Grant Number:	1R01CA197402-01A1 Interpret this number
Primary Investigator:	Satagopan, Jaya
Organization:	Sloan-Kettering Inst Can Research
Project Title:	Study of Exposures and Biomarkers in Cancer Epidemiology
Fiscal Year:	2016

 DESCRIPTION (provided by applicant): It is well recognized that different individuals respond in different ways to the same treatment, and inherited genetic factors play a role on these inter-individual differences. Such genetic factors, referred to as predictive genetic factors, are beginning to enable physicians to make informed therapeutic decisions by tailoring treatments and interventions according to the genetic profiles of patients. When there is an interaction between a genetic factor and treatment or intervention, it means that treatment benefits vary according to the level of the genetic factor. Therefore, epidemiology studies increasingly try to investigate gene-treatment, gene-exposure, and gene-gene interactions in statistical models to identify promising predictive genetic factors. Despite remark- able progress in the identification of etiologic risk factors for cancer, the success rate of identifying interactions and predictive genetic factors remains low. While sample size limitations may partly contribute to this challenge, some significant interactions cannot be replicated because they may be biologically implausible. Therefore, improving the power to detect interactions and developing methodologies to identify practically interpretable interactions and predictive genetic factors are among the critical needs of the field. While there is a large and growing body of work on evaluating interactions for binary outcomes, other richer data types are also be- coming available, and analytic methods to evaluate predictive genetic factors are urgently needed for these settings. The overarching objective of our proposal is to develop formal statistical and mathematical foundations to address these needs. In this R01 project, we propose to show that interactions arising in statistical models corresponding to quantitative expressions for carcinogenesis can be written in a parsimonious manner that can provide insights into the rate at which disease outcome increases in relation to the risk factors. We propose to develop innovative and powerful frequentist and Bayesian statistical techniques to evaluate interactions by harnessing the significant potential of model parsimony. We propose to use these powerful methods to develop well-calibrated models to identify clinically interpretable predictive genetic factors. We also propose to develop and disseminate R libraries that implement our proposed methods. We focus on developing methodologies for count outcomes (measured at a single time point and at two time points) and multiple continuous outcomes measured at a single time point. We will apply our proposed methods to data from three collaborative studies - the study of nevi in children, and cognitive studies of brain and breast cancer patients - and confirm our results using validation data sets.




Neoadjuvant chemotherapy in ovarian cancer: Are there racial disparities in use and survival?
Authors: Amin S.A. , Collin L.J. , Setoguchi S. , Satagopan J.M. , Buckley de Meritens A. , Bandera E.V. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2022-11-21 00:00:00.0; , .
EPub date: 2022-11-21 00:00:00.0.
PMID: 36409506
Related Citations

Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations.
Authors: Dutta D. , Sen A. , Satagopan J. .
Source: Plos One, 2022; 17(12), p. e0276886.
EPub date: 2022-12-30 00:00:00.0.
PMID: 36584096
Related Citations

Factors Associated with Surgery Among South Asian American and Non-Hispanic White Women with Breast Cancer.
Authors: Lo L. , Satagopan J.M. .
Source: American Journal Of Undergraduate Research, 2021 Dec; 18(3), p. 15-23.
PMID: 34970087
Related Citations

Breast cancer among Asian Indian and Pakistani Americans: A surveillance, epidemiology and end results-based study.
Authors: Satagopan J.M. , Stroup A. , Kinney A.Y. , Dharamdasani T. , Ganesan S. , Bandera E.V. .
Source: International Journal Of Cancer, 2020-10-25 00:00:00.0; , .
EPub date: 2020-10-25 00:00:00.0.
PMID: 33099777
Related Citations

Prediagnostic serum polychlorinated biphenyl concentrations and primary liver cancer: A case-control study nested within two prospective cohorts.
Authors: Niehoff N.M. , Zabor E.C. , Satagopan J. , Widell A. , O'Brien T.R. , Zhang M. , Rothman N. , Grimsrud T.K. , Van Den Eeden S.K. , Engel L.S. .
Source: Environmental Research, 2020 Aug; 187, p. 109690.
EPub date: 2020-05-20 00:00:00.0.
PMID: 32474310
Related Citations

Herbicide, fumigant, and fungicide use and breast cancer risk among farmers' wives.
Authors: Werder E.J. , Engel L.S. , Satagopan J. , Blair A. , Koutros S. , Lerro C.C. , Alavanja M.C. , Sandler D.P. , Beane Freeman L.E. .
Source: Environmental Epidemiology (philadelphia, Pa.), 2020 Jun; 4(3), p. e097.
EPub date: 2020-05-27 00:00:00.0.
PMID: 32613154
Related Citations

