We are developing bioinformatics techniques and tools for uncovering the molecular-level pathways involved in complex diseases such as cancer, aiming at determining disease markers and therapeutic targets.
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest form of cancer, for which the best known therapeutic options are currently extremely ineffective. Moreover, the precise details of PDAC pathogenesis are still insufficiently known, requiring the use of high-throughput methods. In the framework of the GENOPACT project (Research of Excellence Program CEEX 56/2005), we analyzed a set of 78 pancreatic cancer-normal sample pairs from the tissue bank of the Fundeni Clinical Institute (ICF), measured with Affymetrix U133 Plus 2.0 microarrays. This is one of the largest available pancreatic ductal adenocarcinoma datasets, thereby allowing a statistically reliable identification of the genes involved in this disease.
We have developed a complex bioinformatic framework for the analysis of this dataset including:
We have performed an in-depth integrated analysis of the resulting set of differentially expressed genes, producing a plausible “model” of the molecular-level mechanisms of PDAC and its progression.
PDAC is especially difficult to study using microarrays due to its strong desmoplastic reaction, which involves a hyperproliferating stroma that effectively "masks" the contribution of the minoritary neoplastic epithelial cells. Thus it is not clear which of the genes that have been found differentially expressed between normal and whole tumor tissues are due to the tumor epithelia and which simply reflect the differences in cellular composition. To address this problem, laser microdissection studies have been performed, but these have to deal with much smaller tissue sample quantities and therefore have significantly higher experimental noise.
We have combined our own large sample whole-tissue study with a previously published smaller sample microdissection study by Grutzmann et al. to identify the genes that are specifically overexpressed in PDAC tumor epithelia.
We have found a number of genes whose over-expression appears to be inversely correlated with patient survival :
which are all specifically upregulated in the neoplastic epithelia, rather than the tumor stroma.
We plan to further refine our current understanding of the molecular-level processes responsible in this disease in the framework of future projects and with the help of a specialized molecular-biology lab by using various high-throughput technologies (not just microarrays) to dissect the pathways involved in PDAC and to test these on cell lines and possibly animal models.
- microarray data analysis (differentially expressed genes, biclustering, metaclustering, gene network inference)
- literature analysis tools (e.g. extracting co-citations)
The microarray data analysis tools reveal only the level of transcription regulation and are strongly affected by noise and normal biological variability. We are therefore using them in conjunction with literature analysis tools for
- validating certain transcriptional influences, as well as
- emphasizing the various (signaling) pathways in which these genes operate.
The partial results are very encouraging. For example, for the squamous cell lung carcinoma we have found essentially two groups of differentially expressed genes:
- a set of upregulated genes involved e.g. in the cell cycle (e.g. E2F and/or p130/retinoblastoma like 2 targets) and/or in the structure and organization of the cytoskeleton (e.g. keratin 5, desmoplakin – specific to the squamous cancer subtype)
- a larger set of down-regulated genes, normally involved in certain developmental stages of the lung.
Apparently, this cancer subtype seems to be due to a defective re-enactment of normal developmental processes (at a wrong time and place).
• Liviu Badea, Vlad Herlea, Simona Dima, Traian Dumitrascu, Irinel Popescu. Combined gene expression analysis of whole-tissue and microdissected pancreatic ductal adenocarcinoma identifies genes specifically overexpressed in tumor epithelia. Hepatogastroenterology. 2008 Nov-Dec;55(88):2016-27.
• Liviu Badea. Extracting Gene Expression Profiles Common to Colon and Pancreatic Adenocarcinoma Using Simultaneous Nonnegative Matrix Factorization. Proc. Pacific Symposium on Biocomputing PSB-2008, pp. 267-278, World Scientific 2008.
• Liviu Badea. Generalized Clustergrams for Overlapping Biclusters. Proceedings of the International Joint Conference on Artificial Intelligence IJCAI-09, Pasadena, pp 1383-1388, 2009.
