Dataset Information

Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer.

ABSTRACT: 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.

SUBMITTER: Anagnostou V

PROVIDER: S-EPMC7514475 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer.

Anagnostou Valsamo V Niknafs Noushin N Marrone Kristen K Bruhm Daniel C DC White James R JR Naidoo Jarushka J Hummelink Karlijn K Monkhorst Kim K Lalezari Ferry F Lanis Mara M Rosner Samuel S Reuss Joshua E JE Smith Kellie N KN Adleff Vilmos V Rodgers Kristen K Belcaid Zineb Z Rhymee Lamia L Levy Benjamin B Feliciano Josephine J Hann Christine L CL Ettinger David S DS Georgiades Christos C Verde Franco F Illei Peter P Li Qing Kay QK Baras Alexander S AS Gabrielson Edward E Brock Malcolm V MV Karchin Rachel R Pardoll Drew M DM Baylin Stephen B SB Brahmer Julie R JR Scharpf Robert B RB Forde Patrick M PM Velculescu Victor E VE

Nature cancer 20200113 1

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 checkpoin ...[more]

PMID: 32984843

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Project description:BackgroundPredictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.ConclusionsThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy.Trial registrationCheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Dataset Information

Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer.

Publications

Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer.

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