1

Optimize the impact of Biostatistics Research for Clinical and Public Health Science

Prof. Benny Chung-Ying ZeeJockey Club School of Public Health and Primary Care

The Chinese University of Hong Kong

Biostatistics in the School of Public Health and Primary Care

Biostatistics Methodology

Clinical Trials & Pharmaceutical 

Statistics

RegulatoryIssues

Clinical Trial  Design  & Operations

Risk‐based and System Modeling

Device Development

Retinal Image Analysis

Exhaled Breath  

Analysis by  SIFT‐MS

Big Data in Health Care

Bioinformatics

Cancer Markers

Infectious Diseases Modeling

AutismBipolar Disorder

Biostatistics in a Cancer Cooperative Group NCIC CTG

• Research in clinical trials methodology– Use of quality of life endpoints in Phase III clinical trials to increase

the value of new drugs for patients• Quality-adjusted time without symptoms or treatment side-effect (Q-

TWiST)• Statistical analysis of longitudinal QOL data

– Use of multiple endpoints in Phase II clinical trials to increase the screening efficiency for new drugs

• Less patients are needed to expose to ineffective study drugs• Faster turn around to test more new drugs• Higher chance of detecting new drugs that warrant further study

• Research in bioinformatics- Microarray analysis in HCC using wavelet

approach to de-noise and extract useful gene expression on prognosis

Research in Clinical Trials Methodology

Multiple endpoints for Phase II trials Zee B, Melnychuck D, Dancey J, Eisenhauer E: Multinomial Phase II cancer trials 

incorporating response and early progression. J Biopharmaceutical Statistics Vol. 9 , No. 2, 351‐363, 1999.

Dent S, Zee B, Dancey J, Hanauske A, Wanders J, Eisenhauer E. “Application of a new Multinomial Phase II Stopping Rule using Response and Early‐Progression. J Clin Oncol19(3): 785‐91, 2001.

Lai X and Zee B “Mixed Response and Time‐to‐event Endpoints for Multi‐stage Single‐arm Phase II Design”, Trials 2014 (in press)

V Non‐inferiority Trial

• Zee B. “Planned Equivalence or Non‐inferiority Trials versus Unplanned Non‐inferiority Claims –Are They Equal?” J Clin Oncol. 2006 Mar 1;24(7):1026‐8

2

Mixed Response and Time-to-event Endpoints for Multi-stage Single-arm Phase II Design

• Cytostatic agents aims to inhibit the growth of tumor and prolong patient survival. Sorafenib in hepatocellular carcinoma (HCC) Response Rate<10% but significantly prolong both progression

free survival (PFS) and overall survival (OS)

• Time-to-event endpoint is recommended for cytostaticagent • Cytotoxic drugs combined with cytostatic therapy

suggests both response and time-to-event endpoints could be useful to evaluate the anti-cancer activity.

Correlation  between endpoints and OS in colorectal cancer (Tang et al. 2007)

Cancer

Cytotoxic drugCytotoxic drug

Cytostatic drug

3

Future direction in clinical trials methodology

• Mixed response and time-to-event endpoints for randomized phase II design

• Design and analysis of bridging study in China

Academic CRO

Academic CRO• Create an Academic CRO in CUHK

– Practical objective: • Attract new and interesting drugs to Hong Kong• Patients in Hong Kong may benefit from effective new drugs

– Academic objective: • Contribute to the design and analysis of new drug development

• Sponsor trials– Novartis, AstraZeneca, Sanofi, Servier, Biogen-Idec, Aegera,

PharmaScience, Otsuka, Bio-Cancer, Lee’s Pharma, SugerDown, IMMD, New A Innovation, DOH, etc.

– Funding• Generated more than 12 million HKD from contract research for last 5

years• Clinical trials sites budgets are a few times more than the CRO

portions

4

Academic CRO

• Obtained accreditation for 10 clinical disciplines from China Food and Drug Administration (CFDA) in the PWH

• Developed a Clinical Research Ethics Committee– Comply to ICH-GCP, FDA, CFDA

• Developed a clinical trials registry in Hong Kong as a partner registry of WHO

• Established the Clinical Trials and Biostatistics Lab in Shenzhen

Centre for Clinical Research and Biostatistics, Hong Kong

Services Provide by the Centre for Clinical Research and Biostatistics (CCRB)

