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1
Themostglobalcancerincidenceinwomen
Rank Cancer Newcasesdiagnosedin2018 %ofallcancers(excl.non-melanomaskincancer)
1 Breast 2,088,849 25.4
2 Colorectal 794,958 9.7
3 Lung 725,352 8.8
4 Cervixuteri 569,847 6.9
• Breast cancer causes the greatest number of cancer-relateddeaths among women. In 2018, it is esGmated that 627,000womendiedfrombreastcancer–thatisapproximately15%ofallcancerdeathsamongwomen.
• While breast cancer rates are higher among women in moredeveloped regions, rates are increasing in nearly every regionglobally.
BreastCancer
CatalanoM,V.,Bertaglia,V.,Tariq,N.,Califano,R.(2014).TreatmentofAdvancedBreastCancer(ABC):TheExpandingLandscapeofTargetedTherapies.JCancerBiolRes,2(1),1036.
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40% 20% 10-15% 15-20%
TripleNegaGve
Humanepidermalgrowthfactor2
HER2PosiGveLuminalA LuminalB
Percentageatdiagnosis
Receptorexpression
Treatmentstrategies
Estrogensandprogesterone
ChemotherapyAnG-HER2therapies
HormonaltherapiesNoveltargettherapies
DiversityofBreastCancer:Subtypes
3
1. EarlydetecKon2. New progression mechanism for
targetedtherapies(e.g.TNBC)3. Heterogeneity4. DrugResistance5. Companiondiagnosis6. Racialdisparity
Clinicalunmetneedsinbreastcancer
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Stage0
Benign StageI
StageII
StageIII
StageIV
TNMStageFiveyearsurvivalrate
>95%
About90%
About25%
About70%
Progression&DiversityofBreastCancer
DCIS:Ductalcarcinomainsitu
TNM Stage T = size of primary tumor N = the extent of spread to nearby lymph nodes M = presence or absence of distant metastases
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DiversityofBreastCancer:RacialDisparity
SurveillanceResearch,2017
Year Year
Rateper100,000
Rateper100,000
Disease Biomarkers From Liquid Biopsy to Precision Medicine
Liquid Biopsy
• Predictive, • Personalized, • Preventive, • Participatory
“Doctors have always recognized that every patient is unique, and doctors have always tried to tailor their treatments as best they can to individuals. You can match a blood transfusion to a blood type — that was an important discovery. What if matching a cancer cure to our genetic code was just as easy, just as standard? What if figuring out the right dose of medicine was as simple as taking our temperature?” - President Obama, January 30, 2015
LiquidBiopsy:•Lessinvasive,•lesscostly,•lessrisky,•containmoredynamicinformaGonthanconvenGonalGssuebiopsies.
Varietyofbiomarkersexistedinbodilyfluidssuchasblood,saliva,urine,ascites,etc
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miR-331andmiR-21=>gastriccancer
8miRNAs=>coloncancer
MicroRNA(miRNA)1. Non-codingRNA2. 17-25nucleoGdes3. Post-transcripGonalregulaGon:
base-pairwithmRNAsandsilencethosemRNAs
4. Appeartotargetabout60%ofthegenesofhuman
miRNABiomarkersinLiquidBiopsy
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1. IdenGfypossiblebiomarkersforearlydetecGonofbreastcancerfromliquidbiopsy(e.g.miRNAs).
2. Generateprofilingonthevariousstagesofbreastcancer. IdenGfy more insigheul informaGonregardingthecriGcalfactorsthatprogresscancertothenextstage.
Problemstobesolved
Comprehensivedata(containingallvariaGon)+High-throughputtechniques(highsensiGvity)+Properdataanalysis(lowsamplenumberissue)+ArGficialintelligenceforopGmizaGon(predictable)
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Currentbiomarkersforbreastcancer
TumorMarker N(Total) SensiKvity
CA15-3 35(145) 24.1%
CA27.29 37(145) 25.5%
CEA 27(145) 18.6%
BreastCancer:3tumormarkers• canceranGgen15-3(CA15-3),• canceranGgen27.29(CA27.29),and• carcinoembryonicanGgen(CEA)
Hou,MF,etal.(1999).EvaluaGonofserumCA27.29,CA15-3andCEAinpaGentswithbreastcancer.KaohsiungJMedSci.,23(1),pp.88-93.
Comprehensivedata
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Wen-HongKuo,MD;PhD
Noncancer Benign 10 DCIS 20
CancerStage
TotalI II III IV
Subtypes
LuminalA 8 8 12 5 33
LuminalB 8 8 12 5 33
HER2+ 8 8 12 5 33
TripleNegaGve 8 8 12 4 32
Total 32 32 48 19 131
Serum 300µL� 3D-Gene®
①
Addition
Vortex & cfg.
