TY - JOUR T1 - Immune Cell Associations with Cancer Risk. JF - iScience Y1 - 2020 A1 - Palomero, Luis A1 - Galván-Femenía, Ivan A1 - de Cid, Rafael A1 - Espín, Roderic A1 - Barnes, Daniel R A1 - Blommaert, Eline A1 - Gil-Gil, Miguel A1 - Falo, Catalina A1 - Stradella, Agostina A1 - Ouchi, Dan A1 - Roso-Llorach, Albert A1 - Violan, Concepció A1 - Peña-Chilet, Maria A1 - Dopazo, Joaquin A1 - Extremera, Ana Isabel A1 - García-Valero, Mar A1 - Herranz, Carmen A1 - Mateo, Francesca A1 - Mereu, Elisabetta A1 - Beesley, Jonathan A1 - Chenevix-Trench, Georgia A1 - Roux, Cecilia A1 - Mak, Tak A1 - Brunet, Joan A1 - Hakem, Razq A1 - Gorrini, Chiara A1 - Antoniou, Antonis C A1 - Lázaro, Conxi A1 - Pujana, Miquel Angel AB -

Proper immune system function hinders cancer development, but little is known about whether genetic variants linked to cancer risk alter immune cells. Here, we report 57 cancer risk loci associated with differences in immune and/or stromal cell contents in the corresponding tissue. Predicted target genes show expression and regulatory associations with immune features. Polygenic risk scores also reveal associations with immune and/or stromal cell contents, and breast cancer scores show consistent results in normal and tumor tissue. SH2B3 links peripheral alterations of several immune cell types to the risk of this malignancy. Pleiotropic SH2B3 variants are associated with breast cancer risk in BRCA1/2 mutation carriers. A retrospective case-cohort study indicates a positive association between blood counts of basophils, leukocytes, and monocytes and age at breast cancer diagnosis. These findings broaden our knowledge of the role of the immune system in cancer and highlight promising prevention strategies for individuals at high risk.

VL - 23 IS - 7 U1 - https://www.ncbi.nlm.nih.gov/pubmed/32622267?dopt=Abstract ER - TY - JOUR T1 - Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models. JF - NPJ Syst Biol Appl Y1 - 2019 A1 - Cubuk, Cankut A1 - Hidalgo, Marta R A1 - Amadoz, Alicia A1 - Rian, Kinza A1 - Salavert, Francisco A1 - Pujana, Miguel A A1 - Mateo, Francesca A1 - Herranz, Carmen A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Computational Biology KW - Computer Simulation KW - Drug discovery KW - Gene Regulatory Networks KW - Humans KW - Internet KW - Metabolic Networks and Pathways KW - Models, Biological KW - Neoplasms KW - Phenotype KW - Software KW - Transcriptome AB -

In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.

VL - 5 U1 - https://www.ncbi.nlm.nih.gov/pubmed/30854222?dopt=Abstract ER - TY - JOUR T1 - Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. JF - Cancer Res Y1 - 2018 A1 - Cubuk, Cankut A1 - Hidalgo, Marta R A1 - Amadoz, Alicia A1 - Pujana, Miguel A A1 - Mateo, Francesca A1 - Herranz, Carmen A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Cell Line, Tumor KW - Cluster Analysis KW - Disease Progression KW - Gene Expression Profiling KW - Gene Expression Regulation, Neoplastic KW - Gene Regulatory Networks KW - Humans KW - Kaplan-Meier Estimate KW - Metabolome KW - mutation KW - Neoplasms KW - Oncogenes KW - Phenotype KW - Prognosis KW - RNA, Small Interfering KW - Sequence Analysis, RNA KW - Transcriptome KW - Treatment Outcome AB -

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. .

VL - 78 IS - 21 U1 - https://www.ncbi.nlm.nih.gov/pubmed/30135189?dopt=Abstract ER -