PharmaceuticalsBiotech CompaniesCooperative Groups

CCRB

Protocol

DevelopCRF

DataMonitoring

Study Coordination

DrugSupply

SAEReport

Database

Data Entry &Verification

StatisticalAnalysis &Report

5

Example: A Phase 1-2, Open-Label Study of the X-Linked Inhibitor of Apoptosis (XIAP) Antisense AEG35156 in Patients with Advanced HCC

Responsibilities– Biostatistics and Data Management– Phase I and randomized Phase II Design– Randomization procedure and program– Sites and PI’s selection (TMH, QEH, QMH, PMH)– Data monitoring– Case-report form– Data management– Statistical analysis– Abstracts and paper– Financial and accounting

Results• XIAP inhibits caspases which are proteases responsible for apoptotic cell

death.• AEG35156 is a second generation antisense oligonucleotide targeting

XIAP mRNA, thus lowers the apoptotic threshold of cancer cells. It also accumulates in the liver.

• Response rate based on Choi’s criteria is 16.1% has a 95% CI: 6%-34%

Best Response AEG35156 + Sorafenib (n=31)

Sorafenib(n=17)

Total (n=48)

CHOI’s criteria

CR 0 0% 0 0% 0 0%PR 5 16.1% 0 0% 5 10.4%SD 14 45.2% 10 58.8% 24 50.0%PD 10 32.3% 7 41.2% 17 35.4%NE 2 6.5% 0 0% 2 4.2%

RECIST criteria

CR 0 0% 0 0% 0 0%PR 3 9.7% 0 0% 3 6.3%SD 18 58.1% 13 76.5% 31 64.6%PD 8 25.8% 4 23.5% 12 25%NE 2 6.5% 0 0% 2 4.2%

0.00

0.25

0.50

0.75

1.00

Survival (month)

0 5 10 15 20 25 30

Figure 4.1a: Progression-free Survival Curve - Evaluable Population (Total=48)

0.00

0.25

0.50

0.75

1.00

Progression-Free Survival (month)

0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5

STRATA: trtarm=AEG35156 + SorafenibCensored trtarm=AEG35156 + Sorafenibtrtarm=SorafenibCensored trtarm=Sorafenib

0.00.10.20.30.40.50.60.70.80.91.0

Progression-Free Survival (month)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

AEG35156 300mgAEG35156 300mg interrupted but Sorafenib administratedDose of AEG35156 has been modifiedSorafenib

0.10.20.30.40.50.60.70.80.91.0

Survival (month)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

6

Publication on Aegera• ASCO 2011 Presentation - A Report of the Phase I portion of the Phase 1-2,

Open-Label Study of X-Linked Inhibitor of Apoptosis (XIAP) Antisense AEG35156 in Combination with Sorafenib in Patients With Advanced HepatocellularCarcinoma (HCC)

• ASCO 2012 Presentation - Randomized Phase II study of the X-linked Inhibitor of Apoptosis (XIAP) Antisense AEG35156 in combination with Sorafenib in Patients with Advanced Hepatocellular Carcinoma (HCC)

• Publication - Lee FA, Zee BC, Cheung FY, Kwong P, Chiang CL, Leung KC, Siu SW, Lee C, Lai M, Kwok C, Chong M, Jolivet J, Tung S. “Randomized Phase II Study of the X-linked Inhibitor of Apoptosis (XIAP) Antisense AEG35156 in Combination With Sorafenib in Patients With Advanced Hepatocellular Carcinoma (HCC)”, Am J Clin Oncol. 2014 Jun 23. [Epub ahead of print]

CFDA accreditation

CFDA Inspection on May 2006, March 2009 and September 2012

2006 Inspection

CFDA visited on 2009 and 2012

CUHK/PWH Accredited by China FDA as Clinical Trial Site• CUHK has obtained approval on 1/8/2006 by

China CFDA on the following 10 disciplines:– Digestive Diseases 消化– Oncology 肿瘤– Endocrinology 内分泌– Ophthalmology 眼科– Cardiology 心血管– Pediatric – hematology 小儿血液– Pediatric – respiratory 小儿呼吸– Pediatric – immunology 小儿免疫– Pediatric – infectious diseases 小儿传染– Bioequivalence trials 生物等效性试验

7

Clinical Research Ethics Committee

Ethics Approval Process

Clinical Research Ethics Committee (CREC)

16 persons:ChairpersonCliniciansPharmacistScientistsLaypersonLawyerTCM

SAE MonitoringSubcommittee

(2 teams of 2 members,review in 2 weeks)

