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Figure 1.
Sensitivity and Dynamic Range of Plasma Droplet Digital Polymerase Chain Reaction (ddPCR) for the Detection of EGFR and KRAS Mutations
Sensitivity and Dynamic Range of Plasma Droplet Digital Polymerase Chain Reaction (ddPCR) for the Detection of EGFR and KRAS Mutations

A, The sensitivity of plasma ddPCR for the detection of EGFR and KRAS mutations increases directly with the number of metastatic sites present in a given patient (P < .001). B, Dynamic range of plasma genotyping using a validated ddPCR-based assay. Wide dynamic range and the absence of false-positive test results are noted for the detection of KRAS G12X and EGFR sensitizing mutations. A small number of false-positive results are seen with the EGFR T790M assay—potentially secondary to tumor heterogeneity with respect to acquired resistance mechanisms (n = 174). Each symbol represents 1 patient with the specific mutation listed on the x-axis. CT indicates computed tomography; ND, not detectable.

Figure 2.
Distinct Patterns of Plasma Droplet Digital Polymerase Chain Reaction (ddPCR) Plasma Response in Patients Undergoing Serial Plasma Genotyping at 2 and 6 Weeks After Treatment
Distinct Patterns of Plasma Droplet Digital Polymerase Chain Reaction (ddPCR) Plasma Response in Patients Undergoing Serial Plasma Genotyping at 2 and 6 Weeks After Treatment

Six patterns of changes in detectable mutation by plasma ddPCR were observed: A, Mutant cell-free DNA (cfDNA) became undetectable at 2 weeks. B, Mutant cfDNA decreased and then became undetectable at 6 weeks. C, Mutant cfDNA decreased progressively but remained detectable at 6 weeks. D, Mutant cfDNA decreased at 2 weeks and then rebounded at 6 weeks. E, Mutant cfDNA increased initially and then decreased at 6 weeks. F, Mutant cfDNA progressively increased. A and B, Patients with complete resolution of mutant cfDNA exhibited a treatment discontinuation rate of 0% (0 of 23) and 4% (1 of 23) at initial and second restaging computed tomographic (CT) scans. C-F, Alternatively, patients without complete resolution had a treatment discontinuation rate of 33% (9 of 27) at initial reimaging and 56% (15 of 27) at second reimaging assessment. Patient genotypes included EGFR sensitizing alone (negative), EGFR sensitizing in the presence of T790M (negative), and KRAS G12X (negative). ND indicates not detectable.

Figure 3.
A Woman in Her 80s With Metastatic EGFR Mutant Non–Small-Cell Lung Cancer With Acquired Resistance to Erlotinib
A Woman in Her 80s With Metastatic EGFR Mutant Non–Small-Cell Lung Cancer With Acquired Resistance to Erlotinib

Symptomatic progression of pulmonary and bone metastases were noted (primary lung lesion labeled). Empirical single-agent chemotherapy or best supportive care alone were considered given the patient’s age and comorbidities. However, plasma droplet digital polymerase chain reaction (ddPCR) was performed and the result returned the next day, revealing 806 copies/mL of EGFR T790M. The patient underwent rebiopsy, which confirmed EGFR T790M, and the patient was able to start therapy with osimertinib—a novel third-generation EGFR kinase inhibitor—with excellent clinical and radiographic response. Importantly, the plasma ddPCR T790M result was returned 24 days before the results of the repeated tissue biopsy were available.

