Dept of Biomedical Informatics University of Pittsburgh Identify Informative Modular Features for Predicting Cancer Clinical Outcomes Songjian Lu, Chunhui Cai, Lujia Chen Dept of Biomedical Informatics University of Pittsburgh

Central Dogma of Gene Expression http://biochemist01.wordpress.com/tag/process-of-protein-synthesis/ 9/21/2018

Protein Functions Proteins are very important molecules in our cells. Proteins are involved in structural support, bodily movement, and defense germs. Some proteins perform in cell growth that could lead to the formation of tumors. Those cancer related proteins could be translated from differentially express genes. 9/21/2018

Cancers: From mutations to signaling Structure of genome, Central Dogama Cellular signal and function carried out by proteins Cancer genomic changes: somatic mutation, copy number alteration, epigenetic modification Task: given genomic and expression data, can we reverse engineer the perturbed pathways

Mutation

Normal vs. Tumor

Differentially expressed genes Information contained in the DNA is different. Microarray analysis is one of the most popular techniques for identifying differentially expressed genes. DNA microarray could measure the expression levels of large number of genes. Compared with the expression levels of genes in normal cells, the expression levels of genes in tumor cells may be up or down. Those genes with apparent change of expression levels may be translated to proteins that lead to the formation of tumors. 9/21/2018

What cause the differential expressions of genes?

Stimuli

Signaling pathway Signaling transduction occurs when an extracellular signaling molecule activates a cell surface receptor. In turn, this receptor alters intracellular signaling cascades and alters metabolic enzyme, gene regulator and cytoskeletal protein. Signal transduction pathways are perceived to be central to biological processes. A large number of disease are attributed to disregulations of signaling pathways that underlie tumor formation and progression. This drives the development of a new generation of anticancer drugs targeted at specific molecular events. 9/21/2018

Cell: works as a molecular factory Cell’s command center Signal perception and transduction system Transcriptional gene regulatory system Upstream Downstream Command reception and transmit Command execution Fast Slow

Cancers: As complex genetic diseases Algorithm, software and information management languages Java, C++, OSs, databases, web Multiple genomic perturbations coexist in a cancer cells Drivers vs passengers What combination of mutations would lead to a perfect storm? Perturbations in multiple biological processes Multiple signaling pathways underlying a biological processes Script languages Python, Perl, Ruby Statistical/math languages R, Matlab, mathematica Can we represent the molecular findings from a tumor at a conceptual level, How to represent a tumor in terms of perturbed biological process, with a suitable granularity Hanahan D, Weinberg RA. Cell. 2011 4;144(5):646-74.

Challenges in studying cancer signaling Identifying perturbed signals Altered gene expressions are molecular phenotypes A large number of differentially expressed genes  a mixture of disparate responses to distinct signals How to de-convolute the signals? Reverse-engineering pathways through perturbation-response modeling Multiple gene mutations per tumor Mutations in an individual tumor tend to disperse in different pathways Mutations within a single pathway tend to be mutually exclusive in a single tumor How to find tumors share a common signal perturbation?

About Dream 7 Challenge DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenges address how we can assess the quality of our descriptions of networks that underlie biological systems, and of our predictions of the outcomes of novel experiments. We participated in Dream 7 Challenge: Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles. Dream 8 is now open: http://www.the-dream-project.org/

Phase 2 Second Best Performing Team: PittTransMed Team Members: Songjian Lu, Chunhui Cai, Hatice Ulku Osmanbeyoglu, Lujia Chen, Roger Day, Gregory Cooper, and Xinghua Lu Affiliation: Department of Biomedical Informatics, School of Medicine, University of Pittsburgh

Strategy Identify functionally coherent gene modules from tumors If a module of genes participate in coherently related processes and are co-regulated in multiple tumors in a coordinated manner  the module is likely regulated by a common signal Identify functionally coherent gene modules from tumors Potential units responding to cellular signals Representing the processes with GO terms  conceptualize molecular findings Identify tumors that share a common responding module Tumors with a common signal perturbation

Flow of our work Step 1: Identify GMs that Contain Co-expressed Genes with Similar Biological Functions in Only a Subset of Patients Gene expression Genes Patients Module hit Gene Modules Patients Step 2: Identify Patient Groups with Different Prognosis Outcome Module hit Gene Modules Patients Patients Patient grouping based on reduced gene modules Survival Risk Prediction Step 3: Associate GMs and Patient Groups with Pathway Mutations GMs hit matrix Gene mutation matrix Patient groups Mutations that drive Each of the GMs A group of patients

Classification of METABRIC Samples Similar clusters can be found in both TCGA and METABRIC data. Distribution and relation to PAM50 maintains across the data set, even though different platform were used to perform

Significant Survival Difference

Classification using selected number of GM features