Individual studies

SAE Reporting within 24 hrs

CREC approvals& amendmentsSAE reporting

- to industry- to CREC- to DOH and other regulatoryagencies

About 600 new studies, 700 amendments, 160 clinical trials, 50% are sponsored trials

About 600 SAE

Expedite Committee (2 members)

Number of Studies Reviewed by the Joint CUHK-NTEC Clinical Research Ethics Committee

403455 474 468 502

554639 606 623

584

0

100

200

300

400

500

600

700

No.

of S

tudi

es

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012Year

Number of Clinical Trials Conducted in the Chinese University of Hong Kong

51

84

111 114125

138

160

182173

020406080100120140160180200

No

. of

Tria

ls

Year2002 2003 2004 2005 2006 2007 … 2010 2011 2012

8

Number of Ethics Applications in 2013 2013 Panel Performance

Ethics Committee Review and Management System• Operations

– All the applications are either in e-form or scanned in the system– All full and expedited reviews and SAE reviews are done online with one

SOP managed by the system• Reduce waste in physical and human resources

– Less photocopying– Easy filing– Less storage space– No need to send files and documents around

• Increase efficiency and accuracy– Faster reviewing time– Better documentation– Ensure SOP compliance– Increase security and privacy– Standardized approval letter format and keep track of all previous

approvals

Results of external audits and inspections

• Department of Health, Hong Kong– Pre-CFDA inspection - June 2012 (minor suggestions)

• Hospital Authority, Hong Kong– HAREC audit – June 2005 (lacking in manpower and space)– HAREC audit by ADAMAS Consulting - July 2012 (minor suggestions)

• China Food and Drug Administration (CFDA)– Inspection – June 2006 (minor suggestion – add a pharmacist)– Inspection – Sept 2012 (No finding)

• PWH Hospital Accreditation– Accreditation visit – Sept 2013 (Research Excellent)

• AAHRPP (Association for the Accreditation of Human Research Protection Programs)– Accreditation to be held in 2015

9

Clinical Trials Registry

Clinical Trials Registry

• International Committee for Medical Journal Editors (ICMJE)– All clinical trials are required to register under a public trial registry

• WHO Registry Platform– CCTCTR of CUHK is a partner registry of the ChiCTR recognized

by WHO– All major journals recognized WHO platform

• We required a copy of the Ethics Committee approval letter for the study– Ensure the trial is real and active– Ensure all patients have access to the information– We try to make this information accessible by general population

in addition to journal editors

10

CCTCTR Webpage – Trials registered

Chinese Title

Audit Trail

Registration details and Change Histories

Registration Details

Change Histories

Clinical Trials and Biostatistics Lab Shenzhen, China

CUHK Shenzhen Research Institute

Located in the Shenzhen Virtual University Park

Current Programs• Collaboration between CUHK and Shenzhen University

• Shenzhen Clinical Trials Consortium• Shenzhen No. 2 People Hospital• Shenzhen TCM Hospital• Shenzhen Longgang No. 9 People Hospital• Shenzhen No. 3 People Hospital

• Biostatistics – Drug and Device Development

• Automatic Retinal Image Analysis System• Breath Analysis System

11

Shenzhen International University Park located in Longgang District, Shenzhen• Chinese University of Hong Kong (Shenzhen)• Peking University, Tsinghua University, Harbin Institute of Technology • Shenzhen Institutes of Information Technology• Under development:

– Massachusetts Institute of Technology, USA– Georgia Institute of Technology, USA– University of British Columbia, Canada– University of Sydney, Australia– University of Queensland, Australia– University of Munich, Germany– Birmingham University, UK– University of Copenhagen, Denmark– Moscow State University, Russia

Biostatistics &Bioinformatics

Partnerships in Clinical Research

Backup serverGig abit Storage NetworkDell 2850Oracle databaseSuSE 9 EnterpriseDell 2 850Applic ation ServerSuSE 9 EnterpriseDell 750Web ServerRedhat 9 Dell 750Email serverWindows 2003PC Cluster co nnected by MyrinetLAN

Centre forClinical Research and 

Biostatistics

Education &Training

Clinical TrialsCoordination 

& Data Management

Global PharmaBiotech

Companies

PWH & CUHKFaculty ofMedicine

Other Hospitals in Hong Kong 

Hospitals andUniversitiesin China

Other CRO’s inChina

Infectious diseases

Bioinformatics

12

Influenza epidemiology• Collaboration with Shenzhen and Zhejiang 

CDC, Prof. Ming‐Liang He, Division of Infectious Diseases

• Influenza, hand‐foot‐mouth diseases 

• Shenzhen CDC provided data of 6,558 subjects over 4 years. Serum antibody level for H1N1, H3N2, Pandemic H1N1, Subtype B/Victoria and B/Yamagata 

• We found that the >60 years age group has delayed immune response by one measurement point than other age groups. 

