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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Vydané v:	Molecular oncology Ročník 18; číslo 3; s. 606 - 619
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States John Wiley & Sons, Inc 01.03.2024 Wiley
Predmet:	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
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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. 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.
AbstractList	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. 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. 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.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.
Audience	Academic
Author	Pedersen, Christina Bligaard Rene, Lasse Rossing, Maria Bagger, Frederik Otzen Campos, Benito Olsen, Lars Rønn Krishnan, Neeraja M. Panda, Binay Vitting‐Seerup, Kristoffer Wegener, Helene Scheel
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38158740$$D View this record in MEDLINE/PubMed
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Snippet	Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in‐depth knowledge of... Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of...
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SubjectTerms	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
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Title	Building flexible and robust analysis frameworks for molecular subtyping of cancers
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