228P - Biomarker discovery via meta-analysis of immunotherapy clinical trials in cancer

Background

Clinical profiling studies have shed light on molecular features and mechanisms that modulate response or resistance to immunotherapy but their predictive value remains largely unclear. We (Bareche et al., Annals of Oncology 2022 ) and others (Litchfield et al., Cell 2021 ) have recently curated a compendium of public datasets of DNA, RNA and clinical profiles of patients treated with immunotherapy.

Methods

Leveraging our compendium of immunotherapy clinical datasets, we developed, PredictIO, an open-source meta-analysis pipeline to assess the predictive value of molecular predictors. We first used PredictIO to compute the association between immunotherapy response and established biomarkers, such as tumor mutation burden (TNB) or CD8 gene expression, and a collection of 91 molecular signatures curated from the literature. Second, we used PredictIO for de novo RNA signature discovery pipeline to build a new predictor of immunotherapy response.

Results

Using molecular and clinical profiles of ∼3600 patients across 12 tumor types, our meta-analysis pipeline revealed thatTMB and ∼50% of the gene signatures were significantly predictive of immunotherapy response across tumor types, although their predictive value were strongly dependent on specific tumour types. We next developed a de novo gene expression signature from our pan-cancer analysis and demonstrated its superior predictive value over other biomarkers. To identify novel targets, we computed the T-cell dysfunction score for each gene within PredictIO and their ability to predict dual PD-1/CTLA-4 blockade in mice. Two genes, F2RL1 and RBFOX2, were concurrently associated with worse ICB clinical outcomes, T-cell dysfunction in ICB-naive patients and resistance to dual PD-1/CTLA-4 blockade in preclinical models.

Conclusions

Our study highlights the potential of large-scale meta-analyses in identifying novel biomarkers and potential therapeutic targets for cancer immunotherapy. These initial results, while promising, suffer from severe limitations in terms of data availability for specific cancer types and the lack of frameworks to develop and validate multi-omics predictors of immunotherapy response in a collaborative and scalable way.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

University Health Network.

Disclosure

B. Haibe-Kains: Financial Interests, Personal, Advisory Board: BreakThorugh Cancer, IONIQ Sciences, CQDM; Financial Interests, Personal, Speaker, Consultant, Advisor: Code Ocean.