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Multiple Hotspot Mutations Scanning by Single Droplet Digital PCR.

Thousands of somatic mutations related to cancer development have been reported (1). Among these, a few are predictive markers of targeted therapy efficacy. For example, the presence of activating KRAS proto-oncogene, GTPase (KRAS) [12]; NRAS proto-oncogene, GTPase (NRAS); and B-Raf proto-oncogene, serine/threonine kinase (BRAF) mutations conveys primary resistance of metastatic colorectal adenocarcinomas to anti-epidermal growth factor receptor (EGFR) antibodies (i.e., cetuximab, panitumumab) (2, 3) and the genetic screening of these mutations is now part of routine practice. The screening of EGFR-activating mutations in metastatic non-small cell lung cancers (NSCLC) [13] is another example of everyday clinical practice since these mutations are predictive of EGFR tyrosine kinase inhibitors efficacy (e.g., gefitinib, erlotinib). The efficiency of molecular profiling as a diagnostic procedure relies on the comprehensive scanning of mutations at hotspot loci of targeted oncogenes. This type of genetic screen is usually performed at diagnosis of metastatic relapses, and it has been shown that the appearance of previously undetected mutations occurring in the gene coding for the drug-targeted factors or in its downstream signaling pathways is a common mechanism of resistance. For instance, emerging KRAS mutants in KRAS wild-type colorectal cancers (4) or EGFR T790M mutations in EGFR-mutated NSCLC (5) are 2 frequently reported resistance mechanisms. Mutational status plasticity in cancer is a key challenge for "precision medicine" in oncology. This calls for the development of noninvasive somatic mutation screens that can be processed repeatedly throughout the course of the disease.

In this context, the analysis of circulating tumor DNA (ctDNA) has gained considerable attention. Multiple studies have demonstrated the high concordance of the mutational status as compared with tumor biopsy (6, 7). Owing to the often low fraction of DNA originating from the tumor in the plasma, usual methods such as Sanger sequencing are usually not analytically sensitive enough to identify mutations from a liquid biopsy. Droplet digital PCR (ddPCR) is a recognized method for PCR-based ctDNA detection because it can detect mutations at frequencies <1% (4, 8-10). However, Food and Drug Administration (FDA) or European Conformity (EC) cleared methods are currently based on amplification-refractory mutation system (ARMS) or quantitative PCR assays (11-14). The principle of the ddPCR technique is to partition samples using limiting dilutions, which results in zero to one molecule in most reactions (15). This decreases the reaction complexity and allows more reliable and analytically sensitive measurement of low mutant fractions. In contrast to real-time PCR, ddPCR provides absolute quantification of targeted sequences (16) and offers greater precision and reproducibility (17).

Currently, ddPCR remains a low-throughput method because it uses specific fluorescent TaqMan reporters recognizing each possible allele version. Because the number of colors is usually limited to 2, this requires a reaction for each specific mutation. This also implies that the mutation is known a priori. Therefore, alternatives enabling multiplex ddPCR are currently being developed. One such approach, based on 2 separate reactions detecting 3 and 4 mutations, was used to screen for the 7 most common KRAS mutations and reported a good efficiency for detecting ctDNA in plasma specimens from patients with metastatic colorectal cancer (CRC) (18). Another ddPCR-multiplexed assay investigated 9 different KRAS mutations in 3 separate reactions and was validated on NSCLC formalin-fixed and paraffin-embedded (FFPE) samples (19). However, these assays still require allele-specific probes: 1 probe against the wild-type (WT) version together with 7 and 9 mutant probes. A more recent study reported the use of a novel WT negative assay to discriminate patients carrying WT BRAF vs BRAF mutations in the V600 codon (20). This assay used a probe targeting only the WT BRAF, thereby distinguishing WT V600 from all BRAF V600 mutant alleles with a limit of detection (LOD) of 0.05%. This type of assay has also been used to detect genome editing events and is referred to as a drop-off assay (21).

To increase the throughput of the ddPCR technique and reduce the quantity of material to be screened, we established 2 new specific drop-off assays covering KRAS exon 2 and EGFR exon 19 mutation hotspot regions, which are ofparticular clinical importance in the context of colorectal and lung cancer. Following optimization, the KRAS and EGFR systems were validated both on solid and liquid biopsy-extracted DNA.

Materials and Methods

TISSUE AND PLASMA SAMPLES

Tissue and plasma samples were obtained, after informed consent, from patients treated at the Institut Curie (Paris, France) or at Nantes University Hospital (France), for which studies were approved by institutions' review boards. All samples except the plasmas from patients with CRC were previously evaluated for KRAS or EGFR mutations through pharmacogenomics assessment and reviewed by a pathologist. All plasma specimens analyzed for KRAS mutation status were collected from patients included in the PRODIGE14 trial (NCT01442935). Plasma was isolated from 7 mL of fresh blood collected in EDTA tubes (BD Vacutainer[R]), extracted within 3 h as previously performed in the laboratory (7, 22), and stored at -80 [degrees]C until needed. For cfDNA extraction using the QIAamp[R] Circulating Nucleic Acid Kit (Qiagen) or the Cobas cell-free DNA Purification Kit (Roche Diagnostics), 2-4 mL was used. Genomic DNA (gDNA) was extracted from tissues with use of the DNA FFPE Tissue Kit (Qiagen). DNA samples were quantified with use of a Qubit[TM] fluorometer (Thermo Fisher Scientific).

