Dominik Glodzik's research at Harvard DBMI is focused on applications of statistical algorithms to understand, treat and detect cancer early.

During his postdoctoral fellowship at the Sanger Institute in the groups of Sir Prof Mike Stratton and Prof Serena Nik-Zainal he became an expert on detecting mutational patterns in cancer genomes. Specifically, he pioneered the use of supervised machine learning methods for the understanding of mutational processes in cancer. His most widely used algorithm is called HRDetect (Nature Medicine, Glodzik et al., 2017). This algorithm identifies cancer patients with homologous recombination deficiency (HRD) from genome sequencing data, widening the population of patients eligible for therapies. Because of his expertise in cancer genome analysis, he was recruited to the pediatric cancer program at the Memorial Sloan Kettering Cancer Center (MSKCC) in New York City, where he led an effort to build a cancer genome analysis platform. Together with the team, he successfully analyzed the MSKCC’s first 100 cancer patient genomes, discovering fusions which define both rare and hyper-mutated outlier pediatric cancers that may in future be candidates for rare patient cancer immunotherapies.