• Implies an earlier vaccination scheme for elderly may be better

• Hospital response:  hospital should prepare additional and longer capacity for elderly during influenza epidemic

1 1 11

1 1 1 11030

50

age0-5

Time

GM

T

2 2

22

2

2

2 23 3

3 3

3 3 3 34 4

4 4 4 4 4 45

5 5 5

5 5

5 5

Mar09 Sep09 Mar10 Sep10 Mar11 Sep11 Mar12 Sep12

1 : H1N12 : H3N23 : B/Y4 : B/V5 : NewH1N1

1 1

1 1

1 1 1 11030

50

age6-15

Time

GM

T

2 2

2 2

2

2

223 3

3 3

3 3 334 4 4 4 4 4 4 4

55

55

5 5

5 5

Mar09 Sep09 Mar10 Sep10 Mar11 Sep11 Mar12 Sep12

1 : H1N12 : H3N23 : B/Y4 : B/V5 : NewH1N1

11

1 11 1 1 110

3050

age16-25

Time

GM

T

22

2 2

2

2

2 2

3

3

3

33 3 3

3

4

4 4 44 4 4 45 5

5 5

5

55 5

Mar09 Sep09 Mar10 Sep10 Mar11 Sep11 Mar12 Sep12

1 : H1N12 : H3N23 : B/Y4 : B/V5 : NewH1N1

1 1 1 11 1 1 110

3050

age26-59

Time

GM

T

22

2 2

2

2

2 2

33

3

3 33 3 3

44 4 4

4 4 4 45 5 5 55 5

5 5

Mar09 Sep09 Mar10 Sep10 Mar11 Sep11 Mar12 Sep12

1 : H1N12 : H3N23 : B/Y4 : B/V5 : NewH1N1

11 1

1

1 1 1 1515

2535

age>60

Time

GM

T

22

2 2

22

2 2

3

3

3

3

33 3 3

4

4

44

4 4 4 45

55 5

5

5

5 5

Mar09 Sep09 Mar10 Sep10 Mar11 Sep11 Mar12 Sep12

1 : H1N12 : H3N23 : B/Y4 : B/V5 : NewH1N1

Figure 1. Times series plot of antibody level of five types of influenza, 2009‐2012

Infectious diseases viral evolution• Genome of virus

• Associate the epidemiological evidence (serological data) and genetic evidence

• The simultaneous mutations at key antibody binding sites match epidemic cycle 

• First stage mutation sites that has dominance prevalence should be included in the influenza vaccine antigen in the coming year

Figure 2. H3N2 HA Gene Amino acids2009 S T N K K T A V A N N N R I D Y E I R P I S I R K T S E S I2009 S T N K K T A V A N N N R I D Y E I R P I S I R K T S E S I2009 S T N K K T A V A N N N R I D Y E I R P I S I R K T S E S I2009 S T N K K T A V A N N N R I D Y E I R P I S I R K T S E S I2009 S T N K K T A V A N N N R I D Y E I R P I S I R K T S E S I2009 S T N K K T A V A N N N R I D Y G I R P I S I R K T S E S I2009 S T N K K T A V A N N N R I D Y E I R P I S I R K T S E S I2009 S T N K K T A V A N N N R I D Y G I R P I S I R K T S E S I2009 S T N E N A V V A N N N R I D Y E I R P I S I R K T S E S V2010 S T N K K T V V A N N N R I D Y E M Q S I S I R K T S E S I2010 S T N K K T V V A N N N R I D Y E M Q S I S I R K T S E S I2010 S T N K K T V V A N N N R I D Y E M Q S I S I R K T S K S I2010 S T N K K T V V A N N N R I D Y E M Q S I S I R K T S E S I2010 S T N E N A V V A N N N R V N H A I R P I S I R K T S E S I2010 S T N E N A V V A N N N R V N H A I R P I S I R K N S E S I2010 S T N E N A V V A N N N R V N H A I R P I S I R K T S E S I2010 S T N E N A V V A N N N R V N H A I R P I S I R K T S E S I2010 S T N E N A V V A N N N R V N H A I R P I S I R K T S E S I2010 S T K E N A V V A N N N R V N H A I R P I S I R K T S E S I2010 S T K E N A V V A N N N R V N H A I R P M S I R K T S E S I2010 S T K E N A V V A N N X R V N H A I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2011 S T N E N A V I S S S N R I D Y E I R P I S I R K T S E S I2011 S T N E N A V I S S S N R I D Y E I R P I S I R K T S E S I2012 S T N E D A V I A N S N R I D Y E I R P I S I R K T S E S I2012 S T N E N A V I S S S N R I D Y E I R P I S I R K T S E S I2012 S T N E N A V I S S S N R I D N E I R P I S I R K T S E R I2012 N T K E N A V I S S S N R I D Y E I R P I S I R K T S E S I2012 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2012 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2012 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2012 N I K E N A V I S S N N R I N Y E I R P I S I R K T S E S I2012 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2012 N I K E N A V I S S N N R I D Y E I R P I S I R K T S E S I2012 N I K E N A V I S S N N R I D Y E I R P I S I R K T T E S I2012 N I K E N A V I S S N N R I D Y E I R P I S I R K T T E S I

