C. M. Perou, Molecular portraits of human breast tumours, Nature, vol.406, pp.747-752, 2000.

, Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours, Nature, vol.490, pp.61-70, 2012.

C. M. Perou, Molecular stratification of triple-negative breast cancers, Oncologist, vol.15, issue.5, pp.39-48, 2010.

A. Prat, Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer, Breast Cancer Res, vol.12, p.68, 2010.

A. Prat, Clinical implications of the intrinsic molecular subtypes of breast cancer, Breast, vol.24, issue.2, pp.26-35, 2015.

A. Van-keymeulen, Reactivation of multipotency by oncogenic PIK3CA induces breast tumour heterogeneity, Nature, vol.525, pp.119-123, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02379276

S. Koren, PIK3CA(H1047R) induces multipotency and multi-lineage mammary tumours, Nature, vol.525, pp.114-118, 2015.

A. Puisieux, R. M. Pommier, A. Morel, and F. Lavial, Cellular pliancy and the multistep process of tumorigenesis, Cancer Cell, vol.33, pp.164-172, 2018.

S. Badve, Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists, Mod. Pathol, vol.24, pp.157-167, 2011.

S. F. Chin, High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer, Genome Biol, vol.8, p.215, 2007.

H. G. Russnes, O. C. Lingjaerde, A. Børresen-dale, and C. Caldas, Breast cancer molecular stratification: from intrinsic subtypes to integrative clusters, Am. J. Pathol, vol.187, pp.2152-2162, 2017.

J. I. Herschkowitz, Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors

, Genome Biol, vol.8, p.76, 2007.

A. Prat and C. M. Perou, Deconstructing the molecular portraits of breast cancer, Mol. Oncol, vol.5, pp.5-23, 2011.

E. Lim, Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers, Nat. Med, vol.15, pp.907-913, 2009.

A. Morel, Generation of breast cancer stem cells through epithelialmesenchymal transition, PLoS ONE, vol.3, p.2888, 2008.

S. A. Mani, The epithelial-mesenchymal transition generates cells with properties of stem cells, Cell, vol.133, pp.704-715, 2008.

A. Morel, EMT inducers catalyze malignant transformation of mammary epithelial cells and drive tumorigenesis towards claudin-low tumors in transgenic mice, PLoS Genet, vol.8, p.1002723, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00712474

A. Morel, A stemness-related ZEB1-MSRB3 axis governs cellular pliancy and breast cancer genome stability, Nat. Med, vol.23, pp.568-578, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01788395

K. Dias, Claudin-low breast cancer; clinical & pathological characteristics, PLoS ONE, vol.12, p.168669, 2017.

P. Van-loo, Allele-specific copy number analysis of tumors, Proc. Natl Acad. Sci. USA, vol.107, pp.16910-16915, 2010.

C. Curtis, The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups, Nature, vol.486, pp.346-352, 2012.

S. Dawson, O. M. Rueda, S. Aparicio, and C. Caldas, A new genome-driven integrated classification of breast cancer and its implications, EMBO J, vol.32, pp.617-628, 2013.

S. Song, qpure: A tool to estimate tumor cellularity from genome-wide single-nucleotide polymorphism profiles, PLoS ONE, vol.7, p.45835, 2012.

C. Biernacki, G. Celeux, G. Govaert, and F. Langrognet, Model-based cluster and discriminant analysis with the MIXMOD software, Comput. Stat. Data Anal, vol.51, pp.587-600, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00069878

L. Scrucca, M. Fop, T. B. Murphy, and A. E. Raftery, mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R, J, vol.8, pp.289-317, 2016.

R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, Diagnosis of multiple cancer types by shrunken centroids of gene expression, Proc. Natl Acad. Sci. USA, vol.99, pp.6567-6572, 2002.

J. Barretina, The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity, Nature, vol.483, pp.603-607, 2012.

R. Akbani, A pan-cancer proteomic perspective on The Cancer Genome Atlas, Nat. Commun, vol.5, p.3887, 2014.

R. Sabatier, Claudin-low breast cancers: clinical, pathological, molecular and prognostic characterization, Mol. Cancer, vol.13, p.228, 2014.
URL : https://hal.archives-ouvertes.fr/inserm-01192813

C. Fougner, H. Bergholtz, J. H. Norum, and T. Sørlie, Re-definition of claudinlow as a breast cancer phenotype, Nat. Commun, vol.11, p.1787, 2020.

L. V. Nguyen, Barcoding reveals complex clonal dynamics of de novo transformed human mammary cells, Nature, vol.528, pp.267-271, 2015.

A. Dhasarathy, D. Phadke, D. Mav, R. R. Shah, and P. A. Wade, The transcription factors Snail and Slug activate the transforming growth factorbeta signaling pathway in breast cancer, PLoS ONE, vol.6, p.26514, 2011.

