Building flexible and robust analysis frameworks for molecular subtyping of cancers

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 sequen...

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Bibliographic Details
Published in:Molecular oncology Vol. 18; no. 3; pp. 606 - 619
Main Authors: Pedersen, Christina Bligaard, Campos, Benito, Rene, Lasse, Wegener, Helene Scheel, Krishnan, Neeraja M., Panda, Binay, Vitting‐Seerup, Kristoffer, Rossing, Maria, Bagger, Frederik Otzen, Olsen, Lars Rønn
Format: Journal Article
Language:English
Published: United States John Wiley & Sons, Inc 01.03.2024
Wiley
Subjects:
Analysis
Bioinformatics
bioinformatics workflows
Biotechnology industry
Breast cancer
Breast Neoplasms - diagnosis
Breast Neoplasms - genetics
Cancer
Classification
clinical bioinformatics
Computational Biology - methods
Estrogens
Female
Gene expression
Gene Expression Profiling - methods
Gene Expression Regulation, Neoplastic
Genomes
Humans
Kinases
Laboratories
molecular subtyping
Principal components analysis
Quality control
RNA
RNA sequencing
sample classification
Tumors
Canada
United States
clinical bioinformatics
sample classification
bioinformatics workflows
molecular subtyping
ISSN:1574-7891, 1878-0261, 1878-0261
Online Access:Get full text
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Summary: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. We provide a generally applicable roadmap for implementing data and bioinformatics tools for robust inference of cancer subtypes in a clinical setting, both from raw and processed RNA sequencing data and on a per‐sample basis. We apply the resulting framework to the PAM50 and CIT breast cancer classifiers.
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ISSN:1574-7891
1878-0261
1878-0261
DOI:10.1002/1878-0261.13580