Table 1.  
Patients Characteristics
Patients Characteristics
Table 2.  
Plasma Droplet Digital Polymerase Chain Reaction Assay Sensitivity, Specificity, and Positive Predictive Value
Plasma Droplet Digital Polymerase Chain Reaction Assay Sensitivity, Specificity, and Positive Predictive Value
1.
Li  T, Kung  HJ, Mack  PC, Gandara  DR.  Genotyping and genomic profiling of non-small-cell lung cancer: implications for current and future therapies.  J Clin Oncol. 2013;31(8):1039-1049.PubMedGoogle ScholarCrossref
2.
Paez  JG, Jänne  PA, Lee  JC,  et al.  EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy.  Science. 2004;304(5676):1497-1500.PubMedGoogle ScholarCrossref
3.
Kwak  EL, Bang  YJ, Camidge  DR,  et al.  Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer.  N Engl J Med. 2010;363(18):1693-1703.PubMedGoogle ScholarCrossref
4.
Cardarella  S, Ortiz  TM, Joshi  VA,  et al.  The introduction of systematic genomic testing for patients with non-small-cell lung cancer.  J Thorac Oncol. 2012;7(12):1767-1774.PubMedGoogle ScholarCrossref
5.
Sholl  LM, Aisner  DL, Varella-Garcia  M,  et al; LCMC Investigators.  Multi-institutional oncogenic driver mutation analysis in lung adenocarcinoma: the Lung Cancer Mutation Consortium experience.  J Thorac Oncol. 2015;10(5):768-777.PubMedGoogle ScholarCrossref
6.
Shaw  AT, Solomon  BJ.  Crizotinib in ROS1-rearranged non-small-cell lung cancer.  N Engl J Med. 2015;372(7):683-684.PubMedGoogle ScholarCrossref
7.
Jänne  PA, Yang  JC, Kim  DW,  et al.  AZD9291 in EGFR inhibitor-resistant non-small-cell lung cancer.  N Engl J Med. 2015;372(18):1689-1699.PubMedGoogle ScholarCrossref
8.
Sequist  LV, Soria  JC, Goldman  JW,  et al.  Rociletinib in EGFR-mutated non-small-cell lung cancer.  N Engl J Med. 2015;372(18):1700-1709.PubMedGoogle ScholarCrossref
9.
Oxnard  GR, Paweletz  CP, Kuang  Y,  et al.  Noninvasive detection of response and resistance in EGFR-mutant lung cancer using quantitative next-generation genotyping of cell-free plasma DNA.  Clin Cancer Res. 2014;20(6):1698-1705.PubMedGoogle ScholarCrossref
10.
Mok  T, Wu  YL, Lee  JS,  et al.  Detection and dynamic changes of EGFR mutations from circulating tumor DNA as a predictor of survival outcomes in NSCLC patients treated with first-line intercalated erlotinib and chemotherapy.  Clin Cancer Res. 2015;21(14):3196-3203.PubMedGoogle ScholarCrossref
11.
Karachaliou  N, Mayo-de las Casas  C, Queralt  C,  et al; Spanish Lung Cancer Group.  Association of EGFR L858R mutation in circulating free DNA with survival in the EURTAC trial.  JAMA Oncol. 2015;1(2):149-157.PubMedGoogle ScholarCrossref
12.
Lee  YJ, Yoon  KA, Han  JY,  et al.  Circulating cell-free DNA in plasma of never smokers with advanced lung adenocarcinoma receiving gefitinib or standard chemotherapy as first-line therapy.  Clin Cancer Res. 2011;17(15):5179-5187.PubMedGoogle ScholarCrossref
13.
Couraud  S, Vaca-Paniagua  F, Villar  S,  et al; BioCAST/IFCT-1002 investigators.  Noninvasive diagnosis of actionable mutations by deep sequencing of circulating free DNA in lung cancer from never-smokers: a proof-of-concept study from BioCAST/IFCT-1002.  Clin Cancer Res. 2014;20(17):4613-4624.PubMedGoogle ScholarCrossref
14.
Bai  H, Zhao  J, Wang  SH,  et al.  The detection by denaturing high performance liquid chromatography of epidermal growth factor receptor mutation in tissue and peripheral blood from patients with advanced non-small cell lung cancer [in Chinese].  Zhonghua Jie He He Hu Xi Za Zhi. 2008;31(12):891-896.PubMedGoogle Scholar
15.
Goto  K, Ichinose  Y, Ohe  Y,  et al.  Epidermal growth factor receptor mutation status in circulating free DNA in serum: from IPASS, a phase III study of gefitinib or carboplatin/paclitaxel in non-small cell lung cancer.  J Thorac Oncol. 2012;7(1):115-121.PubMedGoogle ScholarCrossref
16.
Douillard  JY, Ostoros  G, Cobo  M,  et al.  Gefitinib treatment in EGFR mutated Caucasian NSCLC: circulating-free tumor DNA as a surrogate for determination of EGFR status.  J Thorac Oncol. 2014;9(9):1345-1353.PubMedGoogle ScholarCrossref
17.
Dawson  SJ, Tsui  DW, Murtaza  M,  et al.  Analysis of circulating tumor DNA to monitor metastatic breast cancer.  N Engl J Med. 2013;368(13):1199-1209.PubMedGoogle ScholarCrossref
18.
Misale  S, Yaeger  R, Hobor  S,  et al.  Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer.  Nature. 2012;486(7404):532-536.PubMedGoogle Scholar
19.
Diehl  F, Schmidt  K, Choti  MA,  et al.  Circulating mutant DNA to assess tumor dynamics.  Nat Med. 2008;14(9):985-990.PubMedGoogle ScholarCrossref
20.
Bettegowda  C, Sausen  M, Leary  RJ,  et al.  Detection of circulating tumor DNA in early- and late-stage human malignancies.  Sci Transl Med. 2014;6(224):224ra24.PubMedGoogle ScholarCrossref
21.
Li  J, Wang  L, Mamon  H, Kulke  MH, Berbeco  R, Makrigiorgos  GM.  Replacing PCR with COLD-PCR enriches variant DNA sequences and redefines the sensitivity of genetic testing.  Nat Med. 2008;14(5):579-584.PubMedGoogle ScholarCrossref
22.
Sanmamed  MF, Fernández-Landázuri  S, Rodríguez  C,  et al.  Quantitative cell-free circulating BRAFV600E mutation analysis by use of droplet digital PCR in the follow-up of patients with melanoma being treated with BRAF inhibitors.  Clin Chem. 2015;61(1):297-304.PubMedGoogle ScholarCrossref
23.
Paweletz  CP, Sacher  AG, Raymond  CK,  et al.  Bias-corrected targeted next-generation sequencing for rapid, multiplexed detection of actionable alterations in cell-free DNA from advanced lung cancer patients.  Clin Cancer Res. 2016;22(4):915-922.PubMedGoogle ScholarCrossref
24.
Reck  M, Hagiwara  K, Han  B,  et al. Investigating the utility of circulating-free tumour-derived DNA (ctDNA) in plasma detection of epidermal growth factor receptor (EGFR) mutation status in European and Japanese patients with advanced non-small-cell lung cancer: ASSESS Study. Paper presented at: European Lung Cancer Conference (ELCC); April 15-18, 2015; Geneva, Switzerland.
25.
Zhu  G, Ye  X, Dong  Z,  et al.  Highly sensitive droplet digital PCR method for detection of EGFR-activating mutations in plasma cell-free DNA from patients with advanced non-small cell lung cancer.  J Mol Diagn. 2015;17(3):265-272.PubMedGoogle ScholarCrossref
26.
Thress  KS, Paweletz  CP, Felip  E,  et al.  Acquired EGFR C797S mutation mediates resistance to AZD9291 in non-small cell lung cancer harboring EGFR T790M.  Nat Med. 2015;21(6):560-562.PubMedGoogle ScholarCrossref
27.
Sundaresan  TK, Sequist  LV, Heymach  JV,  et al.  Detection of T790M, the acquired resistance EGFR mutation, by tumor biopsy vs noninvasive blood-based analyses.  Clin Cancer Res. 2016;22(5):1103-1110.PubMedGoogle ScholarCrossref
28.
Thress  K, Yang  J, Ahn  M,  et al. Levels of EGFR T790M in plasma DNA as a predictive biomarker for response to AZD9291, a mutant-selective EGFR kinase inhibitor. Paper presented at: European Society for Medical Oncology (ESMO) Annual Meeting; September 26-30, 2014; Madrid, Spain.
Original Investigation
August 2016

