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Precision medicine is driven by technologies such as rapid genome sequencing and artificial intelligence (AI), according to a presentation at the AACR virtual meeting II.
AI can be applied to the sequencing information derived from advanced cancers to make highly personalized treatment recommendations for patients, said Olivier Elemento, PhD, of Weill Cornell Medicine, New York.
Dr. Elemento described such work during the opening plenary session of the meeting.
Dr. Elemento advocated for whole-genome sequencing (WGS) of metastatic sites, as it can reveal “branched evolution” as tumors progress from localized to metastatic (Nat Genet. 2016 Dec;48[12]:1490-9).
The metastases share common mutations with the primaries from which they arise but also develop their own mutational profiles, which facilitate site-of-origin-agnostic, predictive treatment choices.
As examples, Dr. Elemento mentioned HER2 amplification found in a patient with urothelial cancer (J Natl Compr Canc Netw. 2019 Mar 1;17[3]:194-200) and a patient with uterine serous carcinoma (Gynecol Oncol Rep. 2019 Feb 21;28:54-7), both of whom experienced long-lasting remissions to HER2-targeted therapy.
Dr. Elemento also noted that WGS can reveal complex structural variants in lung adenocarcinomas that lack alterations in the RTK/RAS/RAF pathway (unpublished data).
Application of machine learning
One study suggested that microRNA expression and machine learning can be used to identify malignant thyroid lesions (Clin Cancer Res. 2012 Apr 1;18[7]:2032-8). The approach diagnosed malignant lesions with 90% accuracy, 100% sensitivity, and 86% specificity.
Dr. Elemento and colleagues used a similar approach to predict response to immunotherapy in melanoma (unpublished data).
[embed:render:related:node:225344]
The idea was to mine the cancer genome and transcriptome, allowing for identification of signals from neoantigens, immune gene expression, immune cell composition, and T-cell receptor repertoires, Dr. Elemento said. Integrating these signals with clinical outcome data via machine learning technology enabled the researchers to predict immunotherapy response in malignant melanoma with nearly 90% accuracy.
AI and image analysis
Studies have indicated that AI can be applied to medical images to improve diagnosis and treatment. The approach has been shown to:
- Facilitate correct diagnoses of malignant skin lesions (Nature. 2017 Feb 2;542[7639]:115-8).
- Distinguish lung adenocarcinoma from squamous cell cancer with 100% accuracy (EBioMedicine. 2018 Jan;27:317-28).
- Recognize distinct breast cancer subtypes (ductal, lobular, mucinous, papillary) and biomarkers (bioRxiv 242818. doi: 10.1101/242818; EBioMedicine. 2018 Jan;27:317-28)
- Predict mesothelioma prognosis (Nat Med. 2019 Oct;25[10]:1519-25).
- Predict prostate biopsy results (unpublished data) and calculate Gleason scores that can predict survival in prostate cancer patients (AACR 2020, Abstract 867).
Drug development through applied AI
In another study, Dr. Elemento and colleagues used a Bayesian machine learning approach to predict targets of molecules without a known mechanism of action (Nat Commun. 2019 Nov 19;10[1]:5221).
The method involved using data on gene expression profiles, cell line viability, side effects in animals, and structures of the molecules. The researchers applied this method to a large library of orphan small molecules and found it could predict targets in about 40% of cases.
Of 24 AI-predicted microtubule-targeting molecules, 14 depolymerized microtubules in the lab. Five of these molecules were effective in cell lines that were resistant to other microtubule-targeted drugs.
Dr. Elemento went on to describe how Oncoceutics was developing an antineoplastic agent called ONC201, but the company lacked information about the agent’s target. Using AI, the target was identified as dopamine receptor 2 (DRD2; Clin Cancer Res. 2019 Apr 1;25[7]:2305-13).
With that information, Oncoceutics initiated trials of ONC201 in tumors expressing high levels of DRD2, including a highly resistant glioma (J Neurooncol. 2019 Oct;145[1]:97-105). Responses were seen, and ONC201 is now being tested against other DRD2-expressing cancers.
Challenges to acknowledge
Potential benefits of AI in the clinic are exciting, but there are many bench-to-bedside challenges.
A clinically obvious example of AI’s applications is radiographic image analysis. There is no biologic rationale for our RECIST “cut values” for partial response, minimal response, and stable disease.
