Investigation of Antigen-Specific T-Cell Receptor Clusters in Human Cancers

Clin Cancer Res. 2020 Mar 15;26(6):1359-1371. doi: 10.1158/1078-0432.CCR-19-3249. Epub 2019 Dec 12.


Purpose: Cancer antigen-specific T cells are key components in antitumor immune response, yet their identification in the tumor microenvironment remains challenging, as most cancer antigens are unknown. Recent advance in immunology suggests that similar T-cell receptor (TCR) sequences can be clustered to infer shared antigen specificity. This study aims to identify antigen-specific TCRs from the tumor genomics sequencing data.

Experimental design: We used the TRUST (Tcr Repertoire Utilities for Solid Tissue) algorithm to assemble the TCR hypervariable CDR3 regions from 9,700 bulk tumor RNA-sequencing (RNA-seq) samples, and developed a computational method, iSMART, to group similar TCRs into antigen-specific clusters. Integrative analysis on the TCR clusters with multi-omics datasets was performed to profile cancer-associated T cells and to uncover novel cancer antigens.

Results: Clustered TCRs are associated with signatures of T-cell activation after antigen encounter. We further elucidated the phenotypes of clustered T cells using single-cell RNA-seq data, which revealed a novel subset of tissue-resident memory T-cell population with elevated metabolic status. An exciting application of the TCR clusters is to identify novel cancer antigens, exemplified by our identification of a candidate cancer/testis gene, HSFX1, through integrated analysis of HLA alleles and genomics data. The target was further validated using vaccination of humanized HLA-A*02:01 mice and ELISpot assay. Finally, we showed that clustered tumor-infiltrating TCRs can differentiate patients with early-stage cancer from healthy donors, using blood TCR repertoire sequencing data, suggesting potential applications in noninvasive cancer detection.

Conclusions: Our analysis on the antigen-specific TCR clusters provides a unique resource for alternative antigen discovery and biomarker identification for cancer immunotherapies.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Antigens, Neoplasm / genetics
  • Antigens, Neoplasm / immunology*
  • Biomarkers, Tumor / blood
  • Biomarkers, Tumor / genetics*
  • Computational Biology / methods
  • Databases, Genetic / statistics & numerical data
  • Disease Models, Animal
  • Female
  • Heat Shock Transcription Factors / immunology
  • Heat Shock Transcription Factors / metabolism
  • Heat-Shock Proteins / immunology
  • Heat-Shock Proteins / metabolism
  • Humans
  • Lymphocyte Activation / immunology*
  • Mice
  • Mice, Inbred C57BL
  • Mice, Transgenic
  • Neoplasms / genetics
  • Neoplasms / immunology*
  • RNA-Seq / methods
  • Receptors, Antigen, T-Cell / genetics
  • Receptors, Antigen, T-Cell / immunology*
  • Survival Rate
  • T-Lymphocytes / immunology*
  • Tumor Microenvironment


  • Antigens, Neoplasm
  • Biomarkers, Tumor
  • HSFX1 protein, human
  • Heat Shock Transcription Factors
  • Heat-Shock Proteins
  • Receptors, Antigen, T-Cell