ArticleBioinformatics (Oxford, England)2022
AutoCAT: automated cancer-associated TCRs discovery from TCR-seq data.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A conceptual blueprint for "turning cold to hot" in Osteosarcoma: from TME stratification hypotheses to adaptive therapeutic prospects.Cell communication and signaling : CCS · 2026Review
- Deep learning-driven TCRβ repertoire analysis enhances diagnosis and enables mining of immunological biomarkers in systemic lupus erythematosus.BioData mining · 2025Article
- Peripheral blood TCR repertoire improves early detection across multiple cancer types utilizing a cancer predictor.Frontiers in oncology · 2025Article
- TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes.BMC bioinformatics · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
summaryT 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.
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