Utilization of NGS to Identify Clinically-Relevant Mutations in cfDNA: Meet the NGS Experts Series Part 3

Pancreatic cancer is a uniquely lethal malignancy characterized by frequent mutations in KRAS, CDKN2A, SMAD4, TP53 and many others. We have shown that KRAS mutation can be detected in cell-free, circulating tumor DNA (ctDNA) isolated from the plasma in a subset of patients and is associated with poor prognosis. The ability to simultaneously detect multiple pancreatic cancer-specific mutations in ctDNA would open a new avenue for detection of clinically-relevant mutations. In this study, we performed ultra-deep sequencing of ctDNA from advanced pancreatic cancer patients prior to treatment with Gemcitabine and Erlotinib following target enrichment. Somatic, non-synonymous variants were identified in 29 different genes at allele frequencies typically less than 0.5%. Updated results of ultra-deep NGS analysis will be presented.

1. Clifford Tepper, Ph.D.
Technical Director, Genomics Shared Resource
UC Davis Comprehensive Cancer Center
Research Biochemist
Dept. of Biochemistry and Molecular Medicine
UC Davis School of Medicine
Utilization of NGS to Identify Clinically-
relevant Mutations in Cell-free Circulating
Tumor DNA

4. The UCDCCC Genomics Shared Resource
Goal:To provide comprehensive and integrative genomics and bioinformatics
solutions for Cancer Center members and UCD research community
•Microarray services: Affymetrix, Agilent
•Comprehensive NGS capabilities –Illuminaplatform
–Transcriptomesequencing: Total RNA-Seq, Small RNA-Seq
–Genomic DNA sequencing: Whole genome, whole-exomesequencing
–Epigenomics: ChIP-Seq, MethylC-Seq
–Ampliconsequencing
•Bioinformatics
–Analysis pipelines for all common NGS applications
•Clinical assay development and optimization
–UCD129 Cancer Gene Mutation Assay
–TP53Functional Assay
–HBV Genome Sequencing Assay
–Liquid biopsies–blood genomics: cfDNA, gene expression

5. Personalized Cancer Therapy –Precision Medicine
•Tailoringtherapytoapatient’sspecificneedsby
matchingthetreatmentstrategytomolecular
featuresspecificfortheircancer.
•Standardofcareforseveralcancertypes
possessingwell-definedgenomicaberrations.
–Amplifications,pointmutations,insertions/deletions,
genefusions
–BRAF,ERBB2,EGFR,KRAS,ALKfusions,PIK3CA
•Revolutionizedbynext-generationsequencing
(NGS)
–Rapidandcomprehensivemolecularcharacterization
–Whole-exomesequencing(WES)-mutations,fusions
–RNA-Sequencing(RNA-Seq)–geneexpressionprofiles
•Tumortissueiscommonlyusedforthese
analyses.

6. Blood Genomic Approaches –“Liquid Biopsies”
Based on blood components and the information in which we are
interested in obtaining:
•Molecular characterization of tumors
•Mutations that are tumor-specific
•Expression of tumor-specific RNA transcripts
Analytesof high interest:
•Cell-free nucleic acids -cfDNA and cfRNA, or ctDNA
•Exosomalnucleic acids
•Circulating tumor cells
•Peripheral blood mononuclear cells (PBMCs)
Assay approaches:
•qPCR
•NGS –Target enrichment (amplicon, hybrid capture)
Brock, G. et al. Translational Cancer
Research, Vol. 4, June (2015)

7. Liquid Biopsies and Derivation of ctDNA
Crowley, E. et al. Nat. Rev. Clin. Oncol. 2013;10:472-34

8. Blood Genomic Approaches Address Unmet Clinical
Needs in Precision Medicine
•Blood-basedassaysorscreensforsomaticgenemutationsin
circulatingtumor-derivedcell-freeDNA(cfDNA)
–Minimally-invasiveandandcanbehighlyspecific
–Detectthepresenceofprimarytumorsandtomonitortheirresponsesto
therapy.
–Dependinguponthetechnology,potentialcapabilityforearlierdetectionof
aggressiveprimarycancers,residualdiseasefollowingresection,emerging
therapy-refractorytumors,andmetastaticdisease
•Novelmoleculardiagnostictoolsthataddressesunmetclinical
needsincancercarethrough
–Improveddetectionandmonitoringofcancerstatus
–Facilitatingprecisioncancermedicineparadigms
–Clinicaltrials:M-PACT,NCI-MATCH,andALCHEMIST
•Companiondiagnostic?