Water layer
Organic layer RNA purification
(96 well)
Extraction Concentration
Serum miRNA
4µL
RNA extraction� RNA purification�
miRNA
Exosome
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TakahiroOchiya,PhD�
High-throughputtechniques1. miRNAextracGonfromserum2. miRNAanalysiswithmicroarray
miRNAoligochip(2565miRNA,miRBasever.21)
RNA 2µL
Fluorescent labeling
Hybridization
Wash
Digitizing
Scan RNA labeling �
DNA chip analysis� Analysis�3D-Gene®
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High-throughputtechniques1. miRNAextracGonfromserum2. miRNAanalysiswithmicroarray
miRNAexpressiondata(Rawdata)
1.DifferenGalexpressionanalysis2.ElasGcnet(LASSO&Ridge)
1.Supportvectormachine(SVM)2.Lineardiscriminantanalysis(LDA)3.Generalizedlinearmodel(GLM)
Modeling
NormalizedData
FeaturemiRNAs
1.VariancestabilizingnormalizaGon(VSN)
2.Spike-inVSN
3.CrossNorm
Ochiyaet.al.
NormalizedData
NA-imputedData
FeaturemiRNAs
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RawData
NA-handledData
1.Removing2.ImputaGon(trimmedmean)
ThisStudy
PredicGonmodel PredicGonmodel
PotenKalbiomarkers&PredicKonmodels
1.AccuracyFiltering2.PredicGonmodeling
FurtherModeling
ImputaGon(smallvalue)
CalibraGonwithnegaGvecontrolsandinternalcontrols
DifferenGalexpressionanalysisandexpressionlevelfiltering
FeaturesselecKon
NA-handling
NormalizaKon
DataAnalysisPipeline
Chen-AnTsaiTakahiroOchiya �
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-log 1
0(P-value)
log2(foldchange)
-log 1
0(P-value)
log2(foldchange)
-log 1
0(P-value)
log2(foldchange)
DifferenKalExpressionAnalyses
AdjustedP-values<0.05(Benjamini–Hochbergprocedure) Significantlog2foldchange=1.5
VSN Spike-inVSN CrossNorm
103probesareselected 437probesareselected
269probesareselected
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miRNAsSelectedbyElasKcNetRegression
hsa-miR-4783-5p
miRNAAnalysisFlowChart RawData
NormalizedData
FeaturemiRNAs
1. DifferenGalExpressionAnalysis2. ElasGcnet(LASSO&Ridge)
1. SupportVectorMachine(SVM)2. Lineardiscriminantanalysis(LDA)3. Generalizedlinearmodel(GLM)
PredicGonmodels
NormalizaGon
FeaturesselecGon
Modeling
EvaluaGon:LeaveoneoutCrossvalidaGon
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WholemiRNA 2565
AperremovingNA 2462
NormalizaGon VSN Spike-inVSN
CrossNorm
DifferenGalExpression
0103
307130
0269
ElasGcNet 3231
5014
022
Modelingwithatmost3selectedmiRNAoreachsinglemiRNAs->Construct17,423,153models->Evaluatewith10-foldcrossvalidaGon
ResultsofmiRNAthroughAnalysisPipeline
103 125
1221
1
19
SVM LDA GLM
ModelingwithSelectedmiRNAsSelecGngthemiRNAswithpredicGonability
sensiGvity&specificity>85%
20
hsa-miR-61xxhsa-miR-61xhsa-miR-66xxhsa-miR-42xxhsa-miR-42yyhsa-miR-7xxhsa-miR-3xxhsa-miR-6xxxhsa-miR-5xxhsa-miR-12xxhsa-miR-67xx
1
11
2
2 1
1 1
SVM
LDA GLM
OverlapandConsistencyofEachModelingMethod
SelectedmiRNAswithPredicKonAbility
⇒ 18miRNAs(overlapfrom4methods)
21
NA-omiMngdatawithVSN
PCAandheatmapwithSelected18miRNAs
22
NA-omiMngdatawithVSN
PCAplotsshowselected18miRNAsfit-inearlydetecKon
Further modeling
23