Prospective Validation of Rapid Plasma Genotyping for the Detection of EGFR and KRAS Mutations in Advanced Lung Cancer

Author Affiliations
  • 1Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
  • 2Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
  • 3Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, Massachusetts
  • 4Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
  • 5Harvard T.H. Chan School of Public Health, Boston, Massachusetts
 

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Oncol. 2016;2(8):1014-1022. doi:10.1001/jamaoncol.2016.0173
Abstract

Importance  Plasma genotyping of cell-free DNA has the potential to allow for rapid noninvasive genotyping while avoiding the inherent shortcomings of tissue genotyping and repeat biopsies.

Objective  To prospectively validate plasma droplet digital PCR (ddPCR) for the rapid detection of common epidermal growth factor receptor (EGFR) and KRAS mutations, as well as the EGFR T790M acquired resistance mutation.

Design, Setting, and Participants  Patients with advanced nonsquamous non–small-cell lung cancer (NSCLC) who either (1) had a new diagnosis and were planned for initial therapy or (2) had developed acquired resistance to an EGFR kinase inhibitor and were planned for rebiopsy underwent initial blood sampling and immediate plasma ddPCR for EGFR exon 19 del, L858R, T790M, and/or KRAS G12X between July 3, 2014, and June 30, 2015, at a National Cancer Institute–designated comprehensive cancer center. All patients underwent biopsy for tissue genotyping, which was used as the reference standard for comparison; rebiopsy was required for patients with acquired resistance to EGFR kinase inhibitors. Test turnaround time (TAT) was measured in business days from blood sampling until test reporting.

Main Outcomes and Measures  Plasma ddPCR assay sensitivity, specificity, and TAT.

Results  Of 180 patients with advanced NSCLC (62% female; median [range] age, 62 [37-93] years), 120 cases were newly diagnosed; 60 had acquired resistance. Tumor genotype included 80 EGFR exon 19/L858R mutants, 35 EGFR T790M, and 25 KRAS G12X mutants. Median (range) TAT for plasma ddPCR was 3 (1-7) days. Tissue genotyping median (range) TAT was 12 (1-54) days for patients with newly diagnosed NSCLC and 27 (1-146) days for patients with acquired resistance. Plasma ddPCR exhibited a positive predictive value of 100% (95% CI, 91%-100%) for EGFR 19 del, 100% (95% CI, 85%-100%) for L858R, and 100% (95% CI, 79%-100%) for KRAS, but lower for T790M at 79% (95% CI, 62%-91%). The sensitivity of plasma ddPCR was 82% (95% CI, 69%-91%) for EGFR 19 del, 74% (95% CI, 55%-88%) for L858R, and 77% (95% CI, 60%-90%) for T790M, but lower for KRAS at 64% (95% CI, 43%-82%). Sensitivity for EGFR or KRAS was higher in patients with multiple metastatic sites and those with hepatic or bone metastases, specifically.

