From Bits to Bedside: Translating Big Data into Precision Medicine and Digital Health
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Transcript of 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
Targeted Intervention
TemperatureDiagnostic
Test&Treatparadigmofpersonalizedmedicine
4
Fever
IL1/IL6/TNF/IFN/PGE2
acetaminophen
4
Treatonlydefectivepathways
Disease
Defective pathway
Targeted Intervention
TemperatureDiagnostic
Test&Treatparadigmofpersonalizedmedicine
5
Fever
IL1/IL6/TNF/IFN/PGE2
acetaminophen
5
Treatonlydefectivepathways
To date, the mechanism of action of paracetamol is not completely understood!
Breast Cancer
HER2
trastuzumab
Disease
Defective pathway
Targeted Intervention
MolecularDiagnostic
Treatonlydefectivepathways
Test&Treatparadigmofpersonalizedmedicine
6
Disease
Defective pathway
Targeted Intervention
Diagnostic
Treatonlydefectivepathways
Test&Treatparadigmofpersonalizedmedicine
OurgoalistoapplythesameT&Tparadigmacrossthediseasespectrum
7
Overview:Translatingbigdataintobiomedicalinnovation
• Autism&ADHD(PrivateData)– Functionaldiseasetargets
àDefectivegeneticnetworksàPersonalizedtherapeutics
• SevereDengue(PublicData)– Functionaldiseasesignatures
àPrognosticbiomarkersàPrioritizedtherapeutics
• FutureDirections(DigitalHealth)– OpenBigDataintegrationwithclosedhealthsystems
à Bettercharacterizationsofdiseaseà Rapidproofsofconceptandclinicaltrials
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
11
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
12
Copynumbervariationisamechanismoffunctional geneticvariation
Pointmutation
Micro-duplication
Micro-deletion
SingleNucleotideVariant
CopyNumberVariation
13
MostsignificantCNVRsinADHDhighlightmGluR/GRM
17
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
18
Elia J,Glessner J,WangK,TakahashiN,Shtir,C,HadleyD,etal,NatureGenetics, 2011
P≤4.38x10-10Enrichment =3x
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
23
• 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%
60%
70%
80%
90%
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
29
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
30
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
Overview:Translatingbigdataintobiomedicalinnovation
• Autism&ADHD(PrivateData)– Functionaldiseasetargets
àDefectivegeneticnetworksàPersonalizedtherapeutics
• SevereDengue(PublicData)– Functionaldiseasesignatures
àPrognosticbiomarkersàPrioritizedtherapeutics
• FutureDirections– OpenBigDataintegrationwithclosedhealthsystems
à Bettercharacterizationsofdiseaseà Rapidproofsofconceptandclinicaltrials
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
390Mestimatedinfections
96Mmanifestclinically
2Mcasesprogresstoseveredengue
21Kfatalities!
Dengueis“themostimportantmosquito-borneviraldiseaseintheworld”– WHO
Aedes aegypti Aedes albopictus
3.6Bpeopleatrisk
390Mestimatedinfections
96Mmanifestclinically
2Mcasesprogresstoseveredengue
21Kfatalities!
Dengueis“themostimportantmosquito-borneviraldiseaseintheworld”– WHO
Aedes aegypti Aedes albopictus
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
?
Stanforddenguemoleculardiagnostic
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DENV-1 DENV-2 DENV-3 DENV-4
Δmax
Stanforddenguemoleculardiagnostic
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Δmax
DiagnosticMicrobiology andInfectiousDisease2015,Volume81,Issue2,Pages 105–106
Dengueclinicalcourse
NatRevMicrobiol.2010.GuzmanMGetal
Ourgoalistopredictprognosisintheacutephaseofthedisease
GeneExpressionOmnibushasopendataon1M+‘digitalsamples’
Nucleic AcidsRes.2013Jan;41(Databaseissue):D991–D995.
GEOdatahasderived32Kdifferent publicationscurrently inPubMed !
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
45
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
Robustdiseasesignaturesà prioritizeddrugcandidates
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LibraryofIntegratedNetwork-basedCellularSignatures
Clinicaldevelopmentplanfordengue
• Ongoingvalidationofnovelprognosticbiomarkersfordevelopment
• Developmentofmultiplexed,scalablehumanprognostictestinTrinidadandCuba
• Validationofcandidatedrugtargetsinguineapigandotheranimalmodels
Overview:Translatingbigdataintobiomedicalinnovation
• Autism&ADHD(PrivateData)– Functionaldiseasetargets
àDefectivegeneticnetworksàPersonalizedtherapeutics
• SevereDengue(PublicData)– Functionaldiseasesignatures
àPrognosticbiomarkersàPrioritizedtherapeutics
• FutureDirections(DigitalHealth)– OpenBigDataintegrationwithclosedhealthsystems
à Bettercharacterizationsofdiseaseà Rapidproofsofconceptandclinicaltrials
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
GSM452244gender: male| |age: 41| |days of symptoms: 3| |igm: Neg| |igg: Pos| |pcr/virus isolation: Pos
DHF Patient 2 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
GSM452243gender: male| |age: 41| |days of symptoms: 3| |igm: Neg| |igg: Pos| |pcr/virus isolation: Pos
DHF Patient 1 PBMCs from DHF patient
GSM452244gender: male| |age: 41| |days of symptoms: 3| |igm: Neg| |igg: Pos| |pcr/virus isolation: Pos
DHF Patient 2 PBMCs from DHF 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
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
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.
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)!
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!
IntegratingpublicandprivatedatabaseswithdigitalhealthwilldriveaBigDatatranslationalrevolution!
Basicresearch
Targetidentification
Therapeuticdiscovery
Clinical trial
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]