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The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated dense single-cell and spatial reference maps of the human uterus and three-dimensional endometrial organoid cultures. We dissect the signaling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids reveals the pathways and cell states regulating differentiation of the secretory and ciliated lineages both in vivo and in vitro. In vitro downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. We utilize our cellular maps to deconvolute bulk data from endometrial cancers and endometriotic lesions, illuminating the cell types dominating in each of these disorders. These mechanistic insights provide a platform for future development of treatments for common conditions including endometriosis and endometrial carcinoma. Single-cell and spatial transcriptomic profiling of the human endometrium highlights pathways governing the proliferative and secretory phases of the menstrual cycle. Analyses of endometrial organoids show that WNT and NOTCH signaling modulate differentiation into the secretory and ciliated epithelial lineages, respectively. Correction in: Nature Genetics, 55, page 165 (2023)DOI: 10.1038/s41588-022-01287-6

Many gene fusions are reported in tumours and for most their role remains unknown. As fusions are used for diagnostic and prognostic purposes, and are targets for treatment, it is crucial to assess their function in cancer. To systematically investigate the role of fusions in tumour cell fitness, we utilized RNA-sequencing data from 1011 human cancer cell lines to functionally link 8354 fusion events with genomic data, sensitivity to >350 anti-cancer drugs and CRISPR-Cas9 loss-of-fitness effects. Established clinically-relevant fusions were identified. Overall, detection of functional fusions was rare, including those involving cancer driver genes, suggesting that many fusions are dispensable for tumour fitness. Therapeutically actionable fusions involving RAF1, BRD4 and ROS1 were verified in new histologies. In addition, recurrent YAP1-MAML2 fusions were identified as activators of Hippo-pathway signaling in multiple cancer types. Our approach discriminates functional fusions, identifying new drivers of carcinogenesis and fusions that could have clinical implications., Gene fusions are observed in many cancers but their link to tumour fitness is largely unknown. Here, transcriptomic analysis combined with pharmacological and CRISPR-Cas9 screening of cancer cell lines was used to evaluate the functional linkage between fusions and tumour fitness.

The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated single-cell and spatial reference maps of the human uterus and 3D endometrial organoid cultures. We dissect the signalling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids highlights common pathways regulating the differentiation of secretory and ciliated lineage in vivo and in vitro. We show in vitro that downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. These mechanistic insights provide a platform for future development of treatments for a range of common endometrial disorders including endometriosis and carcinoma.

Genome research 29(8), 1363-1375 (2019). doi:10.1101/gr.240663.118, Published by HighWire Press, Stanford, Calif.

Transcriptional dysregulation induced by aberrant transcription factors (TF) is a key feature of cancer, but its global influence on drug sensitivity has not been examined. Here, we infer the transcriptional activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell lines, combined with publicly available datasets to survey a total of 1,056 cancer cell lines and 9,250 primary tumors. Predicted TF activities are supported by their agreement with independent shRNA essentiality profiles and homozygous gene deletions, and recapitulate mutant-specific mechanisms of transcriptional dysregulation in cancer. By analyzing cell line responses to 265 compounds, we uncovered numerous TFs whose activity interacts with anticancer drugs. Importantly, combining existing pharmacogenomic markers with TF activities often improves the stratification of cell lines in response to drug treatment. Our results, which can be queried freely at dorothea.opentargets.io, offer a broad foundation for discovering opportunities to refine personalized cancer therapies. Significance: Systematic analysis of transcriptional dysregulation in cancer cell lines and patient tumor specimens offers a publicly searchable foundation to discover new opportunities to refine personalized cancer therapies. Cancer Res; 78(3); 769–80. ©2017 AACR.

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. While tens of thousands of phosphorylation sites have been identified in human cells to date, the extent and functional importance of the phosphoproteome remains largely unknown. Here, we have analyzed 6,801 publicly available phospho-enriched mass spectrometry proteomics experiments, creating a state-of-the-art phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, 59 features indicative of proteomic, structural, regulatory or evolutionary relevance were integrated into a single functional score using machine learning. We demonstrate how this prioritization identifies regulatory phosphosites across different molecular mechanisms and pinpoint genetic susceptibilities at a genomic scale. Several novel regulatory phosphosites were experimentally validated including a role in neuronal differentiation for phosphosites present in the SWI/SNF SMARCC2 complex member. The scored reference phosphoproteome and its annotations identify the most relevant phosphorylations for a given process or disease addressing a major bottleneck in cell signaling studies.

