AutoCAT: automated cancer-associated TCRs discovery from TCR-seq data.
Bioinformatics
; 38(2): 589-591, 2022 01 03.
Article
em En
| MEDLINE
| ID: mdl-34529039
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Receptores de Antígenos de Linfócitos T
/
Neoplasias
Tipo de estudo:
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos