John Luk Shanghai Bioforum 2012-05-11
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Transcript of John Luk Shanghai Bioforum 2012-05-11
John M Luk 陸滿晴
肝癌生物标记物及靶点发现中的转化医学研究
BSc (HKU), MPh (HKU), PhD (Karolinska), Postdoc (Harvard)
Translational Medicine in HCC Study
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Clinical Samples
Proteomics
Gene Expression
Genotyping
WGS
Dis. Biol.
Biomarker
Targets
Translational Medicine in HCC Study
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1. Biomarkers discovery(proteins, genes, miRNAs)o Diagnostico Prognostico Tx response
2. Target identification and assessment
3. Understanding of Disease Biology
Development and Progression of Hepatocellular Carcinoma
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: American Cancer Society, 2007.
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HCC is a Chinese malignancy
7CA Cancer J Clin 2005;55:74-108.
New Cancer Cases (Incidence) and Deaths (Mortality) in 2002
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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
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Clinical Outcomes of HCC patients (n=651)
• Mortality rate:• 1-year mortality rate 202/651=31.03% • 2-year mortality rate 345/651=52.99% • 3-year mortality rate 426/651=65.44% • 4-year mortality rate 490/651=75.27% • 5-year mortality rate 548/651=84.18% • 6-year mortality rate 604/651=92.78% • 7-year mortality rate 633/651=97.24% • 8-year mortality rate 651/651=100%
Short: < 1-year survival (31%) Medium: 1-3 year survival (36%) Long: > 3-year survival (35%)
Ke H, Luk JM et al, BMC Cancer (2009)
0 20 40 60 800
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1KM curve: use tstage, TU
Time(Months)E
vent
less
Pro
babi
lity
Chi2 = 19.84 P = 8.42e-006(wt power = 0)
I & II, 103 samplesIII & IV, 127 samples
Clinical stages predict survival
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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)
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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.
Translational Medicine Workflow in HCCClinical specimensTissues; PBMCSera
Molecularstudies
DNA/RNA/Protein analyses
Clinicaldata
Patients infocorrelation
Celllines HypothesisFx testings
AnimalmodelsTranslational
& targetvalidation
ClinicaltrialsRandomized
trials & cross-center validation
HCC
2-D Gel
Proteomics Genomics
cDNA microarray
Gene expression profiling
• CGH• ROMA• SNP-CNV• miRNA
HistopathologyClinical pathological
data
HKU Surgery, Queen Mary Hospital, Hong Kong
Liver Transplant & HBP Surgical Team
Tissue Biobank Team
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HCC
Small
Large
Liver cancer or Hepatocellular carcinoma (HCC)
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OT Surgery
Livertissues
Histopathology
Clinical data andPatient databases
2-D Gel
Proteomics Genomics
cDNA microarray
Gene expression profiling
Patients
OPD Liver Clinic
Blood / Biopsy
Follow-ups
• CGH• ROMA• SNP-CNV• miRNA
FU: 0,3,6,9,12,18,24,@6-12m
BioBanking -Clinical Samples
TU: Tumor AN: Normal
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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
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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 = 33Recurrence free (>1 year), RF = 35
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Experimental workflow for proteomics 2-DE/MS platform
Protein extraction 2-DE Gel
Proteome image
analysis
Sequence with LC/Tandem mass
spectrometry
Search database
Protein identity
Statistical analysis
Molecular biology analysis
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1. Biomarker set distinguishes HCC from normal
Hsp27: AFP and survivals Grp78: tumor venous invasion
Mimic HCC phenotypes
Liver Functions& Structures
Proteomic markers for small (2cm) HCCSpot 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
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SSP3717
SSP1615
Vimentin and Lamin B1 are highly expressed in small HCC
