The development of personalized therapeutic options to cancer patients based on the genetic and molecular markers o ers great promise to improve the outcomes of cancer therapy. Sign cant advances in biotechnology has allowed the measurement of molecular data points across multiple levels (DNA, RNA and protein) from a single tumor sample in a reasonable time frame for making clinical decisions. One of the key data-analytic challenges is to develop holistic approaches that combine information within and across these multiple molecular levels to inform the prognosis and guide evidence-based management of current and future cancer patients. A critical step towards addressing this challenge is to develop a deeper understanding of the underlying molecular mechanisms leading to cancer initiation and progression. This task is complicated by the fact that disease phenotypes and cellular functions are not driven by individual entities (genes/proteins) but rather a consequence of coordinated changes across multiple networks and pathways reflecting various pathobiological processes. The goal of this application is development of systematic, integrated and data-driven approaches for analysis of molecular networks that are perturbed during carcinogenesis. We will accomplish these objectives via four specific aims: 1) develop flexible and computationally efficient techniques for investigating structural dependence within and between large multi-dimensional 'omics datasets; 2) to systematically identify local dependencies of DNA and RNA markers on the topology of functional proteomic networks to improve protein function and pathway prediction; 3) develop pan- cancer models integrating multiple sources of 'omics data spanning multiple tumor lineages; 4) develop translational models to identify major proteomic networks and pathways involved in cancer development and progression. Finally, we plan to develop computationally efficient and tractable software (R/MATLAB) packages which will be delivered and will be regularly updated to maximize impact across both statistics and medicine.
The development of personalized therapeutic options to cancer patients based on the genetic and molecular markers offers great promise to improve the outcomes of cancer therapy. Significant advances in biotechnology have allowed the measurement of molecular data points across multiple levels from a single tumor sample to aid clinical decisions. One of the key data-analytic challenges is to develop holistic approaches that combine information within and across these multiple molecular levels to inform the prognosis and guide evidence-based management of current and future cancer patients.
|Payne, Richard D; Mallick, Bani K (2018) Two-Stage Metropolis-Hastings for Tall Data. J Classif 35:29-51|
|Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran (2018) Inferring network structure in non-normal and mixed discrete-continuous genomic data. Biometrics 74:185-195|
|Bharath, Karthik; Kurtek, Sebastian; Rao, Arvind et al. (2018) Radiologic image-based statistical shape analysis of brain tumours. J R Stat Soc Ser C Appl Stat 67:1357-1378|
|Davenport, Clemontina A; Maity, Arnab; Baladandayuthapani, Veerabhadran (2018) Functional interaction-based nonlinear models with application to multiplatform genomics data. Stat Med 37:2715-2733|
|Kundu, Suprateek; Cheng, Yichen; Shin, Minsuk et al. (2018) Bayesian variable selection with graphical structure learning: Applications in integrative genomics. PLoS One 13:e0195070|
|Sarkar, Abhra; Pati, Debdeep; Chakraborty, Antik et al. (2018) Bayesian Semiparametric Multivariate Density Deconvolution. J Am Stat Assoc 113:401-416|
|Shoemaker, Katherine; Hobbs, Brian P; Bharath, Karthik et al. (2018) Tree-based Methods for Characterizing Tumor Density Heterogeneity. Pac Symp Biocomput 23:216-227|
|Morris, Jeffrey S; Baladandayuthapani, Veerabhadran (2017) Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration. Stat Modelling 17:245-289|
|Morris, Jeffrey S; Baladandayuthapani, Veerabhadran (2017) Rejoinder to statistical contributions to bioinformatics: Design, modelling, structure learning and Integration. Stat Modelling 17:338-357|
|Yu, Kaixian; Zhang, Youyi; Yu, Yang et al. (2017) Radiomic analysis in prediction of Human Papilloma Virus status. Clin Transl Radiat Oncol 7:49-54|
Showing the most recent 10 out of 11 publications