Initial drifts

First stage Simultaneous residue drifts occurred; H3N2 epidemic before peak period

Second stage Additional residues simultaneous drifts occurred; H3N2 epidemic at 4-year peak

H7N9 viral evolution

Zhejiang mutated aa 72 328HaZ1_33_D R R I L K D A I K M Q Q VHaZ6_34_R R R L L K D A I R M Q Q VHaZ5_34_R R R L L K D A I K M Q Q VHaZ8_33_R R R L L K D A I K M Q Q VHaZ9_34_R R R L L K D A I K M Q Q VSX10_34_I R R L L K D A I K M Q Q VSX11_310_I R R L L K D A I K M P Q VSX12_311_D R R L I K D A I K M Q Q VWZ3_34_R R R L L K D S I K M Q Q VHuZ2_33_D R R L L K D A I K M Q Q VHuZ4_33_D R R L L K D A I K M Q Q VHaZ7_34_R K R L L K D A I K M Q R VHuZ15_3_En K K L L K D A I K M Q Q IHaZ16_4_Ch K K L L R G A V K M Q Q VNB13_41_C K K L L K D A I K M Q Q VTZ14_41_D K K L L K D A I K X Q Q V

• Collaboration with Zhejiang CDC

• By monitoring avian mutations, alert human epidemics

• Besides local H7N9 virus, also consider global avian H7N9 virus evolution

• Two proteins mutated in late 2013 and early 2014 in environment, got on human

Next: 

‐ One of the position was among the key antigenic shift position when H7N9 first adapted to human in 2013

‐ Mutation map, by province, in terms of genetic epidemiology

BIOINFORMATICS

• What do we do? 

– Find genetic markers associated with complex disease

– Find genetic markers responding to drug, pharmaceutical targets 

– Disease prognosis/classification, informative to clinical diagnosis 

• What is our specialty?

– Statistical genetics (Vs. lab approach)

– High dimensional data search (Vs. candidate markers)

– Interaction effect (Vs. Single marker evaluation)• Gene‐gene/ gene environment interaction

• Essential for decoding diseases such as diabetes, cancer, autism spectrum disorder

13

Bioinformatics – Classification algorithms

• A hierarchy of procedures 

Narrow down the search of important variables to a small pool

Evaluate high‐order interaction subsets

Use the subsets identified to do classification

An influence measure and BDA(Lo and Zheng 2002)

‐ on variable selections‐ on pairs/triplets 

Forward‐One Method

j

jj YYnI 2)(

1. Breast cancer dataset (van’t veer et al): 4,918 gene expression, 87 subjects, to classify whether a patient has developed distant metastasis. 10 group cross validation error rate: 8%.   Literature error rate: around 30%.   Wang et al.(2012) Bioinformatics

Bioinformatics – Classification algorithms

2.  Breast cancer ER status. (Sotiriou et al. 2003) – 99 patients, 7,649 genes– Y is Estrogen Receptor (ER) Status: {0, 1}– 10 group CV error rate:  6% 

3.  Leukemia classification (AML and ALL) (Golub et al. 1999)– 72 subjects, 7,129 genes– Y‐label is AML(Acute myeloid leukemia) and ALL (Acute lymphoblastic 

leukemia):  {0, 1}– Independent test set error rate:  0%  

(Best error rate in literature 3%, Fan and Lv 2007)

