Master of Science

thesisMultivariate assays using gene expression as their contributing factors, such as the centroid-based PAM50 Breast Cancer Intrinsic Classi er, 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 e ects on the results of an assay due to the intrinsic error in the laboratory process and measuring its contributing factors. While we expect that classi cation of samples in proximity to one of the centroids de ning 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 misclassi cation. Given the serious consequences due to ambiguous results in clinical classi cations, methods to measure the e ects 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 classi cation of centroid-based multivariate assays, is developed and described, using the PAM50 Breast Cancer Intrinsic Classi er as the model multivariate assay. Furthermore, the described method provides a general and individual classi cation con dence measurement that advances multivariate assays towards personalized healthcare by providing personalized con dence measurements on the assay's result. Finally, this study explores whether using parametric versus nonparametric distance measurements is most e ective when using a single gene expression platform, such as microarray or Real-time, quantitative Polymerase Chain Reaction

Evolutionary Pressure on Mitochondrial Cytochrome b Is Consistent with a Role of CytbI7T Affecting Longevity during Caloric Restriction

Background: Metabolism of energy nutrients by the mitochondrial electron transport chain (ETC) is implicated in the aging process. Polymorphisms in core ETC proteins may have an effect on longevity. Here we investigate the cytochrome b (cytb) polymorphism at amino acid 7 (cytbI7T) that distinguishes human mitochondrial haplogroup H from haplogroup U. Principal Findings: We compared longevity of individuals in these two haplogroups during historical extremes of caloric intake. Haplogroup H exhibits significantly increased longevity during historical caloric restriction compared to haplogroup U(p = 0.02) while during caloric abundance they are not different. The historical effects of natural selection on the cytb protein were estimated with the software TreeSAAP using a phylogenetic reconstruction for 107 mammal taxa from all major mammalian lineages using 13 complete protein-coding mitochondrial gene sequences. With this framework, we compared the biochemical shifts produced by cytbI7T with historical evolutionary pressure on and near this polymorphic site throughout mammalian evolution to characterize the role cytbI7T had on the ETC during times of restricted caloric intake. Significance: Our results suggest the relationship between caloric restriction and increased longevity in human mitochondrial haplogroup H is determined by cytbI7T which likely enhances the ability of water to replenish the Q i binding site and decreases the time ubisemiquinone is at the Qo site, resulting in a decrease in the average production rate of radica

Common DNA Variants Accurately Rank an Individual of Extreme Height

Polygenic scores (or genetic risk scores) quantify the aggregate of small effects from many common genetic loci that have been associated with a trait through genome-wide association. Polygenic scores were first used successfully in schizophrenia and have since been applied to multiple phenotypes including multiple sclerosis, rheumatoid arthritis, and height. Because human height is an easily-measured and complex polygenic trait, polygenic height scores provide exciting insights into the predictability of aggregate common variant effect on the phenotype. Shawn Bradley is an extremely tall former professional basketball player from Brigham Young University and the National Basketball Association (NBA), measuring 2.29 meters (7′6″, 99.99999th percentile for height) tall, with no known medical conditions. Here, we present a case where a rare combination of common SNPs in one individual results in an extremely high polygenic height score that is correlated with an extreme phenotype. While polygenic scores are not clinically significant in the average case, our findings suggest that for extreme phenotypes, polygenic scores may be more successful for the prediction of individuals

Pairwise Correlation Analysis of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10−13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses

PAM50 proliferation score as a predictor of weekly paclitaxel benefit in breast cancer

To identify a group of patients who might benefit from the addition of weekly paclitaxel to conventional anthracycline-containing chemotherapy as adjuvant therapy of node-positive operable breast cancer. The predictive value of PAM50 subtypes and the 11-gene proliferation score contained within the PAM50 assay were evaluated in 820 patients from the GEICAM/9906 randomized phase III trial comparing adjuvant FEC to FEC followed by weekly paclitaxel (FEC-P). Multivariable Cox regression analyses of the secondary endpoint of overall survival (OS) were performed to determine the significance of the interaction between treatment and the (1) PAM50 subtypes, (2) PAM50 proliferation score, and (3) clinical and pathological variables. Similar OS analyses were performed in 222 patients treated with weekly paclitaxel versus paclitaxel every 3 weeks in the CALGB/9342 and 9840 metastatic clinical trials. In GEICAM/9906, with a median follow up of 8.7 years, OS of the FEC-P arm was significantly superior compared to the FEC arm (unadjusted HR = 0.693, p = 0.013). A benefit from paclitaxel was only observed in the group of patients with a low PAM50 proliferation score (unadjusted HR = 0.23, p < 0.001; and interaction test, p = 0.006). No significant interactions between treatment and the PAM50 subtypes or the various clinical–pathological variables, including Ki-67 and histologic grade, were identified. Finally, similar OS results were obtained in the CALGB data set, although the interaction test did not reach statistical significance (p = 0.109). The PAM50 proliferation score identifies a subset of patients with a low proliferation status that may derive a larger benefit from weekly paclitaxel. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-013-2416-2) contains supplementary material, which is available to authorized users

Responsiveness of Intrinsic Subtypes to Adjuvant Anthracycline Substitution in the NCIC.CTG MA.5 Randomized Trial

Recent studies suggest that intrinsic breast cancer subtypes may differ in their responsiveness to specific chemotherapy regimens. We examined this hypothesis on NCIC.CTG MA.5, a clinical trial randomizing premenopausal women with node-positive breast cancer to adjuvant CMF (cyclophosphamide-methotrexate-fluorouracil) versus CEF (cyclophosphamide-epirubicin-fluorouracil) chemotherapy