Droplet-Based Digital PCR

CHAPTER THREE

Droplet-Based Digital PCR: Application in Cancer Research G. Perkins*,†, H. Lu*, F. Garlan*, V. Taly*,1 *Universite Sorbonne Paris Cite, INSERM UMR-S1147, CNRS SNC 5014, Centre Universitaire des Saints-Pe`res, Equipe labelisee LIGUE Contre le Cancer, Paris, France † European Georges Pompidou Hospital, AP-HP - Paris Descartes University, Paris, France 1 Corresponding author: e-mail address: [email protected]

Contents 1. Introduction 2. Digital Procedures for Biomarker Testing: A Historical Overview 2.1 Digital PCR in Microchambers 2.2 Droplet-Based Digital PCR 3. Droplet-Based Digital PCR Procedures: Applications to the Detection of Known Targets 3.1 Droplet-Based Digital DNA Amplification for the Analysis of Tumor Heterogeneity and Subclones Analysis 3.2 Droplet-Based Digital PCR for Circulating DNA Quantitative Detection: Cancer Detection in Body Fluids 3.3 Droplet-Based Digital PCR for the Detection of Emerging Biomarkers 3.4 Droplet-Based Digital PCR Procedures: Applications in NGS Strategies 4. Conclusions and Future Perspectives Acknowledgments References

44 46 50 54 63 64 66 72 74 77 77 78

Abstract The efficient characterization of genetic and epigenetic alterations in oncology, virology, or prenatal diagnostics requires highly sensitive and specific high-throughput approaches. Nevertheless, with the use of conventional methods, sensitivity and specificity were largely limited. By partitioning individual target molecules within distinct compartments, digital PCR (dPCR) could overcome these limitations and detect very rare sequences with unprecedented precision and sensitivity. In dPCR, the sample is diluted such that each individual partition will contain no more than one target sequence. Following the assay reaction, the dPCR process provides an absolute value and analyzable quantitative data. The recent coupling of dPCR with microfluidic systems in commercial platforms should lead to an essential tool for the management of patients with cancer, especially adapted to the analysis of precious samples. Applications in cancer research range from the analysis of tumor heterogeneity to that of a

Advances in Clinical Chemistry, Volume 79 ISSN 0065-2423 http://dx.doi.org/10.1016/bs.acc.2016.10.001

#

2017 Elsevier Inc. All rights reserved.

43

44

G. Perkins et al.

range of body fluids. Droplet-based dPCR is indeed particularly appropriate for the emerging field of liquid biopsy analysis. In this review, following an overview of the development in dPCR technology and different strategies based on the use of microcompartments, we will focus particularly on the applications and latest development of microfluidic droplet-based dPCR in oncology.

1. INTRODUCTION Significant advances in the detection and analysis of nucleic acids have been permitted by the development and dissemination of molecular biology techniques, which are nowadays central to the management of cancer patients. Complex networks of genetic alterations including deletions, point mutations, chromosomal rearrangements, amplifications, or epigenetic changes have been identified in most cancers. These alterations are present only in tumor cells and thus could represent specific cancer markers. Based on their application, these markers can be classified into three categories: (i) diagnostic markers allowing for the determination of a disease location or stage, (ii) prognostic markers, being used to determine cancer progression or recurrence, or (iii) predictive markers, permitting to predict response to treatment, pharmacodynamics, or toxicity [1,2]. By allowing the characterization of tumors at the molecular level, such markers are especially pertinent for personalized or precision medicine [3,4]. Numerous targeted therapies have been developed over the last decade, and new genetic markers predictive of sensitivity or resistance to such therapies have been highlighted. The characterization of these markers now makes it possible to propose personalized treatments for patients. In particular, the tracking of tumor-specific genetic alterations during a patients’ follow-up could allow a closer monitoring of cancer evolution, treatment efficacy, or early detection of potential recurrence. Comprehensive use of such markers in clinical oncology requires precision techniques allowing highly sensitive and quantitative detection within different types of biological samples (e.g., tumor biopsies, blood, urine, or feces). In addition, such techniques should permit the detection of altered sequences within a high background of nonaltered sequences liberated from normal cells [5]. Identification and quantification of variations of DNA or RNA are generally achieved with the use of polymerase chain reaction (PCR). This method is generally central to genetic testing but presents various limitations

Droplet-Based Digital PCR

45

including preferential amplification of small fragments, production of chimeric sequences, and difficulty in detecting low abundance or poorly represented sequences [6]. In addition, analysis of a complex DNA mixture by bulk PCR-based technologies leads to an averaged signal. Moreover, these global analyses show a very low sensitivity for the detection of rare sequences within complex DNA mixtures such as those extracted from biological samples [7,8] (Fig. 1, left panel). Such limitations could be overcome with the use of digital PCR (dPCR) (Fig. 1, right panel) where each target DNA molecule is isolated within individual compartment prior to PCR amplification [9].

Fig. 1 Comparison between conventional PCR procedures and digital PCR. Example of the analysis of a sample containing 0.1% of mutated sequences. The PCR experiment is realized with a mixture of probes specific of the mutated sequences (in green) and normal sequence (in red). The conventional qPCR (left) will amplify all target molecules present in the sample. Obtained signal will thus represent an averaged signal corresponding to the different DNA present in the sample; the signal resulting from under represented sequence could thus be hidden by highly represented sequences. The digital PCR (right) in contrast can amplify each target DNA within an independent compartment. The analysis of the fluorescence of each independent compartment allows detecting and quantifying the one containing mutated sequences. The procedure is now quantitative and its sensitivity depends on the number of compartments that can be analyzed. Modified from K. Perez-Toralla, D. Pekin, J.F. Bartolo, F. Garlan, P. Nizard, P. Laurent-Puig, J.C. Baret, V. Taly, Digital PCR compartmentalization I. Singlemolecule detection of rare mutations, Med. Sci. (Paris) 31 (2015) 84–92; V. Taly, D. Pekin, A.E. Abed, P. Laurent-Puig, Detecting biomarkers with microdroplet technology, Trends Mol. Med. 18 (2012) 405–416, with permission from Trends in Molecular Medicine.

46

G. Perkins et al.

The improvement of patient sample characterization could potentially have a very important impact on clinical trials and subsequently on clinical practice [2]. The recent technological breakthrough represented by dPCR in microcompartments allows detection and quantification of rare sequences with a sensitivity and precision unachievable with the methods conventionally used in clinics (Fig. 2). Following a historical overview of the development of dPCR technology and a summary of several recently developed systems, this review will demonstrate droplet-based dPCR as an ideal approach capable of overcoming the limitations of conventional methods [10,14,15]. Indeed, droplet-based dPCR recently emerged as an extremely promising tool for noninvasive quantitative detection of genetic markers, particularly relevant to cancer research. Therefore, a particular focus will be placed on the clinical applications of this technology for cancer research in the following chapters.

2. DIGITAL PROCEDURES FOR BIOMARKER TESTING: A HISTORICAL OVERVIEW Real-time quantitative PCR (qPCR) is, nowadays, the most commonly used method for the detection of genetic or epigenetic alterations in clinical research. This method forms the basis of many clinical diagnostic tests [16] by allowing the amplification of DNA molecules within a sample and the follow-up of this amplification in real-time. However, the detection and characterization of rare genetic alterations in clinical samples become a particular challenge for qPCR which is mainly linked to the complex composition of such biological samples and the fact that tumor-specific sequences are generally largely diluted within nonaltered sequences (differing often only by one nucleotide) from normal cells. To overcome such limitations, Sykes et al. described in 1992 “a general method to quantify the total number of initial targets present in a sample using limiting dilution, PCR, and Poisson statistics” [17]. It consists of the serial dilution of a given sample to reach limiting dilution, where each partition contains at most one target DNA molecule, followed by PCR amplification of each partition (in this case conventional test tubes). The Poisson distribution law, governing statistics of rare events, gives the theoretical distribution of target DNA in the compartments. Although the application of limiting dilutions has been reported before [18], it was the first time that an optimized PCR reaction (leading to an “all-or-none” end-point result where target DNA is present at very low copy numbers) combined

Fig. 2 Potential methods used in clinic for detection of genetic alterations in cancer. Sensitivity of the different techniques has been described in Ref. [10–13]. The sensitivity of real-time qPCR is dependent of the type of assays.

