John Luk, May 11, 2012. Shanghai Bioforum Translational Medicine, Session S4, Shanghai, China

1. 肝癌生物标记物及靶点发现中的转化医学研究
John M Luk 陸 滿 晴
BSc (HKU), MPh (HKU), PhD (Karolinska), Postdoc (Harvard)

2. Translational Medicine in HCC Study
Proteomics
Dis. Biol.
WGS
Clinical Gene
Biomarker
Expression
Samples
Targets
Genotyping
2

3. Translational Medicine in HCC Study
1. Biomarkers discovery
(proteins, genes, miRNAs)
o Diagnostic
o Prognostic
o Tx response
2. Target identification and assessment
3. Understanding of Disease Biology
3

4. Development and Progression of Hepatocellular Carcinoma

5. HCC Statistics: Worldwide
• Worldwide, HCC is the 5th most common
cancer
• Over 700,000 new cases are diagnosed
globally each year
• HCC is the 3rd most common cause of cancer
mortality and the main cause of death in
cirrhotic patients
El-Serag H, Rudolph KL. Gastroenterology. 2007;132:2557-2576; Garcia M, et al. Global Cancer Facts & Figures 2007. Atlanta, GA:
5
American Cancer Society, 2007.

6. HCC is a Chinese malignancy
6

7. New Cancer Cases (Incidence) and Deaths (Mortality) in 2002
CA Cancer J Clin 2005;55:74-108. 7

8. Treatment Options for HCC
 Surgery/hepatic resection (20%)
 Local ablation therapies (20%)
 Trans-arterial chemoembolization (TACE) (25%)
 Liver Transplantation (<5%)
 Systemic chemotherapies/ Palliative care (>30%)
 Molecular targeted therapy- Sorafenib
8

9. Clinical Outcomes of HCC patients (n=651)
• Mortality rate: KM curve: use tstage, TU
• 1-year mortality rate 202/651=31.03% 1
I & II, 103 samples
• 2-year mortality rate 345/651=52.99%
III & IV, 127 samples
• 3-year mortality rate 426/651=65.44% 0.8
• 4-year mortality rate 490/651=75.27%
E v e n tle s s P ro b a b ility
• 5-year mortality rate 548/651=84.18%
• 6-year mortality rate 604/651=92.78% 0.6
• 7-year mortality rate 633/651=97.24%
• 8-year mortality rate 651/651=100% 0.4
0.2
 Short: < 1-year survival (31%) Chi2 = 19.84 P = 8.42e-006(wt power = 0)
 Medium: 1-3 year survival (36%) 0
0 20 40 60 80
 Long: > 3-year survival (35%) Time(Months)
Clinical stages predict survival
Ke H, Luk JM et al, BMC Cancer (2009) 9

10. The Unmet Medical Needs of HCC in China
 3rd leading cause of cancer deaths in China (also in HK and
Singapore)
 ~300,000 new incidences per year
 ~80% HCC patients are inoperable at presentation in clinic
 Recurrence rate ~80% and some in early stages
 Poor prognosis due to:
 Late detection
 High tumor recurrence rate
 Refractory to chemotherapies (Dox ~10% PR)
10

11. How to improve the clinical outcomes for HCC patients
 To detect the cancer earlier
when the tumors can be treated by curative surgery
and/or radiotherapies.
 To stratify high-risk subgroup of patients
that may be benefited from target inhibitors (e.g. Avastin,
Sunitinib/Sutent, Sorafenib/Nexavar)
 To develop new/experimental drugs
that can kill chemo-resistant HCC cancer cells and
show survival advantages.
11

12. Translational Medicine Workflow in HCC
Clinical Molecular Clinical Cell Animal Clinical
specimens studies data lines models trials
Tissues; PBMC DNA/RNA/ Patients info Hypothesis Translational Randomized
Sera Protein correlation Fx testings & target trials & cross-
analyses validation center validation
Clinical pathological
Histopathology data
Proteomics Genomics
cDNA microarray
• CGH
• ROMA
• SNP-CNV
• miRNA
HCC
2-D Gel Gene expression profiling

13. Liver Transplant & HBP Surgical Team
HKU Surgery, Queen Mary Hospital, Hong Kong Tissue Biobank Team

14. Liver cancer or Hepatocellular carcinoma (HCC)
Small
Large
HCC
14

15. BioBanking -Clinical Samples
Patients Follow-ups
OPD Liver Clinic OT Surgery FU: 0,3,6,9,12,18,24,@6-12m
Liver
Blood / Biopsy
tissues
TU: Tumor AN: Normal
Proteomics Histopathology Genomics
cDNA microarray • CGH
• ROMA
Clinical data and • SNP-CNV
Patient databases • miRNA
2-D Gel
15
Gene expression profiling

