Download Isolation of Circulating Tumor DNA from Archived Plasma Samples

Isolation of Circulating Tumor DNA from Archived Plasma Samples Application for Downstream Approaches Andrew B. Nixon, MBA, PhD Duke University Medical Center Disclaimers Nature of Research Presented: •  All data presented are derived using banked, residual samples from retrospective studies and used only for research purposes. None of this data is being used for clinical decision making. •  All products are intended for research use only – not for use in diagnostic procedures Support Received: •  cfDNA purification reagents were supplied by Promega Phase I Biomarker Laboratory Duke University Medical Center Laboratory Themes •  Develop & optimize study-­‐specific blood-­‐based biomarker assays •  Implement operational standardization across studies •  Execute correlative biomarker analyses in a time-­‐ and cost-­‐effective manner •  Correlate biomarkers at baseline and on-­‐treatment with measures of clinical benefit and toxicity •  Assess novel assay technologies and platforms Currently serve as a Molecular Reference Laboratory for the Alliance Oncology Cooperative Group What is Cancer – The Old Paradigm Breast Cancer Lung Cancer Colon Cancer Personalized Medicine “Knowledge of the molecular profile of the tumor is necessary to guide selection of therapy for patient.” -­‐ Richard Schilsky (ASCO president 2008-­‐2009, Chair CALGB (1995-­‐2010)) Current Model A All patients receive standard treatment A Patients B B Clinical trial Personalized Medicine Model Patients Molecular analysis of tumor and patients A B C D Choice of treatment dependent upon molecular profile of tumor Biomarkers •  Types of Biomarkers • 
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Prognostic • 
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Regulatory Predictive Pharmacodynamic Pharmacogenomic •  Uses Optimize drug use Explore mechanisms of action, toxicity, sensitivity, resistance Explore other aspects of biology Should be fit for purpose • 
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Assay: Sample handling, analytic methods Context: Drug, target, pathway, disease and setting Goals: Immediate needs vs long term uses The Translational Gap – Valley of Death •  Translation of basic research findings to clinically applicable situations •  Animal model systems don’t often translate to patients •  Heterogeneity of patients can’t be well modeled •  Specificities of drugs across systems •  New therapies require immune systems to be intact •  Translational research works both ways •  From bench to bedside & bedside back to bench Striving to bridge the Gap Recognizing these difficulties, how do we bridge this gap? B Mellor, Nature 2008 Background -­‐ Circulating Cell-­‐Free DNA (cfDNA) •  Initially described in prenatal settings •  Initial report detecting fetal, circulating DNA in maternal circulation •  Detectable within 5-­‐6 weeks after pregnancy •  Cleared from circulation within 30 min after delivery •  Consists of 3-­‐6% of total cfDNA pool Circulating Cell-­‐Free, Tumor DNA (ctDNA) •  Current identification of ctDNA opens many new doors for cancer researchers •  No biopsies needed • 
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Limits risks and cost to patients Solution for situations in which tumor is unavailable or inaccessible •  Other Advantages • 
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Longitudinal analyses Treat the current situation (i.e., metastatic disease) in real-­‐time Data obtained from primary tissues may not reflect the current status of the patient’s cancer •  Minimizing inherent tumor sample heterogeneity Current Options for ctDNA Analysis •  Little standardization in the field •  Companies such as Guardant Health, Sysmex Inostics, and Foundation Medicine provide ctDNA analysis in the clinical setting •  What about Research Use Only testing? •  Significant $$$/sample •  Hotspot panels for pre-­‐defined genomic alterations •  Less versatility for discovery How do we approach simple questions at reasonable costs? Promise of Biobanking •  Biobanking is growing at a rapid rate •  Increases in biobanks and collections • 
