External validation of the VIGex gene-expression signature (GES) as a novel predictive biomarker for immune checkpoint treatment (ICT).

Abstract

2510 Background: VIGex is a 12- gene GES classifier initially developed on the Nanostring platform and validated for RNA-seq. VIGex classifies samples into Hot, intermediate-Cold (I-Cold) and Cold subgroups. The Hot subgroup as defined by VIGex has been associated with better (PFS) in patients (pts) treated on phase 1 ICT trials at Vall D’Hebron Hospital (VH) (ESMO2020). We investigated the performance of VIGex in pts treated with Pembrolizumab (P) in the INSPIRE clinical trial (NCT02644369) at Princess Margaret Cancer Centre (PM) and compared VIGex with other predictive ICT biomarkers. Methods: Pts with advanced solid tumors were treated with P 200 mg IV Q3wks. RNA-seq from baseline biopsies was performed using the Illumina NextSeq550 platform. Tumor RNA-seq data were transferred from PM to VH and classified by the VIGex algorithm blinded to clinical data. Bespoke circulating tumor DNA (ctDNA) was assayed at baseline (B) and start of cycle 3 (C3) using a pt-specific amplicon-based NGS assay (Signatera). Tumor mutational burden (TMB) was defined as the number of non-synonymous mutations per megabase and PD-L1 was assessed by immunohistochemistry (22C3). Hot subgroup (HOT) was compared to I-Cold + Cold (COLD). We defined 4 groups based on the combination of VIGex subgroups and the change in ctDNA at cycle 3 from baseline (ΔctDNA). Survival times were calculated with the Kaplan–Meier method and Cox proportional-hazard models were constructed. Results: Out of 76 pts, median age was 55y (range 21-81y), M:F 31:45, all ECOG 0-1, 16 High-grade serous ovarian, 12 triple negative breast, 12 head and neck, 10 melanoma and 26 other. Median no. of P cycles was 3 (range 1–35); follow up was 14m (range 1-67); Median PFS 10.9m and median overall survival (OS) 14m. Overall response rate (RECIST 1.1) was 24% in HOT and 10% in COLD (p = 0.22 two-sided Fisher's exact test). The HOT subgroup was significantly associated with higher OS and PFS when included in a multivariate model adjusted by tumor histology, TMB and PD-L1 (HR 0.43; 95%CI 0.23-0.81; p = 0.009) and (HR: 0.48; 95%CI 0.25-0.95; p = 0.036) respectively. A total of 57 pts had both VIGex and ΔctDNA data. The addition of ΔctDNA further improved the predictive performance of VIGex for OS (Table). Conclusions: VIGex maintained its predictive power for ICT outcomes when applied to an independent external dataset using RNA-seq. The predictive information provided by VIGex was independent of PD-L1 and TMB. Our data indicates that the addition of ΔctDNA to baseline VIGex may refine prediction for ICT outcomes. [Table: see text]