Exploring The Association Between Antidepressants and Colorectal Cancer in Administrative Data: Negative Controls, Active Comparators and Algorithms

Some antidepressants, especially Selective Serotonin Reuptake Inhibitors (SSRIs), may prevent colorectal cancer (CRC), but these effects may be drug rather than class specific. Previous epidemiological studies have only examined class-level effects, and all studies used non-user comparisons, which are susceptible to several biases. Examining specific SSRI-CRC associations requires a large sample size and precise prescription records, which are features of administrative data; however, these data do not generally contain pathology confirmed cases and algorithms are required to identify probable cases. The goals of this dissertation were: 1) to examine the class-level associations between three antidepressant classes, including SSRIs, and CRC compared to a negative control, antihypertensive initiators (AHT), 2) to examine the association between specific SSRIs and CRC, and 3) to re-evaluate claims-based CRC-identification algorithms in a contemporary population. To examine the first two goals, we performed a new-user, cohort study using a 20% random sample of Medicare beneficiaries (2007-2013), aged ≥66. We estimated hazard ratios (HRs) and 95% confidence intervals (CI), and controlled measured confounding using a propensity score weighting approach. SSRI initiators had lower CRC rates compared with AHT initiators (aHR=0.85, 95% CI: 0.70-1.02). Paroxetine and fluoxetine initiators had lower CRC rates compared with citalopram users (aHR: 0.78, 95% CI: 0.56-1.06; aHR: 0.74, 95% CI: 0.52-1.05, respectively). Estimates were consistent across sensitivity analyses. We re-evaluated CRC-identification algorithm performance in a ≥65, 2006-2009 North Carolina Medicare population, a proportion of which were cancer registry identified CRC cases. We employed a novel cohort creation strategy, whereby cases contribute information from both their pre-diagnostic non-case and case states to accurately capture CRC incidence. Specificity was lower (98.3-99.4% versus 98.5-99.6%) and Positive Predictive Value (PPV) substantially lower (18-37% versus 45-71%) in this population compared to the original population. Results from the first two goals warrant further investigation into the SSRI-CRC association, including incorporating additional part D data as it becomes available. Algorithms are a necessity when performing a drug-cancer study in administrative data, but should be used cautiously, because they are population and time specific. These CRC-identification algorithms need to be updated to reflect a more contemporary and economically diverse population. Future validation studies should employ strategies to accurately ascertain incidence to avoid overestimating PPV.Doctor of Philosoph

CNV Workshop: an integrated platform for high-throughput copy number variation discovery and clinical diagnostics

<p>Abstract</p> <p>Background</p> <p>Recent studies have shown that copy number variations (CNVs) are frequent in higher eukaryotes and associated with a substantial portion of inherited and acquired risk for various human diseases. The increasing availability of high-resolution genome surveillance platforms provides opportunity for rapidly assessing research and clinical samples for CNV content, as well as for determining the potential pathogenicity of identified variants. However, few informatics tools for accurate and efficient CNV detection and assessment currently exist.</p> <p>Results</p> <p>We developed a suite of software tools and resources (CNV Workshop) for automated, genome-wide CNV detection from a variety of SNP array platforms. CNV Workshop includes three major components: detection, annotation, and presentation of structural variants from genome array data. CNV detection utilizes a robust and genotype-specific extension of the Circular Binary Segmentation algorithm, and the use of additional detection algorithms is supported. Predicted CNVs are captured in a MySQL database that supports cohort-based projects and incorporates a secure user authentication layer and user/admin roles. To assist with determination of pathogenicity, detected CNVs are also annotated automatically for gene content, known disease loci, and gene-based literature references. Results are easily queried, sorted, filtered, and visualized via a web-based presentation layer that includes a GBrowse-based graphical representation of CNV content and relevant public data, integration with the UCSC Genome Browser, and tabular displays of genomic attributes for each CNV.</p> <p>Conclusions</p> <p>To our knowledge, CNV Workshop represents the first cohesive and convenient platform for detection, annotation, and assessment of the biological and clinical significance of structural variants. CNV Workshop has been successfully utilized for assessment of genomic variation in healthy individuals and disease cohorts and is an ideal platform for coordinating multiple associated projects.</p> <p>Availability and Implementation</p> <p>Available on the web at: <url>http://sourceforge.net/projects/cnv</url></p

Overexpression of miR-146a in basal-like breast cancer cells confers enhanced tumorigenic potential in association with altered p53 status