9. Pancreatic Ductal Adenocarcinoma (PDAC)
•Newmethodsofassessmentare
needed.
•4thleadingcauseofcancerdeathinthe
UnitedStates
–Mortalityisrising
–<5%patientsreach5-yearsurvival
–2/3patientsdiewithin1styearof
diagnosis
•Adenocarcinoma–95%ofcases
–NE/Isletcellcarcinoma-<5%ofcases
•Molecularassessmentisessential
–Prognosis
–Evaluatingcandidatepredictivemarkers
•Challengesassociatedwithtumor
biopsy
–Anatomy
–Desmoplasticreaction
–Advancedpresentation

10. Pancreatic Adenocarcinoma is Highly Metastatic
Hezel, A.F., et al. Genes& Dev. 20: 1218-1249 (2006)
PancareFoundation

11. Genomic Features of PDAC
•16 significantly-mutated genes defined:
–Common driver mutations: KRAS, TP53, CDKN2A, SMAD4, MLL3, TGFBR2,
ARID1A, SF3B1
–Chromatin modification: EPC1, ARID2
–DNA damage repair: ATM
–Additional mechanisms: ZIM2, MAP2K4, NALCN, SLC16A4, MAGEA6
Biankin, AV et al. Nature. 491:399-405 (2012)
Lawrence, MS et al. Nature. 499:214–218 (2013)

12. Detection of KRASMutation in Circulating Cell-free
DNA Is a Strong Predictor of Survival
•PhaseIIStudyofGemcitabineandIntermittentErlotinib
•TumorandcirculatingtumorDNA(ctDNA)samplesobtained
•AnalysisofKRASstatus:therascreenKRASRGQPCRKit(Qiagen)
•PatientswithmutantKRAShadsignificantlylowermedianPFS(1.8
vs.4.6months,p=0.014)andoverallsurvival(3.0vs.10.5months,
p=0.003)thanthosewithoutdetectedplasmaKRASmutations
Semrad, T. et al. IntJ ClinOncol20:518-24 (2015).

13. Study Goals
OverallGoal:Performproof-ofprinciplestudiestodemonstratethe
feasibilityofusinganext-generation-sequencing(NGS)-
basedassayforidentifyingmutationsincell-free,
circulatingtumorDNA(ctDNA)obtainedfrompatients
withpancreaticcancer
•ComparetheresultsofKRASmutationanalysisofmatchedtumor
andctDNAsamplesusingtargetedNGStothatperformedwitha
CLIAqPCR-basedassay
•ExpandtheanalysisbeyondKRAStoidentifyadditionalcancer-
relevantenes.

14. Study Design -1
TumorandcirculatingtumorDNA(ctDNA)samples
•ObtainedfrompatientsenrolledinctDNAKRASMutationsin
PancreasCancer,aPhaseIIStudyofGemcitabineandIntermittent
Erlotinib
–N=28patientswithplasmasamples
–N=3correspondingtumorspecimens(FFPE)
•DNAisolations:
–Plasmasamples–ChemagenSystem(PerkinElmer)
–FFPEtumorsamples–QIAampDNAFFPETissueKit(Qiagen)Blood
Sampling Centrifugaon
DNA
Isolaon

15. Study Design -2
•Targeted,AmpliconNGSofCancer-RelevantGenes
–Samples:FFPEtumors,ctDNAsamples
–MultiplexsequencingperformedonIlluminasequencingsystems:
•MiSeq:2x75bp,paired-end,6libraries/run
•HiSeq2000:2x125bp,paired-end,14libraries/lane
•Sequencingdataanalysistoidentifysomaticmutations
–Alternateallelefraction
–Typeofmutation
–Evidenceofsomaticmutation
–Impactofmutation
•Compareresults
–MutationsinctDNAvs.FFPE
–IdentifyotherrelevantmutationsintumorsandctDNAs
–IdentifypotentialtherapeutictargetsSelect
functional
mutations
Non-
synonymous
Missense
Nonsense
Stop gain
Stop loss
Splicing
Filter out
normal
variants
dbSNP
>1% in pop
Mutation
prioritization
COSMIC(+),
TCGA(+)
SIFT
PolyPhen2
FASTQ sequence files
Data Visualization: Integrative
Genomics Viewer (IGV) (Broad Institute)
Qiagen GeneRead
Variant Calling Pipeline
Read mapping – Bowtie2
Variant calling – Genome
Analysis ToolKit (GATK)
Ingenuity Variant Analysis (Qiagen)
Custom Tools (UCDCCC GSR)

