Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma

Christof C. Smith, Kathryn E. Beckermann, Dante S. Bortone, Aguirre A. Cubas, Lisa M. Bixby, Samuel J. Lee, Anshuman Panda, Shridar Ganesan, Gyan Bhanot, Eric M. Wallen, Matthew I. Milowsky, William Y. Kim, Kimryn Rathmell, Ronald Swanstrom, Joel S. Parker, Jonathan S. Serody, Sara R. Selitsky, Benjamin G. Vincent

Research output: Contribution to journalArticlepeer-review

181 Scopus citations

Abstract

Human endogenous retroviruses (hERVs) are remnants of exogenous retroviruses that have integrated into the genome throughout evolution. We developed a computational workflow, hervQuant, which identified more than 3,000 transcriptionally active hERVs within The Cancer Genome Atlas (TCGA) pan-cancer RNA-Seq database. hERV expression was associated with clinical prognosis in several tumor types, most significantly clear cell renal cell carcinoma (ccRCC). We explored two mechanisms by which hERV expression may influence the tumor immune microenvironment in ccRCC: (i) RIG-I-like signaling and (ii) retroviral antigen activation of adaptive immunity. We demonstrated the ability of hERV signatures associated with these immune mechanisms to predict patient survival in ccRCC, independent of clinical staging and molecular subtyping. We identified potential tumor-specific hERV epitopes with evidence of translational activity through the use of a ccRCC ribosome profiling (Ribo-Seq) dataset, validated their ability to bind HLA in vitro, and identified the presence of MHC tetramer-positive T cells against predicted epitopes. hERV sequences identified through this screening approach were significantly more highly expressed in ccRCC tumors responsive to treatment with programmed death receptor 1 (PD-1) inhibition. hervQuant provides insights into the role of hERVs within the tumor immune microenvironment, as well as evidence that hERV expression could serve as a biomarker for patient prognosis and response to immunotherapy.

Original languageEnglish (US)
Pages (from-to)4804-4820
Number of pages17
JournalJournal of Clinical Investigation
Volume128
Issue number11
DOIs
StatePublished - Nov 1 2018

All Science Journal Classification (ASJC) codes

  • General Medicine

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