Aper previous predicGon modeling, we used the union of selecGon from eachpipeline tobuildmorepredicGonmodels. Fisher’s lineardiscriminantanalysis (LDA)was performedwith each of thesemiRNAmarker or a combinaGon of atmost sixmiRNAmarkers.To evaluaGon the predicGon performance, 10-fold cross validaGonwere applied toeachmodel.Weseparatedthedatainto10groups,builtthemodelwith9groupsandused the residual group as tesGng cohort to calculate the predicGon accuracy,sensiGvity and specificity. Aper repeaGng the esGmaGon process with differenttesGng group 10 Gmes, the average values of each test result were calculated formodelevaluaGon.TheresulGngvaluesofthediscriminantfuncGonswereusedtopreparethediagnosGcindex.Indexscore≥0:breastcancerIndexscore<0:non-breastcancerorotherclinicalcondiGons
Modelingwith1miRNA(hsa-miR-614)
FurtherModeling(with1miRNA)
24
Threshold:0.9850Specificity:0.8656±0.0964SensiGvity:0.8700±0.0881Accuracy:0.8683±0.0552AreaUnderCurve:0.9320
PredicGonscore=11.3262-1.5682xhsa-miR-614
Threshold:1.0250Specificity:0.9156±0.0822SensiGvity:0.9307±0.0621Accuracy:0.9250±0.0470AreaUnderCurve:0.9660
FurtherModeling(with3miRNAs)
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PredicGonscore=5.4049-1.7271xhsa-miR-614+0.0937xhsa-miR-42XX+1.1171xhsa-miR-61XX
Threshold:1.4590Specificity:0.9533±0.0635SensiGvity:0.9747±0.0517Accuracy:0.9667±0.0419AreaUnderCurve:0.9871
FurtherModeling(with4miRNAs)
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PredicGonscore=9.225-0.9554xhsa-miR-614+0.8076xhsa-miR-42xx+1.4167xhsa-miR-61xx-1.9153xhsa-miR-66XX
Threshold:1.5280Specificity:0.9611±0.0533SensiGvity:0.9800±0.0396Accuracy:0.9729±0.0326AreaUnderCurve:0.9885
FurtherModeling(with5miRNAs)
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PredicGonscore=8.763-0.665xmiR-614+0.865xmiR-42xx+1.413xmiR-61xx-1.697xmiR-66xx-0.716xmiR-1xxx
Validation • PaGentSerumCollecGon:Healthy,Benign,pre-Cancer&Cancer
• Workflow:1. IsolateRNA2. OpGmizeprimer3. ReversetranscripGon4. AmplifycDNA5. RunqPCR/nanostring/(liquid)chip6. Analyze
29
Conclusions
2. Several serum miRNAs are enough to idenGfy the group(cancerornoncancer)ofapaGentatahighaccuracylevel.Thus,theseselectedmiRNAscouldbeviewedaspotenGalbiomarkersforimplemenGngearlydetecGonofbreastcancer.
1. While applying different analysis pipeline would get quietdifferent outcomes, there are some overlaps, which show theconsistencyofthesemethods.
30
PerspecKves1. The models seem to be precise enough to fit early
detecGon, more validaGons are sGll required toestablishrobustcriteria.
2. The established useful analysis pipeline enablesapplying for other different expression data derivedfromotherdiseases.
3. ThemechanismsoftheseselectedmiRNAsrelatedareunknown. It ismuchmoremeaningful and criGcal forthe understanding of these idenGfied biomarkers. Bycomprehending themolecularmechanismsunderlyingthesebiomarkers, thedevelopingeffecGvetreatmentsandtranslaGonalresearchwouldbepromoted.
Acknowledgements
LabmembersBao-HongLee,PhD
Yu-LingTai,PhD
YumiYasua,PhD
EmilyLo, PhD
Shion-ShanShen, PhD
Shang-JerKuo(PhDstudent)
Chia-YuYang(Master)
Ru-YingFang(Master)
Yi-TingChen(Master)
Dan-JungChuang(Master)
Shi-ChenWang(Master)
MitchLin(Master)AngelaWang(RA)
JeffKu(RA)
CornellDavidC.Lyden,
MD,PhD
AyukoHoshino,PhD
HéctorPeinado,PhD(CNIO,Spain)
NTUKing-JenChang,
MD,PhD
Wen-HongKuo,MD,PhD
Ming-YangWang,MD,PhD
Chen-ChihHsu,PhD
Chen-AnTsai,PhD
JapanTakahiroOchiya,PhD
(NCC)
HidetoshiTahara,PhD(UHiroshima)
KojiUeda,PhD(CancerFound.)
OthersArthurLander,
MD,PhD(UCIrvine)
Kuo-KanLiang(AS)
AliMortazabi,PhD(UCIrvine)
ZhongminZhao,PhD(UTHealth)
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