人工智能驅(qū)動(dòng)的液體活檢技術(shù)在癌癥全程管理中的應(yīng)用

HushanYang,PhD

SidneyKimmelCancerCenter

ThomasJeffersonUniversity

Philadelphia,PAGannanMedicalUniversity

Oct16,2018人工智能驅(qū)動(dòng)的液體活檢技術(shù)

在癌癥全程管理中的應(yīng)用SiravegnaG,etal.NatRevClinOncol,2017LiquidbiopsySpatialandtemporaltumorheterogeneityMinimallyinvasiveprocedureObtainedrepeatedlyIdentify/tracktumor-specificaberrationsEvaluatetreatmentresponse&predictdrugresistancePredictrecurrence/diseaseprogression/vitalstatusChenL,etal.Theranostics,2017MechanismsofCTCgenerationCTCenrichmentmethodologiesChenL,etal.Theranostics,2017CTCidentification:EpCAM+CK+DAPI+CD45-CellSearchsystemCTCsexistasintactcells,clusters,orapoptoticcellsCTCFirstdescribedin1869byThomasAshworth(MedJAustralia,1869)≥5CTC/7.5mLbloodatbaselineandatthefirstfollow-upvisitpredictedpoorprogression-freesurvivalandoverallsurvivalinpatientswithmetastaticbreastcancer(CristofanilliM.,etal.NEnglJMed,2004)CTCclustershave23-to50-foldincreasedmetastaticpotential(AcetoN.,etal.Cell,2014)CTCenrichment(CellSearch)DEPArraySingleCTCisolationLiY,etal.SeminCellDevBiol,2017CTCandCTC-clustersimagesfromtheCellSearchanalyzerb

N-S-001-87c

M-V-001-129

FilamentousCK

DiffuseCK

SpeckledCKadSingleCTCsCTC-clusters

DAPI-CK-PE

CK-PEDAPI

CD45-APC

DAPI-CK-PE

CK-PEDAPI

CD45-APC

DAPI-CK-PE

CK-PEDAPI

CD45-APC

DAPI-CK-PE

CK-PEDAPI

CD45-APCCTCsandCTC-clustersinpredictingbreastcanceroutcomesMuZ,etal.BreastCancerResTreat,2015Progression-freesurvivalChangesofCTCsfrombaselinetofirstfollow-upvisitChangesofCTC-clustersfrombaselinetofirstfollow-upvisit0.000.250.500.751.0001224364860720CTCattwotimepointsor≥50%reductionatfollow-up,32/38ProbabilityofProgression-freeSurvival(%)WeeksPlog-rank=0.066AOtherchanges,36/390.000.250.500.751.0001224364860720CTC-clusterattwotimepointsoranyreductionatfollow-up,53/62ProbabilityofProgression-freeSurvival(%)WeeksPlog-rank=0.044BOtherchanges,15/15Overallsurvival0.000.250.500.751.00012243648607284CProbabilityofOverallSurvival(%)WeeksPlog-rank=0.0780CTCattwotimepointsor≥50%reductionatfollow-up,7/38Otherchanges,11/390.000.250.500.751.00012243648607284ProbabilityofOverallSurvival(%)WeeksPlog-rank=0.019D0CTC-clusterattwotimepointsoranyreductionatfollow-up,12/62Otherchanges,6/15WangC,etal.BreastCancerResTreat,2017WangC,etal.BreastCancerResTreat,201701234CTC-clustercount020406080100048121620242832364044485256606468727680WeeksCTCcountFulvestrant+/-Palbociclib+GoserelinAbraxaneCisplatin(+Zometa)Lapatinib+EpirubicinPalliativecarePDliverPD(mild)liverPDliver,lymphnewinbonePDLiverboneDiePDlymph0501001500500100015000481216202428323640CTC-clustercountCTCcountWeeksFECx6(+Zometa)Taxolx5Navelbine+Afinitor+HerceptinHospicecarePDbonelymphPDnewinlungDiePDbonenewinliverCTCsCTC-clustersSDBAWangC,etal.BreastCancerResTreat,2017HER2atdiagnosis(tissue)HER2atfirstblooddrawn

