1. Exploring Pan-Cancer Network Relationships Between Somatic Changes and Expression Profiles with PACMEN
Presented by
Ms Shila Ghazanfar
School of Mathematics and Statistics
@shazanfar

2. Complex biomedical data and network analysis of cancers
Exploring Pan-Cancer Network Relationships Between Somatic Changes and Expression Profiles
Complex biomedical data and network analysis of cancers
Mutation-expression networks
PACMEN Shiny application for exploration
The focus of this talk is on finding meaningful biological relationships between multiple datasets. In particular, I will describe two specific aspects of my thesis work, which looks at the relationships between gene expression data and mutation data, and a software tool that I have developed.
But before I do that, I will offer some motivation and context to the research area.

3. What does biomedical data look like?
DNA
RNA
Protein
Samples (n)
Samples (n)
Samples (n)
Samples (n)
Somatic Change
Binary, continuous or integer
Gene expression
continuous measurements
Protein cts ~ missing values
Clinical continuous and factors
Genes (p1)
Proteins (p3)
Variables (p4)
Genes (p2)
All these data can boil down to matrices. All with their own characteristics.
How can we find meaningful biological relationships between these multiple datasets?
What other experimental evidence can we use to achieve this?

4. Other experimental evidence: protein-protein interaction networks
6/05/2018
Other experimental evidence: protein-protein interaction networks
BioGRID, Human Interactome (HI-II-14), Human Protein Reference Database (HPRD), iRefWeb, MetaCore
Union PPI with 2237 nodes and edges
Human Protein Reference Database
Keshava Prasad et al. 2009
iRefWeb
Turner et al. 2010
BioGRID
Chatr-aryamontri et al. 2013
MetaCore
From GeneGo Inc.
…and independently curated information about human protein-protein interactions from sources such as HPRD, iRefWeb, BioGRID, and MetaCore.
Additional Information:
As I mentioned before, CSB is about integrating different levels of data. In my study, I integrated the gene expression data with large-scale protein-protein interaction networks: the fundamental tenet being that by doing so, the expression information is organised and contextualised in a global , biologically meaningful way.
So, where does one find a comprehensive list of all the known, protein-protein interactions that occur in a one-step, direct binding manner in humans?
Getting some traction on this part of the project took several months and here’s why.
So, because these large scale molecular interaction networks typically include hundreds or thousands of molecules and tens of thousands of interactions and it can therefore be useful, for example to save computational time, to focus in on specific aspects of the network. In my case, I was interested in the most highly-connected proteins in the network: so-called ‘hubs’. A B
edge
node
node
Prognostic biomarkers in melanoma

5. Detailed clinical and pathological data
More genetic information on the same patients.
Available for multiple cancers
Downloaded data for 4443 samples over 19 cancers
Samples eligible for this study (n.79) were obtained from lymph node specimens in which macroscopic tumor was observed, obtained from patients believed
to be without distant metastases at the time of tumor banking based on clinical examination and computerized axial tomographic scanning of the brain, chest, abdomen, and pelvis.
Specimens were macrodissected at the time of banking and subsequently reviewed to meet minimum criteria for tumor cell content (480%) and amount of necrosis (o30%)

6. Existing network analysis
Single Platform
Multiple Platform
Ideker et al (2002)
Gene Expression
Taylor et al (2009)
Has focused only on gene expression, with networks. Finding significantly disturbed
Mutation
Ciriello et al (2009)
Bashashati et al (2012)

7. Complex biomedical data and network analysis of cancers
Exploring Pan-Cancer Network Relationships Between Somatic Changes and Expression Profiles
Complex biomedical data and network analysis of cancers
Mutation-expression networks
PACMEN Shiny application for exploration
The focus of this talk is on finding meaningful biological relationships between multiple datasets. In particular, I will describe two specific aspects of my thesis work, which looks at the relationships between gene expression data and mutation data, and a software tool that I have developed.
But before I do that, I will offer some motivation and context to the research area.

8. Our idea
To incorporate information from gene expression and mutation in a more direct manner
Look for potential differences due to specific mutated genes, via repeated partitioning
Outcome: directed networks of mutated genes and differentially expressed genes

9. Mutation-expression network construction:
Gene expression matrix
Filtered mutation matrix
samples
samples i
genes
UPPI partners
genes
Now this is some of my latest work, that I will be presenting at an international bioinformatics conference in Jan. The idea is to to construct a directed network linking mutated genes to differentially expressed genes potentially leading to interesting connections.
Among all these, how to deconvolute to identify most useful mutations? In particular, ones that appear to change some aspect of the biology in the cell. In our study we considered gene expression as a measure of this, and aimed to identify a network, made of genes that are mutated, and those whose gene expression differs between those mutated and nonmutated.
UPPI i

