人工智能驱动的液体活检技术在癌症全程管理中的应用

HushanYang,PhD

SidneyKimmelCancerCenter

ThomasJeffersonUniversity

Philadelphia,PAGannanMedicalUniversity

Oct16,2018人工智能驱动的液体活检技术

在癌症全程管理中的应用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循环肿瘤细胞预后预测性能–whattodonext?FurtherfunctionalcharacterizationofisolatedCTCsSingleCTCgenomicanalysis

(mutation,methylation,CNV,RNA)NavinNE,etal.SciTranslMed,2015Whole-genomesingle-cellsequencingoffourtumorcellsfromanestrogenreceptor-positivebreastcancerpatientshowsthatnotwotumorcellsaregeneticallyidenticalBrouwerA,etal.Oncotarget,2016CTCsassnapshotoftheevolvingtumorlandscape单CTC测序突变分析–valueofstatisticalgeneticsBF=P(D|M1)/P(D|M0)

P(D|M1):multipleCTCssharethesamemutationP(D|M0):noCTChasthemutationD:sequencingdatafrommultipleCTCsPriorformutationpatternisderivedfromtheredcurveandblackcurvewhencalculatingP(D|M1)andP(D|M0),respectively.单细胞突变分析指导靶向治疗CTCenumerationindicatedfailureofcurrenttherapy(day200).Experience-basedsubsequenttherapydidnotwork,highlightingtheimportancein-depthgenomiccharacterizationsofCTCs.DynamicchangesofCTCmutationsindicatedfailureofcurrenttherapy(day120).Importantmutationscouldmorepreciselymatchpatientstothebestsubsequenttherapy.单细胞突变分析指导靶向治疗TwoESR1mutationsnotdetectedinctDNAOthermutationsincludePI3KE545K,atargetofEverolimusNCI支持的第一个乳腺癌多中心CTC单细胞分析–1R01CA207468SKCC,PAKennedyHealth,PAReadingHospital,PADoylestownCancerCenter,PASerialimagesandbloodsCTCandctDNAcollectedatbaseline,duringtreatment,andaftertreatmentBaselineCTCsignatureFollow-upCTCsignatureBaselinevs.follow-upfortreatmentresponseER,HER2,PD1dynamicchangeduringtreatmentClinicalvariablesenhanceprognosticationCombineduseofCTCandctDNAintreatmentresponseN=500N=200Externalvalidation,targetedpanelSpecifictherapiesandclinicaltrialstobeanalyzedAromataseinhibitor-containingtherapiesFulvestrant—containingtherapiesAromataseinhibitor+Palbociclib/RibociclibFulvestrant+Palbociclib/RibociclibPD1/PDL1-containingtherapiesExceptionalResponderstrategiestoincreasestatisticalpowerMutationsidentifiedfromnon-exceptionalrespondersneedsmorestringentvalidations本项研究回答的问题与将来的挑战可增强CTC预测性能的临床，分子，和遗传变量

对CTC进行纵向性分析是否可以发现导致各种主流治疗手段的基因组突变CTC与ctDNA的联合分析是否可以更充分发挥液体活检的优势治疗过程中，CTC的ER/HER2/PD1表达是否会产生变化，此变化是否和肿瘤组织的变化一致，是否可以预测相关的主流治疗手段的结果随着治疗产生的CTC的基因组的进化过程对治疗结果的影响如何根据不同肿瘤类型选择相应的CTC检测平台对于CellSearch，如何增强其检测CTC的灵敏性对于其他CTC检测平台，如何保证其检测CTC的特异性以及临床有效性是否可以有效准确的分离CTC-cluster。CTC-Cluster的遗传信息是否和CTC不同，是否有更多的导致转移的基因可通过CTC-cluster发现

根据实时的CTC突变设计治疗是否可以改善病人预后–前瞻性临床试验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新发40万/年，60%晚期，治疗手段贫乏2300万560万70万630万ClinicalsignificanceofearlylivercancerdetectioninChina（共2800万）（共700万）9300万乙肝病毒携带者2800万慢性乙肝患者700万肝硬化患者以早筛将50%的晚期肝癌病人提前诊断到早期来计算，可以将肝癌的五年生存率由~30%提高到~50%每年节约直接医疗成本几百亿元同时大幅降低护理和劳动力丧失等间接成本早、晚期肝癌五年生存率70%vs5%穿刺活检:

诊断金标准；侵入性、异质性、扩散风险早、晚期肝癌平均治疗费用相差>10万临床筛查手段（血检、影像学）性能不足Cancerliquidbiopsy2015

MIT技术评论十大突破技术之一2016美国白宫/NIH“BloodProfilingAtlasinCancer”(BloodPAC)2016福布斯未来五大医疗行业颠覆技术2017达沃斯论坛，世界经济论坛和《科学美国人》评选十大新兴技术第一位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早期和晚期肿瘤的基因组特征有所不同。早期肿瘤基因组变化少，但是更可能富集驱动信号驱动信号是多维度的体现，突变仅是其中一个维度基于已知驱动基因突变发展的技术受限于有限的recurrent基因和突变以及突变的功能机理研究。不依赖于突变的驱动基因并未被完全发现

和点突变相比，CNA影响基因组的长片段，提供更可靠的信号和统计上的信心多组学(CNA,甲基化，突变,etc.）的整合检测有望增加筛查的准确度。如何发展算法进行更有效并可靠的整合是一个巨大的挑战大规模独立人群队列的验证，尤其是前瞻性和纵向性样本数据的重要性Multi-stagestudydesignSupervisedmachinelearningfor

modeldevelopmentUnsupervisedmachinelearningofpublicdatatoassessdriverscores翱锐在肝癌早筛上的领先技术Twolayersofmachinelearningtoconstructdiagnosismodel翱锐在肝癌早筛上的领先技术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