Biology subjects

Lopez-Bigas, N.

Publications and source records attributed to Lopez-Bigas, N..

27 records · Page 2Linked to original sources

bioRxiv2019

Pervasive lesion segregation shapes cancer genome evolution

Cancers arise through the acquisition of oncogenic mutations and grow through clonal expansion1, 2. Here we reveal that most mutagenic DNA lesions are not resolved as mutations within a single cell-cycle. Instead, DNA lesions segregate unrepaired into daughter cells for multiple cell generations, resulting in the chromosome-scale phasing of subsequent mutations. We characterise this process in mutagen-induced mouse liver tumours and show that DNA replication across persisting lesions can generate multiple alternative alleles in successive cell divisions, thereby increasing both multi-allelic and combinatorial genetic diversity. The phasing of lesions enables the accurate measurement of strand biased repair processes, the quantification of oncogenic selection, and the fine mapping of sister chromatid exchange events. Finally, we demonstrate that lesion segregation is a unifying property of exogenous mutagens, including UV light and chemotherapy agents in human cells and tumours, which has profound implications for the evolution and adaptation of cancer genomes.

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↗

bioRxiv2018

A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants

Precision oncology relies on the accurate discovery and interpretation of genomic variants to enable individualized diagnosis, prognosis, and therapy selection. We found that knowledgebases containing clinical interpretations of somatic cancer variants are highly disparate in interpretation content, structure, and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. With the cooperation of experts of the Global Alliance for Genomics and Health (GA4GH) and six prominent cancer variant knowledgebases, we developed a framework for aggregating and harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations covering 3,437 unique variants in 415 genes, 357 diseases, and 791 drugs. We demonstrated large gains in overlap between resources across variants, diseases, and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 56% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide an open and freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

bioinformatics↗

bioRxiv2018

The Repertoire of Mutational Signatures in Human Cancer

Somatic mutations in cancer genomes are caused by multiple mutational processes each of which generates a characteristic mutational signature. Using 84,729,690 somatic mutations from 4,645 whole cancer genome and 19,184 exome sequences encompassing most cancer types we characterised 49 single base substitution, 11 doublet base substitution, four clustered base substitution, and 17 small insertion and deletion mutational signatures. The substantial dataset size compared to previous analyses enabled discovery of new signatures, separation of overlapping signatures and decomposition of signatures into components that may represent associated, but distinct, DNA damage, repair and/or replication mechanisms. Estimation of the contribution of each signature to the mutational catalogues of individual cancer genomes revealed associations with exogenous and endogenous exposures and defective DNA maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes contributing to the development of human cancer including a comprehensive reference set of mutational signatures in human cancer.

cancer biology↗

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↗

bioRxiv2017

Cancer Genome Interpreter Annotates The Biological And Clinical Relevance Of Tumor Alterations

While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Most of the alterations observed in tumors, including those in well-known cancer genes, are of uncertain significance. Moreover, the information on tumor genomic alterations shaping the response to existing therapies is fragmented across the literature and several specialized resources. Here we present the Cancer Genome Interpreter (http://www.cancergenomeinterpreter.org), an open access tool that we have implemented to annotate genomic alterations and interpret their possible role in tumorigenesis and in the response to anti-cancer therapies.

cancer biology↗