48

G. Perkins et al.

with Poisson statistics was demonstrated to truly quantify a specific alteration in an excess of unmodified sequences. They used a two-stage PCR to quantify the rearranged immunoglobulin heavy chain (IgH) gene from a leukemic clone in a background of rearranged IgH genes from normal lymphocytes. The authors reported that two potentially amplifiable leukemic IgH targets could be detected in the presence of 160,000 competing nonleukemic genomes. The number of amplifiable genomes present in a sample was determined with the use of NRAS gene quantification, which also permits the evaluation of DNA degradation. This bulk procedure was relatively simple but, since it involved two sequential PCR, could potentially be prone to contamination. However, this work has paved the way to the use of a “yes-or-no” readout for the analysis and quantification of PCR reactions. Other interesting works using limiting dilutions for highly sensitive detection of nucleic acid targets have also been reported (see Ref. [19–21]). The concept was then extended by Vogelstein et al. who used limiting dilutions to detect very rare sequences with a newly developed microtiter plate-based technology named “dPCR” [9]. This method integrates a detection step with fluorogenic probes (Molecular Beacons) where one probe recognizes amplification products (mutated and nonmutated) and a second probe, bearing a different fluorescent marker, hybridizes to nonmutated sequences. At the end of the reaction, only wells with a fluorescent signal are analyzed. Depending on the presence or absence of the target sequence, each compartment thus presents a positive or a negative signal leading to the name “dPCR.” By calculating the ratio of the wells presenting each color, it is now possible to quantify the fraction of mutated DNA contained in the sample (Fig. 3). Thanks to this binary discrimination, rare events could be detected and quantified at the level of single molecule with the segregation of individual sequences within separate compartments, digital procedures can allow the identification of very rare sequences (Fig. 1, right panel). These procedures consist on the discrete counting of a given event. They give an absolute value and are quantitative. While classical PCR performs one reaction per sample with each sample containing potentially many target molecules, dPCR separates the sample into a large number of partitions (e.g., compartments) where each partition is a positive/negative single molecule reaction. dPCR, through the counting of individual reactions, allows a more reliable and sensitive measurement of the quantity of initial nucleic acid. In addition, single target molecules of interest and nonspecific molecules are separated, thus leading to positive

Droplet-Based Digital PCR

49

Fig. 3 Digital PCR principle. Digital PCR consists in diluting a sample in a large number of partitions and counting the number of partitions where a reaction has occurred. In this example, two DNA are targeted with probes bearing different fluorophores (red or green). Modified from O. Caen, P. Nizard, S. Garrigou, K. Perez-Toralla, E. Zonta, P. LaurentPuig, V. Taly, Digital PCR compartmentalization II. Contribution for the quantitative detection of circulating tumor DNA, Med. Sci. (Paris) 31 (2015) 180–186; M. Baker, Digital PCR hits its stride, Nat. Methods 9 (2012) 541–544, with permission from Nature Methods.

and negative partitions and thereby increasing the signal-to-noise ratio for the positive partitions [7]. With the segregation of individual sequences within separate compartments, dPCR supports the identification of very rare sequences (Fig. 1, right panel and Fig. 3). The sensitivity of dPCR is theoretically very high depending mainly on the number of individual compartments and individual sequences that could be created and analyzed, respectively. Indeed, as the resolution of a digital image depends on the number of pixels, the sensitivity and precision of a dPCR reaction relies on the number of analyzed compartments. Therefore theoretically, to quantify small fractions of mutants within large amount of nonmutated sequences, the number of compartments should be increased in order to provide statistically reliable conclusions [10,14,15]. However practically, the sensitivity also depends on the false-positive rate of each assay as well as on the quantity of amplifiable input DNA. Microtiter plate-based dPCR has been applied to the detection and quantification of mutated genes in different clinical situations. It has been applied to the detection and quantification of mutated KRAS [9] or BAT26 [22] in fecal DNA from patients with colorectal cancer (CRC) (see Ref. [23] for a review). Allelic imbalance (AI) for various chromosomes has also been largely explored and correlated with disease stages of CRC [24–26] or ovarian cancers [27,28]. Loss of heterozygosity [29] or variation in gene expression [30] of APC (adenomatous polyposis coli) has also been analyzed by microtiter plate-based dPCR. In addition, AI has been detected using SNP markers for the early detection of ovarian cancer in plasma [31] or ascitic fluid [32] as well as in other types of cancers. As mentioned earlier, the

50

G. Perkins et al.

limit of detection and accuracy of quantification with the use of microtiterbased methods is largely determined by the number of partitions (and thus reactions) that can be analyzed. It has been stated that, in clinics, an ideal screening method for cancer should be noninvasive, cheap (low volumes of reagents), simple to perform (automatable), and representative of the whole targeted organ(s) [2,14]. In addition, it should provide an unambiguous interpretation of results (high sensitivity, specificity, positive predictive value, and negative predictive value) and be easy to teach and the quality controls should be easy to maintain [33]. Recently, with the use of procedures presenting higher sensitivity it has been proposed that any such test should also be quantitative [34,35]. By performing hundreds to thousands of reactions per sample, dPCR could theoretically fulfill all of these criteria. However, dPCR in microtiter plates, simple in theory, requires rather complex implementation and the use of 384-well microplates with a minimum of 5 μL per reaction requires large volumes of reagents [16] making this method labor intensive and economically nonviable for high sensitivity. Furthermore, the technical constraints associated with the use of microplates with volumes of less than a microliter (such as evaporation or capillary effects) limit the use of this technology for the future [36]. Evaporation becomes significant in microliter volumes (1 μL corresponds to a 1-mm diameter droplet) and capillary action can cause “wicking” and bridging of liquid between wells [37], both of these effects would potentially impact on data sensitivity and integrity. Considering these technical constraints, it was thus obvious that a technological development was needed that would allow bridging of the gap between current microplate technologies and true miniaturization [38]. Various strategies have been developed to bridge this gap including arrays of nanoliter [39–41] to picoliter volumes [42–44] as well as molecular colonies [45] or polonies [46] corresponding to clonal amplification of singletarget DNA molecules within a gel solid medium. The main strategies that have subsequently emerged are those based on the control of reaction volumes either in microchambers or in microdroplets. After a rapid overview of the use of microchambers for dPCR, this review will focus on the potential of droplet-based approaches (see also Ref. [10,14,15]).

2.1 Digital PCR in Microchambers Digital PCR in microchips typically utilizes microchambers for the dPCR reactions, whose volumes are in the nanoliter range. The main advantage of

Droplet-Based Digital PCR

51

Fig. 4 Commercial digital PCR microfluidic platforms using microchambers. Several dPCR systems in microchambers have been developed within which the systems using microvalves (Fluidigm Corporation, Courtaboeuf, France), perforated slides (Life technologies, Carlsberg, United States), and systems using microplates (Formulatrix, Bedford, United States). Modified from K. Perez-Toralla, D. Pekin, J.F. Bartolo, F. Garlan, P. Nizard, P. LaurentPuig, J.C. Baret, V. Taly, Digital PCR compartmentalization I. Single-molecule detection of rare mutations, Med. Sci. (Paris) 31 (2015) 84–92; M. Baker, Digital PCR hits its stride, Nat. Methods 9 (2012) 541–544, with permission from Nature Methods and Medecine/Sciences.

these platforms is their ease of use and the ability to automate the system for all steps from the sample injection to the analysis of the reaction. However, in order to minimize the dimensions of the chip, the number of compartments for the different systems is often limited to a few thousands [40]. Several dPCR systems using microchambers have been developed, among them systems using microvalves and systems using microwells. Fig. 4 illustrates commercially available systems for dPCR based on microcompartments. Pumps and valves are control elements of liquid flow inside the chip. These elements are complex to manufacture from silicon-based technologies and often difficult to integrate in “lab on a chip” systems. With the use of soft lithography technology, which is based on polymer molding techniques, an elegant solution using flexible valves of elastomeric material has been proposed [47]. These valves can be operated in parallel or independently to control the contents of each chamber and facilitate the handling of fluids over the whole chip. The large-scale integration of flexible microvalves within a microfluidic device led to many applications being

