Identification and Utilization of Biomarkers to Predict Response to Immune Checkpoint Inhibitors

Oct 15, 2020The AAPS journal

Using Biomarkers to Predict How Patients Respond to Immune Checkpoint Inhibitors

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Abstract

Immune checkpoint inhibitors (ICPI) have revolutionized cancer therapy, offering clinical benefits to many patients but also posing challenges in response prediction and adverse effects.

Many patients either do not respond or develop resistance to immune checkpoint inhibitors.
Current biomarkers like PD-L1 expression and tumor mutational burden can predict some responses but are insufficient for all cases.
Pembrolizumab received the first tissue-agnostic FDA approval based on tumor microsatellite instability rather than tumor type.
Exploratory biomarkers such as tumor-infiltrating lymphocytes and gene expression profiling are being investigated for their predictive potential.
Combining multiple biomarkers may enhance the ability to predict patient responses to immunotherapy.

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Immune checkpoint inhibitors (ICPI) have revolutionized cancer therapy and provided clinical benefit to thousands of patients. Despite durable responses in many tumor types, the majority of patients either fail to respond at all or develop resistance to the ICPI. Furthermore, ICPI treatment can be accompanied by serious adverse effects. There is an urgent need for identification of patient populations that will benefit from ICPI as single agents and when used in combinations. As ICPI have achieved regulatory approvals, accompanying biomarkers including PD-L1 immunohistochemistry (IHC) and tumor mutational burden (TMB) have also received approvals for some indications. The ICPI pembrolizumab was the first example of a tissue-agnostic FDA approval based on tumor microsatellite instability (MSI)/deficient mismatch repair (dMMR) biomarker status, rather than on tumor histology assessment. Several other ICPI-associated biomarkers are in the exploratory stage, including quantification of tumor-infiltrating lymphocytes (TILs), gene expression profiling (GEP) of an inflamed microenvironment, and neoantigen prediction. TMB and PD-L1 expression can predict a subset of responses, but they fail to predict all responses to checkpoint blockade. While a single biomarker is currently limited in its ability to fully capture the complexity of the tumor-immune microenvironment, a combination of biomarkers is emerging as a method to improve predictive power. Here we review the steadily growing impact of comprehensive genomic profiling (CGP) for development and utilization of predictive biomarkers by simultaneously capturing TMB, MSI, and the status of genomic targets that confer sensitivity or resistance to immunotherapy, as well as detecting inflammation through RNA expression signatures.

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Identification and Utilization of Biomarkers to Predict Response to Immune Checkpoint Inhibitors

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