MUTATION SCREENING

KRAS mutations were screened by Sanger sequencing or next-generation sequencing (NGS) on tumor samples, or by ddPCR on plasma samples. Screening of EGFR mutations was performed by targeted NGS with use of the Ion AmpliSeq[TM] Colon and Lung Cancer panel V2 in conjunction with the AmpliSeq Library Kit 2.0 (Thermo Fisher), or 2 PCR-based methods. The first PCR method was the Therascreen EGFR RGQ assay (Qiagen) for which amplifications were performed on a Rotor-Gene Q 5plex HRM instrument (Qiagen). The [DELTA]Ct cutoffs used for each EGFR mutation reaction were determined as indicated in the performance characteristics part of the application guide. The [DELTA]Ct value is inversely proportional to the percentage of EGFR mutant DNA in the sample. The second PCR method was the Cobas EGFR Mutation Test v2 (Roche), which was performed according to the manufacturer's protocol on a Cobas z480 analyzer (Roche). For plasma mutant samples, the Cobas z480 software reports a semiquantitative index representing the percent of mutation.

DROPLET DIGITAL PCR ANALYSIS

TaqMan probes and primers. Drop-off and reference (REF) probes are contained within the same amplicon that must be shorter than 150 bp for compatibility with ctDNA detection. The following primers were used to amplify (a) KRAS exon 2 (116-bp amplicon): Fwd primer: 5 -CTGAAAATGACTGAATATAAACTT GTGGTA-3'; Rev primer 5'-TCTATTGTTGGAT CATATTCGTCCAC-3'; and (b) EGFR exon 19 (147-bp amplicon): Fwd primer 5'-TCTGGATCCCA GAAGGTGAG-3'; Rev primer: 5'-TGGGCCTGAG GTTCAGA-3'. The following TaqMan probes (Life technologies) with a 5' fluorophore and a 3' nonfluorescent quencher (NFQ) were designed: KRAS REF 5'-(6-FAM)AGTGCCTTGACGATACAGCT-(MGB NFQ)-3'; KRAS drop-off 5'-(VIC)-AGCTGGTGGCGTAGGC(MGB NFQ)-3'; EGFR REF 5'-(FAM)-TGAGTTT CTGCTTTGCTGTGT-(MGB NFQ)-3'; EGFR drop-off 5'-(VIC)-TATCAAGGAATTAAGAGAAGCAACA(MGB NFQ)-3'.

Mixture preparation. According to the manufacturer's protocol, master mix solutions (20 [micro]L) were prepared from 10 [micro]L 2X dUTP-free Supermix for probes (BioRad), 1 [micro]L of a 20X mix of relevant primers and TaqMan probes (Bio-Rad or Life technologies), and DNA plus water (up to 9 [micro]L), and transferred with 70 [micro]L of oil to a DG8 droplet generator cassette, which was then loaded into a QX100 Droplet Generator (Bio-Rad). 40 [micro]L of emulsified PCR reactions were transferred to 96-well PCR plates and run on a C1000 thermal cycler (Bio-Rad) under the following program: 95 [degrees]C 10 min, 40 cycles of [94 [degrees]C 30 s, 63 [degrees]C (KRAS assay) or 58 [degrees]C (EGFR assay) for 60 s], 98 [degrees]C 10 min. Controls with no input DNA and WT controls from peripheral blood mononuclear cells (PBMC) were included in each run. Plates were analyzed on the Bio-Rad QX100 droplet reader.

Analysis. For ddPCR assays developed by Bio-Rad, the concentration of mutant DNA copies was estimated with use of the dedicated workflow available in the QuantaSoft v1.7.4 software. For drop-off ddPCRs, data were exported to comma-separated value files and analyzed with the program developed by Attali and colleagues (20, 23) specifically for this type of assay. This program automated the determination of each droplet as empty, part of the "rain" owing to inefficient amplification, or filled with a real positive signal. The assigned droplets were then used to detect mutant alleles and compute the mutant allele frequency (MAF) in each well.

KRAS AND EGFR DROP-OFF OPTIMIZATION

Optimization was performed on reconstituted MAFs (1 X [10.sup.4] total copies for KRAS and 3 X [10.sup.3] total copies for EGFR) prepared from cell line and PBMC gDNA (KRAS), and Multiplex I cfDNA Reference Standard Set (Horizon Discovery) (KRAS and EGFR). We defined the false-positives mean and associated SD on 100% PBMC gDNA ([MAF.sub.KRAS] = 0.0098%, [SD.sub.KRAS] = 0.0197%; [MAF.sub.EGFR] = 0.0%, [SD.sub.EGFR] = 0.0%) from which the 95% confidence interval was calculated (95% [CI.sub.KRAS] = 0.0073%; 95% [CI.sub.EGFR] = 0.0%) and used to define the limit of blank (LOB) as follows: LOB = false-positive MAF mean + 95% CI ([LOB.sub.KRAS] = 0.017%; [LOB.sub.EGFR] = 0.0%). SW480 (ATCC[R] CCL-228[TM], G12V homozygous) and HCT 116 (ATCC CCL-247[TM], G13D heterozygous) cell lines were cultured in Dulbecco's Modified Eagle Medium, 5% fetal bovine serum, and McCoy's 5a Medium, 5% fetal bovine serum, respectively. gDNA was extracted with use of the DNeasy[TM] Blood and Tissue Kit (Qiagen).