(tissue)PNegativePositiveNegative61(93.9%)3(16.7%)<0.0001Positive4(6.1%)15(83.3%)HER2atfirstblood(tissue)CTC-HER2atfirstbloodPNegativePositiveNegative46(85.2%)19(65.5%)0.038Positive8(14.8%)10(34.5%)Concordance67.5%ComparisonofHER2statusintissuevsCTCGroupHR(95%CI)PNegCTC-HER21.00PosCTC-HER2withtargettherapy1.66(0.85-3.25)0.14PosCTC-HER2withouttargettherapy2.76(0.91-8.41)0.07AmongpatientswhoseHER2statusintissuearenegativeCTC-HER2associatedwithPFS0.000.250.500.751.000501000.000.250.500.751.00050100Plog-rank=0.05CTC–HER2-CTC-HER2+38ProbabilityofProgression-freeSurvival(%)WeeksPlog-rank=0.09ProbabilityofProgression-freeSurvival(%)WeeksCTC–HER2-CTC–HER2+withtargettherapyCTC–HER2+withouttargettherapyLongitudinalanalysisofassociationbetweenCTC-HER2andPFSGroupCrudeHR(95%CI)PAdjustedHR(95%CI)PNegCTC-HER2withtargettherapy1.001.00NegCTC-HER2withouttargettherapy1.62(0.79-3.32)0.192.20(0.94-5.17)0.07PosCTC-HER2withtargettherapy1.72(0.59-5.04)0.321.29(0.36-4.62)0.69PosCTC-HER2withouttargettherapy3.88(1.52-9.91)0.0054.75(1.39-16.31)0.01AdjustedforCTC,age,ethnicity,BMI,menopause,ER,PR,hormonaltherapy,andchemotherapy0.000.250.500.751.000501001500.000.250.500.751.000501001500.000.250.500.751.00050100150Plog-rank<0.001CTC<5CTC≥538Plog-rank=0.09Platelet<400Platelet≥400Plog-rank<0.001CTC<5andPlatelet<400CTC≥5orPlatelet≥400CTC≥5andPlatelet≥400ProbabilityofProgression-freeSurvival(%)WeeksProbabilityofProgression-freeSurvival(%)WeeksProbabilityofProgression-freeSurvival(%)WeeksInteractionsbetweenbaselineCTCandplateletonPFSCrudeHR(95%CI)PPintAdjustedHR(95%CI)PPintCTC<5andplatelet<4001.001.00CTC≥5orPlatelet≥4001.95(1.28-2.97)0.0021.58(1.00-2.50)0.048CTC≥5andPlatelet≥4007.97(3.54-17.97)<0.0010.056.81(2.85-16.30)<0.0010.01循環(huán)腫瘤細(xì)胞預(yù)后預(yù)測(cè)性能–whattodonext?FurtherfunctionalcharacterizationofisolatedCTCsSingleCTCgenomicanalysis

(mutation,methylation,CNV,RNA)NavinNE,etal.SciTranslMed,2015Whole-genomesingle-cellsequencingoffourtumorcellsfromanestrogenreceptor-positivebreastcancerpatientshowsthatnotwotumorcellsaregeneticallyidenticalBrouwerA,etal.Oncotarget,2016CTCsassnapshotoftheevolvingtumorlandscape單CTC測(cè)序突變分析–valueofstatisticalgeneticsBF=P(D|M1)/P(D|M0)

P(D|M1):multipleCTCssharethesamemutationP(D|M0):noCTChasthemutationD:sequencingdatafrommultipleCTCsPriorformutationpatternisderivedfromtheredcurveandblackcurvewhencalculatingP(D|M1)andP(D|M0),respectively.單細(xì)胞突變分析指導(dǎo)靶向治療CTCenumerationindicatedfailureofcurrenttherapy(day200).Experience-basedsubsequenttherapydidnotwork,highlightingtheimportancein-depthgenomiccharacterizationsofCTCs.DynamicchangesofCTCmutationsindicatedfailureofcurrenttherapy(day120).Importantmutationscouldmorepreciselymatchpatientstothebestsubsequenttherapy.單細(xì)胞突變分析指導(dǎo)靶向治療TwoESR1mutationsnotdetectedinctDNAOthermutationsincludePI3KE545K,atargetofEverolimusNCI支持的第一個(gè)乳腺癌多中心CTC單細(xì)胞分析–1R01CA207468SKCC,PAKennedyHealth,PAReadingHospital,PADoylestownCancerCenter,PASerialimagesandbloodsCTCandctDNAcollectedatbaseline,duringtreatment,andaftertreatmentBaselineCTCsignatureFollow-upCTCsignatureBaselinevs.follow-upfortreatmentresponseER,HER2,PD1dynamicchangeduringtreatmentClinicalvariablesenhanceprognosticationCombineduseofCTCandctDNAintreatmentresponseN=500N=200Externalvalidation,targetedpanelSpecifictherapiesandclinicaltrialstobeanalyzedAromataseinhibitor-containingtherapiesFulvestrant—containingtherapiesAromataseinhibitor+Palbociclib/RibociclibFulvestrant+Palbociclib/RibociclibPD1/PDL1-containingtherapiesExceptionalResponderstrategiestoincreasestatisticalpowerMutationsidentifiedfromnon-exceptionalrespondersneedsmorestringentvalidations本項(xiàng)研究回答的問(wèn)題與將來(lái)的挑戰(zhàn)可增強(qiáng)CTC預(yù)測(cè)性能的臨床，分子，和遺傳變量

