DOI: 10.5281/zenodo.4831758
title: barechey/PredictIO.data:

description: <strong>Data for our paper titled "Leveraging Big Data of Immune Checkpoint Blockade Response Identifies Novel Potential Targets".</strong> Bareche et al., Annals of Oncology (2022); https://doi.org/10.1016/j.annonc.2022.08.084 ----------------------------------------------------------------------------------------------------------------------------------------------<br> ---------------------------------------------------------------------------------------------------------------------------------------------- <strong>Background:</strong> The development of immune checkpoint blockade (ICB) has changed the way we treat various cancers. While ICB produces durable survival benefits in a number of malignancies, a large proportion of treated patients do not derive clinical benefit. Recent clinical profiling studies have shed light on molecular features and mechanisms that modulate response to ICB. Nevertheless, none of these identified molecular features were investigated in large enough cohorts to be of clinical value. <strong>Materials and methods:</strong> Literature review was performed to identify relevant studies including clinical dataset of patient treated with ICB (anti-PD1/L1, anti-CTLA4 or the combo) and available sequencing data. Tumor mutational burden (TMB) and 37 previously reported gene expression (GE) signature were computed with respect to the original publication. Biomarker association with ICB response (IR) and survival (PFS/OS) was investigated separately within each study and combined together for meta-analysis. <strong>Results:</strong> We performed a comparative meta-analysis of genomic and transcriptomic biomarkers of immune-checkpoint blockade (ICB) responses in over 3,600 patients across 12 tumor types and implemented an open-source web-application (predictIO.ca) for exploration. Tumor mutation burden (TMB) and 21/37 gene signatures were predictive of ICB responses across tumor types. We next developed a <em>de novo</em> gene expression signature (PredictIO) 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, <em>F2RL1</em> (encoding protease-activated receptor-2) and <em>RBFOX2</em> (encoding RNA-binding motif protein 9), 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. <strong>Conclusions:</strong> Our study highlights the potential of large-scale meta-analyses in identifying novel biomarkers and potential therapeutic targets for cancer immunotherapy. ----------------------------------------------------------------------------------------------------------------------------------------------<br> ---------------------------------------------------------------------------------------------------------------------------------------------- <strong>Data description</strong> <em>mouseModel:</em> Chen: Expression data of the TNBC mouse model study from Chen et al. (PMID:32907939) Meskini: Expression data of the Melanoma mouse model study from Meskini et al. (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE172320) Zemek: Expression data of the AB1 & Renca mouse model study from Zemek et al. (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117358) <em>Discovery_cohort:</em><br> Expression and SNV data of the discovery cohort <em>Validation_cohort:</em><br> Expression and SNV data of the validation cohort