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Eigengenes as biological signatures Dr. Habil Zare, PhD PI of Oncinfo Lab Assistant Professor, Department of Computer Science Texas State University 5.

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Presentation on theme: "Eigengenes as biological signatures Dr. Habil Zare, PhD PI of Oncinfo Lab Assistant Professor, Department of Computer Science Texas State University 5."— Presentation transcript:

1 Eigengenes as biological signatures Dr. Habil Zare, PhD PI of Oncinfo Lab Assistant Professor, Department of Computer Science Texas State University 5 February 2016

2 Outline -Large-scale gene network analysis reveals the role of extracellular matrix pathway and homeobox genes in acute myeloid leukemia. -Similar approaches are useful in identifying low-risk breast cancer cases.

3 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 3 AML Diagram by A. Rad Acute myeloid leukemia (AML) is an aggressive type of blood cancer, which can cause death within months after diagnosis. Cancer here

4 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 4 MDS Diagram by Cazzola Myelodysplastic syndromes (MDS) is less aggressive than AML but it can transform to AML with a risk probability of 30%.

5 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 5 Hypothesis Network analysis can reveal the biological differences between AML and MDS.

6 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 6 Overview of the methodology

7 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 7 Expression data

8 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 8 Expression data -Discovery dataset: Microarray gene expression data of 202 AML-NK and 164 MDS cases from MILE study. -Validation dataset: RNA-seq data of 52 AML-NK and 22 MDS cases from BCCA.

9 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 9 Identifying gene modules

10 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 10 Identifying gene modules - We analyzed 9,166 differentially expressed genes in AML vs. MDS. - We considered a module as a set of highly correlated genes in AML, and identified 33 such modules.

11 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 11 Computing eigengenes

12 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 12 Computing eigengenes -An eigengene summarizes a module. It is a weighted sum (linear combination) of expression of all genes in the corresponding module. -We applied PCA on each module separately to compute its corresponding eigengene.

13 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 13 Computing eigengenes Eigengenes are differentially expressed in AML compared to MDS.

14 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 14 The Bayesian network

15 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 15 Bayesian network The Bayesian network shows the probabilistic dependencies between the modules and the disease type.

16 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 16 The disease associated modules The children of the “Disease” node are enriched in genes associated with AML.

17 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 17 The decision tree

18 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 18 ECM and HOXA&B eigengenes were automatically selected from the set of children of the Disease node to build a predictive model. The decision tree Average expression of 113 genes Average expression of 42 genes

19 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 19 Validation in an independent dataset

20 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 20 We inferred the expression of eigengenes in 52 AML and 22 MDS cases from BCCA dataset. Qualitative validation MILE BCCA

21 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 21 Some of the eigengenes showed expression patterns similar to MILE dataset. Qualitative validation MILE BCCA

22 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 22 With the same thresholds, the tree classifies cases from both datasets. Quantitative validation

23 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 23 We trained our model on MILE microarray dataset, and validated its performance on BCCA RNA-seq dataset. Although the platforms differ, performances are comparable indicating the robustness of our approach. Quantitative validation

24 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 24 Validation using epigenetics Among all genes in ECM pathway, MMP9 has the highest weight in the eigengene.

25 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 25 Validation using epigenetics These 3 genes from matrix metalloproteinase (MMP) family are methylated in AML, which can explain their relatively lower expression.

26 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 26 Validation at the protein level Is the expression of MMP9 protein product different in AML compared to MDS? Work in progress…

27 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 27 Because an eigengene is based on the average expression of several genes, our approach is robust with respect to noise in expression profiles. Robustness to noise

28 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 28 Even when 30% entries of the expression profile are replaced with noise, the accuracy drops only by 2%. Robustness to noise {

29 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 29 Two modules were automatically selected: -A cell cycle associated module with 319 genes. -A mysterious module with 26 genes, 24 in 9q34. Breast cancer risk factors METABRIC discovery dataset METABRIC validation dataset MILLER dataset

30 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 30 Using a similar approach, we could identify low-risk ER+ breast cancer cases with precision > 88% in 3 datasets. Breast cancer risk assessment

31 Dr. Habil Zare, PhD The PI Computational Biologist Dr. Amir Forpushani, PhD Postdoc, Computational Biologist Rupesh Agrihari Grad student, Computer Science Acknowledgments Oncinfo Lab Members 31 Dr. Aly Karsan, MD Hematopathologist Rod Docking Grad student, BCCA & UBC In collaboration with British Columbia Cancer Agency

32 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 5 Feb 2016 32 We can apply a similar approach on fish RNA-seq data. 1.Identify gene modules using all available expression data including normal samples. 2.Compute the eigengenes for each module. 3.Investigate which eigengenes are associated with experiment conditions like dosage or wavelength. 4.Perform overrepresentation analysis on the corresponding modules to determine the most relevant biological processes. Future work Eigengene 5 Dosage

33 References: Cazzola, Mario. "IDH1 and IDH2 mutations in myeloid neoplasms–Novel paradigms and clinical implications." Haematologica 95.10 (2010): 1623-1627. Haferlach, Torsten, et al. "Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group." Journal of Clinical Oncology 28.15 (2010): 2529-2537. Langfelder, Peter, and Steve Horvath. "WGCNA: an R package for weighted correlation network analysis." BMC bioinformatics 9.1 (2008): 1. Curtis, Christina, et al. "The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups." Nature 486.7403 (2012): 346-352. Miller, Lance D., et al. "An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival." Proceedings of the National Academy of Sciences of the United States of America 102.38 (2005): 13550-13555. Instaling BioLinux using VM, Dr. Habil Zare 27 Oct 2015 33

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