From Bits to Bedside: Translating Big Data into Precision Medicine and Digital Health

FromBitstoBedside

TranslatingBigDataintoPrecisionMedicineandDigitalHealth

DexterHadley,MD/PhDAssistant ProfessorofPediatricsInstitute ofComputational Health [email protected]

PrecisionMedicine

“Tonight,I'mlaunchinganewPrecisionMedicineInitiativetobringusclosertocuringdiseaseslikecanceranddiabetes— andtogiveallofusaccesstothepersonalizedinformationweneedtokeepourselvesandourfamilieshealthier.”

— PresidentBarackObama,StateoftheUnion,January20,2015

Disease

Defective pathway

Targeted Intervention

Diagnostic

Test&Treatparadigmofpersonalizedmedicine

Multipledefectivepathwayscanmanifestsimilarly incomplexdisease

3

Treatonlydefectivepathways

Disease

Defective pathway


TemperatureDiagnostic


4

Fever

IL1/IL6/TNF/IFN/PGE2

acetaminophen

4


Disease

Defective pathway


TemperatureDiagnostic


5

Fever

IL1/IL6/TNF/IFN/PGE2

acetaminophen

5


To date, the mechanism of action of paracetamol is not completely understood!

Breast Cancer

HER2

trastuzumab

Disease

Defective pathway


MolecularDiagnostic



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Disease

Defective pathway


Diagnostic



OurgoalistoapplythesameT&Tparadigmacrossthediseasespectrum

7

Overview:Translatingbigdataintobiomedicalinnovation

• Autism&ADHD(PrivateData)– Functionaldiseasetargets

àDefectivegeneticnetworksàPersonalizedtherapeutics

• SevereDengue(PublicData)– Functionaldiseasesignatures

àPrognosticbiomarkersàPrioritizedtherapeutics

• FutureDirections(DigitalHealth)– OpenBigDataintegrationwithclosedhealthsystems

à Bettercharacterizationsofdiseaseà Rapidproofsofconceptandclinicaltrials

Autism&ADHD

Translatingdefectivegeneticnetworks intopersonalized

therapeutics

CAG houses the world’s largest pediatric biobank

Ø > 1M patient visits / year to CHOP

Ø Initial 5-year goal to establish biobank with an emphasis on genomic discovery

Ø Future 5-year vision is to translate discoveriesintotangiblepatientbenefit

10

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Datasets (Genomics EMR)§ Over 75K pediatric and 150K

related adult patients GWAS genotyped with associated longitudinal EMR since 2006

Data Analytics§ End to end internal Next-

Gen sequencing capabilities

§ Integrated bioinformatics§ Rapid identification of

novel genetic biomarkers

Biobank (BB)§ Fully automated robotic

biorepository

Consented Patients • 85% of the BB

patients are consented for longitudinal follow up and are eligible for call back for future studies

§ ~1.2M patient visits/year§ 10% of all R/O disease patients in

N. America are treated at CHOP

CAG’s pediatric biobank contains a high percentage of rare genetic variants

§ Population is unique in that it represents the most severe forms of common diseases

§ Global reach in many therapy areas

In the last 8 years CAG has had over 400 peer reviewed publications focused on novel genetic discoveries

Highly scalable infrastructure to support translational research

ThePediatric Biobank atTheCenter forAppliedGenomics (@CHOP)

Personalizeddrugdiscoverypipeline

CAGbiobank

Geneticscreen

Riskfactors

Defectivepathways

Targetedtherapies

POCclinicaltrials

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Copynumbervariationisamechanismoffunctional geneticvariation

Pointmutation

Micro-duplication

Micro-deletion

SingleNucleotideVariant

CopyNumberVariation

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CNVanalysisworkflow

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Samplepreparation

Illuminagenotyping

PennCNVanalysis

PennCNV todiscoverandcallCNVs

15WangK.,LiM,HadleyD,et.al.GenomeRes.2007;17:1665-1674

GeneticanalysisofADHD

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13K+samples genotyped forCNVs3.5K+cases9K+controls

