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Research published in Cell has revealed 150 genetic drivers of diffuse large B-cell lymphoma (DLBCL).
Among these drivers are 27 genes newly implicated in DLBCL, 35 functional oncogenes, and 9 genes that can be targeted with existing drugs.
Researchers used these findings to create a prognostic model that, they say, outperformed existing risk predictors in DLBCL.
“This work provides a comprehensive road map in terms of research and clinical priorities,” said study author Sandeep Dave, MD, of Duke University in Durham, North Carolina.
“We have very good data now to pursue new and existing therapies that might target the genetic mutations we identified. Additionally, this data could also be used to develop genetic markers that steer patients to therapies that would be most effective.”
Dr Dave and his colleagues began this research by analyzing tumor samples from 1001 patients who had been diagnosed with DLBCL over the past decade and were treated at 12 institutions around the world. There were 313 patients with ABC DLBCL, 331 with GCB DLBCL, and the rest were unclassified DLBCLs.
Using whole-exome sequencing, the researchers pinpointed 150 driver genes that were recurrently mutated in the DLBCL patients. This included 27 genes that, the researchers believe, had never before been implicated in DLBCL.
The team also found that ABC and GCB DLBCLs “shared the vast majority of driver genes at statistically indistinguishable frequencies.”
However, there were 20 genes that were differentially mutated between the 2 groups. For instance, EZH2, SGK1, GNA13, SOCS1, STAT6, and TNFRSF14 were more frequently mutated in GCB DLBCLs. And ETV6, MYD88, PIM1, and TBL1XR1 were more frequently mutated in ABC DLBCLs.
Essential genes
To identify genes essential to the development and maintenance of DLBCL, the researchers used CRISPR. The team knocked out genes in 6 cell lines—3 ABC DLBCLs (LY3, TMD8, and HBL1), 2 GCB DLBCLs (SUDHL4 and Pfeiffer), and 1 Burkitt lymphoma (BJAB) that phenotypically resembles GCB DLBCL.
This revealed 1956 essential genes. Knocking out these genes resulted in a significant decrease in cell fitness in at least 1 cell line.
The work also revealed 35 driver genes that, when knocked out, resulted in decreased viability of DLBCL cells, which classified them as functional oncogenes.
The researchers found that knockout of EBF1, IRF4, CARD11, MYD88, and IKBKB was selectively lethal in ABC DLBCL. And knockout of ZBTB7A, XPO1, TGFBR2, and PTPN6 was selectively lethal in GCB DLBCL.
In addition, the team noted that 9 of the driver genes are direct targets of drugs that are already approved or under investigation in clinical trials—MTOR, BCL2, SF3B1, SYK, PIM2, PIK3R1, XPO1, MCL1, and BTK.
Patient outcomes
The researchers also looked at the driver genes in the context of patient outcomes. The team found that mutations in MYC, CD79B, and ZFAT were strongly associated with poorer survival, while mutations in NF1 and SGK1 were associated with more favorable survival.
Mutations in KLHL14, BTG1, PAX5, and CDKN2A were associated with significantly poorer survival in ABC DLBCL, while mutations in CREBBP were associated with favorable survival in ABC DLBCL.
In GCB DLBCL, mutations in NFKBIA and NCOR1 were associated with poorer prognosis, while mutations in EZH2, MYD88, and ARID5B were associated with better prognosis.
Finally, the researchers developed a prognostic model based on combinations of genetic markers (the 150 driver genes) and gene expression markers (cell of origin, MYC, and BCL2).
The team found their prognostic model could predict survival more effectively than the International Prognostic Index, cell of origin alone, and MYC and BCL2 expression alone or together. 
Research published in Cell has revealed 150 genetic drivers of diffuse large B-cell lymphoma (DLBCL).
Among these drivers are 27 genes newly implicated in DLBCL, 35 functional oncogenes, and 9 genes that can be targeted with existing drugs.
Researchers used these findings to create a prognostic model that, they say, outperformed existing risk predictors in DLBCL.
“This work provides a comprehensive road map in terms of research and clinical priorities,” said study author Sandeep Dave, MD, of Duke University in Durham, North Carolina.
“We have very good data now to pursue new and existing therapies that might target the genetic mutations we identified. Additionally, this data could also be used to develop genetic markers that steer patients to therapies that would be most effective.”
Dr Dave and his colleagues began this research by analyzing tumor samples from 1001 patients who had been diagnosed with DLBCL over the past decade and were treated at 12 institutions around the world. There were 313 patients with ABC DLBCL, 331 with GCB DLBCL, and the rest were unclassified DLBCLs.
Using whole-exome sequencing, the researchers pinpointed 150 driver genes that were recurrently mutated in the DLBCL patients. This included 27 genes that, the researchers believe, had never before been implicated in DLBCL.
The team also found that ABC and GCB DLBCLs “shared the vast majority of driver genes at statistically indistinguishable frequencies.”
However, there were 20 genes that were differentially mutated between the 2 groups. For instance, EZH2, SGK1, GNA13, SOCS1, STAT6, and TNFRSF14 were more frequently mutated in GCB DLBCLs. And ETV6, MYD88, PIM1, and TBL1XR1 were more frequently mutated in ABC DLBCLs.
Essential genes
To identify genes essential to the development and maintenance of DLBCL, the researchers used CRISPR. The team knocked out genes in 6 cell lines—3 ABC DLBCLs (LY3, TMD8, and HBL1), 2 GCB DLBCLs (SUDHL4 and Pfeiffer), and 1 Burkitt lymphoma (BJAB) that phenotypically resembles GCB DLBCL.
