Grant: Op Grant: Analyses of Existing Canadian Cohorts & Databases - Cancer Control

Lung cancer is the leading cause of cancer mortality in Canada. Non-small cell lung cancer (NSCLC) is the most common form of lung cancer and is usually diagnosed at locally advanced (27.4%) or metastatic (47.9%) stage leading to a mortality rate >85%. Prompt diagnosis at the earliest stage is imperative; however, the process of obtaining a tissue diagnosis for NSCLC consumes weeks. Additional molecular phenotyping to determine the Epidermal Growth Factor Receptor (EGFR) or Anaplastic Lymphoma Kinase (ALK) mutation status for eligibility for mutation-specific therapies take an additional 3 weeks' time. Due to the hasty growth of NSCLC, metastatic stage progression can occur during lengthy diagnostic periods rendering many patients incurable. Presently, no biomarker based test of bodily fluids exists for the diagnosis or phenotypic subtyping of NSCLC. Metabolomics consists of the measurement of all low-molecular-weight metabolites in the body. The Manitoba Tumor Bank (MTB) has collected tumour tissue, biofluids, and clinical outcomes from >400 patients with early stage NSCLC and 250 cancer-free controls. A pilot study was recently conducted to assess the capabilities of metabolomics methods using 48 NSCLC cases and 24 controls from the MTB and found >17,500 unique metabolic entities of which 250 biomarkers were present in all subjects. Using a supervised cluster analysis method, a metabolic signature was identified which classified cases from controls with an overall accuracy of 97%. We therefore propose a larger case-control study utilizing 250 cases, and 250 controls. Samples will undergo whole metabolome profiling and an optimized metabolomics profiles will be developed using machine learning which differentiate: 1) NSCLC cases from Controls; 2) Adenocarcinomas from squamous cell carcinoma; and 3) ALK or EGFR mutants from wild-type NSCLC. A separate group of the cohort will be used for validation to assess the predictive accuracy of the optimized profiles.