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.
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Utilization of NGS to Identify Clinically-Relevant Mutations in cfDNA: Meet the NGS Experts Series Part 3
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
2.
3.
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 – Illumina platform
– Transcriptomesequencing:Total RNA-Seq,Small RNA-Seq
– Genomic DNA sequencing: Whole genome, whole-exome sequencing
– Epigenomics: ChIP-Seq,MethylC-Seq
– Amplicon sequencing
• Bioinformatics
– Analysis pipelinesforall common NGS applications
• Clinical assay development and optimization
– UCD129 Cancer Gene Mutation Assay
– TP53 Functional Assay
– HBV Genome SequencingAssay
– Liquid biopsies – blood genomics:cfDNA, gene expression
5. Personalized Cancer Therapy – Precision Medicine
• Tailoring therapy to a patient’s specific needs by
matching the treatment strategy to molecular
features specific for their cancer.
• Standard of care for several cancer types
possessing well-defined genomic aberrations.
– Amplifications, point mutations, insertions/deletions,
gene fusions
– BRAF, ERBB2, EGFR, KRAS, ALK fusions, PIK3CA
• Revolutionized by next-generation sequencing
(NGS)
– Rapid and comprehensive molecular characterization
– Whole-exome sequencing (WES) - mutations, fusions
– RNA-Sequencing (RNA-Seq) – gene expression profiles
• Tumor tissue is commonly used for these
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
Analytes of high interest:
• Cell-free nucleic acids - cfDNA and cfRNA, or ctDNA
• Exosomal nucleic 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-based assays or screens for somatic gene mutations in
circulating tumor-derived cell-free DNA (cfDNA)
– Minimally-invasive and and can be highly specific
– Detect the presence of primary tumors and to monitor their responses to
therapy.
– Depending upon the technology, potential capability for earlier detection of
aggressive primary cancers, residual disease following resection, emerging
therapy-refractory tumors, and metastatic disease
• Novel molecular diagnostic tools that addresses unmet clinical
needs in cancer care through
– Improved detection and monitoring of cancer status
– Facilitating precision cancer medicine paradigms
– Clinical trials: M-PACT, NCI-MATCH, and ALCHEMIST
• Companiondiagnostic?
9. Pancreatic Ductal Adenocarcinoma (PDAC)
• New methods of assessment are
needed.
• 4th leading cause of cancer death in the
United States
– Mortalityisrising
– <5% patientsreach 5-year survival
– 2/3 patients die within 1st year of
diagnosis
• Adenocarcinoma– 95% of cases
– NE/Islet cell carcinoma - <5% of cases
• Molecular assessmentis essential
– Prognosis
– Evaluatingcandidate predictivemarkers
• Challenges associated with tumor
biopsy
– Anatomy
– Desmoplasticreaction
– Advancedpresentation
10. Pancreatic Adenocarcinoma is Highly Metastatic
Hezel,A.F.,etal. Genes & Dev. 20: 1218-1249 (2006)
Pancare Foundation
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 etal. Nature.499:214–218 (2013)
12. Detection of KRAS Mutation in Circulating Cell-free
DNA Is a Strong Predictor of Survival
• Phase II Study of Gemcitabine and Intermittent Erlotinib
• Tumor and circulating tumor DNA (ctDNA) samples obtained
• Analysis of KRAS status: therascreen KRAS RGQ PCR Kit (Qiagen)
• Patients with mutant KRAS had significantly lower median PFS (1.8
vs. 4.6 months, p = 0.014) and overall survival (3.0 vs. 10.5 months,
p = 0.003) than those without detected plasma KRAS mutations
Semrad,T. et al. Int J Clin Oncol 20:518-24 (2015).
13. Study Goals
Overall Goal: Perform proof-of principle studies to demonstrate the
feasibility of using a next-generation-sequencing (NGS)-
based assay for identifying mutations in cell-free,
circulating tumor DNA (ctDNA) obtained from patients
with pancreatic cancer
• Compare the results of KRAS mutation analysis of matched tumor
and ctDNA samples using targeted NGS to that performed with a
CLIA qPCR-based assay
• Expand the analysis beyond KRAS to identify additional cancer-
relevant enes.
