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Updated: Dec 6, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Genome and Transcriptome Biomarkers of Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors
Alexandra Pender1, Emma Titmuss2, Erin D Pleasance2
1Department of Medical Oncology, BC Cancer, Vancouver, British Columbia, Canada.
Purpose:
Immune checkpoint inhibitors (ICI) have revolutionized the treatment of solid tumors with dramatic and durable responses seen across multiple tumor types. However, identifying patients who will respond to these drugs remains challenging, particularly in the context of advanced and previously treated cancers.
Experimental Design:
We characterized fresh tumor biopsies from a heterogeneous pan-cancer cohort of 98 patients with metastatic predominantly pretreated disease through the Personalized OncoGenomics program at BC Cancer (Vancouver, Canada) using whole genome and transcriptome analysis (WGTA). Baseline characteristics and follow-up data were collected retrospectively.
Results:
We found that tumor mutation burden, independent of mismatch repair status, was the most predictive marker of time to progression (P = 0.007), but immune-related CD8+ T-cell and M1-M2 macrophage ratio scores were more predictive for overall survival (OS; P = 0.0014 and 0.0012, respectively). While CD274 [programmed death-ligand 1 (PD-L1)] gene expression is comparable with protein levels detected by IHC, we did not observe a clinical benefit for patients with this marker. We demonstrate that a combination of markers based on WGTA provides the best stratification of patients (P = 0.00071, OS), and also present a case study of possible acquired resistance to pembrolizumab in a patient with non-small cell lung cancer.
Conclusions:
Interpreting the tumor-immune interface to predict ICI efficacy remains challenging. WGTA allows for identification of multiple biomarkers simultaneously that in combination may help to identify responders, particularly in the context of a heterogeneous population of advanced and previously treated cancers, thus precluding tumor type-specific testing.
Insights
Identifying patients who respond to immune checkpoint inhibitors (ICI) is challenging. Whole genome and transcriptome analysis (WGTA) identified tumor mutation burden and immune cell ratios as key biomarkers for predicting treatment outcomes in advanced cancers.
Area of Science:
- Oncology
- Immunotherapy
- Genomics
Background:
- Immune checkpoint inhibitors (ICI) have transformed solid tumor treatment, offering durable responses.
- Predicting patient response to ICI remains a significant challenge, especially in advanced or pretreated cancers.
Purpose of the Study:
- To identify predictive biomarkers for ICI efficacy in a heterogeneous pan-cancer cohort.
- To evaluate the utility of whole genome and transcriptome analysis (WGTA) for stratifying patients for immunotherapy.
Main Methods:
- Retrospective analysis of fresh tumor biopsies from 98 metastatic cancer patients.
- Utilized whole genome and transcriptome analysis (WGTA) to characterize tumor and immune profiles.
- Collected baseline characteristics and follow-up data for correlation with treatment outcomes.
Main Results:
- Tumor mutation burden predicted time to progression (P=0.007).
- CD8+ T-cell and M1-M2 macrophage ratios were more predictive of overall survival (OS; P=0.0014 and P=0.0012).
- Combined WGTA markers offered the best patient stratification for OS (P=0.00071); PD-L1 expression did not correlate with clinical benefit.
Conclusions:
- Interpreting the tumor-immune microenvironment is crucial for predicting ICI efficacy.
- WGTA enables simultaneous identification of multiple biomarkers for improved patient stratification.
- Combined biomarkers from WGTA can identify responders in diverse, advanced cancer populations, potentially reducing the need for tumor-specific tests.
