in the promoter region of the serotonin transporter gene SLC6A4 appear to influence the treatment response and side-effect profiles of selective serotonin reuptake inhibitors (SSRIs). Carriers of the SLC6A4 5-HTTLPR L alleles have fewer side effects⁷⁹ and better response to SSRI treatment, and carriers of the S allele have a higher incidence of antidepressant-induced mania⁸⁰ and poorer response to SSRI treatment.⁸¹

5-HT. Polymorphisms in serotonin receptors (2A and 2C subtypes) appear to influence SSRI response and side effects. Carriers of 5-HT 2A C alleles had more severe adverse effects from paroxetine,⁷¹ but another 5-HT 2A polymorphism common to Asians is associated with better response to antidepressant therapy.⁸² A 5-HT 2C polymorphism was associated with a lower incidence of antipsychotic-induced weight gain.⁸³

Although the understanding of these relationships is incomplete and routine pharmacogenomic testing is not currently recommended, reviews of the pharmacodynamic data have suggested a few practical applications when a patient’s genetic status is already known. One should consider:

• Selecting treatments other than SSRIs for depressed patients known to possess the SLC6A4 variant
• Selecting citalopram for depressed patients known to carry the 5-HT 2A polymorphism
• Avoiding treatment with antipsychotic drugs for patients known to possess the 5-HT 2C polymorphism.

THE FUTURE OF PHARMACOGENOMIC TESTING

The examples discussed in this article provide some insight about how pharmacogenomic testing is maturing and slowly being integrated into the practice of medicine. They also illustrate the complexity of the multiple stages of research that pharmacogenomic applications must go through in order to be adopted as standard practice.

In the future, pharmacogenomic data will continue to accumulate, and the clinical utility of many other pharmacogenomic tests may be uncovered. The FDA provides information on emerging pharmacogenomic tests at its Web site, www.fda.gov.¹¹ Its up-to-date “Table of Valid Genomic Biomarkers in the Context of Approved Drug Labels” includes boxed warnings, recommendations, research outcomes, and relevant population genetics.

If the FDA continues its current policy, prospective randomized trials that show improvement in patient outcomes will remain the gold standard for determining the clinical significance of a pharmacogenomic test. Furthermore, cost-benefit analyses are likely to continue dictating policy regarding pharmacogenomic testing, and cost-benefit profiles should improve as technology advances and as information gathered from a single test becomes applicable to multiple medications and clinical scenarios.

In the meantime, physicians should become familiar with the terms used in medical genetics and pharmacogenomics and begin to understand genetic contributions to the outcomes of drug therapy. For example, understanding the consequences of metabolizer status and the frequency of variants in a given population can be tremendously helpful when advising our patients about anticipating potential problems when taking specific medications and about making informed decisions about pharmacogenomic testing.

This exchange of information alone may go a long way in improving therapy outcomes even when prospective pharmacogenomic testing is not routinely performed. Furthermore, an increasing number of patients will already have genotyping information available when they come to us, and clinicians need to be aware of the many pharmacogenomic applications recommended by the FDA when genetic status is known.¹⁰

In many patients, certain drugs do not work as well as expected, whereas in other patients they cause toxic effects, even at lower doses. For some patients, the reason may be genetic.

Sizeable minorities of the population carry genetic variants—polymorphisms— that affect their response to various drugs. Thanks to genetic research, our understanding of the variability of drug response has advanced markedly in the last decade. Many relevant polymorphisms have been identified, and tests for some of them are available.

See related editorial

Armed with the knowledge of their patients’ genetic status, physicians could predict their response to certain drugs, leading to better efficacy, fewer adverse drug reactions, and a better cost-benefit ratio.

The possible impact is substantial, since more than half of the drugs most commonly involved in adverse drug reactions are metabolized by polymorphic enzymes.¹ Adverse drug reactions remain a significant detriment to public health, having a substantial impact on rates of morbidity and death and on healthcare costs.^2–8 In the United States, adverse drug reactions are a leading cause of death in hospitalized patients⁴ and are annually responsible for hundreds of thousands of deaths and hundreds of billions of dollars in added costs.^2,4,6–8

In the meantime, physicians can educate their patients and promote efforts to incorporate genomic information into standard clinical decision-making.

This article offers an overview of pharmacogenomic testing, discussing implications and limitations of a few validated tests. Specifically, we will discuss testing that is relevant when using warfarin (Coumadin), clopidogrel (Plavix), statins, tamoxifen (Nolvadex), codeine, and psychotropic medications, as well as the future role of pharmacogenomic testing in medicine.

WHAT IS PHARMACOGENOMICS?

Pharmacogenomics is the study of how genetic factors relate to interindividual variability of drug response.

Many clinicians may not be familiar with the background and terminology used in the pharmacogenomic literature. Below, a brief review of the terminology is followed by a schematic describing the various stages of research involved in pharmacogenomics and the advancement of a test into standard practice.

The review and schematic may be helpful for evaluating the clinical significance of pharmacogenomics-related articles.

From genotype to phenotype

Genotype refers to the coding sequence of DNA base pairs for a particular gene, and phenotype (eg, disease or drug response) refers to a trait resulting from the protein product encoded by the gene. The name of a gene often refers to its protein product and is italicized (eg, the CYP3A4 gene encodes for the CYP3A4 enzyme).

