期刊
CANCER RESEARCH
卷 79, 期 7, 页码 1671-1680出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/0008-5472.CAN-18-2292
关键词
-
类别
资金
- National Institute of Allergy and Infectious Diseases [AI097403]
- Cancer Prevention and Research Institute of Texas [RP160157]
- UT Southwestern
- Simmons Comprehensive Cancer Center
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 TCR beta chains. To develop each classifier, we extracted 4-mers from every TCR beta CDR3 and represented each 4-mer using biophysicochemical featuresof 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据