MostsignificantCNVRsinADHDhighlightmGluR/GRM

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Elia J,Glessner J,WangK,TakahashiN,Shtir,C,HadleyD,etal,NatureGenetics, 2011

Cluster 1 74 genes Cluster 3

25 genes

Cluster 4 17 genes

Cluster 5 25 genes

Cluster 7 11 genes

Cluster 10 20 genes

Cluster 11 9 genes

Cluster 13 9 genes

Cluster 15 8 genes

mGluR networkhighlysignificantinADHD

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Elia J,Glessner J,WangK,TakahashiN,Shtir,C,HadleyD,etal,NatureGenetics, 2011

P≤4.38x10-10Enrichment =3x

Geneticanalysisofautismnetworks

19K+samples genotyped forCNVs6.5K+cases12.5K+controls

PopulationstructureofSNPsusedtoassigncontinentalancestry

• Machine learnedannotationofethnicity fromHAPMAPandHGDP

Ancestry Case Control TotalEurope 4,602 4,722 9,324Africa 312 4,169 4,481America 485 276 761Asia 201 350 551Other 27 127 154Grandtotal 5,627 9,644 15,271

ComponentGRMs donotdefinemGluR networksignificance inASDs

CNV gene bands Size(Kb) #SNP #Case #Ctrl P OR

MostsignificantCNVRswithingenesacrossthemGluR networkdup CACNA1B 9q34.3 6.98 2 11 0 4.21E-04 infdup ECHS1 10q26.3 8.89 2 10 0 8.54E-04 infdel PSMD1 2q37.1 10.51 1 14 2 1.77E-03 7.2dup RANBP1 22q11.21 9.62 1 13 3 9.24E-03 4.46dup TUBA3C 13q12.11 4.85 4 17 8 4.70E-02 2.18dup TRAF2 9q34.3 44.67 3 6 1 5.83E-02 6.16del RYR2 1q43 2.01 1 4 0 5.93E-02 infdel TJP1 15q13.1 365.84 62 4 0 5.93E-02 infdup HOMER3 19p13.11 3.76 2 9 3 6.68E-02 3.08dup CNR1 6q15 2.98 1 15 8 9.39E-02 1.93

MostsignificantCNVRswithinGRMhubsofmGluR networkdel GRM1 6q24.3 18.44 3 2 0 2.44E-01 infdel GRM3 7q21.12 44.85 9 1 0 4.94E-01 infdel GRM4 6p21.31 86.47 26 0 1 1.00E+00 0del GRM5 11q14.3 73.18 7 4 0 5.96E-02 infdup GRM6 5q35.3 234.49 51 0 2 5.00E-01 0del GRM7 3p26.1 28.26 11 2 0 2.44E-01 infdel GRM8 7q31.33 52.53 11 1 0 4.94E-01 inf21

mGluR networkalsosignificantinASD

22P<=2.40E-09Enrichment=1.8xHadleyD,etal,NatureCommunications, 2014

therapeutics

NFC-1asaleadtargetedtherapeuticcandidateforADHD&ASD

• Smallmoleculetargetingmetabotropicglutamatereceptors(mGluRs)

• BroadactivityforallthreeclassesofmGluRs invitro

• Anti-amnesiaandanti-depressantactivityinanimalmodels

• PreviousPhaseIIItrialexperience:failedforspecificindicationbutshowntobesafeandhaveeffectsonpsychiatricsymptoms

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• Officialname:Fasoracetam,NS-105,LAM-105

• IUPACname: (5R)-5-(piperidine-1-carbonyl)pyrrolidin-2-one

• Chemical formula:C10H16N2O2• Molecular weight:196.25• Originally developedbyNipponShinyaku,

