Thesis Defense - Mark Ebbert
Apr 11, 2012 1:00 AM
Characterization Of Technical Uncertainty In The Classification Of Centroid-Based Multivariate Assays
Location: HSEB 4100C
Date: Apr. 25, 2012
Time: 2:00 pm
Supervisory Committee: Julio Facelli, Ph.D.; Philip Bernard, M.D.; Kenneth Boucher, Ph.D.; Karen Eilbeck, Ph.D.; Lewis Frey, Ph.D.
Multivariate assays using gene expression as their contributing factors, such as the centroid-based PAM50 Breast Cancer Intrinsic Classifier, are becoming commonly used in assisting treatment decisions in medicine, especially in oncology. Although physicians may rely on these multivariate assays for planning treatment, little is known about the effects on the results of an assay due to the intrinsic error in the laboratory process and measuring its contributing factors. While we expect that classification of samples in proximity to one of the centroids defining the tumor classes will be stable with respect to experimental errors in the gene expression measurements, what happens to the samples not in proximity to a single centroid is unknown. Results reported to the attending physician may be misleading because he or she is receiving no information about the probability for sample misclassification. Given the serious consequences due to ambiguous results in clinical classifications, methods to measure the effects of a multivariate assay’s intrinsic errors need to be established and communicated to attending physicians. In this study, a method to characterize the technical uncertainty in the classification of centroid-based multivariate assays, is developed and described, using the PAM50 Breast Cancer Intrinsic Classifier as the model multivariate assay. Furthermore, the described method provides a general and individual classification confidence measurement that advances multivariate assays towards personalized healthcare by providing personalized confidence measurements on the assay’s result. Finally, this study explores whether using parametric versus non-parametric distance measurements is most effective when using a single gene expression platform, such as microarray or Real-time, quantitative PCR.
In 2007 Mark Ebbert graduated with a Bachelor’s of Science from Brigham Young University in Bioinformatics and a minor in Computer Science. During Mark’s undergraduate degree he was a research assistant in two labs. While working as a research assistant Mark participated numerous research projects, which lead to numerous oral and poster presentations, as well as published manuscripts. Mark also participated in internships at Clemson University and the Harvard-MIT Health Sciences and Technology summer internship studying the temporomandibular joint and asthma, respectively. In 2007 Mark was awarded the Frontiers Scholar Award from BYU’s College of Physical and Mathematical Sciences Department for his research accomplishments as an undergraduate student.
Since his graduation, Mark has been working at ARUP laboratories researching breast cancer and HIV. During his time at ARUP, Mark has played a fundamental role in developing the PAM50 Breast Cancer Intrinsic Classifier – a clinical test developed to subtype breast tumors into one of four major subtypes.
While working at ARUP, Mark enrolled as a Master’s student in the University of Utah Biomedical Informatics Department and expects to complete his degree in the summer of 2012. Following completion of his Master’s degree, Mark will pursue a Ph.D. and seek to one day become a professor researching early-onset diseases in children.