Conclusions and Relevance  Plasma ddPCR detected EGFR and KRAS mutations rapidly with the high specificity needed to select therapy and avoid repeat biopsies. This assay may also detect EGFR T790M missed by tissue genotyping due to tumor heterogeneity in resistant disease.

Introduction

Plasma genotyping uses tumor-derived cell-free DNA (cfDNA) to allow for rapid noninvasive genotyping of tumors. This technology is currently poised to transition into a treatment decision-making tool in multiple cancer types. It is particularly relevant to the treatment of advanced non–small-cell lung cancer (NSCLC), in which therapy hinges on rapid and accurate detection of targetable epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and ROS1 alterations.1-6 Plasma genotyping is capable of circumventing many limitations of standard tissue genotyping including slow turnaround time (TAT), limited tissue for testing, and the potential for failed biopsies. It may be particularly useful in directing the rapid use of new targeted therapies for acquired resistance in advanced EGFR-mutant NSCLC, where the need for a repeat biopsy to test for resistance mechanisms has amplified the inherent limitations of traditional genotyping.7,8

The need to carefully validate the test characteristics of each of the myriad individual plasma genotyping assays before use in clinical decision making is paramount. We have previously reported the development of a quantitative droplet digital polymerase chain reaction (ddPCR)-based assay for the detection of EGFR kinase mutations and KRAS codon 12 mutations in plasma.9 The detection of these mutations has the potential to guide treatment by either facilitating targeted therapy with an EGFR tyrosine kinase inhibitor (TKI) or ruling out the presence of other potentially targetable alterations in the case of KRAS.5 Alternative platforms including Cobas, peptide nucleic acid–mediated PCR, multiplexed next-generation sequencing (NGS), high-performance liquid chromatography, and Scorpion–amplified refractory mutation system have also been examined in retrospective analyses of patient samples.10-22 The test characteristics of these assays have been variable and may be attributable to differences in testing platforms, as well as the retrospective nature of these studies, their smaller size, and the timing of blood collection with respect to disease progression and therapy initiation. The absence of reliable prospective data on the use of specific plasma genotyping assays in advanced NSCLC has left key aspects of its utility largely undefined and slowed its uptake as a tool for clinical care in patients with both newly diagnosed NSCLC and EGFR acquired resistance.

To our knowledge, we have conducted the first prospective study of the use of ddPCR-based plasma genotyping for the detection of EGFR and KRAS mutations. This study was performed in the 2 settings where we anticipate clinical adoption of this assay: (1) patients with newly diagnosed advanced NSCLC and (2) those with acquired resistance to EGFR kinase inhibitors. The primary aim of this study was to prospectively evaluate the feasibility and accuracy of this assay for the detection of EGFR/KRAS mutations in patients with newly diagnosed NSCLC and EGFR T790M in patients with acquired resistance in a clinical setting. Additional end points included test TAT and the effect of sample treatment conditions on test accuracy.

Box Section Ref ID

Key Points

  • Question What is the sensitivity, specificity, turnaround time, and robustness of droplet digital polymerase chain reaction (ddPCR)-based plasma genotyping for the rapid detection of targetable genomic alterations in patients with advanced non–small-cell lung cancer (NSCLC)?

  • Findings In this study of 180 patients with advanced NSCLC (120 newly diagnosed, 60 with acquired resistance to epidermal growth factor receptor [EGFR] kinase inhibitors), plasma genotyping exhibited perfect specificity (100%) and acceptable sensitivity (69%-80%) for the detection of EGFR-sensitizing mutations with rapid turnaround time (3 business days). Specificity was lower for EGFR T790M (63%), presumably secondary to tumor heterogeneity and false-negative tissue genotyping.

  • Meaning The use of ddPCR-based plasma genotyping can detect EGFR mutations with the rigor necessary to direct clinical care. This assay may obviate repeated biopsies in patients with positive plasma genotyping results.

Methods
Trial Design

Patients with advanced NSCLC were prospectively enrolled onto an institutional review board–approved plasma genotyping study protocol (NCT02279004). Patients were eligible for the study if they had biopsy-proven advanced or recurrent nonsquamous NSCLC and were either treatment naive (cohort 1) or had acquired resistance to an EGFR TKI (cohort 2). All patients must have been planned to begin new systemic therapy and have either tissue available for standard genotyping or a planned repeat biopsy. All patients had radiographic evidence of disease, were 18 years of age or older, and signed written informed consent before any study-related procedure. Participant-defined race was recorded given known associations between race and the frequency of EGFR-mutant tumors.

All patients underwent an initial paired blood collection after study enrollment. These 2 tubes of blood subsequently underwent plasma isolation, cfDNA extraction, and ddPCR-based genotyping. One tube of blood was processed and analyzed immediately in accordance with standard operating procedures, and the second was subjected to preplanned variations in specimen handling designed to simulate real-world testing conditions including (1) standard ethylenediaminetetraacetic acid (EDTA) tube shipped overnight on ice and (2) Streck tube shipped at room temperature. Comparison between paired samples was made on the basis of sample quality, total DNA as determined by PicoGreen assay, and quantitative ddPCR result. Differences between paired tubes were analyzed by paired t test. If more than 2 weeks elapsed before initiation of planned systemic therapy, the blood sampling was repeated. The TAT for plasma genotyping was measured in business days from the date of blood sampling until reporting of results to the study investigator.

