department of biomedical informatics mining gene co-expression network for cancer biomarker...

Department of Biomedical Informatics

Mining Gene Co-expression Network for Cancer Biomarker Prediction

Kun HuangDepartment of Biomedical Informatics

OSUCCC Biomedical Informatics Shared Resource


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Outline

• Introduction• Co-expression network for Breast cancer

• Frequent cancer co-expression network• Tissue-tissue network between stroma and

tumor mass• Other applications

• Chronic lymphocytic leukemia• Glioblastoma

• Discussion


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• Correlation / co-expression• Time-course data

• Bayesian network• Boolean network• …


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Boolean Network

Sahoo et al. Genome Biology 2008 9:R157

http://genomebiology.com/2008/9/10/R157/figure/F1


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Gene Co-Expression

HMMR siRNA


r = 1 r = -1

Ranges from 1 to -1.

Pearson Correlation Coefficient


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• Expansion• Negative correlation• Multiple breast cancer datasets• More anchor genes• …

• Is there a way to find all highly correlated genes in multiple datasets?

• Do these genes form clusters?

Gene Co-Expression Network


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Frequent Gene Co-expression Network Mining

• Genes appear in tight networks in multiple disease datasets may indicate functionally related biological modules, therefore can provide insights on the disease cell physiology and new direction for the research.


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Frequent network mining • CODENSE

• Search for frequent coherent dense subgraphs across large numbers of massive graphs

• Unsupervised bottom-up clustering on unweighted, undirected network


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Data selection and correlation

• Selected 23 datasets from Gene Expression Omnibus (GEO): • Search term “metastatic cancer”• Contain both control and tumor, # sample > 8• Only primary tumor biopsy

• Correlation : │PCC│ > 0.75 (really high similarity)• For CODENSE:

• Edge support appears in at least 4 datasets• Connectivity ratio r > 40% (r = L / [n(n-1)/2] )• # of nodes > 20


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Results from CODENSE• 44 networks (clusters) are identified• # of nodes: 21 ~ 74 (average 44)• Connectivity: 0.41 ~ 0.78


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GO Enrichment Analysis on the Networks

• Networks with enriched GO terms associated with at least 1/3 of the genes• Immune response/system – 15• Protein translation (ribosome) – 5• Development – 4• Metabolism and energy (oxidative phosphorylation or

monocarboxylic acid metabolism) – 3• Cell cycle – 2• Muscle contraction – 1

• 14 networks do NOT satisfy the above criterion• Potential new functions• New interactions


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Use cluster 2 to predict survival outcome

• NKI-295 dataset• Supervised clustering: k-means, k=2, 100 random

initialization• Kaplan Meier curve and log-rank test for survival analysis

and comparison• Test for different patient groups


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Predict Survival Outcome


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Predict Survival Outcome


Relation to BRCA1


Finding New Gene FunctionsTest siRNA HDR

(relative)Centrosome

(Hela cell)Centrosome

(B.C. cell)1 Control siRNA 1 2% 4%2 BRCA1 0.16 ND 22%3 BRCA2 0.02 ND ND4 HMMR 1.33 10% 19%5 KIAA0101 0.52 10% 19%6 ASPM 1.0 2% ND7 NUSAP1 0.5 5% ND8 DLG7 0.5 ND ND9 KIF14 0.33 20% ND

10 KIF23 ND 27% 15%


Finding New Gene Functions

KIAA0101


ER-Negative Breast Cancer


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Tumor Microenvironment (TME)

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Cell, Volume 100, Issue 1, 7 January 2000, Pages 57-70

Kalluri et al. Nature Reviews Cancerpublished online 30 March 2006 | doi:10.1038/nrc1877


Bipartite Graph

Network Density (r) For a bipartite network

with M+N nodes (M nodes in one side and N nodes in the other) and K edges

r = K/MN. For a weighted bipartite

network with M+N nodes and K edges

r = Σi=1,…K Wi/MN.

Stroma Tumor


Bipartite Quasi-clique Discovery Algorithm

A Greedy Algorithm Original algorithm for quasi-clique finding is from

Ou and Zhang (2007). A new multimembership clustering method. J. of Ind. and Man. Opt., 3(4): 619-624.

Modified for the bipartite graph Four steps:

1. Set the threshold on edge weight w0 = g•max(wi).2. Initialize a new search: pick the edge with the maximal weight (larger than

w0) that has not been assigned to any network as the first edge of a new network.

3. Grow: alternatively adding nodes to the network from both sides which contribute most to the network density if the contribution to the density is higher than an adaptive threshold defined by two parameters l and t; 3.1. stop when no new node can be added; go to Step 2.

