Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue.
Identifieur interne : 000679 ( Main/Exploration ); précédent : 000678; suivant : 000680Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue.
Auteurs : Jared Ostmeyer [États-Unis] ; Scott Christley [États-Unis] ; Inimary T. Toby [États-Unis] ; Lindsay G. Cowell [États-Unis]Source :
- Cancer research [ 1538-7445 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Complementarity Determining Regions, Receptors, Antigen, T-Cell, alpha-beta.
- chemical , genetics : Receptors, Antigen, T-Cell.
- immunology : T-Lymphocytes.
- Humans, Lymphocytes, Tumor-Infiltrating.
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.See related commentary by Zoete and Coukos, p. 1299.
DOI: 10.1158/0008-5472.CAN-18-2292
PubMed: 30622114
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">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.<i>See related commentary by Zoete and Coukos, p. 1299</i>
.</div>
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