52

G. Perkins et al.

developed, particularly in dPCR [48]. This system allows partitioning of a diluted sample and the associated PCR reagents in a simple and efficient manner. One example of a currently available system is the Digital Array Chip 12.765® (Fluidigm, Courtaboeuf, France), which is capable of analyzing 12 different samples in parallel, each sample being distributed within 765 closed compartments of 6 nL volume. Using this platform, it is possible to perform 9180 PCR reactions simultaneously [49]. Other chips with a greater number of compartments are also available (36,960 parallel reactions in 850 pL volumes) to achieve a higher sensitivity. Thermocycling and reading of fluorescence signals are done directly in a unified platform, which reduces the duration of the experiment and limits the frequency of sample manipulations. This also allows implementation of qPCR protocols to detect possible false-positive signals and improve sensitivity of the analysis. Single cell transcriptional analyses using these systems have also been described [50,51]. Another system for measuring the amplification in real-time developed by Morrison et al. [40] consists of a perforated metal plate the size of a microscope slide. The plate comprises 3072 holes (48 zones of 64 microwells) of 300 μm in diameter and an internal volume of 33 nL. The interior of each hole is treated with a hydrophilic coating, while the outside of the plate is hydrophobic thereby confining the sample. This configuration promotes the isolation of each compartment from its neighbors resulting in reduced contamination from microwell to microwell. Different applications of these systems have been demonstrated using the commercial system (OpenArray®, Thermofischer) especially for real-time detection of point mutations [48,52] or noncoding RNAs [53]. More recently, the same company has introduced a new less expensive miniaturized apparatus that can carry out up to 20,000 reactions simultaneously but at the expense of real-time fluorescence measurement. A recently commercialized system based on a special 96-well microplate contains input wells on the top surface and microfluidic chambers on the bottom surface. After pipetting the reaction mixture (mastermix, primers, and DNA) into the input well, the sealed microplate is placed in the priming drawer of the Constellation Digital PCR system (Formulatrix, Bedford, United-States). The compartmentalization is performed by a system including pins pushing on the plate seal over each well, forcing the liquid into the channels, and allowing individual copies of DNA to be isolated in 496 individual partitions. The microplate is then transferred for thermal cycling of the dPCR reaction. The fluorescence determination is performed by the imaging station of the instrument.

53

Droplet-Based Digital PCR

Finally, the “SlipChip” system, a microfluidic system consisting of two microstructured plates, brought into contact and separation by means of a thin layer of oil. Each plate contains microwells that can be aligned and stacked to create a network of microchannels allowing the filling of the system. By simply sliding the plates together, the wells can be isolated to compartmentalize DNA molecules and mix them with the necessary reagents for PCR (Fig. 5). The number of compartments and the sequence of reactions to be carried out are predefined during the design of the plates. This system has been optimized for the quantitative detection of single molecules of bacterial DNA (nuc gene, Staphylococcus aureus) by dPCR [54]. With 1280 wells of a volume of 2.6 nL, samples with a concentration of 1 fg/μL of DNA were analyzed. The main benefits of such platform are ease of use and low cost making it possible to be used in settings with limited resources.

1.

A 2.

3. + Top plate

Bottom plate Assemble

4.

5. Load with pipette

B

6. Slip

2.

1.

Intensity (au)

3. 600 450 300 150 0 0

400

800

1200

Distance (mm)

Fig. 5 SlipChip principle applied to digital PCR. (A) Schematic drawing: representation of the system of assembly of the two plates, leading to microchannel system constitution after slipping and PCR reagents and DNA loading; (B) Fluorescence images (1 and 2) and line scan (3) at the end-point of amplification on the digital PCR SlipChip.

54

G. Perkins et al.

While the above-described systems are a logical extension of the microtiter plate technology, a significant modification in the experimental approach was recently proposed. It consists of the use of aqueous droplets dispersed in oil for compartmentalization of PCR reactions, opening up the possibility of having a theoretically unlimited number of compartments [6]. The aqueous phase contains the target DNA and the reaction mixture necessary for the PCR reaction. This strategy, called emulsion PCR (ePCR) or droplet PCR, has the advantage of being highly sensitive and theoretically less expensive compared to platforms using microchambers [16].

2.2 Droplet-Based Digital PCR Compartmentalization of biological reactions in aqueous droplets of waterin-oil bulk emulsion [55–57] has been described for many applications [58–60a] but one of the most important applications to date is ePCR [6]. In ePCR, target DNA molecules contained in a tested sample are compartmentalized within aqueous independent droplets of a thermostable emulsion. These aqueous droplets have volumes ranging from femtoliters to nanoliters. After PCR amplification, each droplet will contain a high number of identical copies of the initially segregated DNA molecule. This method is capable of performing millions of single-molecule PCRs in parallel and consequently avoids the bias seen in conventional PCR. The resulting amplification products thus reflect more closely the original composition of nucleic acid mixtures than conventional PCR [14,59]. Initial procedures described for ePCR were using bulk-created emulsions for which the main limitation was the lack of control over droplet sizes. The resulting variations of droplet sizes (polydispersity) limit both the reliability of encapsulating a single DNA molecule per droplet and consequently the possibility of performing truly quantitative measurements. Other limitations of bulk-created emulsions are linked to the inability to manipulate the droplets after their creation as well as to measure reactions kinetics within individual droplets. To circumvent these limitations, different strategies have been developed including the use of magnetic beads to capture amplified DNA and thus manipulate them after the PCR reaction, and more recently the use of microfluidic technologies to generate and manipulate highly monodisperse droplets [59]. Fig. 6 summarizes commercially available systems based on droplet-based dPCR.

Fig. 6 Commercial droplet-based digital PCR microfluidic platforms. * This publication refers to the platform principle and not to its application to digital PCR. The newly developed platform needs to be validated for biological sample analysis. Modified from K. Perez-Toralla, D. Pekin, J.F. Bartolo, F. Garlan, P. Nizard, P. Laurent-Puig, J.C. Baret, V. Taly, Digital PCR compartmentalization I. Single-molecule detection of rare mutations, Med. Sci. (Paris) 31 (2015) 84–92; M. Baker, Digital PCR hits its stride, Nat. Methods 9 (2012) 541–544, with permission from Nature Methods.

56

G. Perkins et al.

2.2.1 Bead-Based PCR in Aqueous Droplets of Bulk Emulsion As mentioned earlier, ePCR can be performed with target DNA coupled to beads. In this system, after PCR amplification in independent droplets, each bead will carry thousands to millions of copies of the target DNA. After breaking the emulsion, the beads can be recovered for further analysis. Such solid phase PCR has been extensively studied [43] and is central to the creation of sequencing templates for high-throughput sequencing [61–64]. ePCR is also the basis of a method called BEAMing (beads, emulsion, amplification, and magnetics). This method was first described in 2003 for the detection and enumeration of genetic variants [65]. It uses magnetic beads functionalized with short single-strand sequences complementary to the target DNA that, once coated by multiple copies of the initially encapsulated DNA, are labeled with fluorescent probes and then analyzed by fluorescence cytometry (Fig. 7). The sensitivity of the technique is mainly linked to the error rate of the polymerase that is used for the preamplification of the targeted sequences. The detection limit of the procedure has thus been described as one mutant DNA molecule in a background of 10,000 wildtype molecules [66]. This method has been applied to the quantitative detection of tumor-derived circulating DNA in clinical samples of cancer patients [67–71]. For example, BEAMing was used for the quantitative detection in plasma of PIK3CA mutations of patients with metastatic breast cancer [72], KRAS mutations in plasma of CRC patients [73,74] as well as of activating and resistant mutations of the EGFR gene for patients with lung adenocarcinoma [75]. An extension of the procedure called methyl-BEAMing has also been described for the sensitive digital quantification of DNA methylation, with a sensitivity of one methylated molecule in
5000 unmethylated molecules in DNA from plasma or fecal samples [76]. The company Sysmex Inostics is now offering an ePCR product based on BEAMing. The BEAMing technique represents an attractive tool for the detection of rare sequences but requires a relatively cumbersome and complicated procedure for routine clinical use [66,77]. Microfluidic systems allowing precise control of droplet volumes in an emulsion have been developed for carrying out reactions in microdroplet dPCR [14]. 2.2.2 Droplet-Based Microfluidics for Digital PCR Microfluidics is a technology that allows the controlled manipulation of small volumes of liquid (109 to 1018 L) in microchannels [78]. Microfluidic systems consist of networks of channels with diameters typically measuring 10–100 μm that allow the efficient and rapid partitioning of single

