1

Steven G RozenProfessor of Cancer & Stem Cell Biology, Duke-NUS Medical School Singapore

Director, Duke-NUS Centre for Computational Biology

Associate Dean of Research Informatics

1. Integrative analysis of activity, blood lipids,

and cardiac imaging in healthy volunteers

2. Widespread exposure to an herbal

medicine mutagen in Asian cancers

Sept 7, 2017Weekly Precision Medicine Forum, Duke University

Part 1 from Weng Khong LIMChief Bioinformatics Lead, PRISM (SingHealth and Duke-NUS Precision Medicine Institute)

Singapore

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• Small: 50 km (31 mi) east-west26 km (16 mi) north-south

• 5.8 million people

• Rich: per capita GDP > US (purchasing power parity, includes non-citizens)

• Good public health

• Life expectancy at birth 85 (US is 79.8)

• Infant mortality: 2.4/1,000 (US is 5.8/1,000)http://kids.britannica.com/comptons/art-55105/Singapore

(From CIA World Factbook, https://www.cia.gov/library/publications/the-world-factbook, Mar 2017)

Duke-NUS Medical School

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• Nonprofit partnership– Duke in North Carolina, USA– National University of Singapore (NUS)– SingHealth, largest public healthcare

provider, 3 hospitals, 3,100 beds• Same campus as National Cancer Centre,

National Heart Centre, National Eye Centre, etc.

• 60 MDs / year• 15 PhD students / year• PhD program in biostatistics and

bioinformatics: tinyurl.com/dnus-ibb

Precision Medicine In Singapore

• Strong interest on the part of Ministry of Health and Ministry of Trade and Industry (Biomedical Research Council)

• There is a National Precision Medicine Alliance

• Research community in process of “self-organizing” with several early-stage initiatives (many are genomics oriented), including PRISM (Patrick Tan will visit in October [?])

• Singapore does not have a single-payor system; EHR not centralized

• Trying to learn from other small countries (e.g. Finland) – “fast follower”

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Part 1

Integrative analysis of activity, blood lipids,

and cardiac imaging in healthy volunteers

Weng Khong LIMChief Bioinformatics Lead, PRISM (SingHealth and Duke-NUS Precision Medicine Institute)

Serum

Lipidomics

Activity/Sleep

Monitoring

(Wearables)

SPECTRA – A Genome/Phenome

Encyclopedia for Asian Patient Normality

Collaboration with National Heart Centre Biobank, SingHEART and

Singapore Infocom Development Authority (PIs : Stuart Cook and Yeo Khung

Keong)

Consented for Research,

Incidental Findings,

and Long Term Follow UpVolunteer

Lifestyle Factors

and clinical tests incl. ECGImaging

Studies

(MRI, Calcium)

Volunteer Characteristics

Characteristic Female (n=137, 58.8%) Male (n=96, 41.2%) Test

Age, years 47.49 (11.44) 44.36 (12.63) 0.051

Ethnicity 0.257

Chinese 127 (92.7) 85 (88.5)

Malay 4 ( 2.9) 3 ( 3.1)

Indian 2 ( 1.5) 6 ( 6.2)

Others 4 ( 2.9) 2 ( 2.1)

BMI, kg/m2 22.68 (3.89) 24.65 (3.98) <0.001

Waist Circumference, cm 78.33 (10.14) 88.54 (10.88) <0.001

SBP, mmHg 122.80 (17.36) 133.81 (15.64) <0.001

DBP, mmHg 72.88 (12.50) 83.12 (11.51) <0.001

RestingHR, (Fitbit, bpm) 70.37 (6.85) 68.72 (6.80) 0.07

ECG HR, bpm 64.87 (9.58) 63.45 (11.13) 0.304

Total Cholesterol, mmol/l 5.33 (1.02) 5.26 (0.85) 0.581

LDL, mmol/l 3.28 (0.84) 3.37 (0.92) 0.471

HDL, mmol/l 1.60 (0.34) 1.33 (0.32) <0.001

Triglycerides, mmol/l 0.98 (0.49) 1.34 (0.88) <0.001

Glucose, mmol/L 5.24 (0.41) 5.44 (0.64) 0.005

DailySteps, (Fitbit, x1000) 10.74 (4.13) 11.00 (3.66) 0.612

Fitbit ActivityClass 0.799

Cat I 14 (10.2) 10 (10.4)

Cat II 57 (41.6) 36 (37.5)

Cat III 54 (39.4) 38 (39.6)

