Statistical methods for the study of etiologic heterogeneity

Traditionally, cancer epidemiologists have investigated the causes of disease under the premise that patients with a certain site of disease can be treated as a single entity. Then risk factors associated with the disease are identified through case-control or cohort studies for the disease as a whole. However, with the rise of molecular and genomic profiling, in recent years biologic subtypes have increasingly been identified. Once subtypes are known, it is natural to ask the question of whether they share a common etiology, or in fact arise from distinct sets of risk factors, a concept known as etiologic heterogeneity. This dissertation seeks to evaluate methods for the study of etiologic heterogeneity in the context of cancer research and with a focus on methods for case-control studies. First, a number of existing regression-based methods for the study of etiologic heterogeneity in the context of pre-defined subtypes are compared using a data example and simulation studies. This work found that a standard polytomous logistic regression approach performs at least as well as more complex methods, and is easy to implement in standard software. Next, simulation studies investigate the statistical properties of an approach that combines the search for the most etiologically distinct subtype solution from high dimensional tumor marker data with estimation of risk factor effects. The method performs well when appropriate up-front selection of tumor markers is performed, even when there is confounding structure or high-dimensional noise. And finally, an application to a breast cancer case-control study demonstrates the usefulness of the novel clustering approach to identify a more risk heterogeneous class solution in breast cancer based on a panel of gene expression data and known risk factors

Examining the common aetiology of serous ovarian cancers and basal-like breast cancers using double primaries

Background: The somatic molecular profiles of basal-like breast cancers and high-grade serous ovarian cancers share many similarities, leading to the hypothesis that they have similar aetiologies, in which case they should occur together in the same patient more often than expected. Methods: We identified 545 women with double independent primary cancers of the breast and ovary reported to the California Cancer Registry from 1999 to 2013 and examined the coincidence of subtype combinations. Results: For most subtype combinations the observed frequencies were similar to their expected frequencies, but in 103 observed cases vs 43.8 expected (O/E=2.35; 95% CI 1.90–2.81) a triple-negative breast tumour (typically basal-like) was matched with a serous ovarian tumour (typically high-grade). Conclusions: The results provide compelling evidence that basal-like breast cancer and high-grade serous ovarian cancer share a much more similar aetiology than breast and ovarian cancers more broadly. Further research is needed to clarify the influence of germ-line BRCA1 mutations and other risk factors on these results

Guidelines for reporting of statistics for clinical research in urology

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148242/1/bju14640.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148242/2/bju14640_am.pd

Genomic investigation of etiologic heterogeneity: methodologic challenges

Background: The etiologic heterogeneity of cancer has traditionally been investigated by comparing risk factor frequencies within candidate sub-types, defined for example by histology or by distinct tumor markers of interest. Increasingly tumors are being profiled for molecular features much more extensively. This greatly expands the opportunities for defining distinct sub-types. In this article we describe an exploratory analysis of the etiologic heterogeneity of clear cell kidney cancer. Data are available on the primary known risk factors for kidney cancer, while the tumors are characterized on a genome-wide basis using expression, methylation, copy number and mutational profiles. Methods: We use a novel clustering strategy to identify sub-types. This is accomplished independently for the expression, methylation and copy number profiles. The goals are to identify tumor sub-types that are etiologically distinct, to identify the risk factors that define specific sub-types, and to endeavor to characterize the key genes that appear to represent the principal features of the distinct sub-types. Results: The analysis reveals strong evidence that gender represents an important factor that distinguishes disease sub-types. The sub-types defined using expression data and methylation data demonstrate considerable congruence and are also clearly correlated with mutations in important cancer genes. These sub-types are also strongly correlated with survival. The complexity of the data presents many analytical challenges including, prominently, the risk of false discovery. Conclusions: Genomic profiling of tumors offers the opportunity to identify etiologically distinct sub-types, paving the way for a more refined understanding of cancer etiology. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-138) contains supplementary material, which is available to authorized users

Initial Reactions to Tobacco Use and Risk of Future Regular Use

Introduction: Studies suggest that initial smoking pleasure influences future smoking behavior. We investigated how initial reactions to cigarettes or Swedish smokeless tobacco (snus) were associated with future use among 10,708 adults from the Swedish Twin Registry

Proceedings of the third international molecular pathological epidemiology (MPE) meeting