The tumor suppressor p53 is the most frequently mutated gene in human cancers, mutated in 25–30% of breast cancers. However, mutation rates differ according to breast cancer subtype, being more prevalent in aggressive estrogen receptor-negative tumors and basal-like and HER2-amplified subtypes. This heterogeneity suggests that p53 may function differently across breast cancer subtypes. We used RNAi-mediated p53 knockdown (KD) and antagomir-mediated KD of microRNAs to study how gene expression and cellular response to p53 loss differ in luminal versus basal-like breast cancer. As expected, p53 loss caused downregulation of established p53 targets (e.g. p21 and miR-34 family) and increased proliferation in both luminal and basal-like cell lines. However, some p53-dependent changes were subtype specific, including expression of miR-134, miR-146a and miR-181b. To study the cellular response to miR-146a upregulation in p53-impaired basal-like lines, antagomir KD of miR-146a was performed. KD of miR-146a caused decreased proliferation and increased apoptosis, effectively ablating the effects of p53 loss. Furthermore, we found that miR-146a upregulation decreased NF-κB expression and downregulated the NF-κB-dependent extrinsic apoptotic pathway (including tumor necrosis factor, FADD and TRADD) and antagomir-mediated miR-146a KD restored expression of these components, suggesting a plausible mechanism for miR-146a-dependent cellular responses. These findings are relevant to human basal-like tumor progression in vivo, since miR-146a is highly expressed in p53 mutant basal-like breast cancers. These findings suggest that targeting miR-146a expression may have value for altering the aggressiveness of p53 mutant basal-like tumors

Dermcidin expression is associated with disease progression and survival among breast cancer patients

Improved diagnostic screening has led to earlier detection of many tumors, but screening may still miss many aggressive tumor types. Proteomic and genomic profiling studies of breast cancer samples have identified tumor markers that may help improve screening for more aggressive, rapidly growing breast cancers. To identify potential blood-based biomarkers for the early detection of breast cancer, we assayed serum samples via matrix-assisted laser desorption ionization–time of flight mass spectrometry from a rat model of mammary carcinogenesis. We found elevated levels of a fragment of the protein dermcidin (DCD) to be associated with early progression of N-methylnitrosourea-induced breast cancer, demonstrating significance at weeks 4 (p = 0.045) and 5 (p = 0.004), a time period during which mammary pathologies rapidly progress from ductal hyperplasia to adenocarcinoma. The highest serum concentrations were observed in rats bearing palpable mammary carcinomas. Increased DCD was also detected with immunoblotting methods in 102 serum samples taken from women just prior to breast cancer diagnosis. To validate these findings in a larger population, we applied a 32-gene in vitro DCD response signature to a dataset of 295 breast tumors and assessed correlation with intrinsic breast cancer subtypes and overall survival. The DCD-derived gene signature was significantly associated with subtype (p < 0.001) and poorer overall survival [HR (95 % CI) = 1.60 (1.01–2.51), p = 0.044]. In conclusion, these results present novel evidence that DCD levels may increase in early carcinogenesis, particularly among more aggressive forms of breast cancer

Impact of Tumor Microenvironment and Epithelial Phenotypes on Metabolism in Breast Cancer

Cancer cells have altered metabolism, with increased glucose uptake, glycolysis, and biomass production. This study performed genomic and metabolomic analyses to elucidate how tumor and stromal genomic characteristics influence tumor metabolism

Tumor Intrinsic Subtype Is Reflected in Cancer-Adjacent Tissue

Overall survival of early-stage breast cancer (BC) patients is similar for those who undergo breast conserving therapy (BCT) and mastectomy, however, 10-15% of women undergoing BCT suffer ipsilateral breast tumor recurrence. The risk of recurrence may vary with BC subtype. Understanding the gene expression of the cancer-adjacent tissue and the stromal response to specific tumor subtypes is important for developing clinical strategies to reduce recurrence risk

Age-associated gene expression in normal breast tissue mirrors qualitative age-at-incidence patterns for breast cancer.

BackgroundAge is the strongest breast cancer risk factor, with overall breast cancer risk increasing steadily beginning at approximately 30 years of age. However, while breast cancer risk is lower among younger women, young women's breast cancer may be more aggressive. Although, several genomic and epidemiologic studies have shown higher prevalence of aggressive, estrogen-receptor negative breast cancer in younger women, the age-related gene expression that predisposes to these tumors is poorly understood. Characterizing age-related patterns of gene expression in normal breast tissues may provide insights on etiology of distinct breast cancer subtypes that arise from these tissues.MethodsTo identify age-related changes in normal breast tissue, 96 tissue specimens from patients with reduction mammoplasty, ages 14 to 70 years, were assayed by gene expression microarray.ResultsSignificant associations between gene expression levels and age were identified for 802 probes (481 increased, 321 decreased with increasing age). Enriched functions included "aging of cells," "shape change," and "chemotaxis," and enriched pathways included Wnt/beta-catenin signaling, Ephrin receptor signaling, and JAK/Stat signaling. Applying the age-associated genes to publicly available tumor datasets, the age-associated pathways defined two groups of tumors with distinct survival.ConclusionThe hazard rates of young-like tumors mirrored that of high-grade tumors in the Surveillance, Epidemiology, and End Results Program, providing a biologic link between normal aging and age-related tumor aggressiveness.ImpactThese data show that studies of normal tissue gene expression can yield important insights about the pathways and biologic pressures that are relevant during tumor etiology and progression

Impact of Tumor Microenvironment and Epithelial Phenotypes on Metabolism in Breast Cancer

Cancer cells have altered metabolism, with increased glucose uptake, glycolysis, and biomass production. This study performed genomic and metabolomic analyses to elucidate how tumor and stromal genomic characteristics influence tumor metabolism