and cardiac imaging in healthy volunteers 2. Widespread exposure to an herbal medicine mutagen in Asian cancers

Transcription

1 1. Integrative analysis of activity, blood lipids, and cardiac imaging in healthy volunteers 2. Widespread exposure to an herbal medicine mutagen in Asian cancers Steven G Rozen Professor of Cancer & Stem Cell Biology, Duke-NUS Medical School Singapore Director, Duke-NUS Centre for Computational Biology Associate Dean of Research Informatics Part 1 from Weng Khong LIM Chief Bioinformatics Lead, PRISM (SingHealth and Duke-NUS Precision Medicine Institute) Sept 7, 2017 Weekly Precision Medicine Forum, Duke University 1

2 Singapore Small: 50 km (31 mi) east-west 26 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) (From CIA World Factbook, Mar 2017) 2

3 Duke-NUS Medical School 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 3

4 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 4

5 Part 1 Integrative analysis of activity, blood lipids, and cardiac imaging in healthy volunteers Weng Khong LIM Chief Bioinformatics Lead, PRISM (SingHealth and Duke-NUS Precision Medicine Institute)

6 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) Volunteer Consented for Research, Incidental Findings, and Long Term Follow Up Lifestyle Factors and clinical tests incl. ECG Imaging Studies (MRI, Calcium) Activity/Sleep Monitoring (Wearables) Serum Lipidomics

7 Volunteer Characteristics Characteristic Female (n=137, 58.8%) Male (n=96, 41.2%) Test Age, years (11.44) (12.63) Ethnicity 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/m (3.89) (3.98) <0.001 Waist Circumference, cm (10.14) (10.88) <0.001 SBP, mmhg (17.36) (15.64) <0.001 DBP, mmhg (12.50) (11.51) <0.001 RestingHR, (Fitbit, bpm) (6.85) (6.80) 0.07 ECG HR, bpm (9.58) (11.13) Total Cholesterol, mmol/l 5.33 (1.02) 5.26 (0.85) LDL, mmol/l 3.28 (0.84) 3.37 (0.92) 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

8 BMI, kg/m (3.89) (3.98) <0.001 Waist Circumference, cm (10.14) (10.88) <0.001 SBP, mmhg (17.36) (15.64) <0.001 Volunteer Characteristics DBP, mmhg (12.50) (11.51) <0.001 RestingHR, (Fitbit, bpm) (6.85) (6.80) 0.07 ECG HR, bpm (9.58) (11.13) Total Cholesterol, mmol/l 5.33 (1.02) 5.26 (0.85) LDL, mmol/l 3.28 (0.84) 3.37 (0.92) 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) DailySteps, (Fitbit, x1000) (4.13) (3.66) Fitbit ActivityClass 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) Physical GPPAQ Activity Score Questionnaire 1.25 (1.12) 1.84 (1.15) <0.001 Left LVM, ventricular g mass, g (14.49) (21.29) <0.001 Left LVEDV, ventricular ml end-diastolic vol. ml (16.90) (25.37) <0.001 Right RVEDV, ventricular ml end-diastolic vol. ml (19.00) (22.65) <0.001 Aortic AoF, ml forward flow, ml (9.37) (12.72) <0.001

9 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

10 Cluster MidDay PM AM Wearable activity trackers provide insights on behavioral and demographic stratification of volunteers -- 3 clusters based on daily activity patterns Low activity High activity

11 Average of Each Activity Cluster

12 Association between Age and Activity Cluster * (p < 0.05) ** (p < 0.01)

13 Relationships among Daily Steps, Age and Gender Daily steps (1,000s)

14 Electrocardiogram Resting HR (bpm) Tracker HR (bpm) Wearable activity trackers correlate with clinical heart rate measurements and selfreported activity levels Activity Tracker Resting HR (bm) Self-reported activity level

15 Association of Daily Steps with Cardiovascular and Metabolic Disease Risk Markers High Fasting Blood Glucose OR for each additional 1000 steps

16 Association of Activity Tracker Resting Heart Rate with Cardiovascular and Metabolic Disease Risk Markers High Fasting Blood Glucose OR for each additional BPM

17 Association of Cardiac Remodeling with Daily Steps Activity Left Ventricular Mass Left Ventricular End-Diastolic Volume

18 Association of Cardiac Remodeling with Daily Steps Activity Right Ventricular End-Diastolic Volume Aortic Forward Flow

19 Consistent with Previous Larger Study Association with on Cardiac Remodeling

20 Lipidomics: Sphingolipids Correlated with Daily Steps Sphingolipid DailySteps (x1000) FBG p-value β rs p-value β rs Cer(d18:1/20:0)* Cer(d18:0/20:0) Cer(d18:1/24:1(15Z)) Cer(d18:1/18:0)* Cer(d18:0/24:1(15Z)) Cer(d18:1/16:0) Cer(d18:1/22:0) SM(36:0)* Cer(d18:1/24:0) SM(36:1)* GlcCer(d18:1/16:0) SM(36:2)* Cer, ceramide; SM, sphingomyelin; GlcCer, glucosylceramide

21 Lipidomics: Sphingolipids Correlated with Daily Steps and Blood Glucose Cer, ceramide; SM, sphingomyelin; GlcCer, glucosylceramide

22 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?)