Results

DROP-OFF ddPCR REQUIRES A UNIQUE PAIR OF TaqMan[R] OLIGOPROBES TO SCAN ALL MUTATIONS OF A HOTSPOT REGION

In the conventional ddPCR design, a probe recognizing the WT allele competes with 1 or multiple probes recognizing the mutant alleles (Fig. 1A). The probe with higher affinity will hybridize and release its fluorophore through the exonuclease activity of the Taq polymerase, indicating the nature of the corresponding allele (here VIC+ = MUT, [FAM.sup.+] = WT). In a hotspot region with a variety of potential adjacent mutations, the number of fluorophores that can be used limits the analysis. The design of the drop-off ddPCR is based on the high affinity needed for hybridization of the TaqMan probes. A single nucleotide change in the targeted sequence is enough to destabilize the duplex formation at standard conditions used for conventional ddPCR. Taking advantage of this perfect-match requirement for hybridization, our drop-off assays used a single probe covering the mutated region (drop-off probe) to detect all the mutations contained in that region (Fig. 1B). This probe was designed complementary to the WT sequence of the targeted region and indicated the presence of mutant alleles that trigger suboptimal hybridization. A reference probe designed over a nonmutated region and located within the same amplicon was used to quantify the total number of molecules present in the sample. WT molecules are thus double positive ([VIC.sup.+]/[FAM.sup.+]). The suboptimal hybridization of the drop-off VIC probe, in case of mismatch due to the mutation, lead to VIC signal decrease ([VIC.sup.low]/[FAM.sup.+]). A shift of the droplet cloud toward a single FAM-positive population was therefore observed proportional to the fraction of mutant molecules (Fig. 1B). The quantity of DNA loaded in the reaction must have ensured that most droplets contained either 0 or 1 copy of targeted genes for an optimal separation of the WT vs MUT clouds. The drop-off ddPCR detected multiple mutations simultaneously in a single experiment while consuming a minimum of the patient sample. In addition, with this type of assay the mutation did not have to be known a priori. Here, we established 2 specific assays covering KRAS exon 2 and EGFR exon 19 mutation hotspot regions, which are of clinical importance in the context of colorectal and lung cancer.

THE KRAS DROP-OFF ASSAY IS HIGHLY SPECIFIC AND REACHES AN ANALYTICAL SENSITIVITY OF <0.1%

We designed a KRAS drop-off probe that interrogated a 16-bp region encompassing multiple mutations in exon 2 of the KRAS gene. This region covers the sequence corresponding to codons 12 and 13, which carry more than 95% of the KRAS mutations in human cancers [Fig. 2A, (24)]. The reference probe was 20 bp in length and located 3 bp downstream. To optimize our assay, we used DNA extracted from cell lines containing 2 of the most frequently mutated KRAS amino acids: G12V (c.35G>T, SW480) and G13D (c.38G>A, HCT 116), which represent 23% and 12.6% of KRAS mutations, respectively, found in human cancer (24). The G12V mutation is actually one of the most abundant KRAS alterations in colorectal cancer (>21% of CRC cases) (24). We first tested the analytical specificity of our probes on pure mutant DNA and pure WT DNA from PBMC (Fig. 2B). With the SW480 cell line being homozygous for KRAS G12V, while HCT 116 is heterozygous for KRAS G13D, we confirmed that we could detect 100% and 50% of mutant alleles, respectively, while none was detected in PBMC control samples. We further tested the analytical sensitivity and the specificity of the KRAS drop-off assay using reconstitution experiments of various mutant allelic fractions diluted in PBMC WT DNA. We verified that we obtained concordant results comparing the KRAS drop-off assay with assays commercialized by Bio-Rad and commonly used to target KRAS mutations: simple duplexes targeting specifically G12V or G13D, as well as the ddPCR KRAS Screening Multiplex Kit (25), which targets the 7 most common mutations in that region (highlighted with a star in Fig. 2A). By testing 100%, 10%, and 0% of mutant cell line/PBMC DNA mixtures, we confirmed that all assays performed well to detect expected MAFs and that KRAS drop-off results agreed with conventional tests (Fig. 2C). We also observed that the SW480 cell line carried a duplication of the region encompassing KRAS. The quantification of the "SW480 10%" mutant fraction was twice as high (20%) than the proportion of mutant DNA incorporated in the test, which was confirmed in all assays. In addition, we noted that the multiplex KRAS from Bio-Rad gave a higher background in WT DNA compared to the simple duplexes or the KRAS drop-off assay (Fig. 2D, droplet numbers detailed in Table 1 in the Data Supplement that accompanies the online version of this article at http:// www.clinchem.org/content/vol64/issue2). The higher number of probes used in the multiplex KRAS assay (8 vs 2 in the others) could explain this result. The LOB of the KRAS drop-off assay was determined through the analysis of multiple replicates of WT gDNA (PBMC), as previously reported (20, 26, 27) (see Table 1 in the online Data Supplement; Fig. 2, E and F; and Methods in the main manuscript). We next used multiple serial dilutions of mutant DNA in WT DNA to test decreasing MAFs from 5% down to 0.01% and define the LOD of the KRAS drop-off. The correlation between observed and expected MAF for the 2 mutations was [R.sup.2] = 0.976 for G12V and [R.sup.2] = 0.984 for G13D. We considered MAF values statistically significant when replicates presented average values and SD above the LOB, identifying an LOD of 0.04% for G12V and 0.08% for G13D (Fig. 2E and 2F). Droplet numbers and data plot examples for each MAF tested are shown in Table 2 and Fig. 1, A and B in the online Data Supplement. This represented a theoretical discrimination of 1 KRAS mutant molecule in 1250 WT molecules and demonstrated that the assay reached the level of sensitivity needed to screen even low input samples. Because the DNA template used above originated from cell line genomic DNA, we further validated the efficiency of KRAS drop-off to detect 5%, 1%, and 0.1% MAFs from templates with a similar structure to cfDNA with use of the Horizon cfDNA reference standard set (see Fig. 1C and Table 2 in the online Data Supplement).

