Biology subjects

Gonzalez-Perez, A.

Publications and source records attributed to Gonzalez-Perez, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Variable latency between the founder genetic event and rhabdoid tumor expansion

Rhabdoid tumors are very aggressive rare pediatric cancers with poor survival affecting very young children. They are characterized by the bi-allelic loss of SMARCB1 or SMARCA4, which is suspected to occur prenatally. However, their genomic evolution is not well understood. Here we assembled the largest cohort of whole-genome sequenced rhabdoid tumors to date, comprising 97 tumors from 88 children. We discovered that, in 42% of cases, the bi-allelic inactivation of the driver gene occurred via a Copy Number Neutral-Loss of Heterozygosity (CN-LOH). We exploited these CN-LOH events and the steady accumulation of age-related mutations in the tumor genomes to estimate the age of donors at the time of occurrence of the driver event and at the time of emergence of the clonal expansion. Across all cases with CN-LOH, the loss of the driver gene occurred very early during prenatal development. However, the clonal expansion that ultimately gave rise to the tumor occurred at different times during infancy, even several years after the acquisition of the founder event. These results indicate that probably other factors, besides the genetic driver event, are required to promote rhabdoid tumorigenesis.

genomics↗

bioRxiv2025

Oncodrive3D: Fast and accurate detection of structural clusters of somatic mutations under positive selection

Identifying the genes capable of driving tumorigenesis in different tissues is one of the central goals of cancer genomics. Computational methods that exploit different signals of positive selection in the pattern of mutations observed in genes across tumors have been developed to this end. One such signal of positive selection is clustering of mutations in areas of the three-dimensional (3D) structure of the protein above the expectation under neutrality. Methods that exploit this signal have been hindered by the paucity in proteins with experimentally solved 3D structures covering their entire sequence. Here, we present Oncodrive3D, a computational method that by exploiting AlphaFold 2 structural models extends the identification of proteins with significant mutational 3D clusters to the entire human proteome. Oncodrive3D shows sensitivity and specificity on par with state-of-the-art cancer driver gene identification methods exploiting mutational clustering, and clearly outperforms them in computational efficiency. We demonstrate, through several examples, how significant mutational 3D clusters identified by Oncodrive3D in different known or potential cancer driver genes can reveal details about the mechanism of tumorigenesis in different cancer types and in clonal hematopoiesis.

bioinformatics↗

bioRxiv2024

Implications of noncoding regulatory functions in the development of insulinomas

Insulinomas are rare neuroendocrine tumours arising from the pancreatic {beta}-cells. While retaining the ability to produce insulin, insulinomas feature aberrant proliferation and altered hormone secretion resulting in failure to maintain glucose homeostasis. With the aim of uncovering the role of noncoding regulatory regions and their aberrations to the development of these tumors, we coupled epigenetic and gene expression profiling with whole-genome sequencing. As a result, we mapped H3K27ac sites in the tumoral tissue and unraveled overlapping somatic mutations associated with changes in regulatory functions. Critically, these regions impact insulin secretion, tumor development and epigenetic modifying genes, including key components of the polycomb complex. Chromatin remodeling is apparent as insulinoma-selective regions are mostly clustered in regulatory domains, shared across patients and containing a specific set of regulatory sequences dominated by the binding motif of the transcription factor SOX17. Moreover, a large fraction of these regions are H3K27me3-repressed in unaffected {beta}-cells, suggesting that tumoral transition is coupled with derepression of {beta}-cell polycomb-targeted domains. Our work provides a compendium of aberrant cis-regulatory elements and transcription factors that alter {beta}-cell function and fate in their progression to pancreatic neuroendocrine tumors and a framework to identify coding and noncoding driver mutations.

genomics↗

bioRxiv2022

Copy number footprints of platinum-based anticancer therapies

Recently, distinct mutational footprints observed in metastatic tumors, secondary malignancies and normal human tissues have been demonstrated to be caused by the exposure to several chemotherapeutic drugs. These characteristic mutations originate from specific lesions caused by these chemicals to the DNA of exposed cells. However, it is unknown whether the exposure to these chemotherapies leads to a specific footprint of larger chromosomal aberrations. Here, we address this question exploiting whole genome sequencing data of metastatic tumors obtained from patients exposed to different chemotherapeutic drugs. As a result, we discovered a specific copy number footprint across tumors from patients previously exposed to platinum-based therapies. This footprint is characterized by a significant increase in the number of chromosomal fragments of copy number 1-4 and size smaller than 10 Mb in exposed tumors with respect to their unexposed counterparts (median 14-387% greater across tumor types). The number of chromosomal fragments characteristic of the platinum-associated CN footprint increases significantly with the activity of the well known platinum-related footprint of single nucleotide variants across exposed tumors.