23 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 Research Institute Singapore and National 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)

24 Part 2 Widespread exposure to an herbal medicine mutagen in Asian cancers 27

25 Herbal remedy contains aristolochic acids and related compounds collectively AA Aristolochia Plants Herbal remedies Aristolochic acid I AA (multiple variants) Adenine DNA adducts, Adenine > Thymine mutations Also a nephrotoxin causes kidney failure Adapted from Poon et al, Science Translational Medicine,

26 Cost per human genome (US $) Mutation signature analysis enabled by cheap next generation sequencing of cancer genomes $10 million In ,000 X drop in price Year $1.5 thousand today 29

27 Mutational signature in an AA-exposed upper tract urothelial carcinoma (UTUC) CAG>CTG TAG>TTG CAA>CTA Transcriptional strand bias Poon et al, Science Translational Medicine, 2013 T > A on transcribed strand T > A On NON transcribed strand 30

28 A few years ago: AA exposure in upper tract urothelial cancer Upper Tract Urothelial (Taiwan) 31

29 A year ago: AA exposure in multiple tumor types Liver (China) Bile duct (Singapore) Kidney (Taiwan) Upper Tract Urothelial (Taiwan) Bladder (Taiwan) 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) 32

30 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 33

31 AA signature in Taiwan HCCs 34

32 Principal components analysis shows many Taiwan HCCs have spectra similar to AA bladder and AA UTUC from Taiwan 35

33 Overlays of mutations due to different exogenous mutagens or endogenous mutagenic processes -- Computational separation AA Bladder spectrum from Poon et al, Genome Medicine,

34 Overlays of mutations due to different exogenous mutagens or endogenous mutagenic processes -- Computational separation AA Non-negative matrix factorization and related approaches + ABOBEC signatures + background signature Bladder spectrum from Poon et al, Genome Medicine,

35 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 38

36 78% of Taiwan liver cancers have the AA signature 39

37 78% of Taiwan liver cancers have the AA signature 40

38 78% of Taiwan liver cancers have the AA signature 41

39 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 42

40 43

41 44

42 Proportions of liver cancers with AA 45

43 Numbers of mutations due to AA (log scale) Taiwan China SE Asia Vietnam Korea Japan North America Europe Mayo Clinic No information 46

44 Asia, especially Taiwan most affected 47

45 Non-molecular evidence that AA 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 48

46 Evidence of use of AA-containing plants in South Asia 49

47 Evidence of use of AA-containing plants in South Asia Cultivated AA plant or AA plant product purchased in market 50

48 AA in Central American snake bottle Aristolochia trilobata Battus polydamas Photograph: Kimera Corporation Photograph by Donald Hall University of Florida AA 51

49 AA containing plants readily available on the internet 52

50 Theoretically banned/restricted but multiple plant species and parts readily available on web ( 广防己, guǎng fáng jǐ) 53

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

52 Theoretically banned/restricted but multiple plant species and parts readily available on web ( 马兜铃 mǎ dōu líng) 55

53 Asarum sp. 细辛, xì xīn

54 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) 57

55 Part 2 Acknowledgements Chang Gung Memorial Hospital Jacob See-Tong Pang Sen-Yung Hiseh Hao-Yi Huang Ming-Chin Yu Ying-Hsu Chang Kai-Jie Yu Kwai-Fong Ng Ching-Fang Wu Cheng-Lung Hsu Cheng-Keng Chuang Singapore (Duke NUS, National Cancer Centre Singapore, and others) Song Ling Poon Willie Yu Mi Ni Huang Alvin Ng Apinya Jusakul John McPherson Swe Swe Myint Lay Guat Ng John SP Yuen Patrick Tan Ben Tean Teh Alex Chang Funding Singapore 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 58

56 END 59

57 96 mutation types 96 mutation types N signatures Non negative matrix factorization (NMF) Mutation signatures Mutations contributed by each signature Mutations not present in the reconstructed catalog Observed somatic mutation catalog of a tumor genome T tumors Level of exposure of 1 tumor to 1 signature W X H T tumors N signatures V (observed mutations) 60

58 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 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