THE KRAS DROP-OFF ASSAY RECAPITULATES RESULTS OBSERVED WITH CONVENTIONAL TESTS IN PATIENT SAMPLES

We further validated our assay on DNA extracted from 16 FFPE tumor tissues and 17 plasma samples (Table 1). Sanger sequencing or NGS was previously performed to assess the mutated status of tumor samples (P-1 to P-16). Plasma samples (P-17 to P-33) were screened by Bio-Rad simple duplex ddPCR designed for specific KRAS mutations, previously characterized on corresponding tumor tissues. This set of samples covered a total of 11 different KRAS mutations and 2 types of cancers: NSCLC (8 patients) and CRC (25 patients). We confirmed the mutant status of all FFPE tumors and plasma samples demonstrating high concordance with MAFs previously observed by NGS or Bio-Rad ddPCRs (Table 1). Fig. 3A shows examples of KRAS drop-off results for 6 plasma samples at various mutant fractions. In addition, our assay could monitor mutations that were not detected by the currently commercialized assays, including the BioRad multiplex KRAS kit. In fact, the Bio-Rad multiplex KRAS kit contains specific probes for single substitutions (highlighted in Fig. 2A) and did not properly detect double substitutions found in patients P-4, P-9, and P-10, on which the mutant probes did not anneal (Table 1). These double substitutions could, however, be monitored with the KRAS drop-off, with accurate MAF evaluation as compared to NGS data in the case of P-10 (Fig. 3B). In addition, P-7 displayed a single substitution that was not covered by the Bio-Rad multiplex KRAS kit (marked with an * in Fig. 2A). Consistently, we observed that this mutation could only be detected with the KRAS drop-off (Fig. 3B).

THE EGFR DROP-OFF REACHES AN ANALYTICAL SENSITIVITY OF <0.1% AND RECAPITULATES RESULTS OBSERVED WITH OTHER METHODS

Next, we designed the EGFR drop-off assay to scan for all activating deletions of EGFR exon-19. Such a test would be of particular interest for lung cancer therapy decisions and especially for NSCLC, in which 45% of EGFR-mutated patients carry one of these deletions (28). The assay used a 25-bp-long drop-off probe designed over the recurrent deleted region together with a REF probe of 21-bp, located 31-bp downstream in the same amplicon (Fig. 4A). With this design, the EGFR drop-off can potentially detect all deletions overlapping with the region covered by the drop-off probe. The background tested in 24 PBMC controls appeared to be nonexistent, and the LOB was determined to be equal to 0.0% (see Table 1 and Fig. 2B in the online Data Supplement, and Methods). For lack of accessibility to cell lines carrying EGFR-deletions, we tested the analytical sensitivity of the EGFR drop-off on mutant serial dilutions prepared from the Horizon reference cfDNA (p.Glu746_Ala750del, Fig. 4B; and see Fig. 2 and Table 2 in the online Data Supplement). The correlation between observed and expected MAF was [R.sup.2] = 0.9894. Statistically significant MAF values were obtained to an LOD of 0.02%. Droplet numbers and data plot examples for each MAF tested are shown in Table 2 and Fig. 2A in the online Data Supplement. Next, we tested 48 samples (25 FFPE, 1 pleural effusion, 1 serum, and 21 plasma) from patients affected by NSCLC and previously tested for deletions of EGFR exon-19 by current assays available for molecular diagnosis (Table 2). The first 7 samples (P-34-P-40), which were initially screened by NGS, covered 6 different deletions encompassing 10 -20-bp (Fig. 4A). We were able to detect all 6 deletions with the EGFR drop-off assay (Table 2, Fig. 4C) and confirmed mutant frequencies demonstrating high concordance with MAFs previously observed by NGS ([R.sup.2] = 0.98; see Fig. 3Ain the online Data Supplement). We further compared the performance of the EGFR drop-off assay with 2 FDA/EC-cleared methods: the Therascreen EGFR RGQ assay, which is routinely used to screen for EGFR mutation on FFPE samples, and the Cobas EGFR Mutation Test (v2), which is also recommended for screening of body fluids such as plasma. We validated the EGFR Del19 mutant status for all tissues and plasma samples tested (Table 2, Fig. 4C), and indicative measures of the mutant concentrations given by the Therascreen ([DELTA]Ct) or the Cobas (semi-quantitative index) correlated with the MAFs observed with the EGFR drop-off ([R.sup.2] = 0.94 and 0.82, respectively; see Fig. 3, B and C in the online Data Supplement). This demonstrated the power of the EGFR drop-off assay to detect virtually all deletions of exon-19 within a single reaction and using a single pair of probes.

Discussion

To fulfill the need for universal tools for molecular cancer diagnosis, we established 2 ddPCR assays detecting, in a single reaction, all genomic alterations within KRAS exon 2 or EGFR exon 19 mutation hotspots, which are of clinical importance in colorectal and lung cancer. The KRAS drop-off assay covered more than 95% of KRAS mutations reported in human cancers (24) and reached an LOD of <0.1% (0.04% for G12V and 0.08% for G13D mutations). The EGFR assay screened for all in-frame deletions of exon 19, which are found in 45% of EGFR-mutated NSCLC (28) and also reached an LOD of <0.1% for the c.2235_2249 deletion. In addition, we demonstrated that drop-off ddPCRs potentially lead to less background noise than multiplexed assays. We further validated the 2 systems on several DNAs extracted from FFPE tumor samples and various body fluids. Drop-off results matched those obtained by conventional and FDA/EC-cleared methods in all samples tested with a high concordance in terms of MAF detected. Here, we showed that the drop-off ddPCR is as analytically sensitive as classical ddPCR assays and can be applied to many types of molecular alterations (single nucleotide variant, deletion) in a single reaction, thereby consuming a minimal amount of patient sample. This makes it a highly relevant technique to screen for mutations in tissues but also liquid biopsies, in which mutant alleles are diluted in WT background. A previous study reported the possibility of using a combination of coamplifications using probes matching wild-type sequences (12). Castellanos-Rizaldos et al. performed the amplification at lower denaturation temperature (COLD-PCR) to increase the mutant allele detection limit (12). Here, we showed that our KRAS and EGFR drop-off ddPCRs could be performed at standard ddPCR conditions. Bidshahri et al. (20) reported an LOD of 0.05% for their BRAF assay but did not test it on body fluids. The 165-bp BRAF amplicon they used might interfere with an accurate quantification in plasma. Indeed, ctDNA detection requires amplicons shorter than 150 bp (data not shown), owing to the fragmentation of circulating DNA with a mean size of 167 bp (29). Here, we demonstrate the compatibility of drop-off ddPCRs with liquid-biopsy-based mutation screening. Downstream characterization still needs to be performed separately to identify the exact mutation carried by mutant alleles. However, because therapeutic decisions are primarily based on the mutational status of hotspot regions of targetable oncogenes (WT vs mutant), its ability to immediately identify mutations makes the drop-off ddPCR a powerful diagnostic tool. Moreover, this method provides direct quantification of mutant and WT alleles and does not require any computational analysis. In addition, these tests can be combined with other specific ddPCR assays targeting single point-mutations such as the resistance-associated T790M mutation found in EGFR.