If AI can measure subtle changes on imaging that correlate with tumor biology (i.e., radiomics), we stand a better chance of predicting treatment outcomes than we can with conventional measurements of shrinkage of arbitrarily selected “target lesions.”
A tremendous amount of work is needed to build the required large image banks. During that time, AI will only improve – and without the human risks of fatigue, inconsistency, or burnout.
Those human frailties notwithstanding, AI cannot substitute for the key discussions between patient and clinician regarding goals of care, trade-offs of risks and benefits, and shared decision-making regarding management options.
[embed:render:related:node:225068]
At least initially (but painfully), complex technologies like WGS and digital image analysis via AI may further disadvantage patients who are medically disadvantaged by geography or socioeconomic circumstances.
In the discussion period, AACR President Antoni Ribas, MD, of University of California, Los Angeles, asked whether AI can simulate crosstalk between gene pathways so that unique treatment combinations can be identified. Dr. Elemento said those simulations are the subject of ongoing investigation.
The theme of the opening plenary session at the AACR virtual meeting II was “Turning Science into Life-Saving Care.” Applications of AI to optimize personalized use of genomics, digital image analysis, and drug development show great promise for being among the technologies that can help to realize AACR’s thematic vision.
Dr. Elemento disclosed relationships with Volastra Therapeutics, OneThree Biotech, Owkin, Freenome, Genetic Intelligence, Acuamark Diagnostics, Eli Lilly, Janssen, and Sanofi.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
Precision medicine is driven by technologies such as rapid genome sequencing and artificial intelligence (AI), according to a presentation at the AACR virtual meeting II.
AI can be applied to the sequencing information derived from advanced cancers to make highly personalized treatment recommendations for patients, said Olivier Elemento, PhD, of Weill Cornell Medicine, New York.
Dr. Elemento described such work during the opening plenary session of the meeting.
Dr. Elemento advocated for whole-genome sequencing (WGS) of metastatic sites, as it can reveal “branched evolution” as tumors progress from localized to metastatic (Nat Genet. 2016 Dec;48[12]:1490-9).
The metastases share common mutations with the primaries from which they arise but also develop their own mutational profiles, which facilitate site-of-origin-agnostic, predictive treatment choices.
As examples, Dr. Elemento mentioned HER2 amplification found in a patient with urothelial cancer (J Natl Compr Canc Netw. 2019 Mar 1;17[3]:194-200) and a patient with uterine serous carcinoma (Gynecol Oncol Rep. 2019 Feb 21;28:54-7), both of whom experienced long-lasting remissions to HER2-targeted therapy.
Dr. Elemento also noted that WGS can reveal complex structural variants in lung adenocarcinomas that lack alterations in the RTK/RAS/RAF pathway (unpublished data).
Application of machine learning
One study suggested that microRNA expression and machine learning can be used to identify malignant thyroid lesions (Clin Cancer Res. 2012 Apr 1;18[7]:2032-8). The approach diagnosed malignant lesions with 90% accuracy, 100% sensitivity, and 86% specificity.
Dr. Elemento and colleagues used a similar approach to predict response to immunotherapy in melanoma (unpublished data).
[embed:render:related:node:225344]
The idea was to mine the cancer genome and transcriptome, allowing for identification of signals from neoantigens, immune gene expression, immune cell composition, and T-cell receptor repertoires, Dr. Elemento said. Integrating these signals with clinical outcome data via machine learning technology enabled the researchers to predict immunotherapy response in malignant melanoma with nearly 90% accuracy.
AI and image analysis
Studies have indicated that AI can be applied to medical images to improve diagnosis and treatment. The approach has been shown to:
- Facilitate correct diagnoses of malignant skin lesions (Nature. 2017 Feb 2;542[7639]:115-8).
- Distinguish lung adenocarcinoma from squamous cell cancer with 100% accuracy (EBioMedicine. 2018 Jan;27:317-28).
- Recognize distinct breast cancer subtypes (ductal, lobular, mucinous, papillary) and biomarkers (bioRxiv 242818. doi: 10.1101/242818; EBioMedicine. 2018 Jan;27:317-28)
- Predict mesothelioma prognosis (Nat Med. 2019 Oct;25[10]:1519-25).
- Predict prostate biopsy results (unpublished data) and calculate Gleason scores that can predict survival in prostate cancer patients (AACR 2020, Abstract 867).
