A pan-cancer clinical platform to predict immunotherapy outcomes and prioritize immuno-oncology combinations in early-phase trials

Abstract

Immunooncology; Predictive biomarkers; Tumor microenvironment

Inmunooncología; Biomarcadores predictivos; Microambiente tumoral

Immunooncologia; Biomarcadors predictius; Microambient tumoral

Background Immunotherapy is effective, but current biomarkers for patient selection have proven modest sensitivity. Here, we developed VIGex, an optimized gene signature based on the expression level of 12 genes involved in immune response with RNA sequencing. Methods We implemented VIGex using the nCounter platform (Nanostring) on a large clinical cohort encompassing 909 tumor samples across 45 tumor types. VIGex was developed as a continuous variable, with cutoffs selected to detect three main categories (hot, intermediate-cold and cold) based on the different inflammatory status of the tumor microenvironment. Findings Hot tumors had the highest VIGex scores and exhibited an increased abundance of tumor-infiltrating lymphocytes as compared with the intermediate-cold and cold. VIGex scores varied depending on tumor origin and anatomic site of metastases, with liver metastases showing an immunosuppressive tumor microenvironment. The predictive power of VIGex-Hot was observed in a cohort of 98 refractory solid tumor from patients treated in early-phase immunotherapy trials and its clinical performance was confirmed through an extensive metanalysis across 13 clinically annotated gene expression datasets from 877 patients treated with immunotherapy agents. Last, we generated a pan-cancer biomarker platform that integrates VIGex categories with the expression levels of immunotherapy targets under development in early-phase clinical trials. Conclusions Our results support the clinical utility of VIGex as a tool to aid clinicians for patient selection and personalized immunotherapy interventions.

A.H.C. would like to acknowledge fellowship funding from the Spanish Society of Medical Oncology (SEOM), CRIS Contra el Cancer and Hold'em For Life Oncology Fellowship. This research has been funded by the Comprehensive Program of Cancer Immunotherapy & Immunology II (CAIMI-II) supported by the BBVA Foundation (grant 53/2021) and the 2020–2021 Division of Medical Oncology and Hematology (DMOH) Fellowship award at Princess Margaret Cancer Centre. VHIO would like to acknowledge the Cellex Foundation for providing research facilities and equipment and the CERCA Programme from the Generalitat de Catalunya for their support of this research. Authors from VHIO acknowledge the State Agency for Research (Agencia Estatal de Investigación) for providing financial support as a Center of Excellence Severo Ochoa (CEX2020-001024-S/AEI/10.13039/501100011033). A.V. was the recipient of a project award from the FAECC (AVP/18/AECC/3219) and received funding from the Advanced Molecular Diagnostic (DIAMAV) program from the FERO Foundation. Graphical abstract was created with BioRender.com. Diagram in Figure 3B was created with SankeyMATIC (sankeymatic.com).