Project 3: Statistical/Computational Methods for Pharmacogenomics and Indi- vidualized Therapy PROJECT SUMMARY The broad, long-term objectives of this research are the development of novel and high-impact statistical and computational tools for discovering genetic variants associated with interindividual differences in the efficacy and toxicity of cancer medications and for optimizing drug therapy on the basis of each patient's genetic consti- tution.
The specific aims of this renewal application include: (1) investigating statistical methods to assess the effects of DNA variations on the occurrence of adverse clinical events (e.g., neuropathy, neutropenia and hyper- tension) in cancer clinical trials under complex censoring and sampling schemes; (2) exploring statistical tools to integrate multiple types of genomics data (e.g., copy number alteration, point mutation, DNA methylation, RNA and microRNA expressions, and protein expressions) in understanding the influence of genomic profile on treatment response; (3) pursuing statistical methods to discern tumor cell subclones and to relate intra-tumor heterogeneity to clinical outcomes; and (4) developing machine learning techniques to discover and validate biomarkers that can distinguish groups of patients with different treatment response. All of these aims have been motivated by the investigators' applied research experiences and address the most timely and important issues in pharmacogenomics and individualized therapy. The proposed solutions are built on sound statistical and data-mining principles. The theoretical properties of the new methods will be established rigorously via modern empirical process theory and other advanced mathematical arguments. Efficient and stable numerical algorithms will be devised to implement the new methods. Extensive simulation studies will be conducted to evaluate the operating characteristics of the new inferential and numerical procedures in realistic settings. Ap- plications will be provided to a large number of cancer studies, most of which are carried out at Duke University (Duke) and the University of North Carolina at Chapel Hill (UNC-CH). In collaboration with Core B, practical and user-friendly software will be developed and disseminated freely to the general public. This research will change the ways pharmacogenomic studies and individualized therapy trials are designed and analyzed, which will lead to optimal treatments for patients in cancer and other diseases.
Project 3: Statistical/Computational Methods for Pharmacogenomics and Indi- vidualized Therapy PROJECT NARRATIVE This research intends to develop novel and high-impact statistical and computational tools for discovering ge- netic variants associated with interindividual differences in the efficacy and toxicity of cancer medications and for optimizing drug therapy on the basis of each patient's genetic constitution. The specific aims include assess- ment of genetic influence on adverse clinical events, integration of multiple genomics platforms, discernment of intra-tumor heterogeneity, and discovery of biomarkers. This research will change the ways pharmacogenomic studies and individualized therapy trials are designed and analyzed, which will lead to optimal treatments for patients in cancer and other diseases.
|Wang, Xiaofei; Wang, Xiaoyi; Hodgson, Lydia et al. (2017) Validation of Progression-Free Survival as a Surrogate Endpoint for Overall Survival in Malignant Mesothelioma: Analysis of Cancer and Leukemia Group B and North Central Cancer Treatment Group (Alliance) Trials. Oncologist 22:189-198|
|Zhang, Danjie; Chen, Ming-Hui; Ibrahim, Joseph G et al. (2017) Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials. J Comput Graph Stat 26:121-133|
|Kang, Suhyun; Lu, Wenbin; Song, Rui (2017) Subgroup detection and sample size calculation with proportional hazards regression for survival data. Stat Med 36:4646-4659|
|Zhao, Jingkang; Li, Dongshunyi; Seo, Jungkyun et al. (2017) Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding. Res Comput Mol Biol 10229:336-352|
|Silva, Grace O; Siegel, Marni B; Mose, Lisle E et al. (2017) SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling. Genome Biol 18:66|
|Stinchcombe, Thomas E; Zhang, Ying; Vokes, Everett E et al. (2017) Pooled Analysis of Individual Patient Data on Concurrent Chemoradiotherapy for Stage III Non-Small-Cell Lung Cancer in Elderly Patients Compared With Younger Patients Who Participated in US National Cancer Institute Cooperative Group Studies. J Clin Oncol 35:2885-2892|
|Li, Zhiguo (2017) Comparison of adaptive treatment strategies based on longitudinal outcomes in sequential multiple assignment randomized trials. Stat Med 36:403-415|
|Ding, Jieli; Lu, Tsui-Shan; Cai, Jianwen et al. (2017) Recent progresses in outcome-dependent sampling with failure time data. Lifetime Data Anal 23:57-82|
|Liang, Baosheng; Tong, Xingwei; Zeng, Donglin et al. (2017) SEMIPARAMETRIC REGRESSION ANALYSIS OF REPEATED CURRENT STATUS DATA. Stat Sin 27:1079-1100|
|Kong, Dehan; Maity, Arnab; Hsu, Fang-Chi et al. (2017) Rejoinder to ""A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine"". Biometrics :|
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