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Introduction Clinical trials, although academically accepted as the most effective treatment available for cancer patients, poor accrual to clinical trials remains a significant problem. A clinical trials navigator (CTN) program was piloted where patients and/or their healthcare professionals could request a search and provide a list of potential cancer clinical trials in which a patient may be eligible based on their current status and disease. Objectives This study examined the outcomes of a pilot program to try to improve clinical trials accrual with a focus on patients at medium to small sized cancer programs. Outcomes examined included patient disposition (referral to and accrual to interventional trials), patient survival, sites of referral to the CTN program. Methods One 0.5 FTE navigator was retained. Stakeholders referred to the CTN through the Canadian Cancer Clinical Trials Network. Demographic and outcomes data were recorded. Results Between March 2019 and February 2020, 118 patients from across Canada used the program. Seven per cent of patients referred were enrolled onto treatment clinical trials. No available trial excluded 39% patients, and 28% had a decline in their health and died before they could be referred or enrolled onto a clinical trial. The median time from referral to death was 109 days in those that passed. Conclusion This novel navigator pilot has the potential to increase patient accrual to clinical trials. The CTN program services the gap in the clinical trials system, helping patients in medium and small sized cancer centres identify potential clinical trials at larger centres.

Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. Analysis of normal versus malignant prostate cancer data sets indicates differential expression of the genes HEATR5B, DDC, and GABPB1-AS1 as potential prostate cancer biomarkers. Our study also supports PTGFR, NREP, SCARNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease.