The Project

WE WANT TO FIGHT AGAINST THE INHERITANCE OF CANCER USING BIOINFORMATICS

With the PanCanRisk project we try to expand scientific horizons in this area of health and chart a new course for the entire field of cancer research to point the way to more effective individualized approaches.

Cancer susceptibility variants have not been studied extensively at a genome wide scale, which is clearly visible when comparing the number of somatic driver genes and germline susceptibility genes in the COSMIC database, (>400 vs. <100). Our proposal aims to close this gap, advancing the knowledge on the landscape of susceptibility variants and genes across several cancers in large cohorts of patients.

To this end, we have the ambitious goal to mine the pan cancer panel of the International Cancer Genome Consortium (ICGC)/The Cancer Genome Atlas (TCGA), encompassing 2,000 WGS and 8,000 WES or cancer cases across more than 12 cancer types and to replicate the resulting candidate genes in an independent large cohort of cancer patients, building on an innovative MTGS approach.

This effort requires new methods that need to be equally suited for the analysis of large cohorts of patients (pan-cancer study), as well as for the application in clinical settings (diagnostic analysis using MGTS at DEXEUS and Fundación Pública Galega de Medicina Xenómica).

We propose to advance the bioinformatics algorithms included in this platform in multiple ways:

New statistical methods for the identification of rare susceptibility variants.


Algorithms for the interrogation of intergenic regulatory variants using epigenomic information.


Integrative analysis of susceptibility variants with somatic driver mutations, a broad range of molecular phenotypes and clinical phenotypes.


In combination, these innovations have the potential to change the way cancer risk is assessed in the clinics, to significantly expand the set of known risk genes and pathways, and to provide a better understanding of their involvement in cancer susceptibility.

We will further advance the use of innovative sequencing based methods for the experimental validation of computational predictions. This includes laboratory evolution experiments coupled with NGS to study tumour evolution in different susceptibility backgrounds, massively parallel reporter assays for direct disruption of regulatory motifs in vitro and novel techniques for studying gene regulation. The proposal has also a substantial collaborative component to maximally exploit these data. Moreover, collectively, the consortium has a very broad range of different expertise and specialized knowledge for state-of-the-art computational and experimental methodology.