Cat IV 12 ( 8.8) 12 (12.5)

GPPAQ Score 1.25 (1.12) 1.84 (1.15) <0.001

LVM, g 64.13 (14.49) 93.16 (21.29) <0.001

LVEDV, ml 107.79 (16.90) 137.36 (25.37) <0.001

RVEDV, ml 106.21 (19.00) 141.74 (22.65) <0.001

AoF, ml 65.62 (9.37) 78.39 (12.72) <0.001

Volunteer Characteristics

Characteristic Female (n=137, 58.8%) Male (n=96, 41.2%) Test

Age, years 47.49 (11.44) 44.36 (12.63) 0.051

Ethnicity 0.257

Chinese 127 (92.7) 85 (88.5)

Malay 4 ( 2.9) 3 ( 3.1)

Indian 2 ( 1.5) 6 ( 6.2)

Others 4 ( 2.9) 2 ( 2.1)

BMI, kg/m2 22.68 (3.89) 24.65 (3.98) <0.001

Waist Circumference, cm 78.33 (10.14) 88.54 (10.88) <0.001

SBP, mmHg 122.80 (17.36) 133.81 (15.64) <0.001

DBP, mmHg 72.88 (12.50) 83.12 (11.51) <0.001

RestingHR, (Fitbit, bpm) 70.37 (6.85) 68.72 (6.80) 0.07

ECG HR, bpm 64.87 (9.58) 63.45 (11.13) 0.304

Total Cholesterol, mmol/l 5.33 (1.02) 5.26 (0.85) 0.581

LDL, mmol/l 3.28 (0.84) 3.37 (0.92) 0.471

HDL, mmol/l 1.60 (0.34) 1.33 (0.32) <0.001

Triglycerides, mmol/l 0.98 (0.49) 1.34 (0.88) <0.001

Glucose, mmol/L 5.24 (0.41) 5.44 (0.64) 0.005

DailySteps, (Fitbit, x1000) 10.74 (4.13) 11.00 (3.66) 0.612

Fitbit ActivityClass 0.799

Cat I 14 (10.2) 10 (10.4)

Cat II 57 (41.6) 36 (37.5)

Cat III 54 (39.4) 38 (39.6)

Cat IV 12 ( 8.8) 12 (12.5)

GPPAQ Score 1.25 (1.12) 1.84 (1.15) <0.001

LVM, g 64.13 (14.49) 93.16 (21.29) <0.001

LVEDV, ml 107.79 (16.90) 137.36 (25.37) <0.001

RVEDV, ml 106.21 (19.00) 141.74 (22.65) <0.001

AoF, ml 65.62 (9.37) 78.39 (12.72) <0.001

Physical Activity Questionnaire

Left ventricular mass, g

Left ventricular end-diastolic vol. ml

Right ventricular end-diastolic vol. ml

Aortic forward flow, ml

Wearable Activity Tracker Data Collected

•Step counts (15-minute resolution)

•Heart rate (5-minute resolution)

•Sleep sessions (session start/end time)

• Volunteers tracked for:•Average of 5 days•With 3 days of complete data

Wearable activity trackers provide insights on behavioral and demographic stratification of volunteers --3 clusters based on daily activity patterns

High activity

Low activity

Clu

ster

Mid

Day

PM

A

M

Average of Each Activity Cluster

Association between Age and Activity Cluster

* (p < 0.05)

** (p < 0.01)

Dai

ly s

tep

s (1

,00

0s)

Relationships among Daily Steps, Age and

Gender

Wearable activity trackers correlate with

clinical heart rate measurements and self-

reported activity levels

Ele

ctro

card

iogr

amR

esti

ng

HR

(b

pm

)

Activity Tracker Resting HR (bm)

Trac

ker

HR

(b

pm

)

