FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients

Abstract

A patient’s response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational burden (TMB)) are routinely used for patient selection for ICI response in clinical practice. Although clinical utility of these biomarkers has been demonstrated in ample clinical trials, many variables involved in using these biomarkers have poised serious challenges in daily practice. Furthermore, the predicted responders by these three biomarkers only have a small percentage of overlap, suggesting that each biomarker captures different contributing factors to ICI response. Optimized use of currently FDA-approved biomarkers and development of a new generation of predictive biomarkers are urgently needed. In this review, we will first discuss three widely used FDA-approved predictive biomarkers and their optimal use. Secondly, we will review four novel gene signature biomarkers: T-cell inflamed gene expression profile (GEP), T-cell dysfunction and exclusion gene signature (TIDE), melanocytic plasticity signature (MPS) and B-cell focused gene signature. The GEP and TIDE have shown better predictive performance than PD-L1, and PD-L1 or TMB, respectively. The MPS is superior to PD-L1, TMB, and TIDE. The B-cell focused gene signature represents a previously unexplored predictive biomarker to ICI response. Thirdly, we will highlight two combined predictive biomarkers: TMB+GEP and MPS+TIDE. These integrated biomarkers showed improved predictive outcomes compared to a single predictor. Finally, we will present a potential nucleic acid biomarker signature, allowing DNA and RNA biomarkers to be analyzed in one assay. This comprehensive signature could represent a future direction of developing robust predictive biomarkers, particularly for the cold tumors, for ICI response.

RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples

“…Furthermore, WES-TMB data obtained for tumor biosamples with healthy controls correlated well with the data for the same biosamples without healthy controls (Figure S4). This is in line with the broad clinical practice of using targeted NGS TMB panels like FoundationOne CDx assay that does not require a healthy norm to estimate TMB (2).…”

“…Tumor mutation burden (TMB) per million base pairs is a well-known efficacy predictor for checkpoint inhibitor immunotherapy ( 1 ). TMB can be calculated in several ways ( 2 ). For example, in commercial FDA-approved FoundationOne CDx test for unpaired single tumor samples, TMB is defined as the number of somatic mutations per million base pairs (megabase) of the protein-coding sequence analyzed—including both substitutions and indels, but irrespective of the functional consequences of the variants ( 3 ).…”

“…Furthermore, this was done for the matched pairs of tumors and adjacent normal tissues, while matching healthy samples are not frequently available in the routine clinical practice. For example, the FoundationOne CDx test utilizes single tumor-only biosamples to return TMB ( 2 ).…”

The Adaptive Immune Landscape of the Colorectal Adenoma–Carcinoma Sequence

“…To verify whether the decrease in adaptive immune cell counts could be explained by the establishment of an immunosuppressive microenvironment, we analyzed the expression of the PD-L1 immune checkpoint protein in tumor stroma cells [ 25 ]. Among the different immune checkpoint proteins available, PD-L1 was chosen because it is the most widespread used predictive biomarker of response to immunotherapy.…”

Immunotherapies in Genitourinary Oncology: Where Are We Now? Where Are We Going?

“…Because of this, there is an urgent need for predictive biomarkers for ICIs. Three promising biomarkers approved by the FDA for patient selection to respond to ICIs, as mentioned in this article, include PD-L1, MSI-H/dMMR, and TMB [144]. Although data support the notion that the presence of these biomarkers may render cancer more susceptible ICIs, conflicting data exist.…”