Two alleles per autosomal gene (one paternal and one maternal) form the genotype. Heterozygotes possess two different alleles, and homozygotes possess two of the same alleles. The most common allele in a population is referred to as the wild type, and allele frequencies can vary greatly in different populations.⁹

Most sequence variations are single nucleotide polymorphisms (SNPs, pronounced “snips”), a single DNA base pair substitution that may result in a different gene product. SNPs can be classified as structural RNA polymorphisms (srSNPs), regulatory polymorphisms (rSNPs), or polymorphisms in coding regions (cSNPs)¹⁰: srSNPs alter mRNA processing and translation, rSNPs alter transcription, and cSNPs alter protein sequence and function.

Recently, genetic associations with a phenotype have been done on a large scale, with millions of SNPs measured in each of many subjects. This approach, called a genomewide association study or GWAS, has revealed countless candidate genes for clinical traits, but only a few have resulted in a practical clinical application. SNPs may by themselves exert a pharmacokinetic effect (ie, how the body processes the drug), a pharmacodynamic effect (ie, how the drug affects the body), or both, or they may act in concert with other genetic factors. Pharmacodynamic effects can result from a pharmacokinetic effect or can result from variations in a pharmacologic target.

Establishing a genotype-phenotype association can involve clinical studies, animal transgenic studies, or molecular and cellular functional assays.

Clinical applications are emerging

WARFARIN: IMPORTANCE OF CYP2C9, VKORC1

Warfarin is used for the long-term treatment and prevention of thromboembolic events.

This drug has a narrow therapeutic window and shows substantial interpatient dose variability. The start of warfarin therapy is associated with one of the highest rates of adverse events and emergency room visits of any single drug.¹² More than 2 million patients start warfarin each year in the United States alone,¹³ and about 20% of them are hospitalized within the first 6 months because of bleeding due to overanticoagulation.¹⁴

The findings from a recent study suggest that pharmacogenomic testing may eventually allow more patients to safely benefit from warfarin therapy. In this large, nationwide, prospective study, hospitalization rates were 30% lower when pharmacogenomic testing was used.¹⁴ However, no reduction was seen in the time needed to reach the target international normalized ratio (INR) or in the need for INR checks at 6 months. Furthermore, this study used historical control data, and some or all of the reduction in hospitalization rates may be attributed to more frequent INR checks in the patients who underwent testing than in the historical control group.

A relationship between warfarin dose requirements and the genetic status of CYP2C9, which encodes a major drug-metabolizing enzyme, has been demonstrated in retrospective and prospective studies.^15–17

S-warfarin is metabolized by CYP2C9, which is polymorphic

Warfarin contains equal amounts of two isomers, designated S and R. S-warfarin, which is more potent, is metabolized principally by CYP2C9, while R-warfarin is metabolized by CYP1A2, CYP2C19, and CYP3A4.

People who possess two copies of the wild type CYP2C9 gene CYP2C9*1 metabolize warfarin very well and so are called “extensive warfarin metabolizers.” Carriers of the allelic variants CYP2C9*2 and CYP2C9*3 (which have point mutations in exons 3 and 7 of CYP2C9, respectively), have less capacity. Compared with those who are homozygous for the wild-type gene, homozygous carriers of CYP2C9* 3 clear S-warfarin at a rate that is 90% lower, and those with the CYP2C9*1/*3, CYP2C9* 1/*2, CYP2C9*2/*2, or CYP2C9*2/*3 genotypes clear it at a rate 50% to 75% lower. A meta-analysis of 12 studies found that the CYP2C9 genotype accounted for 12% of the interindividual variability of warfarin dose requirements.¹⁸

About 8% of whites carry at least one copy of CYP2C9*2, as do 1% of African Americans; the allele is rare in Asian populations. The frequency of CYP2C9*3 is 6% in whites, 1% in African Americans, and 3% in Asians.^19,20 People with CYP2C9*4 or CYP2C9*5 have a diminished capacity to clear warfarin; however, these variants occur so infrequently that their clinical relevance may be minimal.

Warfarin’s target, VKOR, is also polymorphic

Genetic variation in warfarin’s pharmacologic target, vitamin K 2,3-epoxide reductase (VKOR), also influences dose requirements. Warfarin decreases the synthesis of vitamin-K-dependent clotting factors by inhibiting VKOR. This inhibition depends on the patient’s C1 subunit gene, VKORC1. Patients with a guanine-to-adenine SNP 1,639 bases upstream of VKORC1 (−1639G>A) need lower warfarin doses—an average of 25% lower in those with the GA genotype (ie, one allele has guanine in the −1639 position and the other allele has adenine in that position) and 50% lower in those with the AA genotype compared with the wild-type genotype GG.²¹ This promoter SNP, positioned upstream (ie, before the gene-coding region), greatly influences VKORC1 expression.

