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Updated: Mar 11, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Next generation predictive biomarkers for immune checkpoint inhibition
Yulian Khagi1, Razelle Kurzrock2, Sandip Pravin Patel2
1Center for Personalized Cancer Therapy, Division of Hematology and Oncology, University of California San Diego Moores Cancer Center, 3855 Health Sciences Dr., La Jolla, CA, 92093, USA. ykhagi@ucsd.edu.
Abstract:
With the advent of targeted therapies, there has been a revolution in the treatment of cancer across multiple histologies. Immune checkpoint blockade has made it possible to take advantage of receptor-ligand interactions between immune and tumor cells in a wide spectrum of malignancies. Toxicity in healthy tissue, however, can limit our use of these agents. Immune checkpoint blockade has been approved in advanced melanoma, renal cell cancer, non-small cell lung cancer, relapsed refractory Hodgkin's lymphoma, and urothelial cancer. Though FDA-approved indications for use of some of these novel agents depend on current protein-based programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) assays, detection methods come with several caveats. Additional predictive tools must be interrogated to discern responders from non-responders. Some of these include measurement of microsatellite instability, PD-L1 amplification, cluster of differentiation 8 (CD8) infiltrate density, and tumor mutational burden. This review serves to synthesize biomarker detection at the DNA, RNA, and protein level to more accurately forecast benefit from these novel agents.
Insights
Immune checkpoint blockade revolutionizes cancer treatment but has toxicities. This review synthesizes biomarkers at DNA, RNA, and protein levels to predict patient response to these therapies.
Area of Science:
- Oncology
- Immunology
- Molecular Biology
Background:
- Targeted therapies, including immune checkpoint blockade (ICB), have transformed cancer treatment across various histologies.
- ICB leverages receptor-ligand interactions between immune and tumor cells but can cause toxicity in healthy tissues.
- Current FDA-approved indications for ICB often rely on programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) protein assays, which have limitations.
Purpose of the Study:
- To review and synthesize biomarker detection methods for predicting response to immune checkpoint blockade therapies.
- To explore predictive tools beyond current protein-based assays for identifying responders versus non-responders.
Main Methods:
- Review of literature synthesizing biomarker detection at the DNA, RNA, and protein levels.
- Analysis of established and emerging predictive biomarkers for ICB efficacy.
- Discussion of limitations of current diagnostic assays and the need for complementary tools.
Main Results:
- Current PD-1/PD-L1 assays have caveats, necessitating additional predictive tools.
- Emerging biomarkers include microsatellite instability, PD-L1 amplification, CD8 infiltrate density, and tumor mutational burden.
- Biomarker detection across DNA, RNA, and protein levels offers a multi-faceted approach to forecasting treatment benefit.
Conclusions:
- Accurate prediction of response to immune checkpoint blockade is crucial for optimizing patient outcomes and minimizing toxicity.
- Integrating diverse biomarker data (DNA, RNA, protein) can enhance the precision of patient selection for ICB.
- Further research and validation of these predictive tools are essential for clinical implementation.