Drug development through applied AI
In another study, Dr. Elemento and colleagues used a Bayesian machine learning approach to predict targets of molecules without a known mechanism of action (Nat Commun. 2019 Nov 19;10[1]:5221).
The method involved using data on gene expression profiles, cell line viability, side effects in animals, and structures of the molecules. The researchers applied this method to a large library of orphan small molecules and found it could predict targets in about 40% of cases.
Of 24 AI-predicted microtubule-targeting molecules, 14 depolymerized microtubules in the lab. Five of these molecules were effective in cell lines that were resistant to other microtubule-targeted drugs.
Dr. Elemento went on to describe how Oncoceutics was developing an antineoplastic agent called ONC201, but the company lacked information about the agent’s target. Using AI, the target was identified as dopamine receptor 2 (DRD2; Clin Cancer Res. 2019 Apr 1;25[7]:2305-13).
With that information, Oncoceutics initiated trials of ONC201 in tumors expressing high levels of DRD2, including a highly resistant glioma (J Neurooncol. 2019 Oct;145[1]:97-105). Responses were seen, and ONC201 is now being tested against other DRD2-expressing cancers.
Challenges to acknowledge
Potential benefits of AI in the clinic are exciting, but there are many bench-to-bedside challenges.
A clinically obvious example of AI’s applications is radiographic image analysis. There is no biologic rationale for our RECIST “cut values” for partial response, minimal response, and stable disease.
If AI can measure subtle changes on imaging that correlate with tumor biology (i.e., radiomics), we stand a better chance of predicting treatment outcomes than we can with conventional measurements of shrinkage of arbitrarily selected “target lesions.”
A tremendous amount of work is needed to build the required large image banks. During that time, AI will only improve – and without the human risks of fatigue, inconsistency, or burnout.
Those human frailties notwithstanding, AI cannot substitute for the key discussions between patient and clinician regarding goals of care, trade-offs of risks and benefits, and shared decision-making regarding management options.
[embed:render:related:node:225068]
At least initially (but painfully), complex technologies like WGS and digital image analysis via AI may further disadvantage patients who are medically disadvantaged by geography or socioeconomic circumstances.
In the discussion period, AACR President Antoni Ribas, MD, of University of California, Los Angeles, asked whether AI can simulate crosstalk between gene pathways so that unique treatment combinations can be identified. Dr. Elemento said those simulations are the subject of ongoing investigation.
The theme of the opening plenary session at the AACR virtual meeting II was “Turning Science into Life-Saving Care.” Applications of AI to optimize personalized use of genomics, digital image analysis, and drug development show great promise for being among the technologies that can help to realize AACR’s thematic vision.
Dr. Elemento disclosed relationships with Volastra Therapeutics, OneThree Biotech, Owkin, Freenome, Genetic Intelligence, Acuamark Diagnostics, Eli Lilly, Janssen, and Sanofi.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
Precision medicine is driven by technologies such as rapid genome sequencing and artificial intelligence (AI), according to a presentation at the AACR virtual meeting II.
AI can be applied to the sequencing information derived from advanced cancers to make highly personalized treatment recommendations for patients, said Olivier Elemento, PhD, of Weill Cornell Medicine, New York.
Dr. Elemento described such work during the opening plenary session of the meeting.
Dr. Elemento advocated for whole-genome sequencing (WGS) of metastatic sites, as it can reveal “branched evolution” as tumors progress from localized to metastatic (Nat Genet. 2016 Dec;48[12]:1490-9).
The metastases share common mutations with the primaries from which they arise but also develop their own mutational profiles, which facilitate site-of-origin-agnostic, predictive treatment choices.
As examples, Dr. Elemento mentioned HER2 amplification found in a patient with urothelial cancer (J Natl Compr Canc Netw. 2019 Mar 1;17[3]:194-200) and a patient with uterine serous carcinoma (Gynecol Oncol Rep. 2019 Feb 21;28:54-7), both of whom experienced long-lasting remissions to HER2-targeted therapy.
Dr. Elemento also noted that WGS can reveal complex structural variants in lung adenocarcinomas that lack alterations in the RTK/RAS/RAF pathway (unpublished data).
Application of machine learning
One study suggested that microRNA expression and machine learning can be used to identify malignant thyroid lesions (Clin Cancer Res. 2012 Apr 1;18[7]:2032-8). The approach diagnosed malignant lesions with 90% accuracy, 100% sensitivity, and 86% specificity.
