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Finding noncoding cancer drivers

Periodic Reporting for period 2 - NONCODRIVERS (Finding noncoding cancer drivers)

Reporting period: 2018-06-01 to 2019-11-30

Tumors contain thousands of somatic mutations in their genomes. Most of these mutations are not involved in the disease, but few of them, which we call cancer driver mutations, are directly involved in the development of the tumor. Identifying these cancer driver mutations is key to understand cancer biology and to progress towards personalized cancer medicine.

Most of the work on cancer research have been focused in the study of the coding genome, which comprise less than 2% of the genome sequence. This has allowed us to identify several hundreds of genes involved in tumorigenesis through mutations that affect their coding sequence. On the other hand, only a handful of non-coding driver elements have been identified to date. Our project aims to study what is the role of somatic mutations in the non-coding genome in cancer development.
We have developed novel computational methodologies able to identify genomic elements, in coding and non-coding regions of the genome, with cancer driver mutations. These methods exploit the principles of Darwinian evolution of tumors. Briefly, genomic elements with cancer driver mutations during tumorigenesis present mutational profiles that deviate in several respects from the expected distribution of mutations under neutrality. We call these deviations signals of positive selection, and we have developed several methods to identify them in the observed mutational patterns of genomic elements across cohorts of tumors.

We have also collected data from thousands of tumor whole-genomes from different sources.

As part of the analysis of mutations in non-coding regions we have discovered that the rate at which mutations accumulate in different regions of the genome is highly variable at the local level. We have advanced significantly in understanding the reasons of these variability in terms of accumulation of DNA damage and the activity of DNA repair along the genome. These are important basic biology results and are also important to accurately identify of cancer driver mutations.
We will improve our understanding of the local variability of DNA damage, DNA repair and mutation rates along the genome.

We will improve the computational methodologies to identify cancer driver mutations by incorporating the knowledge obtained about the local variability on the accumulation of mutations in the calculation of the background mutation rate.

We continue the analyses of tumor whole-genomes with the newly collected tumor whole-genomes and the new methods.

We will validate functionally the most promising novel candidate non-coding cancer driver mutations.
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