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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA142538-08
Application #
9265407
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
2017-04-01
Budget End
2018-03-31
Support Year
8
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Type
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Jung, Sin-Ho; Lee, Ho Yun; Chow, Shein-Chung (2018) Statistical Methods for Conditional Survival Analysis. J Biopharm Stat 28:927-938
Jiang, Yuchao; Wang, Rujin; Urrutia, Eugene et al. (2018) CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing. Genome Biol 19:202
Kim, Soyoung; Zeng, Donglin; Cai, Jianwen (2018) Analysis of multiple survival events in generalized case-cohort designs. Biometrics :
Chung, Yunro; Ivanova, Anastasia; Hudgens, Michael G et al. (2018) Partial likelihood estimation of isotonic proportional hazards models. Biometrika 105:133-148
Liang, Shuhan; Lu, Wenbin; Song, Rui (2018) Deep advantage learning for optimal dynamic treatment regime. Stat Theory Relat Fields 2:80-88
Chen, Xiaolin; Cai, Jianwen (2018) Reweighted estimators for additive hazard model with censoring indicators missing at random. Lifetime Data Anal 24:224-249
Yang, Yuchen; Huh, Ruth; Culpepper, Houston W et al. (2018) SAFE-clustering: Single-cell Aggregated (From Ensemble) Clustering for Single-cell RNA-seq Data. Bioinformatics :
Nasution, Marlina D; Wang, Xiaofei (2018) Statistical issues and advances in cancer precision medicine research. J Biopharm Stat 28:215-216
Wu, Yuan; Chambers, Christina D; Xu, Ronghui (2018) Semiparametric sieve maximum likelihood estimation under cure model with partly interval censored and left truncated data for application to spontaneous abortion. Lifetime Data Anal :
Ibrahim, Joseph G; Kim, Sungduk; Chen, Ming-Hui et al. (2018) Bayesian multivariate skew meta-regression models for individual patient data. Stat Methods Med Res :962280218801147

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