Ovarian cancer is the abnormal development of cells found in the ovaries. It is the fifth most fatal cancer amongst woman and has an overall five-year survival rate of 45% (American Cancer Society, 2016). For women with newly-diagnosed, advanced stage ovarian cancer, the current standard of care is surgery – to remove as much of the cancer as possible – followed by chemotherapy – to kill the remaining tumour cells (Cancer.Net Editorial Board, 2016). However, chemotherapy can have devastating side-effects such as infection, nausea, reduced cognitive
function, and death (Sun, et al., 2005). Using patients’ genomic profiles to predict how well they will respond to the standard of care will be valuable for patients when deciding whether to pursue standard or alternative forms of treatment. This study uses ovarian cancer patient data compiled by The Cancer Genome Atlas (TCGA). Clinical data – such as patient age, gender, ethnicity, disease severity and treatment undergone – is used to define which patients responded well to chemotherapy. Patient gene expression data – which gives insight into which genes are up- or down-regulated – will be used to identify markers of chemotherapy response. This will be done using differential gene expression analysis – to identify individual genes that contribute to chemotherapy-response – and network analysis – to understand how the expression of these genes functions as a system. To make the results of the study clinically relevant, chemotherapy-response markers will be correlated to single nucleotide polymorphisms – a form of genetic variation that is much quicker to test for in a patient than gene expression.
Gene expression analysis reveals a strong signature of an interferon-induced pathway in childhood lymphoblastic leukemia as well as in breast and ovarian cancer