Self-reported activity level

Association of Daily Steps with

Cardiovascular and Metabolic Disease Risk Markers

OR for each additional 1000 steps

High Fasting Blood Glucose

Association of Activity Tracker

Resting Heart Rate with Cardiovascular and

Metabolic Disease Risk Markers

OR for each additional BPM

High Fasting Blood Glucose

Association of Cardiac Remodeling with

Daily Steps Activity

Left Ventricular MassLeft Ventricular

End-Diastolic Volume

Association of Cardiac Remodeling with

Daily Steps Activity

Right VentricularEnd-Diastolic Volume Aortic Forward Flow

Consistent with Previous Larger Study

Association with on Cardiac Remodeling

Lipidomics: Sphingolipids Correlated with Daily Steps

SphingolipidDailySteps (x1000) FBG

p-value β rs p-value β rs

Cer(d18:1/20:0)* 0.002 -0.073 -0.284 0.031 0.112 0.208

Cer(d18:0/20:0) 0.004 -0.066 -0.282 0.434 0.044 -0.001

Cer(d18:1/24:1(15Z)) 0.004 -0.067 -0.278 0.391 0.045 0.083

Cer(d18:1/18:0)* 0.005 -0.071 -0.305 0.024 0.112 0.197

Cer(d18:0/24:1(15Z)) 0.009 -0.062 -0.268 0.502 0.035 0.042

Cer(d18:1/16:0) 0.013 -0.061 -0.270 0.575 0.028 0.117

Cer(d18:1/22:0) 0.014 -0.060 -0.277 0.055 0.095 0.197

SM(36:0)* 0.015 -0.056 -0.233 0.024 0.123 0.204

Cer(d18:1/24:0) 0.023 -0.053 -0.204 0.188 0.067 0.081

SM(36:1)* 0.027 -0.051 -0.202 0.045 0.109 0.189

GlcCer(d18:1/16:0) 0.043 -0.043 -0.175 0.066 -0.113 -0.144

SM(36:2)* 0.048 -0.045 -0.211 0.021 0.125 0.215

Cer, ceramide; SM, sphingomyelin; GlcCer, glucosylceramide

Lipidomics: Sphingolipids Correlated

with Daily Steps and Blood Glucose

Cer, ceramide; SM, sphingomyelin; GlcCer, glucosylceramide

Conclusions

This proof of concept study looked at activity questionnaire and clinical lab tests, consumer grade wearable activity trackers, cardiac imaging, serum lipidomics

Consumer grade activity trackers provide useful data linked to other information

Activity tracker resting heart rate seems more informative than daily steps (integrates activity and other parameters over months / years?)

Acknowledgements

PRISM

• Patrick Tan (PI)• Stuart Cook (PI)• TEH Bin Tean• Steve Rozen• Sonia Davila• Teo Jing Xian• YANG Chengxi• Chris Bloecker• LIM Jing Quan

National Heart ResearchInstitute Singapore andNational Heart Centre Singapore

• YEO Khung Keong (PI)• Calvin Chin• Anders Sahlen• Tan Swee Yaw• Jonathan Yap• Edmund Pua• Kong Siew Ching (CRC)• Ho Pei Yi (CRC)

Part 2

Widespread exposure

to an herbal medicine mutagenin Asian cancers

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Herbal remedy – contains aristolochic acids and related compounds – collectively “AA”

Aristolochia PlantsHerbal remedies

Aristolochic acid IAA

(multiple variants)

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AdenineDNA adducts,

Adenine > Thymine mutations

Adapted from Poon et al, Science Translational Medicine, 2013

Also a nephrotoxin –causes kidney failure

Mutation signature analysis enabled by cheapnext generation sequencing of cancer genomes

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$1.5 thousandtoday

6,000 Xdrop in price

Mutational signature in an AA-exposed upper tracturothelial carcinoma (UTUC)

30Poon et al, Science Translational Medicine, 2013

CAG>CTG

CAA>CTATAG>TTG

Transcriptional strand bias

T > A ontranscribedstrand

T > A On NONtranscribedstrand

A few years ago:AA exposure in upper tract urothelial cancer

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Upper Tract Urothelial (Taiwan)

A year ago: AA exposure in multiple tumor types

Upper Tract Urothelial (Taiwan)

Bile duct (Singapore)

Liver (China)

Poon et al., 2013 and subsequent data (HCC)Zou et al., 2015 (CCA)Scelo et al., 2014 and Jelakovic et al., 2014 (RCC)Poon et al., 2013, Hoang et al., 2013 (UTUC)Poon et al., 2015 (Bladder)

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Kidney (Taiwan)

Bladder (Taiwan)

Why look for AA exposure in Taiwan liver cancer?