Molecular pathological epidemiology (MPE) is a transdisciplinary and relatively new scientific discipline that integrates theory, methods and resources from epidemiology, pathology, biostatistics, bioinformatics and computational biology. The underlying objective of MPE research is to better understand the etiology and progression of complex and heterogeneous human diseases with the goal of informing prevention and treatment efforts in population health and clinical medicine. Although MPE research has been commonly applied to investigating breast, lung, and colorectal cancers, its methodology can be used to study most diseases. Recent successes in MPE studies include: 1) the development of new statistical methods to address etiologic heterogeneity; 2) the enhancement of causal inference; 3) the identification of previously unknown exposure-subtype disease associations; and 4) better understanding of the role of lifestyle/behavioral factors on modifying prognosis according to disease subtype. Central challenges to MPE include the relative lack of transdisciplinary experts, educational programs, and forums to discuss issues related to the advancement of the field. To address these challenges, highlight recent successes in the field, and identify new opportunities, a series of MPE meetings have been held at the Dana-Farber Cancer Institute in Boston, MA. Herein, we share the proceedings of the Third International MPE Meeting, held in May 2016 and attended by 150 scientists from 17 countries. Special topics included integration of MPE with immunology and health disparity research. This meeting series will continue to provide an impetus to foster further transdisciplinary integration of divergent scientific fields

Prevalence and Co-Occurrence of Actionable Genomic Alterations in High-Grade Bladder Cancer

We sought to define the prevalence and co-occurrence of actionable genomic alterations in patients with high-grade bladder cancer to serve as a platform for therapeutic drug discovery

Dataset for: A comparison of statistical methods for the study of etiologic heterogeneity

Cancer epidemiologic research has traditionally been guided by the premise that certain diseases share an underlying etiology, or cause. However, with the rise of molecular and genomic profiling, attention has increasingly focused on identifying subtypes of disease. As subtypes are identified, it is natural to ask the question of whether they share a common etiology or in fact arise from distinct sets of risk factors. In this context, epidemiologic questions of interest include 1) whether a risk factor of interest has the same effect across all subtypes of disease and 2) whether risk factor effects differ across levels of each individual tumor marker of which the subtypes are comprised. A number of statistical models have been proposed to address these questions. In an effort to determine the similarities and differences among the proposed methods, and to identify any advantages or disadvantages, we employ a simplified data example to elucidate the interpretation of model parameters and available hypothesis tests, and we perform a simulation study to assess bias in effect size, type I error, and power. The results show that when the number of tumor markers is small enough that the cross-classification of markers can be evaluated in the traditional polytomous logistic regression framework, then the statistical properties are at least as good as the more complex modeling approaches that have been proposed. The potential advantage of more complex methods is in the ability to accommodate multiple tumor markers in a model of reduced parametric dimension

Breast Cancer in the United States: A Cross-Sectional Overview

Introduction. Breast cancer remains the most commonly diagnosed malignancy in women. It encompasses considerable heterogeneity in pathology, patient clinical characteristics, and outcome. This study describes factors associated with overall survival (OS) of breast cancer in an updated national database. Methods. We conducted a retrospective analysis of patients with breast cancer diagnosed between 2004 and 2016 based on the National Cancer Database. Categorical variables were summarized using frequencies/percentages, whereas continuous variables were summarized using the median/interquartile range (IQR). OS was explored using the Kaplan-Meier method. Results. Data from n=2,671,549 patients were analyzed. The median age at diagnosis was 61 years (range 18-90). 75% were non-Hispanic (NH) White; 11% were NH-Black; 4.7% were Hispanic-White; 0.1% were Hispanic-Black; and 3.4% were Asian. Most cases (73%) presented with ductal carcinoma histology; while 15% with lobular carcinoma. Rarer subtypes included epithelial-myoepithelial, fibroepithelial, metaplastic, and mesenchymal tumors. OS was associated with molecular subtype, histologic subtype, and AJCC clinical staging. Survival also correlated with race: a cohort including Asians and Pacific Islanders had the best survival, while Black patients had the worst. Finally, facility type also impacted outcome: patients at academic centers had the best survival, while those at community cancer programs had the worst. Conclusion. This large database provides a recent and comprehensive overview of breast cancer over 12 years. Molecular subtype, histologic subtype, stage, race, and facility type were correlated with OS. In addition to the educational perspective of this overview, significant factors impacting the outcome identified here should be considered in future cancer research on disparities