@article {807, title = {HMGA1 regulates trabectedin sensitivity in advanced soft-tissue sarcoma (STS): A Spanish Group for Research on Sarcomas (GEIS) study.}, journal = {Cell Mol Life Sci}, volume = {81}, year = {2024}, month = {2024 May 17}, pages = {219}, abstract = {

HMGA1 is a structural epigenetic chromatin factor that has been associated with tumor progression and drug resistance. Here, we reported the prognostic/predictive value of HMGA1 for trabectedin in advanced soft-tissue sarcoma (STS) and the effect of inhibiting HMGA1 or the mTOR downstream pathway in trabectedin activity. The prognostic/predictive value of HMGA1 expression was assessed in a cohort of 301 STS patients at mRNA (n = 133) and protein level (n = 272), by HTG EdgeSeq transcriptomics and immunohistochemistry, respectively. The effect of HMGA1 silencing on trabectedin activity and gene expression profiling was measured in leiomyosarcoma cells. The effect of combining mTOR inhibitors with trabectedin was assessed on cell viability in vitro studies, whereas in vivo studies tested the activity of this combination. HMGA1 mRNA and protein expression were significantly associated with worse progression-free survival of trabectedin and worse overall survival in STS. HMGA1 silencing sensitized leiomyosarcoma cells for trabectedin treatment, reducing the spheroid area and increasing cell death. The downregulation of HGMA1 significantly decreased the enrichment of some specific gene sets, including the PI3K/AKT/mTOR pathway. The inhibition of mTOR, sensitized leiomyosarcoma cultures for trabectedin treatment, increasing cell death. In in vivo studies, the combination of rapamycin with trabectedin downregulated HMGA1 expression and stabilized tumor growth of 3-methylcholantrene-induced sarcoma-like models. HMGA1 is an adverse prognostic factor for trabectedin treatment in advanced STS. HMGA1 silencing increases trabectedin efficacy, in part by modulating the mTOR signaling pathway. Trabectedin plus mTOR inhibitors are active in preclinical models of sarcoma, downregulating HMGA1 expression levels and stabilizing tumor growth.

}, keywords = {Animals, Antineoplastic Agents, Alkylating, Cell Line, Tumor, Drug Resistance, Neoplasm, Female, Gene Expression Regulation, Neoplastic, HMGA1a Protein, Humans, Leiomyosarcoma, Mice, Prognosis, Sarcoma, Signal Transduction, TOR Serine-Threonine Kinases, Trabectedin, Xenograft Model Antitumor Assays}, issn = {1420-9071}, doi = {10.1007/s00018-024-05250-y}, author = {Moura, David S and Mondaza-Hernandez, Jose L and Sanchez-Bustos, Paloma and Pe{\~n}a-Chilet, Maria and Cordero-Varela, Juan A and Lopez-Alvarez, Maria and Carrillo-Garcia, Jaime and Martin-Ruiz, Marta and Romero-Gonzalez, Pablo and Renshaw-Calderon, Marta and Ramos, Rafael and Marcilla, David and Alvarez-Alegret, Ramiro and Agra-Pujol, Carolina and Izquierdo, Francisco and Ortega-Medina, Luis and Martin-Davila, Francisco and Hernandez-Leon, Carmen Nieves and Romagosa, Cleofe and Salgado, Maria Angeles Vaz and Lavernia, Javier and Bagu{\'e}, Silvia and Mayodormo-Aranda, Empar and Alvarez, Rosa and Valverde, Claudia and Martinez-Trufero, Javier and Castilla-Ramirez, Carolina and Gutierrez, Antonio and Dopazo, Joaquin and Hindi, Nadia and Garcia-Foncillas, Jesus and Martin-Broto, Javier} } @article {712, title = {A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.}, journal = {PLoS Comput Biol}, volume = {17}, year = {2021}, month = {2021 02}, pages = {e1008748}, abstract = {

MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.

}, keywords = {Algorithms, Cell Line, Tumor, Computational Biology, Databases, Factual, Gene Expression Profiling, Genomics, High-Throughput Nucleotide Sequencing, Humans, Models, Theoretical, mutation, RNA-seq, Signal Transduction, Software, Transcriptome, whole exome sequencing, Workflow}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008748}, author = {Garrido-Rodriguez, Mart{\'\i}n and L{\'o}pez-L{\'o}pez, Daniel and Ortuno, Francisco M and Pe{\~n}a-Chilet, Maria and Mu{\~n}oz, Eduardo and Calzado, Marco A and Dopazo, Joaquin} } @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 = {

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. .

}, 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 = {

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).

}, 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} } @article {505, title = {Mammosphere formation in breast carcinoma cell lines depends upon expression of E-cadherin.}, journal = {PLoS One}, volume = {8}, year = {2013}, month = {2013}, pages = {e77281}, abstract = {

Tumors are heterogeneous at the cellular level where the ability to maintain tumor growth resides in discrete cell populations. Floating sphere-forming assays are broadly used to test stem cell activity in tissues, tumors and cell lines. Spheroids are originated from a small population of cells with stem cell features able to grow in suspension culture and behaving as tumorigenic in mice. We tested the ability of eleven common breast cancer cell lines representing the major breast cancer subtypes to grow as mammospheres, measuring the ability to maintain cell viability upon serial non-adherent passage. Only MCF7, T47D, BT474, MDA-MB-436 and JIMT1 were successfully propagated as long-term mammosphere cultures, measured as the increase in the number of viable cells upon serial non-adherent passages. Other cell lines tested (SKBR3, MDA-MB-231, MDA-MB-468 and MDA-MB-435) formed cell clumps that can be disaggregated mechanically, but cell viability drops dramatically on their second passage. HCC1937 and HCC1569 cells formed typical mammospheres, although they could not be propagated as long-term mammosphere cultures. All the sphere forming lines but MDA-MB-436 express E-cadherin on their surface. Knock down of E-cadherin expression in MCF-7 cells abrogated its ability to grow as mammospheres, while re-expression of E-cadherin in SKBR3 cells allow them to form mammospheres. Therefore, the mammosphere assay is suitable to reveal stem like features in breast cancer cell lines that express E-cadherin.

}, keywords = {Breast Neoplasms, Cadherins, Cell Line, Tumor, Cell Proliferation, Cluster Analysis, Female, gene expression, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Knockdown Techniques, Humans, MCF-7 Cells, Neoplastic Stem Cells, Spheroids, Cellular, Tumor Cells, Cultured}, issn = {1932-6203}, doi = {10.1371/journal.pone.0077281}, author = {Manuel Iglesias, Juan and Beloqui, Izaskun and Garcia-Garcia, Francisco and Leis, Olatz and Vazquez-Martin, Alejandro and Eguiara, Arrate and Cufi, Silvia and Pavon, Andres and Menendez, Javier A and Dopazo, Joaquin and Martin, Angel G} }