Rubio, A. (Ángel)
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- DrugSniper, a Tool to Exploit Loss-Of-Function Screens, Identifies CREBBP as a Predictive Biomarker of VOLASERTIB in Small Cell Lung Carcinoma (SCLC)(2020) Cendoya-Garmendia, X. (Xabier); Pio, R. (Rubén); Serrano, D. (Diego); Castilla, C. (Carlos); Campuzano, L. (Lucía); Carazo-Melo, F.(Fernando); Montuenga-Badia, L.M. (Luis M.); Bertolo, C. (Cristina); Planes-Pedreño, F.J. (Francisco Javier); Gimeno-Combarro, M. (Marian); Rubio, A. (Ángel)The development of predictive biomarkers of response to targeted therapies is an unmet clinical need for many antitumoral agents. Recent genome-wide loss-of-function screens, such as RNA interference (RNAi) and CRISPR-Cas9 libraries, are an unprecedented resource to identify novel drug targets, reposition drugs and associate predictive biomarkers in the context of precision oncology. In this work, we have developed and validated a large-scale bioinformatics tool named DrugSniper, which exploits loss-of-function experiments to model the sensitivity of 6237 inhibitors and predict their corresponding biomarkers of sensitivity in 30 tumor types. Applying DrugSniper to small cell lung cancer (SCLC), we identified genes extensively explored in SCLC, such as Aurora kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to polo-like kinase 1 (PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association in vitro using four mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of CREBBP. Besides, DrugSniper was validated in-silico with several known clinically-used treatments, including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to FLT3 and BRAF mutant cells, respectively. These findings show the potential of genome-wide loss-of-function screens to identify new personalized therapeutic hypotheses in SCLC and potentially in other tumors, which is a valuable starting point for further drug development and drug repositioning projects.
- Integration of CRISPR-Cas9, shRNA with other genomic data provides reliable predicions of gene essentiality(Elsevier, 2020) Rubio, A. (Ángel)Recent genome-wide loss-of-function screening studies have provided an unprecedented amount of information to perform functional analysis of the genome. Two main strategies have been followed to perform these experiments: shRNA and, more recently, CRISPR-Cas9. Both technologies have shown their ability to knock down genes and, in the case of CRISPR-Cas9, the ability to create new cell-line strands with different phenotypes
- Development of a novel splice array platform and its application in the identification of alternative splice variants in lung cancer(BioMed Central, 2010-06-03) Pajares, M.J. (María José); Anton, M.A. (Miguel Ángel); Pio, R. (Rubén); Lozano, M.D. (María Dolores); Gomez-Roman, J. (Javier); Agorreta, J. (Jackeline); Subirada, F. (Francesc); Durany, O. (O.); Lopez-Picazo, J.M. (José M.); Blanco, D. (Daniel); Ezponda, T. (Teresa); Montuenga-Badia, L.M. (Luis M.); Aibar, E. (Elena); Maes, T. (Tamara); Rubio, A. (Ángel)Abstract Background Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array approach was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events. Results The array consisted of exon probes and thermodynamically balanced junction probes. Suboptimal probes were tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms in lung cancer samples compared to matched normal lung tissue. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer. Conclusions This methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies.
- FactorY, a bioinformatic resource for genome-wide promoter analysis(Elsevier, 2009) Corrales, F.J. (Fernando José); Guruceaga, E. (Elizabeth); Segura, V. (Víctor); Rubio, A. (Ángel)The interpretation of the complex molecular descriptions generated by high-throughput gene expression technologies is still challenging. The development of new tools to identify common regulatory mechanisms involved in the control of the expression of a set of co-expressed genes, might enhance our capacity to extract functional information from genomic data sets. Here we present FactorY, a website that allows identification of enriched transcription factor binding sites (TFBSs) in the proximal promoter of a cluster of genes, as well as functional interpretation, and intuitive visualization of the results.
