Abstract
Recently proposed tumor fitness measures, based on profiling neoepitopes for reactive viral epitope similarity, have been proposed to predict response to immune checkpoint inhibitors in melanoma and small-cell lung cancer. Here we applied these checkpoint based fitness measures to the matched checkpoint treatment naive Cancer Genome Atlas (TCGA) samples where cytolytic activity (CYT) imparts a known survival benefit. We observed no significant survival predictive power beyond that of overall patient tumor mutation burden, and furthermore, found no association between checkpoint based fitness and tumor T-cell infiltration, cytolytic activity, and abundance (tumor infiltrating lymphocyte, TIL, burden). In addition, we investigated the key assumption of viral epitope similarity driving immune response in the hepatitis B virally infected liver cancer TCGA cohort, and uncovered suggestive evidence that tumor neoepitopes actually dominate viral epitopes in putative immunogenicity and plausibly drive immune response and recruitment.
Highlights
Proposed tumor fitness measures, based on profiling neoepitopes for reactive viral epitope similarity, have been proposed to predict response to immune checkpoint inhibitors in melanoma and small-cell lung cancer
Putative immunogenicity is modeled via a nonlinear function of in silico derived major histocompatibility complex (MHC)-I binding affinities arising from the clonal somatic mutation spectrum, while the T-cell receptor (TCR) recognition likelihood is set to scale with sequence similarity to known, infectious, virally derived epitopes from the Immune Epitope Database (IEDB, www.iedb.org)[5]
We found that skin-cutaneous and melanoma (SKCM) patients with viral-like epitopes had a significantly worse survival rate, and correlated with lower tumor mutation burden (TMB), while no survival associations were observed for non-small cell lung cancer (NSCLC)
Summary
Proposed tumor fitness measures, based on profiling neoepitopes for reactive viral epitope similarity, have been proposed to predict response to immune checkpoint inhibitors in melanoma and small-cell lung cancer. Putative immunogenicity is modeled via a nonlinear function of in silico derived MHC-I binding affinities arising from the clonal somatic mutation spectrum, while the TCR recognition likelihood is set to scale with sequence similarity to known, infectious, virally derived epitopes from the Immune Epitope Database (IEDB, www.iedb.org)[5]. Given these assumptions, a combined measure of tumor fitness is defined as the inverse of the maximum clonal immunogenicity potential, Ϊ, weighted across subclones in the tumor (see Methods, Fig. 1B). Developing and deploying new in-silico methods, we carried this test out using the hepatitis B virally positive portion (HBV+) of the liver hepatocellular carcinoma (LIHC) TCGA liver cancer cohort
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