2026-04-13T01:08:44Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/357372025-12-18T01:17:47Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Porta-Pardo, Eduard author Kamburov, Atanas author Tamborero Noguera, David author Pons, Tirso author Grases, Daniela author Valencia, Alfonso author López Bigas, Núria author Getz, Gad author Godzik, Adam author 2018-11-13T09:56:54Z 2018-11-13T09:56:54Z 2017 Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes. E.P.-P. and A.G. acknowledge the support from the Cancer Center grants P30 CA030199 (to our institute) and R35 GM118187 (A.G.). A.K. was supported by startup funds of G.G. and by a collaboration with Bayer AG. D.T. is supported by project SAF2015-74072-JIN, which is funded by the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER). N.L.-B. acknowledges funding from the European Research Council (consolidator grant 682398). A.V. and T.P. acknowledge funding by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 305444 (RD-Connect) Algorismes Carcinogènesi Cromosomes Tumors Comparison of algorithms for the detection of cancer drivers at subgene resolution