16. GeneReadHuman Comprehensive Cancer Panel –
Gene List

17. GeneReadDNAseqComprehensive Cancer Panel V2
•Targetedsequencinganalysisofthecompleteexonicregionsof160
cancer-relevantgenes,including:
–KRAS,BRAF,ALK,EGFR,ROS1,PIK3CA,MET,etc.
•Ampliconsequencingapproach–PCR-basedenrichmentoftargeted
genomicregions
–7,951 regions/ampliconstargeted by the panel cover 745 kbpof
genomic content.
–Barcoded sequencing libraries prepared from amplified and enriched
regions

18. 18
Overview of GeneReadTargeted Panel Protocol
GeneRead DNAseqTargeted Panel V2
PCR primer mix
Add genomic DNA (10 ng/reaction)and
GeneRead DNAseqPanel PCR Kit V2
Pool reactions for each sample
and purify (AMPure
®
bead purification)
PCR amplification
3 hours
NGSlibrary preparation

19. GeneReadDNAseqTargeted Panels V2
•Multiplex PCR-enabled enrichment of any region, gene, or set of genes in the
human genome
•Average ampliconsize = 150 bp
•Need just 10 ngof DNA/pool –40 ngfor human Comprehensive Panel
•Takes only 3 hours for target enrichment
•Integrated data analysis and biological interpretation

20. NGS Data Analysis Pipeline for Variant Calling
•Primarygoalofanalysisistoidentifysomaticmutationshavinga
functionalimpact
•ApipelineisassembledusingvariouscomputationaltoolsSelect
functional
mutations
Non-
synonymous
Missense
Nonsense
Stop gain
Stop loss
Splicing
Filter out
normal
variants
dbSNP
>1% in pop
Mutation
prioritization
COSMIC(+),
TCGA(+)
SIFT
PolyPhen2
FASTQ sequence files
Data Visualization: Integrative
Genomics Viewer (IGV) (Broad Institute)
Qiagen GeneRead
Variant Calling Pipeline
Read mapping – Bowtie2
Variant calling – Genome
Analysis ToolKit (GATK)
Ingenuity Variant Analysis (Qiagen)
Custom Tools (UCDCCC GSR)

21. Biological and Technical Considerations
•Amount of tumor DNA shed into blood
–Tumor burden
–Type of tumor, treatment type status and type
•Tumor heterogeneity –mutant allele fraction
•Amount of normal DNA in blood
•Fragment size of DNA
•Assay design
–PCR, NGS, Enrichment, Input requirements
•Focused tests are more practical –Targeted

22. Workflow with GeneReadDNAseqTargeted PanelsPanels

23. Detailed Workflow with GeneReadDNAseqTargeted
Panels
AMPurebead purification
GeneRead amplification
GeneRead Library Prep
GeneReadDNAseq Panel
GeneRead Size Selection
QIAquickPCR Purification
RUO Hybrid Workflow
GeneRead Library Quant Kit
GeneReadQuantiMIZEKit
FFPE DNA isolation
Sequencing
CLC Cancer Work bench
(hh:mm)
3:45
2:00
3:00
1:00
2:00
1:15
1:00
0:45
0:30
3:00
24:00
5:00
Day 1
Day 2
Day 3
For 12 samples
Turnaround time:
4 days
AMPurebead purification
Day 4

24. IGV Visualization of MiSeqData from Matched Tumor-
ctDNASamples from Patients 1 and 2
Patient 1
Tumor
Patient 1
ctDNA
Patient 2
ctDNA
Patient 2
Tumor

25. Investigation of the Sensitivity of Targeted NGS for
Detection of KRAS mutations in circulating, cell-free
DNA
•Both the ARMS and NGS-based assays do not always detect KRAS mutations that
were found in the tumor by each assay. Patient
ARMS
Tumor
MiSeq
Tumor
HiSeq
Tumor
1 G12R G12R G12R
2 G12V G12V G12V
3 G12V G12V G12V
ARMS
Plasma
G12R
Not Detected
Not Detected
GeneRead
Plasma
Not Detected
G12V
G12V