genomics↗

bioRxiv2022

Signatures 1 and 17 show increased propensity to create mutational hotspots in the human genome

The ability to study mutation rate variability at nucleotide resolution is impaired by the sparsity of observed mutational events across the genome. To circumvent this problem, here we investigated the propensity of 14 different mutational processes to form recurrently mutated sites across tumour samples (hotspots). We found that mutational signatures 1 (SBS1) and 17 (SBS17a and SBS17b) have the highest propensity to form hotspots, generating 5-78 times more than other common somatic mutational processes. After accounting for trinucleotide mutational probabilities, sequence composition and heterogeneity of mutation rates at 10 Kbp, the majority (89-95%) of SBS17a and b hotspots remain unexplained. This suggests that local genomic features play a significant role in SBS17a and b hotspot propensity, among which we identify CTCF binding as a minor contributor. In the case of SBS1, we demonstrate that including genome-wide distribution of methylated CpGs sites into our models can explain most (80-100%) of its hotspot propensity. We also observe an increased hotspot propensity of SBS1 in normal tissues from mammals, as well as in de novo germline mutations. We demonstrate that hotspot propensity is a useful readout to assess the accuracy of mutation rate models at nucleotide resolution. This new approach and the findings derived from it open up new avenues for a range of somatic and germline studies investigating and modelling mutagenesis.

genomics↗

bioRxiv2021

Disentangling heterogeneity of Malignant Pleural Mesothelioma through deep integrative omics analyses

Malignant Pleural Mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Using the largest series of whole-genome sequencing data integrated with transcriptomic and epigenomic data using multi-omic factor analysis, we demonstrate that MPM heterogeneity arises from four sources of variation: tumor cell morphology, ploidy, adaptive immune response, and CpG island methylator phenotype. Previous genomic studies focused on describing only the tumor cell morphology factor, although we robustly find the three other sources in all publicly available cohorts. We prove how these sources of variation explain the biological functions performed by the cancer cells, and how genomic events shape MPM molecular profiles. We show how these new sources of variation help understand the heterogeneity of the clinical behavior of MPM and drug responses measured in cell lines. These findings unearth the interplay between MPM functional biology and its genomic history, and ultimately, inform classification, prognostication and treatment. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/461908v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@b6c43corg.highwire.dtl.DTLVardef@10d45a9org.highwire.dtl.DTLVardef@58746eorg.highwire.dtl.DTLVardef@143f880_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data

MotivationDNA Methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods employ either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. ResultsHere we present DeepMP, a convolutional neural network (CNN)-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on E. coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. AvailabilityDeepMP is implemented and freely available under MIT license at github.com/pepebonet/DeepMP Contactjose.bonet@irbbarcelona.org -- mandiche@kth.se

bioinformatics↗

bioRxiv2020

The evolution of hematopoietic cells under cancer therapy

Chemotherapies may influence the evolution of somatic tissues through the introduction of genetic variation in cells and by changing the selective pressures they face. However, the contributions of chemotherapeutic agents to the mutation burden of healthy cells and to clonal expansions in somatic tissues are not clear. Here, we exploit the mutational footprint of some chemotherapies to explore their influence on the evolution of hematopoietic cells. Cells of Acute Myeloid Leukemia (AML) secondary to treatment with platinum-based drugs showed a clear mutational footprint of these drugs, indicating that healthy blood cells received chemotherapy mutations. In contrast, no trace of 5-fluorouracil (5-FU) mutational signature was found in AML secondary to exposure to 5-FU, suggesting that cells establishing the AML were quiescent during treatment. We used the platinum-based mutational signature as a barcode to precisely time clonal expansions with respect to the moment of exposure to the drug. The enrichment for clonal mutations among treatment-related mutations in all platinum-treated AMLs shows that these secondary neoplasms begin their clonal expansion after the start of the cytotoxic treatment. In contrast, the absence of detectable platinum-related mutations in healthy blood samples with clonal hematopoiesis is consistent with a clonal expansion that predates the exposure to the cytotoxic agent, which favours particular pre-existing clones.

cancer biology↗

bioRxiv2020

Discovering the drivers of clonal hematopoiesis

Mutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) in certain conditions drive clonal hematopoiesis (CH). While some CH drivers have been identified experimentally or through epidemiological studies, the compendium of all genes able to drive CH upon mutations in HSCs is far from complete. We propose that identifying signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, similarly as done to identify cancer genes. Using a reverse somatic variant calling approach, we repurposed whole-genome and whole-exome blood/tumor paired samples of more than 12,000 donors from two large cancer genomics cohorts to identify blood somatic mutations. The application of IntOGen, a robust driver discovery pipeline, to blood somatic mutations across both cohorts, and more than 24,000 targeted sequenced samples yielded a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers all known CH genes, and discovers novel candidates. Generating this compendium is an essential step to understand the molecular mechanisms of CH and to accurately detect individuals with CH to ascertain their risk to develop related diseases.

