Utilization of NGS to Identify Clinically-Relevant Mutations in cfDNA: Meet the NGS Experts Series Part 3
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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
- 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)
- 16. GeneRead Human Comprehensive Cancer Panel – Gene List
- 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
- 22. Workflow with GeneRead DNAseq Targeted Panels Panels
- 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
- 27. Summary NGS Statistics for Matched ctDNA- Tumor Pairs Tumor 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 <0.5 78.95 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-wt Melanoma and ctDNA Samples Gene 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 Change CT014396 CT009511 CT012559 CT012908 CT013170 CT013095 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 c.4487G>A p.R1496Q c.353C>T p.A118V c.620A>G p.N207S 0.12 0.15 0.17 0.17 0.45 0.53
- 29. Molecular Characterization of Cell-free DNA Specimens from Patients with Pancreatic Cancer 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 CT010180 CT012001 CT008959 CT8073 CT013900 CT012350 CT012187 CT013095 CT012908 CT011131 CT012907 CT8195 CT008571 CT013413 CT012354 CT012458 CT010097 CT012827 CT010980 CT008933 CT009511 CT012459 CT008541 CT012455 CT008945 CT011422 CT010328 CT012689 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 4 9 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 4 5 26 23 46 11 16 41 2 0 3 3 0 0 1 0 2 0 1 4 1 4 3 1 2 0 12 3 2 3 1 10 2 2 3 17
- 30. Molecular Characterization of Cell-free DNA Specimens from Patients with Pancreatic Cancer • Several PDAC ctDNA samples with the highest number of aberrations possess somatic mutations in DNA damage checkpoint genes: ATM and TP53 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$Count 58 64 30 1 1 21 8 9 10 12 43 13 1 7 12 5 2 1 3 5 1 1 5 1 7 2 1 52 4 3 2 6 40 16 6 0 1 4 2 0 2 5 2 1 2 4 59 50 16 17 4 0 55 1 2 4 9 1 1 10 24 16 2 7 6 5 14 12 50 1 7 1 0 1 0 1 0 1 3 41 67 5 0 70 35 1 4 4 1 13 2 1 3 9 8 57 1 4 20 2 2 4 9 6 4 10 13 11 4 5 2 6 23 46 1 1 1 6 4 1 2 0 3 3 0 0 1 0 2 0 1 4 1 4 3 1 2 0 12 3 2 3 1 10 2 2 3 17 Gene$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 CT010180 CT012001 CT008959 CT8073 CT013900 CT012350 CT012187 CT013095 CT012908 CT011131 CT012907 CT8195 CT008571 CT013413 CT012354 CT012458 CT010097 CT012827 CT010980 CT008933 CT009511 CT012459 CT008541 CT012455 CT008945 CT011422 CT010328 CT012689 15 1 8 13 5 8 6 4 3 0 11 21 8 9 1 0 1 2 4 3 1 3 17 1 2 52 13 51 15 17 21 5 2 43 26 4 0 1 6 60 14 20 25 21 24 5 9 5 0 1 6 1 7 40 55 12 49 11 1 0 24 1 6 27 65 14 12 50 17 10 10 10 13 4 1 6 7 50
- 31. Rules-based Selection of Therapeutic Targets Based On Integrated Analysis of WES and RNA-seq Data 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 VariantType Actionabl e Functional
- 32. Rules-based Selection of Therapeutic Targets Based on Targeted NGS Analysis of ctDNA 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