Understanding the interplay of mutations and selection during cancer evolution

Cancer is a genetic disease, subject to population genetics forces like mutation, selection and stochasticity. Our lab has recently demonstrated that coding sequences of cancer tumors not only exhibit positively selected mutations that drive cancer (www.biorxiv.org/content/10.1101/485292v1), but that there exist genes that the tumor cannot afford to lose to the mutational pressure (www.nature.com/articles/ng.3987). In addition to genes, we have also identified cancer driver loci in the non-coding part of the genome (www.nature.com/articles/s41467-017-00100-x). Both coding and non-coding selection can act to promote cancer defense mechanisms against therapy, which can be unveiled through the analysis of time-sequence data of cell-free DNA and of patient survival data.

Our lab is particularly interested in how the evolution and survival of cancer cell populations relies on mutation influx and in the selection inference from allele frequency information. To this end, our lab develops mathematical and computational approaches to estimate mutation rates and selection. We use whole-exome and whole-genome sequencing data repositories to analyze selection on coding and non-coding sequences. In addition, we analyze cell-free DNA from tumors and their temporal evolution in response to therapy. Estimates of the strength of selection in cancer allow for a prioritization of genes and non-coding regions by their disease relevance, with the ultimate goal of promoting therapeutic advances.

The Evolutionary Processes Modeling Group was established in October 2018 and is part of the “Bioinformatics and Genomics” program at the CRG in Barcelona, Spain. Further information can be found at: https://www.crg.eu/en/programmes-groups/weghorn-lab.

Whom would we like to hire?

We are looking for a PhD student to join the lab to help elucidate cancer evolutionary dynamics using population genetics predictions and simulations together with recently published and unpublished cancer sequencing data. Research interests within the field of population genetics but outside this specific topic can also be considered.

• The ideal candidate should be highly motivated and eager to work on biological problems using theoretical and computational approaches.
• Candidates should have a University Degree and a Master’s Degree in physics, mathematics, statistics, genetics, bioinformatics, computer science or related disciplines within the European Higher Education System (minimum 300 ECTS) or equivalent by September 2020.
• The candidate needs to be proficient in English.

We provide a highly stimulating environment with state-of-the-art infrastructure, and unique professional career development opportunities. The successful applicant will enrol in the very active CRG International PhD program, which includes science and practical courses, a wide range of complementary skills training, access to many courses, mentoring via a thesis committee, and active participation in the organization of seminars, symposia and retreats.

Application

Applications are accepted exclusively online. Please, click the "Visit Website" button to access our online application system.

The deadline for the receipt of complete applications (including reference letters) is November 4, 2019. Proposals must be written in English. CRG offers and promotes a diverse and inclusive environment and welcomes applicants regardless of age, disability, gender, nationality, race, religion or sexual orientation.

Candidates may contact [Email Address Removed] for informal enquiries regarding the application and academic enquiries, as well as address scientific enquiries to [Email Address Removed].

All personal data requested by CRG from applicants will be treated in accordance with the principles of the Data Protection Act (15/13 December 1999).