A meta-analysis of 10 studies found that the VKORC1 polymorphism accounts for 25% of the interindividual variation in warfarin dose.¹⁸ In one study, the frequency of the AA genotype in a white population was 14%, AG 47%, and GG 39%; in a Chinese population the frequency of AA was 82%, AG 18%, and GG 0.35%.²²

CYP4F2 and GGCX also affect warfarin’s dose requirements

Genetic variations in the enzymes CYP4F2 and gamma-glutamyl carboxylase (GGCX) also influence warfarin dose requirements. Although the data are limited and the effects are smaller than those of CYP2C9 and VKORC1, people with a SNP in CYP4F2 need 8% higher doses of warfarin, while those with a SNP in GGCX need 6% lower doses.²³

CYP2C9 and VKORC1 testing is available

Currently, the clinical pharmacogenetic tests relevant for warfarin use are for CYP2C9 and VKORC1.¹⁰

The FDA has approved four warfarin pharmacogenetic test kits, but most third-party payers are reluctant to reimburse for testing because it is not currently considered a standard of care. Testing typically costs a few hundred dollars, but it should become less expensive as it becomes more commonplace. The current FDA-approved product label for warfarin does not recommend routine pharmacogenomic testing for determining initial or maintenance doses, but it does acknowledge that dose requirements are influenced by CYP2C9 and VKORC1 and states that genotype information, when available, can assist in selecting the starting dose.²⁴

Clinical trials of warfarin pharmacogenomic testing are under way

Although genetic status can greatly influence an individual patient’s warfarin dosing requirement, routine prospective pharmacogenomic testing is not endorsed by the FDA or by other expert panels²⁶ because there is currently insufficient evidence to recommend for or against it.

Several large prospective trials are under way. For example, the National Heart, Lung, and Blood Institute began a prospective trial in about 1,200 patients to evaluate the use of clinical plus genetic information to guide the initiation of warfarin therapy and to improve anticoagulation control for patients.²⁷ The results, expected in September 2011, and those of other large prospective trials should provide adequate evidence for making recommendations about the clinical utility of routine pharmacogenetic testing for guiding warfarin therapy.

Several recent cost-utility and cost-effectiveness studies have attempted to quantify the value of pharmacogenomic testing for warfarin therapy,^28–30 but their analyses are largely limited because the benefit (clinical utility) is yet to be sufficiently characterized.

The relevance of such analyses may soon be drastically diminished, as several non-vitamin-K-dependent blood thinners such as rivaroxaban (Xarelto), dabigatran (Pradaxa), and apixaban are poised to enter clinical practice.³¹

CLOPIDOGREL IS ACTIVATED BY CYP2C19

Clopidogrel, taken by about 40 million patients worldwide, is used to prevent atherothrombotic events and cardiac stent thrombosis when given along with aspirin.

Studies of clopidogrel pharmacogenomics

A recent genome-wide association study conducted in a cohort of 429 healthy Amish persons revealed a SNP in CYP2C19 to be associated with a diminished response to clopidogrel and to account for 12% of the variation in drug response.³³ Traditional factors (the patient’s age, body-mass index, and cholesterol level) combined accounted for less than 10% of the variation.

Findings were similar in a subsequent investigation in 227 cardiac patients receiving clopidogrel: 21% of those with the variant had a cardiovascular ischemic event or died during a 1-year follow-up period compared with 10% of those without the variant (hazard ratio 2.42, P = .02).³³

A 12-year prospective study investigating clopidogrel efficacy in 300 cardiac patients under the age of 45 used cardiovascular death, nonfatal myocardial infarction, and urgent coronary revascularization as end points. It concluded that the only independent predictor of these events was the patient’s CYP2C19 status.³⁴

A study in 2,200 patients with recent myocardial infarction examined whether any of the known allelic variations that modulate clopidogrel’s absorption (ABCB1), metabolic activation (CYP3A4/5 and CYP2C19), or biologic activity (P2RY12 and ITGB3) was associated with a higher rate of the combined end point of all-cause mortality, nonfatal myocardial infarction, or stroke. None of the SNPs in CYP3A4/5, P2RY12, or ITGB3 that were evaluated was associated with a higher risk at 1 year. However, the allelic variations modulating clopidogrel’s absorption (ABCB1) and metabolism (CYP2C19) were associated with higher event rates. Patients with two variant ABCB1 alleles had a higher adjusted hazard ratio (95% confidence interval [CI] 1.2–2.47) than those with the wild-type allele. Patients who had one or two CYP2C19 loss-of-function alleles had a higher event rate than those with two wild-type alleles (95% CI 1.10–3.58 and 1.71–7.51, respectively).³⁵

Conversely, researchers from the Population Health Research Institute found no association between poor-metabolizer status and treatment outcomes when CYP2C19 analysis was retrospectively added to the findings of two large clinical trials (combined N > 5,000). However, patients with acute coronary syndrome benefited more from clopidogrel treatment if they were ultra-rapid metabolizers (possessing the gain-of-function allele CYP2C19*17).³⁶

Current status of clopidogrel testing: Uncertain

A current FDA boxed warning states that poor CYP2C19 metabolizers may not benefit from clopidogrel and recommends that prescribers consider alternative treatment for patients in this category.³⁷ However, routine CYP2C19 testing is not recommended, and no firm recommendations have been established regarding dose adjustments for CYP2C19 status.