Ltd.• Materials patenthassinceexpired

NFC-1clinicaltrialdesignforADHDWeek 1Day 7±2

Week 2Day 14±2

Week 3Day 21±2

Week 4Day 28±2

Week 5Day 35±2

Week 9Day 75±2

Adverse event monitoring X X X X X phoneLaboratory Safety Tests (blood and urine)A X X X X XPhysical Examination X X X X XVital Signs: BP, HR, RR X X X X XBody Weight (all points) & Height (week 1 only) X X X X X12-lead ECG X X X X XUrine b-hCG test (menstruating females only) X X X X XContraception verification (selected females) X X X X XVanderbilt Parent Rating Scale X X X X XBREIF (Parent; Self) X X X X XQuotientâADHD test X X X X XPERMP-Math test X X X X XActigraphy (continuous monitoring) X X X X XCGI-S & CGI-I X X X X XDispense study drugB X X X XNFC-1 or placebo administration at homeC Placebo bid 50 mg bid 100 mg bid 200 mg bid 400 mg bidRetrieve pill bottle/pill count X X X X X

A. Blood draws for hematology (RBC, WBC with differential, platelet count) and clinical chemistry (electrolytes, albumin, ALT, AST, alkaline phosphatase, bilirubin, BUN, creatinine, glucose,

B. Study drug for Week 1 administered at end of PK study; study drug for next week dispensed at each clinic visitC. Dose escalations to be determined by CGI-S and CGI-I scores at end of each week of treatment; maximum doses indicated

30mGluR+ADHDchildrenhavecompleted5weeksondrug(FPI01/23/15)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1 2 3 4 5

CGI-I: Proportion of Responders at Each Weekfor All Subjects

Week Week Week Week Week

CGI-I, Clinical Global Impression of Symptom Improvement

Responder – Global rating of much or very much improved

NFC-1ADHDStudyResults– ClinicianRatingScale

P<0.001

0%

10%

20%

30%

40%

50%

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Week 1 Week 2 Week 3 Week 4 Week 5

Vanderbilt Scores: Proportion of Patients Improved from Pre-study baselinefor All Patients

Improvement defined as 25% improvement in hyperactivity/inattention domains

NFC-1ADHDStudyResults– ParentRatingScale

P<0.001

Current clinical trials are expensive and inefficient

27No Response Response

Non-targeted efficacy(generalized population):

20 / 100 = 20%$$$$$$$$

Big data to disrupt clinical trials by minimizing cost with maximal efficacy

28No-pathway defect Targeted pathway defect Response

Non-targeted efficacy(generalized population):

20 / 100 = 20%$$$$$$$$

Targeted efficacy(personalized population):

20 / 25 = 80%$$

Genomics

Definingthemolecularsynaptopathologyspaceforneuropsychiatricdisease

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0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.0

0.00.20.40.60.81.0

GRM

NRXN

GABAR

Towardsbettercharacterizationofneuropsychiatricdisease

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0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.0

0.00.20.40.60.81.0

GRM

NRXN

GABAR

SCZASD

ADHD

BP

ASD






• FutureDirections– OpenBigDataintegrationwithclosedhealthsystems


SevereDengue

Translatingdiseasesignatures intoprognosticbiomarkers andnoveltherapeutics

Dengueis“themostimportantmosquito-borneviraldiseaseintheworld”– WHO

• Dengue viruscauses aflu-like illness thatcanprogress tofatalsevere dengue

• Epidemic breakouts arealeading cause ofpediatric deathsamongdeveloping AsianandLatinAmerican countries!

• Noprognostic assaysordrugsareavailable… treatment islargelysupportive anddirected atsymptoms

• Neglected tropicaldisease

• Spreading totheUSmainland!

Aedes aegypti Aedes albopictus

3.6Bpeopleatrisk

390Mestimatedinfections

96Mmanifestclinically

2Mcasesprogresstoseveredengue

21Kfatalities!

3.6Bpeopleatrisk




21Kfatalities!



3.6Bpeopleatrisk




21Kfatalities!



Wewanttopredictthe2%ofpeoplethatwillgetsick!