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

Prediction of transcription factor (TF) activities from the gene expression of their targets (i.e. TF regulon) is becoming a widely-used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and datasets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are: (i) manually curated repositories, (ii) interactions derived from ChIP-seq binding data, (iii) in silico prediction of TF binding on gene promoters, and (iv) reverse-engineered regulons from large gene expression datasets. However, it is not known how these different sources of regulons affect the TF activity estimations, and thereby downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark datasets. We assembled a collection of TF-target interactions among 1,541 TFs, and evaluated how the different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities or mode of interaction with the chromatin, affect the predictions of TF activity changes. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies with a confidence score, as a resource for enhanced prediction of TF activities.

En PLoS ONE. San Francisco (California, United States) : PLOS. Vol. 13 (october 2018), n. 10, art. e0202926. Este artículo se encuentra disponible en la página web de la revista en la siguiente URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0202926 También participan en la elaboración de este artículo científico: José Carbonell-Caballero , Esperanza Such, Luz García-Alonso, Alessandro Liquori, María López-Pavía, Marta Llop, Carmen Alonso, Eva Barragán, Inés Gómez-Seguí, Alexander Neef, David Hervás, Pau Montesinos, Guillermo Sanz, Miguel Angel Sanz, Joaquín Dopazo y José Cervera. Acute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways.

Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages.Finally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks’ predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.

Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome re-sequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.

The Scientific Foundation ‘Asociación Española Contra el Cáncer’ (AECC, Stable Coordinated Group, Hereditary Cancer); the BBVA Foundation; the Eugenio Rodríguez Pascual Foundation grant 2012; Generalitat de Catalunya AGAUR SGR 2012 grants 283, 290 and 312, and SGR 2014 grants 364, 530, and 535; Spanish Ministry of Health ISCIII FIS grants PI10/00057, PI10/00222, PI10/01422, PI12/01528, PI13/00132, and PI14/00336. ISCIII RTICC grants RD06/0020/1051, RD12/0036/0007, RD12/0036/0008 and RD12/0036/0063; Spanish Ministry of Science and Innovation, ‘Fondo Europeo de Desarrollo Regional (FEDER), una manera de hacer Europa’, MINECO grants SAF2010-20203 and SAF2013-46196; and the Telemaraton 2014 ‘Todos Somos Raros, Todos Somos Únicos’ grant P35 (...), Mateo, F.; Arenas, E. J.; Aguilar, H.; Serra-Musach, J.; de Garibay, G. Ruiz; Boni, J.; Maicas, M.; Du, S.; Iorio, F.; Herranz-Ors, C.; Islam, A.; Prado, X.; Llorente, A.; Petit, A.; Vidal, A.; Catala, I.; Soler, T.; Venturas, G.; Rojo-Sebastian, A.; Serra, H.; Cuadras, D.; Blanco, I.; Lozano, J.; Canals, F.; Sieuwerts, A. M.; de Weerd, V.; Look, M. P.; Puertas, S.; Garcia, N.; Perkins, A. S.; Bonifaci, N.; Skowron, M.; Gomez-Baldo, L.; Hernandez, V.; Martinez-Aranda, A.; Martinez-Iniesta, M.; Serrat, X.; Ceron, J.; Brunet, J.; Barretina, M. P.; Gil, M.; Falo, C.; Fernandez, A.; Morilla, I.; Pernas, S.; Pla, M. J.; Andreu, X.; Segui, M. A.; Ballester, R.; Castella, E.; Nellist, M.; Morales, S.; Valls, J.; Velasco, A.; Matias-Guiu, X.; Figueras, A.; Sanchez-Mut, J. V.; Sanchez-Cespedes, M.; Cordero, A.; Gomez-Miragaya, J.; Palomero, L.; Gomez, A.; Gajewski, T. F.; Cohen, E. E. W.; Jesiotr, M.; Bodnar, L.; Quintela-Fandino, M.; Lopez-Bigas, N.; Valdes-Mas, R.; Puente, X. S.; Vinals, F.; Casanovas, O.; Graupera, M.; Hernandez-Losa, J.; Ramon y Cajal, S.; Garcia-Alonso, L.; Saez-Rodriguez, J.; Esteller, M.; Sierra, A.; Martin-Martin, N.; Matheu, A.; Carracedo, A.; Gonzalez-Suarez, E.; Nanjundan, M.; Cortes, J.; Lazaro, C.; Odero, M. D.; Martens, J. W. M.; Moreno-Bueno, G.; Barcellos-Hoff, M. H.; Villanueva, A.; Gomis, R. R.; Pujana, M. A.