對(duì)CTC進(jìn)行縱向性分析是否可以發(fā)現(xiàn)導(dǎo)致各種主流治療手段的基因組突變CTC與ctDNA的聯(lián)合分析是否可以更充分發(fā)揮液體活檢的優(yōu)勢(shì)治療過(guò)程中，CTC的ER/HER2/PD1表達(dá)是否會(huì)產(chǎn)生變化，此變化是否和腫瘤組織的變化一致，是否可以預(yù)測(cè)相關(guān)的主流治療手段的結(jié)果隨著治療產(chǎn)生的CTC的基因組的進(jìn)化過(guò)程對(duì)治療結(jié)果的影響如何根據(jù)不同腫瘤類型選擇相應(yīng)的CTC檢測(cè)平臺(tái)對(duì)于CellSearch，如何增強(qiáng)其檢測(cè)CTC的靈敏性對(duì)于其他CTC檢測(cè)平臺(tái)，如何保證其檢測(cè)CTC的特異性以及臨床有效性是否可以有效準(zhǔn)確的分離CTC-cluster。CTC-Cluster的遺傳信息是否和CTC不同，是否有更多的導(dǎo)致轉(zhuǎn)移的基因可通過(guò)CTC-cluster發(fā)現(xiàn)

根據(jù)實(shí)時(shí)的CTC突變?cè)O(shè)計(jì)治療是否可以改善病人預(yù)后–前瞻性臨床試驗(yàn)SiravegnaG,etal.GenomeBiol,2014ctDNACrowleyE,etal.NatRevClinOncol,2013ReleaseandextractionofcfDNAfromthebloodHeitzerE,etal.ClinChem,2015DiazLAJr,etal.JClinOncol,2014CrowleyE,etal.NatRevClinOncol,2013ctDNAtomonitorresponseandrelapsewithtargetedtherapiesDiazLAJr,etal.JClinOncol,2014DiehlF,etal.NatMed,2008DawsonSJ,etal.NEnglJMed,2013ctDNAtoassesstumordynamicsSchemeofbloodanalysisforCTCsandctDNAinpatientswithcancerPantelK,etal.CancerRes,2013CTCsctDNA/RNAAssessmentofpre/post-analyticalvariabilityYesYesDetectionofsomaticmutations,InDels,copy-numberalterationsandgene-fusionsYesYesEvaluationofmethylationpatternsYesYesAnalysisofmRNA/miRNA/lncRNA/RNAsplicevariantsYesYesAnalysisofRNAexpressionYesNoCellmorphologyandfunctionalstudiesexvivoYesNoDemonstrationofsignalcolocalizationYesNoProteomicsanalysisYesNoComparisonbetweentheapplicationsofCTCsandctDNASiravegnaG,etal.NatRevClinOncol,2017SiravegnaG,etal.GenomeBiol,2014RepresentativepatientA:ctDNAmutationinTP53andKRASsignificantlyloweredafterregimenchange,butstartedtoincrease,whichwascorrelatedtodiseaseprogressionandpatientdeath.RepresentativepatientB:Recurrentpatient,diseasekeepingprogressionuntilmetastasiswasidentified.Regimenchangeafterthat,alongwithdecreaseinctDNAmutationsinTP53andKRAS.Patientstillalive4monthsafterregimenchange.ProgressionduringChemotherapyMetastasis:PET-CT,regimenchange,addingXelodaMetastasis:PET-CT;regimenchange,addingAbraxaneTP53_1TP53_2IDH1KRASDNMT3AAPC%Metastasis,(PET-CT)Regimenchange,addingEpirubicinRegimenchange,addingHerceptinDeathTP53SEPTBP1PICK3CA/PTENKRAS%DayDayCancerTypeChina2015US2015US2017Incidence（10,000）Death（10,000）Ratio*Incidence（10,000）Death（10,000）Ratio*Incidence（10,000）Death（10,000）Ratio*LungColorectalBreastProstateLiverGastricEsophagealPancreatic7338276476848961197.12.74250387.984%50%26%45%89%74%79%88%221323223.62.51.74.91654.12.82.51.11.64.173%38%18%13%69%44%94%84%221426164.12.81.75.41254.12.72.91.11.64.355%36%16%17%71%43%94%80%Overall42928166%1665936%1696036%HighcancermortalityinChina*Notanidealindicator,forintuitivecomparisonsCACancerJClin,2015

