Use of cluster analysis as translational pharmacogenomics tool for breast cancer guided therapy
Creators: name: Nwana, Ngozi
Publisher: name: No Publisher Supplied
Year: 2014

description: Breast cancer epidemiology and disease diversity continue to be a major health concern today. Breast cancer is a tumor that develops from the breast cells and which has become malignant. It is currently considered the second leading cause of cancer death in women, exceeded only by lung cancer as death rates have been declining since 1989 (with larger decreases in women younger than 50). The decline is believed to be as a result of early detection through screening, increased awareness and also improved treatment. However despite all the advances in early detection and current treatment options to date, the race for sustainable, effective, personalized, treatment and ultimately cure still continues to be a challenge.  Varying levels of breast cancer disease biology and treatment information currently exist today. So far, this has enabled varying levels of treatment success. However, despite spectacular examples of prolonged disease remission with some of the treatments available today, the statistical survival benefit in metastatic breast cancer patients currently is still estimated in months and not years. A patient’s potential to survive continues to vary greatly and depend on factors such as type, stage (spread) of cancer, degree of disease aggressiveness in addition to the minimally analyzed genetic make- up. This treatment gap has been attributed to the diversity of breast cancer disease (diverse subtypes), and at the granular level, limitations with disease understanding and pharmacogenomics. All these factors present treatment limitations and challenges thus resulting in increased incidence for the disease.      Undoubtedly, the area mostly lacking and impactful in today’s general breast cancer treatment is inadequate incorporation of the genetic aspects of disease for sustainable personalized treatments for the patients in spite of the tremendous amount of genetic data that is available today.  This gap generated my interest to research breast cancer gene expression data in order to extract relevant genetic information for disease understanding.    Today, breast cancer data are hugely generated mainly using DNA microarrays. This provides the opportunity to extract from these gene expression data previously unrecognized biological structure and meaning. However, one of the major challenges with huge genetic data is that of understanding and then analyzing the resulting gene expression data in order to understand gene behavior and extract significant patterns of disease from relevant gene activity. Several approaches for mining genetic data include some existing unsupervised clustering techniques. Incidentally, some of these existing unsupervised clustering methods have often been classified either as non-robust and/or lack the ability to discover subtle, context-dependent biological patterns, and hence have not proven to be optimal methods for analyzing cDNA microarray breast cancer gene expression data.    In this study, NMF (non-negative matrix factorization) Consensus algorithm from MIT’s GenePattern analysis module, a robust clustering methodology designed for class discovery and clustering validation is presented as the main clustering methodology for the analyses of breast cancer DNA microarray gene expression data obtained from the Broad Cancer Institute of Harvard and MIT. NMF is sensitive and adaptive to huge genetic data, and provides biological relevance and sensitivity to the resulting clusters. It was used in conjunction with preparatory unsupervised and sparse hierarchical clustering, both of which were used in the initial classification and baseline clustering before NMF clustering was performed. Comparative gene marker selection analysis was also run to evaluate other genes and pathways for other types of cancer other than breast cancer in order to determine if there are comparable genes/biomarkers that may share similar pathways as breast cancer genes.     This study puts forth some important recommendations that would constitute a science-based, long-term, sustainable breast cancer data analysis approach, critical for disease understanding and for implementing effective sustainable and personalized treatment solutions for all breast cancer patients. Some of the components of these recommendations are primarily lacking in traditional treatment and some of the recommendations are somewhat already incorporated into similar genetic- based breast cancer treatment today. However, they are yet to be adequately incorporated in standard breast cancer disease treatment for all patients and for all subtypes.