This revealed 1956 essential genes. Knocking out these genes resulted in a significant decrease in cell fitness in at least 1 cell line.
The work also revealed 35 driver genes that, when knocked out, resulted in decreased viability of DLBCL cells, which classified them as functional oncogenes.
The researchers found that knockout of EBF1, IRF4, CARD11, MYD88, and IKBKB was selectively lethal in ABC DLBCL. And knockout of ZBTB7A, XPO1, TGFBR2, and PTPN6 was selectively lethal in GCB DLBCL.
In addition, the team noted that 9 of the driver genes are direct targets of drugs that are already approved or under investigation in clinical trials—MTOR, BCL2, SF3B1, SYK, PIM2, PIK3R1, XPO1, MCL1, and BTK.
Patient outcomes
The researchers also looked at the driver genes in the context of patient outcomes. The team found that mutations in MYC, CD79B, and ZFAT were strongly associated with poorer survival, while mutations in NF1 and SGK1 were associated with more favorable survival.
Mutations in KLHL14, BTG1, PAX5, and CDKN2A were associated with significantly poorer survival in ABC DLBCL, while mutations in CREBBP were associated with favorable survival in ABC DLBCL.
In GCB DLBCL, mutations in NFKBIA and NCOR1 were associated with poorer prognosis, while mutations in EZH2, MYD88, and ARID5B were associated with better prognosis.
Finally, the researchers developed a prognostic model based on combinations of genetic markers (the 150 driver genes) and gene expression markers (cell of origin, MYC, and BCL2).
The team found their prognostic model could predict survival more effectively than the International Prognostic Index, cell of origin alone, and MYC and BCL2 expression alone or together.
Research published in Cell has revealed 150 genetic drivers of diffuse large B-cell lymphoma (DLBCL).
Among these drivers are 27 genes newly implicated in DLBCL, 35 functional oncogenes, and 9 genes that can be targeted with existing drugs.
Researchers used these findings to create a prognostic model that, they say, outperformed existing risk predictors in DLBCL.
“This work provides a comprehensive road map in terms of research and clinical priorities,” said study author Sandeep Dave, MD, of Duke University in Durham, North Carolina.
“We have very good data now to pursue new and existing therapies that might target the genetic mutations we identified. Additionally, this data could also be used to develop genetic markers that steer patients to therapies that would be most effective.”
Dr Dave and his colleagues began this research by analyzing tumor samples from 1001 patients who had been diagnosed with DLBCL over the past decade and were treated at 12 institutions around the world. There were 313 patients with ABC DLBCL, 331 with GCB DLBCL, and the rest were unclassified DLBCLs.
Using whole-exome sequencing, the researchers pinpointed 150 driver genes that were recurrently mutated in the DLBCL patients. This included 27 genes that, the researchers believe, had never before been implicated in DLBCL.
The team also found that ABC and GCB DLBCLs “shared the vast majority of driver genes at statistically indistinguishable frequencies.”
However, there were 20 genes that were differentially mutated between the 2 groups. For instance, EZH2, SGK1, GNA13, SOCS1, STAT6, and TNFRSF14 were more frequently mutated in GCB DLBCLs. And ETV6, MYD88, PIM1, and TBL1XR1 were more frequently mutated in ABC DLBCLs.
Essential genes
To identify genes essential to the development and maintenance of DLBCL, the researchers used CRISPR. The team knocked out genes in 6 cell lines—3 ABC DLBCLs (LY3, TMD8, and HBL1), 2 GCB DLBCLs (SUDHL4 and Pfeiffer), and 1 Burkitt lymphoma (BJAB) that phenotypically resembles GCB DLBCL.
This revealed 1956 essential genes. Knocking out these genes resulted in a significant decrease in cell fitness in at least 1 cell line.
The work also revealed 35 driver genes that, when knocked out, resulted in decreased viability of DLBCL cells, which classified them as functional oncogenes.
The researchers found that knockout of EBF1, IRF4, CARD11, MYD88, and IKBKB was selectively lethal in ABC DLBCL. And knockout of ZBTB7A, XPO1, TGFBR2, and PTPN6 was selectively lethal in GCB DLBCL.
In addition, the team noted that 9 of the driver genes are direct targets of drugs that are already approved or under investigation in clinical trials—MTOR, BCL2, SF3B1, SYK, PIM2, PIK3R1, XPO1, MCL1, and BTK.
Patient outcomes
The researchers also looked at the driver genes in the context of patient outcomes. The team found that mutations in MYC, CD79B, and ZFAT were strongly associated with poorer survival, while mutations in NF1 and SGK1 were associated with more favorable survival.
Mutations in KLHL14, BTG1, PAX5, and CDKN2A were associated with significantly poorer survival in ABC DLBCL, while mutations in CREBBP were associated with favorable survival in ABC DLBCL.
In GCB DLBCL, mutations in NFKBIA and NCOR1 were associated with poorer prognosis, while mutations in EZH2, MYD88, and ARID5B were associated with better prognosis.
Finally, the researchers developed a prognostic model based on combinations of genetic markers (the 150 driver genes) and gene expression markers (cell of origin, MYC, and BCL2).
The team found their prognostic model could predict survival more effectively than the International Prognostic Index, cell of origin alone, and MYC and BCL2 expression alone or together.