We are a multi-disciplinary hybrid wet/dry lab at the University of Pittsburgh and affiliated with the Department of Biomedical Informatics, Bioengineering and UPMC Hillman Cancer Center. Primary focus of our group is developing integrative statistical and machine learning approaches for extracting therapeutic insight from highly heterogenous omic datasets, clinical and drug response data for the purpose of precision medicine. Our projects are in the areas of cancer systems biology, epigenetics of drug response, and immunotherapy and are executed through multi-disciplinary collaborations. What makes our research unique is the ability to effectively condense massive compendia of biological data into highly interpretable and predictive computational models capable of capturing functional heterogeneity and context-specific dynamics, thereby allowing new discoveries.

Fundamental questions motivate research in our group:

· Why do some patients respond to treatment, while others not?

· Why are some treatments effective initially, but fail over time?

· How do cancer cells acquire the ability to spread from one part of the body to another?

Research Highlights

  • Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers. Osmanbeyoglu HU#, Shimizu F*, Rynne-Vidal A*, Alonso-Curbelo D*, Chen HA, Wen HY, Yeung TL, Jelinic P, Ravazi P, Lowe SW, Mok SC, Chiosis G, Levine DA, Leslie CS#. Nature Communications (2019).(#=co-corresponding authors)
  • An ATR and CHK1 kinase signaling mechanism that limits origin firing during unperturbed DNA replication. Tatiana N Moiseeva, Yandong Yin, Michael J Calderon, Chenao Qian, Sandra Schamus-Haynes, Norie Sugitani, Hatice U Osmanbeyoglu, Eli Rothenberg, Simon C Watkins, Christopher J Bakkenist. Proceedings of the National Academy of Sciences (2019).
  • PI3K pathway regulates ER-dependent transcription in breast cancer through the epigenetic regulator KMT2D. Toska E, Osmanbeyoglu HU*, Castel P*, Chan C, Dickler M, Hendrickson RC, Scaltriti M, Leslie CS, Armstrong SA, Baselga J . Science (2017).
  • Pan-cancer modeling predicts the context-specific impact of somatic mutations on transcriptional programs. Osmanbeyoglu HU, Toska E, Chan C, Baselga J, Leslie CS. Nature Communications (2017).
  • Linking signaling pathways to transcriptional response in breast cancer. Osmanbeyoglu HU, Pelossof R, Bromberg JF, Leslie CS. Genome Res (2014).