BUSCADOR RECOLECTA

The cancer bioMarkers database is curated and maintained by several clinical and scientific experts in the field of precision oncology supported by the European Union’s Horizon 2020 funding. This database is currently being integrated with knowledge databases of other institutions in a collaborative effort of the Global Alliance for Genomics and Health.

A tool aimed at the rational design of cancer gene panels. It estimates the cost-effectiveness of the designed panel on a cohort of tumors and provides reports on the importance of individual mutations for tumorigenesis or therapy.

Analyses somatic mutations in thousands of tumor genomes to identify cancer driver genes.

OncodriveCLUST is a method aimed to identify genes whose mutations are biased towards a large spatial clustering. This method is designed to exploit the feature that mutations in cancer genes, especially oncogenes, often cluster in particular positions of the protein. We consider this as a sign that mutations in these regions change the function of these proteins in a manner that provides an adaptive advantage to cancer cells and consequently are positively selected during clonal evolution of tumours, and this property can thus be used to nominate novel candidate driver genes./nThe method does not assume that the baseline mutation probability is homogeneous across all gene positions but it creates a background model using silent mutations. Coding silent mutations are supposed to be under no positive selection and may reflect the baseline clustering of somatic mutations. Given recent evidences of non-random mutation processes along the genome, the assumption of homogenous mutation probabilities is likely an oversimplication introducing bias in the detection of meaningful events.

Machine-learning based approach to classify cancer driver genes into to Activating or Loss of Function roles for cancer gene development.

Oncodrive-CIS is a method aimed to identify those copy number alterations (CNAs) leading to larger in cis expression changes that may be useful in elucidating the role of these aberrations in cancer. This is based on the hypothesis that a gene driving oncogenesis through copy number changes is more prone to bias towards overexpression (or underexpression) as compared to bystanders. The effect of the gene dosage is assessed by observing expression changes not only among tumors but also taking into account normal samples data, when available./nOncodrive-CIS has several potential benefits: first, it did not examine the frequency of the CNAs across samples and therefore the detection of low-recurrent driver alterations was not impaired. Second, amplifications and deletions were evaluated separately to obtain a fair ranking of genes, because the expression change measured in deletions was lower than the one obtained from multi-copy amplifications. Third, the expression of genes in tumor samples was analyzed according to the copy number status but was also compared to normal samples, thus better revealing the gene misregulation role of CNAs in cancer cells. And finally, it should be emphasized that the relationship between expression changes and their functional impact is complex, thus Oncodrive-CIS is proposed as a method to elucidate the role of CNAs in cancer which may be complementary to analyses based on other criteria.

This database contains information on the genes identified as drivers in Rubio-Perez and Tamborero et al. (2015). It contains information on driver identification at mutational, CNA and gene fusion level. Additional ancillary information about the role and major clonality of drivers is also included. A table is also provided with the list of datasets used for mutational driver identification.

This file lists the High Confidence Drivers identified as part of the pan-cancer12 initiative, published in the paper Comprehensive identification of mutational cancer driver genes across 12 tumor types" Scientific Reports 3:2650, 2013, doi:10.1038/srep02650

Bioinformatics method to identify individual driver mutations.

This database contains the results of the driver analysis performed by the Cancer Genome Interpreter across 6,792 exomes of a pan-cancer cohort of 28 tumor types. Validated oncogenic mutations are identified according to the state-of-the-art clinical and experimental data, whereas the effect of the mutations of unknown significance is predicted by the OncodriveMUT method.