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AutoCAT: automated cancer-associated TCRs discovery from TCR-seq data.

ABSTRACT:

Summary

T cells participate directly in the body's immune response to cancer, allowing immunotherapy treatments to effectively recognize and target cancer cells. We previously developed DeepCAT to demonstrate that T cells serve as a biomarker of immune response in cancer patients and can be utilized as a diagnostic tool to differentiate healthy and cancer patient samples. However, DeepCAT's reliance on tumor bulk RNA-seq samples as training data limited its further performance improvement. Here, we benchmarked a new approach, AutoCAT, to predict tumor-associated TCRs from targeted TCR-seq data as a new form of input for DeepCAT, and observed the same level of predictive accuracy.

Availability and implementation

Source code is freely available at https://github.com/cew88/AutoCAT, and data is available at 10.5281/zenodo.5176884.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Wong C

PROVIDER: S-EPMC10060699 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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AutoCAT: automated cancer-associated TCRs discovery from TCR-seq data.

Wong Christina C Li Bo B

Bioinformatics (Oxford, England) 20220101 2

<h4>Summary</h4>T cells participate directly in the body's immune response to cancer, allowing immunotherapy treatments to effectively recognize and target cancer cells. We previously developed DeepCAT to demonstrate that T cells serve as a biomarker of immune response in cancer patients and can be utilized as a diagnostic tool to differentiate healthy and cancer patient samples. However, DeepCAT's reliance on tumor bulk RNA-seq samples as training data limited its further performance improvemen ...[more]

PMID: 34529039

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