A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer
Authors: Mason Mike , Lapuente-Santana Óscar , Halkola Anni S. , Wang Wenyu , Mall Raghvendra , Xiao Xu , Kaufman Jacob , Fu Jingxin , Pfeil Jacob , Banerjee Jineta , Chung Verena , Chang Han , Chasalow Scott D. , Lin Hung Ying , Chai Rongrong , Yu Thomas , Finotello Francesca , Mirtti Tuomas , Mäyränpää Mikko I. , Bao Jie , Verschuren Emmy W. , Ahmed Eiman I. , Ceccarelli Michele , Miller Lance D. , Monaco Gianni , Hendrickx Wouter R.L. , Sherif Shimaa , Yang Lin , Tang Ming , Gu Shengqing Stan , Zhang Wubing , Zhang Yi , Zeng Zexian , Sahu Avinash Das , Liu Yang , Yang Wenxian , Bedognetti Davide , Tang Jing , Eduati Federica , Laajala Teemu D. , Geese William J. , Guinney Justin , Szustakowski Joseph D. , Carbone David P. , Vincent Benjamin G. 
Published: December 2022

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.

Context summary

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.

Relevance