What We Do
SPHERE applies a multidisciplinary approach to breast cancer research with an emphasis on big data and stratification of populations to inform a precision medicine model of care. This model uses real world evidence to reveal patterns and trends within a disease to inform better care management. Genetic, lifestyle and environmental factors are used to identify and predict an individual patient's prognosis, response and outcomes.
To improve outcomes for breast cancer patients, SPHERE focuses on three key research priorities: precision medicine, big data and machine learning; improving outcomes for special populations; and best practices for patient-centered care.
Research Themes
Precision medicine, big data and machine learning
Precision medicine, big data and machine learning
Precision medicine, big data and machine learning
SPHERE’s research leverages vast amounts of health data to create machine learning algorithms that can help with the prevention, prediction and treatment of breast cancer. Health data includes information such as routinely-collected clinical data, electronic health records, retrospective studies and chart reviews, and it is obtained through Alberta Health Services.
Big data analytics gives researchers the ability to identify trends and patterns of breast cancer and accurately stratify patients into populations based on genetic, lifestyle and environmental factors.
By grouping patients together, researchers can look at the unique characteristics of each group, and more precisely predict their risk, treatment response and outcomes. SPHERE has recently looked into predicting recurrence and pathologic complete response through the use of big data analytics.
Improving outcomes for special populations
Precision medicine, big data and machine learning
Precision medicine, big data and machine learning
SPHERE uses big data analytics to obtain a complete and accurate picture of breast cancer and how it affects different patient populations. Specifically, SPHERE looks at women aged 40 and under to determine the unique characteristics of young-onset breast cancer, such as its biomarkers and lifestyle factors.
Young-onset breast cancer is unique because its genetic basis is currently unclear beyond BRCA1 and BRCA2 mutations. A better understanding of this disease and the impact of diagnosis and treatment on young women is needed to improve outcomes.
SPHERE collaborates with researchers from the pan-Canadian cohort study, Reducing the Burden of Breast Cancer in Young Women (RUBY), to study young-onset breast cancer, and conducts similar studies looking at its etiology to inform better care management.
Best practices for patient-centered care
Precision medicine, big data and machine learning
Best practices for patient-centered care
Patient-centered care involves collaboration and coordination between health systems and patients. SPHERE looks at standardized clinical pathways and identifies areas of opportunity in shared decision-making, integration, access and communication, so that the patient has the best outcome and experience.
SPHERE also participates in studies looking at the emotional, mental, social and financial impacts of breast cancer and creates interventions that can improve the way care is delivered.
Patient self-management, pathway improvements, patient reported outcomes and patient reported experience measures are a main area of research for SPHERE. Active involvement and feedback from patients is key to creating recommendations for patient-centered care practices.