Droplet-Based Digital PCR

57

Fig. 7 BEAMing principle. Streptavidin-coated magnetic beads displaying biotinylated primers (specific of wild-type allele and of mutated allele) are emulsified with template DNA and all components needed for DNA amplification. Each aqueous compartment contains an average of no more than one template molecule and no more than one bead. Red and green templates represent two DNA molecules with sequences differing by one or more nucleotides. After thermocycling, the beads are coated with thousands of identical copies of the original target DNA molecule. The emulsion is then broken and the beads are recovered using a magnet. After DNA denaturation, the beads are incubated with fluorescently labeled oligonucleotide probes (represented in red and green) specific for the different template sequences. Sorting of beads coated with the different target sequences is performed using flow cytometry (red and green regions), allowing quantification of the different populations. The beads that failed to hybridize a probe since not coated with target sequence are represented in the larger, gray region in the lower left corner. The right corner image is a capture of fluorescence measurement of beads coated with wild-type sequence (green), mutated sequence (red) and nontarget sequence. Modified from V. Taly, D. Pekin, A.E. Abed, P. Laurent-Puig, Detecting biomarkers with microdroplet technology, Trends Mol. Med. 18 (2012) 405–416, with permission from Trends Molecular Medicine.

molecules (or single cells or beads) from complex samples. The use of microfluidics also extends the capabilities of droplet-based procedures in term of efficiency, throughput, and sensitivity [14,79,80]. Highly monodisperse droplets can be produced at high frequency [81] and are stabilized by the use of surfactants [82]. These droplets act as independent microreactors, which can then be manipulated in microchannels through the implementation of different modules for merging, splitting, incubating, or sorting [36]. Implementation of the detection chemistries used in qPCR (fluorescently labeled probes or DNA binding molecules for example) permits detection

58

G. Perkins et al.

of individual targeted sequences by fluorescence as well as multiplex detection with high sensitivity [83,84]. Manufactured microfluidic systems made from materials compatible with fluorescence detection are particularly suitable for this type of detection [43]. Microfluidic systems are being developed for use in several areas, including diagnostics and organic synthesis [59,80,85–87]. Real-time monitoring of DNA amplification has been performed directly in droplets using fluorescent probes in microfluidic systems [88–91]. An example of an integrated system combining production, thermocycling, and on-chip droplet analysis has been developed by Beer et al. [90]. Droplets (
70 pL volume) are immobilized within a long channel by stopping the flow, and the whole chip is then submitted to temperature cycles allowing DNA amplification and real-time measurement of fluorescent signal of amplification products. One of the drawbacks of this static configuration was the limited number of analyzable droplets (
1000). Kiss et al. developed a system capable of thermocycling and dynamic analysis of droplets in continuous flow, in a real-time basis [88]. This system is able to detect one target molecule at a concentration of 0.003 pg/μL (one molecule detected every 167 droplets). Although this system can offer a complete analysis in 35 min, the calibration step remains delicate. Moreover, the follow-up of an individual droplet in real-time is not feasible. Devices capable of performing on-chip amplification present the advantages of rapid PCR amplification and better droplet integrity during PCR amplification. The main reported drawbacks are difficulties in creating parallel reactions and adjusting the number of cycles once design is complete, as well as the high cost of such devices [39]. Thermocycling of microchips using conventional PCR instruments [54,92,93] as well as off-chip incubations [94,95] have also been described. To increase the analysis throughput, the droplets can be collected and thermocycled off-chip. Such a procedure has been demonstrated for the highly sensitive detection of mutated DNA in a quantitative manner within complex mixtures of DNA. Reinjection of PCR amplified droplets into a new microfluidic analysis device allows individual droplets to pass through a laser beam, with subsequent generation of a discriminating fluorescent signal from the different allelic variants. With a strategy based on the allelic discrimination approach of dual TaqMan® probes, several targets can be detected simultaneously [14]. Several thousand PCR reactions can be performed in parallel using microliter to nanoliter droplet volumes [96–98]. dPCR can also be

Droplet-Based Digital PCR

59

performed using picoliter droplets that can be generated and manipulated at kHz frequencies in microfluidic systems. Such a reduction of ePCR reaction volumes decreases reagent consumption and improves statistics if all reactions are processed [90]. Performing millions of parallel PCR reactions also leads to an increased sensitivity. Droplet-based dPCR using picoliter size droplets has been applied to the detection of rare mutations [94,95]. Pekin et al. have described a method based on a droplet-based microfluidic system to perform millions of single molecule PCR in millions of picoliter droplets. In the developed strategy, genomic DNA is compartmentalized in droplets with two TaqMan® probes labeled with different fluorophores, one specific for the mutant and the other specific for the wild-type DNA. After emulsion generation the emulsion is thermocycled. The ratio of mutant to wild-type genes is then determined by counting the droplets containing fluorescent signal from each probe. The procedure is based on the measure of end-point fluorescence within each droplet. This procedure demonstrated a sensitivity of up to 1 mutant sequence within 200,000 nonmutated/wild-type sequences for the detection of the mutated KRAS oncogene (mutated in more than 30% of all adenocarcinomas) (Fig. 8A and B). The use of the same materials (probes, target DNA) in conventional bulk reaction-based qPCR gave a sensitivity of approximately 10% [11,94]. The technique also enabled the determination of mutant allele-specific imbalance in several cancer cell lines [94]. Picoliter droplet-based dPCR technology is now commercialized by Raindance Technologies (Lexington, MA, United States). Other studies have shown the possibility of achieving equivalent sensitivities using nanoliter droplets [99]. The authors detected one mutant BRAF V600E sequence in an excess of 100,000 wild-type sequences. Copy number variations in HapMap or breast tissue samples (normal and tumor) using Taqman duplex assays have also been detected and circulating fetal and maternal DNA from cell-free plasma were also quantified. Biorad (Hercules, CA, United States) now commercializes this system. An extension of the picoliter droplet-based dPCR procedure is the ability to detect and quantify multiple somatic mutations in parallel in a single experiment [94]. This procedure was developed for the multiplex detection of the six mutations of the KRAS oncogene codon 12 in a single experiment. It is based on one-to-one fusion of droplets containing genomic DNA with any one of seven different types of droplets, each containing a TaqMan® probe specific for a different KRAS mutation, or wild-type KRAS, as well as an optical code [94]. In this system, even if the different

60

G. Perkins et al.

Fig. 8 Droplet-based digital PCR in duplex (A and B) or in multiplex (C). The first strategy will allow, for example, to quantify one mutated sequence (tumor) over the normal (nonmutated, coming from normal cells) sequence. The second, multiplex strategy, will permit to detect and quantify multiple target sequences. Modified from V. Taly, P. Nizard, P. Laurent-Puig, Circulating DNA, digital PCR and colorectal cancers, Correspondances en Onco-Theranostic 2 (2013) 188–193, permission from Lab on Chip.

mutations are assessed within a single experiment, each target DNA molecule is tested for one mutation only. An alternative procedure to the one above analyses a single target DNA molecule simultaneously for multiple different mutations within a single microreactor (Fig. 8C). This multiplexing approach is performed by varying the concentration of different fluorogenic probes within the total reaction, leading to the identification of the targeted gene alterations on the basis of fluorescence color and intensity. This highly efficient dPCR multiplexing procedure was demonstrated in a five-plex assay for spinal muscular atrophy (SMA) with just two fluorophores. The simultaneous measurement of the copy number of two genes (SMN1 and SMN2) and genotyping of a single nucleotide polymorphism (c.815A > G, SMN1) were performed. This multiplexing has been applied to a pilot study with SMA patients [95]. Furthermore in a subsequent study of circulating tumor DNA (ctDNA) in CRC patients, results from