Development and optimization of more "universal" ddPCR assays targeting the other key mutated regions used for therapeutic decision will improve molecular cancer diagnosis.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met thefollowing3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: M. Ychou, Merck Serono.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: C. Proudhon, Institut Curie SiRIC (grant INCaDGOS-4654).

Expert Testimony: None declared.

Patents: None declared.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or final approval of manuscript.

References

(1.) Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJR, Behjati S, Biankin AV, et al. Signatures of mutational processes in human cancer. Nature 2013;500:415-21.

(2.) Amado RG, Wolf M, Peeters M, Van Cutsem E, Siena S,

Freeman DJ, etal. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J Clin Oncol 2008;26:1626-34.

(3.) Karapetis CS, Khambata-Ford S,Jonker DJ, O'Callaghan

CJ, Tu D, Tebbutt NC, etal. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 2008;359:1757-65.

(4.) Siravegna G, Mussolin B, Buscarino M, Corti G, Cassingena A, Crisafulli G, et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat Med 2015;21:795-801.

(5.) Kobayashi S, Boggon TJ, Dayaram T, Janne PA, Kocher O, Meyerson M, et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N Engl J Med 2005;352:786-92.

(6.) Bidard F-C, Madic J, Mariani P, Piperno-Neumann S, Rampanou A, Servois V, et al. Detection rate and prognostic value of circulating tumor cells and circulating tumor DNA in metastatic uveal melanoma. Int J Cancer 2014;134:1207-13.

(7.) Lebofsky R, Decraene C, Bernard V, Kamal M, Blin A, Leroy Q, etal. Circulating tumor DNA as a non-invasive substitute to metastasis biopsy for tumor genotyping and personalized medicine in a prospective trial across all tumor types. Mol Oncol 2015;9:783-90.

(8.) Vogelstein B, Kinzler KW. Digital PCR. Proc Natl Acad Sci 1999;96:9236-41.

(9.) Garcia-Murillas I, Schiavon G, Weigelt B, Ng C, Hrebien S, CuttsRJ, etal. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci Transl Med 2015;7:302ra133.

(10.) Riva F, Bidard F-C, Houy A, Saliou A, Madic J, Rampanou A, et al. Patient-specific circulating tumor DNA detection during neoadjuvant chemotherapy in triple-negative breast cancer. Clin Chem 2017;63:691-9.

(11.) Douillard J-Y, Ostoros G, Cobo M, Ciuleanu T, Cole R, McWalter G, etal. Gefitinib treatment in EGFR mutated caucasian NSCLC: circulating-free tumor DNA as a surrogate for determination of EGFR status. J Thorac Oncol 2014;9:1345-53.

(12.) Castellanos-Rizaldos E, Paweletz C, Song C, Oxnard GR, Mamon H, Janne PA, et al. Enhanced ratio of signals enables digital mutation scanning for rare allele detection. J Mol Diagn 2015;17:284-92.

(13.) Karlovich C, GoldmanJW,SunJM, Mann E,SequistLV, Konopa K, etal. Assessment of EGFR mutation status in matched plasma and tumor tissue of NSCLC patients from a phase I study of rociletinib (CO-1686). Clin Cancer Res 2016;22:2386-95.

(14.) ValleeA, Le Loupp A-G, Denis MG. Efficiency of the Therascreen[R] RGQ PCR kit for the detection of EGFR mutations in non-small cell lung carcinomas. Clin ChimActa 2014;429:8-11.

(15.) Sykes PJ, Neoh SH, Brisco MJ, Hughes E, Condon J, Morley AA. Quantitation of targets for PCR by use of limiting dilution. BioTechniques 1992;13:444-9.

(16.) Hindson CM, Chevillet JR, Briggs HA, Gallichotte EN, Ruf IK, Hindson BJ, et al. Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat Methods 2013;10:1003-5.

(17.) Whale AS, Devonshire AS, Karlin-Neumann G, Regan J, Javier L, Cowen S, et al. International inter-laboratory digital PCR study demonstrating high reproducibility for the measurement of a rare sequence variant. Anal Chem 2017;89:1724-33.

(18.) Taly V, Pekin D, Benhaim L, Kotsopoulos SK, Le Corre D, Li X, et al. Multiplex picodroplet digital PCR to detect KRAS mutations in circulating DNA from the plasma of colorectal cancer patients. Clin Chem 2013;59:1722-31.

(19.) Pender A, Garcia-Murillas I, Rana S, Cutts RJ, Kelly G, Fenwick K, et al. Efficient Genotyping of KRAS mutant non-small cell lung cancer using a multiplexed droplet digital PCR approach. PLoS One 2015;10:e0139074 -18.

(20.) Bidshahri R, Attali D, Fakhfakh K, McNeil K, Karsan A, Won JR, etal. Quantitative detection and resolution of BRAF V600 status in colorectal cancer using droplet-digital PCR and a novel wild-type negative assay. J Mol Diagn 2016;18:190-204.

(21.) Findlay SD, Vincent KM, Berman JR, Postovit L-M.A digital PCR-based method for efficient and highly specific screening of genome edited cells. PLoS One 2016;11: e0153901.

(22.) Madic J, Kiialainen A, Bidard F-C, Birzele F, Ramey G, Leroy Q, et al. Circulating tumor DNA and circulating tumor cells in metastatic triple negative breast cancer patients. Int J Cancer 2015;136:2158-65.

