Breast and ovarian cancers are significant causes of disease and death among North American women. Tumours in these cancers can acquire different mutations, resulting in cells that may respond differently to therapy. However, this genetic diversity within tumours is rarely considered when it comes to treatment, even though it is believed to contribute to drug resistance and disease progression. The breakthroughs in sequencing technology has led to precise identification of mutations in a tumour’s genome and shed light on the nature of tumour evolution. However, in order to gain accurate insights to how evolutionary processes contribute to clinical characteristics in cancer, novel and accurate statistical models/software is under unmet need and the lack of it has become a fundamental limitation. This project developed innovative statistical models/software (TITAN and PyClone) to address the lack of statistical frameworks to quantitatively infer cancer evolutionary patterns. The application and validation of these software tools in different tumour types led to multiple publications including a landmark publication in Nature (using breast cancer patient xenografts). Ongoing research projects in cancer evolution in Dr. Shah’s lab applying these tools are funded by multiple funding agencies including CIHR, TFRI and CCSRI. By the end of the project, eleven collaborations with PIs from BCCA, UBC, OICR and Denmark will directly use the software tools.