Hanna works on integrative computational methods for the identification of new oncology therapeutic targets and biomarkers, maximizing their translational potential via combined analyses of large-scale drug/functional-genomics screening data and genomic profiles of cancer patients.
Within Open Targets, Hanna is responsible for the development of new algorithms and computational tools for optimising the genomic characterization of large panels of cancer in vitro models. This involves the design of scoring methods and assessment routines for evaluating the relevance of immortalised cancer cell lines with respect to matched primary diseases. The aim of this work is to enable researchers to make appropriate informed choices about model inclusion/exclusion in retrospective and future studies, and to identify cancer subtypes currently lacking representative in vitro models.
Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets.
Nature communications 2019;10;1;5817