Patient samples from cohort 1 underwent ddPCR-based plasma genotyping for EGFR exon 19 del, L858R, and KRAS codon 12 mutations. Cohort 2 samples underwent testing for EGFR exon 19 del, L858R, and T790M. Plasma genotyping results were compared with tissue genotyping results from initial biopsy (cohort 1) or rebiopsy at acquired resistance (cohort 2) as the reference standard.

Patients from both cohorts who had a mutation detected by ddPCR-based genotyping subsequently underwent 2 follow-up blood draws at 1 to 2 weeks and 4 to 6 weeks after beginning systemic therapy. These samples underwent serial quantitative genotyping by ddPCR for the detected mutation.

Plasma Genotyping

Venous blood samples were collected in EDTA tubes and underwent centrifugation within 1 hour of sample collection and plasma preparation as previously described.9 Immediate extraction of cfDNA was then performed using the QIAmp circulating nucleic acid kit (QIagen) according to the manufacturer’s protocol. DNA was eluted in 100 μL of AVE buffer and stored at −80°C until genotyping was performed. Genotyping of cfDNA was performed by ddPCR (BioRad) and primer/probes were custom ordered from Life Technologies. The development of this assay has been previously described.9 Briefly, cfDNA is emulsified into approximately 20 000 droplets, mixed with appropriate primer/probe mixes, and then undergoes PCR to end point. Droplets are then read in a flow cytometer and fluorescence signal quantified to determine the number of copies of mutant allele per milliliter (eMethods in the Supplement). To simulate standard clinical practice, the assay was performed twice weekly (Monday and Thursday). Laboratory personnel performing plasma ddPCR were blinded to tissue genotyping results.

Tissue Genotyping

Clinical tumor genotyping was performed for all patients on initial biopsy material (cohort 1) or rebiopsy material following development of acquired resistance (cohort 2). Turnaround time for tissue genotyping was measured from the date of the initial genotyping order until the reporting of the final genotyping result. In cases in which a repeat biopsy was required to successfully complete tissue genotyping, the time required to perform the repeat biopsy was included in the TAT measurement.

Statistical Analysis

From a total of 120 patients with NSCLC studied in cohort 1, we estimated that 24 and 30 would have EGFR and KRAS mutations, respectively, based on prior data at our institution. Concordance between tumor and plasma genotyping results had at least 80% power to detect a κ statistic of 0.85 (compared with a null of 0.60) while controlling for a 1-sided type 1 error rate of .15.

For the 60 patients with acquired resistance who were planned for cohort 2, we estimated that half would harbor T790M detected in their resistance biopsy. An expanded target of 80 patients was originally planned but was revised to 60 patients as a result of feasibility concerns. Concordance between tumor and plasma genotyping results for T790M had 88% power to detect a κ coefficient of 0.85 (compared with a null of 0.60) while controlling for a 1-sided type 1 error rate of .05. Categorical variables were compared using the Fisher exact test, and continuous variables were compared using the Wilcoxon rank sum test or Kruskal-Wallis test. No adjustments have been made for multiple comparisons.

Results
Patients

A total of 180 patients with advanced NSCLC were enrolled in the study with either newly diagnosed disease (n = 120) or acquired resistance to an EGFR TKI (n = 60). Most patients had adenocarcinoma histologic subtype (169 [94%]), and only a few had either NSCLC not otherwise specified (5 [3%]) or adenosquamous histologic subtype (6 [3%]) (Table 1). Patients were predominantly female (112 [62%]) and primarily white (152 [84%]) or Asian (20 [11%]). Patients who did not complete their initial blood sampling or any tissue genotyping were excluded from the analysis (eFigure 1 in the Supplement). An additional 28 patients did not have sufficient tissue available for KRAS G12X testing after completion of initial EGFR testing and were excluded from the KRAS G12X analysis.

The confirmed tissue genotypes of the 115 eligible patients with newly diagnosed NSCLC included 14 EGFR exon 19 del, 13 EGFR L858R, 26 KRAS G12X, and 62 EGFR/KRAS wild type (Table 1). The 54 eligible patients with acquired resistance possessed a range of EGFR sensitizing mutations (37 EGFR exon 19 del, 18 EGFR L858R, 5 rare) and 35 (58%) of these patients were EGFR T790M positive according to tissue genotyping performed on rebiopsy specimens.

