External validation of the VIGex gene-expression signature as a novel predictive biomarker for immune checkpoint treatment

Abstract

Abstract Immune gene expression signatures are emerging as potential biomarkers for immunotherapy. Yet, their limited predictive performance and complexity limit routine clinical implementation. VIGex is a 12-gene expression classifier developed in both nCounter (Nanostring) and RNA-Seq assays and analytically validated across laboratories. VIGex classifies tumor samples into Hot, Intermediate-Cold (I-Cold) and Cold subgroups. VIGex-Hot has been associated with better immunotherapy (IO) treatment outcomes. Here we investigated the performance of VIGex and other IO biomarkers in an independent dataset of patients treated with Pembrolizumab in the INSPIRE phase 2 clinical trial (NCT02644369). Patients with advanced solid tumors were treated with Pembrolizumab 200 mg IV every 3 weeks. Tumor RNA-seq data from baseline tumor samples were classified by the VIGex algorithm. Circulating tumor DNA (ctDNA) was measured at baseline and start of cycle 3 using the bespoke Signatera™ assay. VIGex-Hot was compared to VIGex Intermediate-Cold + Cold and 4 groups were defined based on the combination of VIGex subgroups and the change in ctDNA at cycle 3 from baseline (ΔctDNA). Seventy-six patients were enrolled including 16 ovarian, 12 breast, 12 head and neck cancers, 10 melanoma and 26 other tumor types. Objective response rate was 24% in VIGex-Hot and 10% in I-Cold/Cold. VIGex-Hot subgroup was associated with higher OS (HR: 0.43; p = 0.009) and PFS (HR: 0.49; p = 0.036) when included in a multivariable model adjusted for tumor type, tumor mutational burden (TMB) and PD-L1 immunohistochemistry. The addition of ΔctDNA improved the predictive performance of the baseline VIGex classification for both OS and PFS. Our data indicate that the addition of ΔctDNA to baseline VIGex may refine prediction for IO.