4. Merge: iteratively merge networks with more than 50% overlap (w.r.t. to the smaller one).


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Select a breast cancer dataset from GEO:GSE5847 contains 47 samples with separate microarray data for stroma and tumor separated using laser capture microdissection

Compute Pearson Correlation Coefficients (PCC) for every pair of gene between the stroma and the tumor

Select the top 10 networks for further analysis

Use the PCC values as the weights for the edges and set the three parameters (0.7)g , (2)l , and t (2) to run the bipartite quasi-clique finding algorithm

Workflow


• Stroma-tumor networkStroma GO BP (p-values) Tumor GO BP (p-values) Common

1 19 M phase/cell cycle (1e-6) 68 Cell cycle/M phase (0) 13

2 35 Cell-cell signaling (8e-6)Neuropeptide signaling pathway (0.000686)

49 Cell-cell signaling (0.000549)Synaptic transmission (0.000084)

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3 33 Immune response (0)Response to virus (0)

36 Immune response (0)Response to virus (0)

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4 29 Immune response (0) 29 Immune response (0)Positive regulation of B cell proliferation (0.000536)

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5 23 Cell-cell signaling (0.003158)Synaptic transmission (0.005969)

28 Secretion to cell (0.000218)Cell-cell signaling (0.000441)

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6 17 M phase (0)Cell cycle (2e-6)

31 M phase (0)Mitosis (0)

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7 12 Immune response(8e-6) 27 Follicle-stimulation hormone secretion (33e-6)

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8 13 Extracellular space (1e-6) (CC) 19 Extracellular space (9.7e-5) (CC) 7

9 7 Wound healing (0.000114) 25 Amine metabolic process (3.5e-6) 6

10 5 24 Extracellular region part (0.000267) (CC) 4

Results


• Tumor microenvironmentNetwork Stroma Tumor

8 AMPH, DCN, ELN, FBN1, HTRA1, LRRC17, OMD, PDGFRL, SFRP4, SGCD, SPON1, TGFB3, ZFHX4

AMPH, ANGPTL2, BNC2, COL1A1, DPT, ECM2, EHD2, FAT4, GLT8D2, GUCY1B3, HTRA1, KCNJ8, LRRC17, MMP2, OLFML1, PDGFRL, SFRP4, SPON1, ZFHX4

Extracellular Matrix Network


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Outline

• Introduction• Co-expression network for Breast cancer

• Frequent cancer co-expression network• Tissue-tissue network between stroma and

tumor mass• Other applications

• Chronic lymphocytic leukemia (CLL)• Glioblastoma

• Discussion


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CLL Prognostic Biomarker

• CLL is the most common adult leukemia in the western world. It is highly heterogeneous, can be indolent or progressive.

• Prognosis at early stage is crucial to progressive patient survival as well as to indolent patients to avoid unnecessary adverse treatment.

• Biological prognostic markers:• Serum markers (TK, B2M, sCD23)• FISH cytogenetics• IgVH mutational status - Determination is time consuming and expansive

• CD38 expression - Actually independent of IgVH mutational status

• ZAP-70 expression - Not 100% correlated to IgVH mutational status, only accurate when patients in the progressive stage


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Network 17

• 51 genes, including ZAP-70 and CD38• r = 0.4142• Including known ZAP-70 interacting genes - CD8A, CD3G,

CD3D, CD247


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Highly enriched Functions of Immune Response


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Workflow of CLL Prognostic Biomarker Selection

Further select prognostic biomarkers by testing on separate CLL dataset

Genes with exp fold change > 1.5p <0.05

Test the prediction accuracy of each gene on IgVH mutation status

Cross validation

Select a group of feature genes that can

differentiate IgVH mu +/- groups

mRMR40

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Identify potential prognostic biomarkers 5

Compute gene exp level difference on IgVH

mu+/- groups

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6

40


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Differential Expression of Genes between IgVH mutation +/-

Genesp-values

(Unmutated vs Mutated)

Mean fold change

(Unmutated vs Mutated)

p-value (Patients vs

Normal)

SH2D1AIL2RBKLRK1CD247GZMBCD3GCD3DGZMKCD8ANKG7ZAP70LAG3

1.3E-38.1E-54.9E-31.6E-43.1E-30.0171.4E-40.0229.9E-58.3E-47.9E-40.023

1.9441.8211.8131.8071.7191.6851.6211.5861.5761.560-1.403-1.598

0.0894.8E-160.00797.1E-86.2E-11

0.414.3E-169.2E-113.5E-91.3E-9

5.5E-120.028


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Prediction of IgVH Mutational Status with Individual Genes

