229 research outputs found

    Wnt and vitamin D at the crossroads in solid cancer

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    The Wnt/β-catenin signaling pathway is aberrantly activated in most colorectal cancers and less frequently in a variety of other solid neoplasias. Many epidemiological and experimental studies and some clinical trials suggest an anticancer action of vitamin D, mainly against colorectal cancer. The aim of this review was to analyze the literature supporting the interference of Wnt/β-catenin signaling by the active vitamin D metabolite 1α,25-dihydroxyvitamin D3. We discuss the molecular mechanisms of this antagonism in colorectal cancer and other cancer types. Additionally, we summarize the available data indicating a reciprocal inhibition of vitamin D action by the activated Wnt/β-catenin pathway. Thus, a complex mutual antagonism between Wnt/β-catenin signaling and the vitamin D system seems to be at the root of many solid cancers. Abnormal activation of the Wnt/β-catenin pathway is common in many types of solid cancers. Likewise, a large proportion of cancer patients have vitamin D deficiency. In line with these observations, Wnt/β-catenin signaling and 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3), the active vitamin D metabolite, usually have opposite effects on cancer cell proliferation and phenotype. In recent years, an increasing number of studies performed in a variety of cancer types have revealed a complex crosstalk between Wnt/β-catenin signaling and 1,25(OH)2D3. Here we review the mechanisms by which 1,25(OH)2D3 inhibits Wnt/β-catenin signaling and, conversely, how the activated Wnt/β-catenin pathway may abrogate vitamin D action. The available data suggest that interaction between Wnt/β-catenin signaling and the vitamin D system is at the crossroads in solid cancers and may have therapeutic applications.The work in the authors’ laboratory is funded by the Agencia Estatal de Investigación (PID2019-104867RB-I00/AEI/10.13039/501100011033), the Agencia Estatal de Investigación—Fondo Europeo de Desarrollo Regional (SAF2016-76377-R, MINECO/AEI/FEDER, EU), the Ministerio de Economía y Competitividad (SAF2017-90604-REDT/NuRCaMeIn), and the Instituto de Salud Carlos III—Fondo Europeo de Desarrollo Regional (CIBERONC; CB16/12/00273

    Leveraging big data resources and data integration in biology: applying computational systems analyses and machine learning to gain insights into the biology of cancers

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    Recently, many "molecular profiling" projects have yielded vast amounts of genetic, epigenetic, transcription, protein expression, metabolic and drug response data for cancerous tumours, healthy tissues, and cell lines. We aim to facilitate a multi-scale understanding of these high-dimensional biological data and the complexity of the relationships between the different data types taken from human tumours. Further, we intend to identify molecular disease subtypes of various cancers, uncover the subtype-specific drug targets and identify sets of therapeutic molecules that could potentially be used to inhibit these targets. We collected data from over 20 publicly available resources. We then leverage integrative computational systems analyses, network analyses and machine learning, to gain insights into the pathophysiology of pancreatic cancer and 32 other human cancer types. Here, we uncover aberrations in multiple cell signalling and metabolic pathways that implicate regulatory kinases and the Warburg effect as the likely drivers of the distinct molecular signatures of three established pancreatic cancer subtypes. Then, we apply an integrative clustering method to four different types of molecular data to reveal that pancreatic tumours can be segregated into two distinct subtypes. We define sets of proteins, mRNAs, miRNAs and DNA methylation patterns that could serve as biomarkers to accurately differentiate between the two pancreatic cancer subtypes. Then we confirm the biological relevance of the identified biomarkers by showing that these can be used together with pattern-recognition algorithms to infer the drug sensitivity of pancreatic cancer cell lines accurately. Further, we evaluate the alterations of metabolic pathway genes across 32 human cancers. We find that while alterations of metabolic genes are pervasive across all human cancers, the extent of these gene alterations varies between them. Based on these gene alterations, we define two distinct cancer supertypes that tend to be associated with different clinical outcomes and show that these supertypes are likely to respond differently to anticancer drugs. Overall, we show that the time has already arrived where we can leverage available data resources to potentially elicit more precise and personalised cancer therapies that would yield better clinical outcomes at a much lower cost than is currently being achieved

    Integrative analysis of genomic data

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    This thesis is composed of three different projects, and aims to predict substrates which transported by transmembrane proteins, understand the effects caused by copy number alterations (CNAs) on target proteins of antineoplastic (AN) agents, and on the genes in antineoplastic resistance pathways in cancer patients. In the first project, we propose a computational method to classify membrane transporters from three organisms (Escherichia coli, Saccharomyces cerevisiae and Homo sapiens) according to their transported substrates. Our method focuses on neighboring genes that show high co-expression with query gene. Then, we identified frequent gene ontology (GO) terms among these co-expressed neighbors and used a support vector machine classifier to annotate the substrate specificity of the query gene. The second project analyses CNAs and clinical data of 31 tumor types from The Cancer Genome Atlas (TCGA). We found that the genome sequences of tumor patients generally contain more recurrently deleted CNAs than recurrently amplified CNAs. We observed certain signs of apparently compensating effects of CNAs. The third project continues the idea of chemoresistance as suggested in the second one. This project utilized TCGA CNAs data from both normal and tumor tissues. We found that the genome sequences of tumor tissues contain more recurrently amplified CNAs of genes in cancer antineoplastic resistance pathways than normal tissues.Diese Arbeit besteht aus drei verschiedenen Projekten, die darauf abzielen Substrate die von Transmembranproteinen transportiert werden vorherzusagen, die Auswirkungen sog. Kopienzahlvariationen (CNAs) sowohl auf Zielproteine von Antineoplastischen Medikamenten als auch auf die zugehörigen Gene in den entsprechenden Resistenzwegen von Krebspatienten zu verstehen. Im ersten Projekt wird eine computergestützte Methode zur Klassifizierung von Transmembrantransportern dreier Organismen (Escherichia coli, Saccharomyces cerevisiae und Homo sapiens) anhand der von ihnen transportierten Substrate vorgestellt. Im zweiten Projekt wurden CNAs und klinische Daten von 31 Tumorarten die aus dem Cancer Genome Atlas (TCGA) stammen analysiert. Dabei stellte sich heraus, daß die genomischen Sequenzen von Tumorpatienten im allgemeinen mehr wiederkehrend deletierte CNAs aufweisen als wiederkehrend amplifizierte CNAs. Ebenfalls beobachtet wurden bestimmte Anzeichen für offensichtlich kompensatorische Effekte durch CNAs. Wie im vorgehenden Projekt wurde auch im dritten Teil der Arbeit die Idee der Chemoresistenz weiterverfolgt. Hierbei wurden CNA-Daten von normalem Gewebe, als auch von Tumorgewebe aus dem TCGA verwendet. Dabei wurde festgestellt, daß die genomischen Sequenzen von Tumorgewebe mehr wiederkehrend amplifizierte CNAs von Genen aufweisen, welche sich in Resistenzwegen von Antineoplastica befinden, als dies in normalem Gewebe der Fall ist
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