• IBM international bioinformatics algorithm competition, Boston (IBM Improver 2012, ranked no.9 in psoriasis category)

Bioinformatics – Interaction effect• The reason of the good classification result is the consideration of 

interaction effect between markers. • Gene‐gene,  Gene‐environment interactions• Example:  ‐ Interaction Map • When system of genes are considered jointly, we are able to group their 

effect and draw inference on complex diseases 

Gene 6 Gene 9

Gene 7 Gene 17

Gene 2

Gene 4 Gene 5

Gene 19

Age

Autism spectrum disorder

• PhD student Rui Sun project 

• Collaboration with Biomedical Scientists in CUHK

• From public available genetic dataset– Welcome trust consortium bipolar data set (~ 3000 subjects) 

– NIH bipolar and schizophrenia data set (~ 2000 subjects)

• Identify markers with interaction effect

• Biomedical scientists will validate the markers in mouse model – Animal ethics approval already obtained

2

1 00

11

)ˆ1/(ˆ)ˆ1/(ˆlog

I

ii

ii

ii SEppppW

W‐stat I‐score RF MDR Lasso

FDR 25% 25% 37.5% 40% 70%

Time (s) 123 152 146 1799 72+

14

Pharmaceutical Bioinformatics • Collaborating with Beijing Genome Institute (BGI)

– BGI did sequencing and primary data mining

– We will do thorough search considering interaction effect

– Example:• ~500 Patients from Pfizer Oncology, CA, Merck, Boston

• RNA microarray with 37,582 genes

• HCC Tumor subject: 249, and non‐tumor subject 255

• Nanjing First hospital, and Prof. Ming‐Liang He– ~1,000 gastric cancer clinical pathology samples

– They analyzed by single markers but couldn’t get significant results

– Identify interaction genes related to cancer patient survival 

Pharmaceutical Bioinformatics

• Gastric cancer drug effect project, CUHK– Over 700 miRNA

– Identified 7 miRNAs that are significantly down regulated in cancer progression environment, but up‐regulated by a certain drug

– One of the miRNAs passed mice model and human testing

– In Revision by Cancer Research.

Figure: Down‐regulation of miRNA in cancer environment

Medical Device DevelopmentqNANO

15

57

Get individual particle information of particle size, concentration and relative surface charge

Particle size: absolute size (50nm‐10μm+)

Particle composition: any synthetic or biological particle dispersed in a conducting fluid, typically an aqueous electrolyte

Real‐Time monitoring & confirmation of particle interactions

Wide range of particle types

Physiological conditions. Small sample size

Compact, portable, rapid

Cost effective

Particles

• All Particle types, Biological & Synthetic :– Polymeric, PLGA, Hydrogels, Emulsions, Polystyrene, PMMA

– Liposomes, Micelles, Lipids, Cubosomes, Milk, Virus-like-Particles ..

– Exosomes, Vesicles, Protein-Protein conplexes, Blood Platelets ..

– Bacteria, E.Coli, Plankton, Shewanella, Bacilli, Cocci, Yeast, Probiotics ..

– Viruses - Adenovirus, Lentivirus, Baculovirus, HIV, H1N1, H7N3, CMV, Dengue, Flu viruses, Phage, EV71, ..

– Magnetic, Titania, Silica, Pt, Gold, Pigments, ..

Particle Size range ~ 50 nm to 10 microns

Cur

rent

(nA

)

Time

Magnitude

Duration

Measurement in Real-time

60

Particles of different sizes produce distinctly different signals when measured using qNANO.

Each event corresponds to the measurement of a single particle.

The signal magnitude response provides a direct correlation to the size of each particle.

16

61

Particle diameter vs

Population percentage

Particle diameter vs

Blockade baseline duration

62

Particle diameter vs

Population percentage

Particle diameter vs

Blockade baseline duration

Overlap the results of different samples

Automatic Retinal Image Analysis System (ARIAS)for 

StrokeDiabetes Retinopathy (DR)

Age‐related Macular Degeneration (AMD)

Eye retina: A glimpse of our body

• Retina reflects– Eye diseases, e.g. diabetic retinopathy– Systematic diseases, e.g. diabetes, stroke, cardiovascular

disease

Normal Abnormal

17

Can we identify Vascular Diseases before stroke happens using a non-invasive and inexpensive method?

