Cancer genomic studies have been extensively conducted using high-throughput profiling techniques. Molecular signatures identified from these studies have been used to assist clinical practice including diagnosis, prognosis prediction, and selection of treatment regimens. Despite promising successes, these signatures often suffer from a lack of reproducibility and reliability. A major cause of this problem is the relatively small sample sizes and hence lack of power of individual studies. A cost-effective remedy is to pool and analyze data from multiple studies. Available methods for analyzing multiple datasets have serious drawbacks. There is an urgent need for novel statistical methodologies that can effectively analyze and extract useful information from multiple cancer genomic studies. This project will be among the first to systematically develop and implement integrative analysis methodologies. The proposed methods will be able to effectively analyze heterogeneous high-dimensional datasets from multiple cancer genomic studies. They will be able to account for the joint effects of multiple genomic measurements and the pathway structure in modeling cancer development, and be able to properly adjust for clinical and environmental risk factors. Dissemination through the development of R package and public website will make our research accessible to the general biomedical community. Analysis of data on multiple cancer clinical outcomes will lead to identification of clinically useful markers. Specifically, we plan to (1) Develop penalized marginal screening methods for integrative analysis of multiple heterogeneous cancer genomic datasets;(2) Develop individual-marker based penalization methods for integrative analysis of multiple heterogeneous cancer genomic datasets;(3) Develop pathway based penalization methods for integrative analysis of multiple heterogeneous cancer genomic datasets;(4) Develop integrative analysis methods that can properly accommodate partially linear clinical and environmental covariate effects;(5) Disseminate the proposed methods, analyze data on multiple cancers, and identify cancer markers. The proposed study will emphasize equally development of novel methodologies and their practical applications. It will make significant contributions to methodologies for integrative analysis of multiple heterogeneous datasets, and enable researchers to more efficiently extract useful information from cancer genomic studies.

Public Health Relevance

This study will be among the first to systematically develop and implement novel integrative analysis methods, which can effectively analyze multiple heterogeneous and high-dimensional cancer genomic studies. It will enrich the family of methodologies for integrative analysis, enable researchers to more efficiently extract useful information from existing data, and lead to a better understanding of cancer genomics. Applications of the proposed methods will lead to identification of clinically useful cancer markers.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA142774-03
Application #
8272701
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Tricoli, James
Project Start
2010-09-01
Project End
2014-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
3
Fiscal Year
2012
Total Cost
$322,630
Indirect Cost
$87,003
Name
Yale University
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
Shi, Xingjie; Liu, Jin; Huang, Jian et al. (2014) Integrative analysis of high-throughput cancer studies with contrasted penalization. Genet Epidemiol 38:144-51
Jiang, Dingfeng; Huang, Jian (2014) Majorization Minimization by Coordinate Descent for Concave Penalized Generalized Linear Models. Stat Comput 24:871-883
Wang, Yu; Ma, Shuangge (2014) Racial differences in mantle cell lymphoma in the United States. BMC Cancer 14:764
Zhu, Ruoqing; Zhao, Hongyu; Ma, Shuangge (2014) Identifying gene-environment and gene-gene interactions using a progressive penalization approach. Genet Epidemiol 38:353-68
Shi, Xingjie; Liu, Jin; Huang, Jian et al. (2014) A penalized robust method for identifying gene-environment interactions. Genet Epidemiol 38:220-30
Liu, Jin; Huang, Jian; Zhang, Yawei et al. (2014) Integrative analysis of prognosis data on multiple cancer subtypes. Biometrics 70:480-8
Wang, Yu; Ma, Shuangge (2014) Risk factors for etiology and prognosis of mantle cell lymphoma. Expert Rev Hematol 7:233-43
Liu, Jin; Huang, Jian; Ma, Shuangge (2014) Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization. Scand Stat Theory Appl 41:87-103
Wu, Cen; Cui, Yuehua; Ma, Shuangge (2014) Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model. Stat Med 33:4988-98
Guolo, A (2014) The SIMEX approach to measurement error correction in meta-analysis with baseline risk as covariate. Stat Med 33:2062-76

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