In 2010, the American College of Cardiology Foundation Task Force on Clinical Expert Consensus Documents and the American Heart Association collectively pronounced the current evidence base insufficient for recommending routine pharmacogenomic testing.³⁹

Needed are large-scale studies examining the cost-effectiveness and clinical utility of genotype-guided clopidogrel therapy compared with other therapy options such as prasugrel (Effient), an analogue not metabolized by CYP2C19. One such study, sponsored by Medco Health Solutions, plans to enroll 14,600 cardiac patients and has an estimated completion date in June 2011.⁴⁰ The expectation that clopidogrel will be available in generic form in 2012 adds to the uncertainty regarding the cost-effectiveness of CYP2C19 testing for clopidogrel therapy.

STATINS: SLC01B1*5 INCREASES MYOPATHY RISK

Statins lower the concentration of low-density lipoprotein cholesterol (LDL-C), resulting in a relative-risk reduction of about 20% for each 1 mmol/L (39 mg/dL) decrement in LDL-C.⁴¹ They are one of the most commonly prescribed classes of drugs, but their side effects can limit their appeal: statin-induced myopathy occurs in about 1:1,000 to 1:10,000 patients and is difficult to predict.

SLC01B1. The Study of the Effectiveness of Additional Reductions in Cholesterol and Homocysteine (SEARCH), a genome-wide association study, recently found a SNP (SLCO1B1* 5) in the SLC01B1 gene to be associated with a higher risk of statin-induced myopathy in cardiac patients receiving simvastatin (Zocor) 40 or 80 mg daily.⁴² The SLC01B1 gene, located on chromosome 12, influences the extent of the drug’s hepatic uptake and its serum concentration. Only the SLC01B1*5 SNP emerged as a predictor of statin-induced myopathy across the entire genome.⁴²

The authors believe the findings are likely to apply to other statins. The mechanisms leading to statin-induced myopathy and the impact of statin pharmacogenomics are still unclear.⁴³

CYP3A4. Other genetic variants may play a vital role in determining response to statin therapy. Carriers of a newly identified CYP3A4 polymorphism (intron 6 SNP, rs35599367, C>T) required significantly lower statin doses (0.2–0.6 times less) for optimal lipid control. The analyses included atorvastatin (Lipitor), simvastatin, and lovastatin (Mevacor), and the association was robust (P = .019).⁴⁴

Statin pharmacogenomic testing is not routinely recommended

Routine pharmacogenomic testing for statin therapy is not recommended. Additional studies are needed to determine the clinical utility and cost-effectiveness of pharmacogenomic testing (involving a combination of several polymorphisms) in various patient populations delineated by type of statin, dose, and concomitant use of other drugs.

TAMOXIFEN IS ACTIVATED BY CYP2D6

Tamoxifen is prescribed to prevent the recurrence of estrogen-receptor-positive breast cancer, to treat metastatic breast cancer, to prevent cancer in high-risk populations, and to treat ductal carcinoma in situ.

Tamoxifen is metabolized to form endoxifen, which has much higher potency and higher systemic levels than tamoxifen.⁴⁵ Both CYP2D6 and CYP3A4/5 are required to produce endoxifen via two intermediates, but CYP2D6 catalyzes the critical step leading to metabolic activation.

The CYP2D6 gene is highly polymorphic, with more than 75 allelic variants identified. Extensive literature is available describing the influence of CYP2D6 polymorphisms on tamoxifen metabolism and therapy outcomes.^46–52 Several CYP2D6 variants result in reduced or no enzyme activity, and people who have more than two normally functioning alleles have exaggerated enzyme activity (gene amplification).

Classification of CYP2D6 status

Several systems have been developed to categorize the phenotypic activity of CYP2D6 based on genotype.

A genetic basis for the observed diversity in the metabolism of cytochrome P450 substrates was recognized more than 30 years ago. People were categorized as either extensive or poor metabolizers, reflecting normal vs impaired ability to metabolize the CYP2D6 substrates sparteine and debrisoquine. Later work expanded this system to include categories for intermediate (between poor and extensive) and ultra-rapid (better than extensive) metabolizers.

The genetic basis for these categories includes homozygosity for dysfunctional variants (the poor-metabolizer group) or extra copies of normal functioning variants (the ultra-rapid-metabolizer group).

Newer systems have been described for characterizing the CYP2D6 activity phenotype whereby CYP2D6 variants are assigned activity scores.^53–56 The various scoring systems have been reviewed by Kirchheiner.⁵⁷

A recent version of the activity scoring system also takes into consideration the many drugs that inhibit CYP2D6, such as amiodarone (Cordarone) and fluoxetine (Prozac) that can reduce the action of tamoxifen if given with it (Table 4).⁵⁸ For example, the tamoxifen exposure (as predicted by the CYP2D6-activity score) experienced by a CYP2D6 extensive metabolizer taking a CYP2D6-inhibiting drug may be similar to the exposure experienced by a CYP2D6 poor metabolizer receiving the same tamoxifen dose but not taking a CYP2D6-inhibiting drug.

Likewise, the activity score of a CYP2D6 intermediate metabolizer taking a CYP2D6-inducing drug may be similar to that of a CYP2D6 ultra-rapid metabolizer not taking a CYP2D6-inducing drug. Examples of CYP2D6 inducers are dexamethasone, rifampin, and hyperforin (St. John’s wort).

While the newer systems are reported to provide better correlations between genotype and phenotype scores, the older scoring systems and the categorical names are still widely used (eg, in the FDA-approved AmpliChip CYP450 test from Roche,⁵⁹ which includes genotype data for CYP2D6 and CYP2C19).