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60% of all biobanks created in past 10 years High quality samples Collected and stored properly Clinical annotation is key •  Samples used for planned analyses, then stored •  What about extra material that is collected for future, unspecified research? • 
Rich source for biomarker development and discovery •  As technology advances, hard to know what ‘best science’ will be 5-­‐10 years in the future Leveraging the Wealth of the Biorepositories •  If banked samples from retrospective studies could provide ctDNA that was amenable for downstream technologies: •  Validation of pre-­‐specified hypotheses •  Discovery sample sets •  New information gained allows for better questions •  Archived samples may hold the answers for why previous trials failed Goal: Develop a process for obtaining ctDNA from 1 ml or less of frozen plasma Feasibility Issues •  Identification and availability of appropriate, retrospective sample sets •  Utilization of archived samples that were not collected for ctDNA analyses •  Ability to utilize smaller, available amounts of tissue •  Realistic asks for the biobanks •  Type of samples utilized •  Frozen, biobanked samples – multiple blood fractions available •  Control over the sample, markers, panels, etc. Key Considerations for Process •  Automation vs standard protocols •  New automated technologies vs standard column/bead approaches •  Better consistency •  Increased yields •  Cost •  Comparable to standard processing – need to consider effort savings •  Quantity required •  Looking for systems that can utilize ~1 ml (or less) Maxwell RSC System Key Features •  Obtain high yields of amplifiable ctDNA using the Maxwell RSC ccfDNA Plasma Kit* •  Save time & minimize errors • 
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No pre-­‐processing Add sample → start automated run Easy to incorporate into work flow •  Can be adapted to use larger volumes •  16 samples processed per run •  Quantification by Quantus Fluorometer* •  Cost is ~ $13 per sample *For Research Use Only. Not for use in diagnostic procedures Initial Characterization Experimental Considerations •  Sample input •  How much plasma is needed to provide sufficient material for downstream analyses? •  Cancer patient plasma samples vs healthy volunteer plasma samples •  Different types of plasma (EDTA, citrate, heparin, ACD, etc.) •  Serum samples vs plasma samples •  gDNA contamination – limit noise •  Quantitation methods •  Nanodrop •  Fluorescence (Quantus) •  Real-­‐time PCR Not all tubes are created equal ctDNA Levels From Healthy Volunteer Samples & Cancer Patient Samples 45 Total Yield (ng) 40 Avg: 20.3 ng StDev: 18.8 ng 35 30 25 20 15 Avg: 7.4 ng StDev: 2.5 ng 10 5 0 Healthy Volunteers Cancer Patients ctDNA Levels From Healthy Volunteer Samples & Cancer Patient Samples Normalized Yield (pg/µl Input) 60 50 Avg: 27.5 pg/µl StDev: 27.8 pg/µl 40 30 20 Avg: 9.2 pg/µl StDev: 3.2 pg/µl 10 0 Healthy Volunteers Cancer Patients Normalized Yield (pg/µl Input) ctDNA Levels From Matched Serum & Plasma Samples 90 80 70 Avg: 43.7 pg/µl StDev: 39.0 pg/µl 60 50 Avg: 20.7 pg/µl StDev: 12.2 pg/µl 40 30 20 10 0 Serum Plasma ctDNA Levels From Different Types of Plasma Samples Normalized Yield (pg/µl Input) 60 50 Avg: 27.5 pg/µl StDev: 27.8 pg/µl 40 Avg: 21.5 pg/µl StDev: 17.3 pg/µl 30 20 10 0 EDTA Plasma Citrate Plasma With ctDNA in Hand, Now What? •  Data in the literature suggest that levels of ctDNA may reflect overall tumor burden in some disease settings – prognostic marker •  Other previously reported data indicate ctDNA could be used to monitor residual disease •  Need to move from simply quantifying levels of ctDNA to utilizing it for downstream molecular profiling approaches Molecular Profiling Approaches •  Different technologies for various downstream uses •  Key Issues to consider •  Input yields •  Price • 
