@article {405,
	title = {Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.},
	journal = {Cancer Res},
	volume = {78},
	year = {2018},
	month = {2018 Nov 01},
	pages = {6059-6072},
	abstract = {<p>Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .</p>},
	keywords = {Cell Line, Tumor, Cluster Analysis, Disease Progression, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Humans, Kaplan-Meier Estimate, Metabolome, mutation, Neoplasms, Oncogenes, Phenotype, Prognosis, RNA, Small Interfering, Sequence Analysis, RNA, Transcriptome, Treatment Outcome},
	issn = {1538-7445},
	doi = {10.1158/0008-5472.CAN-17-2705},
	author = {Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Pujana, Miguel A and Mateo, Francesca and Herranz, Carmen and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin}
}
@article {474,
	title = {Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.},
	journal = {Sci Rep},
	volume = {5},
	year = {2015},
	month = {2015 Dec 18},
	pages = {18494},
	abstract = {<p>Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA). </p>},
	keywords = {Algorithms, Antineoplastic Agents, biomarkers, Cell Line, Tumor, Cell Survival, gene expression, Humans, Lethal Dose 50, Neoplasms, Phosphorylation, Proteins, Signal Transduction},
	issn = {2045-2322},
	doi = {10.1038/srep18494},
	author = {Amadoz, Alicia and Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Salavert, Francisco and Dopazo, Joaquin}
}