No firm recommendations for CYP2D6 testing in tamoxifen users

The different genotypes and phenotypes vary in prevalence in different ethnic groups, and significantly different activity levels for endoxifen formation are observed. Tamoxifen lacks efficacy in those who are poor CYP2D6 metabolizers—ie, about 7% of the white population.

However, the FDA has not made firm recommendations about CYP2D6 testing for prescribing tamoxifen because the evidence of benefit, although suggestive, has been considered insufficient.

Clinicians should be aware that tamoxifen’s efficacy is greatly reduced by concomitant therapy with CYP2D6-inhibiting drugs (Table 4).

Other genes affecting tamoxifen: CYP3A4/5, SULT1A1, and UGT2B15

Some investigators propose that polymorphisms in additional genes encoding enzymes in the tamoxifen metabolic and elimination pathways (eg, CYP3A4/5, SULT1A1, and UGT2B15) also need to be considered to account adequately for interindividual variation in drug response.

For example, CYP3A4 and CYP3A5 are also polymorphic, and large interindividual variation exists in their enzyme activities. These enzymes have overlapping substrate specificities, represent the most abundant drug-metabolizing enzymes in the human liver, and are involved in the biotransformation of a broad range of endogenous substrates and most drugs.⁶⁰

Clinical studies evaluating the impact of CYP3A4/5 polymorphisms have been inconsistent in their conclusions, which is generally attributed to the relatively low functional impact or the low prevalence of the SNPs evaluated. Many of the nearly 100 CYP3A4/5 polymorphisms identified have not yet been characterized regarding their functional impact on enzyme expression or activity. CYP-3A4/5 enzyme activity is highly variable between individuals and warrants further study of its role in outcomes of tamoxifen therapy. Ongoing and future prospective clinical trials evaluating CYP2D6, CYP3A4/5, and other relevant polymorphisms are necessary to define their clinical relevance before routine genetic testing for tamoxifen can be justified.

CODEINE IS ALSO ACTIVATED BY CYP2D6

Codeine also depends on the CYP2D6 gene, as it must be activated to its more potent opioid metabolites, including morphine. Poor CYP2D6 metabolizers do not benefit from codeine therapy.

The pharmacogenomics of codeine has become a hot topic, especially regarding breast-feeding mothers. The debate was ignited with the publication in 2006 of a case report of an infant’s death, apparently the result of metabolic polymorphisms.⁶¹ The evolution of this debate and the outcome of the case may be noteworthy to clinicians, as they illustrate the gravity of public and patient interest in pharmacogenomic testing. In this case, the breast-feeding mother had taken codeine regularly for about 14 days when her 13-day-old infant died from toxic levels of morphine. Unknown to her and the prescriber, both the mother and infant were ultra-rapid CYP2D6 metabolizers, resulting in a more rapid and extensive conversion of codeine to morphine.

A logical strategy for preventing similar deaths would be routine CYP2D6 genotyping when prescribing codeine to breast-feeding mothers. However, after several investigations examined the metabolic and excretion pathways of codeine in their entirety, the FDA did not recommend routine CYP2D6 testing when prescribing codeine to breastfeeding mothers because several other factors, including rare genetic variations of other enzymes, proved necessary for reaching the opioid toxicity leading to the infant’s death.⁶²

PHARMACOGENOMICS OF PSYCHOTROPIC DRUGS

Pharmacogenomic testing has clinical utility for some psychotropic drugs.

HLA-B and carbamazepine

Considered a standard of care, HLA-B genotyping is appropriate before prescribing carbamazepine (Tegretol, Equetro) to patients in populations in which HLAB*1502 is likely to be present, such as Asians. Carriers of HLAB* 1502 are at higher risk of life-threatening skin reactions such as Stevens-Johnson syndrome.¹¹

Several other pharmacogenomic applications for psychotropic medications have been suggested, but routine testing has not been recommended by the FDA or endorsed by any expert panel because sufficient clinical utility and cost-effectiveness have not been demonstrated. A brief summary of study findings and a few practical suggestions follow.

Polymorphisms in metabolizing enzymes have been investigated in patients receiving psychotropic drugs.

CYP2D6 and antidepressants

Many antidepressants show significant differences in plasma drug levels with CYP2D6 polymorphisms (in descending order of influence)⁵⁵:

• Imipramine (Tofranil)
• Doxepin (Adapin, Silenor, Sinequan)
• Maprotiline (Deprilept, Ludiomil, Psymion)
• Trimipramine (Surmontil)
• Desipramine (Noraprim)
• Nortriptyline (Aventyl, Pamelor)
• Clomipramine (Anafranil)
• Paroxetine (Paxil)
• Venlafaxine (Effexor)
• Amitriptyline (Elavil)
• Mianserin
• Trazadone (Desyrel)
• Bupropion (Wellbutrin)
• Nefazodone (Serzone)
• Citalopram (Celexa)
• Sertraline (Zoloft).

CYP2D6 and antipsychotics

Several antipsychotics are also influenced by CYP2D6 polymorphisms (also in descending order of influence)⁵⁵:

• Perphenazine (Trilafon)
• Thioridazine (Mellaril)
• Olanzapine (Zyprexa)
• Zuclopenthixol (Cisordinol, Clopixol, Acuphase)
• Aripiprazole (Abilify)
• Flupentixol (Depixol, Fluanxol)
• Haloperidol (Haldol)
• Perazine (Taxilan)
• Risperidone (Risperdal)
• Pimozide (Orap).