Dengueclinicalcourse

NatRevMicrobiol.2010.GuzmanMGetal

Ourgoalistopredictprognosisintheacutephaseofthedisease

CurrentWHOrecommendations

WHO2009guidelines

WHO Sensitivity Specificity

1997 95.4%(9-.9-98.2) 36.0%(29.4-43.1)

2009 79.9%(72.7-85.9) 57.0%(49.8-64.0)

Largedifferentialdiagnosisofundifferentiatedfebrileillness

• Influenza

• Measles

• Rubella

• Malaria

• Typhus

• Leptospirosis

• Rickettsial Infections

• Chikungunya

• Sindbis

• Mayaro

• RossRiver

• WestNile

• O’nyongnyong

FieldsVirology,5th Edition

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Stanforddenguemoleculardiagnostic

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Δmax

StanfordminedBigDatafromGenBank fordenguevirusgenome

Stanforddenguemoleculardiagnostic

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DiagnosticMicrobiology andInfectiousDisease2015,Volume81,Issue2,Pages 105–106

Dengueclinicalcourse

NatRevMicrobiol.2010.GuzmanMGetal

Ourgoalistopredictprognosisintheacutephaseofthedisease

GeneExpressionOmnibushasopendataon1M+‘digitalsamples’

Nucleic AcidsRes.2013Jan;41(Databaseissue):D991–D995.

GEOdatahasderived32Kdifferent publicationscurrently inPubMed !

GEOishighestqualityNIHR01fundeddatathathasgenerated32K+publications

OpenDengueSampleInventory

Study CountryUncomplicated Severe Grand

TotalTotal Total DHF DSS

GSE13052 Vietnam 9 9 9 18GSE17924 Cambodia 16 32 13 19 48GSE18090 Brazil 8 10 10 18GSE25001 Vietnam 89 37 37 126GSE25226 Nicaragua 20 14 6 8 34GSE38246 Nicaragua 50 45 26 19 95GSE40628 Vietnam 6 7 6 1 13GSE43777 Venezuela 154 43 43 197GrandTotal 352 197 104 93 549

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Filtered foracutesamples within7daysofillness

Personalizedbiomarker/drugdiscoverypipeline

NCBIGEO

Meta-Analysis

Diseasesignatures Biomarkers Targeted

therapies

POCclinicaltrials

46

Robustdiseasesignatureforseveredenguemeta SAM

myGeneSym dir TE p k ABH pop |Fcmax| pGSE ΣGSECOLCA1 up 1.31 1.02E-09 3 1.10E-05 5 1.23 0.50 2CEACAM8 up 0.98 3.36E-05 8 1.55E-02 9 2.18 0.57 7LTF up 0.84 2.79E-04 8 4.98E-02 12 2.01 0.57 7ELANE up 0.81 1.63E-04 8 3.83E-02 13 1.15 0.57 7HTATSF1P2 down -0.72 1.72E-05 3 1.05E-02 16 0.67 0.50 2LINC00668 up 0.65 3.64E-06 3 4.25E-03 19 0.68 0.50 2FOXO3B down -0.65 1.61E-05 3 1.05E-02 20 0.64 0.50 2CTSG up 0.61 6.31E-05 10 2.38E-02 22 1.21 0.50 8ADAM1A down -0.61 1.71E-04 3 3.90E-02 22 0.66 0.50 2LOC100505711 up 0.60 1.29E-04 3 3.46E-02 23 0.52 0.50 2PCOLCE-AS1 up 0.58 4.88E-05 3 1.97E-02 24 0.58 0.50 2TEX41 up 0.58 5.82E-07 3 1.29E-03 24 0.59 0.50 2LINC01134 up 0.54 1.51E-05 3 1.02E-02 28 0.54 0.50 2LOC729173 up 0.53 9.76E-06 3 7.46E-03 29 0.49 0.50 2LINC00959 down -0.52 2.17E-06 3 3.43E-03 30 0.51 0.50 2ARG1 up 0.52 4.13E-06 9 4.58E-03 30 1.37 0.50 8FCRL6 down -0.41 2.54E-08 5 1.88E-04 50 0.92 0.75 4

47

LTF

48

ELANE

49

LTF,ELANEandotherneutrophilbiomarkersvalidatedlongago…yetnoprognostic!