CACancerJClin,2016

CACancerJClin,2017新發(fā)40萬(wàn)/年，60%晚期，治療手段貧乏2300萬(wàn)560萬(wàn)70萬(wàn)630萬(wàn)ClinicalsignificanceofearlylivercancerdetectioninChina（共2800萬(wàn)）（共700萬(wàn)）9300萬(wàn)乙肝病毒攜帶者2800萬(wàn)慢性乙肝患者700萬(wàn)肝硬化患者以早篩將50%的晚期肝癌病人提前診斷到早期來(lái)計(jì)算，可以將肝癌的五年生存率由~30%提高到~50%每年節(jié)約直接醫(yī)療成本幾百億元同時(shí)大幅降低護(hù)理和勞動(dòng)力喪失等間接成本早、晚期肝癌五年生存率70%vs5%穿刺活檢:

診斷金標(biāo)準(zhǔn)；侵入性、異質(zhì)性、擴(kuò)散風(fēng)險(xiǎn)早、晚期肝癌平均治療費(fèi)用相差>10萬(wàn)臨床篩查手段（血檢、影像學(xué)）性能不足Cancerliquidbiopsy2015

MIT技術(shù)評(píng)論十大突破技術(shù)之一2016美國(guó)白宮/NIH“BloodProfilingAtlasinCancer”(BloodPAC)2016福布斯未來(lái)五大醫(yī)療行業(yè)顛覆技術(shù)2017達(dá)沃斯論壇，世界經(jīng)濟(jì)論壇和《科學(xué)美國(guó)人》評(píng)選十大新興技術(shù)第一位LiquidbiopsyofcancerCalabuig-FarinasS,etal.TranslLungCancerRes,2016；SiravegnaG,etal.NatRevClinOncol,2017ctDNA/RNACTCsExosomesPotentialtofullyrecapitulatespatialandtemporaltumorheterogeneityYesNoNoAssessmentofpre/post-analyticalvariabilityYesYesYesDetectionofsomaticmutations,InDels,copy-numberalterationsandgene-fusionsYesYesYesEvaluationofmethylationpatternsYesYesYesAnalysisofmRNA/miRNA/lncRNA/RNAsplicevariantsYesYesYesAnalysisofRNAexpressionNoYesYesCellmorphologyandfunctionalstudiesexvivoNoYesNoDemonstrationofsignalcolocalizationNoYesNoProteomicsanalysisNoYesYescfDNA-basedliquidbiopsyandearlycancerdetectionCopynumberaberrationDrivermutationMethylationClinicaldata

e.g.,AFPDiagnosismodelIndependentvalidationSuccessofNIPTAppearinearly-stagecancerNotaffectedbyageAppearinearly-stagecancerAffectedbyageTissue-of-originClonalhematopoiesisrelatedmutationsAffectedbyageUnclearclinicalrelevanceoflow-frequencymutationsOpportunitiesNon-invasiveReal-timelongitudinalmonitoringPatientadherenceChallengesLowcfDNAinbloodofearly-stagetumorsConfoundingfrombackgroundsignalsHigh-throughputdataanalysisandalgorithmClearinterventionandlead-timebiascfDNA

liquidbiopsyandearlycancerdetectionApplymachinelearninginwholegenomeanalysisforearlycancerdetection早期和晚期腫瘤的基因組特征有所不同。早期腫瘤基因組變化少，但是更可能富集驅(qū)動(dòng)信號(hào)驅(qū)動(dòng)信號(hào)是多維度的體現(xiàn)，突變僅是其中一個(gè)維度基于已知驅(qū)動(dòng)基因突變發(fā)展的技術(shù)受限于有限的recurrent基因和突變以及突變的功能機(jī)理研究。不依賴于突變的驅(qū)動(dòng)基因并未被完全發(fā)現(xiàn)