(23.) Attali D, Bidshahri R, Haynes C, Bryan J. ddPCR: an R package and web application for analysis of droplet digital PCR data. F1000Res 2016;5:1411-1.

(24.) Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, etal. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res 2017;45:D777-83.

(25.) Janku F, Huang HJ, Fujii T, Shelton DN, Madwani K, Fu S, et al. Multiplex KRASG12/G13 mutation testing of unamplified cell-free DNA from the plasma of patients with advanced cancers using droplet digital polymerase chain reaction. Ann Oncol 2017;28:642-50.

(26.) Zonta E, Garlan F, Pecuchet N, Perez-Toralla K, Caen O, Milbury C, etal. Multiplex detection of rare mutations by picoliter droplet based digital PCR: sensitivity and specificity considerations. PLoS One 2016; 11:e0159094-20.

(27.) Milbury CA, Zhong Q, Lin J, Williams M, Olson J, Link DR, etal. Determining lower limits of detection of digital PCR assays for cancer-related gene mutations. Biomol Detect Quantif 2014;1:8-22.

(28.) Sharma SV, Bell DW, Settleman J, Haber DA. Epidermal growth factor receptor mutations in lung cancer. Nat Rev Cancer 2007;7:169-81.

(29.) Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J. Cell-free DNA Comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell 2016;164: 57-68.

Charles Decraene, [1,2] Amanda B. Silveira, [1] Francois-Clement Bidard, [1,3] Audrey Vallee, [4] Marc Michel, [1] Samia Melaabi, [5] Anne Vincent-Salomon, [5,6] Adrien Saliou, [1] Alexandre Houy, [1,7] Maud Milder, [1,8] Olivier Lantz, [1,8,9] Marc Ychou, [10] Marc G. Denis, [4] Jean-Yves Pierga, [1,3,11] Marc-Henri Stern, [1,7] ([dagger])

[1] Circulating Tumor Biomarkers Laboratory, SiRIC, Translational Research Department, Institut Curie, PSL Research University, Paris, France; [2] CNRS UMR144, Institut Curie, PSL Research University, Paris, France; [3] Department of Medical Oncology, Institut Curie, PSL Research University, Paris, France; [4] Department of Biochemistry and INSERM U1232, Nantes University Hospital, Nantes, France; [5] Department of Biopathology, Institut Curie, PSL Research University, Paris, France; [6] Inserm U934, Institut Curie, PSL Research University, Paris, France; [7] Inserm U830, Institut Curie, PSL Research University, Paris, France; [8] Inserm CIC BT 1418, Institut Curie, PSL Research University, Paris, France; [9] Inserm U932, Institut Curie, PSL Research University, Paris, France; [10] Department of Medical Oncology, Montpellier Cancer Institute, Montpellier, France; [11] University Paris Descartes, Paris, France.

* Address correspondence to this author at: Translational Research Department, Institut Curie, 26 rue d'Ulm 75005 Paris, France. E-mail Charlotte.Proudhon@curie.fr.

([dagger])] Marc-Henri Stern and Charlotte Proudhon contributed equally to this work.

Received February 3,2017; accepted October 10,2017.

Previously published online at DOI: 10.1373/clinchem.2017.272518

[C] 2017 American Association for Clinical Chemistry

[12] Human Genes: KRAS, KRAS proto-oncogene, GTPase; NRAS, NRAS proto-oncogene, GTPase; BRAF, B-Raf proto-oncogene, serine/threonine kinase; EGFR, epidermal growth factor receptor.

[13] Nonstandard abbreviations: ddPCR, droplet digital PCR; FDA, Food and Drug Administration; EC, European Conformity; NSCLC, non-small cell lung cancers; ctDNA, circulating tumor DNA; WT, wild-type; FFPE, formalin-fixed and paraffin embedded; CRC, colorectal cancer; cfDNA, cell-free DNA; gDNA, genomic DNA; NGS, next-generation sequencing; PBMC, peripheral blood mononuclear cells; MAF, mutant allele frequency; REF, reference; NFQ, nonfluorescent quencher; LOB, limit of blank; LOD, limit of detection.

Caption: Fig. 1. The drop-off ddPCR requiring a unique pair of TaqMan oligoprobes to scan all mutations of a hotspot region. Assay designs with typical results displayed as two-dimension plots showing droplet fluorescence intensity. Conventional ddPCR with multiplexed mutant(MUT1-3) and wild-type (WT) probes targeting the same region (A). The drop-off ddPCR contains a drop-off probe targeting the mutated region but complementary to the WT sequence and a reference (REF) probe annealing to a nonmutated region within the same amplicon (B).

Caption: Fig. 2. The KRAS drop-off assay specificity and analytical sensitivity of <0.1%. KRAS drop-off assay design (A). * = mutations targeted by the Bio-Rad multiplex KRAS kit. KRAS drop-off tested on DNA from 100% SW480, 100% HCT 116, and 100% PBMC WT cells(B). Drop-off ddPCR vs ddPCR assays specific for G12V, G13D, or multiplex KRAS (n [greater than or equal to] 5) (C-D). Tests for decreasing MAFs (100%, 50%, 20%, 10%, and 0%) (C). Background observed with the 4 assays (D). Observed versus expected MAFs in mutant serial dilutions (5%, 2.5%, 1.25%, 0.63%, 0.31%, 0.16%, 0.08%, 0.04%, 0.02%, 0.01%) for G12V and G13D, respectively (E-F). Black line: false-positives mean = 0.0098%; dashed line: limit of blank = 0.017%, n = 29.

Caption: Fig. 3. The KRAS drop-off assay recapitulation of results observed with conventional tests. Results for 6 plasma samples validated with the KRAS drop-off assay at various mutant fractions (A). Performance of KRAS drop-off versus Bio-Rad multiplex KRAS assays on double or single substitutions not covered by the multiplex KRAS assay (B). Mutant fractions determined by ddPCR are written on each plot.

