Combined Transcriptome and Circulating Tumor DNA Longitudinal Biomarker Analysis Associates With Clinical Outcomes in Advanced Solid Tumors Treated With Pembrolizumab

Abstract

Purpose: Immune gene expression signatures are emerging as potential biomarkers for immunotherapy (IO). VIGex is a 12-gene expression classifier developed in both nCounter (Nanostring) and RNA sequencing (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 IO treatment outcomes. Here, we investigated the performance of VIGex and other IO biomarkers in an independent data set of patients treated with pembrolizumab in the INSPIRE phase II clinical trial (ClinicalTrials.gov identifier: NCT02644369).

Materials and methods: Patients with advanced solid tumors were treated with pembrolizumab 200 mg IV once 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 with VIGex I-Cold + Cold and four groups were defined on the basis of the combination of VIGex subgroups and the change in ctDNA at cycle 3 from baseline (ΔctDNA).

Results: 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 overall survival (OS) and progression-free survival (PFS) when included in a multivariable model adjusted for tumor type, tumor mutation burden, and PD-L1 immunohistochemistry. The addition of ΔctDNA improved the predictive performance of the baseline VIGex classification for both OS and PFS.

Conclusion: Our data indicate that the addition of ΔctDNA to baseline VIGex may refine prediction for IO.

FIG 1.

Association of VIGex-Hot with immunotherapy outcomes in INSPIRE. (A) Consort diagram. (B) Distribution of tumor types across VIGex categories. (C) Distribution of responses across VIGex categories. (D) Kaplan-Meier curves of OS stratified by VIGex categories. (E) Kaplan-Meier curves of PFS stratified by VIGex categories. (F) Table showing multivariable analysis for OS. (G) Table showing multivariable analysis for PFS. aHR, adjusted hazard ratio; CR, complete response; HNSCC, head and neck squamous cell carcinoma; I-Cold, intermediate-cold; MM, metastatic melanoma; MST, mixed solid tumors; NE, not evaluable; OS, overall survival; OV, ovarian cancer; PD, progressive disease; PFS, progression-free survival; PR, partial response; SD, stable disease; TNBC, triple-negative breast cancer.

FIG 2.

Association of VIGex categories and ▵ctDNA with immunotherapy outcomes in INSPIRE. (A) Consort diagram. (B) Distribution of VIGex categories across subgroups defined by ctDNA clearance. (C) Plot showing objective response rate in each of the groups defined by VIGex and ▵ctDNA categories. (D) Kaplan‐Meier curve of OS stratified by VIGex and ▵ctDNA categories. Survival analyses were performed from cycle 3. (E) Kaplan-Meier curve of PFS stratified by VIGex and ▵ctDNA categories. Survival analyses were performed from cycle 3. (F) Forest plot for Cox proportional hazards model for OS, reporting HR and the 95% confidence intervals for each covariate in the model. (G) Forest plot for Cox proportional hazards model for PFS, reporting HR and the 95% CIs for each covariate in the model. CR, complete response; ctDNA, circulating tumor DNA; HR, hazard ratio; I-Cold, intermediate-cold; OS, overall survival; PFS, progression-free survival; PR, partial response.

FIG 2.

Association of VIGex categories and ▵ctDNA with immunotherapy outcomes in INSPIRE. (A) Consort diagram. (B) Distribution of VIGex categories across subgroups defined by ctDNA clearance. (C) Plot showing objective response rate in each of the groups defined by VIGex and ▵ctDNA categories. (D) Kaplan‐Meier curve of OS stratified by VIGex and ▵ctDNA categories. Survival analyses were performed from cycle 3. (E) Kaplan-Meier curve of PFS stratified by VIGex and ▵ctDNA categories. Survival analyses were performed from cycle 3. (F) Forest plot for Cox proportional hazards model for OS, reporting HR and the 95% confidence intervals for each covariate in the model. (G) Forest plot for Cox proportional hazards model for PFS, reporting HR and the 95% CIs for each covariate in the model. CR, complete response; ctDNA, circulating tumor DNA; HR, hazard ratio; I-Cold, intermediate-cold; OS, overall survival; PFS, progression-free survival; PR, partial response.

FIG 3.

Association of VIGex categories with other approved predictive biomarkers for immunotherapy. (A) Scatter plot showing the association of VIGex continuous score and tumor mutational burden. (B) Scatter plot showing the association of VIGex continuous score and PD-L1 by Qualtek MPS score. (C) Bar plot showing the association of VIGex and PD-L1 score. (D) Bar plot showing the association of VIGex and QualTek TIL score. (E) Bar plot showing the association of 9p21 loss and VIGex categories. HD, homozygous deletion; I-Cold, intermediate-cold; MPS, modified proportion score; NA, not applicable; TIL, tumor-infiltrating lymphocyte; TMB, tumor mutation burden; WT, wild type.

FIG A1.

Association of VIGex categories with immune cell infiltration assessed by CIBERSORT. I-Cold, intermediate-cold; TIL, tumor-infiltrating lymphocyte.