Transcriptional dysregulation is a key feature of cancer. Transcription factors (TFs) are the main link between signalling pathways and the transcriptional regulatory machinery of the cell, positioning them as key oncogenic inductors and therefore potential targets of therapeutic intervention. We implemented a computational pipeline to infer TF regulatory activities from basal gene expression and applied it to publicly available and newly generated RNA-seq data from a collection of 1,010 cancer cell lines and 9,250 primary tumors. We show that the predicted TF activities recapitulate known mechanisms of transcriptional dysregulation in cancer and dissect mutant-specific effects in driver genes. Importantly, we show the potential for predicted TF activities to be used as markers of sensitivity to the inhibition of their upstream regulators. Furthermore, combining these inferred activities with existing pharmacogenomic markers significantly improves the stratification of sensitive and resistant cell lines for several compounds. Our approach provides a framework to link driver genomic alterations with transcriptional dysregulation that helps to predict drug sensitivity in cancer and to dissect its mechanistic determinants.

The post-translational regulation of proteins is mainly driven by two molecular events, their modification by several types of moieties and their interaction with other proteins. These two processes are interdependent and together are responsible for the function of the protein in a particular cell state. Several databases focus on the prediction and compilation of protein-protein interactions (PPIs) and no less on the collection and analysis of protein post-translational modifications (PTMs), however, there are no resources that concentrate on describing the regulatory role of PTMs in PPIs. We developed several methods based on residue co-evolution and proximity to predict the functional associations of pairs of PTMs that we apply to modifications in the same protein and between two interacting proteins. In order to make data available for understudied organisms, PTMcode v2 (http://ptmcode.embl.de) includes a new strategy to propagate PTMs from validated modified sites through orthologous proteins. The second release of PTMcode covers 19 eukaryotic species from which we collected more than 300 000 experimentally verified PTMs (>1 300 000 propagated) of 69 types extracting the post-translational regulation of >100 000 proteins and >100 000 interactions. In total, we report 8 million associations of PTMs regulating single proteins and over 9.4 million interplays tuning PPIs.

Chromosomal rearrangements, despite being detrimental to normal organismal fitness, are ubiquitous in cancer and often act as driver events. Copy number variations (CNVs) in the autosomal chromosomes are not known to have specific balancing mechanists but cancer cells can thrive with large dosage imbalances. Since the effect of CNVs on the cellular proteome of tumours is poorly understood, we analyzed recently generated proteogenomic data-sets on 282 patient samples of solid tumours, and investigated the implication of CNVs in the proteome of these cells. We found that the impact of CNVs is post-transcriptionally attenuated in 23-33% of the proteins with a strong enrichment for protein complex subunits. Interaction partners have a high co-regulation of their abundances and some complex subunits act as rate-limiting steps of complex assembly, indirectly controlling the abundance of other complex members. We identified 48 such regulatory interactions providing insight into assembly pathways of protein complexes involved in relevant biological processes in cancer. Lastly, we found that a gene-signature of the proteome attenuation is associated with increased resistance to chaperone and proteasome inhibitors. This study presents novel insights into the widespread importance of post-transcriptional mechanisms in cancer that allow cells to cope with their altered genomic profile.