Many labs can’t afford to do extensive sequencing for all samples •  However, with ctDNA in hand, individual or pre-­‐specified work can now be done in a cost-­‐effective manner •  Focused analyses can lead to broader analyses •  Data in-­‐hand, rationale confirmed •  Flexibility Digital Droplet PCR (ddPCR) •  Increased sensitivity versus conventional real-­‐time PCR •  Input ~1 ng/reaction with varying levels of multiplexing possible (~2-­‐5 targets/reaction) •  Ideal for answering directed questions with minimum sample input •  Highly versatile for DNA analyses •  Mutations, amplifications, deletions Initial Evaluation of ddPCR Can we isolate ctDNA from archived samples and detect cMET amplification? •  Amplification of this gene is a clinically-­‐relevant genomic modification that can guide the course of treatment for colorectal cancer patients •  Developed a PCR-­‐based assay to measure genomic amplification of cMET in circulating DNA isolated from the plasma of patients with colorectal cancer •  We had access to archived, frozen samples that had previously been evaluated by Guardant Health to serve as external validation of our results Initial Evaluation of ddPCR •  We selected 4 cancer patient samples that had previously had ctDNA testing performed •  2 patients were cMET amplified, 2 patients were cMET unamplified (normal) •  Initial testing done by Guardant Health utilized 10 ml vacutainer – Streck tube shipped overnight to laboratory •  Our samples were ‘research samples’ •  Collected in EDTA vacutainers, processed on site, stored in 1 ml aliquots at -­‐80°C •  Samples collected for other purposes, but residual sample reallocated to ctDNA testing •  Isolated DNA from both cMET amplified and unamplified cell lines as controls •  All work performed in our lab is research-­‐use only ddPCR Results – Raindance System Sample Avg Gene/Control Ratio Our Result Expected/External Validation Cell-­‐Line Control 1 1.02 Unamplified Unamplified Cell-­‐Line Control 2 11.47 Amplified Amplified Sample 1 1.13 Unamplified Amplified (1.15 Expected) Sample 2 3.83 Amplified Amplified (3.2 Expected) Sample 3 0.80 Unamplified Unamplified Sample 4 1.05 Unamplified Unamplified Agreement Notes: Expected cell-­‐line control results were derived from the literature. Samples 1-­‐4 were all evaluated by Guardant Health. Sample 1 was not called amplified due to variability in our results. ddPCR Results – Life Technologies Sample Avg Gene/Control Ratio Our Result Expected/External Validation Cell-­‐Line Control 1 0.91 Unamplified Unamplified Cell-­‐Line Control 2 7.40 Amplified Amplified Sample 1 0.81 Unamplified Amplified (1.15 Expected) Sample 2 3.40 Amplified Amplified (3.2 Expected) Sample 3 0.89 Unamplified Unamplified Sample 4 0.68 Unamplified Unamplified Agreement Notes: Expected cell-­‐line control results were derived from the literature. Samples 1-­‐4 were all evaluated by Guardant Health. ddPCR Results •  Initial attempts to perform ddPCR from ctDNA isolated from archived, frozen plasma samples from cancer patients were successful •  We developed cell-­‐line controls for both amplified and unamplified variants •  3/4 samples aligned across all three technologies •  1 sample did not align •  Low degree of amplification •  Variability •  No optimization Next Generation Sequencing (NGS) •  State-­‐of-­‐the-­‐art •  Comprehensive – many analysis options •  Whole genome •  Whole exome •  Hot spot • 
Panels relevant, but fixed •  Cost still a barrier D. Goldman, Int J Neuropsychopharmacol 2014;17:1717-25
