De Novo Pathway-Based Classification of Breast Cancer Subtypes

  • Markus ListEmail author
  • Nicolas Alcaraz
  • Richa Batra
Part of the Methods in Molecular Biology book series (MIMB, volume 2074)


Breast cancer is a heterogeneous disease for which various clinically relevant subtypes have been reported. These subtypes are characterized by molecular differences which direct treatment selection. The state of the art for breast cancer subtyping utilizes histochemistry or gene expression to measure a few selected markers. However, classification based on molecular pathways (rather than individual markers) is a more robust way to classify breast cancer samples into known subtypes.

Here, we present PathClass, a web application that allows its users to predict breast cancer subtypes using various traditional as well as advanced methods. This includes methods based on classical gene expression panels as well as de novo pathway-based predictors. Users can predict labels for datasets in the Gene Expression Omnibus or upload their own expression profiling data.


Key words

De novo pathways Disease subtyping Breast cancer Classification 



R.B. would like to thank 676858-IMCIS for funding.


  1. 1.
    Perou CM, Sørlie T, Eisen MB et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752CrossRefGoogle Scholar
  2. 2.
    Donnenberg VS, Donnenberg AD (2005) Multiple drug resistance in cancer revisited: the cancer stem cell hypothesis. J Clin Pharmacol 45:872–877CrossRefGoogle Scholar
  3. 3.
    Parker JS, Mullins M, Cheang MCU et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167CrossRefGoogle Scholar
  4. 4.
    Slodkowska EA, Ross JS (2009) MammaPrint™ 70-gene signature: another milestone in personalized medical care for breast cancer patients. Expert Rev Mol Diagn 9:417–422CrossRefGoogle Scholar
  5. 5.
    Cronin M, Sangli C, Liu M-L et al (2007) Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin Chem 53:1084–1091CrossRefGoogle Scholar
  6. 6.
    Thakur S, Das AM, Das BC (2016) Utility of gene expression signature in treatment decision of breast cancer. Transl Cancer Res 5:S1469–S1472CrossRefGoogle Scholar
  7. 7.
    Wirapati P, Sotiriou C, Kunkel S et al (2008) Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res 10:R65CrossRefGoogle Scholar
  8. 8.
    Staiger C, Cadot S, Kooter R et al (2012) A critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancer. PLoS One 7:e34796CrossRefGoogle Scholar
  9. 9.
    Allahyar A, de Ridder J (2015) FERAL: network-based classifier with application to breast cancer outcome prediction. Bioinformatics 31:i311–i319CrossRefGoogle Scholar
  10. 10.
    Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30CrossRefGoogle Scholar
  11. 11.
    Joshi-Tope G, Gillespie M, Vastrik I et al (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33:D428–D432CrossRefGoogle Scholar
  12. 12.
    Chatr-aryamontri A, Breitkreutz B-J, Oughtred R et al (2015) The BioGRID interaction database: 2015 update. Nucleic Acids Res 43:D470–D478CrossRefGoogle Scholar
  13. 13.
    Kotlyar M, Pastrello C, Sheahan N, Jurisica I (2016) Integrated interactions database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Res 44:D536–D541CrossRefGoogle Scholar
  14. 14.
    Alcaraz N, List M, Batra R et al (2017) De novo pathway-based biomarker identification. Nucleic Acids Res 45:e151–e151CrossRefGoogle Scholar
  15. 15.
    Alcaraz N, List M, Dissing-Hansen M et al (2016) Robust de novo pathway enrichment with KeyPathwayMiner 5. F1000Res 5:1531CrossRefGoogle Scholar
  16. 16.
    Barbie DA, Tamayo P, Boehm JS et al (2009) Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462:108–112CrossRefGoogle Scholar
  17. 17.
    Wittkop T, Emig D, Lange S et al (2010) Partitioning biological data with transitivity clustering. Nat Methods 7:419–420CrossRefGoogle Scholar
  18. 18.
    Diaz-Uriarte R (2009) varSelRF: Variable selection using random forests. R package version 0 7-1.
  19. 19.
    Atlas TCG (2012) Comprehensive molecular portraits of human breast tumours. Nature 490:61–70CrossRefGoogle Scholar
  20. 20.
    Keshava Prasad TS, Goel R, Kandasamy K et al (2009) Human protein reference database—2009 update. Nucleic Acids Res 37:D767–D772CrossRefGoogle Scholar
  21. 21.
    Brown KR, Jurisica I (2007) Unequal evolutionary conservation of human protein interactions in interologous networks. Genome Biol 8:R95CrossRefGoogle Scholar
  22. 22.
    Isserlin R, El-Badrawi RA, Bader GD (2011) The biomolecular interaction network database in PSI-MI 2.5. Database 2011:baq037CrossRefGoogle Scholar
  23. 23.
    Licata L, Briganti L, Peluso D et al (2012) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 40:D857–D861CrossRefGoogle Scholar
  24. 24.
    Bovolenta LA, Acencio ML, Lemke N (2012) HTRIdb: an open-access database for experimentally verified human transcriptional regulation interactions. BMC Genomics 13:405CrossRefGoogle Scholar
  25. 25.
    Lee I, Blom UM, Wang PI et al (2011) Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res 21:1109–1121CrossRefGoogle Scholar
  26. 26.
    Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550CrossRefGoogle Scholar
  27. 27.
    Kamburov A, Stelzl U, Lehrach H, Herwig R (2013) The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res 41:D793–D800CrossRefGoogle Scholar
  28. 28.
    Haibe-Kains B, Desmedt C, Loi S et al (2012) A three-gene model to robustly identify breast cancer molecular subtypes. J Natl Cancer Inst 104:311–325CrossRefGoogle Scholar
  29. 29.
    Sørlie T, Tibshirani R, Parker J et al (2003) Repeated observation of breast tumor subtypes in independent gene expression datasets. Proc Natl Acad Sci U S A 100:8418–8423CrossRefGoogle Scholar
  30. 30.
    Hu Z, Fan C, Oh DS et al (2006) The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 7:96CrossRefGoogle Scholar
  31. 31.
    Desmedt C, Haibe-Kains B, Wirapati P et al (2008) Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res 14:5158–5165CrossRefGoogle Scholar
  32. 32.
    Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–210CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.TUM School of Life SciencesTechnical University of MunichFreisingGermany
  2. 2.Department of Biology, The Bioinformatics CentreUniversity of CopenhagenCopenhagenDenmark
  3. 3.Helmholtz Zentrum München, Research Center for Environmental HealthInstitute of Computational BiologyNeuherbergGermany
  4. 4.Department of Dermatology and AllergyTechnical University of MunichMünchenGermany

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