Manitoba Onco-Multiomics initiative (MOMi): profiling a “mixed Manitoba cohort” of cancer patients with highly aggressive disease that feature poor prognoses and treatment outcomes.

Aim(s):

• Acquire an initial molecular snapshot of highly aggressive and poor outcome solid cancer subtypes at the earliest of stages of the patients’ cancer journey
• Using a multiomics approach (genomics, transcriptomics, proteomics, phospho-proteomics) combined with advanced AI-based analytical and processing tools to interrogate fresh surgically resected tumour samples from Manitoban cancer patients.
• Patients may achieve better outcomes by using their tumour’s multiomic signature (genomic and molecular data), and considers their corresponding clinical health data, to “match” onto clinical trials and/or with novel drug combination therapies earlier in the patient’s cancer treatment journey.
  

Summary:

A significant number of cancer patients with highly-aggressive tumour subtypes continue to show poor outcomes in spite standard-of-care treatment; necessitating a critical rethinking of the prescribed clinical interventions. Our multi-disciplinary research team will profile these aggressive solid tumour subtypes at its earliest stages (prior to any treatment) and identify opportunities for new multiomics data-driven drug interventions and/or clinical trials that may alter these patients’ treatment trajectories and outcomes. Using this approach, we hope to establish a new “data-driven standard-of-care” methodology to guide treatment and improve prognoses thereby saving the lives of more cancer patients. We expect that our findings could also lead to identification of new biomarkers of disease that can inform “prevention” and “early detection” research studies involving population screening and early intervention initiatives.

Anticipated outcomes and impact: 

Multiomics profiling of aggressive tumours/cancer subtypes early in their disease course may uncover actionable genetic and molecular targets, vulnerabilities and determinants that can be used to better predict which patients will benefit from clinical and/or drug trials. This may empower patients and physicians to alter treatment trajectories and outcomes using a precision oncology approach for these cancer subtypes.