The fundamental premise of this proposal is that cancer types based on anatomic site may contain sub-types that are etiologically distinct. Indeed a lot of evidence for this has emerged in recent years. The goal of the proposal is to develop a strategy for optimally identifying such etiologically distinct tumor sub-types, and to develop the statistical techniques needed to accomplish this. In addition to clarifying cancer etiology, such an approach offers the promise of a more powerful strategy for detecting new risk factors, by focusing studies to discover these new risk factors on the sub-types that possess distinct etiology. Our research plan is motivated by a crucial new result regarding the occurrence of double primary malignancies. We show that the odds ratio linking tumor sub-types of pairs of independently occurring cancers is directly related to the underlying population risk heterogeneity of the sub-types. Consequently data from studies of double primaries can be used to determine optimal tumor sub-classification from an etiologic perspective. In this proposal we build upon this result to develop multivariate clustering techniques that optimize the etiologic heterogeneity of the resulting clusters (Aim 1). We will develop analogous techniques for creating sub-types that maximize the degree of etiologic heterogeneity on the basis of known risk factors for use in settings where data on multiple primary cancers are unavailable or unobtainable (Aim 2). We will determine the implications of the use of sub-typing as a strategy for detecting new risk factors from the perspective of statistical power (Aim 3). Finally, we will develop freely-available software to allow other investigators easy access to the methods that we develop (Aim 4). The research will lead ultimately to a conceptual framework for investigating etiologic heterogeneity, and a suite of statistical tools for conducting the dat analyses.
Our research plan has the potential to change the landscape of how cancer epidemiologic investigations are conducted, by focusing on etiologic heterogeneity as a tool for improving the efficiency and statistical power of cancer epidemiologic investigations. As such, it can lead to greater speed in the discovery of factors affecting cancer risk.
| Begg, Colin B; Ostrovnaya, Irina; Geyer, Felipe C et al. (2018) Contralateral breast cancers: Independent cancers or metastases? Int J Cancer 142:347-356 |
| Mauguen, Audrey; Seshan, Venkatraman E; Ostrovnaya, Irina et al. (2018) Estimating the probability of clonal relatedness of pairs of tumors in cancer patients. Biometrics 74:321-330 |
| Begg, Colin B; Rice, Megan S; Zabor, Emily C et al. (2017) Examining the common aetiology of serous ovarian cancers and basal-like breast cancers using double primaries. Br J Cancer 116:1088-1091 |
| Cunanan, Kristen M; Iasonos, Alexia; Shen, Ronglai et al. (2017) An efficient basket trial design. Stat Med 36:1568-1579 |
| Mauguen, Audrey; Zabor, Emily C; Thomas, Nancy E et al. (2017) Defining Cancer Subtypes With Distinctive Etiologic Profiles: An Application to the Epidemiology of Melanoma. J Am Stat Assoc 112:54-63 |
| Zabor, Emily C; Begg, Colin B (2017) A comparison of statistical methods for the study of etiologic heterogeneity. Stat Med 36:4050-4060 |
| Cunanan, Kristen M; Gonen, Mithat; Shen, Ronglai et al. (2017) Basket Trials in Oncology: A Trade-Off Between Complexity and Efficiency. J Clin Oncol 35:271-273 |
| Thomas, Nancy E; Edmiston, Sharon N; Kanetsky, Peter A et al. (2017) Associations of MC1R Genotype and Patient Phenotypes with BRAF and NRAS Mutations in Melanoma. J Invest Dermatol 137:2588-2598 |
| Mauguen, Audrey; Begg, Colin B (2016) Using the Lorenz Curve to Characterize Risk Predictiveness and Etiologic Heterogeneity. Epidemiology 27:531-7 |
| Begg, Colin B; Ostrovnaya, Irina; Carniello, Jose V Scarpa et al. (2016) Clonal relationships between lobular carcinoma in situ and other breast malignancies. Breast Cancer Res 18:66 |
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