71 research outputs found
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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
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
ASO Author Reflections: Careful Development and Thoughtful Interpretation are Needed when Developing Online Prognostic Tools
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
Evidence for etiologic subtypes of breast cancer in the Carolina Breast Cancer Study
Background: Distinctions in the etiology of triple-negative versus luminal breast cancer have become well established using immunohistochemical surrogates [notably estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)]. However, it is unclear whether established immunohistochemical subtypes are the sole or definitive means of etiologically subdividing breast cancers. Methods: We evaluated clinical biomarkers and tumor suppressor p53 with risk factor data from cases and controls in the Carolina Breast Cancer Study, a population-based study of incident breast cancers. For each individual marker and combinations of markers, we calculated an aggregate measure to distinguish the etiologic heterogeneity of different classification schema. To compare schema, we estimated subtype-specific case–control odds ratios for individual risk factors and fit age-at-incidence curves with two-component mixture models. We also evaluated subtype concordance of metachronous contralateral breast tumors in the California Cancer Registry. Results: ER was the biomarker that individually explained the greatest variability in risk factor profiles. However, further subdivision by p53 significantly increased the degree of etiologic heterogeneity. Age at diagnosis, nulliparity, and race were heterogeneously associated with ER/p53 subtypes. The ER-/ p53þ subtype exhibited a similar risk factor profile and age-at-incidence distribution to the triple-negative subtype. Conclusions: Clinical marker–based intrinsic subtypes have established value, yet other schema may also yield important etiologic insights. Impact: Novel environmental or genetic risk factors may be identifiable by considering different etiologic schema, including cross-classification based on ER/p53
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