Droplet-Based Digital PCR

61

multiplex and duplex dPCR analysis were found highly correlated (r2 ¼ 0.98) [100]. This strategy has been extended to the analysis of clinical samples both to qualify the integrity of tumor tissue DNA and also to quantify the presence of each of the most frequent mutations of the KRAS oncogene within those samples. Sample integrity was evaluated before next generation sequencing (NGS) analysis by targeting four amplicons of different sizes located throughout the genome [101]. The multiplex detection of the seven most frequent mutations of the KRAS oncogene and the wild-type sequence using a two-panel assay system has been validated both for the quantitative detection of ctDNA in the plasma of patients with advanced cancer [100] and the quantitative detection of rare mutated subclones within CRC tumor tissues [102]. This latter work gave insights into the clinical significance of the presence of rare subclones within patient tumors (see later in the manuscript). Jerome et al. have also demonstrated the development of a three-plex assay using a similar strategy in nanoliter droplets for the detection of human cytomegalovirus (CMV), human adenovirus species F, and an internal control. This internal control can be useful to evaluate efficiency of PCR reactions especially in the context of samples prone to assay inhibition [103]. Finally, the use of Eva Green DNA binding dye has also been described for droplet dPCR. By choosing amplicons with different sizes for each targeted DNA, McDermott et al. demonstrated that they could detect two different targets [104]. Ji et al. have described a method for quantifying copy number and point mutations with the use of a DNA binding dye [105]. This single color system is based on the use of different lengths of 50 primers that are specific for the different tested alleles in a droplet-based dPCR using a common 30 primer. The experiment results in the generation of amplicons of different sizes that will thus present different fluorescent signals after dye incorporation. The authors exemplify the use of this relatively simple and cost effective system (as compared with the use of Taqman probe-based assays) for the analysis of copy number in the protooncogene FLT3 and for the detection of the BRAF V600E mutations in control samples and cancer cell lines. They demonstrated sensitivity of less than 1% for the detection of mutations (DNA bearing target mutation diluted in DNA from normal cells). Many clinically pertinent works have now described the use of dropletbased dPCR for the detection of specific tumor mutations (previously identified in the patient’s tumor) within body effluents. Being able to efficiently

62

G. Perkins et al.

track mutations in frequently mutated DNA regions without previous knowledge of the tumor genotype would, however, be of high benefit for patients. It would eliminate the necessity of designing assays for each individual patient and would also allow for the testing of patients when the molecular profiles of a patient’s tumor are not available or accessible. For CRC patients, the regions of interest would include, among others, the 12–13 codons of the exon 2 of the KRAS oncogene (predictive of nonresponse to anti-EGFR therapies) or frequently mutated regions of the TP53 or APC tumor suppressor genes. Makrigiorgos et al., developer of COLD-PCR, a method based on coamplification at lower denaturation temperatures, have combined their approach with droplet-based dPCR. The procedure is based on the use of two hydrolysis probes labeled with different fluorophores matching wild-type sequences (at two targeted locations) and allows for mutation scanning of the sequences targeted by these probes. The original COLD-PCR approach suppresses wild-type sequences and enables preferential amplification of mutation-containing DNA for mutations within the amplicon [106–108]. In this modified version of the technology, the method interrogates the sequences covered by the hydrolysis probes and relies on detecting changes in the ratio of COLDddPCR signals caused by the presence of mutation(s) within the probed region (
50 bp section of a specific sequence target could be scanned) [109]. The authors demonstrated it for the scanning of multiple mutations in TP53 and EGFR at <1% mutation abundance with just two fluorophores. Finally, it is also important to mention that, even if dPCR in microdroplets provides unprecedented levels of sensitivity, accuracy of experiments will rely on the will of the scientists to follow common rules for assay design, experimental controls as well as experimental analysis. Such rules or guidelines will enhance the impact of the technology. In 2009, Huggett et al. described a series of guidelines called MIQE, Minimum Information for publication of Quantitative real-time PCR Experiments. The guidelines were designed to provide a consensus on “how best to perform and interpret quantitative real-time experiments” and aim to help ensuring the integrity of the scientific literature, promote consistency between laboratories, and to increase experimental transparency. It describes the minimum information necessary for the evaluation of qPCR experiments. In 2013, the same authors published an extension of these guidelines called the digital MIQE guidelines dedicated this time to the realization and publication of dPCR experiments [110]. As for qPCR,

Droplet-Based Digital PCR

63

these guidelines aim at helping scientists to perform robust, meaningful experiments using careful design, and adequate controls. In addition to good practices in the realization of experiments, proper data analysis and determination of limit of blank (LOB) and limit of detection (LOD) of dPCR molecular detection assays have to be adopted. LOB is defined by the frequency of positive droplets measured in normal control DNA samples with no mutant DNA present. A process to perform accurate determination of LOD of dPCR molecular detection assays has been described by Milbury et al. [111]. The authors described an approach for calculating LOD using two example mutation-detection assays with hydrolysis probes targeting the epidermal growth factor receptor gene (EGFR) (p.L858R and p.T790M). In addition, 16 additional cancer-related mutation assays were also described in this work and explored by the same approach.

3. DROPLET-BASED DIGITAL PCR PROCEDURES: APPLICATIONS TO THE DETECTION OF KNOWN TARGETS There are many advantages of using droplet-based dPCR for the analysis of biological samples that are complex in nature. In addition to the higher sensitivity over qPCR, dPCR possesses other advantages including substantially higher resistance to a variety of inhibitors that could be contained within biological samples and improved comparison between different laboratories’ results thanks to the high reproducibility of the obtained data [110]. The commercialization of droplet-based dPCR apparatus also opens up the possible adoption of dPCR technology in a clinical research environment, especially for the detection of tumor markers (Figs. 7 and 8). The increased tolerance of droplet-based dPCR to PCR inhibitory molecules makes it an attractive alternative to qPCR for medical applications [16,112]. In the context of viral research, Jerome et al. described dropletbased PCR as having tolerance to clinically relevant inhibitors (i.e., SDS, EDTA, or heparin). Indeed, they demonstrated in a CMV assay that dPCR was more tolerant to SDS and heparin than qPCR assays and proposed that the droplet compartmentalization may be responsible for this reduced susceptibility. This work highlights that droplet dPCR could offer interesting advantages over qPCR when dealing with inhibition-prone samples. Such specimens could include stool, sputum, or tissues. The conventional utilization of qPCR for such sample analysis usually involves high dilution of the samples to reduce the risk of residual inhibitor effect. The authors have

64

G. Perkins et al.

demonstrated that such dilutions were not needed for stool samples when tested by droplet dPCR, to lead to pertinent results [103]. The multiplex assay that they developed included an internal plasmid control that could allow the evaluation of such inhibition and demonstrated better performance on stool samples than qPCR. The improved accuracy and precision offered by droplet-based dPCR have to date led to rapid application to clinical challenges [113]. Dropletbased dPCR has thus found increasing applications in oncology, prenatal diagnostics, and infectious disease [14,114–117]. The following section will mainly present, in a nonexhaustive way, the application of commercial microfluidic droplet-based dPCR for cancer research using illustrative examples.

3.1 Droplet-Based Digital DNA Amplification for the Analysis of Tumor Heterogeneity and Subclones Analysis The high sensitivity and quantitativity of picoliter droplet-based dPCR have been exploited to understand the clinical relevance of the presence of KRAS-mutated subclones within tumors of patients with advanced CRC [102]. KRAS mutations have been highlighted as a predictive factor of nonresponse to anti-EGFR therapies in advanced CRC. However, half of the patients with a KRAS wild-type tumor do not respond to such therapies and KRAS-mutated subclones that are not detectable by conventional qPCR methods have been suggested to be responsible for this resistance. There is thus a need to test such a hypothesis using a more sensitive and precise technique than conventional qPCR to accurately quantify the fraction of mutated DNA. A study involving 177 patients with a metastatic CRC has been designed to give insights into the clinical significance of the presence of low frequency KRAS tumor subclones. Multiplex picoliter dropletbased dPCR enabled the detection of low level KRAS mutations in patients with previously undetectable KRAS-mutated alleles as determined by conventional methods. It was also possible to determine the individual mutated KRAS alleles’ fractions of each included patient. It was demonstrated that the fraction of KRAS-mutated alleles was inversely correlated with antiEGFR therapy response rate. In addition, higher fractions of mutated alleles were associated with worse progression-free survival (PFS) and overall survival (OS). This work highlighted the possibility of defining a biological threshold for the fraction of KRAS-mutated subclones allowing to predict benefit from anti-EGFR therapies. BRAF V600E, also recently described as

