IMR Press / FBL / Volume 22 / Issue 10 / DOI: 10.2741/4566

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on imrpress.com as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Review

Pathway-based classification of breast cancer subtypes

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1 Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR), Milan, Italy
2 Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy
3 Department of Tumor Biology, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
4 Department of Gastroenterological Surgery, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
5 Institute of Clinical Medicine, University of Oslo, Oslo, Norway
6 SYSBIO Centre of Systems Biology (SYSBIO), 20126 Milan, Italy

Academic Editors: Isabella Castiglioni, Gloria Bertoli, Claudia Cava

Front. Biosci. (Landmark Ed) 2017, 22(10), 1697–1712; https://doi.org/10.2741/4566
Published: 1 March 2017
(This article belongs to the Special Issue Frontiers in biomarker for theranostics)
Abstract

Cancer heterogeneity represents a major hurdle in the development of effective theranostic strategies, as it prevents to devise unique and maximally efficient diagnostic, prognostic and therapeutic procedures even for patients affected by the same tumor type. Computational techniques can nowadays leverage the huge and ever increasing amount of (epi)genomic data to tackle this problem, therefore providing new and valuable instruments for decision support to biologists and pathologists, in the broad sphere of precision medicine. In this context, we here introduce a novel cancer subtype classifier from gene expression data and we apply it to two different Breast Cancer datasets, from TCGA and GEO repositories. The classifier is based on Support Vector Machines and relies on the information about the relevant pathways involved in breast cancer development to reduce the huge variable space. Among the main results, we show that the classifier accuracy is preserved at excellent values even when the variable space is reduced by a 20-fold, hence providing a precious tool for cancer patient profiling even in case of limited experimental resources.

Keywords
Cancer Subtypes Classification
Breast Cancer
BC
Pathway Enrichment
Differentially Expressed Genes
DEG
Review
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