Alessio Locallo

Name: Alessio Locallo

Nationality: Italian

Academic Background: Bachelor of Science degree in Biomolecular Sciences and Technology and Master of Science degree in Quantitative and Computational Biology, University of Trento (Italy).

Project Title: Clonal evolution reconstruction of brain tumors from genomic and Hi-C data

Project Background : Glioblastoma (GBM) is the most frequent malignant primary brain cancer in adults and it often rapidly recurs after initial therapy. As for most of the tumors, it typically evolves as a heterogeneous mixture of genetic clones and subclones and this could potentially contribute to treatment failures. The underlying evolutionary processes remain poorly understood. Thus, disentangle the relationship between tumor growth and genetic evolution is of crucial importance, as the identification of the chronology of molecular alterations may identify milestones in carcinogenesis. Recently developed computational approaches that exploit next-generation sequencing data from temporally and/or spatially separated tumor resections from the same patient, with the aim to reconstruct tumor phylogeny, exhibit some notable limitations. Additionally, none of them currently exploits the statistically rich amount of information coming from transcriptomic and 3D chromatin organization in the genome.

Project Aim: The aims of this research project regard the clonal evolution analysis of brain tumor samples, in order to identify recurrent mechanisms of treatment resistance and novel therapeutic targets. Moreover, I will develop and apply ad-hoc methods to reconstruct the clonal evolution and to identify clinical trajectories. Paired, temporally separated whole genome sequencing (WGS) data will be used to infer the cancer cell fraction and the clonal status of mutations. In addition, I will develop and implement novel methodologies to infer the clonality of 3D chromatin conformation changes in tumor cells using Hi-C data. The integration of mutations and chromatin interactions from WGS and Hi-C in tumors will increase the ability to better understand how a tumor evolves over time, in order to correctly address the therapeutic approaches in the context of precision medicine.

Expected Outcome: This study will hopefully lead to the development of new computational tools and strategies featuring the ability to integrate different data sources, with the aim of establishing maps of cancer evolution, which might inform clinical risk stratification and treatment strategies.

Contact: alessio.locallo@bric.ku.dk