Evidence map›Paper›PMID 40857351›Full record

ArticlePloS one2025

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

Cu Nguyen, Trinh Nguyen, Gloria Trivitt, Brian Capaldo, Chunhua Yan, Qingrong Chen, Nicholas Renzette, Umit Topaloglu, Daoud Meerzaman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Cu NguyenThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America.
Trinh NguyenThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America.
Gloria TrivittThe Clinical and Translational Research Informatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America.
Brian CapaldoThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America.
Chunhua YanThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America.
Qingrong ChenThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America.
Nicholas RenzetteThe Clinical and Translational Research Informatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America.ORCID https://orcid.org/0009-0002-1789-9780
Umit TopalogluThe Clinical and Translational Research Informatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America.
Daoud MeerzamanThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Biomarkers, TumorCloud ComputingComputational BiologyImmunotherapyNeoplasmsClinical Trials as TopicExome SequencingHumansReproducibility of ResultsBiomarkers, Tumor

Identifiers

PMID40857351
PMCPMC12380322

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.