Droplet-Based Digital PCR

65

a predictive factor of resistance to anti-EGFR therapies, was also tested on these samples. Non-small cell lung cancer (NSCLC) treatments based on EGFR tyrosine kinase inhibitors (TKI) have led to increased survival rates [118]. Patients with activating mutations of EGFR will initially present a high response to TKI therapies but most of them will present secondary resistance [119]. Acquired resistance to treatment (gefitinib or erlotinib) is associated with the EGFR T790M mutation (50% of resistances) [120,121] or to the amplification of the MET oncogene (15% of resistances). For the amplification of MET, the presence of minority subclone(s) within tumor cells (<0.1% in a cell line and <1% in human lung tumor before treatment) has been described before the beginning of the treatment [122]. It has been hypothesized that the T790M mutation preexists at the beginning of the specific treatments. The use of low sensitivity conventional methods for the detection of mutations demonstrated that a small fraction of the patients presented T790M-mutated tumors at the beginning of the specific treatments [123,124]. However, the use of more sensitive procedures has highlighted higher fractions of mutated patients [125,126]. The clinical prevalence of pretreatment T790M clones needs to be further validated using large cohorts of patients and truly quantitative procedures such as picoliter droplet-based dPCR. Watanabe et al. have recently described the use of picoliter droplet-based dPCR to evaluate the possibility of the detection of T790M subclones within the tumor of NSCLC patients of a cohort of 373 surgically resected patients [127]. In this study, the authors described that the majority of the tested patients exhibited T790M-mutated tumors with most of them presenting very low fractions of mutated DNA (0.01–0.1%). Intratumor heterogeneity of ovarian low-grade serous carcinomas (LGSC) has been analyzed by target sequencing and picoliter droplet-based PCR in a study involving 11 patients [128]. The authors made an analysis of the detectable tumor mutations over space and time. While in most of the evaluable cases (n ¼ 5, presenting detectable mutations) mutations were detected in all samples, the authors found there was heterogeneity in presumed drivers of disease in one case (i.e., KRAS or BRAF mutations), even though this patient did not receive a treatment that could have driven a specific clonal evolution. More work needs to be done to confirm if the heterogeneity in a minority of LGSC is a general phenomenon. In which case this could eventually lead to a modification in the way that tumor sampling is performed for mutational status analysis as a means to guide a more appropriate treatment decision.

66

G. Perkins et al.

In melanoma, BRAF inhibitors showed dramatic response rates in patients with BRAF V600E-positive metastatic melanoma [129]. BRIM-3 phase 3 study compared vemurafenib to chemotherapy treatment dacarbazine [130,131]. Both OS and PFS were significantly improved for patients harboring BRAF V600E or V600K mutations. Lamy et al. analyzed 47 paraffin-embedded cutaneous melanoma biopsies, comparing dPCR, qPCR methods, and pyrosequencing. dPCR appeared as the most sensitive method with the lowest LOD, measured at 0.3% [132]. Furthermore, single cell RT-PCR in droplets could represent a highly pertinent tool for investigating tumor heterogeneity. For example, recently, Raji cells expressing PTPRC mRNA were identified (85% detected) within a mixture of PC3 cells which do not express PTPRC mRNA (0.3% detected) [133]. Combined with droplet sorting [134], such method could be used to enrich a rare cell type [135].

3.2 Droplet-Based Digital PCR for Circulating DNA Quantitative Detection: Cancer Detection in Body Fluids Circulating nucleic acids could be detected in biological effluents including blood, urine, feces, saliva, or synovial fluid [15]. Circulating DNA is present in healthy subjects at concentrations ranging from 0 to 100 ng/mL [136]. For cancer patients, these concentrations vary from 0 to 5000 ng/mL [137]. Circulating DNA consists of genomic DNA fragments from 150 to 2000 base pairs among which the smallest (150–180 bases) ones are reported to originate from apoptosis and largest ones from necrosis, lysis, or cellular fragmentation [138,139]. Measurements of DNA integrity and of the elevation of circulating DNA in the blood of patients have been proposed as cancer markers [140], but reported results are contradictory. For example, a high variability is observed for circulating DNA quantity depending on sample handling, patient physiological state, or gender [137,138]. Being able to follow free ctDNA within the blood of cancer patients is a highly attractive opportunity for research in oncology (Figs. 9 and 10). Indeed, such noninvasive (or minimally invasive) follow-up has long been a major goal in oncology [70]. The detection of ctDNA is generally performed via the detection of specific tumor alteration(s) (previously identified within the patient tumor tissue). Such detection has been demonstrated by many studies [70,141]. ctDNA is said to originate from the release of genetic material in the circulation from dying tumor cells and thus contains the same

67

Droplet-Based Digital PCR

Diagnosis

Prediction

Prognosis

Analysis of the intra- and inter tumor heterogeneity Early detection Tumor dynamic follow-up Analysis of cancer molecular profile Identification of multiple tumor sites

Assessment of cancer recurrence risk Identification of genetic markers for the establishment of targeted therapies Correlation with tumor burden

Residual disease follow-up

Early evaluation of the patient’s response to treatment(s) Detection of the resistance to treatments (emergence of genetic alteration(s)) in real time

Fig. 9 Clinical applications of the detection of circulating tumor DNA. Modified from K. Perez-Toralla, D. Pekin, J.F. Bartolo, F. Garlan, P. Nizard, P. Laurent-Puig, J.C. Baret, V. Taly, Digital PCR compartmentalization I. Single-molecule detection of rare mutations, Med. Sci. (Paris) 31 (2015) 84–92; M. Baker, Digital PCR hits its stride, Nat. Methods 9 (2012) 541–544; E. Heitzer, P. Ulz, J.B. Geigl, Circulating tumor DNA as a liquid biopsy for cancer, Clin. Chem. 61 (2015) 112–123, permission from Clinical Chemistry.

Fig. 10 Examples of applications ctDNA for the management of cancer patients. Modified from A. Vallee, M. Denis, Cell-free circulating DNA and the management of non-small-cell lung cancer, Correspondances en Onco-Theranostic 2 (2013) 174–179, permission from Nature Reviews Cancer.

molecular signatures as the patients’ tumor [140]. It has been proposed that the longitudinal follow-up of such genetic circulating markers could provide insight into the tumor evolution, to determine treatment efficiency or to detect potential recurrence [73] (Fig. 10). The decrease in ctDNA levels after complete tumor resection has similarly been described as well as its increase when new lesions appear [5]. The follow-up of circulating genetic markers could thus be used as a means of preventing multiple surgical

68

G. Perkins et al.

interventions or relying on archived primary tumor tissues. Classic biopsies, impossible if the tumor is not accessible, are invasive procedures and have thus been proposed as being responsible for tumor dissemination [142]. The use of blood as liquid biopsy has also been suggested to be more representative of the whole pathological state of the patient [143]. ctDNA should provide a snapshot of clonal tumor heterogeneity at a particular time point, indicating the presence of potential metastasis as well as other tumor sites [141]. The use of liquid biopsy is thus an advantage compared to classic biopsies, which can lead to underestimation of genetic diversity [144]. Fig. 10 illustrates example of the follow-up of ctDNA for cancer patient management. The DNA released by the tumor is highly diluted within circulating DNA coming from the dying normal cells. It can indeed represent as low as 0.01% of total circulating DNA for early stage cancers [67]. This very low representation of ctDNA requires tools that are more sensitive and more specific than the conventionally used ones (qPCR and sequencing). In addition, accurate monitoring of ctDNA level variations requires highly quantitative methods. Recent developments of dPCR have now made possible the highly sensitive and quantitative detection of ctDNA within biological effluents. Recent developments in droplet-based microfluidics have allowed the detection of many genetic markers in the blood of cancer patients. As mentioned earlier, droplet-based dPCR represents an especially pertinent tool for the detection and quantification of minority markers including rare subclones within ctDNA [100]. Many studies have demonstrated the value of dPCR for the follow-up of different cancer types [70,145–147] via the use of blood samples. The next sections consist of a nonexhaustive review of illustrative examples of the use of microfluidic droplet-based dPCR detection of ctDNA. The utility of multiplex picoliter dPCR for the detection of KRASmutated tumor DNA in plasmatic circulating DNA of patients with metastatic CRC have been investigated recently [100]. In this study, 50 plasma samples were analyzed using either a two-panel multiplex procedure capable of detecting any of the seven most frequent mutations of the KRAS oncogene as well as the wild-type sequences or by duplex PCR (one mutated allele and wild-type allele). The tumor genotype had been assessed previously and 19 patients presented a KRAS-mutated allele within their tumor tissue. Among the corresponding plasma samples, 14 had a mutation that matched the one identified in the tumor tissue, 1 sample had a different