Caption: Fig. 4. The EGFR drop-off analytical sensitivity of <0.1% and recapitulation of results observed with other methods. EGFR exon-19 assay design (A). The 6 deleted alleles represented correspond to the deletions of patients P-34-P-40 screened by NGS. Observed versus expected MAFs in mutant serial dilutions (2.5%, 1.25%, 0.63%, 0.31%, 0.16%, 0.08%, 0.04%, 0.02%, 0.01%) obtained from the Horizon cfDNA reference standard set carrying the p.Glu746_Ala750del (n = 3 for MAF 2.5%, n = 7 for MAF 1.25% to 0.01%) (B). False-positives mean = 0.0%, limit of blank = 0.0%, n = 24. Results for 7 FFPE tissues and 5 body fluid samples validated with the EGFR drop-off assay at various mutant fractions (Table 2) (C). Mutant fractions identified by EGFR drop-off are written on each plot.
Table l. KRAS drop-off recapitulates results observed with other
conventional tests in patient samples. (a)

Patient    Tumor                                          MAF%
ID         type         KRAS mutation        Sample    (Drop-off)

P-1        NSCLC       c.34G>A,p.G12S         FFPE        28.5
P-2         CRC        c.34G>C,p.G12R         FFPE        41.1
P-3        NSCLC       c.34G>T,p.G12C         FFPE        59.3
P-4#       CRC#       c.35 37GTG>TTT,#       FFPE#       13.7#
                        p.G12V_G13C#
P-5         CRC        c.35G>A,p.G12D         FFPE        37.9
P-6         CRC        c.35G>C,p.G12A         FFPE        7.65
P-7       NSCLC#      c.37G>T,p.G13C#         FFPE       9.74#
P-8         CRC        c.38G>A,p.G13D         FFPE        22.9
P-9        CRC#     c.37_38GG>CT,p.G13L#     FFPE#       42.2#
P-10      NSCLC#    c.34_35GG>TT,p.G12F#     FFPE#       10.9#
P-11       NSCLC       c.34G>T,p.G12C         FFPE        33.4
P-12       NSCLC       c.35G>A,p.G12D         FFPE        54.4
P-13       NSCLC       c.37G>T,p.G13C         FFPE        10.6
P-14        CRC        c.35G>T,p.G12V         FFPE        35.8
P-15       NSCLC       c.35G>A,p.G12D         FFPE        4.04
P-16        CRC        c.38G>A,p. G13D        FFPE        48.4
P-17        CRC        c.34G>A p.G12S        Plasma       20.9
P-18        CRC        c.34G>T p.G12C        Plasma       0.37
P-19       CRC#       c.35G> A p.G12D#      Plasma#      4.14#
P-20       CRC#       c.35G> A p.G12D#      Plasma#      0.12#
P-21        CRC         c.35G>Ap.G12D        Plasma       18.4
P-22        CRC        c.35G>C p.G12A        Plasma       0.19
P-23       CRC#       c.35G>T p.G12V#       Plasma#      11.1#
P-24       CRC#       c.35G>T p.G12V#       Plasma#      1.02#
P-25        CRC         c.35G>Tp.G12V        Plasma       1.56
P-26        CRC         c.35G>Tp.G12V        Plasma      No mut
P-27        CRC        c.38G>A p.G13D        Plasma       0.88
P-28        CRC        c.38G>A p.G13D        Plasma       5.65
P-29       CRC#       c.38G> A p.G13D#      Plasma#      0.30#
P-30       CRC#       c.38G> A p.G13D#      Plasma#      0.74#
P-31        CRC         c.35G>Tp.G12V        Plasma       1.83
P-32        CRC         c.35G>Tp.G12V        Plasma      No mut
P-33        CRC         c.35G>Ap.G12D        Plasma       15.4

Patient    MAF%            Mutant
ID        (Conv.)     detection (Conv.)

P-1         NA            Sanger#
P-2         NA            Sanger#
P-3         NA            Sanger#
P-4#       NA#            Sanger#
P-5         NA            Sanger#
P-6         NA            Sanger#
P-7        NA#            Sanger#
P-8         NA            Sanger#
P-9        NA#            Sanger#
P-10       10#              NGS#
P-11        30              NGS#
P-12        50              NGS#
P-13        15              NGS#
P-14        35              NGS#
P-15         5              NGS#
P-16        50              NGS#
P-17       24.9     ddPCR simple duplex#
P-18        1.1     ddPCR simple duplex#
P-19       4.8#     ddPCR simple duplex#
P-20      0.14#     ddPCR simple duplex#
P-21       19.7     ddPCR simple duplex#
P-22        0.3     ddPCR simple duplex#
P-23       10.6     ddPCR simple duplex#
P-24       1.37     ddPCR simple duplex#
P-25        2.0     ddPCR simple duplex#
P-26      No mut    ddPCR simple duplex#
P-27        0.8     ddPCR simple duplex#
P-28        7.6     ddPCR simple duplex#
P-29       0.49     ddPCR simple duplex#
P-30       0.75     ddPCR simple duplex#
P-31        2.1     ddPCR simple duplex#
P-32      No mut    ddPCR simple duplex#
P-33       15.0     ddPCR simple duplex#

(a) Patients showed as example in Fig. 3, A and B are highlighted in
bold text.

NA = not available.

Note: Patients showed as example in Fig. 3, A and B are highlighted in
bold text is indicated with #.