Sun S et al. J Proteome Res. 2010
LMB1
VIM
Circulating VIM detects small HCC in serum
Gradient titration curve
y = 0.0003x + 0.0173R2 = 0.9977
0.000
0.050
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0.150
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0.250
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0 200 400 600 800 1000 1200
Vimentin ng/ml
Abs
orba
nce
415n
mSun S et al. J Proteome Res. 2010
Predictive performance of vimentin and AFP for the detection of HCC
Non-neoplasm vs small HCC Non-neoplasm vs all HCC
Statistical parameters
Vimentin AFP Vimentin AFP
(≥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%
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
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)
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
miRNA profiling of HCC tumor and adjacent non-tumor tissues
(n = 96)
Selection of 6 miRNAs
Measurement of miRNAs in culture supernatant of HCC cell
lines panel
Detection of miRNAs in logitudinalHCC serum samples before and after surgical removal of tumors
(n = 15)
Validation in an independent cohort:• Healthy controls (n = 30)• Chronic hepatitis B carriers (n = 29)• HCC patients (n = 57)
Selection of 4 miRNAs
miR-15b and miR-130b
Expl
orat
ion
Sele
ctio
n/Fi
lterin
gVa
lidat
ion
Study Approach
Tumor Adjacent non-tumor
miR-15b
miR-21
miR-130bmiR-224
miR-301
miR-183
microRNAs biomarkers for AFP-low HCC
(AN)(TU)
AFP-low TU tissues (<400 ng/ml)
Adjacent non-tumor tissues>Selection criteria: 2-fold
Candidate miRNA biomarkers for AFP-low HCC
miRNAs are readily detected in culture medium of HCC cells
• miR-301 and miR-224 had very low abundance in the culture medium
0
200
400
600
800
1000
1200
1400
pre-opera on post-opera on
Copi
es /
ng
of R
NA
miR-15b
p = 0.06370
100000
200000
300000
400000
500000
600000
pre-opera on post-opera on
Copi
es /
ng
of R
NA
miR-21
p = 0.0648
0
50
100
150
200
250
300
pre-opera on post-opera on
Copi
es /
ng
of R
NA
miR-130b
p = 0.01580
200
400
600
800
1000
1200
pre-opera on post-opera on
Copi
es /
ng
of R
NA
miR-183
p = 0.0084
Changes of serum miRNAs before and after surgery
Measurements of serum miRNAs in an independent cohort (n=116)
a b
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
The classifier could detect AFP-low HCC cases
HCC serum samples
AFP, 400 20 100
HepB and healthy controls
The classifier could detect early-stage HCC cases
Liu AM et al., BMJ 2012
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)
Conclusion 1:
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421: healthy, 2: Cirrhotic; 3: AN; 4:TU
Tumor pattern
Genome-wide HCC data overview
Metabolic process
ECM, wound healing, inflammation, vadcular,,
Cell growth/ Proliferation/
survival
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Prognostic genes can be identified from tumor and adjacent normal tissues
C.Zhang
AN has more prognostic genes
100 genes in TU
100 genes in AN
HepaPRINT: predicting prognostic outcomes
HepaPRINT: cross-validation in NCI samples
0 10 20 30 40 50 60 700
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1KM curve: OS, both 2 metaTU and metaAN, 60 matched TU and AN NCI sample
Time(Months)
Sur
viva
l Pro
babi
lity
Chi2 = 10.08 P = 0.0015
HighLow
0 20 40 60 80 1000
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1KM curve: DFS, both 2 metaTU and metaAN, 31 matched TU and AN NCI sa
Time(Months)
Sur
viva
l Pro
babi
lity
Chi2 = 4.19 P = 0.0408
HighLow
N=60 N=31
Overall Survival Disease-free Survival
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• 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
Summary:
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Merck• Hongyue Dai• Ron Chen• Carolyn Busser• James Hardwick• Andrey Loboda• Ke Hao• Chunsheng Zhang
Collaborators 合作伙伴
北京医科大学人民医院
•冷希圣教授
FHCRC/Sage• Stephen Friend
• Lee Hartwell
HKU• ST Fan• RT Poon
• Nikki Lee
• TJ Yao
• Angela Liu
复旦大学中山医院
•王建华教授
Pfizer• Mao Mao
NUS• Ken Sung• Tony Wong
• Charlie Lee
• Pramila
EHPH (Shanghai)• C Gao
Thank You
23 Dec 2010