Introduction Translating our understanding of genetic alterations in cancer into clinical care remains a major challenge. The discovery of gene fusions such as EML4-ALK in lung cancer and BCR-ABL1 in chronic myeloid leukaemia have already led to changes in clinical care. Advances in next-generation sequencing have accelerated the rate at which novel gene fusions are discovered, but important questions remain about their roles in promoting oncogenic phenotypes and their relevance in drug response. Here, we combine RNA sequencing, CRISPR/Cas9 screens and high-throughput drug sensitivity data in a panel of 1000 human cancer cell lines to examine the occurrence and functional relevance of gene fusions in cancer. Material and methods We performed RNA-sequencing on 1015 human cancer cell lines, representing 42 cancer types. We called fusions using three algorithms: TopHat Fusion, DeFuse and RNA-STAR fusion. Further, we integrate high-throughput drug screening data across >350 compounds, single-nucleotide variants, copy number variation, gene expression data and genome-wide CRISPR/Cas9 dropout screening data to systematically search for gene fusions with functional relevance. Results and discussions We find 8546 distinct gene fusion events across our panel of cell lines. These include well understood gene fusions (e.g. ALK-fusions, BCR-ABL1 and EWSR1-FLI1), as well as novel fusions that involve known cancer driver genes. We are able to recapitulate previously identified gene fusion-drug response associations using an unguided statistical analysis. Furthermore, we developed a systematic unguided approach of using CRISPR/Cas9 gene essentiality data to identify essential gene fusions. This approach recapitulates known essential gene fusions and provides evidence for the oncogenic relevance for previously poorly understood gene fusions. Conclusion In this study, we provide an annotation of gene fusions in 1015 human cancer cell lines. Our systematic analysis of the functional role of gene fusions captures the oncogenic and therapeutic relevance of known gene fusions, and highlights potential therapeutic opportunities of previously uncharacterised gene fusions.

Drug resistance is an almost inevitable consequence of cancer therapy and ultimately proves fatal for the majority of patients. In many cases this is the consequence of specific gene mutations that have the potential to be targeted to re-sensitize the tumor. The ability to uniformly saturate the genome with point mutations without chromosome or nucleotide sequence context bias would open the door to identify all putative drug resistance mutations in cancer models. Here we describe such a method for elucidating drug resistance mechanisms using genome-wide chemical mutagenesis allied to next-generation sequencing. We show that chemically mutagenizing the genome of cancer cells dramatically increases the number of drug-resistant clones and allows the detection of both known and novel drug resistance mutations. We have developed an efficient computational process that allows for the rapid identification of involved pathways and druggable targets. Such a priori knowledge would greatly empower serial monitoring strategies for drug resistance in the clinic as well as the development of trials for drug resistant patients.

Dopazo, Joaquín et al., Recent results from large-scale genomic projects suggest that allele frequencies, which are highly relevant for medical purposes, differ considerably across different populations. The need for a detailed catalog of local variability motivated the whole-exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population. Like in other studies, a considerable number of rare variants were found (almost one-third of the described variants). There were also relevant differences in allelic frequencies in polymorphic variants, including ~10,000 polymorphisms private to the Spanish population. The allelic frequencies of variants conferring susceptibility to complex diseases (including cancer, schizophrenia, Alzheimer disease, type 2 diabetes, and other pathologies) were overall similar to those of other populations. However, the trend is the opposite for variants linked to Mendelian and rare diseases (including several retinal degenerative dystrophies and cardiomyopathies) that show marked frequency differences between populations. Interestingly, a correspondence between differences in allelic frequencies and disease prevalence was found, highlighting the relevance of frequency differences in disease risk. These differences are also observed in variants that disrupt known drug binding sites, suggesting an important role for local variability in population-specific drug resistances or adverse effects. We have made the Spanish population variant server web page that contains population frequency information for the complete list of 170,888 variant positions we found publicly available (http://spv.babelomics.org/), We show that it if fundamental to determine population-specific variant frequencies to distinguish real disease associations from population-specific polymorphisms. © 2016 The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution., The MGP is a joint initiative between the Consejería de Salud de la Junta de Andalucía and Roche, supported by the “Programa Nacional de Proyectos de investigación Aplicada,” I+D+i 2008, “Subprograma de actuaciones Científicas y Tecnológicas en Parques Científicos y Tecnológicos” (ACTEPARQ 2009), and European Regional Development Funds (ERDF). This work is also supported by grants BIO2014-57291-R and BFU2012-38236 from the Spanish Ministry of Economy and Competitiveness and “Plataforma de Recursos Biomoleculares y Bioinformáticos” PT 13/0001/0030 from the ISCIII, both cofunded with ERDF; grants PI1102923 and PI1001290 from the Fondo de Investigación Sanitaria, PROMETEOII/2014/025 from the Generalitat Valenciana (GVA-FEDER), FP7-PEOPLE-2012-ITN MLPM2012 318861 from the EU FP7, Fundació la Marató TV3 [20133134], and by Direcció General de Recerca, Generalitat de Catalunya (2014SGR1311). The CIBER de Enfermedades Raras is an Instituto de Salud Carlos III initiative.

Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations.

Summary: CellMaps is an HTML5 open-source web tool that allows displaying, editing, exploring and analyzing biological networks as well as integrating metadata into them. Computations and analyses are remotely executed in high-end servers, and all the functionalities are available through RESTful web services. CellMaps can easily be integrated in any web page by using an available JavaScript API. Availability and Implementation: The application is available at: http://cellmaps.babelomics.org/ and the code can be found in: https://github.com/opencb/cell-maps. The client is implemented in JavaScript and the server in C and Java. Contact: jdopazo@cipf.es Supplementary information: Supplementary data are available at Bioinformatics online.

Background The molecular mechanisms leading to sporadic medullary thyroid carcinoma (sMTC) and juvenile papillary thyroid carcinoma (PTC), two rare tumours of the thyroid gland, remain poorly understood. Genetic studies on thyroid carcinomas have been conducted, although just a few loci have been systematically associated. Given the difficulties to obtain single-loci associations, this work expands its scope to the study of epistatic interactions that could help to understand the genetic architecture of complex diseases and explain new heritable components of genetic risk. Methods We carried out the first screening for epistasis by Multifactor-Dimensionality Reduction (MDR) in genome-wide association study (GWAS) on sMTC and juvenile PTC, to identify the potential simultaneous involvement of pairs of variants in the disease. Results We have identified two significant epistatic gene interactions in sMTC (CHFR-AC016582.2 and C8orf37-RNU1-55P) and three in juvenile PTC (RP11-648k4.2-DIO1, RP11-648k4.2-DMGDH and RP11-648k4.2-LOXL1). Interestingly, each interacting gene pair included a non-coding RNA, providing thus support to the relevance that these elements are increasingly gaining to explain carcinoma development and progression. Conclusions Overall, this study contributes to the understanding of the genetic basis of thyroid carcinoma susceptibility in two different case scenarios such as sMTC and juvenile PTC. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0160-7) contains supplementary material, which is available to authorized users.

Despite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated., Author Summary Until now, most efforts in cancer genomics have focused on identifying genes and pathways driving tumor development. Although this has been unquestionably a success, as evidenced by the fact that we now have an extensive catalogue of cancer driver genes and pathways, there is still a poor understanding of why patients with the same affected driver genes may have different disease outcomes or drug responses. This is precisely the aim of this work-to show how by considering proteins as multifunctional factories instead of monolithic black boxes, it is possible to identify novel cancer driver genes and propose molecular hypotheses to explain such heterogeneity. To that end we have mapped the mutation profiles of 5,989 cancer patients from TCGA to more than 10,000 protein structures, leading us to identify 103 protein interaction interfaces enriched in somatic mutations. Finally, we have integrated clinical annotations as well as proteomics data to show how tumors apparently driven by the same gene can display different behaviors, including patient outcomes, depending on which specific interfaces are mutated.

The importance of the context of interactions in the proteins mutated in cancer is long known. However, our knowledge on how mutations affecting to protein-protein interactions (PPIs) are related to cancer occurrence and progression is still poor. Here, we extracted the missense somatic mutations from 5920 cancer patients of 33 different cancer types, taken from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), and mapped them onto a structurally resolved interactome, which integrates three-dimensional atomic-level models of domain-domain interactions with experimentally determined PPIs, involving a total of 7580 unique interacting domains that participate in 13160 interactions connecting 4996 proteins. We observed that somatic nonsynonymous mutations tend to concentrate in ordered regions of the affected proteins and, within these, they have a clear preference for the interacting interfaces. Also, we have identified more than 250 interacting interfaces candidate to drive cancer. Examples demonstrate how mutations in the interacting interfaces are strongly associated with patient survival time, while similar mutations in other areas of the same proteins lack this association. Our results suggest that the perturbation caused by cancer mutations in protein interactions is an important factor in explaining the heterogeneity between cancer patients.

Copy-number variations (CNVs) are ubiquitous in cancer and often act as driver events, but the effects of CNVs on the proteome of tumors are poorly understood. Here, we analyze recently published genomics, transcriptomics, and proteomics datasets made available by CPTAC and TCGA consortia on 282 breast, ovarian, and colorectal tumor samples to investigate the impact of CNVs in the proteomes of these cells. We found that CNVs are buffered by post-transcriptional regulation in 23%–33% of proteins that are significantly enriched in protein complex members. Our analyses show that complex subunits are highly co-regulated, and some act as rate-limiting steps of complex assembly, as their depletion induces decreased abundance of other complex members. We identified 48 such rate-limiting interactions and experimentally confirmed our predictions on the interactions of AP3B1 with AP3M1 and GTF2E2 with GTF2E1. This study highlights the importance of post-transcriptional mechanisms in cancer that allow cells to cope with their altered genomes., Cell Systems, 5 (4), ISSN:2405-4720

Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins.