Genes Accuracy Subcellular

location

SH2D1AIL2RBKLRK1CD247GZMBCD3GCD3DGZMKCD8ANKG7ZAP70LAG3

ZAP70+IL2RBZAP70+IL2RB+CD8A

57.32%68.84%63.67%66.03%57.13%62.52%64.27%57.58%68.31%64.94%68.46%59.53%73.22%74.62%

cytoplasmicmembranemembranemembranesecreted

membranemembranesecreted

membranemembrane

cytoplasmicmembrane

--

• Two groups of patients (GDS1494): 49 IgVH mu- ; 51 IgVH mu+• Each gene / gene set was tested independently• A linear classifier with 20% hold out and 100 repeats


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Top Ten Genes Selected by mRMR

Rank Name mRMR Score

123456789

10

IL2RBLAG3

RASGRP1CD8AXCL1ZAP70CD79AFMNL1KLRK1CST7

0.1010.0200.0290.0210.0110.0180.0010.0000.0000.002


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Cross Check with Outcome Data

• LAG3 : involved in T-cell-dependent B-cell activation, reported recently to be highly correlated to IgVH mutational status

• IL2RB: involved in endocytosis and transduction of mitogenic signal of IL2, expression on B-cells was linked to CLL

• CD8A and CD247: expression of CD8A on B-cells has been linked to CLL

• KLRK1: involved in immune surveillance exerted by T/B-cells

Using GSE10138


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Application to GBM

Cluster p-value # Genes

A 0.0010946 79

B 0.00054934 87

C 0.0016763 23

D 0.0063116 466

E 0.0057298 154

F 0.000957 79

G 0.0010599 29

H 0.0086392 303

I 0.00098224 39

J 0.0097023 21

K 0.0061901 97

L 0.000352 42


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ID Name P-value

Term in Query

Term in Genome

1 GO:0002376 immune system process

5.380E-37

119 1258

2 GO:0006955 immune response 4.421E-35

95 832

3 GO:0006952 defense response 1.172E-22

75 760

4 GO:0002684 positive regulation of immune system

process

2.644E-21

47 303

5 GO:0002682 regulation of immune system

process

1.374E-20

58 493

6 GO:0050776 regulation of immune response

1.825E-17

41 277

7 GO:0009611 response to wounding

4.149E-17

62 658

8 GO:0050778 positive regulation of immune response

6.545E-16

33 188

9 GO:0006954 inflammatory response

1.640E-15

47 414

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GO:0002252 immune effector process

4.530E-14

36 260

Functional Enrichment analysis using IPA for cluster D.


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IDName

P-value

Term in

Query

Term in Genome

1GO:0008219 cell death 8.245

E-328 1385

2GO:0016265 death 8.829

E-328 1390

3GO:0010033 response to organic

substance1.299E-2

17 605

4GO:0012501 programmed cell death 1.735

E-226 1279

5GO:0034097 response to cytokine

stimulus1.742E-2

7 93

6GO:0009628 response to abiotic

stimulus2.393E-2

14 443

7GO:0048545 response to steroid

hormone stimulus2.469E-2

10 225

8GO:0051093 negative regulation of

developmental process3.889E-2

18 728

9GO:0006915 apoptosis 4.244

E-225 1265

Functional Enrichment analysis using IPA for cluster E. The x-axis shows the log (base 10) of p-values of the enriched terms using the Fisher’s exact tests.

GO Enrichment results using ToppGene for Cluster E (GO: Biological Processes)


Summary and Future Work

Gene co-expression networks provide rich information in predicting gene functions and disease mechanisms

Need to be integrated with other networks such as PPI



Ongoing work 1: More biological and clinical validation Tissue microarray – at protein level

Ongoing work 2: Multiple tissue network for TME Microarray data for epithelial cells, fibroblast cells,

endothelial cells, macrophages Moving to RNA-seq

Ongoing work 3: Biclique mining algorithm using frequent item set

and graph summarization



Ongoing work 4: Integrating multiple networks – disease network,

phenotype network

Barabasi A-L, Network medicine – from obesity to “Diseasome”, NEJM, 357(4): 404-407, 2007.

department of biomedical informatics mining gene co-expression network for cancer biomarker...

Documents

undirected network

networks networks

cancer initiation

gene expression omnibus

expression hmmr sirna

similar expression profiles

tight networks

codense44 networks clusters