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@article{Ep-MES,
	Abstract = {Epithelial-mesenchymal transition (EMT) is a reversible and dynamic process hypothesized to be co-opted by carcinoma during invasion and metastasis. Yet, there is still no quantitative measure to assess the interplay between EMT and cancer progression. Here, we derived a method for universal EMT scoring from cancer-specific transcriptomic EMT signatures of ovarian, breast, bladder, lung, colorectal and gastric cancers. We show that EMT scoring exhibits good correlation with previously published, cancer-specific EMT signatures. This universal and quantitative EMT scoring was used to establish an EMT spectrum across various cancers, with good correlation noted between cell lines and tumours. We show correlations between EMT and poorer disease-free survival in ovarian and colorectal, but not breast, carcinomas, despite previous notions. Importantly, we found distinct responses between epithelial- and mesenchymal-like ovarian cancers to therapeutic regimes administered with or without paclitaxelin vivo and demonstrated that mesenchymal-like tumours do not always show resistance to chemotherapy. EMT scoring is thus a promising, versatile tool for the objective and systematic investigation of EMT roles and dynamics in cancer progression, treatment response and survival.},
	Address = {Oxford, UK},
	An = {PMC4287932},
	Author = {Tan, Tuan Zea and Miow, Qing Hao and Miki, Yoshio and Noda, Tetsuo and Mori, Seiichi and Huang, Ruby Yun-Ju and Thiery, Jean Paul},
	Date = {2014},
	Date-Added = {2017-11-14 06:29:11 +0000},
	Date-Modified = {2017-11-14 06:29:11 +0000},
	Db = {PMC},
	Doi = {10.15252/emmm.201404208},
	Isbn = {1757-4676; 1757-4684},
	J1 = {EMBO Mol Med},
	Journal = {EMBO Molecular Medicine},
	Month = {10},
	Number = {10},
	Pages = {1279--1293},
	Publisher = {BlackWell Publishing Ltd},
	Title = {Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients},
	Ty = {JOUR},
	U1 = {25214461{$[$}pmid{$]$}},
	Url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287932/},
	Volume = {6},
	Year = {2014},
	Year1 = {2014/09/11},
	Year2 = {2014/04/28/received},
	Year3 = {2014/08/07/revised},
	Year4 = {2014/08/08/accepted},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287932/},
	Bdsk-Url-2 = {http://dx.doi.org/10.15252/emmm.201404208}}

@article{Foroutanmolcanres.0313.2016,
	Abstract = {Most cancer deaths are due to metastasis, and epithelial-to-mesenchymal transition (EMT) plays a central role in driving cancer cell metastasis. EMT is induced by different stimuli, leading to different signaling patterns and therapeutic responses. Transforming growth factor beta (TGFbeta) is one of the best-studied drivers of EMT, and many drugs are available to target this signalling pathway. A comprehensive bioinformatics approach was employed to derive a signature for TGFbeta-induced EMT which can be used to score TGFbeta-driven EMT in cells and clinical specimens. Considering this signature in pan-cancer cell and tumour datasets, a number of cell lines (including Basal B breast cancer and cancers of the central nervous system) show evidence for TGFbeta-driven EMT and carry a low mutational burden across the TGFbeta signalling pathway. Further, significant variation is observed in the response of high scoring cell lines to some common cancer drugs. Finally, this signature was applied to pan-cancer data from The Cancer Genome Atlas to identify tumour types with evidence of TGFbeta-induced EMT. Tumour types with high scores showed significantly lower survival rates compared to those with low scores, and also carry a lower mutational burden in the TGFbeta pathway. The current transcriptomic signature demonstrates reproducible results across independent cell line and cancer datasets and identifies samples with strong mesenchymal phenotypes likely to be driven by TGFbeta. Implications: The TGFbeta-induced EMT signature may be useful to identify patients with mesenchymal-like tumours that could benefit from targeted therapeutics to inhibit pro-mesenchymal TGFbeta signalling and disrupt the metastatic cascade.},
	Author = {Foroutan, Momeneh and Cursons, Joseph and Hediyeh-Zadeh, Soroor and Thompson, Erik W and Davis, Melissa J},
	Date-Added = {2017-11-14 02:39:41 +0000},
	Date-Modified = {2017-11-14 02:39:41 +0000},
	Doi = {10.1158/1541-7786.MCR-16-0313},
	Eprint = {http://mcr.aacrjournals.org/content/early/2017/01/21/1541-7786.MCR-16-0313.full.pdf},
	Issn = {1541-7786},
	Journal = {Molecular Cancer Research},
	Publisher = {American Association for Cancer Research},
	Title = {A Transcriptional Program for Detecting TGFbeta-induced EMT in Cancer},
	Url = {http://mcr.aacrjournals.org/content/early/2017/01/21/1541-7786.MCR-16-0313},
	Year = {2017},
	Bdsk-Url-1 = {http://mcr.aacrjournals.org/content/early/2017/01/21/1541-7786.MCR-16-0313},
	Bdsk-Url-2 = {http://dx.doi.org/10.1158/1541-7786.MCR-16-0313}}

@article{barretina2012cancer,
	Annote = {@article{barretina2012cancer,
  title={The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity},
  author={Barretina, Jordi and Caponigro, Giordano and Stransky, Nicolas and Venkatesan, Kavitha and Margolin, Adam A and Kim, Sungjoon and Wilson, Christopher J and Leh{\'a}r, Joseph and Kryukov, Gregory V and Sonkin, Dmitriy and others},
  journal={Nature},
  volume={483},
  number={7391},
  pages={603--607},
  year={2012},
  publisher={Nature Research}
}},
	Author = {Barretina, Jordi and Caponigro, Giordano and Stransky, Nicolas and Venkatesan, Kavitha and Margolin, Adam A and Kim, Sungjoon and Wilson, Christopher J and Leh{\'a}r, Joseph and Kryukov, Gregory V and Sonkin, Dmitriy and others},
	Date-Added = {2017-11-14 02:35:06 +0000},
	Date-Modified = {2017-11-14 02:36:22 +0000},
	Journal = {Nature},
	Number = {7391},
	Pages = {603--607},
	Title = {The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity},
	Volume = {483},
	Year = {2012}}

	@article{TCGAcite,
	Abstract = {We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at > 10{\%} incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.},
	An = {PMC3465532},
	Author = {The Cancer Genome Atlas Network,},
	Date = {2012},
	Date-Added = {2018-02-28 07:54:42 +0000},
	Date-Modified = {2018-02-28 07:55:19 +0000},
	Db = {PMC},
	Doi = {10.1038/nature11412},
	Isbn = {0028-0836; 1476-4687},
	J1 = {Nature},
	Journal = {Nature},
	Month = {10},
	Number = {7418},
	Pages = {61--70},
	Title = {Comprehensive molecular portraits of human breast tumors},
	Ty = {JOUR},
	U1 = {23000897{$[$}pmid{$]$}},
	Url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465532/},
	Volume = {490},
	Year = {2012},
	Year1 = {2012/09/23},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465532/},
	Bdsk-Url-2 = {http://dx.doi.org/10.1038/nature11412}}


@article{Foroutan231217,
	  title={Single sample scoring of molecular phenotypes},
  author={Foroutan, Momeneh and Bhuva, Dharmesh D and Lyu, Ruqian and Horan, Kristy and Cursons, Joseph and Davis, Melissa J},
  journal={BMC bioinformatics},
  volume={19},
  number={1},
  pages={404},
  year={2018},
  publisher={BioMed Central},
  Doi = {10.1186/s12859-018-2435-4}
}
