My slides on: Identification of cancer drivers across tumor types

//My slides on: Identification of cancer drivers across tumor types

My slides on: Identification of cancer drivers across tumor types

Yesterday I gave a talk at the PRBB Computational Genomics Seminars Series. In that talk I summarized our work of this year in the lab. Basically, we have developed methods to identify cancer driver genes and we have applied them to thousands of tumor resequenced genomes. Here, I leave you the slides, and I summarize the talk below.



I started explaining that the ultimate goal of cancer research is to provide better healthcare to patients and that this involves the development of new targeted therapies and the advance towards personalized/precision cancer medicine. I started with the example of BRAF and Vemurafenib. Then, I explained that thousands of tumor genomes/exomes are currently being sequenced as part of the International Cancer Genome Consortium (ICGC), The Cancer Genome Atlas (TCGA) and other initiatives. One of the main objectives of these consortia is to identify mutations driving the tumorigenic process and the proteins they affect; these are the potential targets for novel therapies. Tumor cells, however, accumulate large numbers of mutations, most of which are not directly involved in the development of the tumor phenotype.


We have developed computational methods (OncodriveFM and OncodriveCLUST) able to identify signals of positive selection in the pattern of tumor somatic mutations, which point to genes and pathways directly involved in the development of the tumors.


We have created a pipeline that takes the list of somatic mutations detected across cohorts of tumors of the same type to identify driver mutations, genes and pathways. We have applied this pipeline to more than 4500 tumors originated in 13 different cancer sites (including all data from TCGA, ICGC and other independent initiatives). The results of this large-scale analysis are available through the web at The pipeline is available to analyze newly resequenced tumor genomes, providing the advantage that the results can be analyzed within context of the thousands of tumor genomes already analyzed by IntOGen-mutations.


Among the novel cancer driver genes identified there are chromatin regulatory factors (CRFs), which are emerging as important genes in cancer development and are regarded as interesting candidates for novel targets for cancer treatment. We did an exhaustive search for CRFs driver genes across 4623 tumors, and we studied the pattern of mutations in chromatin regulatory complexes (Gonzalez-Perez et al., 2013).


As part of the TCGA Pan-Cancer effort, we have combined the results of 5 complementary methods (OncodriveFM, OncodriveCLUST, MuSiC, ActiveDriver and MutSig) applied to 3025 tumors from 12 different cancer types with the objective to identify a comprehensive and reliable list of cancer driver genes acting on those tumors (Tamborero et al., 2013). We identified 291 high-confidence cancer driver genes, among which there are well-known cancer genes as well as novel candidates, some of which have important functional interactions to known cancer genes. The results of this analysis, including the mutations in the tumors of the TCGA Pan-Cancer and their predicted impact, the list of 291 high-confidence cancer driver genesĀ and their evidences can be browsed at


Finally I acknowledge the whole team, who did an impressive effort in the last year to generate all these results.



Tamborero et al., Comprehensive identification of mutations cancer driver genes across 12 tumor types. Scientific Reports 2013. 3:2650 doi:10.1038/srep02650

Gonzalez-Perez et al., IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods 2013 doi:10.1038/nmeth.2642

Gonzalez-Perez et al., The mutational landscape of chromatin regulatory factors across 4623 tumor types. Genome Biology 2013 4(9):r106


By | 2013-10-25T10:42:21+00:00 October 25th, 2013|Categories: BG News|Tags: , , , |0 Comments

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