Table 2. The EGFR drop-off recapitulates results observed with other
methods used for molecular diagnosis. (a)

Patient ID   Tumor type   Del EGRF
                                                           Sample

P-34#         NSCLC#         c.2239_2248delinsC,#          FFPE#
                           p.Leu747_Ala750delinsPro#

P-35           NSCLC         c.2235 2252delinsAAT,          FFPE
                            p.Glu746_Thr751delinsIle

P-36#         NSCLC#         c.2239_2248delinsC,#        Pleural#
                           p.Leu747_Ala750delinsPro#     effusion#

P-37           NSCLC            c.2237_2254del,             FFPE
                            p.Glu746_Ser752delinsAla

P-38           NSCLC            c.2240_2257del,            Serum
                            p.Leu747_Pro753delInsSer

P-39           NSCLC          c.2237_2255delinsT,          Plasma
                            p.Glu746_Ser752delinsVal

P-40#         NSCLC#           c.2235_2249del,#            FFPE#
                              p.Glu746_Ala750del#

P-41#         NSCLC#                  NA#                  FFPE#

P-42           NSCLC                   NA                   FFPE

P-43           NSCLC                   NA                   FFPE

P-44           NSCLC                   NA                   FFPE

P-45           NSCLC                   NA                   FFPE

P-46           NSCLC                   NA                   FFPE

P-47           NSCLC                   NA                   FFPE

P-48           NSCLC                   NA                   FFPE

P-49#         NSCLC#                  NA#                  FFPE#

P-50           NSCLC                   NA                   FFPE

P-51           NSCLC                   NA                   FFPE

P-52           NSCLC                   NA                   FFPE

P-53           NSCLC                   NA                   FFPE

P-54           NSCLC                   NA                   FFPE

P-55           NSCLC                   NA                   FFPE

P-56           NSCLC                   NA                   FFPE

P-57           NSCLC                   NA                   FFPE

P-58#         NSCLC#                  NA#                  FFPE#

P-59           NSCLC                   NA                   FFPE

P-60           NSCLC                   NA                   FFPE

P-61#         NSCLC#                  NA#                  FFPE#

P-62           NSCLC                   NA                  Plasma

P-63#         NSCLC#                  NA#                 Plasma#

P-64#         NSCLC#                  NA#                 Plasma#

P-65#         NSCLC#                  NA#                 Plasma#

P-66           NSCLC                   NA                  Plasma

P-67#         NSCLC#                  NA#                 Plasma#

P-68           NSCLC                   NA                  Plasma

P-69           NSCLC                   NA                  Plasma

P-70           NSCLC                   NA                  Plasma

P-71           NSCLC                   NA                  Plasma

P-72           NSCLC                   NA                  Plasma

P-73           NSCLC                   NA                  Plasma

P-74           NSCLC                   NA                  Plasma

P-75           NSCLC                   NA                  Plasma

P-76           NSCLC                   NA                  Plasma

P-77           NSCLC                   NA                  Plasma

P-78           NSCLC                   NA                  Plasma

P-79           NSCLC                   NA                  Plasma

P-80           NSCLC                   NA                  Plasma

P-81           NSCLC                   NA                  Plasma

                      MAF%/Mutant Detection
Patient ID
              Drop-off    NGS    Therascreen     COBAS

P-34#          31.1#      30#       NA#          NA#

P-35            4.39       5         NA           NA

P-36#          1.86#      3#        NA#          NA#

P-37            43.8       56        NA           NA

P-38            98.9       95        NA           NA

P-39            87.3       90        NA           NA

P-40#          24.4#      29#       NA#          NA#

P-41#          1.35#      NA#     Ex19Del#     Ex19Del#

P-42           10.30       NA      Ex19Del      Ex19Del

P-43           65.20       NA      Ex19Del      Ex19Del

P-44           15.80       NA      Ex19Del      Ex19Del

P-45           37.80       NA      Ex19Del      Ex19Del

P-46           20.60       NA      Ex19Del      Ex19Del

P-47           51.00       NA      Ex19Del      Ex19Del

P-48           12.80       NA      Ex19Del      Ex19Del

P-49#          9.75#      NA#     Ex19Del#     Ex19Del#

P-50           20.60       NA      Ex19Del      Ex19Del

P-51           35.90       NA      Ex19Del      Ex19Del

P-52           21.30       NA      Ex19Del      Ex19Del

P-53           52.80       NA      Ex19Del        NA

P-54           19.20       NA      Ex19Del      Ex19Del

P-55           20.60       NA      Ex19Del        NA

P-56           26.30       NA      Ex19Del      Ex19Del

P-57           46.00       NA      Ex19Del        NA

P-58#         18.20#      NA#     Ex19Del#       NA#

P-59           64.00       NA      Ex19Del      Ex19Del

P-60           33.20       NA      Ex19Del     Ex19Del

P-61#          4.72#      NA#     Ex19Del#     Ex19Del#

P-62            6.54       NA        NA         Ex19Del

P-63#          0.00#      NA#       NA#        No mut#

P-64#          3.20#      NA#       NA#        Ex19Del#

P-65#          0.80#      NA#       NA#        Ex19Del#

P-66            0.22       NA        NA         Ex19Del

P-67#          0.40#      NA#       NA#        Ex19Del#

P-68            3.42       NA        NA         Ex19Del

P-69            4.38       NA        NA         Ex19Del

P-70            5.45       NA        NA         Ex19Del

P-71           16.50       NA        NA         Ex19Del

P-72           20.90       NA        NA         Ex19Del

P-73            0.55       NA        NA         Ex19Del

P-74           70.60       NA        NA         Ex19Del

P-75            0.00       NA        NA         No mut

P-76            0.00       NA        NA         No mut

P-77            0.11       NA        NA         Ex19Del

P-78            0.00       NA        NA         No mut

P-79            0.00       NA        NA         No mut

P-80            6.07       NA        NA         Ex19Del

P-81            0.00       NA        NA         No mut

(a) Patients showed as example in Fig. 4Care highlighted in bold text.

NA = not available.

Note: Patients showed as example in Fig. 4Care highlighted in bold text,
is indicated with #.
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Title Annotation:Cancer Diagnostics
Author:Decraene, Charles; Silveira, Amanda B.; Bidard, Francois-Clement; Vallee, Audrey; Michel, Marc; Mela
Publication:Clinical Chemistry
Date:Feb 1, 2018
Words:6850
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