TNF-alphafromtop10genes

Clyde K et al. J. Virol. 2006;80:11418-11431

Pathogenesis of dengue virus infection

80%AUCbySVMcrossvalidation(nofeatureselectionortuningyet)

Robustdiseasesignaturesà prioritizeddrugcandidates

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LibraryofIntegratedNetwork-basedCellularSignatures

Clinicaldevelopmentplanfordengue

• Ongoingvalidationofnovelprognosticbiomarkersfordevelopment

• Developmentofmultiplexed,scalablehumanprognostictestinTrinidadandCuba

• Validationofcandidatedrugtargetsinguineapigandotheranimalmodels






• FutureDirections(DigitalHealth)– OpenBigDataintegrationwithclosedhealthsystems


Futuredirections

Massively collaborativebiggerdataanalysis tofuelclinicalinnovation

anddisruptmedicine

Manybigdatastoresintranslationalbioinformatics tostudy

Basicresearch

Targetidentification

Drugdiscovery

Clinical trial

BigDataProbleminBiomedicine• Biomedicalbigdataiscomplex,

oftenpoorlyannotated,andnotstructuredforbigdataanalytics

• Textminingandotherstrictlycomputationalapproachestostructurethedataarenotpreciseenoughtobeclinicalgrade

• Onesolution:OpenandeasilyaccessibleonlinetoolstotointerpretBigDatatowardstranslationalopportunities !

GEOhasfreetextattributeswithnostructuredbio-ontology

GSE Caseannotation ControlannotationGSE13052 DSS uncomplicateddengueGSE17924 DSS|DHF DFGSE18090 DHF DFGSE25001 dengueshocksyndrome uncomplicateddengue

GSE25226 dengueshocksyndrome|denguehemorrhagicfever denguefever

GSE38246 DSS|DHF DFGSE40628 WHOstage3|WHOstage4 WHOstage1|WHOstage2GSE43777 DHF DF

Wemadeanappforthat:STARGEO.org

Tag samplesGene Expression Profiling During Early Acute Febrile Stage of Dengue Infection Can Predict The Disease Outcome: GSE18090Background: We report the detailed development of biomarkers to predict the clinical outcome under dengue infection. Transcriptional signatures from... More.

Tag: (Dengue hemorrhagic fever)DHF

All (26) DHF(10) Unmatched (16)

Tag Value Sample_acc sample_characteristics sample_title sample_source_name

GSM452242gender: female| |age: 23| |days of symptoms: 7| |igm: Pos| |igg: Neg| |pcr/virus isolation: Pos

DF Patient 8 PBMCs from DF patient

GSM452243gender: male| |age: 41| |days of symptoms: 3| |igm: Neg| |igg: Pos| |pcr/virus isolation: Pos

DHF Patient 1 PBMCs from DHF patient



DHF

DHF

Column Regex Saveall ▼ DHF

DHF

DHF

DHF

DHF

NCIBD2KFunded(PI:Hadley)

Wemadeanappforthat:STARGEO.org

Tag samplesGene Expression Profiling During Early Acute Febrile Stage of Dengue Infection Can Predict The Disease Outcome: GSE18090Background: We report the detailed development of biomarkers to predict the clinical outcome under dengue infection. Transcriptional signatures from... More.

Tag: (Dengue hemorrhagic fever)DHF

All (26) DHF(10) Unmatched (16)

Tag Value Sample_acc sample_characteristics sample_title sample_source_name

GSM452242gender: female| |age: 23| |days of symptoms: 7| |igm: Pos| |igg: Neg| |pcr/virus isolation: Pos

DF Patient 8 PBMCs from DF patient





DHF

DHF

Column Regex Saveall ▼ DHF

DHF

DHF

DHF

DHF

Search free text attributes of human microarray expression

11,903 Series à465,770 Samples

Tag samples across multiple studies to annotate features278 Tags à5,798 Series annotations à490,110 Sample annotations

Analyze genomic signatures by meta-analysis

1,682 microarray platforms à28,254,323 gene probes

NCIBD2KFunded(PI:Hadley)