14. Study Design - 1
Tumor and circulating tumor DNA (ctDNA) samples
• Obtained from patients enrolled in ctDNA KRAS Mutations in
Pancreas Cancer, a Phase II Study of Gemcitabine and Intermittent
Erlotinib
– N = 28 patients with plasma samples
– N = 3 corresponding tumor specimens (FFPE)
• DNA isolations:
– Plasma samples – Chemagen System (Perkin Elmer)
– FFPE tumor samples – QIAamp DNA FFPE Tissue Kit (Qiagen)
Blood
Sampling Centrifuga on
DNA
Isola on
15. Study Design - 2
• Targeted, Amplicon NGS of Cancer-Relevant Genes
– Samples: FFPE tumors, ctDNA samples
– Multiplex sequencing performed on Illumina sequencing systems:
• MiSeq: 2 x 75bp, paired-end, 6 libraries/run
• HiSeq 2000:2 x 125bp,paired-end, 14 libraries/lane
• Sequencing data analysis to identify somatic mutations
– Alternate allele fraction
– Type of mutation
– Evidence of somatic mutation
– Impact of mutation
• Compare results
– Mutations in ctDNA vs. FFPE
– Identify other relevant mutations in tumors and ctDNAs
– Identify potential therapeutic targets
Select
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)
17. GeneRead DNAseq Comprehensive Cancer Panel V2
• Targeted sequencing analysis of the complete exonic regions of 160
cancer-relevantgenes,including:
– KRAS, BRAF, ALK, EGFR, ROS1, PIK3CA, MET, etc.
• Amplicon sequencing approach – PCR-based enrichment of targeted
genomic regions
– 7,951 regions/amplicons targeted by the panel cover 745 kbp of
genomic content.
– Barcoded sequencing libraries prepared from amplified and enriched
regions
18. 18
Overview of GeneRead Targeted Panel Protocol
GeneRead DNAseq Targeted Panel V2
PCR primer mix
Add genomic DNA (10 ng/reaction) and
GeneRead DNAseq Panel PCR Kit V2
Pool reactions for each sample
and purify (AMPure®
bead purification)
PCR amplification
3 hours
NGS library preparation
19. GeneRead DNAseq Targeted Panels V2
• Multiplex PCR-enabled enrichment of any region, gene, or set of genes in the
human genome
• Average amplicon size = 150 bp
• Need just 10 ng of DNA/pool – 40 ng for human Comprehensive Panel
• Takes only 3 hours for target enrichment
• Integrated data analysis and biological interpretation
20. NGS Data Analysis Pipeline for Variant Calling
• Primary goal of analysis is to identify somatic mutations having a
functional impact
• A pipeline is assembled using various computational tools
Select
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, treatmenttype statusand 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
23. Detailed Workflow with GeneRead DNAseq Targeted
Panels
AMPure bead purification
GeneRead amplification
GeneRead Library Prep
GeneRead DNAseq Panel
GeneRead Size Selection
QIAquick PCR Purification
RUO Hybrid Workflow
GeneRead Library Quant Kit
GeneRead QuantiMIZE Kit
FFPE DNAisolation
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
AMPure bead purification
Day 4
24. IGV Visualizationof MiSeq Data from Matched Tumor-
ctDNA Samples from Patients 1 and 2
Patient1
Tumor
Patient1
ctDNA
Patient2
ctDNA
Patient2
Tumor
25. Investigationof 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. Investigationof the Sensitivity of Targeted NGS for
Detection of KRAS mutations in circulating,cell-free
DNA
Sample 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
33. Summary of Findings
• The results demonstrate the feasibility of using a new targeted NGS
assay for the simultaneous identification of mutations in 160 cancer-
related genes in ctDNA.
• While the specificity of the NGS-based assay is very high, achieving high
sensitivity for detection of mutations in circulating cfDNA derived from
low frequency alleles remainsa challenge.
• The sensitivity of the assay can be increased by various approaches,
including deeper sequencing, inclusion of mutation-specific
primers/probes,etc.
• Routine primary and secondary NGS data analysis is now quite
straightforward and can be efficiently and quickly performed with in-
house pipelines and commercially-available packages, including
GeneRead Variant Calling Pipeline and Ingenuity Variant Analysis.
• In the future, we will examine larger sets of matched ctDNA-tumor
sample pairs in order to more rigorously evaluate the power of a cfDNA-
based test for the molecular characterization, detection, and/or
screeningof cancers.
• Further optimization may allow for a “liquid biopsy” of multiple types of
cancer.
34. Acknowledgements
UC Davis Comprehensive Cancer Center
QIAGEN
• Felicity Hall
• Julie Deschênes
• Shawn Clairmont
• Raed N. Samara
Hematology and Oncology
• Thomas J. Semrad
• Philip C. Mack
• Irene M. Hutchins
• Rebekah Tsai
Support:
• NCI Cancer Center Support Grant P30CA093373 (de Vere White)
Genomics Shared Resource
• Ryan R. Davis
• Stephenie Y. Liu
• Jeffrey P. Gregg
Department of Pathology and
Laboratory Medicine
• Irmi Feldman
• Regina Gandour-Edwards