Prediagnostic serum organochlorine insecticide concentrations and primary liver cancer: A case-control study nested within two prospective cohorts.
Authors: Engel L.S. , Zabor E.C. , Satagopan J. , Widell A. , Rothman N. , O'Brien T.R. , Zhang M. , Van Den Eeden S.K. , Grimsrud T.K. .
Source: International Journal Of Cancer, 2019-11-01 00:00:00.0; 145(9), p. 2360-2371.
EPub date: 2019-02-14 00:00:00.0.
PMID: 30701531
Related Citations

Genetic variants and cognitive functions in patients with brain tumors.
Authors: Correa D.D. , Satagopan J. , Martin A. , Braun E. , Kryza-Lacombe M. , Cheung K. , Sharma A. , Dimitriadoy S. , O'Connell K. , Leong S. , et al. .
Source: Neuro-oncology, 2019-10-09 00:00:00.0; 21(10), p. 1297-1309.
PMID: 31123752
Related Citations

Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design.
Authors: Ni A. , Satagopan J.M. .
Source: Human Heredity, 2019; 84(2), p. 90-108.
EPub date: 2019-10-21 00:00:00.0.
PMID: 31634888
Related Citations

A pilot study of neuropsychological functions, APOE and amyloid imaging in patients with gliomas.
Authors: Correa D.D. , Kryza-Lacombe M. , Zhou X. , Baser R.E. , Beattie B.J. , Beiene Z. , Humm J. , DeAngelis L.M. , Orlow I. , Weber W. , et al. .
Source: Journal Of Neuro-oncology, 2018 Feb; 136(3), p. 613-622.
EPub date: 2017-11-22 00:00:00.0.
PMID: 29168082
Related Citations

Insecticide Use and Breast Cancer Risk among Farmers' Wives in the Agricultural Health Study.
Authors: Engel L.S. , Werder E. , Satagopan J. , Blair A. , Hoppin J.A. , Koutros S. , Lerro C.C. , Sandler D.P. , Alavanja M.C. , Beane Freeman L.E. .
Source: Environmental Health Perspectives, 2017-09-06 00:00:00.0; 125(9), p. 097002.
EPub date: 2017-09-06 00:00:00.0.
PMID: 28934092
Related Citations

A reconstructed melanoma data set for evaluating differential treatment benefit according to biomarker subgroups.
Authors: Satagopan J.M. , Iasonos A. , Kanik J.G. .
Source: Data In Brief, 2017 Jun; 12, p. 667-675.
EPub date: 2017-05-05 00:00:00.0.
PMID: 28560273
Related Citations

Measuring differential treatment benefit across marker specific subgroups: The choice of outcome scale.
Authors: Satagopan J.M. , Iasonos A. .
Source: Contemporary Clinical Trials, 2017-02-22 00:00:00.0; , .
EPub date: 2017-02-22 00:00:00.0.
PMID: 28254404
Related Citations

COMT, BDNF, and DTNBP1 polymorphisms and cognitive functions in patients with brain tumors.
Authors: Correa D.D. , Satagopan J. , Cheung K. , Arora A.K. , Kryza-Lacombe M. , Xu Y. , Karimi S. , Lyo J. , DeAngelis L.M. , Orlow I. .
Source: Neuro-oncology, 2016 10; 18(10), p. 1425-33.
EPub date: 2016-04-18 00:00:00.0.
PMID: 27091610
Related Citations

The study of nevi in children: Principles learned and implications for melanoma diagnosis.
Authors: Scope A. , Marchetti M.A. , Marghoob A.A. , Dusza S.W. , Geller A.C. , Satagopan J.M. , Weinstock M.A. , Berwick M. , Halpern A.C. .
Source: Journal Of The American Academy Of Dermatology, 2016 Oct; 75(4), p. 813-823.
EPub date: 2016-06-17 00:00:00.0.
PMID: 27320410
Related Citations

Quantifying Treatment Benefit In Molecular Subgroups To Assess A Predictive Biomarker
Authors: Iasonos A. , Chapman P.B. , Satagopan J.M. .
Source: Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research, 2016-05-01 00:00:00.0; 22(9), p. 2114-20.
PMID: 27141007
Related Citations

Statistical Interactions from a Growth Curve Perspective.
Authors: Devlin S.M. , Satagopan J.M. .
Source: Human Heredity, 2016; 82(1-2), p. 21-36.
EPub date: 2017-07-26 00:00:00.0.
PMID: 28743105
Related Citations

Predictors and long-term reproducibility of urinary phthalate metabolites in middle-aged men and women living in urban Shanghai.
Authors: Starling A.P. , Engel L.S. , Calafat A.M. , Koutros S. , Satagopan J.M. , Yang G. , Matthews C.E. , Cai Q. , Buckley J.P. , Ji B.T. , et al. .
Source: Environment International, 2015 Nov; 84, p. 94-106.
PMID: 26255822
Related Citations

Statistical Interactions And Bayes Estimation Of Log Odds In Case-control Studies
Authors: Satagopan J.M. , Olson S.H. , Elston R.C. .
Source: Statistical Methods In Medical Research, 2015-01-12 00:00:00.0; , .
PMID: 25586327
Related Citations