Next-generation predictive signatures for breast cancer

Professor Hallett and his team propose to use genomics and bioinformatics methods to develop clinical tests to better target treatments offered to patients with various types of breast cancer. 22,000 women in Canada, including 5,500 in Québec, will be diagnosed with breast cancer this year. Certain types of breast cancer respond well to certain treatments and not to others. Furthermore, once the primary tumour has been surgically removed, many patients would not need additional treatments that often have short and long-term negative secondary effects. The decision to treat or not and with what, relies on the ability to predict which patients would benefit from treatments and which should be spared.

Michael Hallett’s team has developed gene signatures specific to each of the three principal types of breast cancer. These signatures make it possible to adapt the treatment for each patient for the most common subtype of breast cancers, estrogen receptor positive (~70% of cases) and also identify patients at risk for the other two types of breast cancer. The team proposes to use these signatures to create a diagnostic test that surpasses in precision all tests actually on the market.

Co-applicants: