Women with high grade serous ovarian cancer HGSOC represent 70% of all ovarian cancers and suffer high morbidities and poor response to standard of care treatment. The five-year survival rate after diagnosis is <50%. Newer classes of therapy, called immunotherapy, stimulate patients’ own immune systems to recognize and kill cancer cells. Unfortunately, immunotherapies in HGSOC have thus far proven ineffective. Both standard of care and immunotherapy, specific molecular mechanisms that determine response are generally unknown and poorly understood. Thus there is an urgent and critical unmet need to understand the nature of how and why cancer cells respond to treatments to improve clinical outcomes for HGSOC patients.
Our team has made important progress in uncovering HGSOC biology. We found that HGSOC patients can be grouped into four biologically distinct subtypes by the specific nature of how mutations accumulate in their genomes. The subtypes are meaningful for two key reasons: i) they show differences treatment outcomes and ii) they show differences in immune system response to cancer cells. We also note that HGSOC cancer cells evolve extensively prior to treatment, leading to profound diversity between cancer cells in the same patient. This is thought to be a major determinant of treatment resistance. Cancer evolution itself is driven by mutations accruing in the genomes of malignant cells and is shaped by the nature and levels of immune system recognition and infiltration.
We have designed a new program of research that will link the properties of cancer evolution and immune system response to mutational processes as key factors driving HGSOC drug response. Our team is comprised of leading experts in ovarian cancer oncology, surgery, pathology; and cancer genomics, cancer evolution, immunology, single cell measurements and computational biology. We will bring to bear leading edge technology, measuring the genomes of thousands of individual single cells per cancer, leading to an ultra-high resolution view of how HGSOC change as a result of therapy. Aim 1 will investigate how different mutational processes confer ‘capacity’ of HGSOC to evolve; Aim 2 will investigate how different mutational processes stimulate immune cells to recognize and infiltrate cancers; and Aim 3 will investigate how mutational processes induce differences in how malignant cells and immune cells change after receiving a) standard of care chemotherapy b) combination immuno- and chemo- therapy in a clinical trial setting.
Our work will advance the field providing a novel ‘cellular dynamics’ view linking cancer evolution with immune response. We anticipate improved mechanistic knowledge of therapeutic response will lay the groundwork for novel therapeutic approaches and development biomarkers to optimally demarcate which patients are best suited to chemo and immune therapeutic strategies, ultimately leading to improved outcomes for women diagnosed with HGSOC.
This grant was made possible in part by a generous donation from Ovarian Cycle New York.
Sohrab Shah was appointed to MSK in Apr 2018 as the inaugural Chief of the Computational Oncology Service and is the incumbent of the Nicholls-Biondi Chair. He received his BSc degree in biology from the Queens University in Ontario Canada and his BSc and MSc in computer science from the University of British Columbia. He obtained a PhD in computer science from the University of British Columbia in 2008 and was appointed as a Principal Investigator to The British Columbia Cancer Agency and the University of British Columbia in 2010 where he developed the roots of his research program. He is a University of British Columbia Killam laureate and a Susan G. Komen Foundation Scholar. His research focuses on understanding how tumors evolve over time through integrative approaches involving genomics and computational modeling. He has made seminal contributions to understanding the clonal evolution of ovarian cancers and has discovered that specific mutational patterns in the genomes of ovarian cancers are prognostic. Dr. Shah has also pioneered computational methods for identifying mutations in cancer genomes as well as deciphering patterns of cancer evolution. He has led the development of a novel experimental platform for single cell genome analysis as well as novel statistical models, algorithms, and computational approaches to analyze large, high dimensional genomics and transcriptomic data sets, from both patient tumors and model systems. These resources have led to recent progress in molecular profiling of cancer cells at single cell resolution. Dr. Shah has been at the forefront of studying tumor evolution in breast, ovary and lymphoid malignancies. His work has been published in many of the major scholarly journals. In 2018 Dr. Shah was a Highly Cited Researchers with Clarivate Analytics. He is widely regarded as a leader and his appointment has greatly enhanced the reputation of MSK as a leading force in the field of computational oncology.