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Modular and cloud-based bioinformatics pipelines for high-confidence biomarker detection in cancer immunotherapy clinical trials

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Publicado en:PLoS One vol. 20, no. 8 (Aug 2025), p. e0330827
Autor principal: Nguyen, Cu
Otros Autores: Nguyen, Trinh, Trivitt, Gloria, Capaldo, Brian, Yan, Chunhua, Chen, Qingrong, Renzette, Nicholas, Topaloglu, Umit, Daoud Meerzaman
Publicado:
Public Library of Science
Materias:
Cancer
Clinical trials
Accuracy
Datasets
Toxicity
Precision medicine
Immunotherapy
Bioinformatics
Open source software
Quality control
Laboratories
Data analysis
Genomes
Oncology
Software upgrading
Cancer immunotherapy
Clinical outcomes
Computer centers
Quality standards
Pipelines
Gene sequencing
Biomarkers
Medical research
Whole genome sequencing
Lymphoma
Reproducibility
Social
Acceso en línea:Citation/Abstract
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Resumen:BackgroundThe Cancer Immune Monitoring and Analysis Centers – Cancer Immunologic Data Center (CIMAC-CIDC) network aims to improve cancer immunotherapy by providing harmonized molecular assays and standardized bioinformatics analysis.ResultsIn response to evolving bioinformatics standards and the migration of the CIDC to the National Cancer Institute (NCI), we undertook the enhancement of the CIDC’s extant whole exome sequencing (WES) and RNA sequencing (RNA-Seq) pipelines. Leveraging open-source tools and cloud-based technologies, we implemented modular workflows using Snakemake and Docker for efficient deployment on the Google Cloud Platform (GCP). Benchmarking analyses demonstrate improved reproducibility, precision, and recall across validated truth sets for variant calling, transcript quantification, and fusion detection.ConclusionThis work establishes a scalable framework for harmonized multi-omic analyses, ensuring the continuity and reliability of bioinformatics workflows in multi-site clinical research aimed at advancing cancer biomarker discovery and personalized medicine.
ISSN:1932-6203
DOI:10.1371/journal.pone.0330827
Fuente:Health & Medical Collection