Droplet-Based Digital PCR

69

mutation from the corresponding tissue and 4 had no detectable mutations in the multiplex assay. Among the plasma samples expected to be negative from the characterization of their tumor tissue, multiplex analysis revealed that two were mutated for KRAS. The fraction of mutated alleles ranges from 0.16% to 43% (corresponding to 0.08–38 ng of mutant DNA per mL of plasma). Using the duplex approach, two negative plasma samples appeared positive with low fraction of mutated alleles (0.04% and 0.17%). The rest of the observed results were confirmed with a high correlation for the observed fractions of mutated alleles (r2 ¼ 0.99). In addition, BRAF V600E-mutated allele was also screened for five plasmatic samples of patients with BRAF V600E-mutated tumors. The five plasma samples appeared positive. The overall concordance between primitive tumor and plasma analysis was thus 92%. Using a pipeline combining NGS to identify structural somatic variants and droplet dPCR to perform follow-up of 3–6 identified variants, Reinert et al. have studied the pertinence of ctDNA analysis to monitor disease burden in CRC patients (n ¼ 11, more than 150 plasma samples analyzed) [148]. The impact of clonal heterogeneity was also studied. Among the 11 patients studied, 6 had relapsed. The quantification of the level of ctDNA was correlated with clinical findings. By using up to six personalized assays per patient, the authors observed a mean lead time compared to conventional follow-up of 10 months for the detection of metastatic recurrence. Droplet-based dPCR approaches have also been applied to the detection of mutated alleles within the plasma of patients with melanoma [149–152]. ctDNA analysis in melanoma could represent a particular interest since the screening of a relatively small number of common mutations (BRAF V600E or V600K and NRAS Q61R, Q61H, or Q61K) could allow, if efficient, to detect up to 75% of the melanoma patients. Almost 50% of the melanoma patients presented a tumor that is mutated for BRAF V600E [149,150,153,154]. In a recent work, Chang-Hao Tsao et al. assessed droplet-based dPCR for the monitoring of the therapeutic response of patients with melanoma [149]. In this work, the authors followed the ctDNA levels of six patients with stage IV melanoma during their treatments (presenting a tumor biopsy with either a BRAF V600E, V600K or a NRAS Q61H mutation). The patients received different treatments. They showed that the ctDNA level monitoring (here examined as number of copies of mutated alleles per mL of plasma) was consistent and potentially more informative than the lactate dehydrogenase marker, conventionally used in such cancer, to follow cancer progression. In another recent study, Sanmamed

70

G. Perkins et al.

et al. used droplet-based dPCR detection of BRAF V600E-mutated circulating DNA to perform follow-up of cutaneous melanoma patients treated by BRAF inhibitors. Using 8 controls and 20 patients with advanced melanoma with tumor bearing BRAF V600E mutations, the authors followed the BRAF V600E-mutated ctDNA at baseline, first month, best response, and progression. The authors observed an agreement of 84.3% between tumor tissue and plasma for the presence of the targeted mutation. In addition, the plasma levels of BRAF V600E alleles (per mL of plasma) were significantly associated with patients’ response and survival [150]. Droplet-based dPCR has also been used to detect BRAF V600E and V600K mutations in circulating tumor cells of patients with metastatic melanoma [151]. Briefly, in this work, the authors used immunomagnetic bead-based enrichment of CTC from blood of patients with confirmed BRAF-mutated tumors. In total, the authors tested 30 enriched CTC fractions from 15 patients with BRAF V600E mutations and 18 from 5 patients with BRAF V600K mutations. After whole genome amplification of the DNA extracted from the enriched CTC fractions, the authors detected BRAF V600E and BRAF V600K in 77% and 44% of the cases, respectively, of enriched CTC fractions from metastatic melanoma patients carrying the corresponding mutations. Even if a high proportion of the work published so far had focused on the study of advanced cancers where ctDNA could represent a higher fraction of the total circulating DNA, droplet dPCR has the potential to allow followup of ctDNA in localized cancers. Such detection could be of high interest in oncology and could potentially be of great help for treatment management and follow-up of early stage cancer patients. Beaver et al. have recently performed a study where they used picoliter droplet-based PCR for the detection of tumor DNA within plasma of patients with early stage breast cancers [155]. In the described prospective study, the authors analyzed primary breast tumor tissues as well as matched pre- and postsurgery plasma samples of 29 breast cancer patients. The PIK3CA mutational status of the tumors was analyzed by Sanger sequencing and picoliter droplet-based dPCR assays targeting the specific detected mutations. Following that, the plasma DNA was then screened by picoliter droplet-based dPCR. Interestingly, the use of droplet-based dPCR confirmed all mutations observed by Sanger sequencing and identified five additional mutations. For the presurgery plasma samples matching PIK3CA-mutated tumors, 14/15 presented detectable mutant ctDNA, while none of the presurgery plasma samples matching PIK3CA wild-type tumors presented detectable ctDNA. Among the

Droplet-Based Digital PCR

71

postsurgery samples of patients with detectable ctDNA before surgery, 10 were analyzed by droplet-based dPCR for the corresponding mutation and half of them presented detectable ctDNA. This work needs to be validated on a higher number of patients to evaluate the pertinence of the detection of ctDNA to identify patients with higher risk of recurrence. Similarly, Olsson et al. assessed the serial monitoring of ctDNA for early detection of metastasis in a retrospective study of 20 patients diagnosed with primary breast cancer and with a long follow-up [156]. After whole exome sequencing of the primary tumor, the authors performed the quantitative detection of the tumor-specific rearrangements by droplet dPCR. Detection of ctDNA was associated with a poor prognosis. They demonstrated that the ctDNA monitoring was pertinent for the detection of recurrence. The ctDNA detection of the disease recurrence was observed with an average time of 11 months compared to clinical detection. BRAF V600E mutation is found with a high frequency in the tumor tissue of patients with Langerhans cell histiocytosis (LCH) and Erdheim– Chester disease (ECD) [157,158]. Patients with BRAF-mutated tumor can respond to BRAF inhibitors, but the difficulties to assess the tumor status of the primitive tumors (either because of low histiocytes content of the biopsy or unavailability of a tissue biopsy for example) can preclude BRAF status evaluation [159,160]. Janku et al. investigated the possibility of performing plasma and urine circulating DNA analysis by picoliter dropletbased PCR for six ECD patients [160]. For three patients, the tumor mutational status was available and in accordance with both plasma and urine analysis. For three additional patients, two presented BRAF V600E circulating DNA in urine and one of them in plasma, the last patient was not positive for mutated BRAF. Such work suggested that the mutational status of the patients with ECD could be determined in urine or plasma circulating DNA as an alternative to tissue DNA. A prospective, blinded study was performed by Hyman et al. with the use of picoliter droplet-based dPCR targeting BRAF V600E mutations in the plasma and urine circulating DNA of LCH or ECD patients (n ¼ 30) [159]. A two-step PCR assay was designed to target very short amplicons (31 bp) and thus enhance the detection of rare tumor-derived alleles in the ctDNA. The authors described a 100% concordance between the tissue and urine-free DNA genotype in treatment naı¨ve samples and demonstrated the possibility of tracking disease burden during patient treatment. Few works report the use of digital for urine sample analysis, and the use of urine for the analysis of ctDNA has been limited to malignancies of the genitourinary tract. However, it could present

72

G. Perkins et al.

significant advantages both because it represents a truly noninvasive procedure as well as a source of tumor DNA. It has been reported that urine contains high concentrations of short DNA and that urine analysis could present significant advantages for the analysis of ctDNA including higher sample stability and ease of serial collection [159].