• AA signature in Taiwan

– Upper tract urothelial

– Bladder

– Kidney

• AA signature in other geographical regions in

– Liver (China)

– Bile duct cancer (China, Singapore)

– Kidney (Balkans)

• Taiwan a likely hotspot for AA exposure, but liver cancer not examined

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AA signature in Taiwan HCCs

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Principal components analysis shows many Taiwan HCCs have spectra similar to AA bladder and AA UTUC from Taiwan

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Overlays of mutations due to different exogenous mutagens or endogenous mutagenic processes-- Computational separation

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Bladder spectrum from Poon et al, Genome Medicine, 2015

AA

Overlays of mutations due to different exogenous mutagens or endogenous mutagenic processes-- Computational separation

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Bladder spectrum from Poon et al, Genome Medicine, 2015

AA

+ ABOBEC signatures

+ background signature

Non-negative matrix factorization andrelated approaches

New statistical approach

• mSigAct (mutational signature activity)

• Determines whether the observed mutations are significantly better explained with a contribution from the AA mutational signature than without

• Uses likelihood ratio test – compares likelihood under

– Null hypothesis: AA signature did not contribute to the observed mutations

– Alternative hypothesis: AA signature did contribute to the observed mutations

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78% of Taiwan liver cancers have the AA signature

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78% of Taiwan liver cancers have the AA signature

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78% of Taiwan liver cancers have the AA signature

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How extensive is AA exposure in liver cancer? • A great deal of publicly available somatic mutation data from

liver cancers

• Examined somatic mutations from 1,400 HCCs

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43

44

Proportions of liver cancers with AA

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Numbers of mutations due to AA (log scale)

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Euro

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Asia, especially Taiwan most affected

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Non-molecular evidence thatAA exposure might be more widespread

• India / South Asia, Aristolochia indica plants used in traditional medicine (population > 1 billion)

• South / Central America, Aristolochia plants used in traditional medicine; extent unclear

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Evidence of use of AA-containing plants in South Asia

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Evidence of use of AA-containing plants in South Asia

Cultivated AA plant or AA plant

product purchased in market

AA inCentral American “snake bottle”

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Photograph by Donald HallUniversity of Florida

Aristolochia trilobata Battus polydamas

Photograph: Kimera Corporation

AA

AA containing plants readily available on the internet

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Theoretically banned/restricted but multiple plant species and parts readily available on web(广防己, guǎng fáng jǐ)

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漢防己/汉防己, hàn fáng jǐ(Stephania – no AA)

Theoretically banned/restricted but multiple plantspecies and parts readily available on web(马兜铃mǎ dōu líng)

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Asarum sp. 细辛, xì xīn

2017 07 19

http://qiherbs.com/xi-xin.html

Clinical implications of widespread AA exposure

• Primary prevention (avoiding AA)

– Regulation, education

– Possible: unlike tobacco, presumably non-addictive

– Possible: unlike aflatoxin, ingested deliberately

• Secondary prevention: focused screening for people with known or likely exposure based on

– Geography

– Use of AA-containing remedies

– Kidney failure

– Previous AA-related cancer (e.g. based on signature)

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Part 2 AcknowledgementsSingapore (Duke NUS, National Cancer Centre Singapore, and others)Song Ling PoonWillie YuMi Ni HuangAlvin NgApinya JusakulJohn McPhersonSwe Swe MyintLay Guat NgJohn SP YuenPatrick TanBen Tean TehAlex Chang

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Chang Gung Memorial HospitalJacob See-Tong PangSen-Yung HisehHao-Yi HuangMing-Chin YuYing-Hsu ChangKai-Jie YuKwai-Fong NgChing-Fang WuCheng-Lung HsuCheng-Keng Chuang

FundingSingapore National Medical Research Council; A*STAR and the Singapore Ministry of Health through the Duke-NUS Signature Research Programs; Singapore Millennium Foundation; Lee Foundation, the National Cancer Centre Research Fund; The Verdant Foundation; Cancer Science Institute Singapore

END

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Non negative matrix factorization (NMF)

Mutation signaturesMutations

contributed by

each signature

Mutations not

present in the

reconstructed

catalog

Observed somatic

mutation catalog of a

tumor genome

W X

≈N signatures

96

mu

tati

on

typ

es

96

mu

tati

on

typ

es

N s

ign

atu

resT tumors

T tumors

Level of exposure of 1 tumor to 1 signature

H

V (observed mutations)

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Important points on NMF

• NMF is only a tool – best (lowest error) approximate factorization does not necessarily correspond to any biological reality

• Models derived by NMF should be useful – provide information on exposures or mutational processes; "All models are wrong but some are useful.“1

• Signature extraction and activity (exposure) assignment are separate; can have good signature extraction but poor activity assignment, because factorization is usually underdetermined

• Must combine NMF with additional information to find useful models

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1 George E. P Box, G. E. P. (1979), "Robustness in the strategy of scientific model building", in Launer, R. L.; Wilkinson, G. N., Robustness in Statistics, Academic Press, pp. 201–236.