Biophysicochemical motifs in T-cell receptor sequences distinguish repertoires from tumor-infiltrating lymphocyte and adjacent healthy tissue

Jared Ostmeyer, Scott Christley, Inimary T. Toby, Lindsay G. Cowell

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRβ chains. To develop each classifier, we extracted 4-mers from every TCRβ CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays. Significance: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.

Original languageEnglish (US)
Pages (from-to)1671-1680
Number of pages10
JournalCancer research
Volume79
Issue number7
DOIs
StatePublished - Apr 1 2019

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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