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TREMSUCS-TCGA – an integrated workflow for the identification of biomarkers for treatment success

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Title: TREMSUCS-TCGA – an integrated workflow for the identification of biomarkers for treatment success
Authors: Balogh Gabor, Jorge Natasha, Dupain Célia, Kamal Maud, Servant Nicolas, Le Tourneau Christophe, Stadler Peter F., Bernhart Stephan H.
Source: Journal of Integrative Bioinformatics, Vol 21, Iss 4, Pp 993-8 (2024)
Publisher Information: De Gruyter, 2024.
Publication Year: 2024
Collection: LCC:Biotechnology
Subject Terms: tcga, scc, biomarker, precision medicine, differential expression, differentially methylated regions, Biotechnology, TP248.13-248.65
More Details: Many publicly available databases provide disease related data, that makes it possible to link genomic data to medical and meta-data. The cancer genome atlas (TCGA), for example, compiles tens of thousand of datasets covering a wide array of cancer types. Here we introduce an interactive and highly automatized TCGA-based workflow that links and analyses epigenomic and transcriptomic data with treatment and survival data in order to identify possible biomarkers that indicate treatment success. TREMSUCS-TCGA is flexible with respect to type of cancer and treatment and provides standard methods for differential expression analysis or DMR detection. Furthermore, it makes it possible to examine several cancer types together in a pan-cancer type approach. Parallelisation and reproducibility of all steps is ensured with the workflowmanagement system Snakemake. TREMSUCS-TCGA produces a comprehensive single report file which holds all relevant results in descriptive and tabular form that can be explored in an interactive manner. As a showcase application we describe a comprehensive analysis of the available data for the combination of patients with squamous cell carcinomas of head and neck, cervix and lung treated with cisplatin, carboplatin and the combination of carboplatin and paclitaxel. The best ranked biomarker candidates are discussed in the light of the existing literature, indicating plausible causal relationships to the relevant cancer entities.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1613-4516
2024-0031
Relation: https://doaj.org/toc/1613-4516
DOI: 10.1515/jib-2024-0031
Access URL: https://doaj.org/article/95572e2bae4447ca90547ed0200ee842
Accession Number: edsdoj.95572e2bae4447ca90547ed0200ee842
Database: Directory of Open Access Journals
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  Data: Journal of Integrative Bioinformatics, Vol 21, Iss 4, Pp 993-8 (2024)
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  Data: Many publicly available databases provide disease related data, that makes it possible to link genomic data to medical and meta-data. The cancer genome atlas (TCGA), for example, compiles tens of thousand of datasets covering a wide array of cancer types. Here we introduce an interactive and highly automatized TCGA-based workflow that links and analyses epigenomic and transcriptomic data with treatment and survival data in order to identify possible biomarkers that indicate treatment success. TREMSUCS-TCGA is flexible with respect to type of cancer and treatment and provides standard methods for differential expression analysis or DMR detection. Furthermore, it makes it possible to examine several cancer types together in a pan-cancer type approach. Parallelisation and reproducibility of all steps is ensured with the workflowmanagement system Snakemake. TREMSUCS-TCGA produces a comprehensive single report file which holds all relevant results in descriptive and tabular form that can be explored in an interactive manner. As a showcase application we describe a comprehensive analysis of the available data for the combination of patients with squamous cell carcinomas of head and neck, cervix and lung treated with cisplatin, carboplatin and the combination of carboplatin and paclitaxel. The best ranked biomarker candidates are discussed in the light of the existing literature, indicating plausible causal relationships to the relevant cancer entities.
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