CYP2C19 and antidepressants

CYP2C19 polymorphisms are likewise associated with differences in drug metabolism for many antidepressants, such as (in descending order of CYP2C19-mediated influence)⁵⁵:

• Trimipramine
• Doxepin
• Amitriptyline
• Imipramine
• Citalopram (Celexa)
• Clomipramine
• Moclobemide (Aurorix, Manerix)
• Sertraline
• Fluvoxamine (Luvox).

Clinical relevance of CYP2D6 and CYP2C19

Several studies have demonstrated that poor and intermediate CYP2D6 metabolizers have a higher incidence of adverse effects when taking CYP2D6-dependent antidepressants^63–68; however, an almost equal number of studies did not find statistically significant associations.^69–72 Likewise, several studies have found an association between ultra-rapid CYP2D6 metabolizer status and diminished response to antidepressants,^65,73,74 but no association was found in a larger retrospective study.⁷⁵

Routine CYP2D6 and CYP2C19 screening is not recommended when prescribing psychotropic drugs. However, reviews of the pharmacokinetic data have suggested a few practical applications when genetic status is already known. In general, clinicians can consider reducing the dose of tricyclic antidepressants by about 50% when prescribing to CYP2D6-poor-metabolizers.^55,76–78

Genes that affect serotonin metabolism

Several genes in the serotonin pathway have been investigated to determine whether they influence patients’ susceptibility to depression and adverse effects and response to psychotropic medications.

SLC6A4. Polymorphisms in the promoter region of the serotonin transporter gene SLC6A4 appear to influence the treatment response and side-effect profiles of selective serotonin reuptake inhibitors (SSRIs). Carriers of the SLC6A4 5-HTTLPR L alleles have fewer side effects⁷⁹ and better response to SSRI treatment, and carriers of the S allele have a higher incidence of antidepressant-induced mania⁸⁰ and poorer response to SSRI treatment.⁸¹

5-HT. Polymorphisms in serotonin receptors (2A and 2C subtypes) appear to influence SSRI response and side effects. Carriers of 5-HT 2A C alleles had more severe adverse effects from paroxetine,⁷¹ but another 5-HT 2A polymorphism common to Asians is associated with better response to antidepressant therapy.⁸² A 5-HT 2C polymorphism was associated with a lower incidence of antipsychotic-induced weight gain.⁸³

Although the understanding of these relationships is incomplete and routine pharmacogenomic testing is not currently recommended, reviews of the pharmacodynamic data have suggested a few practical applications when a patient’s genetic status is already known. One should consider:

• Selecting treatments other than SSRIs for depressed patients known to possess the SLC6A4 variant
• Selecting citalopram for depressed patients known to carry the 5-HT 2A polymorphism
• Avoiding treatment with antipsychotic drugs for patients known to possess the 5-HT 2C polymorphism.

THE FUTURE OF PHARMACOGENOMIC TESTING

The examples discussed in this article provide some insight about how pharmacogenomic testing is maturing and slowly being integrated into the practice of medicine. They also illustrate the complexity of the multiple stages of research that pharmacogenomic applications must go through in order to be adopted as standard practice.

In the future, pharmacogenomic data will continue to accumulate, and the clinical utility of many other pharmacogenomic tests may be uncovered. The FDA provides information on emerging pharmacogenomic tests at its Web site, www.fda.gov.¹¹ Its up-to-date “Table of Valid Genomic Biomarkers in the Context of Approved Drug Labels” includes boxed warnings, recommendations, research outcomes, and relevant population genetics.

If the FDA continues its current policy, prospective randomized trials that show improvement in patient outcomes will remain the gold standard for determining the clinical significance of a pharmacogenomic test. Furthermore, cost-benefit analyses are likely to continue dictating policy regarding pharmacogenomic testing, and cost-benefit profiles should improve as technology advances and as information gathered from a single test becomes applicable to multiple medications and clinical scenarios.

In the meantime, physicians should become familiar with the terms used in medical genetics and pharmacogenomics and begin to understand genetic contributions to the outcomes of drug therapy. For example, understanding the consequences of metabolizer status and the frequency of variants in a given population can be tremendously helpful when advising our patients about anticipating potential problems when taking specific medications and about making informed decisions about pharmacogenomic testing.

This exchange of information alone may go a long way in improving therapy outcomes even when prospective pharmacogenomic testing is not routinely performed. Furthermore, an increasing number of patients will already have genotyping information available when they come to us, and clinicians need to be aware of the many pharmacogenomic applications recommended by the FDA when genetic status is known.¹⁰

A 21-year-old man has had cough and hemoptysis for 3 days. For the past 3 years he has smoked one pack of cigarettes a day. His medical history is unremarkable, and he has had no chest trauma or thoracic surgery.

The patient says he was born full-term, and he has never been aware of any congenital anomalies.

Q: Which is the most likely diagnosis?

• Fractured rib
• Poland syndrome
• Paget disease
• Bifid rib

A: Bifid rib, a congenital anomaly, is the correct answer (see below).

