Evaluation and Optimization of the Translational Potential of Array-Based Molecular Diagnostics

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Kernagis, Dawn


Datto, Michael B

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The translational potential of diagnostic and prognostic platforms developed using expression microarray technology is evident. However, the majority of array-based diagnostics have yet to make their way into the clinical laboratory. Current approaches tend to focus on development of multi-gene classifiers of disease subtypes, but very few studies evaluate the translational potential of these assays. Likewise, only a handful of studies focus on development of approaches to optimize array-based tests for the ultimate goal of clinical utility. Prior to translation into the clinical setting, molecular diagnostic platforms should demonstrate a number of characteristics to ensure optimal and efficient testing and patient care. Assays should be accurate and precise, technically and biologically robust, and should take into account normal sources of biological variance that could ultimately affect test results. The overarching goal of the research presented in this dissertation is to develop methods for evaluating and optimizing the translational potential of molecular diagnostics developed using expression microarray technology.

The first research section of this dissertation is focused on our evaluation of the impact of intratumor heterogeneity on precision in microarray-based assays in breast cancer. We conducted genome-wide expression profiling on 50 needle core biopsies from 18 breast cancer patients. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Array-based measures of estrogen (ER) and progesterone receptor (PR) status were compared to immunohistochemistry. The precision of genomic predictors of ER pathway status, recurrence risk, and sensitivity to chemotherapeutics were evaluated by interclass correlation. Results demonstrated that intratumor variation was substantially less than the total variation observed across the patient population. Nevertheless, a fraction of genes exhibited significant intratumor heterogeneity in expression. A high degree of reproducibility was observed in single gene predictors of ER (intraclass correlation coefficient (ICC)=0.94) and PR expression (ICC=0.90), and in a multi-gene predictor of ER pathway activation (ICC=0.98) with high concordance with immunohistochemistry. Substantial agreement was also observed for multi-gene signatures of cancer recurrence (ICC=0.71), and chemotherapeutic sensitivity (ICC=0.72 and 0.64). Together, these results demonstrated that intratumor heterogeneity, although present at the level of individual gene expression, does not preclude precise micro-array based predictions of tumor behavior or clinical outcome in breast cancer patients.

Leading into the second research section, we observed that in some cancer types, certain genes behave as molecular switches and have either an "on" or "off" expression state. Specifically, we observed these molecular switch genes exist in breast cancer as robust diagnostic and prognostic markers, including ER, PR, and HER2, and define tumor subtypes associated with different treatment and patient survival. We hypothesized that clinically relevant molecular switch (bimodal) genes exist in epithelial ovarian cancer, a type of cancer with no established molecular subgroups. To test this hypothesis, we applied a bimodal discovery algorithm to a publically available ovarian cancer expression microarray dataset (GSE9891:285 tumors; 246 malignant serous (MS), 20 endometrioid (EM), 18 low malignant potential (LMP) ovarian carcinomas). Genes with robust bimodal expression were identified across all ovarian tumor types and within selected subtypes. Of these bimodal genes, 73 demonstrated differential expression between LMP vs. MS and EM, and 22 genes distinguished MS from EM. Fourteen bimodal genes had significant association with survival among MS tumors. When these genes were combined into a single survival score, the median survival for patients with a favorable versus unfavorable score was 65 versus 29 months (p<0.0001, HR=0.4221). Two independent datasets (n=53 high grade, advanced stage serous and n=119 advanced stage ovarian tumors) validated the survival score performance. Taken together, the results of this study revealed that genes with bimodal expression patterns not only define clinically relevant molecular subtypes of ovarian carcinoma, but also provide ideal targets for translation into the clinical laboratory.

Finally, the third research section of this dissertation focuses on development of robust blood-based molecular markers of decompression stress (DS). DS is defined as the pathophysiological response to inert gas coming out of solution in the blood and tissues when a body experiences a reduction in ambient pressure. To date, there are no established molecular markers of DS. We hypothesized that comparing gene expression before and after human decompression exposures by genome-wide expression profiling would identify candidate molecular markers of DS. Peripheral blood was collected 1hr before and 2hr after 93 hyperoxic, heliox experimental dives (n=54). Control arms included samples collected 1 hour before and 2 hours after high pressure oxygen breathing (n= 9) and surface exercise (n=9), and samples collected at 7am and 5pm for time of day (n=11). Pre and post-dive expression data collected from normoxic nitrox experimental dives were utilized for independent validation. Blood samples were collected into PaxGene RNA tubes. RNA was extracted and processed for globin reduction prior to cDNA synthesis and Affymetrix U133A GeneChip hybridization. 746 genes were differentially expressed following hyperoxic, heliox decompression exposures (permutation adjusted p-value cutoff 1.0E-4). After filtering control significant genes, 726 genes remained. Pathway analysis demonstrated a significant portion of genes were associated with innate immune response (p<0.0001). A 362 multi-gene signature of significant, covariant genes was then applied to the independent dataset and demonstrated differentiation between pre and post-dive samples (p=0.0058). There was no significant correlation between signature and venous bubble grade or bottom time in the validation study. Our results showed that expression profiling of peripheral blood following decompression exposures, while controlling for experimental and normal sources of biological variance, identifies a reproducible multi-gene signature of differentially expressed genes, primarily comprising genes associated with innate immune response and independent of venous bubble grade or dive profile.

Taken together, the research and results presented in this dissertation represent considerable advances in the development of approaches to guide microarray-based diagnostics towards the ultimate goal of clinical translation.






Kernagis, Dawn (2012). Evaluation and Optimization of the Translational Potential of Array-Based Molecular Diagnostics. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/5536.


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