• Retinal vessels are the only visible vessels• Retinal vessels have the same 

• embryo origin• histological structure• pathological change caused by diabetes

• Hypertensive retinopathies are associated with stroke 

Where is the most common usage of retinal images as a diagnostic tool?

No lesion

Multiple lesion

Screening of diabetes patients for detecting possible diabetic retinopathy

No lesion

Mild lesion

Multiple lesion

Severe lesion

Symptoms for Diabetic Retinopathy

67

Current Issues in Screening

68

Scarcity of ophthalmologists

Inter‐observer variability for the DR diagnosis 

Long waiting time for result after screening and before confirmation for treatment

High cost in screening and referral process

18

What is Our Solution?

• A fully automated system for screening• Standard retinal images are transmitted through internet to

server installed with the algorithm• Read the images pixel-by-pixel• Automatically use advance biostatistics method to analyze these

pixels to measure the symptoms• Advantages:

69

1. Outperform Human Accuracy

2. Reduce Workload and Cost

3. Fast Result 

What are the special characteristics for the estimation of risk of stroke?

Retinal Imaging Signs

Vessel signs

Pattern signs

Retina and Stroke

DiabetesHypertension

19

Odds ratios (95% CI) Incident stroke Prevalent stroke

Retinopathy  2.1(1.7‐2.6) 2.5(1.4‐4.3)

Arteriolar narrowing  0.9(0.8‐1.1)

Venular widening  1.4(1.1‐1.7)

Decreased AVR  1.4(0.9‐2.0) 1.2(1.1‐1.3)

Retinal artery occlusion  2.9(1.6‐5.1)

Retinal vein occlusion  1.2(0.8‐1.9) 3.8(1.9‐7.6)

Arterio‐venular nicking  1.06(1.03~2.47) 1.89(1.22~2.92)

Doubal FN, Hokke PE, Wardlaw JM. Retinal microvascular abnormalities and stroke: a systematic review. J NeurolNeurosurg Psychiatry 2009;80(2):158‐65.

Known Risk Factors on Retina and stroke

Zamir M. Nonsymmetrical bifurcations in arterial branching. J Gen Physiol 1978;72(6):837‐45.

New retina characteristics

• Vessels diameter• Branching bifurcation angle• Branching asymmetry• Tortuosity

exudates

Occlusion

hemorrhages

Arteriole‐venulenicking (nipping)

• Occlusion

• Hemorrhages

• Exudates

• Arteriole‐venule nicking

20

Detection of Exudates Automatic Retinal Image Analysis System (ARIAS)

• Innovative method for the detection of new vessels

Results

Stroke Risk Assessment • Sensitivity = 90%, Specificity = 85%

Diabetic Retinopathy Assessment• Detection of Exudates

• (Sensitivity = 95.8%, Specificity = 98.4%)• Detection of New Vessels 新生血管的检测

• (Sensitivity = 96.3%, Specificity = 99.1%)

21

Risk Equation for Diabetes Patients: 5-year Probability of Stroke

• N = 640

• Risk score of stroke = 0.0634 × age (years)+ 0.0897 × HbA1C + 0.5314 × log10 (ACR) (mg/mmol) + 0.5636 × history of CHD (1 if yes)

• log(5-year Probability for stroke) = 2.007 + 0.086 × DR (equals to 1 if DR is present) + 0.19 × BSTDv– 1.942 × FDa– 1.135 × FDv– 0.003 × AAa– 0.075 × JEv– 0.009 × LDRa

Predicted value based on retinal image alone using ARIAS versus logarithmic 5-year probability for stroke based on risk equation of clinical data (R of 0.725)

Screening for Age-related Macular Degeneration (AMD)

Retinal Images of Dry and Wet AMD

22

Databases for images in Normal, AMD, Diabetic Retinopathy and Other eye diseases

Classification of AMD and Diabetic Retinopathy

Note: 63/66 (95.45%) wet AMD were correctly classified as AMD

Important features associated with AMD

23

Important features associated with AMD Automatic Retinal Image Analysis System (ARIAS)

• Filing of patents– US Non-provisional (Patent No. 20120257164) “Method and

device for retinal image analysis”, US Non-Provisional Patent No. 20120257164 filed on 6 April 2012, granted July 2014

– Chinese Patent Appln No. 201280015796.7 “视网膜图像分析方法和装置”, filed on 18 December 2013.

