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While durable responses and prolonged survival have been demonstrated in some lung cancer patients treated with immuno-oncology (I-O) anti-PD-1 therapy, there remains a need to improve the ability to predict which patients are more likely to receive benefit from treatment with I-O. The goal of this challenge is to leverage clinical and biomarker data to develop predictive models of response to I-O therapy in lung cancer and ultimately gain insights that may facilitate potential novel monotherapies or combinations with I-O.

Immuno-oncology (I-O) therapy targeting the PD-1 pathway has transformed the treatment landscape in advanced non-small cell lung cancer (NSCLC), with the combination of I-O with or without chemotherapy being the current standard of care in the first-line setting for those patients who are ineligible for targeted therapy.1-5 While durable responses and prolonged survival have been demonstrated in some patients treated with I-O, there remains a high disease burden and a need to improve the ability to predict which patients are more likely to receive benefit from treatment with I-O.

The Bristol Myers Squibb-Sage Bionetworks Anti–PD-1 Response Prediction DREAM Challenge is the first DREAM initiative and collaboration in the I-O space. Like other DREAM Challenges, the Anti–PD-1 Challenge is a crowdsourced effort that looks to advance our understanding of foundational questions in biomedicine through open-science collaboration. We invite experts and innovators in genomics, computational biology, and translational biomarker development to participate in this Challenge that aims to identify predictive biomarkers for I-O therapy in lung cancer. The deidentified, validation dataset for this Challenge comes from an international, randomized, open-label phase 3 trial (CheckMate 026) of anti-PD-1 nivolumab vs platinum-based chemotherapy in patients with previously untreated advanced NSCLC.6 By exploring RNA-sequencing data for predictive signals of efficacy and resistance to nivolumab, the Anti–PD-1 Challenge seeks to improve our ability to appropriately select patients most likely to benefit from I-O treatment and to gain insights that may facilitate potential novel monotherapies or combinations with I-O.