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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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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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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