– Taiwan Patent No. 101112189 “Method and device for retinal image analysis”, filed on 6 April 2012

• Business plan and investment competitions– Local (won VCCE 2011 championship)– International ("E Craig Nemec Achievement Challenges - Austin

Ventures" in the Venture Lab Investment Competition (VLIC) at Texas Austin on May 2011

• Press release on 6 Sep 2012 – Stroke

Research Projects on ARIAS• Diabetes Complications

– Stroke and CHD in diabetes (collaborate with Prof Juliana Chan, Endocrinology, CUHK)– Diabetic retinopathy grading and stroke prediction with 2000+ diabetes patients from ADDITION trial

(collaborate with Prof Rebecca Simmons, Cambridge, Leicester, UK; Denmark, Netherland)– Diabetic neuropathy study (collaborate with Shenzhen Hospital)

• Cardiovascular diseases screening– Stroke screening program using ARIAS for 10,000 individuals (collaborate with Shenzhen People No. 2

Hospital)– Retrospective study on 230,000 healthy individuals with about 790 stroke cases (collaborate with MJ

Group, Taiwan)– CHD Early Classification in Primary Care (collaborate with Prof. Katrina Tsang, CUHK)– Stroke recurrence study (collaborate with Shenzhen TCM Hospital)– Association between retinal image and subclinical brain lesion from MRI for stroke in community-based

elderly population in Hong Kong: A Cohort Study (collaborate with Prof Vincent Mok, Neurology, CUHK)

• Eye diseases non-diabetes related– Age-related macular degeneration (AMD) (collaborate with HKEH and private clinics in HK)– Glaucoma using cub-to-disc ratio as screening tool (collaborate with HA hospitals)

• Head and Neck and Brain Cancer– Brain tumor and Intraocular Pressure Study (collaborate with Prof WS Poon, Neurosurgery, CUHK)– NPC screening (collaborate with Clinical Oncology, CUHK)

• Rural Medicine– ARIAS application in rural Canada– ARIAS application in rural China

Breath AnalysisSelected Ion Flow Tube – Mass Spectrometry 

(SIFT‐MS)

24

Breath Analysis – SIFT-MS Each sample was analyzed by three precursor ions（H30+，NO+，O2

+）in the range of 10-180amu by count per second（cps）

Estimate blood creatinine value using breath sample alone (precursor H3O+)• Dialysis patients samples from New Zealand with both

blood creatinine level (black lines) versus breath analysis estimate using new biostatistics method (red lines)

95

0 5 10 15 20 25 30 35

200

400

600

800

1000

Train

samples

crea

tinin

e va

lue

predicted Ytrue Y

1 2 3 4 5

300

400

500

600

700

800

900

Test

samples

crea

tinin

e va

lue

predicted Ytrue Y

Conditions under study Proposed Title

Renal DialysisIdentification of Volatile Organic Compounds (VOC) marker on breath test for evaluate the adequacy of hemodialysis in retinal failure patients.

CancersAnalysis of Volatile Organic Compounds in Exhaled Breath of Multiple Cancer Patients by Selected Ion Flow Tube Mass Spectrometry (SIFT-MS)

Ketamine A Pilot Study on Using Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) as Ketamine Rapid Test

Dementia

Utilizing Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) to analyze exhaled breath of community-based elderly population in Hong Kong: A pilot approach for screening Dementia

Infectious Disease 利用呼气测试中的选择离子质谱法作早期肺癌病患者的诊断

DiabetesEarly detection for patients with type 2 diabetes using selected ion flow tube mass spectrometry on breath tests (CREC Ref. No. 2013.037)

25

Biostatistics in the School of Public Health and Primary Care

Biostatistics Methodology

Clinical Trials & Pharmaceutical 

Statistics

RegulatoryIssues

Clinical Trial  Design  & Operations

Risk‐based and System Modeling

Device Development

Retinal Image Analysis

Exhaled Breath  

Analysis by  SIFT‐MS

Big Data in Health Care

Bioinformatics

Cancer Markers

Infectious Diseases Modeling

AutismBipolar Disorder

Potential Collaborations

• Public Health Screening Programmes using ARIAS– AMD– Diabetes

• Diabetic Retinopathy• Risk of cardiovascular diseases

– Hypertension• Hypertensive retinopathy• Risk of cardiovascular diseases

• Global and Rural Health Projects– ARIAS as an automatic tool can be made available on the cloud– No time zone difference– Non-invasive– High accuracy