Most of us know someone who struggled with breast cancer, or at least heard about the struggles facing patients who are fighting against breast cancer. Breast cancer is the most frequent cancer among women, impacting 2.1 million women each year. Breast cancer causes the greatest number of cancer-related deaths among women. In 2018 alone, it is estimated that 627,000 women died from breast cancer.
The most important part of a process of clinical decision-making in patients with cancers, in general, is the accurate estimation of prognosis and survival duration. Breast cancer patients with the same stage of the disease and the same clinical characteristics can have different treatment responses and overall survival, but why?
Cancers are associated with genetic abnormalities. Gene expression measures the level of gene activity in a tissue and gives information about its complex activities. Comparing the genes expressed in normal and diseased tissue can bring better insights into the cancer prognosis and outcomes. Using machine learning techniques on genetic data has the potentials of giving the correct estimation of survival time and can prevent unnecessary surgical and treatment procedures.
The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database is a Canada-UK Project which contains targeted sequencing data of 1,980 primary breast cancer samples. Clinical and genomic data was downloaded from cBioPortal.
The dataset was collected by Professor Carlos Caldas from Cambridge Research Institute and Professor Sam Aparicio from the British Columbia Cancer Centre in Canada and published on Nature Communications (Pereira et al., 2016).