MS defense at 2016 Fall

1. Multi-omic data integration to
stratify population in hepatocellular
carcinoma (HCC)
Masters Defense presented by Liangqun Lu
Molecular Biosciences & Bioengineering
Committee:
Dr. Lana Garmire (Chair)
Dr. Maarit Tiirikainen
Dr. Herbert Yu

2. Hepatocellular carcinoma (HCC)
•Liver cancer is the 6th
common cancer worldwide
•Liver cancer being 2nd leading cause of cancer death globally
•HCC accounting for approximately 80% liver cancer cases
•Five-year survival rate: 17%
•According to statistical reports from World Health Organization:
• Estimation of new cases: 782,500 (554,400 males and 228,100 females)
•Estimation of deaths: 745,500 (521,000 males and 224,500 females)

3. Key events:
Telomere shortening
Loss and/or mutation
of p53
Genomic instability
HCC etiology
HCC etiology, from(Farazi & DePinho, 2006)

4. Somatic alteration studies on HCC
* WGS: whole genome sequencing, WES: whole exome sequencing
Study Group Technique Sample size Candidate onco-event
Li et al.(M. Li et al., 2011) China WES 10 ARID2 (inactivation)
Guichard(Guichard et al., 2012) France WES 24 ARID1A, RPS6KA3, NFE2L2,
IRF2 (new)
J. Huang et al.(J. Huang et al., 2012) China WES 110 ARID1A
Cleary et al.(Cleary et al., 2013) US WES 87 MLL
Totoki et al.(Totoki et al., 2011) Japan WGS 10 TSC1 (nonsense substitution)
Fujimoto et al.(Fujimoto et al., 2012) Japan WGS 27 ARID1A, ARID1B, ARID2,
KMT2A, MLL3
Kan et al.(Kan et al., 2013) US WGS 88 Wnt pathway, JAK/STAT pathway
Ahn et al.(Ahn et al., 2014) Korea WES 231 TP53, CTNNB1, AXIN1,
RPS6KA3, RB1
Schulze et al.(Schulze et al., 2015) France WES 243 CTNNB1; TP53; AXIN1

5. Somatically altered pathways
Cell cycle
Wnt signaling
Oxidative stress
Epigenetic and chromatin remodeling
AKT-mTOR-MAPK signaling
Telomere maintenance
HCC progression and somatic alterations, from the Hepatocellular carcinoma Nature Reviews Disease Primers
(Llovet et al., 2016).

6. Research goal / specific aims
•Specific aim 1: Clinical and transcriptomics associations of putative
driver mutations in hepatocellular carcinoma
•Specific aim 2: Integrating multi-omic data sets to determine
subtypes and build predictive models of new patients
•Our goal in this study is to associate the somatic mutations with
clinical characteristics and gene expression, and build molecular
classification from multi-omic data integration on HCC samples.

7. TCGA Samples
•TCGA Liver hepatocellular carcinoma (LIHC) data
Data types Platforms Samples
Exome-sequencing Illumina Genome Analyzer DNA Sequencing 198 (T)
198 (N)
Copy-number Variation Affymetrix Genome-Wide Human SNP Array 6.0 371 (T)
83 (N)
RNA-seq Illumina HiSeq 2000 RNA Sequencing Version 2
analysis
371 (T)
50 (N)
DNA Methylation Illumina Infinium Human DNA Methylation 450 377 (T)
50 (N)
miRNA-seq Illumina HiSeq 2000 miRNA Sequencing 372 (T)
51 (N)
Clinical Age, Gender, Race, Stage, Grade and Risks 377 cases
* T: tumor samples; N: normal samples (solid tissue)

8. Aim 1 – Clinical and transcriptomics associations of
putative driver mutations in hepatocellular carcinoma
•Cancer is initiated and developed due to the accumulation of somatic
genomic alterations through the widespread impact on gene products
and phenotypic properties.
•Although significantly mutated genes have been identified in a series of
cohort studies in liver cancer, the association with gene expression and
clinical characteristics has not been investigated.
•Data sets: Exome-seq; RNA-seq and miRNA-seq

9. Coxproportionalhazard
m
odel
Putative driver genes
Phenotype: survival
Phenotype: clinical
characteristics
Gene
expression
miRNA
expression
Linear regression
(On binary status)
Fisherexacttest
Workflow
Exome-seq (TCGA)
MutSigCV

10. Exome-seq – Significantly mutated genes
The illustration of the method MutSig, from Lawrence et al., (Lawrence et al., 2013)

11. Multi-variable linear regression model
yg1 X
1
X
2
X
i
yg2 ygn
=
X
0
Sample
Sample
Sample
Sample

12. Results
•Detection of putative driver genes
• Mutation point
• Significance and Frequency
• Lollipop plot of point mutation
•Associations among clinical characteristics and putative driver genes
•Associations between mRNA transcriptome and putative driver gene
mutations
• Gene count
• Pathway Enrichment
•The associations between miRNA expression and putative driver gene
mutations

13. Figure 1. Putative driver genes in TCGA HCC cohort and their KEGG pathways

14. Figure 2. Mutations effect at the protein level
R249S
D32G/N/V/Y
T432I/L/S

15. Figure 3. Putative driver genes associated with clinical characteristics

16. Figure 4. Multiple variable survival model on putative driver genes

17. Figure 5. The count of significant genes on each putative driver gene

18. Figure 6. Enriched KEGG pathways among significant genes

19. Figure 7. miRNAs whose expression is modelled significantly

20. Summary
•From TCGA somatic mutation data, we selected 13 putative driver
genes and did a systematic analysis on mutation points, mutation
frequency and pathways.
•In the association with clinical characteristics and survival, we showed
that these putative driver genes are related to clinical features, and the
multiple genes demonstrating prediction on survival.
•In the association with gene expression, we showed that some genes
regulate some pathways in common, which might explain the mutation
exclusivity.

21. Aim 2 – Multi-omic data integration to stratify
population in hepatocellular carcinoma
•Population stratification helps guide precise targeted therapeutics
•No comprehensive data integration considering complementary
information from different omic data has been investigated to build
classification on HCC.
•Data sets: RNA-seq, miRNA-seq, DNA methylation and copy number
variation from TCGA

22. Similarity
Network Fusion
+ Clustering
Survival
Training model
on each dataset
Predictive model
Features
Survival
RNA-seq
DNA-methy
miRNA-seq
Copy number

23. Data integration(SNF) and Clustering (Spectral Clustering)
Similarity Network Fusion from (B. Wang et al., 2014)
Spectral Clustering
From online slideshare

24. Results
•Integrative clustering of HCC multi-omic data
• Unsupervised clustering on integrative data
• Different number of clusters
• Association with survival
•SNF subtypes associated with clinical characteristics
• Survival curves
•SNF subtypes associated with putative driver genes
•SNF subtype predictive model on each dataset
•SNF subtype feature on gene expression profiling

25. Figure 1: Unsupervised clustering

26. Figure 2: Unsupervised clustering on different numbers

27. Figure 3: 5 subtypes on HCC samples and Survival curves

28. Figure 4: HCC subtypes associated with putative driver genes.

29. Figure 5. HCC subtypes predictive model concordance

30. Figure 6. active genes and active pathways in subtypes from gene expression level

31. Summary
•Through the four omic-data integration, we optimized five subtypes
based on survival association.
•We built predictive models on each omic dataset and the
concordance between one omic data and fused subtypes ranges
56-87%.
•The subtypes have different active biological processes at
transcriptional level.

32. Thank you for your attention!