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

2

Clinical Samples

Proteomics

Gene Expression

Genotyping

WGS

Dis. Biol.

Biomarker

Targets

Translational Medicine in HCC Study

3

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.

5

6

HCC is a Chinese malignancy

7CA Cancer J Clin 2005;55:74-108.

New Cancer Cases (Incidence) and Deaths (Mortality) in 2002

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

9

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

0.2

0.4

0.6

0.8

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

0.100

0.150

0.200

0.250

0.300

0.350

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

0.1

0.2

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0.5

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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

0.1

0.2

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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