X. Chen, A. Pappo, and M. A. Dyer, Pediatric solid tumor genomics and developmental pliancy, Oncogene, vol.34, pp.5207-5215, 2015.

K. Zhang, Oncogenic K-Ras upregulates ITGA6 expression via FOSL1 to induce anoikis resistance and synergizes with ?V-Class integrins to promote EMT, Oncogene, vol.36, pp.5681-5694, 2017.

X. Zhang, Q. Cheng, H. Yin, and G. Yang, Regulation of autophagy and EMT by the interplay between p53 and RAS during cancer progression (Review)

, Int. J. Oncol, vol.51, pp.18-24, 2017.

D. C. Voon and .. , EMT-induced stemness and tumorigenicity are fueled by the EGFR/Ras pathway, PLoS ONE, vol.8, p.70427, 2013.

P. Zhang, ATM-mediated stabilization of ZEB1 promotes DNA damage response and radioresistance through CHK1, Nat. Cell Biol, vol.16, pp.864-875, 2014.

P. Farmer, A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer, Nat. Med, vol.15, pp.68-74, 2009.

S. Drápela, J. Bouchal, M. K. Jolly, Z. Culig, and K. Sou?ek, ZEB1: a critical regulator of cell plasticity, DNA damage response, and therapy resistance, Front. Mol. Biosci, vol.7, p.36, 2020.

. R-core-team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019.

Z. Gu, R. Eils, and M. Schlesner, Complex heatmaps reveal patterns and correlations in multidimensional genomic data, Bioinformatics, vol.32, pp.2847-2849, 2016.

S. Dray and A. Dufour, The ade4 Package: implementing the duality diagram for ecologists, J. Stat. Softw, vol.22, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00434575

R. Lebret, Rmixmod: The R package of the model-based unsupervised, supervised, and semi-supervised classification mixmod library, J. Stat. Softw, vol.67, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00919486

D. M. Gendoo, Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer, Bioinformatics, vol.32, pp.1097-1099, 2016.

B. Haibe-kains, A three-gene model to robustly identify breast cancer molecular subtypes, J. Natl Cancer Inst, vol.104, pp.311-325, 2012.

P. Wirapati, Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures, Breast Cancer Res, vol.10, p.65, 2008.

J. S. Parker, Supervised risk predictor of breast cancer based on intrinsic subtypes, J. Clin. Oncol, vol.27, pp.1160-1167, 2009.

Z. Hu, The molecular portraits of breast tumors are conserved across microarray platforms, BMC Genomics, vol.7, p.96, 2006.

E. R. Paquet and M. T. Hallett, Absolute assignment of breast cancer intrinsic molecular subtype, J. Natl Cancer Inst, vol.107, p.357, 2015.

A. Subramanian, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl Acad. Sci. USA, vol.102, pp.15545-15550, 2005.

A. A. Sergushichev, An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation, 2016.

S. Hänzelmann, R. Castelo, and J. Guinney, GSVA: gene set variation analysis for microarray and RNA-Seq data, BMC Bioinformatics, vol.14, p.7, 2013.

T. Z. Tan, Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients, EMBO Mol. Med, vol.6, pp.1279-1293, 2014.

B. S. Carvalho and R. A. Irizarry, A framework for oligonucleotide microarray preprocessing, Bioinformatics, vol.26, pp.2363-2367, 2010.

M. E. Ritchie, limma powers differential expression analyses for RNAsequencing and microarray studies, Nucleic Acids Res, vol.43, p.47, 2015.

G. Sturm, Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology, Bioinformatics, vol.35, pp.436-445, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02281980

D. Aran, Z. Hu, and A. J. Butte, xCell: digitally portraying the tissue cellular heterogeneity landscape, Genome Biol, vol.18, p.220, 2017.

E. Becht, Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression, Genome Biol, vol.17, p.218, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01398093

E. Cerami, The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data, Cancer Discov, vol.2, pp.401-404, 2012.

J. Gao, Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal, Sci. Signal, vol.6, p.1, 2013.

T. Fleischer, DNA methylation at enhancers identifies distinct breast cancer lineages, Nat. Commun, vol.8, p.1379, 2017.

J. Li, TCPA: a resource for cancer functional proteomics data, Nat. Methods, vol.10, pp.1046-1047, 2013.

B. Pereira, The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes, Nat. Commun, vol.7, p.11479, 2016.

K. Ellrott, Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines, Cell Syst, vol.6, pp.271-281, 2018.

W. Yang, Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells, Nucleic Acids Res, vol.41, pp.955-961, 2013.

R. M. P-;-l, J. K. , E. T. , A. F. , A. S. et al., provided scientific content. F.H. provided critical evaluation of the manuscript and scientific content. A.P. conceived the project, designed analyses, interpreted data, and wrote the manuscript