A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer
Creators: name: Mason, Mike
name: Halkola, Anni S.
name: Zhang, Yi
name: Mall, Raghvendra
name: Monaco, Gianni
name: Chai, Rongrong
name: Miller, Lance D.
name: Finotello, Francesca
name: Das Sahu, Avinash
name: Pfeil, Jacob
name: Verschuren, Emmy W.
name: Szustakowski, Joseph D.
name: Chung, Verena
name: Xiao, Xu
name: Ahmed, Eiman I.
name: Chasalow, Scott D.
name: Mirtti, Tuomas
name: Sherif, Shimaa
name: Vincent, Benjamin G.
name: Ceccarelli, Michele
name: Carbone, David P.
name: Zeng, Zexian
name: Mäyränpää, Mikko I.
name: Yu, Thomas
name: Bao, Jie
name: Tang, Ming
name: Lapuente-Santana, Óscar
name: Bedognetti, Davide
name: Banerjee, Jineta
name: Hendrickx, Wouter R. L.
name: Chang, Han
name: Kaufman, Jacob
name: Lin, Hung Ying
name: Guinney, Justin
name: Liu, Yang
name: Zhang, Wubing
name: Fu, Jingxin
name: Yang, Wenxian
name: Eduati, Federica
name: Laajala, Teemu D.
name: Yang, Lin
name: Geese, William J.
name: Gu, Shengqing Stan
name: Wang, Wenyu
name: Tang, Jing
Publisher: name: Springer Nature
Year: 2024

description: Background Predictive 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. 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 best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. Conclusions This 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 registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.