A new strategy in cancer prognosis is to base the decision on integrated information from different sources, including the traditional clinical and demographic information of patients, such as age, grade, and tumor size, etc, and the recently emerged genetic information like expression of gene or protein markers. Implementation of such a strategy requires efficient quantitative models that integrate the clinical measurements and genetic measurements together for prognosis. The long-range goal of this application is to improve risk predication, treatment selection, and subtype classification in cancer prevention, diagnosis, and prognosis. The short-term objective is to improve prediction of treatment response for cancer patients by developing innovative statistical models that integrate three different types of data, including two subtypes of informatics data, namely protein pathway data and high-throughput protein expression data, and a third type, which is the standard clinical and demographic data. We will accomplish the objective of this application by pursuing the following five specific aims: 1) Develop Bayesian parametric models that integrate a known genetic pathway with high-throughput protein expression measurements. 2) Develop Bayesian nonparametric model that integrate multiple genetic pathways with protein expression measurements. 3) Develop Bayesian classification procedures based on the Bayesian models proposed in previous two aims. 4) Integrate clinical and demographic measurements into the Bayesian models and apply the Bayesian classification procedures using a comprehensive data set that contains protein expression measurements and clinical measurements for more than 500 patients with leukemia. 5) Validate statistical findings by performing biological experiments, which will be done by our collaborating biologists. The proposed research is expected to provide quantitative prognostic tools for oncologists based on integrated information. The impact of the proposed research will be significant because models developed in this application can be applied to various cancer types and thus potentially improve the prognosis for patients with different types of cancer.

Public Health Relevance

Integrating the protein expression data, the protein pathway data, and the clinical data is expected to significantly improve medical decision making such as treatment selection. The improved decisions are expected to improve the overall patient care. For example, by accurately predicting that certain treatment will not be effective for a cancer patient, this patient will no longer waste time trying out the treatment and will have a better chance finding some other more effective therapies.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA132897-04
Application #
8105108
Study Section
Special Emphasis Panel (ZRG1-HOP-T (02))
Program Officer
Dunn, Michelle C
Project Start
2008-09-15
Project End
2012-03-05
Budget Start
2011-08-01
Budget End
2012-03-05
Support Year
4
Fiscal Year
2011
Total Cost
$192,700
Indirect Cost
Name
University of Texas MD Anderson Cancer Center
Department
Biostatistics & Other Math Sci
Type
Other Domestic Higher Education
DUNS #
800772139
City
Houston
State
TX
Country
United States
Zip Code
77030
Müller, Peter; Xu, Yanxun; Thall, Peter F (2017) Clinical Trial Design as a Decision Problem. Appl Stoch Models Bus Ind 33:296-301
Morita, Satoshi; Müller, Peter (2017) Bayesian population finding with biomarkers in a randomized clinical trial. Biometrics 73:1355-1365
Manching, Heather; Sengupta, Subhajit; Hopper, Keith R et al. (2017) Phased Genotyping-by-Sequencing Enhances Analysis of Genetic Diversity and Reveals Divergent Copy Number Variants in Maize. G3 (Bethesda) 7:2161-2170
Sengupta, Subhajit; Gulukota, Kamalakar; Zhu, Yitan et al. (2016) Ultra-fast local-haplotype variant calling using paired-end DNA-sequencing data reveals somatic mosaicism in tumor and normal blood samples. Nucleic Acids Res 44:e25
Xu, Yanxun; Trippa, Lorenzo; Müller, Peter et al. (2016) Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials. Stat Biosci 8:159-180
Lee, Juhee; Müller, Peter; Sengupta, Subhajit et al. (2016) Bayesian inference for intratumour heterogeneity in mutations and copy number variation. J R Stat Soc Ser C Appl Stat 65:547-563
Xu, Yanxun; Müller, Peter; Telesca, Donatello (2016) Bayesian inference for latent biologic structure with determinantal point processes (DPP). Biometrics 72:955-64
Lee, Juhee; Thall, Peter F; Ji, Yuan et al. (2016) A decision-theoretic phase I-II design for ordinal outcomes in two cycles. Biostatistics 17:304-19
Xu, Yanxun; Müller, Peter; Wahed, Abdus S et al. (2016) Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times. J Am Stat Assoc 111:921-935
Lee, Juhee; Müller, Peter; Zhu, Yitan et al. (2016) A Nonparametric Bayesian Model for Nested Clustering. Methods Mol Biol 1362:129-41

Showing the most recent 10 out of 48 publications