The influence of podocalyxin expression on immune response to ovarian cancer and the efficacy of an antibody-drug conjugate in immunotherapy

Despite significant advances in the treatment of many cancers, ovarian cancer still claims hundreds of lives in Canada every year. A molecule called podocalyxin is “switched on” by a high percentage of tumors from various cancer types including ovarian cancer and its expression is associated with poor prognosis. Since the immune system has a key influence in the control of tumor growth, one of my objectives will be to study how podocalyxin influences the immune response against tumors.
In addition, Dr. McNagny’s team recently developed an antibody, called PODO447, which recognizes an exquisitely tumor-specific form of podocalyxin. Accordingly, my second objective will be to explore the use of this antibody as a method to either attract immune cells to cancer cells and kill them or as a tool to deliver toxins and chemotherapeutic agents specifically to tumor cells while sparing normal tissue. Preliminary experiments in animal models already are suggesting the efficacy of the latter approach. In conclusion, the results obtained in this project will allow us to take one more step toward the objective of ultimately treating ovarian cancer patients with the podocalyxin targeting therapies.

AI-driven integration of omics and histopathology for biomarker discovery in cancer

Tumors of the same cell type, origin, and stage have unique genetic features that impact course of disease and treatment response. However, management of cancer is still largely dictated by a patient’s tumor cell type and stage without further refinement.

We intend to take advantage of the unique opportunities afforded by BC’s cancer care system (with a single payer system and uniform treatment protocols, together with high quality patient outcome data) to build an artificial intelligence (AI)-based cancer biomarker discovery platform. The proposed platform will integrate the images of the tumor tissues along with their genetic markers through AI to identify novel biomarkers for cancer patient risk stratification and management. Our program will:

  1. Improve efficiency in pathology laboratories.
  2. Identify tissue image features that correlate with tumor genetics which can rapidly and accurately classify patients into clinically relevant groups.
  3. Generate new biomarkers for precision medicine by combining tumor genetics and tissue imaging.

Ultimately, this program will improve patient outcomes, alleviate the need to perform expensive genetic profiling tests, and lead to significant cost-savings in the healthcare system.