- Identification of a gene-pathway associated with non-alcoholic steatohepatitis(Elsevier, 2007) Sevilla, J.L. (José L.); Martin-Duce, A. (Antonio); Corrales, F.J. (Fernando José); Martinez-Arrieta, F. (Félix); Lu, S.C. (Shelly C.); Rodriguez, M. (Manuel); Martinez-Cruz, L.A. (L. Alfonso); Torres, L. (Luis); Guruceaga, E. (Elizabeth); Ariz, U. (Usue); Podhorski, A. (Adam); Caballeria, J. (Juan); Vazquez-Chantada, M. (Mercedes); Segura, V. (Víctor); Mato, J.M. (José María); Sandoval, J. (Juan); Rubio, A. (Ángel); Martinez-Chantar, M.L. (María Luz); Aillet, F. (Fabienne)BACKGROUND/AIMS: We have integrated gene expression profiling of liver biopsies of NASH patients with liver samples of a mouse model of steatohepatitis (MAT1A-KO) to identify a gene-pathway associated with NASH. METHODS: Affymetrix U133 Plus 2.0 microarrays were used to evaluate nine patients with NASH, six patients with steatosis, and six control subjects; Affymetrix MOE430A microarrays were used to evaluate wild-type and MAT1A-KO mice at 15 days, 1, 3, 5 and 8 months after birth. Transcriptional profiles of patients with NASH and MAT1A-KO mice were compared with those of their proficient controls. RESULTS: We identified a gene-pathway associated with NASH, that accurately distinguishes between patients with early-stage NASH and controls. Patients with steatosis have a gene expression pattern intermediate between that of NASH and controls. Promoter analysis revealed that 34 of the genes associated with NASH contained an Sp1 element. We found that Sp1 binding to these genes is increased in MAT1A-KO mice. Sp1 is also hyperphosphorylated in MAT1A-KO as well as in patients with NASH and steatosis. CONCLUSIONS: A gene-pathway associated with NASH has been identified. We speculate that hyperphosphorylation of Sp1 may be involved in the genesis of steatosis and that other factors, such as oxidative stress, may trigger its progression to NASH.
- Quantification of miRNA-mRNA interactions(Public Library of Science, 2012) Vazquez, M. (Miguel); Pascual-Montano, A. (Alberto); Muniategui, A. (Ander); Luttun, A. (Aernout); Nogales-Cadenas, R. (Rubén); Prosper-Cardoso, F. (Felipe); Aguirre-Ena, X. (Xabier); Rubio, A. (Ángel); Aranguren, X.L. (Xabier L.)miRNAs are small RNA molecules (' 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO).We used TaLasso on two public datasets that have paired expression levels of human miRNAs and mRNAs. The top ranked interactions recovered by TaLasso are especially enriched (more than using any other algorithm) in experimentally validated targets. The functions of the genes with mRNA transcripts in the top-ranked interactions are meaningful. This is not the case using other algorithms.TaLasso is available as Matlab or R code. There is also a web-based tool for human miRNAs at http://talasso.cnb.csic.es/.
- Precision oncology: a review to assess interpretability in several explainable methods(Oxford University Press, 2023) Sada-del-Real, K. (Katyna); Gimeno-Combarro, M. (Marian); Rubio, A. (Ángel)Great efforts have been made to develop precision medicine-based treatments using machine learning. In this field, where the goal is to provide the optimal treatment for each patient based on his/her medical history and genomic characteristics, it is not sufficient to make excellent predictions. The challenge is to understand and trust the model's decisions while also being able to easily implement it. However, one of the issues with machine learning algorithms-particularly deep learning-is their lack of interpretability. This review compares six different machine learning methods to provide guidance for defining interpretability by focusing on accuracy, multi-omics capability, explainability and implementability. Our selection of algorithms includes tree-, regression- and kernel-based methods, which we selected for their ease of interpretation for the clinician. We also included two novel explainable methods in the comparison. No significant differences in accuracy were observed when comparing the methods, but an improvement was observed when using gene expression instead of mutational status as input for these methods. We concentrated on the current intriguing challenge: model comprehension and ease of use. Our comparison suggests that the tree-based methods are the most interpretable of those tested.
