John Luk Shanghai Bioforum 2012-05-11
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John Luk Shanghai Bioforum 2012-05-11

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

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

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John Luk Shanghai Bioforum 2012-05-11 John Luk Shanghai Bioforum 2012-05-11 Presentation Transcript

  • 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)
  • Translational Medicine in HCC Study Proteomics Dis. Biol.WGS Clinical Gene Biomarker Expression Samples Targets Genotyping 2
  • 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
  • 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 patientsEl-Serag H, Rudolph KL. Gastroenterology. 2007;132:2557-2576; Garcia M, et al. Global Cancer Facts & Figures 2007. Atlanta, GA: 5American Cancer Society, 2007.
  • HCC is a Chinese malignancy 6
  • New Cancer Cases (Incidence) and Deaths (Mortality) in 2002 CA Cancer J Clin 2005;55:74-108. 7
  • 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
  • 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
  • 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
  • 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
  • Translational Medicine Workflow in HCCClinical Molecular Clinical Cell Animal Clinicalspecimens studies data lines models trialsTissues; PBMC DNA/RNA/ Patients info Hypothesis Translational RandomizedSera 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
  • Liver Transplant & HBP Surgical TeamHKU Surgery, Queen Mary Hospital, Hong Kong Tissue Biobank Team
  • Liver cancer or Hepatocellular carcinoma (HCC)SmallLarge HCC 14
  • 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
  • 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
  • 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
  • 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
  • 1. Biomarker set distinguishes HCC from normal 20
  • Hsp27: AFP and survivals Grp78: tumor venous invasion
  • Mimic HCCphenotypesLiver Functions& Structures
  • 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
  • Vimentin and Lamin B1 are highly expressed in small HCC LMB1 SSP3717 SSP1615 VIM 24 Sun S et al. J Proteome Res. 2010
  • 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
  • Predictive performance of vimentin and AFP for the detection of HCC Non-neoplasm vs small HCC Non-neoplasm vs all HCC Vimentin AFP Vimentin AFPStatistical 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 0.154Positive likeihood ratio, LR+ 3.273 1.099 2.909 2.041Negative likeihood ratio, LR_ 0.675 0.983 0.727 0.819Positive 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 markerso Identify miRNA biomarkers in both tissues & serumo miRNAs are relatively stable in blood plasma and serumo 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 ofpatients 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
  • Study ApproachExploration 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)
  • 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
  • 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
  • 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
  • Measurements of serum miRNAs in an independent cohort (n=116)
  • 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
  • The classifier could detect AFP-low HCC cases HCC serum samples HepB and healthy controlsAFP, 400 100 20
  • The classifier could detect early-stage HCC cases Liu AM et al., BMJ 2012
  • Conclusion 1:o The miRNA biomarkers are of great potential in detecting HCC of low AFP levelo 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
  • 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
  • Prognostic genes can be identified from tumor and adjacent normal tissues AN has more prognostic genes C.Zhang 43
  • 100 genes in TU
  • 100 genes in AN
  • HepaPRINT: predicting prognostic outcomes
  • HepaPRINT: cross-validation in NCI samples Overall Survival Disease-free SurvivalKM 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
  • 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
  • Collaborators 合作伙伴 NUS HKUMerck• 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
  • Thank You 23 Dec 2010