OICR-supported researchers quantify common prostate cancer outcome predictor
Advances in cancer research have opened the door to new tests to better assess tumours and help recommend the most appropriate course of treatment for a patient. Research pathologists play a critical role in turning scientific knowledge into tests that can be used in an everyday clinical setting.
“Scientists are constantly advancing our understanding of cancer, but that understanding cannot help patients unless it’s applied in practice,” says Dr. Tamara Jamaspishvili, Research Pathologist at Queen’s Cancer Research Institute. “Our role as research pathologists is to bridge that gap, and transform discoveries into more accurate diagnoses and prognoses for patients that could be implemented and actionable in practice.” Jamaspishvili’s work is supported by the Ontario Molecular Pathology Research Network, an OICR-funded province-wide network that conducts high-quality cancer research focussed on clinical impact.
An example of the challenge of clinical translation is found in PTEN testing. PTEN is a cancer-preventing gene that – when absent in a cell – may lead to uncontrolled tumour growth. Research has shown that the loss of PTEN within a prostate tumour could help predict the severity of a man’s prostate cancer, but PTEN is not routinely tested.
“Simply put, some cells in a tumour sample may have PTEN loss and some cells don’t, but nobody has clearly quantified how the ratio of cells with or without PTEN contribute to a patient’s health,” says Jamaspishvili.
Jamaspishvili teamed up with collaborators to address the subjectivity of PTEN testing. Her collaborators include Drs. David Berman, Palak Patel, Robert Siemens, Paul Peng, and Yi Niu from Queen’s Cancer Research Institute, Drs. Fred Saad and Anne-Marie Mes-Masson from the University of Montreal, Dr. Tamara Lotan from Johns Hopkins University, and Dr. Jeremy Squire and colleagues at the University of São Paulo.
Their study, recently published in the Journal of the National Cancer Institute, proposes a new quantitative approach to assess PTEN. They clarify how pathologists can predict the severity of a patient’s prostate cancer based on the number of cells with PTEN loss. These findings can help standardize PTEN testing, but their approach can also be applied to other pathology tests that are still highly subjective.
“Quantifying qualitative tests helps us move towards automated pathology techniques,” says Jamaspishvili. “This is the future of pathology.”
Jamaspishvili is now working to automate PTEN digital pathology analysis in collaboration with Dr. Stephanie Harmon and colleagues in Dr. Baris Turkbey’s lab as part of the National Cancer Institute’s Molecular Imaging Program.
“Now, we can apply machine learning image analysis tools to analyze PTEN loss and make better predictions for the benefit of patients. We look forward to using artificial intelligence in digital pathology to help fill the gaps between research and clinical practice.”