Biology subjects

Muinos, F.

Publications and source records attributed to Muinos, F..

9 recordsLinked to original sources

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↗

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↗

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

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↗

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↗

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↗

bioRxiv2017

The whole-genome panorama of cancer drivers

The advance of personalized cancer medicine requires the accurate identification of the mutations driving each patients tumor. However, to date, we have only been able to obtain partial insights into the contribution of genomic events to tumor development. Here, we design a comprehensive approach to identify the driver mutations in each patients tumor and obtain a whole-genome panorama of driver events across more than 2,500 tumors from 37 types of cancer. This panorama includes coding and non-coding point mutations, copy number alterations and other genomic rearrangements of somatic origin, and potentially predisposing germline variants. We demonstrate that genomic events are at the root of virtually all tumors, with each carrying on average 4.6 driver events. Most individual tumors harbor a unique combination of drivers, and we uncover the most frequent co-occurring driver events. Half of all cancer genes are affected by several types of driver mutations. In summary, the panorama described here provides answers to fundamental questions in cancer genomics and bridges the gap between cancer genomics and personalized cancer medicine.

cancer biology↗