Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer
Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumor...
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| Veröffentlicht in: | Nature cancer 2020-01, Vol.1 (1), p.99-111 |
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| creator | Anagnostou, Valsamo Niknafs, Noushin Marrone, Kristen Bruhm, Daniel C White, James R Naidoo, Jarushka Hummelink, Karlijn Monkhorst, Kim Lalezari, Ferry Lanis, Mara Rosner, Samuel Reuss, Joshua E Smith, Kellie N Adleff, Vilmos Rodgers, Kristen Belcaid, Zineb Rhymee, Lamia Levy, Benjamin Feliciano, Josephine Hann, Christine L Ettinger, David S Georgiades, Christos Verde, Franco Illei, Peter Li, Qing Kay Baras, Alexander S Gabrielson, Edward Brock, Malcolm V Karchin, Rachel Pardoll, Drew M Baylin, Stephen B Brahmer, Julie R Scharpf, Robert B Forde, Patrick M Velculescu, Victor E |
| description | Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in nonresponding tumors in three immunotherapy treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature and human leukocyte antigen status provided an improved predictor of response to immunotherapy that was independently validated. |
| doi_str_mv | 10.1038/s43018-019-0008-8 |
| format | Article |
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Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in nonresponding tumors in three immunotherapy treated cohorts. 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Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in nonresponding tumors in three immunotherapy treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature and human leukocyte antigen status provided an improved predictor of response to immunotherapy that was independently validated.</description><subject>Biomarkers, Tumor - genetics</subject><subject>Carcinoma, Non-Small-Cell Lung - drug therapy</subject><subject>Humans</subject><subject>Immune Checkpoint Inhibitors - pharmacology</subject><subject>Immunotherapy - methods</subject><subject>Lung Neoplasms - drug therapy</subject><issn>2662-1347</issn><issn>2662-1347</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkctKJTEQhsMwoqI-gBvJ0k3GXPskG2GQuQiKG12HdLr6mDGXM0m34Nvbh6Oiq6qi_vqrig-hU0Z_MCr0RZOCMk0oM4RSqon-hg5513HChFx9_5QfoJPW_i0arhhTRu-jA8GNllqKQ7S-neMUUhlcxGvIJQWPR3DTXKHhTYUh-AmXefIlAS4jDinNGbB_BP-0KSFPuI_FP7kBcMg4l0xacjESDzHiOOc19i57qMdob3SxwclbPEIPv3_dX_0lN3d_rq9-3hAvjJmIGztpeqM7PiqvJVXKOK_ZwKXsmdbABVc9XyruwFPuO9eNg1oNcjRaDEKLI3S5893MfYLBQ56qi3ZTQ3L1xRYX7NdODo92XZ7tSjEpV2oxOH8zqOX_DG2yKbTtNy5DmZtddnfGMEXNImU7qa-ltQrjxxpG7ZaR3TGyCyO7ZWS39519vu9j4p2IeAWj646z</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Anagnostou, Valsamo</creator><creator>Niknafs, Noushin</creator><creator>Marrone, Kristen</creator><creator>Bruhm, Daniel C</creator><creator>White, James R</creator><creator>Naidoo, Jarushka</creator><creator>Hummelink, Karlijn</creator><creator>Monkhorst, Kim</creator><creator>Lalezari, Ferry</creator><creator>Lanis, Mara</creator><creator>Rosner, Samuel</creator><creator>Reuss, Joshua E</creator><creator>Smith, Kellie N</creator><creator>Adleff, Vilmos</creator><creator>Rodgers, Kristen</creator><creator>Belcaid, Zineb</creator><creator>Rhymee, Lamia</creator><creator>Levy, Benjamin</creator><creator>Feliciano, Josephine</creator><creator>Hann, Christine L</creator><creator>Ettinger, David S</creator><creator>Georgiades, Christos</creator><creator>Verde, Franco</creator><creator>Illei, Peter</creator><creator>Li, Qing Kay</creator><creator>Baras, Alexander S</creator><creator>Gabrielson, Edward</creator><creator>Brock, Malcolm V</creator><creator>Karchin, Rachel</creator><creator>Pardoll, Drew M</creator><creator>Baylin, Stephen B</creator><creator>Brahmer, Julie R</creator><creator>Scharpf, Robert B</creator><creator>Forde, Patrick M</creator><creator>Velculescu, Victor E</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9480-3047</orcidid><orcidid>https://orcid.org/0000-0002-5069-1239</orcidid><orcidid>https://orcid.org/0000-0003-1195-438X</orcidid></search><sort><creationdate>20200101</creationdate><title>Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer</title><author>Anagnostou, Valsamo ; 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| subjects | Biomarkers, Tumor - genetics Carcinoma, Non-Small-Cell Lung - drug therapy Humans Immune Checkpoint Inhibitors - pharmacology Immunotherapy - methods Lung Neoplasms - drug therapy |
| title | Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer |
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