1020 Locust Street
Suite 374
Philadelphia, PA 19107
The focus of my laboratory is to leverage novel techniques from disciplines such as cell biology, multiomics, “big data,” and computational biology to study tumor biology and responses to radiotherapy in both patient samples and preclinical model systems in a variety of tumor types.
Research Projects
Characterizing tumor evolution in response to radiotherapy
click for larger image
Tumors are functionally complex and heterogeneous entities, and our understanding of cancer biology has expanded to now include tumor evolution as a key driver of tumorigenesis and response to therapy. Specifically, treatments such as radiotherapy impose selection pressure on tumors which can potentiate the expansion of resistant subclones that underpin treatment failures and poor patient outcomes. My laboratory studies this biology using bioinformatic approaches including next-gen sequencing, ctDNA studies, and novel bioinformatic pipelines to conduct studies on patient samples.
Enhancing efficacy of both radiotherapy and systemic therapy through novel combinatorial therapeutic approaches
click for larger image
Approximately 50% of patients with cancer will receive radiotherapy during the course of their treatment. Importantly, this radiation is often delivered with standardized dose regimens that are “one size fits all.” However, with the growth of molecular testing and targeted therapeutics, personalized oncology is now possible to treat an individual patient’s tumor. In that context, a major focus of my laboratory is applying these principles of personalized medicine, as well as the resulting novel targeted therapies, to enable personalized radiotherapy. Additionally, studying this biology may yield novel insights into the way that tumors acquire treatment resistance which drives cancer recurrence.
In silico modeling of tumor evolution and response to therapy
click for larger image
Traditional cancer biology studies require significant time, infrastructure and resources to generate and test hypotheses. Therefore, improved efficiency in the ability to generate preliminary data would enhance oncologic research. In that context, a major focus of my laboratory is using computational biology to model cancer biology in silico to enable rapid generation of preliminary data to validate downstream bioinformatics pipelines and contribute to the generation of novel hypotheses.