Frozen and formalin-fixed paraffin embedded tissues offer researchers collections of data from which to study and predict the progression of disease. One of the more promising areas of research that depends on biobanks to proceed is using microarray technology to understand the development of breast cancer. More than 1.15 million cases of breast cancer have been diagnosed worldwide. It is a heterogeneous disease that takes many forms and produces an array of clinical outcomes. There are only a small number of predictive factors used presently in the clinic to manage patients with the disease. The development of additional genetic factors for prognostication is an important area of research that depends crucially on biorepositories from which microarray studies can be sourced. Additionally, most patients diagnosed with this disease receive some form of chemotherapy. All patients that obtain this therapy are exposed to toxicity but only some benefit from it. Microarray technology can aid in the determination of which patients might be better candidates for chemotherapy and which might elect to skip the therapy.
Thirty percent of cancer in women is related to the breast making it the most prevalent form of the disease. It represents a significant health problem for women and is almost always related to genetic abnormalities and variation in certain high-penetrance genes. A number of genes have been identified to be associated with breast cancer. The cells all display a phenotype of uncontrolled cell growth. However, this phenotype may arise from many different combinations of genetic abnormalities and mutations. By investigating how cells evolve in various tumors, new diagnostics and therapies can be developed by identifying important mutational events for different classes of tumor.
Studies have demonstrated that breast cancer can be classified into various types by examining the expression profile of the disease. Perou used 40 breast tumors and matched pairs of samples along with 476 genes that variably expressed between the tumors and matched pairs. In another study, clustering and segregation were obtained by Chang using 70 tumors and 78 genes. Four major subgroups were found based on expression levels. Finally, Gruvberger profiled 58 grossly dissected tumors and used machine learning to determine ER status via expression profiles.
In addition to using gene expression profiles to relate genes to specific tumor types, microarrays can be used to tune the patients that receive chemotherapy in the clinic. So called adjuvant systemic therapy is common after surgery. Sixty percent of early breast cancer patients receive some form of chemotherapy but only a minority benefit from it. Predictive indicators, based on microarray studies are needed to select the most effective form of therapy for the best candidates. High-throughput microarray techniques allow for the parallel analysis of thousands of genes and have resulted in an increased understanding of the complex nature of breast cancer progression. A number of multigene signatures have been related to various systemic therapies and tied to prognosis and patient response. These signatures can be used to validate which patients stand to benefit most from chemotherapy.
The Stanford group related histopathological features to distinct molecular subtypes and cemented the idea that breast cancer is not simply a single disease. Perou and his colleagues published their seminal research on gene expression profiles and class discovery in breast cancer and demonstrated that these cancers can be separated into at least five distinct molecular subtypes. A number of groups have demonstrated that ER-positive and ER-negative disease types are governed by distinct genetic processes.
Human tissues samples for cancer research are the basic materials for microarray studies that are being used for a diverse array of clinical ends. From determining the subtype of cancer from expression profiles to determining ER status to understanding which patients will benefit most from chemotherapy, microarray studies, sourced from biorepositories of oncology tissues, are driving progress that extends directly into the clinic and enhances patient care.