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[1]M Gerlinger, AJ Rowan, S Horswell, J Larkin, D Endesfelder, E Gronroos, P Martinez, N Matthews, A Stewart, P Tarpey, I Varela, B Phillimore, S Begum, NQ McDonald, A Butler, D Jones, K Raine, C Latimer, CR Santos, M Nohadani, AC Eklund, B Spencer-Dene, G Clark, L Pickering, G Stamp, M Gore, Z Szallasi, J Downward, PA Futreal, C Swanton: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med, 366(10):883-92 (2012)
[2]R Fisher, L Pusztai, C Swanton: Cancer heterogeneity: implications for targeted therapeutics. Br J Cancer, 108(3):479-85 (2013)
[3]RA Burrell, C Swanton: Tumour heterogeneity and the evolution of polyclonal drug resistance. Mol Oncol, 8(6):1095-111 (2014)
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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.
Pathway-based classification of breast cancer subtypes
, Claudia Cava 1, Gloria Bertoli 1, Bastian Fromm 3, Kjersti Flatmark 3,4,5, Giancarlo Mauri 2,6, Isabella Castiglioni 11 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
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
References
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- [21] SG Zhao, M Shilkrut, C Speers, M Liu, K Wilder-Romans, TS Lawrence, LJ Pierce, FY Feng: Development and validation of a novel platform-independent metastasis signature in human breast cancer. PLoS One, 10(5):e0126631 (2015)
- [22] C Cava, I Zoppis, G Mauri, M Ripamonti, F Gallivanone, C Salvatore, MC Gilardi, I Castiglioni: Combination of gene expression and genome copy number alteration has a prognostic value for breast cancer. Conf Proc IEEE Eng Med Biol Soc, 2013:608-11 (2013)
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- [24] A Colaprico, C Cava, G Bertoli, G Bontempi, I Castiglioni: Integrative Analysis with Monte Carlo Cross-Validation Reveals miRNAs Regulating Pathways Cross-Talk in Aggressive Breast Cancer. Biomed Res Int, 2015:831314 (2015)
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- [26] F Rapaport, A Zinovyev, M Dutreix, E Barillot, JP Vert: Classification of microarray data using gene networks. BMC Bioinformatics, 8:35 (2007)
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