Turnaround Time and Repeat Biopsy

Plasma genotyping was completed successfully in all patients. The median (range) TAT from blood collection to report delivery was 3 (1-7) business days in patients with newly diagnosed NSCLC and 2 (1-4) business days in patients with acquired resistance. In comparison, the median (range) TAT for tissue genotyping in patients with newly diagnosed NSCLC was significantly longer at 12 (1-54) business days (P < .001). The median (range) TAT for tissue genotyping was similarly longer in patients with acquired resistance to EGFR kinase inhibitors at 27 (1-146) business days. A repeat biopsy was required in 22 (19%) patients with newly diagnosed NSCLC to obtain sufficient tissue to complete genotyping. Similarly, 12 (21%) patients with acquired resistance required multiple biopsies to obtain sufficient tissue for EGFR T790M genotyping. Turnaround time measurements included the time required to obtain an additional biopsy if necessary due to failure of 1 or more biopsy attempts.

Assay Characteristics

The accuracy of the EGFR exon 19 del, L858R, and KRAS G12X assays was studied first in patients with newly diagnosed NSCLC (n = 115) (eFigure 1 in the Supplement). Plasma ddPCR exhibited high specificity for the detection of EGFR exon 19 del (100% [101 of 101]), L858R (100% [102 of 102]), and KRAS G12X (100% [62 of 62]). Positive predictive value was similarly high for all assays at 100% (Table 2). Assay sensitivity was more modest for EGFR exon 19 del (86% [12 of 14]), L858R (69% [9 of 13]), and KRAS G12X (64% [16 of 25]) (Table 2). Concordance was 0.91 (P = .01) for EGFR exon 19 del, 0.80 (P = .08) for L858R, and 0.72 (P = .13) for KRAS G12X. Assay sensitivity among patients with newly diagnosed disease and acquired resistance was similar for the detection of EGFR exon 19 del (82% [41 of 50]) and L858R (74% [23 of 31]) (Table 2). A single false-positive result was initially reported for EGFR exon 19 del testing (132 copies/mL), which occurred in a young never-smoker with a scant tumor biopsy sample that was negative for any EGFR mutations. A second biopsy was then performed, and subsequent tumor genotyping confirmed an EGFR exon 19 del mutation.

The accuracy of the EGFR T790M assay was studied in patients with acquired resistance to EGFR TKI. The detection of this resistance mutation by plasma ddPCR exhibited a lower specificity (63% [12 of 19]) and positive predictive value (79% [27 of 34]) than was seen for EGFR sensitizing mutations when compared with tumor genotyping of the resistance biopsy; thus, concordance was also lower for the detection of EGFR T790M (κ statistic, 0.4; P = .10). The sensitivity of this assay was similar to that observed for EGFR sensitizing mutations (77% [27 of 35]) (Table 2). The test characteristics for the detection of EGFR sensitizing mutations were similar in patients with acquired resistance compared with patients with newly diagnosed NSCLC (Table 2).

Predictors of Test Sensitivity and Dynamic Range

Patient and disease characteristics potentially associated with increased test sensitivity were examined using a composite test sensitivity variable combining both EGFR and KRAS assay sensitivity results. Of the variables listed in Table 1, a significant association was demonstrated between test sensitivity and the presence of hepatic metastases (P = .001), bone metastases (P = .007), and increasing number of metastatic sites (P = .001) (Figure 1).

The relationship between detected mutant EGFR or KRAS cfDNA copy number and clinical characteristics was next examined as a marker of tumor cfDNA shed. Given the wide dynamic range noted with this assay (Figure 1), significant associations between clinical characteristics and log10-transformed mutant cfDNA copy number in patients with detected mutant cfDNA were sought. Only increasing number of metastatic sites was associated with a higher mutant cfDNA copy number (P = .03).

Paired Analysis

Multiple real-world sample treatment conditions were tested using paired samples drawn from the same patient at the same point in time. The use of an EDTA tube that was shipped on ice overnight before processing revealed identical qualitative assay results and not significantly different total DNA (P = .38) and mutant allele copy number (P = .26) compared with immediate processing (n = 25 patients). Similarly, use of a Streck DNA preservation tube shipped at room temperature overnight yielded identical qualitative assay results and there was no significant difference in total DNA (P = .25) or mutant allele copy number (P = .32) compared with standard processing (n = 20 patients) (eFigure 2 in the Supplement).

Exploratory Patterns of Mutant cfDNA Changes in Response to Systemic Therapy

Patients with a detectable mutation by means of plasma ddPCR underwent serial blood sampling on treatment. A total of 50 patients completed at least 1 follow-up blood sampling on treatment. Serial quantitative plasma ddPCR among these patients revealed clear changes in the level of detectable mutant allele frequency during treatment (Figure 2). Changes in detectable mutation by plasma ddPCR fell into several recurrent, descriptive patterns including complete resolution of detectable mutant cfDNA either at initial repeated blood sampling (Figure 2A) or subsequently (Figure 2B), residual detectable mutant cfDNA following initial decrease (Figure 2C), initial decrease followed by increase (Figure 2D), or initial increase that was either transient (Figure 2E) or maintained (Figure 2F). Patients with complete resolution of mutant cfDNA at either 2 or 6 weeks exhibited a treatment discontinuation rate of 0% (0 of 23) at initial and 4% (1 of 23) at second reimaging assessment. Patients without complete resolution exhibited a treatment discontinuation rate of 33% (9 of 27) at initial reimaging and 56% (15 of 27) at second reimaging assessment. Treatment discontinuation decisions were made by treating clinicians who were blinded to serial plasma genotyping results. Objective data on overall survival and progression-free survival are presently immature. These patterns of change in plasma response are exploratory at present but provide a potential framework for future analysis of the correlation between changes in detectable mutant cfDNA and response to therapy.