26. Investigation of the Sensitivity of Targeted NGS for
Detection of KRAS mutations in circulating, cell-free
DNASample Mutation # Reads # Alterations % Altered
CT008933 G12R 6139 1535 25
Sample Mutation # Reads # Alterations % Altered
CT009511 G12R 4892 327 6.68
CT008541 G12V 4212 222 5.27
CT012350 G12V 6349 188 2.96
CT012001 G12D 4224 12 0.28
CT012689 G12V 5059 3 0.06
CT013900 G12R 3072 1 0.03
CT012907 G12D 4203 0 0.00
Average 4573 108 2.18
Using standard cutpoints, 3 of 10 KRAS mutations detected by NGS
Expanding analysis of 7 “Negative” Samples

27. Summary NGS Statistics for Matched ctDNA-
Tumor PairsTumor ctDNA Tumor ctDNA Tumor ctDNA
Non-Synonymous Variants 19 13 26 17 25 19
ctDNA Variants in Tumor
ctDNA Variants in Tumor (%)
Number of Variants <0.5 15 8 21 14 13 14
Percentage of variants <0.578.95 61.54 80.77 82.35 52.00 73.68
0.53 0.50 0.68
Patient 1 Patient 2 Patient 3
10 13 17

28. Comparison of Variant Allele Fractions Found in
BRAF-wtMelanoma and ctDNASamplesGene Name
Codon
Change AA Change
Tumor ctDNA Tumor ctDNA Tumor ctDNA
Patient 1 Patient 2 Patient 3
ALK
Gene Name
c.4623C>G p.V1541
Codon
Change AA ChangeCT014396CT009511CT012559CT012908CT013170CT013095
0.53 0.52
CDK12
CDK12
c.1632T>C p.P544
c.1614T>C p.P538
0.36 0.52 0.32
0.34 0.36 0.53
CHEK2
EGFR
c.1626G>C p.L542
c.1496G>A p.C499Y
0.31 0.33 0.53 0.45
0.29 0.31 0.30 0.35 0.32 0.26
FANCD2
FANCE
FH
GNAQ
GNAQ
HRAS
c.2259T>C p.D753
c.1478T>C p.M493T
c.1358T>A p.L453Q
c.162G>C p.T54
c.175A>C p.M59L
c.287A>G p.Y96C
0.23 0.25 0.15 0.18 0.24 0.25
0.25 0.27 0.28 0.31 0.26 0.25
0.11 0.10
0.15 0.19 0.25 0.17
0.15 0.19 0.25 0.17
0.26 0.20 0.28 0.17 0.29
IL6ST
JAK1
JAK1
c.819T>G p.P273
c.457A>G p.S153G
c.456C>T p.A152
0.15 0.35 0.14 0.26 0.22
0.21 0.20 0.20 0.11
0.21 0.20 0.20 0.11
MLH1
MLL2
MLL2
NF1
NOTCH1
NOTCH1
c.1896G>C p.E632D
c.14367T>C p.S4789
c.2259C>T p.S753
c.3200A>T p.D1067V
c.3270C>T p.T1090
c.4251C>T p.P1417
0.27 0.34 0.27 0.32 0.29 0.24
0.44 0.33 0.15
0.56 0.39
0.19 0.20 0.23 0.18
0.54 0.48
0.52 0.51
PBRM1
SMAD4
SMARCB1
SMARCB1
TP53
TP53
TP53
TSC2
TSC2
XPC
c.4487G>A p.R1496Q
c.353C>T p.A118V
c.620A>G p.N207S
c.1098C>T p.R366
c.80C>A p.A27D
c.102C>T p.P34
c.182A>G p.H61R
c.2010T>G p.P670
c.2001A>G p.T667
c.2527C>T p.R843W
0.12 0.15 0.17
0.17
0.45 0.53
0.49 0.49
0.17
0.20
0.44
0.41 0.43
0.49
0.13