Using ctDNA for NGS Applications •  NGS in collaboration with Swift Biosciences •  Protocol optimized for 10 ng of ctDNA input Adapted from www.swiftbiosci.com Gene Coverage in the 56G Oncology Panel The table depicts the genes represented, followed by the number of amplicons for each gene. •  Contiguous, overlapping coverage is included for APC, ATM, EGFR, FBXW7, FGFR3, HNF1A, KIT, MSH6, PIK3CA, PTEN, SMAD4, and TP53. •  Comprehensive coding exon coverage is included for TP53. Adapted from www.swiftbiosci.com Isolation of ctDNA for NGS Analysis •  Samples derived from gastric cancer patients treated with capecitabine, oxaliplatin, bevacizumab1 •  Samples were archived, retrospective samples •  Plasma samples collected at baseline and during treatment •  EDTA and citrate plasma types are represented •  All samples were stored as double-­‐spun, platelet-­‐poor plasma at -­‐80°C for ≥3 years 1Uronis HE, et al. (2103) Oncologist. 18(3):271-­‐2. Isolation of ctDNA •  ctDNA was quantified using the Quantus fluorometer and ONE dsDNA kit •  Average pg ctDNA isolated/µl plasma ± standard deviation: 26.0 ± 25.5 •  Range: 3.4 – 130.0 •  Average total yield of ctDNA (ng) ± standard deviation: 19.9 ± 17.7 •  Range: 2.9 – 83.0 •  Range of plasma input (µl) : 350-­‐1200 Normalized Yield (pg/µl Input) ctDNA Recoveries 140 120 100 80 60 40 20 0 1 11 21 31 41 Sample # 51 61 71 Comparison of Plasma Types •  59 EDTA samples •  Average total yield of ctDNA (ng) ± standard deviation: 20.3 ± 18.8 •  Range: 2.9 – 83.0 ng •  21 Citrate samples •  Average total yield of ctDNA (ng) ± standard deviation: 18.6 ± 14.5 •  Range: 4.5 – 46.0 ng Comparison of Plasma Types Sample •  For 17 samples -­‐ both EDTA and citrate plasma were available at the same timepoint •  Average % difference (12.8%) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 EDTA Yield Citrate Yield Avg. Yield % (ng) (ng) (ng) Difference 8.1 7.2 7.6 5.3 4.5 5.3 4.9 8.3 20.5 17.4 18.9 7.7 10.6 6.1 8.4 21.2 10.6 10.3 10.4 1.4 18.5 32.7 25.6 38.4 6.8 7.9 7.4 8.1 63.5 44.5 54.0 15.0 38.0 43.4 40.7 7.1 13.4 10.5 11.9 10.7 12.5 4.5 8.5 32.1 20.0 21.1 20.5 2.6 31.8 26.2 29.0 8.7 33.4 46.0 39.7 18.9 11.7 12.7 12.2 4.3 23.6 18.0 20.8 12.0 16.4 11.2 13.8 15.7 Total Yield (ng)
ctDNA Quantities Required for NGS 90
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54/80 = 68% of samples yielded sufficient ctDNA for optimal NGS All samples yielded >1 ng ctDNA Next Steps – Data Expectations •  Preparing libraries •  Send to Swift Biosciences for sequencing •  56G Oncology Panel •  Data analysis •  Interpretation and meaningfulness of data •  Retrospective statistical analyses of the clinical study •  Association with outcome •  Resistance factors over time Conclusions •  Isolation of ctDNA from archived, frozen plasma specimens is feasible •  Comparisons between sample types indicated: •  Cancer patient samples have 3X more cfDNA than healthy volunteer samples •  Serum samples have 2X more cfDNA than plasma samples •  EDTA and citrate matched samples yield similar cfDNA •  ddPCR analyses -­‐ 3/4 samples aligned across technologies •  NGS of ctDNA from gastric cancer patient samples underway Future Directions If successful processes are developed utilizing archived, biobanked specimens, the biomarker pipeline will expand rapidly. Retrospective studies can be mined and biomarker discovery and validation can be expedited. The potential for longitudinal monitoring of molecular profiles in the blood can provide real-­‐time monitoring of genomic alterations, potentially helping to develop personalized therapies for patients along the continuum of care. Acknowledgments GI and Phase I Team Herb Hurwitz, MD John Strickler, MD Hope Uronis, MD Christy Arrowood Biomarker Lab Ace Hatch, PhD* Chris Brady Mark Starr Yingmiao Liu, PhD Jennifer Choe MD, PhD Statistical Team Kouros Owzar, PhD Alex Sibley Jiang Chen, PhD Dadong Zhang, PhD Jeremy Gresham All the clinical fellows and undergrads who have contributed to the lab