TheSTARGEO applicationmakesiteasytorunanalyses,givenannotations

analysis_name:Severedengue

case_query:DSSorDHF

control_query:DF

description:Severedengue

Hadleyetal.,inreview

Usingsocialnetworksforrecruitmentinbiomedicine

12/1/14 3/1/16

500,110sample annotations

$10K

≈6w

360K

anno

tatio

ns

Rapidandpreciseannotationofopensamplesovervariedphenotypes

TCGA

STA

RG

EO

R2 =0.77;p<=0.001

108 10892

STARGEO TCGA

Topunder-expressedgenes

102 10298

STARGEO TCGA

Topover-expressedgenes

FunctionalGenomicValidationofAnnotationsvsTCGA

Functionalnosologytomolecularlycharacterizedisease

>10Kdigital samples annotated togenerate thistree

Functionalnosologytomolecularlycharacterizedisease

• Cancerisfunctionallydistinct(>5Ksamples)

• PACandHCCclustertogether

• Infectionsdistributedthroughout

• ConvergestoanclinicallyusefulICD

>10Kdigital samples annotated togenerate thistree

Medulloblastoma subtyping

CourtesyJamesPan,MDCandidate(neurosurgery)

Medulloblastoma subtyping

CourtesyJamesPan,MDCandidate(neurosurgery)

Asthmaendotyping

Asthmaendotyping

Novelasthmaendotypes?

CollaborationwithEstéban GonzélezBurchard,MDMPH





Onemorething…

Onemorething…

DigitalHealth

Digital health is the convergence of the digital and genomic revolutions with health, healthcare, living, and society. Digital health is empowering people to better track, manage, and improve their own and their family's health, live better, more productive lives, and improve society.

EmergingtechnologiestogenerateBiggerDataforDigitalHealth

Realtimesensors!

Emergingtechnologiesformassivebiomedicaloutreachandrecruitment

• OpensourceAPI– Platformindependent

• Informedconsent– Instantenrollment

• HIPPAgradesecurity– Hardwareencryption

• Livesurveysandfeedback– Instantsharing

Apple’sResearchKit

• Earlyaccuratediagnosisimprovesmelanomaoutcomes– Deadliest canceramong youngadultswithincreasing incidence– 5thmostcommon typeofcancer inAmerica

• 73,000newcasesestimatedthisyear• 9,000deathsareexpectedtooccur

– >97%survivablewithearlydetection

• Overdiagnosis isaproblem– Currentclinicalmethods subjective

• Poorspecificity(<60%)– Imprecise histopathology standard

• Poorprecisionamongpathologists (<30%)• 36biopsies foreveryonemelanomaconfirmed

• Poordiagnosticprecisionaddsanestimated$673millioninoverallcosttomanagethedisease

Melanomadiagnosislacksprecision…

Ourgoalistodevelopanobjectiveclinical-gradediagnostic!

1B+selfiestakenlastyear!• 93M+takendailyin2014

– OnAndroidalone!

• 25K+perlifetimeofayoungadults(18-35)– 30%ofphotostakenbyyoungadults

• Australia>US>Canada– 2/3Australianwomentakeselfies

• Morepeoplehavediedbytakingselfies(12)in2015thanbysharkattacks(8)!

AIcontinuestooutperformhumans

AIcontinuestooutperformhumans

DeeplearningonbigdatamakesitthatAIpossible

• DeeplearningisAIbasedonneuralnetworksdevelopedsincethe1980s

• EmergenttodaybecauseofthereadyavailabilityofmodernGPUcomputation

• ComplexmodelsrequireBigDatatotrainon(pixels,text,speech)forprecision

Deeplearningonbigdatamakesobjectiveclassificationpossible

• In2012DLsignificantlyoutperformedtraditionalalgorithms– >1MlabeledimagesfromImageNet

– <20%errorrates

• In2015,DLsignificantlyoutperformed humansatimageclassification– <5%errorrates

Currentmelanoma(mis)diagnosis• Dermatologists

– Problem:Havehighsensitivity, butlowspecificity

– Solution: Aggressive excision ofskin lesions

• GeneralPractitioners– Problem: Lackdermatology

expertise– Solution:Quickreferralto

dermatologist

• Residents– Problem: Variable performance in

predicting melanoma– Solution: Dermatology consult

ThecommonmethodforidentifyingconcerningmolesisusingtheABCDErule,whichfocusesonAsymmetry,Border,Color,Diameter,andEvolutionofaskinlesion.

