This application addresses broad Challenge Are """"""""(06) Enabling Technologies"""""""" and Specific Challenge Topic """"""""06-CA-106 Data integration and visualization methods and tools"""""""". Prostate cancer is a highly prevalent disease in older men of the Western world, whose initiation, unregulated growth, invasion and metastasis is characterized by multiple complex events. Gathering genomic, proteomic and metabolomic expression data offers the possibility of deciphering the molecular networks that distinguish progressive from non-progressive forms of the disease;in addition, they provide insight into the biology of aggressive prostate cancer and help in identifying biomarkers that in turn will aid in the selection of patients for treatment. Using prostate cancer as a target application, we propose developing statistical and bioinformatics methodology for the integration and analysis of these three matched data sources (genomic, proteomic and metabolomic expression profiles). These will be complemented by computational and visualization tools to enable robustness in the integrative pipeline. Specifically, a framework for carrying out inference about the identification of enriched pathways from integrated Omics data is developed based on graph theoretic ideas and mixed linear statistical models. Further, the associated estimation of parameters of interest, hypothesis testing and algorithmic issues, are also addressed. Techniques for the identification of genomic/proteomic/metabolomic biomarkers are also discussed. Finally, the scientific findings from the analysis in the form of altered pathways will be validated through testing in associated cell lines and animal models. The results obtained from the proposed studies will provide a suite of computational approaches for integrating diverse Omics data and identify altered pathways in prostate cancer progression. Overall, the results of such integration will enable the use of a combination of molecular markers (genomics, proteomics and metabolomics) as endogenous sensors for cancer progression. Additionally, the methods developed by this proposal for integrative data will advance the field of bioinformatics by building the platform for amalgamation of other types of high throughput Omics data generated using next generation sequencing, comparative genomic hybridization, DNA methylation etc.
This project aims to develop statistical and bioinformatics methodology to integrate data obtained from different Omics platforms (genomic, proteomic, metabolomic data), in order to decipher the molecular networks that distinguish between progressive from non-progressive forms of prostate cancer. The methodology will be implemented into easy to use computational and visualization tools to enable a seamless and robust integrative pipeline. Further, the scientific findings of the analysis will result in a list of altered pathways that would subsequently be validated in cell lines and animal models. The results of this data integration progress will enable the routine use of combinations of molecular markers (genomic, proteomic, metabolomic) as endogenous sensors for cancer progression.
| Piyarathna, Danthasinghe Waduge Badrajee; Rajendiran, Thekkelnaycke M; Putluri, Vasanta et al. (2018) Distinct Lipidomic Landscapes Associated with Clinical Stages of Urothelial Cancer of the Bladder. Eur Urol Focus 4:907-915 |
| Jin, Feng; Thaiparambil, Jose; Donepudi, Sri Ramya et al. (2017) Tobacco-Specific Carcinogens Induce Hypermethylation, DNA Adducts, and DNA Damage in Bladder Cancer. Cancer Prev Res (Phila) 10:588-597 |
| Roy, Sandipan; Atchadé, Yves; Michailidis, George (2017) Change point estimation in high dimensional Markov random-field models. J R Stat Soc Series B Stat Methodol 79:1187-1206 |
| Kaushik, Akash K; Shojaie, Ali; Panzitt, Katrin et al. (2016) Inhibition of the hexosamine biosynthetic pathway promotes castration-resistant prostate cancer. Nat Commun 7:11612 |
| Guo, Jian; Levina, Elizaveta; Michailidis, George et al. (2015) Graphical Models for Ordinal Data. J Comput Graph Stat 24:183-204 |
| Shojaie, Ali; Jauhiainen, Alexandra; Kallitsis, Michael et al. (2014) Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles. PLoS One 9:e82393 |
| ?enbabao?lu, Yasin; Michailidis, George; Li, Jun Z (2014) Critical limitations of consensus clustering in class discovery. Sci Rep 4:6207 |
| Lim, Nehemy; Senbabaoglu, Yasin; Michailidis, George et al. (2013) OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks. Bioinformatics 29:1416-23 |
| Michailidis, George; d'Alché-Buc, Florence (2013) Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues. Math Biosci 246:326-34 |
| Jauhiainen, Alexandra; Nerman, Olle; Michailidis, George et al. (2012) Transcriptional and metabolic data integration and modeling for identification of active pathways. Biostatistics 13:748-61 |
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