genomics↗

bioRxiv2020

In silico saturation mutagenesis of cancer genes

Extensive bioinformatics analysis of datasets of tumor somatic mutations data have revealed the presence of some 500-600 cancer driver genes. The identification of all potential driver mutations affecting cancer genes is essential to implement precision cancer medicine and to understand the interplay of mutation probability and selection in tumor development. Here, we present an in silico saturation mutagenesis approach to identify all driver mutations in 568 cancer genes across 66 tumor types. For most cancer genes the mutation probability across tissues --underpinned by active mutational processes-- influences which driver variants have been observed, although this differs significantly between tumor suppressor and oncogenes. The role of selection is apparent in some of the latter, the observed and unobserved driver mutations of which are equally likely to occur. The number of potential driver mutations in a cancer gene roughly determines how many mutations are available for detection across newly sequenced tumors.

cancer biology↗

bioRxiv2020

Autosomal Recessive Alzheimer disease (arAD): homozygosity mapping of genomic regions containing arAD loci

Long runs of homozygosity (ROH) are contiguous stretches of homozygous genotypes, which are a footprint of recent inbreeding and recessive inheritance. The presence of recessive loci is suggested for Alzheimers disease (AD). However, the search for recessive variants has been poorly assessed to date. To investigate homozygosity in AD, we performed a fine-scale ROH analysis including 21,100 individuals from 10 cohorts of European ancestry (11,919 AD cases and 9,181 controls). We detected an increase of homozygosity in AD cases compared to controls [{beta}FROH (CI95%) = 0.051 (0.023 - 0.078); P = 3.25 x 10-4]. ROHs increasing the risk of AD (OR > 1) were significantly overrepresented compared to ROHs increasing protection (p < 2.20 x 10-16). The top associated ROH with AD risk ({beta} (CI95%) = 1.09 (0.48 - 1.48), p value = 9.03 x 10-4) was detected upstream the HS3ST1 locus (chr4:11,189,482-11,305,456), previously related to AD. Next, to construct a homozygosity map of AD cases, we selected ROHs shared by inbred AD cases extracted from an outbred population. We used whole-exome sequencing data from 1,449 individuals from the Knight-ADRC-NIA-LOAD (KANL) cohort to identify potential recessive variants in candidate ROHs. We detected a candidate marker, rs117458494, mapped in the SPON1 locus, which has been previously associated with amyloid metabolism. Here, we provide a research framework to look for recessive variants in AD using outbred populations. Our results showed that AD cases have enriched homozygosity, suggesting that recessive effects may explain a proportion of AD heritability.

genetics↗

bioRxiv2020

Bayesian non-parametric clustering of single-cell mutation profiles

The high resolution of single-cell DNA sequencing (scDNA-seq) offers great potential to resolve intra-tumor heterogeneity by distinguishing clonal populations based on their mutation profiles. However, the increasing size of scDNA-seq data sets and technical limitations, such as high error rates and a large proportion of missing values, complicate this task and limit the applicability of existing methods. Here we introduce BnpC, a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles. BnpC employs a Dirichlet process mixture model coupled with a Markov chain Monte Carlo sampling scheme, including a modified split-merge move and a novel posterior estimator to predict clones and genotypes. We benchmarked our method comprehensively against state-of-the-art methods on simulated data using various data sizes, and applied it to three cancer scDNA-seq data sets. On simulated data, BnpC compared favorably against current methods in terms of accuracy, runtime, and scalability. Its inferred genotypes were the most accurate, and it was the only method able to run and produce results on data sets with 10,000 cells. On tumor scDNA-seq data, BnpC was able to identify clonal populations missed by the original cluster analysis but supported by supplementary experimental data. With ever growing scDNA-seq data sets, scalable and accurate methods such as BnpC will become increasingly relevant, not only to resolve intra-tumor heterogeneity but also as a pre-processing step to reduce data size. BnpC is freely available under MIT license at https://github.com/cbg-ethz/BnpC.