10. Mutation-expression network construction:
Gene expression matrix
Filtered mutation matrix
samples
samples i
genes
UPPI partner genes
Now this is some of my latest work, that I will be presenting at an international bioinformatics conference in Jan. The idea is to to construct a directed network linking mutated genes to differentially expressed genes potentially leading to interesting connections.
Among all these, how to deconvolute to identify most useful mutations? In particular, ones that appear to change some aspect of the biology in the cell. In our study we considered gene expression as a measure of this, and aimed to identify a network, made of genes that are mutated, and those whose gene expression differs between those mutated and nonmutated.
UPPI i
Samples without gene i mutated
Samples with gene i mutated
Test for differential expression

11. Mutation-expression network construction:
For each mutated gene j and UPPI partner k, the linear model is formed for sample i,
where Mij is 1 if gene j is mutated in sample i,
Yik is the gene expression of sample i and gene k, equal to αk for samples with no mutation in gene j and αk + βjk for samples with a mutation in gene j, and εi random noise.
We are testing the hypothesis

12. Example: PTPRC in melanoma data
down up
PTPRC mutated in 32/325 SKCM
In top 5% mutated
Previously linked to T-cell acute lymphoblastic leukemia (COSMIC)
SKCM dataset, focus on just one mutated gene (mutated in about 10% of samples) and look at their gene expression effects
library(igraph)
dat = matrix(c("PTPRC","JAK2","PTPRC","MAPK3","PTPRC","IFNAR1","PTPRC","FCGR3A"),ncol=2,byrow=TRUE)
net = graph.edgelist(dat)
E(net)$color = c("red","blue","red","red")
V(net)$color = c("grey",rep("lightpink",4))
V(net)$size = 40
plot(net,layout=layout.davidson.harel)

13. Mutation-expression network considerations
Parameter 1:
What top percentile of mutated genes should be interrogated?
Parameter 2:
Do we include highly connected (but lowly mutated) genes to be interrogated?
Parameter 3: Significance threshold (FDR). How much evidence do we observe of differential gene expression?
As we know, with any statistical procedure or model, there are a number of parameters that can affect the results, and we need to understand how these can change the results.

14. Mutation-expression networks are enriched for cancer-related genes compared to randomly permuted networks
Highlight one row
The heatmap displays comparisons of the
non-empty networks generated for each cancer against networks generated through randomly
permuting the sample and gene labels. The top heatmap is for the HIPPIE network and
bottom is for UPPI. Cancers are ordered in increasing overall mutation rate. Enrichment of
genes among the NCG 4.0 [38], CGC [39] and DrugBank [40] databases, as well as the driver
nodes identied through DriverNet algorithm are displayed, using all nodes in the directed
network, the tail nodes and head nodes respectively. Cell colors correspond to empirical p-
values, blue closer to 1 and white closer to 0, with empirical p-values less than 0.1 enumerated. 28
Overall mutation load

15. Results: pan-cancer observations
More mutated genes shared among cancers than DE genes
Lowly mutated ---- highly mutated

16. Complex biomedical data and network analysis of cancers
Exploring Pan-Cancer Network Relationships Between Somatic Changes and Expression Profiles
Complex biomedical data and network analysis of cancers
Mutation-expression networks
PACMEN Shiny application for exploration
The focus of this talk is on finding meaningful biological relationships between multiple datasets. In particular, I will describe two specific aspects of my thesis work, which looks at the relationships between gene expression data and mutation data, and a software tool that I have developed.
But before I do that, I will offer some motivation and context to the research area.

17. shiny.maths.usyd.edu.au/PACMEN/

18. Acknowledgements Melanoma program at MIA/WMI/RPA Graham Mann
6/05/2018
Acknowledgements
Melanoma program at MIA/WMI/RPA
Graham Mann
Sarah-Jane Schramm
School of Mathematics and Statistics
Statistical Bioinformatics Group
Jean Yang John Ormerod
Samuel Mueller Dario Strbenac
Emi Tanaka Kevin Wang
Sarah Romanes Mark Greenaway
Weichang Yu
This work wouldn’t be possible without a large team of interdisciplinary researchers working in a team.
prof rs and his team provide the samples and assoc. clinic-path data
Many people are involved with the generation of multiple sets of omics data
Many collegue and students were involved with the anlaysis of different individual omics platform as it comes before integration.
This highlights the power of interdisciplinary collaboration how statistics and statistical bioinformatics contributes to biomedical research.
Reference: Ghazanfar, S., & Yang, J. Y. H. (2016). Characterizing mutation-expression network relationships in multiple cancers. Computational Biology and Chemistry.
Prognostic biomarkers in melanoma

19. University of Sydney Statistical Bioinformatics Group
Statistical Bioinformatics Group consists of senior and junior statisticians and bioinformaticians who share the interests in developing statistical and computational methodologies to study biology and complex diseases at the systems level. The group has joint strengths and expertise ranging from genomics, epigenomics, proteomics and beyond uniquely positions the individuals and the group as a whole to tackle the foremost significant challenges posed by modern biology and medicine.
Meet our senior and junior research leaders:
Our group also consists:
Senior research associates: 2; PhD candidates: 8; Honours and TSP students: 5
Contact us:
Jean Yang
John Ormerod
Samuel Müller
Lamiae Azizi
Pengyi Yang
Uri Keich