Discussion

In this prospective study, we demonstrate the highly specific and rapid nature of plasma genotyping. No false-positive test results were seen for driver mutations in EGFR or KRAS, and TAT from when the specimen was obtained to result was a matter of days. This assay exhibited 100% positive predictive value for the detection of these mutations. Sensitivity was more modest and was directly correlated with both number of metastatic sites and the presence of liver or bone metastases. This newly demonstrated relationship is likely related to increased cfDNA shed in the setting of more extensive disease where tumor cfDNA shed is the chief driver of assay sensitivity and determines its upper limit. The characteristics of plasma ddPCR prospectively demonstrated in this study were similar or improved compared with previous retrospective reports of other cfDNA genotyping assays.10-13,15,16,24,25 These retrospective studies are smaller, frequently examined a mix of tumor types and/or stages, and lack the careful prospective design needed to demonstrate the readiness of this technology to transition to a tool for selecting therapy. Studies that use retrospective samples from clinical trials that enrolled only EGFR-mutant patients are further limited by an inability to both blind laboratory investigators to tissue genotype and to generalize their assay test characteristics to a genetically heterogeneous real-world patient population.11 These differences and the multiple platforms examined previously have led to variable test characteristics and uncertainty regarding the clinical application of these technologies. This study is the first to prospectively demonstrate the ability of a ddPCR-based plasma genotyping assay to rapidly and accurately detect EGFR and KRAS mutations in a real-world clinical setting with the rigor necessary to support the assertion that use of this assay is capable of directing clinical care.

Even with a diagnostic sensitivity of less than 100%, such a rapid assay with 100% positive predictive value carries the potential for immense clinical utility. The 2- to 3-day TAT contrasts starkly with the 27-day TAT for tumor genotyping seen in patients needing a new tumor biopsy. This long TAT is due largely to the practical reality that many patients with newly diagnosed NSCLC require a repeat biopsy to obtain tissue for genotyping, as do all patients with acquired resistance. Consider the case of 1 study participant, an octogenarian with metastatic NSCLC who had developed acquired resistance to erlotinib with painful bone metastases (Figure 3). Due to the patient’s age and comorbidities, significant concerns existed about the risks of a biopsy and further systemic therapy. A plasma sample was obtained, and within 24 hours ddPCR demonstrated 806 copies/mL of EGFR T790M. A confirmatory lung biopsy was performed, which confirmed EGFR T790M. Treatment with a third-generation EGFR kinase inhibitor, osimertinib mesylate, was subsequently initiated and the patient had a partial response to therapy that was maintained for more than 1 year. The potential of this technology to obviate repeated biopsy in both patients with newly diagnosed NSCLC with insufficient tissue, as well as patients with acquired resistance, is considerable.

A key limitation of plasma ddPCR is that although this method is adept at rapidly detecting specific targetable mutations, it cannot easily detect copy number alterations and rearrangements. The ddPCR panel assessed in this study thus cannot currently detect targetable alterations in either ALK or ROS1. This limitation may potentially be addressed by using targeted NGS of cfDNA for broad, multiplexed detection of complex genomic alterations including ALK and ROS1 rearrangements, although this method is potentially slower than ddPCR-based methods and has been less thoroughly evaluated.23 The potential exists to use these technologies in tandem in advanced NSCLC to facilitate rapid initiation of therapy. Tissue genotyping and repeated biopsy would be specifically used to direct therapy in cases in which plasma genotyping was uninformative due to limitations of assay sensitivity. This approach would be particularly useful in cases of EGFR acquired resistance in which a repeated biopsy for T790M testing could be avoided entirely in many patients. Beyond detecting targetable alterations in order to drive therapy, the identification of nontargetable oncogenic drivers such as KRAS mutations that preclude the presence of other targetable alterations may guide a clinician to rapidly initiate alternative therapies such as chemotherapy or immunotherapy.5 The finding that assay sensitivity is highest in patients with more extensive metastatic disease suggests that those patients most in need of rapid treatment initiation would also be least likely to have false-negative results.

