Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clinical success of immune checkpoint blockades in diverse malignancies. However, effective tools for comprehensively interpreting multi-omics data are still warranted to provide increased granularity into the intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Therefore, we developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing a comprehensive investigation of the estimation of reported or user-built signatures, TME deconvolution, and signature construction based on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, and signatures generated from single-cell RNA sequencing (scRNA-seq) data in different cancer settings. Additionally, IOBR integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool for leveraging multi-omics data to facilitate immuno-oncology exploration and to unveil tumor-immune interactions and accelerating precision immunotherapy.

BackgroundDurable efficacy of immune checkpoint blockade (ICB) occurred in a small number of patients with metastatic gastric cancer (mGC) and the determinant biomarker of response to ICB remains unclear.MethodsWe developed an open-source TMEscore R package, to quantify the tumor microenvironment (TME) to aid in addressing this dilemma. Two advanced gastric cancer cohorts (RNAseq, N=45 and NanoString, N=48) and other advanced cancer (N=534) treated with ICB were leveraged to investigate the predictive value of TMEscore. Simultaneously, multi-omics data from The Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD) and Asian Cancer Research Group (ACRG) were interrogated for underlying mechanisms.ResultsThe predictive capacity of TMEscore was corroborated in patient with mGC cohorts treated with pembrolizumab in a prospective phase 2 clinical trial (NCT02589496, N=45, area under the curve (AUC)=0.891). Notably, TMEscore, which has a larger AUC than programmed death-ligand 1 combined positive score, tumor mutation burden, microsatellite instability, and Epstein-Barr virus, was also validated in the multicenter advanced gastric cancer cohort using NanoString technology (N=48, AUC=0.877). Exploration of the intrinsic mechanisms of TMEscore with TCGA and ACRG multi-omics data identified TME pertinent mechanisms including mutations, metabolism pathways, and epigenetic features.ConclusionsCurrent study highlighted the promising predictive value of TMEscore for patients with mGC. Exploration of TME in multi-omics gastric cancer data may provide the impetus for precision immunotherapy.

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