1. 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

2. Central Dogma of Gene Expression
9/21/2018

3. 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

4. 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

5. Mutation

6. Normal vs. Tumor

7. 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

8. What cause the differential expressions of genes?

9. Stimuli

10. 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

11. 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

12. 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 ;144(5):

13. 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?

14. 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:

15. 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

16. 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

17. 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

18. 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

19. Significant Survival Difference

20. Classification using selected number of GM features