29. Molecular Characterization of Cell-free DNA
Specimens from Patients with Pancreatic CancerGene$Symbol
AKT1
ALK
ALK
APC
AR
ASXL1
ATM
BRAF
BTK
CBL
CDH1
CDKN2A
CIC
CREBBP
CYLD
ECT2L
EP300
ERBB4
ESR1
FAM46C
FGFR2
FGFR3
GPC3
GRIN2A
JAK2
JAK3
KDR
KIT
KRAS
U2AF1
MEN1
MET
MTOR
MYC
NF1
NFE2L2
NOTCH1
NOTCH2
PAX5
PBRM1
PDGFRA
PMS2
PPP2R1A
PRKAR1A
PTCH1
PTPN11
RB1
RET
ROS1
SETD2
SMAD4
SMARCA4
SMARCB1
SUFU
TP53
Mutation$Count
C
T
0
1
0
1
8
0
C
T
0
1
2
0
0
1
C
T
0
0
8
9
5
9
C
T
8
0
7
3
C
T
0
1
3
9
0
0
C
T
0
1
2
3
5
0
C
T
0
1
2
1
8
7
C
T
0
1
3
0
9
5
C
T
0
1
2
9
0
8
C
T
0
1
1
1
3
1
C
T
0
1
2
9
0
7
C
T
8
1
9
5
C
T
0
0
8
5
7
1
C
T
0
1
3
4
1
3
C
T
0
1
2
3
5
4
C
T
0
1
2
4
5
8
C
T
0
1
0
0
9
7
C
T
0
1
2
8
2
7
C
T
0
1
0
9
8
0
C
T
0
0
8
9
3
3
C
T
0
0
9
5
1
1
C
T
0
1
2
4
5
9
C
T
0
0
8
5
4
1
C
T
0
1
2
4
5
5
C
T
0
0
8
9
4
5
C
T
0
1
1
4
2
2
C
T
0
1
0
3
2
8
C
T
0
1
2
6
8
9
15 18
13
58
64 30 11
21 8 9 10 12
43 13 17
12 52 13 51 15
17
21
52
43
26
40
16
60
14
20
25
21
24
59
50
16
17
40
55 12
49 11
10
24 16 27 65
14
12
50
17
10
10 10
13
41 67
50
70
35
14
41
13 21 39
8
57
14
20
22
49
64
10 13
11
45
26
23 46 11 16 41
2033001020141431201232311022317

30. Molecular Characterization of Cell-free DNA
Specimens from Patients with Pancreatic Cancer
•SeveralPDACctDNAsampleswiththehighestnumberofaberrations
possesssomaticmutationsinDNAdamagecheckpointgenes:ATMandTP53Gene$Symbol
AKT1
ALK
ALK
APC
AR
ASXL1
ATM
BRAF
BTK
CBL
CDH1
CDKN2A
CIC
CREBBP
CYLD
ECT2L
EP300
ERBB4
ESR1
FAM46C
FGFR2
FGFR3
GPC3
GRIN2A
JAK2
JAK3
KDR
KIT
KRAS
U2AF1
MEN1
MET
MTOR
MYC
NF1
NFE2L2
NOTCH1
NOTCH2
PAX5
PBRM1
PDGFRA
PMS2
PPP2R1A
PRKAR1A
PTCH1
PTPN11
RB1
RET
ROS1
SETD2
SMAD4
SMARCA4
SMARCB1
SUFU
TP53
Mutation$Count
C
T
0
1
0
1
8
0
C
T
0
1
2
0
0
1
C
T
0
0
8
9
5
9
C
T
8
0
7
3
C
T
0
1
3
9
0
0
C
T
0
1
2
3
5
0
C
T
0
1
2
1
8
7
C
T
0
1
3
0
9
5
C
T
0
1
2
9
0
8
C
T
0
1
1
1
3
1
C
T
0
1
2
9
0
7
C
T
8
1
9
5
C
T
0
0
8
5
7
1
C
T
0
1
3
4
1
3
C
T
0
1
2
3
5
4
C
T
0
1
2
4
5
8
C
T
0
1
0
0
9
7
C
T
0
1
2
8
2
7
C
T
0
1
0
9
8
0
C
T
0
0
8
9
3
3
C
T
0
0
9
5
1
1
C
T
0
1
2
4
5
9
C
T
0
0
8
5
4
1
C
T
0
1
2
4
5
5
C
T
0
0
8
9
4
5
C
T
0
1
1
4
2
2
C
T
0
1
0
3
2
8
C
T
0
1
2
6
8
9
15 18
13
58
64 30 11
21 8 9 10 12
43 13 17
12 52 13 51 15
17
21
52
43
26
40
16
60
14
20
25
21
24
59
50
16
17
40
55 12
49 11
10
24 16 27 65
14
12
50
17
10
10 10
13
41 67
50
70
35
14
41
13 21 39
8
57
14
20
22
49
64
10 13
11
45
26
23 46 11 16 41
2033001020141431201232311022317
Gene$Symbol
AKT1
ALK
ALK
APC
AR
ASXL1
ATM
BRAF
BTK
CBL
CDH1
CDKN2A
CIC
CREBBP
CYLD
ECT2L
EP300
ERBB4
ESR1
FAM46C
FGFR2
FGFR3
GPC3
GRIN2A
JAK2
JAK3
KDR
KIT
KRAS
U2AF1
MEN1
MET
MTOR
MYC
NF1
NFE2L2
NOTCH1
NOTCH2
PAX5
PBRM1
PDGFRA
PMS2
PPP2R1A
PRKAR1A
PTCH1
PTPN11
RB1
RET
ROS1
SETD2
SMAD4
SMARCA4
SMARCB1
SUFU
TP53
Mutation$Count
C
T
0
1
0
1
8
0
C
T
0
1
2
0
0
1
C
T
0
0
8
9
5
9
C
T
8
0
7
3
C
T
0
1
3
9
0
0
C
T
0
1
2
3
5
0
C
T
0
1
2
1
8
7
C
T
0
1
3
0
9
5
C
T
0
1
2
9
0
8
C
T
0
1
1
1
3
1
C
T
0
1
2
9
0
7
C
T
8
1
9
5
C
T
0
0
8
5
7
1
C
T
0
1
3
4
1
3
C
T
0
1
2
3
5
4
C
T
0
1
2
4
5
8
C
T
0
1
0
0
9
7
C
T
0
1
2
8
2
7
C
T
0
1
0
9
8
0
C
T
0
0
8
9
3
3
C
T
0
0
9
5
1
1
C
T
0
1
2
4
5
9
C
T
0
0
8
5
4
1
C
T
0
1
2
4
5
5
C
T
0
0
8
9
4
5
C
T
0
1
1
4
2
2
C
T
0
1
0
3
2
8
C
T
0
1
2
6
8
9
15 18
13
58
64 30 11
21 8 9 10 12
43 13 17
12 52 13 51 15
17
21
52
43
26
40
16
60
14
20
25
21
24
59
50
16
17
40
55 12
49 11
10
24 16 27 65
14
12
50
17
10
10 10
13
41 67
50
70
35
14
41
13 21 39
8
57
14
20
22
49
64
10 13
11
45
26
23 46 11 16 41
2033001020141431201232311022317