Fractured rib. The patient has no history of chest trauma or thoracic surgery, nor any evidence on chest x-ray to suggest a fractured rib. Also, he has no evidence of osteoporosis to suspect a spontaneous rib fracture. His hemoptysis is most likely due to acute bronchitis.

Poland syndrome is a unilateral deficiency of the pectoralis muscle, variably associated with ipsilateral thoracic and upper limb anomalies. Bilateral hypoplasia or aplasia of the pectoralis muscle and upper-limb defects in association with variable thoracic muscles, chest wall deformities, and lower-limb defects has been infrequently reported in the literature. The diagnosis is usually based on the physical examination (asymmetric chest) or on chest x-ray (unilateral hyperlucent lung).¹ This is not the case in our patient.

Paget disease is a chronic, abnormal bone-remodeling process that leads to enlarged, less-dense, brittle bones. The spine, femur, pelvis, skull, clavicle, and humerus are most commonly affected. In the United States, the prevalence is 3% to 4% in people over age 40. Black Americans have a higher prevalence rate than black Africans, and the disease is rare in Asians.

Pain is the most common symptom, but Paget disease is usually asymptomatic. Paget disease can lead to insufficiency fractures, pathologic fractures, secondary arthritis, and nerve impingement in the spine or the base of the skull. Sarcomatous degeneration of the affected bone has been reported, but is rare.

Radiographic findings are often diagnostic. The skull and long bones typically show evidence of osteolysis from the epiphysis and advancing along the diaphysis. Radiographic findings in the sclerotic phase typically involve the axial skeleton and include trabecular coarsening and distortion and cortical thickening.

Rib abnormalities may be observed; these may either be isolated or may be a sign of multi-system malformations. However, in our patient, the radiographic finding of a bony bridge does not fit the description of Paget disease.²

BIFID RIB

The overall prevalence of bifid rib is estimated at 0.15% to 3.4% (mean 2%), and it accounts for up to 20% of all congenital rib anomalies.³ It is usually unilateral. Wattanasirichaigoon et al⁴ described patterns of rib defects in 47 cases, with bifid rib accounting for 28% of cases.

As with Paget disease, rib anomalies may occur in isolation or in association with multi-system malformations. Since the ribs originate from the mesoderm, it is not surprising that the costal defects are associated with malformations in other organs of the same origin, such as the heart and the kidneys.³ Bifid ribs are also seen in several genetic disorders such as Gorlin-Goltz (ie, basal cell nevus) syndrome, which affects multiple organs including bones, skin, eye, and neural system.⁵ Occasionally, it is encountered as a part of Jobs syndrome (ie, high levels of immunoglobulin E and recurrent infections),⁶ and Kindler syndrome, a rare genodermatosis.⁷

The literature contains little information about the clinical significance of bifid rib. Patients should undergo a thorough physical examination, including oral and cutaneous evaluation, to rule out a genetic syndrome. Physical findings such as palmar pits, subcutaneous calcifications, or odontogenic cyst warrant a more intensive radiologic and genetic investigation.⁵ If the physical examination is normal and if the patient is asymptomatic, additional clinical or radiologic investigation is of low yield. And as in our patient, the anomaly may go unnoticed on computed tomography of the chest.

A 21-year-old man has had cough and hemoptysis for 3 days. For the past 3 years he has smoked one pack of cigarettes a day. His medical history is unremarkable, and he has had no chest trauma or thoracic surgery.

The patient says he was born full-term, and he has never been aware of any congenital anomalies.

Q: Which is the most likely diagnosis?

• Fractured rib
• Poland syndrome
• Paget disease
• Bifid rib

A: Bifid rib, a congenital anomaly, is the correct answer (see below).

Fractured rib. The patient has no history of chest trauma or thoracic surgery, nor any evidence on chest x-ray to suggest a fractured rib. Also, he has no evidence of osteoporosis to suspect a spontaneous rib fracture. His hemoptysis is most likely due to acute bronchitis.

Poland syndrome is a unilateral deficiency of the pectoralis muscle, variably associated with ipsilateral thoracic and upper limb anomalies. Bilateral hypoplasia or aplasia of the pectoralis muscle and upper-limb defects in association with variable thoracic muscles, chest wall deformities, and lower-limb defects has been infrequently reported in the literature. The diagnosis is usually based on the physical examination (asymmetric chest) or on chest x-ray (unilateral hyperlucent lung).¹ This is not the case in our patient.

Paget disease is a chronic, abnormal bone-remodeling process that leads to enlarged, less-dense, brittle bones. The spine, femur, pelvis, skull, clavicle, and humerus are most commonly affected. In the United States, the prevalence is 3% to 4% in people over age 40. Black Americans have a higher prevalence rate than black Africans, and the disease is rare in Asians.

Pain is the most common symptom, but Paget disease is usually asymptomatic. Paget disease can lead to insufficiency fractures, pathologic fractures, secondary arthritis, and nerve impingement in the spine or the base of the skull. Sarcomatous degeneration of the affected bone has been reported, but is rare.

Radiographic findings are often diagnostic. The skull and long bones typically show evidence of osteolysis from the epiphysis and advancing along the diaphysis. Radiographic findings in the sclerotic phase typically involve the axial skeleton and include trabecular coarsening and distortion and cortical thickening.