16. I: Biomarkers Discovery for HCC
 Biomarkers for separating tumors from non-
malignant liver tissues
 Biomarkers for small-size (<2cm) HCC tumors
 Biomarkers for early tumor recurrence
 Biomarkers for prognostic outcomes
16

17. Clinical samples for the biomarker study:
A)Serum
• HCC n =120
• Cirrhosis n=120
• Healthy n=120
B) Resected Tissues
• HCC n =103
• Matched non-tumor n=103
• Normal liver n=16
C) Recurrence (1/4 -1 year), ER = 33
Recurrence free (>1 year), RF = 35
17

19. Experimental workflow for proteomics 2-DE/MS platform
2-DE Gel
Protein extraction
Proteome
image
analysis
Molecular biology
analysis
Statistical
analysis
Sequence with
LC/Tandem mass
spectrometry
Protein identity
19
Search database

20. 1. Biomarker set distinguishes HCC from normal
20

21. Hsp27: AFP and survivals Grp78: tumor venous invasion

22. Mimic HCC
phenotypes
Liver Functions
& Structures

23. Proteomic markers for small (2cm) HCC
Spot Number Protein Name (by MALDI-ToF/ToF MS/MS
SSp1615 Vimentin_HUMAN
SSp2603 Heat shock 90kDa protein_HUMAN
SSp2618 Glucose-regulated protein_78 HUMAN
SSp3211 Cathepsin D
SSp3717 Lamin B1_HUMAN
SSp4111 Alternative splicing factor ASF-2-HUMAN
SSp5605 Chain H, Cys302ser mutant of human mitochondrial
aldehyde dehydrogenase complexed with Nad+ and
Mg2
SSp6305 Keratin 10_HUMAN
SSp7605 Mitochondrial aldehyde dehydrogenase 2,
precursor_HUMAN
SSp8613 Transferrin_HUMAN
SSp9405 Phosphoinositol 4-phosphate adaptor protein-
2_HUMAN
SSp9612 Aldehyde dehydrogenase 4A1, precursor_HUMAN

24. Vimentin and Lamin B1 are highly expressed in small HCC
LMB1
SSP3717
SSP1615
VIM
24
Sun S et al. J Proteome Res. 2010

25. Circulating VIM detects small HCC in serum
Gradient titration curve
0.350
y = 0.0003x + 0.0173
0.300
R2 = 0.9977
Absorbance 415nm
0.250
0.200
0.150
0.100
0.050
0.000
0 200 400 600 800 1000 1200
Vim entin ng/m l
Sun S et al. J Proteome Res. 2010

26. Predictive performance of vimentin and AFP for the detection of HCC
Non-neoplasm vs small HCC Non-neoplasm vs all HCC
Vimentin AFP Vimentin AFP
Statistical parameters (≥245ng/ml) (≥400ng/ml) (≥245ng/ml) (≥400ng/ml)
Sensitivity, SEN 40.91% 16.28% 36.36% 30.23%
Specificity, SPE 87.50% 85.19% 87.50% 85.19%
False positive rate, FPR 12.50% 14.81% 12.50% 14.81%
False negative rate, FNR 59.09% 83.72% 63.64% 69.77%
Accuracy, AC 68.51% 42.86% 57.89% 43.36%
Youden index 0.284 0.015 0.239 0.154
Positive likeihood ratio, LR+ 3.273 1.099 2.909 2.041
Negative likeihood ratio, LR_ 0.675 0.983 0.727 0.819
Positive Predictive Value, PPV+ 69% 64% 80% 87%
Negative Predictive Value, PPV－ 68% 39% 50% 28%

27. Next step: multicenter clinical validation
• Original dataset from Hong Kong
• Multiethnic group test in Singapore
• Biomarker assay development (MRM, Alphascreen,
ELISA, biosensor)
• International biomarker network: USA, EU, Africa

28. II. miRNA as a diagnostic markers
o Identify miRNA biomarkers in both tissues & serum
o miRNAs are relatively stable in blood plasma and
serum
o Tumor-derived miRNAs were detected in blood in
mouse xenograft model (Mitchell P. et al., PNAS,
2008)
o Diseases, such as colorectal cancer, lung cancer,
and diabetes, had specific serum-miRNA profiles
(Chen X. et al., Cell Res., 2008)

29. Table I: . Clinical characteristics of
patients included in this study
o miRNA as a diagnostic
biomarker in HCC
o Especially in AFP normal
patients
o look for miRNAs highly up-
regulated in AFP normal
tumor