- Integrative Oncogenomic Analysis of Microarray Data in Hematologic Malignancies(Humana Press, 2010) Fresquet, V. (Vicente); Ortiz, M. (María); Martinez-Climent, J.A. (José Ángel); Fontan, L. (Lorena); Robles, E.F. (Eloy Francisco); Rubio, A. (Ángel)During the last decade, gene expression microarrays and array-based comparative genomic hybridization (array-CGH) have unraveled the complexity of human tumor genomes more precisely and comprehensively than ever before. More recently, the simultaneous assessment of global changes in messenger RNA (mRNA) expression and in DNA copy number through "integrative oncogenomic" analyses has allowed researchers the access to results uncovered through the analysis of one-dimensional data sets, thus accelerating cancer gene discovery. In this chapter, we discuss the major contributions of DNA microarrays to the study of hematological malignancies, focusing on the integrative oncogenomic approaches that correlate genomic and transcriptomic data. We also present the basic aspects of these methodologies and their present and future application in clinical oncology.
- Discovering the mechanism of action of drugs with a sparse explainable network(2023) Sada-del-Real, K. (Katyna); Rubio, A. (Ángel)Background Although Deep Neural Networks (DDNs) have been successful in predicting the efficacy of cancer drugs, the lack of explainability in their decision-making process is a significant challenge. Previous research proposed mimicking the Gene Ontology structure to allow for interpretation of each neuron in the network. However, these previous approaches require huge amount of GPU resources and hinder its extension to genome-wide models. Methods We developed SparseGO, a sparse and interpretable neural network, for predicting drug response in cancer cell lines and their Mechanism of Action (MoA). To ensure model generalization, we trained it on multiple datasets and evaluated its performance using three cross-validation schemes. Its efficiency allows it to be used with gene expression. In addition, SparseGO integrates an eXplainable Artificial Intelligence (XAI) technique, DeepLIFT, with Support Vector Machines to computationally discover the MoA of drugs. Findings SparseGO's sparse implementation significantly reduced GPU memory usage and training speed compared to other methods, allowing it to process gene expression instead of mutations as input data. SparseGO using expression improved the accuracy and enabled its use on drug repositioning. Furthermore, gene expression allows the prediction of MoA using 265 drugs to train it. It was validated on understudied drugs such as parbendazole and PD153035. Interpretation SparseGO is an effective XAI method for predicting, but more importantly, understanding drug response.
- Integration of CLIP experiments of RNAbinding proteins: a novel approach to predict context-dependent splicing factors from transcriptomic data(Springer Science and Business Media LLC, 2019) Carazo-Melo, F.(Fernando); Ferrer-Bonsoms, J.A. (Juan A.); Gimeno-Combarro, M. (Marian); Rubio, A. (Ángel)Background: Splicing is a genetic process that has important implications in several diseases including cancer. Deciphering the complex rules of splicing regulation is crucial to understand and treat splicing-related diseases. Splicing factors and other RNA-binding proteins (RBPs) play a key role in the regulation of splicing. The specific binding sites of an RBP can be measured using CLIP experiments. However, to unveil which RBPs regulate a condition, it is necessary to have a priori hypotheses, as a single CLIP experiment targets a single protein. Results: In this work, we present a novel methodology to predict context-specific splicing factors from transcriptomic data. For this, we systematically collect, integrate and analyze more than 900 CLIP experiments stored in four CLIP databases: POSTAR2, CLIPdb, DoRiNA and StarBase. The analysis of these experiments shows the strong coherence between the binding sites of RBPs of similar families. Augmenting this information with expression changes, we are able to correctly predict the splicing factors that regulate splicing in two gold-standard experiments in which specific splicing factors are knocked-down. Conclusions: The methodology presented in this study allows the prediction of active splicing factors in either cancer or any other condition by only using the information of transcript expression. This approach opens a wide range of possible studies to understand the splicing regulation of different conditions. A tutorial with the source code and databases is available at https://gitlab.com/fcarazo.m/sfprediction.