Bardet–Biedl syndrome (BBS) is a model ciliopathy characterized by a wide range of clinical variability. The heterogeneity of this condition is reflected in the number of underlying gene defects and the epistatic interactions between the proteins encoded. BBS is generally inherited in an autosomal recessive trait. However, in some families, mutations across different loci interact to modulate the expressivity of the phenotype. In order to investigate the magnitude of epistasis in one BBS family with remarkable intrafamilial phenotypic variability, we designed an exome sequencing–based approach using SOLID 5500xl platform. This strategy allowed the reliable detection of the primary causal mutations in our family consisting of two novel compound heterozygous mutations in McKusick–Kaufman syndrome (MKKS) gene (p.D90G and p.V396F). Additionally, exome sequencing enabled the detection of one novel heterozygous NPHP4 variant which is predicted to activate a cryptic acceptor splice site and is only present in the most severely affected patient. Here, we provide an exome sequencing analysis of a BBS family and show the potential utility of this tool, in combination with network analysis, to detect disease-causing mutations and second-site modifiers. Our data demonstrate how next-generation sequencing (NGS) can facilitate the dissection of epistatic phenomena, and shed light on the genetic basis of phenotypic variability Instituto de Salud Carlos III (ISCIII) Spanish Ministry of Economy and Competitiveness (PI1102923) Regional Ministry of Economy, Innovation, Science and Employment of the Autonomous Government of Andalusia (CTS-03687) Regional Ministry of Health of the Autonomous Government of Andalusia (PI100154), (PCT-30000-2009-12) INNPLANTA (PCT-300000-2010-007) Ciber de Enfermedades raras (CIBERER) Foundation Ramon Areces (CIVP16A1856) Spanish Ministry of Science and Innovation (BIO2011-27069) Conselleria de Educacio of the Valencia Community (PROMETEO/2010/001)

Despite it has been reported that several loci are involved in Hirschsprung’s disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung’s disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations., This work was supported by the Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Economy and Competitiveness, Spain (PI1001290); Spanish Ministry of Economy and Competitiveness (BIO2011-27069), GVA-FEDER (PROMETEO/2010/001) and Consejeria de Innovación Ciencia y Empresa de la Junta de Andalucia (CTS-7447). The CIBER de Enfermedades Raras is an initiative of the ISCIII, Spanish Ministry of Economy and Competitiveness. LG-A is supported by fellowship PFIS FI10/00020 from the ISCIII.

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.

Finding gene associations in rare diseases is frequently hampered by the reduced numbers of patients accessible. Conventional gene-based association tests rely on the availability of large cohorts, which constitutes a serious limitation for its application in this scenario. To overcome this problem we have used here a combined strategy in which a pathway-based analysis (PBA) has been initially conducted to prioritize candidate genes in a Spanish cohort of 53 trios of short-segment Hirschsprung’s disease. Candidate genes have been further validated in an independent population of 106 trios. The study revealed a strong association of 11 gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other HSCR-related processes. Among the preselected candidates, a total of 4 loci, RASGEF1A, IQGAP2, DLC1 and CHRNA7, related to signal transduction and migration processes, were found to be significantly associated to HSCR. Network analysis also confirms their involvement in the network of already known disease genes. This approach, based on the study of functionally-related gene sets, requires of lower sample sizes and opens new opportunities for the study of rare diseases., This work was supported by the Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Economy and Competitiveness, Spain (PI1001290); Spanish Ministry of Economy and Competitiveness (BIO2008-04212), GVA-FEDER (PROMETEO/2010/001) and Consejeria de Innovación Ciencia y Empresa de la Junta de Andalucia (CTS-7447). The CIBER de Enfermedades Raras is an initiative of the ISCIII, Spanish Ministry of Economy and Competitiveness. LG-A and MVE-R are supported by fellowships PFIS FI10/00020 and FI11/00533 from ISCIII respectively.