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Building flexible and robust analysis frameworks for molecular subtyping of cancers

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Title: Building flexible and robust analysis frameworks for molecular subtyping of cancers
Authors: Christina Bligaard Pedersen, Benito Campos, Lasse Rene, Helene Scheel Wegener, Neeraja M. Krishnan, Binay Panda, Kristoffer Vitting‐Seerup, Maria Rossing, Frederik Otzen Bagger, Lars Rønn Olsen
Source: Mol Oncol
Molecular Oncology, Vol 18, Iss 3, Pp 606-619 (2024)
Pedersen, C B, Campos, B, Rene, L, Wegener, H S, Krishnan, N M, Panda, B, Vitting-Seerup, K, Rossing, M, Bagger, F O & Olsen, L R 2024, ' Building flexible and robust analysis frameworks for molecular subtyping of cancers ', Molecular Oncology, vol. 18, no. 3, pp. 606-619 . https://doi.org/10.1002/1878-0261.13580
Publisher Information: Wiley, 2024.
Publication Year: 2024
Subject Terms: 0301 basic medicine, 0303 health sciences, Gene Expression Profiling, Sample classification, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Computational Biology, sample classification, Breast Neoplasms, bioinformatics workflows, Clinical bioinformatics, 3. Good health, Gene Expression Regulation, Neoplastic, molecular subtyping, 03 medical and health sciences, Bioinformatics workflows, Humans, RNA, Female, name=SDG 3 - Good Health and Well-being, Molecular subtyping, clinical bioinformatics, RC254-282, Research Articles
Description: Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in‐depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.
Document Type: Article
Other literature type
File Description: application/pdf
Language: English
ISSN: 1878-0261
1574-7891
DOI: 10.1002/1878-0261.13580
Access URL: https://pubmed.ncbi.nlm.nih.gov/38158740
https://doaj.org/article/91af8075ca0347618bf363e33adca13d
https://orbit.dtu.dk/en/publications/26c5857a-2c8c-46d1-aec5-bd093285566b
Rights: CC BY
Accession Number: edsair.doi.dedup.....d78cf5d6920f03e8b6ec0dff7f1a0ade
Database: OpenAIRE
Description
Abstract:Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in‐depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.
ISSN:18780261
15747891
DOI:10.1002/1878-0261.13580