• Liviu Badea. Combining Gene Expression and Transcription Factor Regulation Data using Simultaneous Nonnegative Matrix Factorization. Proc. BIOCOMP-2007, pp. 127-131, CSREA Press 2007.
• Liviu Badea, Doina Tilivea. Stable Biclustering of Gene Expression Data with Nonnegative Matrix Factorizations. Proceedings of the International Joint Conference on Artificial Intelligence IJCAI-07, Hyderabad, India, AAAI Press, 2007, pp. 2651-2656. ISSN: 0921-7126
• Liviu Badea. Combining DNA Copy Number and Gene Expression Data to Reveal Sample-Specific Genetic Abnormalities in Pancreatic Cancer. Studies in Informatics and Control, Vol. 15 No. 4, (2006), pp.403-413, ISSN 1220-1766.
• Liviu Badea, Doina Tilivea, Meta-clustering Gene Expression Data with Positive Tensor Factorizations. Proceedings European Conference on Artificial Intelligence ECAI-06, p. 787, IOS Press 2006.
• Liviu Badea, Semantic Web reasoning for analyzing gene expression profiles. PRINCIPLES AND PRACTICE OF SEMANTIC WEB REASONING. LECTURE NOTES IN COMPUTER SCIENCE, Vol. 4187, pp. 78-89, Springer 2006.
• Liviu Badea, Doina Tilivea. Sparse Factorizations of Gene Expression Data guided by Binding Data. Proceedings of the Pacific Symposium on Biocomputing PSB-2005, pp. 447-458.
• F. Bry, C. Koch, T. Furche, S. Schaffert, L. Badea, S. Berger: Querying the Web Reconsidered: Design Principles for Versatile Web Query Languages. INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 1(2): pp. 1-21 (2005) ISSN: 1552-6283
• Liviu Badea, Clustering and metaclustering with nonnegative matrix decompositions. MACHINE LEARNING: ECML 2005, PROCEEDINGS. LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, 3720, pp. 10-20 Springer, 2005.
• Liviu Badea, Determining the Direction of Causal Influence in Large Probabilistic Networks: A Constraint-Based Approach. Proceedings of the 14th European Conference on Artificial Intelligence ECAI 2004, IOS Press, pp. 263-267.
• Badea, L, Tilivea, D, Hotaran, A, Semantic Web reasoning for ontology-based integration of resources. PRINCIPLES AND PRACTICE OF SEMANTIC WEB REASONING, PROCEEDINGS. LECTURE NOTES IN COMPUTER SCIENCE, Vol. 3208, pp. 61-75, Springer, 2004.
• Rolf Backofen, Mike Badea, Pedro Barahona, Liviu Badea, François Bry, Gihan Dawelbait, Andreas Doms, François Fages, Carole Goble, Andreas Henschel, Anca Hotaran, Bingding Huang, Ludwig Krippahl, Patrick Lambrix, Werner Nutt, Michael Schroeder, Sylvain Soliman, Sebastian Will. Towards a semantic web for bioinformatics. (Poster) In: Proceedings of "Bioinformatics 2004", Linköping, Sweden (3rd - 6th June 2004), SocBIN - Society for Bioinformatics in the Nordic countries.
• Liviu Badea. Functional Discrimination of Gene Expression Patterns in Terms of the Gene Ontology. Pacific Symposium on Biocomputing 2003: 565-576
• Liviu Badea, Doina Tilivea, Integrating biological process modelling with gene expression data and ontologies for functional genomics (position paper). COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS. LECTURE NOTES IN COMPUTER SCIENCE, Vol. 2602, pp. 187-193, Springer Verlag 2003.
• Liviu Badea, Doina Tilivea, Intelligent Information Integration as a Constraint Handling Problem Proc. of the Fifth International Conference on Flexible Query Answering Systems (FQAS-2002), Lecture Notes In Computer Science, Vol. 2522, pp. 12-27, Springer Verlag, 2002. ISBN:3-540-00074-7.