Dr. Nick Semenkovich joined the Department of Medicine in the Division of Oncology as an instructor since July 2023. He is a physician scientist and computer scientist with broad expertise spanning internal medicine, genetics, machine learning, and information security. He completed his undergraduate studies at M.I.T. with dual degrees in Computer Science (“Course 6”) & Biology, followed by an MD/PhD at Washington University in Genetics. His Ph.D. work was with Dr. Jeffrey Gordon, where he leveraged ATAC-seq to study the epigenetic impacts of the gut microbiota. He completed his Internal Medicine Residency at the Harvard-affiliated Brigham & Women’s Hospital, where he was awarded the prestigious 2018-2019 Resident as Mentor award. Following residency, he completed a clinical fellowship in Endocrinology, Metabolism, and Lipid Research and was selected for the Physician Scientist Training Program with postdoctoral mentorship under Dr. Aadel Chaudhuri. During his graduate work, Dr. Semenkovich adapted and applied ATAC-seq (then a nascent technology) to interrogate the epigenetic landscape of host cells interacting with the gut microbiota. This work involved extensive analysis of multi-omics datasets and systems modeling to identify metabolic pathways and transcription factor networks influenced by the gut microbiota.
He developed key research infrastructure and data management systems, including a university-wide metabolic pathway database and a system for management of microbial genomes and related metadata. His pivotal study was published in PNAS, defining key microbe-host immune epigenetic interactions, and establishing one of the first open source packages for identifying super-enhancers in epigenetic data. As a postdoctoral scholar and clinical fellow, Dr. Semenkovich spearheaded the analysis of a massive multi-institutional data lake of clinical information, liquid biopsies, and paired tumor sequences to better risk-stratify oligometastatic non-small cell lung cancer (NSCLC). He discovered that minimally invasive analysis of cfDNA can be used to identify micrometastatic disease earlier than current gold standard imaging approaches, with this work directly leading to efforts to re-triage oligometastatic NSCLC patients prior to radiation therapy and identify those who are more appropriate for clinical trials. His current work includes extensive development of machine learning approaches to identify markers of immune exhaustion, subclinical infections, and end-organ damage in critically ill patients within the ICU. This includes the development of reproducible, performant computational pipelines to analyze and visualize terabyte-scale multi-omic sequencing data.
He is developing infrastructure and systems to integrate complex multi-platform datasets (including FACS, CyTOF, and epigenetic sequencing data) with clinical parameters from the EMR to build risk models of end organ damage and other key complications. Dr. Semenkovich also maintains a number of cross-disciplinary collaborations within both academia and industry, spanning a range of disease areas and clinical realms. These include work studying tumor ecotypes in ovarian cancer through integration of scRNA-seq and bulk RNA-seq datasets. In collaboration with colleagues in ENT, he has developed a decision support model guiding treatment in head and neck surgical resections, by using clinical and genomic parameters to build gradient-boosted models of residual disease risk. He also has a passion for and research interests in information security and its role in healthcare and medical devices. Outside the hospital, Dr. Semenkovich enjoys coffee and wrangling his four tiny humans.