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

Christina Wong, Bo Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)589-591
Number of pages3
JournalBioinformatics
Volume38
Issue number2
DOIs
StatePublished - Jan 15 2022

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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