30. Study Approach
Exploration
miRNA profiling of HCC tumor
and adjacent non-tumor tissues
(n = 96)
Selection of 6 miRNAs
Measurement of miRNAs in
Selection/Filtering
culture supernatant of HCC cell
lines panel
Selection of 4 miRNAs
Detection of miRNAs in logitudinal
HCC serum samples before and
after surgical removal of tumors
(n = 15)
miR-15b and miR-130b
Validation
Validation in an independent cohort:
• Healthy controls (n = 30)
• Chronic hepatitis B carriers (n = 29)
• HCC patients (n = 57)

31. microRNAs biomarkers for AFP-low HCC
AFP-low TU tissues (<400 ng/ml)
Selection criteria: > 2-fold
Adjacent non-tumor tissues
Tumor (TU) Adjacent non-tumor (AN)
miR-15b
miR-224
miR-130b
miR-301
miR-21
miR-183

32. Candidate miRNA biomarkers for AFP-low HCC

33. miRNAs are readily detected in
culture medium of HCC cells
• miR-301 and miR-224 had very low abundance in the culture medium

34. Changes of serum miRNAs before and after surgery
miR-15b miR-21
1400 600000
1200
500000
Copies / ng of RNA
Copies / ng of RNA
1000
400000
800
300000
600
200000
400
200 100000
p = 0.0637 p = 0.0648
0 0
pre-opera on post-opera on pre-opera on post-opera on
miR-130b
miR-183
300
1200
250
1000
Copies / ng of RNA
Copies / ng of RNA
200
800
150 600
100 400
50 200
p = 0.0158 p = 0.0084
0 0
pre-opera on post-opera on pre-opera on post-opera on

35. Measurements of serum miRNAs in an independent cohort (n=116)

36. miR-15b and miR-130b as a classifier in detecting HCC cases
• Four miRNAs tested: miR-15b, miR-21, miR-130b, and miR-183
• Logistic regression: miR-15b and miR-130b
a b

37. The classifier could detect AFP-low HCC cases
HCC serum samples HepB and healthy controls
AFP, 400 100 20

38. The classifier could detect early-stage HCC cases
Liu AM et al., BMJ 2012

39. Conclusion 1:
o The miRNA biomarkers are of great potential in detecting HCC
of low AFP level
o Independent validation with separate cohort of HCC serum
samples (n=116) showed superior detection sensitivity and
specificity of miR-15b and miR-130b classifiers (ROC >0.98)

40. 40

42. Genome-wide HCC data overview
Cell growth/
Proliferation/
survival
Metabolic
process
ECM, wound healing,
inflammation, vadcular,,
Tumor
pattern 42
1: healthy, 2: Cirrhotic; 3: AN; 4:TU

43. Prognostic genes can be identified from tumor and
adjacent normal tissues
AN has more
prognostic genes
C.Zhang 43

44. 100 genes in TU

45. 100 genes in AN

46. HepaPRINT: predicting prognostic outcomes

47. HepaPRINT: cross-validation in NCI samples
Overall Survival Disease-free Survival
KM curve: OS, both 2 metaTU and metaAN, 60 matched TU and AN NCI sample curve: DFS, both 2 metaTU and metaAN, 31 matched TU and AN NCI sa
KM
1 1
High High
0.9 Low 0.9 Low
0.8 0.8
0.7 0.7
Survival Probability
Survival Probability
0.6 0.6
0.5 0.5
0.4 0.4
0.3 0.3
0.2 0.2
0.1 Chi2 = 10.08 P = 0.0015 0.1 Chi2 = 4.19 P = 0.0408
0 0
0 10 20 30 40 50 60 70 0 20 40 60 80 100
Time(Months) Time(Months)
N=60 N=31

48. Summary:
• Liver cancer is an aggressive malignancy with poor
outcome. Early detection can save many lives and
improve patients quality of life.
Molecular profiling has allowed us to identify candidate
biomarkers and molecular targets for detection and
intervention of HCC
Gene signature is potential clinically useful biomarkers
for HCC outcome prediction
WGS allows us to better understand the disease biology
of HBV-associated HCC
48

49. Collaborators 合作伙伴
NUS HKU
Merck
• Hongyue Dai • Ken Sung • ST Fan
• Ron Chen • Tony Wong • RT Poon
• Carolyn Busser • Nikki Lee
• Charlie Lee
• James Hardwick
• Pramila • TJ Yao
• Andrey Loboda
• Ke Hao • Angela Liu
• Chunsheng Zhang
FHCRC/Sage EHPH (Shanghai)
Pfizer
• Stephen Friend • C Gao
• Mao Mao
• Lee Hartwell
复旦大学中山医院 北京医科大学人民医院
• 王建华教授 • 冷希圣教授
49

50. Thank You
23 Dec 2010