Abstract
Purpose Predictive biomarkers of immune checkpoint inhibitors (ICIs) 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.
Methods Participants 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.
Results A 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 bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1.
Conclusion This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs 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 ICIs efficacy.
Key objective Not all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC.
Knowledge generated Models submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response.
Competing Interest Statement
Han Chang and Hung Ying Lin are employees of Bristol Myers Squibb. Mikko I. Mayranpaa has received lecture and/or advisory board fees from Boehringer Ingelheim, Bristol Myers Squibb, MSD, Takeda, Bayer, Amgen, Roche, and Aiforia Technologies Oy. Wenxian Yang is a shareholder of Aginome Scientific. Justin Guinney is an employee of Tempus Labs. Shengqing Stan Gu is the recipient of a fellowship from the Sara Elizabeth O Brien Trust. Joseph Szustakowski, Scott Chasalow, and William Geese are employees and shareholders of Bristol Myers Squibb. Benjamin G. Vincent received consulting fees from GeneCentric Therapeutics. David Carbone has received advisory board fees, consulting fees, presentation fees, or consulting fees from Regeneron, Novartis, AbbVie, AstraZeneca, Bristol Myers Squibb, Roche, Arcus Biosciences, Mirati, Iovance Biotherapeutics, Pfizer, Onc Live, InThought, Glaxo-Smith Kline, Intellisphere, Sanofi, Merck KGgA, Merck/EMD Serono, Johnson & Johnson, Jazz, Janssen, Curio Science, PDD development, G1 Therapeutics, OncoHost, Eisai, Flame Biosciences, Novocure, Merck, DaiiChi Sankyo, and Boehringer Ingelheim. Other authors do not have competing interests.
Footnotes
↵* Co-senior authors from the Anti–PD-1 Response Prediction DREAM Challenge steering committee
Previous presentation: Results from this study have been presented, in part, at the 14th annual RECOMB/ISCB Conference on Regulatory and Systems Genomics with DREAM Challenges RSGDREAM 2022), November 8–9, 2022, Las Vegas, Nevada, USA.