TheUglyDucklingmethodisanewerclassificationmodelthatlooksatthesurrounding molepatterntofindtheoutliersthatmightbecancerous.

StandardMoleCheckup

WearebuildingaDLmobileapptoscreenformelanoma

UCSFInauguralMarcusAwardforPrecisionMedicine(PIs:Judson,Wei,&Hadley)

SkinDeep surveillanceforMelanoma• Aims

– Digitalscreeningandmolecularconfirmationofskincancerwithcompleteprecisionandaccuracy

– Real-timedatacollectionplatformformultimodalsurveillanceandanalyticsofskinlesionevolution

• Approach– Physicianprescribessmartphone appfor

patienttofollowtheirsuspicions moles– Appanalyzesthepatient-capturedimageusing

aDLscreeningalgorithmandalertsphysicianofresults

– Physiciancanelectforournon-invasivemoleculardiagnosticforconfirmation

• Innovation– Multimodaldeeplearning(DL)andpredictive

algorithms– Augmented realitycaptureandimageanalysis

inamobileapplication– Non-invasivemolecularprofilingoftumor

UCSFInauguralMarcusAwardforPrecisionMedicine(PIs:Judd,Wei,&Hadley)

SkinDeep PrecisionDiagnostics• Selflearningexpertsystemfor

precisemelanomadiagnosis– 83%accuracywith<200images

scrapedfromGoogle– Convergestocomplete precision

withuse

• Serialdigitalimagingcurrentlyoutperformsageneralpractitioner– Expected >90%accuracywith

enough trainingdata

• Serialmolecularprofilingforcompleteaccuracy– Multimodal DLalgorithms

• FirstsuccessledtomelanomaexcisioninUCSBCSprof!

CourtesyAbhishekBhattacharya,UCSBUndergraduate(CS/Bio,honors)

Personalized translational discoverypipeline

SkinDeepcapture

Pixelfeatures

DeepLearningAnalysis

Candidatemelanomafeatures

SkinDeepprediction

POCclinicaltrials

91

Summary• Wecanuseprotected BigData(hospital-based) tomolecularly dissect disease and

personalize noveldrugsandbiomarker discovery– Ex:ADHD&Autism

• Butwealreadyhavealotofopenbiomedical BigDatathatcanbeusedtobettercharacterize disease anddiscover noveldrugsandbiomarkers ifstructuredproperly

– Ex:SevereDengue

• Web-based tools areemerging toempower physician scientists tostructure opendataandformulate genomics hypotheses aboutdisease

– Ex:STARGEO nosology

• Emergingmobile technologies will facilitate biggerdatacollections andmassiverecruitment facilitate digital health

– SkinIQ melanomasurveillance

WecantranslatetheBigDataintoBiomedicalInnovation toDISRUPTMEDICINE!

MobileisExploding

Thefutureofmedicinemustinvolvemobileanddigitalhealth

IntegratingpublicandprivatedatabaseswithdigitalhealthwilldriveaBigDatatranslationalrevolution!

Basicresearch

Targetidentification

Therapeuticdiscovery

Clinical trial

Acknowledgements

Acknowledgements

STARGEO Acknowledgements

SkinDeep Acknowledgements

• AbhishekBhattacharya– UCSBundergraduateCS/Biohonors

• MariaWei,MD,PhD– Director,UCSFMelanomaClinic

• SimoneStalling,MD,PhD– PrivatePracticeDermatology

“Dataispower,dataisrevolution,dataisfrozenknowledge”-- Atul Butte,MD,PhD

DexterHadley,MD/[email protected]

Health & Medicine

From Bits to Bedside: Translating Big Data into Precision Medicine and Digital Health