02422nas a2200481 4500008004100000022001400041245004700055210004600102260001600148300001100164490000700175520106100182100001901243700002701262700001901289700002001308700002201328700002101350700002001371700001901391700002401410700001501434700002501449700002301474700002401497700002001521700002701541700002401568700002001592700002101612700002201633700002201655700002901677700001801706700001301724700001701737700001601754700002001770700002501790700001901815700002601834856008001860 2020 eng d a2589-004200aImmune Cell Associations with Cancer Risk.0 aImmune Cell Associations with Cancer Risk c2020 Jul 24 a1012960 v233 a
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.
1 aPalomero, Luis1 aGalván-Femenía, Ivan1 ade Cid, Rafael1 aEspín, Roderic1 aBarnes, Daniel, R1 aBlommaert, Eline1 aGil-Gil, Miguel1 aFalo, Catalina1 aStradella, Agostina1 aOuchi, Dan1 aRoso-Llorach, Albert1 aViolan, Concepció1 aPeña-Chilet, Maria1 aDopazo, Joaquin1 aExtremera, Ana, Isabel1 aGarcía-Valero, Mar1 aHerranz, Carmen1 aMateo, Francesca1 aMereu, Elisabetta1 aBeesley, Jonathan1 aChenevix-Trench, Georgia1 aRoux, Cecilia1 aMak, Tak1 aBrunet, Joan1 aHakem, Razq1 aGorrini, Chiara1 aAntoniou, Antonis, C1 aLázaro, Conxi1 aPujana, Miquel, Angel uhttps://www.clinbioinfosspa.es/content/immune-cell-associations-cancer-risk02824nas a2200397 4500008004100000022001400041245011600055210006900171260000900240300000600249490000600255520157600261653002601837653002401863653001901887653002901906653001101935653001301946653003601959653002301995653001402018653001402032653001302046653001802059100001802077700002202095700001902117700001602136700002402152700002202176700002102198700002002219700003102239700002002270856013602290 2019 eng d a2056-718900aDifferential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.0 aDifferential metabolic activity and discovery of therapeutic tar c2019 a70 v53 aIn 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.
10aComputational Biology10aComputer Simulation10aDrug discovery10aGene Regulatory Networks10aHumans10aInternet10aMetabolic Networks and Pathways10aModels, Biological10aNeoplasms10aPhenotype10aSoftware10aTranscriptome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aRian, Kinza1 aSalavert, Francisco1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/differential-metabolic-activity-and-discovery-therapeutic-targets-using-summarized-metabolic02950nas a2200445 4500008004100000022001400041245009500055210006900150260001500219300001400234490000700248520156700255653002101822653002101843653002401864653003001888653004301918653002901961653001101990653002602001653001502027653001302042653001402055653001402069653001402083653001402097653002702111653002702138653001802165653002202183100001802205700002202223700001902245700002202264700002102286700002002307700003102327700002002358856012602378 2018 eng d a1538-744500aGene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.0 aGene Expression Integration into Pathway Modules Reveals a PanCa c2018 11 01 a6059-60720 v783 aMetabolic 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. .
10aCell Line, Tumor10aCluster Analysis10aDisease Progression10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aGene Regulatory Networks10aHumans10aKaplan-Meier Estimate10aMetabolome10amutation10aNeoplasms10aOncogenes10aPhenotype10aPrognosis10aRNA, Small Interfering10aSequence Analysis, RNA10aTranscriptome10aTreatment Outcome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/gene-expression-integration-pathway-modules-reveals-pan-cancer-metabolic-landscape