|Grant Number:||7R01CA132897-05 Interpret this number|
|Primary Investigator:||Ji, Yuan|
|Organization:||Northshore University Healthsystem|
|Project Title:||Bayesian Models for Cancer Prognosis By Integrating Diverse Types of Data|
Project Summary/Abstract 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.
A Nonparametric Bayesian Model for Local Clustering with Application to Proteomics.
Authors: Lee J, Müller P, Zhu Y, Ji Y
Source: J Am Stat Assoc, 2013 Jan 1;108(503), p. null.
Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.
Authors: Trentini F, Ji Y, Iwamoto T, Qi Y, Pusztai L, Müller P
Source: PLoS One, 2013;8(7), p. e68071.
EPub date: 2013 Jul 12.
Toward breaking the histone code: bayesian graphical models for histone modifications.
Authors: Mitra R, Müller P, Liang S, Xu Y, Ji Y
Source: Circ Cardiovasc Genet, 2013 Aug;6(4), p. 419-26.
EPub date: 2013 Jun 7.
Screening for SNPs with Allele-Specific Methylation based on Next-Generation Sequencing Data.
Authors: Hu B, Ji Y, Xu Y, Ting AH
Source: Stat Biosci, 2013 May;5(1), p. 179-197.
BM-BC: a Bayesian method of base calling for Solexa sequence data.
Authors: Ji Y, Mitra R, Quintana F, Jara A, Mueller P, Liu P, Lu Y, Liang S
Source: BMC Bioinformatics, 2012;13 Suppl 13, p. S6.
EPub date: 2012 Aug 24.
BM-Map: an efficient software package for accurately allocating multireads of RNA-sequencing data.
Authors: Yuan Y, Norris C, Xu Y, Tsui KW, Ji Y, Liang H
Source: BMC Genomics, 2012;13 Suppl 8, p. S9.
EPub date: 2012 Dec 17.
Bayesian continual reassessment method for dose-finding trials infusing T cells with limited sample size.
Authors: Ji Y, Feng L, Liu P, Shpall EJ, Kebriaei P, Champlin R, Berry D, Cooper LJ
Source: J Biopharm Stat, 2012;22(6), p. 1206-19.
integIRTy: a method to identify genes altered in cancer by accounting for multiple mechanisms of regulation using item response theory.
Authors: Tong P, Coombes KR
Source: Bioinformatics, 2012 Nov 15;28(22), p. 2861-9.
EPub date: 2012 Sep 26.
A Bayesian adaptive design for multi-dose, randomized, placebo-controlled phase I/II trials.
Authors: Xie F, Ji Y, Tremmel L
Source: Contemp Clin Trials, 2012 Jul;33(4), p. 739-48.
EPub date: 2012 Mar 9.
BM-map: Bayesian mapping of multireads for next-generation sequencing data.
Authors: Ji Y, Xu Y, Zhang Q, Tsui KW, Yuan Y, Norris C Jr, Liang S, Liang H
Source: Biometrics, 2011 Dec;67(4), p. 1215-24.
EPub date: 2011 Apr 22.
Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data.
Authors: Baladandayuthapani V, Ji Y, Talluri R, Nieto-Barajas LE, Morris JS
Source: J Am Stat Assoc, 2010 Dec;105(492), p. 1358-1375.
Adaptive dose insertion in early phase clinical trials.
Authors: Hu B, Bekele BN, Ji Y
Source: Clin Trials, 2013 Apr;10(2), p. 216-24.
EPub date: 2010 Sep 6.
Mechanism of Fas signaling regulation by human herpesvirus 8 K1 oncoprotein.
Authors: Berkova Z, Wang S, Wise JF, Maeng H, Ji Y, Samaniego F
Source: J Natl Cancer Inst, 2009 Mar 18;101(6), p. 399-411.
EPub date: 2009 Mar 10.