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Cancer BiologybioRxiv
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A common set of structurally distinct endogenous retroviruses predict survival in multiple human tumor types

Alexandra L. MassaiD, Himanshu ChintalapudiiD, Evelyn C. Coppenbarger, Jake M. PetersoniD, Walter N. MossiD, Hildreth Robert FrostiD, Steven D. LeachiD

Abstract

Accumulating evidence suggests that viral mimicry induction in cancer cells through the expression of human endogenous retroviral elements (ERVs) enhances tumor immunogenicity and improves response to immunotherapy. However, the characterization of ERV expression across multiple tumor types, their clinical relevance, and their role in tumor immunity remain limited. Here, we assessed the prognostic and immune-related consequences of ERV expression across 8 different tumor types from The Cancer Genome Atlas (TCGA). We applied a recently developed tool, Telescope, to quantify the expression of 14,968 ERVs in bulk tumor RNA-sequencing data. Approximately 15-22% of Telescopes ERVs (navg = 2,736) were expressed in each tumor cohort. We identified both tissue-specific and commonly expressed ERVs across cohorts. Over 50% of tissue-specific ERVs were ubiquitously expressed (n = 1,709) across all 8 tumor types. Using patient clinical data, we identified a subset of 94 ERVs whose expression levels were significantly associated with overall survival, of which 42 were favorably prognostic. Prognostic ERVs displayed unique structural characteristics, including more frequent inverted repeats and more stable secondary structures. Patients were classified based on ERV expression, allowing identification of patient cohorts with greater than 6-fold differences in overall survival. We also found that expression of positively prognostic ERVs was associated with enrichment for selected anti-tumor immune response gene expression signatures. This analysis extends previous results identifying patterns of ERV expression in bulk tumor RNA-seq datasets, providing further insight into the role of ERVs in tumor clinicopathology and immunogenicity.

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02 /Specification & Classification

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