Down-regulation and reduced surface-presentation of neoantigens in cancer stem cells decide the fate of cancer immunotherapy: An in-silico mechanistic hypothesis

Immune evasion properties of cancer stem cells (CSCs) have raised the possibility of their role in imparting variable clinical responses to immune checkpoint inhibitor (ICI) therapy. Here, adopting in-silico approaches, we aimed at unfolding the probable mechanisms underlying the role of CSCs in immunotherapy failure. Pan-cancer patient-related statistical, transcriptome, and protein expression data were collected from databases, including ClinicalTrails.gov, R2: Genomics Analysis and Visualization Platform, GEPIA, and cBioPortal. Subsequently, a series of in-silico approaches were used to evaluate the relationships between cancer stemness and ICI-therapy outcome, patient survival, tumor mutational burden (TMB), neoantigen burden as well as immune cell infiltration, thereby, predicting the fate of ICI therapy across cancers. The Human Protein Atlas database was used to assess PDL1 expression in pan-cancer condition and available datasets from R2 database were used to map tSNE-plots (or UMAPs). Our data indicate that present FDA-approved biomarkers, viz., TMB, MSI, T cell infiltration status, and low antigenicity of the tumor, are insufficient to predict the success of ICI therapy across cancer, since there is inconsistency in tissue expression or bio-availability of the relevant biomarkers and therapy outcome. At this juncture, our in-silico findings and observed correlations portray CSCs as deciding factor for ICI therapy response. Our data postulate that CSCs might escape immune system via two mechanisms: (i) up-regulating DNA-mismatch repair (D-MMR), thereby reducing TMB, (ii) thwarting tumor neoantigen generation, and (ii) down-regulating major histocompatibility complex-I (MHC-I) molecules thus lessening surface-availability of neoantigens. As a combined effect, CSCs might evoke low antigenicity and escape immune-recognition, resulting in immune-evasion. Our data analytical approach indicates that CSCs might have decisive roles in predicting ICI-therapy success. Therefore, targeting resistant CSCs in individual cancer patient, might broaden the clinical utility, ensure precise application, thereby, improving and enhancing the translational relevance of cancer immunotherapy.