31. Rules-based Selection of Therapeutic Targets Based
On Integrated Analysis of WES and RNA-seqData
Hard Filters
Somatic Variants –non-
dbSNP
Alternate Allele count >
20
Read Depth >30
Non-synonymous +
Indels
“Actionable” Filter: Therapeutic Target, Driver
Pathway
Node(s)
Expression (>10
FPKM)
Copy Number
Variants
Allele Frequency –Tumor Composition
Rearrangements
Functional
Impact
Clinical
Relevance
Technical
Variant Type
Actionabl
e
Functional

32. Rules-based Selection of Therapeutic Targets Based
on Targeted NGS Analysis of ctDNASamples

33. Summary of Findings
•TheresultsdemonstratethefeasibilityofusinganewtargetedNGS
assayforthesimultaneousidentificationofmutationsin160cancer-
relatedgenesinctDNA.
•WhilethespecificityoftheNGS-basedassayisveryhigh,achievinghigh
sensitivityfordetectionofmutationsincirculatingcfDNAderivedfrom
lowfrequencyallelesremainsachallenge.
•Thesensitivityoftheassaycanbeincreasedbyvariousapproaches,
includingdeepersequencing,inclusionofmutation-specific
primers/probes,etc.
•RoutineprimaryandsecondaryNGSdataanalysisisnowquite
straightforwardandcanbeefficientlyandquicklyperformedwithin-
housepipelinesandcommercially-availablepackages,including
GeneReadVariantCallingPipelineandIngenuityVariantAnalysis.
•Inthefuture,wewillexaminelargersetsofmatchedctDNA-tumor
samplepairsinordertomorerigorouslyevaluatethepowerofacfDNA-
basedtestforthemolecularcharacterization,detection,and/or
screeningofcancers.
•Furtheroptimizationmayallowfora“liquidbiopsy”ofmultipletypesof
cancer.

34. Acknowledgements
UC Davis Comprehensive Cancer Center
QIAGEN
•Felicity Hall
•Julie Deschênes
•Shawn Clairmont
•RaedN. Samara
Hematology and Oncology
•Thomas J. Semrad
•Philip C. Mack
•Irene M. Hutchins
•Rebekah Tsai
Support:
•NCI Cancer Center Support Grant P30CA093373 (de VereWhite)
Genomics Shared Resource
•Ryan R. Davis
•StephenieY. Liu
•Jeffrey P. Gregg
Department of Pathology and
Laboratory Medicine
•IrmiFeldman
•Regina Gandour-Edwards