Format

Send to

Choose Destination
Clin Cancer Res. 2019 Dec 12. doi: 10.1158/1078-0432.CCR-19-3249. [Epub ahead of print]

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

Author information

1
Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas.
2
Department of Pathology, UT Southwestern Medical Center, Dallas, Texas.
3
Beijing Institute of Basic Medical Sciences, Beijing, China.
4
Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.
5
Department of Clinical Science, UT Southwestern Medical Center, Dallas, Texas.
6
Department of Pathology, UT Southwestern Medical Center, Dallas, Texas. bo.li@utsouthwestern.edu yang-xin.fu@utsouthwestern.edu.
7
Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas. bo.li@utsouthwestern.edu yang-xin.fu@utsouthwestern.edu.
8
Department of Immunology, UT Southwestern Medical Center, Dallas, Texas.
#
Contributed equally

Abstract

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.

Supplemental Content

Full text links

Icon for HighWire
Loading ...
Support Center