bioinformatics↗

bioRxiv2019

The mutational footprints of cancer therapies

Some cancer therapies damage DNA and cause mutations both in cancer and healthy cells of the patient1. These therapy-induced mutations may underlie some of the long-term and late side effects of the treatment, such as mental disabilities, organ toxicities and secondary neoplasms. Currently we ignore the mutation pattern and burden caused by different cancer treatments. Here we identify mutational signatures, or footprints of six widely-used anti-cancer therapies with the study of whole-genomes from more than 3500 metastatic tumors originated in different organs. These include previously known and new mutational signatures generated by platinum-based drugs, and a novel signature of treatment with nucleoside metabolic inhibitors. Exploiting these mutational footprints, we estimate the contribution of different treatments to the mutation burden of tumors and their risk of causing coding and likely driver mutations in the genome. In summary, the mutational footprints identified here open a window to precisely appraise the mutational risk of different cancer therapies to understand their late side effects.

genomics↗

bioRxiv2018

Disruption of ubiquitin mediated proteolysis is a widespread mechanism of tumorigenesis

E3 ligases and degrons --the sequences they recognize in target proteins-- are key parts of the ubiquitin-mediated proteolysis system. There are several examples of alterations of these two components of the system that play a role in cancer. Here, we uncovered the landscape of the contribution of such alterations to tumorigenesis across cancer types. We first systematically identified novel instances of degrons across the human proteome using a random forest classifier, and validated them exploiting somatic mutations across more than 7,000 tumors. We detected signals of positive selection across these novel degrons and revealed new instances involved in cancer development. Overall, we estimated that at least one in seven driver mutations across primary tumors affect either degrons or E3 ligases.

bioinformatics↗

bioRxiv2018

OncodriveCLUSTL: a sequence-based clustering method to identify cancerdrivers

SummaryThe identification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes. We have developed OncodriveCLUSTL, a new sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the simulation of mutations accounting for the composition of tri- or penta-nucleotide context substitutions observed in the cohort under study. Our method is able to identify known clusters and bona-fide cancer drivers across cohorts of tumor whole-exomes, outperforming the existing OncodriveCLUST algorithm and complementing other methods based on different signals of positive selection. We show that OncodriveCLUSTL may be applied to the analysis of non-coding genomic elements and non-human mutations data.\n\nAvailability and implementationOncodriveCLUSTL is available as an installable Python 3.5 package. The source code and running examples are freely available at https://bitbucket.org/bbglab/oncodriveclustl under GNU Affero General Public License.\n\nContactnuria.lopez@irbbarcelona.org

bioinformatics↗

bioRxiv2018

Genome Wide Meta-Analysis identifies new loci associated with cardiac phenotypes and uncovers a common genetic signature shared by heart function and Alzheimer’s disease

AimsEchocardiography has become an indispensable tool for the study of heart performance, improving the monitoring of individuals with cardiac diseases. Diverse genetic factors associated with echocardiographic measures of heart structure and functions have been previously reported. The impact of several apoptotic genes in heart development identified in experimental models prompted us to assess their potential association with indicators of human cardiac function. This study started with the aim to investigate the possible association of variants of apoptotic genes with echocardiographic traits and to identify new genetic markers associated with cardiac function.\n\nMethods and resultsGenome wide data from different studies were obtained from public repositories. After quality control and imputation, association analyses confirm the role of caspases and other apoptosis related genes with cardiac phenotypes. Moreover, enrichment analysis showed an over-representation of genes, including some apoptotic regulators, associated with Alzheimers disease (AD). We further explored this unexpected observation which was confirmed by genetic correlation analyses.\n\nConclusionsOur findings show the association of apoptotic gene variants with echocardiographic indicators of heart function and reveal a novel potential genetic link between echocardiographic measures in healthy populations and cognitive decline later on in life. These findings may have important implications for preventative strategies combating Alzheimers disease.

genetics↗

bioRxiv2017

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

genomics↗

bioRxiv2017

A pan-cancer landscape of interactions between solid tumors and infiltrating immune cell populations

Throughout their development, tumors are challenged by the immune system and acquire features to evade its surveillance. A systematic view of these traits is still lacking. Here, we identify genomic and transcriptomic traits associated to the immune-phenotype of 9,403 tumors of 29 solid cancers. In highly cytotoxic immune-phenotypes we found tumors with low clonal heterogeneity enriched by alterations of genes involved in epigenetic regulation, ubiquitin mediated proteolysis, antigen-presentation and cell-cell communication, which may drive resistance. Tumors with immune-phenotypes with mid cytotoxicity present an over-activation of processes involved in invasion and remodeling of neighboring tissues that may foster the recruitment of immune-suppressive cells. Tumors with poor cytotoxic immune-phenotype tend to be of more advanced stages and present frequent alterations in cell cycle, hedgehog, beta-catenin and TGF-beta pathways, which may drive the immune depletion. These results may be exploited to develop novel combinatorial targeting strategies involving immunotherapies.

bioinformatics↗