One surprising result of our study was evidence of recurrent false-positive results for EGFR T790M in patients with acquired resistance, despite no false-positive test results for other mutations studied. The sensitivity of the EGFR T790M assay was comparable to that of the EGFR sensitizing mutation assays and similarly related to both disease burden and the presence of liver or bone metastases, which are likely predictive of increased tumor cfDNA shed. We hypothesize that the lower assay specificity is due to the genomic heterogeneity whereby the T790M status of the biopsied site is not representative of all metastatic sites in a patient, a phenomenon supported by mounting evidence in the acquired resistance setting.26,27 This is consistent with the finding that a minority of patients with apparently EGFR T790M tissue-negative disease respond to therapy with third-generation EGFR kinase inhibitors.7,8,28 These observations raise questions regarding the fallibility of tissue-based genotyping as the reference standard for T790M status. The use of plasma genotyping to detect EGFR T790M thus has great potential to identify patients who would benefit from newly approved third-generation EGFR kinase inhibitors but would be unable to access them based on falsely negative tissue genotyping results. Indeed, plasma genotyping may allow more reliable assessment of both T790M status as well as the mechanisms of resistance across all sites of a heterogeneous cancer as opposed to a tissue biopsy and is likely to be an essential tool for future trials targeting drug resistance. The potential to avoid a repeat biopsy entirely in patients in whom plasma ddPCR detects T790M further strengthens the utility of this technology, although a repeat biopsy would still be needed in patients with uninformative plasma ddPCR due to limitations with respect to assay sensitivity.

This study also examined the potential of the quantitative nature of ddPCR-based plasma genotyping to allow for the early prediction of treatment response. Distinct patterns of change in mutant allele copy number were observed as early as 2 weeks after treatment and were similar to those reported in other tumor types.19,20 We hypothesize that these distinct patterns of change in this study will correlate with specific patterns of radiographic response and emergence of acquired resistance and plan to report these data once mature. The observed differences in treatment discontinuation rates observed in this study comparing patients with complete resolution of detectable mutant cfDNA with those with incomplete resolution support this hypothesis. The use of this technology to monitor disease status in real time has potential utility for both routine clinical care, as well as use as an integrated biomarker in early-phase clinical trials.10

Conclusions

Droplet digital polymerase chain reaction–based plasma genotyping is a technology that is ready to be used for clinical decision making in patients with advanced NSCLC. This assay is capable of rapidly detecting EGFR and KRAS mutations with minimal false-positive test results and with the robustness needed for real-world testing. It has great utility for the detection of actionable genomic alterations in patients who are unable to a undergo repeat biopsy and may even detect mutations missed by standard tissue genotyping due to tissue heterogeneity. As third-generation EGFR T790M inhibitors come into clinical use, the need for rebiopsy and potential role of plasma genotyping will expand dramatically. Furthermore, the potential combination of rapid ddPCR-based plasma genotyping assays with plasma NGS assays for more comprehensive noninvasive genotyping may represent a new paradigm for clinical genotyping.

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Article Information

Correction: This article was corrected online May 12, 2016, to fix an error in the Funding/Support information.

Accepted for Publication: January 22, 2016.

Corresponding Author: Adrian G. Sacher, MD, and Geoffrey R. Oxnard, MD, Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA 02215 (ags2185@cumc.columbia.edu, geoffrey_oxnard@dfci.harvard.edu).

Published Online: April 7, 2016. doi:10.1001/jamaoncol.2016.0173.

Author Contributions: Drs Sacher and Oxnard had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Sacher, Paweletz, Dahlberg, Oxnard.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Sacher, Paweletz, Dahlberg, Oxnard.

Critical revision of the manuscript for important intellectual content: Sacher, Paweletz, Alden, O’Connell, Feeney, Mach, Jänne, Oxnard.

Statistical analysis: Sacher, Dahlberg.

Obtained funding: Oxnard.

Administrative, technical, or material support: Sacher, Alden, O’Connell, Mach, Jänne, Oxnard.

Study supervision: Sacher, Paweletz, Jänne, Oxnard.

Conflict of Interest Disclosures: Dr Sacher has received travel funding from AstraZeneca and Genentech-Roche. Dr Paweletz has received travel funding and honoraria from Bio-Rad Laboratories and Clovis Oncology. Dr Jänne is a consultant for Boehringer Ingelheim, AstraZeneca, Genentech, Pfizer, Merrimack Pharmaceuticals, Clovis Oncology, Roche, Sanofi, and Chugai and has stock ownership in Gatekeeper Pharmaceuticals. Drs Paweletz, Jänne, and Oxnard are inventors on a pending patent related to findings described in this manuscript. Dr Oxnard is a consultant/advisory board member for Ariad, AstraZeneca, Boehringer Ingelheim, Clovis Oncology, and Sysmex and has received honoraria from AstraZeneca, Boehringer Ingelheim, and Chugai. No other disclosures are reported.

Funding/Support: This research was supported in part by the US Department of Defense, the National Cancer Institute of the National Institutes of Health (grants R01CA135257, R01CA114465, and P50CA090578), the Phi Beta Psi Sorority, the Stading-Younger Cancer Foundation, the International Association for the Study of Lung Cancer, the Canadian Institutes of Health Research, the Canadian Association of Medical Oncologists, and the Kaplan Research Fund.

Role of the Funder/Sponsor: These funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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