Eigengenes as biological signatures Dr. Habil Zare, PhD PI of Oncinfo Lab Assistant Professor, Department of Computer Science Texas State University 15 September 2017 Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 15 September 2017

Bioinformatics: Computational and statistical analysis of biological data Biologists Data Genotypes / Phenotypes Results Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 15 September 2017 2

A highly collaborative team (External collaborators) Dr. Aly Karsan, MD Immunopathologist, British Columbia Cancer Agency Dr. Ron Walter Geneticist, Texas State University Dr. Kavitha Venkatesan, PhD Bioinformatician, Novartis Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 15 September 2017

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. Eigengenes as biological signature, Dr. Habil Zare, Oncinfo Lab Texas State University, 15 September 2017

AML Diagram by A. Rad Cancer here Acute myeloid leukemia (AML) is an aggressive type of blood cancer, which can cause death within months after diagnosis.

MDS Diagram by Cazzola Myelodysplastic syndromes (MDS) is less aggressive than AML but it can transform to AML with a risk probability of 30%.

Hypothesis Network analysis can reveal the biological differences between AML and MDS.

Overview of the methodology

Gene expression data

Gene expression data

Machine learning view Features

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.

Identifying gene modules

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.

Computing eigengenes

Principal component analysis Summarizes the information of a high dimensional dataset (say, d=100) into a few vectors (usually 2-3 principal components). http://austingwalters.com/pca-principal-component-analysis/

Principal component analysis Summarizes the information of a high dimensional dataset (say, d=100) into a few vectors (usually 2-3 principal components)

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.

Computing eigengenes Eigengenes are differentially expressed in AML compared to MDS.

The Bayesian network

Bayesian network The Bayesian network shows the probabilistic dependencies between the modules and the disease type.

The decision tree

The decision tree Average expression of 113 genes Average expression of 42 genes ECM and HOXA&B eigengenes were automatically selected from the set of children of the Disease node to build a predictive model.

Validation in an independent dataset

Qualitative validation MILE BCCA We inferred the expression of eigengenes in 52 AML and 22 MDS cases from BCCA dataset.

Qualitative validation MILE BCCA Some of the eigengenes showed expression patterns similar to MILE dataset.

Quantitative validation With the same thresholds, the tree classifies cases from both datasets.

Quantitative validation 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.

Validation using epigenetics Among all genes in ECM pathway, MMP9 has the highest weight in the eigengene.

Validation using epigenetics These 3 genes from matrix metalloproteinase (MMP) family are methylated in AML, which can explain their relatively lower expression.

Validation at the protein level The expression of MMP9 protein is different in AML compared to MDS.

Robustness to noise 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 { Even when 30% entries of the expression profile are replaced with noise, the accuracy drops only by 2%.

Kaplan-Meier survival curve PMCID: PMC3059453 Shows the cumulative probability of survival at a given time.

Kaplan-Meier survival curve

Breast cancer risk factors METABRIC discovery dataset METABRIC validation dataset MILLER dataset 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 assessment Using a similar approach, we could identify low-risk ER+ breast cancer cases with precision > 88% in 3 datasets.

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

Published in 2017

Alumni Oncinfo Lab Members Dr. Amir Forpushani, PhD Rupesh Agrihari Postdoc, Computational Biologist Rupesh Agrihari Grad student, Computer Science Now: Software Developer at Wells Fargo, California Now: Computer Scientist at Laboratory of Immunology, NIAID, NIH

Current members Dr. Habil Zre, PhD The PI Computational Biologist Gabriel Hurtado Undergrad student, Computer Science Bryan Shaw Grad student, Hanie Samimi

Future work We can apply a similar approach on fish RNA-seq data. Identify gene modules using all available expression data including normal samples. Compute the eigengenes for each module. Investigate which eigengenes are associated with experiment conditions like dosage or wavelength. Perform overrepresentation analysis on the corresponding modules to determine the most relevant biological processes. Dosage Eigengene 5

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