Dr. Elemento and colleagues used a similar approach to predict response to immunotherapy in melanoma (unpublished data).
[embed:render:related:node:225344]
The idea was to mine the cancer genome and transcriptome, allowing for identification of signals from neoantigens, immune gene expression, immune cell composition, and T-cell receptor repertoires, Dr. Elemento said. Integrating these signals with clinical outcome data via machine learning technology enabled the researchers to predict immunotherapy response in malignant melanoma with nearly 90% accuracy.
AI and image analysis
Studies have indicated that AI can be applied to medical images to improve diagnosis and treatment. The approach has been shown to:
- Facilitate correct diagnoses of malignant skin lesions (Nature. 2017 Feb 2;542[7639]:115-8).
- Distinguish lung adenocarcinoma from squamous cell cancer with 100% accuracy (EBioMedicine. 2018 Jan;27:317-28).
- Recognize distinct breast cancer subtypes (ductal, lobular, mucinous, papillary) and biomarkers (bioRxiv 242818. doi: 10.1101/242818; EBioMedicine. 2018 Jan;27:317-28)
- Predict mesothelioma prognosis (Nat Med. 2019 Oct;25[10]:1519-25).
- Predict prostate biopsy results (unpublished data) and calculate Gleason scores that can predict survival in prostate cancer patients (AACR 2020, Abstract 867).
Drug development through applied AI
In another study, Dr. Elemento and colleagues used a Bayesian machine learning approach to predict targets of molecules without a known mechanism of action (Nat Commun. 2019 Nov 19;10[1]:5221).
The method involved using data on gene expression profiles, cell line viability, side effects in animals, and structures of the molecules. The researchers applied this method to a large library of orphan small molecules and found it could predict targets in about 40% of cases.
Of 24 AI-predicted microtubule-targeting molecules, 14 depolymerized microtubules in the lab. Five of these molecules were effective in cell lines that were resistant to other microtubule-targeted drugs.
Dr. Elemento went on to describe how Oncoceutics was developing an antineoplastic agent called ONC201, but the company lacked information about the agent’s target. Using AI, the target was identified as dopamine receptor 2 (DRD2; Clin Cancer Res. 2019 Apr 1;25[7]:2305-13).
With that information, Oncoceutics initiated trials of ONC201 in tumors expressing high levels of DRD2, including a highly resistant glioma (J Neurooncol. 2019 Oct;145[1]:97-105). Responses were seen, and ONC201 is now being tested against other DRD2-expressing cancers.
Challenges to acknowledge
Potential benefits of AI in the clinic are exciting, but there are many bench-to-bedside challenges.
A clinically obvious example of AI’s applications is radiographic image analysis. There is no biologic rationale for our RECIST “cut values” for partial response, minimal response, and stable disease.
If AI can measure subtle changes on imaging that correlate with tumor biology (i.e., radiomics), we stand a better chance of predicting treatment outcomes than we can with conventional measurements of shrinkage of arbitrarily selected “target lesions.”
A tremendous amount of work is needed to build the required large image banks. During that time, AI will only improve – and without the human risks of fatigue, inconsistency, or burnout.
Those human frailties notwithstanding, AI cannot substitute for the key discussions between patient and clinician regarding goals of care, trade-offs of risks and benefits, and shared decision-making regarding management options.
[embed:render:related:node:225068]
At least initially (but painfully), complex technologies like WGS and digital image analysis via AI may further disadvantage patients who are medically disadvantaged by geography or socioeconomic circumstances.
In the discussion period, AACR President Antoni Ribas, MD, of University of California, Los Angeles, asked whether AI can simulate crosstalk between gene pathways so that unique treatment combinations can be identified. Dr. Elemento said those simulations are the subject of ongoing investigation.
The theme of the opening plenary session at the AACR virtual meeting II was “Turning Science into Life-Saving Care.” Applications of AI to optimize personalized use of genomics, digital image analysis, and drug development show great promise for being among the technologies that can help to realize AACR’s thematic vision.
Dr. Elemento disclosed relationships with Volastra Therapeutics, OneThree Biotech, Owkin, Freenome, Genetic Intelligence, Acuamark Diagnostics, Eli Lilly, Janssen, and Sanofi.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
FROM AACR 2020