Rib abnormalities may be observed; these may either be isolated or may be a sign of multi-system malformations. However, in our patient, the radiographic finding of a bony bridge does not fit the description of Paget disease.²

BIFID RIB

The overall prevalence of bifid rib is estimated at 0.15% to 3.4% (mean 2%), and it accounts for up to 20% of all congenital rib anomalies.³ It is usually unilateral. Wattanasirichaigoon et al⁴ described patterns of rib defects in 47 cases, with bifid rib accounting for 28% of cases.

As with Paget disease, rib anomalies may occur in isolation or in association with multi-system malformations. Since the ribs originate from the mesoderm, it is not surprising that the costal defects are associated with malformations in other organs of the same origin, such as the heart and the kidneys.³ Bifid ribs are also seen in several genetic disorders such as Gorlin-Goltz (ie, basal cell nevus) syndrome, which affects multiple organs including bones, skin, eye, and neural system.⁵ Occasionally, it is encountered as a part of Jobs syndrome (ie, high levels of immunoglobulin E and recurrent infections),⁶ and Kindler syndrome, a rare genodermatosis.⁷

The literature contains little information about the clinical significance of bifid rib. Patients should undergo a thorough physical examination, including oral and cutaneous evaluation, to rule out a genetic syndrome. Physical findings such as palmar pits, subcutaneous calcifications, or odontogenic cyst warrant a more intensive radiologic and genetic investigation.⁵ If the physical examination is normal and if the patient is asymptomatic, additional clinical or radiologic investigation is of low yield. And as in our patient, the anomaly may go unnoticed on computed tomography of the chest.

A 21-year-old man has had cough and hemoptysis for 3 days. For the past 3 years he has smoked one pack of cigarettes a day. His medical history is unremarkable, and he has had no chest trauma or thoracic surgery.

The patient says he was born full-term, and he has never been aware of any congenital anomalies.

Q: Which is the most likely diagnosis?

• Fractured rib
• Poland syndrome
• Paget disease
• Bifid rib

A: Bifid rib, a congenital anomaly, is the correct answer (see below).

Fractured rib. The patient has no history of chest trauma or thoracic surgery, nor any evidence on chest x-ray to suggest a fractured rib. Also, he has no evidence of osteoporosis to suspect a spontaneous rib fracture. His hemoptysis is most likely due to acute bronchitis.

Poland syndrome is a unilateral deficiency of the pectoralis muscle, variably associated with ipsilateral thoracic and upper limb anomalies. Bilateral hypoplasia or aplasia of the pectoralis muscle and upper-limb defects in association with variable thoracic muscles, chest wall deformities, and lower-limb defects has been infrequently reported in the literature. The diagnosis is usually based on the physical examination (asymmetric chest) or on chest x-ray (unilateral hyperlucent lung).¹ This is not the case in our patient.

Paget disease is a chronic, abnormal bone-remodeling process that leads to enlarged, less-dense, brittle bones. The spine, femur, pelvis, skull, clavicle, and humerus are most commonly affected. In the United States, the prevalence is 3% to 4% in people over age 40. Black Americans have a higher prevalence rate than black Africans, and the disease is rare in Asians.

Pain is the most common symptom, but Paget disease is usually asymptomatic. Paget disease can lead to insufficiency fractures, pathologic fractures, secondary arthritis, and nerve impingement in the spine or the base of the skull. Sarcomatous degeneration of the affected bone has been reported, but is rare.

Radiographic findings are often diagnostic. The skull and long bones typically show evidence of osteolysis from the epiphysis and advancing along the diaphysis. Radiographic findings in the sclerotic phase typically involve the axial skeleton and include trabecular coarsening and distortion and cortical thickening.

Rib abnormalities may be observed; these may either be isolated or may be a sign of multi-system malformations. However, in our patient, the radiographic finding of a bony bridge does not fit the description of Paget disease.²

BIFID RIB

The overall prevalence of bifid rib is estimated at 0.15% to 3.4% (mean 2%), and it accounts for up to 20% of all congenital rib anomalies.³ It is usually unilateral. Wattanasirichaigoon et al⁴ described patterns of rib defects in 47 cases, with bifid rib accounting for 28% of cases.

As with Paget disease, rib anomalies may occur in isolation or in association with multi-system malformations. Since the ribs originate from the mesoderm, it is not surprising that the costal defects are associated with malformations in other organs of the same origin, such as the heart and the kidneys.³ Bifid ribs are also seen in several genetic disorders such as Gorlin-Goltz (ie, basal cell nevus) syndrome, which affects multiple organs including bones, skin, eye, and neural system.⁵ Occasionally, it is encountered as a part of Jobs syndrome (ie, high levels of immunoglobulin E and recurrent infections),⁶ and Kindler syndrome, a rare genodermatosis.⁷

The literature contains little information about the clinical significance of bifid rib. Patients should undergo a thorough physical examination, including oral and cutaneous evaluation, to rule out a genetic syndrome. Physical findings such as palmar pits, subcutaneous calcifications, or odontogenic cyst warrant a more intensive radiologic and genetic investigation.⁵ If the physical examination is normal and if the patient is asymptomatic, additional clinical or radiologic investigation is of low yield. And as in our patient, the anomaly may go unnoticed on computed tomography of the chest.