DNA Methylation Biomarkers Predict Objective Responses to PD-1/PD-L1 Inhibition Blockade

Immune checkpoint inhibitor (ICI) treatment could bring long-lasting clinical benefits to patients with metastatic cancer. However, only a small proportion of patients respond to PD-1/PD-L1 blockade, so predictive biomarkers are needed. Here, based on DNA methylation profiles and the objective response rates (ORRs) of PD-1/PD-L1 inhibition therapy, we identified 269 CpG sites and developed an initial CpG-based model by Lasso to predict ORRs. Notably, as measured by the area under the receiver operating characteristic curve (AUC), our model can produce better performance (AUC = 0.92) than both a model based on tumor mutational burden (TMB) (AUC = 0.77) and a previously reported TMB model (AUC = 0.71). In addition, most CpGs also have additional synergies with TMB, which can achieve a higher prediction accuracy when joined with TMB. Furthermore, we identified CpGs that are associated with TMB at the individual level. DNA methylation modules defined by protein networks, Kyoto Encylopedia of Genes and Genomes (KEGG) pathways, and ligand-receptor gene pairs are also associated with ORRs. This method suggested novel immuno-oncology targets that might be beneficial when combined with PD-1/PD-L1 blockade. Thus, DNA methylation studies might hold great potential for individualized PD1/PD-L1 blockade or combinatory therapy.

Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

The Tumor Inflammation Signature (TIS) is an investigational use only (IUO) 18-gene signature that measures a pre-existing but suppressed adaptive immune response within tumors. The TIS has been shown to enrich for patients who respond to the anti-PD1 agent pembrolizumab. To explore this immune phenotype within and across tumor types, we applied the TIS algorithm to over 9000 tumor gene expression profiles downloaded from The Cancer Genome Atlas (TCGA). As expected based on prior evidence, tumors with known clinical sensitivity to anti-programmed cell death protein 1 (PD-1) blockade had higher average TIS scores. Furthermore, TIS scores were more variable within than between tumor types, and within each tumor type a subset of patients with elevated scores was identifiable although with different prevalence associated with each tumor type, the latter consistent with the observed clinical responsiveness to anti PD-1 blockade. Notably, TIS scores only minimally correlated with mutation load in most tumors and ranking tumors by median TIS score showed differing association to clinical sensitivity to PD-1/PD-1 ligand 1 (PD-L1) blockade than ranking of the same tumors by mutation load. The expression patterns of the TIS algorithm genes were conserved across tumor types yet appeared to be minimally prognostic in most cancers, consistent with the TIS score serving as a pan-cancer measurement of the inflamed tumor phenotype. Characterization of the prevalence and variability of TIS will lead to increased understanding of the immune status of untreated tumors and may lead to improved indication selection for testing immunotherapy agents.