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According to the , nearly half of Canadians are expected to receive a cancer diagnosis in their lifetime. Early detection and close monitoring of treatment response are crucial factors to recovery and survival. However, the effectiveness of the current ‘one size fits all’ approach to cancer treatment is limited, often providing generic data on the presence of cancer, with no specific guidance. To advance personalized cancer treatment and improve patient outcomes, Queen’s has become a founding member of the new Digital Technology Supercluster project .

With a total investment of $17.8 million, CanDETECT will use artificial intelligence (AI) and machine learning to develop a precision oncology software that will more effectively monitor cancer reoccurrence in survivors and provide real-time assessments of a patient’s response to targeted therapies. Led by Imagia Canexia Health, the CanDETECT project is bringing Queen’s together with BC Cancer Research, DNAstack, Microsoft, University Health Network, Illumnia, Kingston General Health Research Institute, Kingston Health Sciences Centre, and Oxford Nanopore Technologies in a collaborative partnership. Under the Supercluster model, academic institutions, not-for-profit organizations, and companies of all sizes work together with support from the federal government to accelerate innovation and create jobs.

Researcher (Pathology and Molecular Medicine; Queen’s Cancer Research Institute) will advance several key research components for the project. Leveraging Queen’s connection to the Ontario Institute for Cancer Research ()-funded OCTANE network, Dr. Feilotter’s team will oversee the collection of patient samples from across Ontario to provide high throughput sequencing of DNA results from patient tumour and plasma specimens.

Queen’s researchers will also report relevant results back to clinicians and patients and assist in the development of AI tools generated from CanDETECT results. The novel software will use predictive analytics to help oncologists tailor treatments to individual patients, ensuring the best possible outcome, more accurately and at less cost than current solutions.

This is not the first time Dr. Feilotter has advanced research innovations through a Supercluster. In summer 2020, she was a member of Access to Cancer Testing & Treatment in Response to COVID-19 () as part of the Digital Technology Supercluster’s response to COVID-19 program. This project developed a minimally invasive blood test that assessed circulating tumor DNA (ctDNA) through remote delivery to detect cancer and guide treatment decisions. Project ACTT exceeded its goal of distributing 2,000 patient liquid biopsy tests across the country. Successfully generating a large amount of data, the project can now move into the critical stage of evaluating the test method for potential clinical implementation and widespread adoption.

In both Supercluster projects, Dr. Feilotter considers the collaboration with industry partners a winning combination. "My team and I are committed to advancing research and development that will improve the lives of Canadian cancer patients," says Dr. Feilotter. "The idea that we could be part of a national group aiming to identify biomarkers of early cancer recurrence is of great importance to all of us. Networking opportunities that bring together academic and private sector partners, that span provinces across the country, and that seek to find common ground between multiple disciplines are best maximized when everyone sees the importance of the clinical question at the heart of all of it and is driven to solve it."

To maintain the balanced approach to research and industry applications in the Supercluster model, Queen’s Partnerships and Innovation (QPI) has been a key internal partner for Dr. Feilotter and her team. From helping to build and manage the relationships, to responding to opportunities and fulfilling funding obligations, QPI has provided the supports needed to let the research be the focus for the Queen’s team, helping to advance their goals for innovation in cancer detection.

To learn more about CanDETECT, see the .