和點(diǎn)突變相比，CNA影響基因組的長(zhǎng)片段，提供更可靠的信號(hào)和統(tǒng)計(jì)上的信心多組學(xué)(CNA,甲基化，突變,etc.）的整合檢測(cè)有望增加篩查的準(zhǔn)確度。如何發(fā)展算法進(jìn)行更有效并可靠的整合是一個(gè)巨大的挑戰(zhàn)大規(guī)模獨(dú)立人群隊(duì)列的驗(yàn)證，尤其是前瞻性和縱向性樣本數(shù)據(jù)的重要性Multi-stagestudydesignSupervisedmachinelearningfor

modeldevelopmentUnsupervisedmachinelearningofpublicdatatoassessdriverscores翱銳在肝癌早篩上的領(lǐng)先技術(shù)Twolayersofmachinelearningtoconstructdiagnosismodel翱銳在肝癌早篩上的領(lǐng)先技術(shù)Novelweightedrandomforest-driverstatisticalmodelConventionalrandomforestGiniscoreusedtoselecttreesplitsinrandomforestWeightedrandomforestGiniscoreandiDriverweightstodeterminetreesplitsinrandomforestVSAdjustGiniscoreusingpenalty

GISTIC2score（TCGA）DriverSCNAs（TCGA）iDriverscoreRFscoreSCNAsprofiling（realdata）

ExternalevidenceInternalevidenceDrivergenes（TCGA）EvaluationofthewRF-driverframeworkAdjustpenaltyofimportantfeaturetochangeweight,thuschangingtreestructureWeightedmodelincreasediagnosisperformanceCharacteristicsDiscoverycohortValidationcohort1Validationcohort2Total#20978105GenderFemale,N(%)41(19.6)15(19.2)25(23.8)Male,N(%)168(80.4)63(80.8)80(76.2)Age(years)Mean51.75050Range31-7927-6724-83AFPvalue<25ng/ml(%)136(65.1)55(70.5)64(61.0)≥25ng/ml(%)58(27.8)19(24.4)36(34.3)NA15(7.2)4(5.1)5(4.8)StageHCC:StageI,N(%)46(22.0)22(28.2)47(44.8)HCC:StageII,N(%)29(13.9)17(21.8)5(5.8)HCC:StageIII,N(%)25(12.0)0(0)0(0)HCC:StageIV,N(%)8(3.8)0(0)0(0)HBVHBV:Cirrhosis(No),N(%)45(21.5)15(19.2)29(27.6)HBV:Cirrhosis(Yes),N(%)56(26.8)24(30.8)23(21.9)Tumorsize(longestdim)Mean(cm)6.613.323.11Confidenceintervals0.630.350.41StudyparticipantsDifferentSCNAsprofilingpatternsindifferentpatientsNon-cirrhoticHBVpatientsRarevisibleSCNAsSomecirrhoticHBVpatientsexhibitvisibleSCNAs,cannotexcludepossibilityofundiagnosedHCCStageIHCCpatientsSCNAprofilesimilartothatofHBVpatientsDifficulttodifferentiateStageIIIHCCpatientsSCNAprofiledifferentfromthatofHBVpatientsEasytodifferentiatectDNAburdenandearlyHCCdetectionSensitivitySpecificityDiscovery0.5830.950StageI0.3830.950StageII-IV0.7380.950Validation10.1800.974Validation20.2600.962HighspecificityLowsensitivityAUCDiscovery0.774StageI0.670StageII-IV0.855Validation10.577Validation20.614Lowperformanceforearly-stagecancerTrainingcohortValidationcohortsRF-basedSCNAmodelctDNAburdendoesnotconsidersequencingdepth.wRF-basedmodelincorporatesequencingdepthinformation,enhancingperformanceforearly-stagetumordetectionSensitivitySpecificityAUCDiscovery0.894StageI0.6000.9500.842StageII-IV0.7500.9500.934SensitivitySpecificityAUCDIscovery0.5830.9500.774StageI0.3830.9500.670StageII-IV0.7380.9500.855RF-basedSCNAmodelctDNAburdenwRF-driver-basedSCNAmodelValidationcohortshavemuchearlier-stagepatients,smallertumors,andlowerctDNAburdenAUCValidation1Validation

2wRF-driver0.8980.788wRF-driverwRF-driverplusAFPFactorsinfluencingwRF-driverperformanceComparingwithusingstageII-IVpatientsastrainingset，usingstageIpatientsastrainingsetincreasesdiagnost