OncodriveROLE predicts activating and loss of function cancer driver genes

ONCODRIVESWe are pleased to announce that the Oncodrive methods family has a new member: OncodriveROLE, an approach to classify cancer drivers into loss of function and activating roles. OncodriveROLE joins and complements the previously developed methods that identify cancer driver genes from the list of somatic mutations in cohort of tumors (OncodriveCLUST and OncodriveFM).

Cancer driver genes come in two main flavors: those that contain driver alterations which cause the loss of function (LoF) of the gene product (for instance, in tumor suppressor genes like TP53 or CDKN2A), and those with driver alterations that increase or change the activity or function of the protein product, such as oncogenes like PIK3CA or BRAF.

Distinguishing between these two classes of driver genes is very important to understand tumorigenesis in patients and has profound implications for therapeutic decision making and for the development of targeted drugs.

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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.


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OncodriveCLUST: a method to reveal cancer drivers based on mutation clustering

We have recently developed a novel method, named OncodriveCLUST, aimed to analyse the mutations observed in sets of tumor samples and identify genes involved in the disease.diana It is based on the feature that driver mutations in cancer genes, especially oncogenes, often cluster in particular positions of the protein. We consider this as a signal that mutations in these regions change the function of the protein in a manner that provides an adaptive advantage to cancer cells and consequently are positively selected during clonal evolution of tumors, and this property can thus be used to nominate driver genes.

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How to identify functional genetic variants in cancer genomes?


The possibility to rapidly and inexpensively sequence tumor genomes is opening important new avenues for cancer research. One of the main objectives when sequencing tumor genomes is to identify the somatic alterations that have a relevant role in developing and maintaining the cancer phenotype. However the analysis of this data is hindered by the large number of mutations detected in tumors (often in the order of thousands) and the large molecular heterogeneity observed between tumor samples.

As part of the International Cancer Genome Consortium (ICGC), during the last 2-3 years I have been co-leading (together with Lincoln Stein) a working group focused on discussing how to analyze this data. The group is formed by 48 Members from 10 different countries, and we have held one teleconference nearly every month. We have now written the results of these discussions in a perspective manuscript that has been published in the current issue of Nature Methods.

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New IntOGen Somatic Mutations Analysis version available

We are proud to announce the brand new version of the IntOGen Somatic Mutations Analysis (IntOGen SM) pipeline. We call it version 2.0.0 as it has been completely rewritten from scratch with a strong focus on quality, efficiency and scalability.

The IntOGen SM pipeline addresses the challenge of identifying which somatic mutations are important for the development of tumors. The input for the analysis is a list of somatic mutations detected in a cohort of tumors. Read the rest of this entry »

Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.

One of the current challenges of oncogenomics is to elucidate which of the aberrations observed in the cancer genome is involved in the disease and which of them are just bystanders occurring stochastically due to the cell genomic instability, i.e. to distinguish driver events from passengers. This is indeed demanding when analyzing copy number alterations (CNAs), in which very large regions of the DNA (and thus a large number of genes) may be affected. The current approach for dealing with this issue is to assess the recurrence of the alteration across multiple samples, since those alterations occurring more than expected by chance should point out significant events in terms of the disease. However, this exhibits some drawbacks, as the underestimation of low-recurrent drivers, the difficulty of assessing the background model and, finally, that it does not take into account how the alteration may impact the normal behavior of the gene.

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How to evaluate the performance of computational methods to identify driver mutations?

We have recently published transFIC, a computational method to assess the functional impact of somatic cancer mutations (see this post). To evaluate the performance of transFIC we needed a dataset of driver and passanger mutations. However, we faced a common problem in this field: there is not such dataset that can be trusted and is not biased. Thus, it was a challenge to properly evaluate the performance of transFIC and compare it to other methods with similar aim.

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