Breast cancer and ovarian cancer are hormone driven and are known to have some predisposition genes in common such as the two well known cancer genes BRCA1 and BRCA2. The objective of this study is to compare the coexpression network modules of both cancers, so as to infer the potential cancer-related modules. We applied the eigen-decomposition to the matrix that integrates the gene coexpression networks of both breast cancer and ovarian cancer. With hierarchical clustering of the related eigenvectors, we obtained the network modules of both cancers simultaneously. We identified 43 modules that are enriched by at least one of the four types of enrichments. The structure of 29 modules in both cancers is significantly different with p -values less than 0.
Many breast imaging centers have launched high risk screening clinics to augment their existing services. This has already become the new standard, as organizations look for justification to expand patient services, recommend breast MRI screening exams, and provide referrals for genetic counseling. Offering a service dedicated to screening patients for high risk, without the proper tools, can put more stress on the breast imaging workflow. The problem is that many systems are not set up to function on this plane.
We are moving into the era of risk-based screening and prevention for breast cancer and increased patient expectations that we can find out that risk. To do so, we need to have accepted methods for estimating the risk of developing breast cancer over time and the risk of having a mutation in a cancer-causing gene. Each model uses different groups of presumed breast cancer risk factors to make an estimate. The models assign a weight to each factor and then use an algorithm to combine these weighted risks to all the factors studied.
Progeny Clinical includes validated risk assessment models to calculate 5-year and lifetime cancer risk, as well as gene mutation probabilities for any member of a pedigree. Any missing or invalid data required to run these models are automatically identified for you. Risk calculation results can be easily saved and timestamped within the database or saved as a pdf file at any time.