3.3 Droplet-Based Digital PCR for the Detection of Emerging Biomarkers Sensitivity and accuracy levels reached by dPCR also make this technique a perfect tool for the precise determination of the absolute representation of a given nucleic acid sequence. These features find application, among others, in gene copy number evaluation, quantitative analysis of transcription (either mRNA, miRNA, or other noncoding RNA), and methylated sequences detection [14]. In different types of cancer, MET inhibition is found to be effective in some patients with amplification of MET in the tumor. However acquired and de novo resistance mechanisms are involved in resistance to MET inhibitors. Kwak et al. performed molecular analysis of tumor biopsies from patients with MET amplified oesogastric cancer treated with MET kinase inhibitors. Coamplification of MET and EGFR and/or HER2 present in the same tumor cells (determined using FISH experiments) seems to be related to resistance. In a patient presenting a response to MET inhibition followed by a progression, MET amplification was found to be present in the tumor without EGFR coamplification. MET and EGFR copy number level evaluation in ctDNA was performed by droplet-based dPCR. In this patient, elevated MET copy number was detected in ctDNA prior to treatment and then decreased during the first 2 months of therapy as tumor responded to MET inhibition. However, increased EGFR copy number was also detectable prior to initiation of MET inhibitor treatment (even if, as described earlier, this EGFR amplification was not detected in the patient tumor). These results suggest that there existed tumor heterogeneity with the presence of EGFR amplified clones prior to treatment. Moreover, a marked increase in EGFR copy number detectable in ctDNA was also observed throughout treatment [161]. Altered expression patterns of circulating miRNAs have been frequently reported in cancer [162] and have shown great promise as tissue-based markers for cancer classification [163]. In particular, several studies have shown that miRNAs might play a role in CRC development and progression [164]. Tumor-derived miRNAs circulating in plasma or serum are

Droplet-Based Digital PCR

73

emerging as novel blood-based markers for the detection of human cancers even at an early stage [165]. Their remarkable stability and their relatively easy accessibility in patients’ samples in a noninvasive manner make them ideal cancer biomarkers with enormous potential [166]. Droplet-based dPCR has recently been applied to the detection of miRNA expression modification [167] and used, within other applications, to evaluate candidate circulating miRNA as cancer biomarkers [168–171]. Wang et al. reported a droplet dPCR system based on an oil saturated PDMS microfluidic chip platform. The authors analyzed miR-126 presence in paired early stage lung cancer and adjacent nontumor tissues, comparing dPCR to qPCR. MiR126 was detected at a higher level in normal tissues than in paired tumor tissues by both methods. dPCR was more accurate to classify tumor vs normal lung tissue, compared to qPCR [172]. The work of Zhang et al. showed that miRNA could be precisely quantified by a droplet-based detection with exponential amplification reaction at a LOD of 50 copies per mL, and a good correlation was found between this method and RT-qPCR in a test of three lung cancer patients’ plasma [173]. Extracellular vesicles (EVs), including exosomes and microvesicles, have been shown to contain a variety of biomolecules of interest including mRNA, microRNA, and other noncoding RNAs [174]. Recently, Chen et al. have used BEAMing and picoliter droplet-based PCR to detect mutant IDH1 mRNA in the serum and cerebrospinal fluid EVs of glioma patients [175]. The authors demonstrated that mutant IDH1 mRNA could be detected in CSF-derived EVs at time of brain tumor surgery for patients with mutant IDH1 glioma tumors but not in the serum of the same patients. Armstrong et al. analyzed miRNA in tumor tissue and different body fluids (plasma and urine exosomes, white blood cells) from a bladder cancer patients cohort with the use of Nanostring miRNA assays and droplet dPCR for validation. When looking for upregulated miRNAs, the authors found that in a same patient these miRNAs were common to different biospecimen sources (FFPE-tumor tissue, plasma, urine exosomes, and white blood cells). In particular, a significant association between tumor and urine exosome miRNAs was noted, but no association was found between tumor and plasma exosome miRNAs [176]. DNA hypermethylation in the regulatory regions of specific genes could be a potential early cancer marker, and strategies for diagnostic or cancer screening procedures are under development [177]. Few reports describe the use of droplet-based digital approaches for this purpose [76]. Barault et al. evaluated the prognostic and predictive value of MGMT

74

G. Perkins et al.

(O-6-methylguanine-DNA methyltransferase) methylation in ctDNA from glioma and CRC patients treated by alkylating agents with the use of methyl-BEAMing technology and showed that the detection of MGMT methylation was associated with a better outcome [178].

3.4 Droplet-Based Digital PCR Procedures: Applications in NGS Strategies Recent developments in droplet-based dPCR have also been directed toward the integration of this technology within the NGS workflow. NGS is now implemented in many clinical laboratories for mutation detection in tissue replacing Sanger and qPCR approaches for RAS and BRAF status determination in CRC for example. In this context, droplet-based dPCR represents a highly valuable and accurate tool to evaluate target sample quality, to perform library amplification, to determine library quality and concentration, as well as to validate obtained results. 3.4.1 Microfluidic Droplet-Based PCR for Sample Qualification Before Library Analysis The assessment of DNA integrity/quality and quantity is still a bottleneck of many genotyping technologies including NGS. Didelot et al. have developed a multiplex assay that is capable of determining amplifiable DNA molecule contents of biological samples with very low amounts of input DNA. This assay is based on the multiplex amplification of four different sequences with increasing sizes (78, 159, 197, and 550 bp). These amplicons are located within four different targets (located in different genomic areas). The assay was applied to the analysis of formalin-fixed paraffin-embedded (FFPE) tissue samples that, despite representing a major source of tumor DNA, can be of very poor qualities and lead to low quality or failed results when submitted to NGS analysis (see also Ref. [179] for a review on DNA integrity measurements). Such assays can be used to down select samples and make efficient use of precious samples to achieve more reliable genome sequencing with more efficiency, lower costs, and efforts. 3.4.2 Microfluidic Droplet-Based PCR for Library Analysis Once NGS libraries are generated, their concentrations need to be determined prior loading on the sequencer instrument. An efficient quantification of sample concentration leads to improved sample loading and thus would allow saving time and cost on NGS. Genome Scan/Service XS have recently described the use of an intercalating dye (EvaGreen) picoliter

Droplet-Based Digital PCR

75

droplet-based dPCR assay for determining the loading concentrations of the library. A comparison with the Agilent Bioanalyzer system showed a higher accuracy of the dPCR procedure for the evaluation of the library’s concentrations as demonstrated by NGS sequencing (Illumina Hiseq). While an overestimation of library concentration results in lower cluster densities than desired, an underestimation might cause higher cluster densities than desired and thus lead to poor cluster resolution (both scenarios are responsible for the suboptimal utilization of sequencing capabilities). In this work, dPCR provided more accurate concentration determination resulting in higher clustering consistency (cluster/pM) [180]. 3.4.3 Microfluidic Droplet-Based PCR for NGS Data Verification Droplet-based PCR has widely been used as an orthogonal method to confirm NGS results including validation of low frequency mutations within samples presenting small amounts of DNA. As an example, Alakus et al. have used picoliter droplet-based PCR to confirm and validate whole exome sequencing results [181]. The authors used this strategy to understand mutational landscapes and copy number aberrations for patients with mucinous carcinomatosis peritonei of appendiceal origins. With the fact that genetic alterations of these rare tumors were being poorly characterized and that most of the therapeutic opportunities were based on CRC, there was a need for new insights toward the molecular landscape of such tumors. The authors demonstrated that the genetic alterations present in such tumors were distinct from the ones of CRC. An alternative approach for the combined workflow using NGS and droplet-based dPCR involves the identifications of mutations in a first screen targeting a large spectrum of genomic alterations in patients’ samples such as primary tumor or metastases followed by the quantitative analysis of the evolution of individual identified mutations in samples such as liquid biopsy or recurrent tissues. The former aspect utilizes NGS, while the latter aspect utilizes the droplet dPCR. With the use of this strategy, different studies were able to investigate disease relapse throughout serial monitoring of ctDNA during the follow-up of patients with, for example, breast cancer [156] or CRC [148]. In a study involving ovarian low-grade carcinoma patients [128], spatiotemporal intratumor heterogeneity was investigated by evaluating the presence of alterations specific to the primary tumor in recurrent samples. Such a combined strategy was able to facilitate a precise and long-term monitoring of the disease evolution in a noninvasive manner. This type of work could pave the way for new therapeutic opportunities.

76

G. Perkins et al.

3.4.4 Microfluidic Droplet-Based dPCR for Target-Based Enrichments Even though droplet-based dPCR presents unprecedented sensitivity and accuracy, most of the strategies described so far are based on the detection of few known mutations. Multiple assays have thus to be designed to detect subgroups of patients. The analysis of the majority of patients within a single assay is however possible using NGS strategies (recent developments of which have been enabled by the advent of droplet dPCR or ePCR developments). Massively parallel sequencing strategies have recently been developed and optimized for the detection of low represented DNA sequences [182,183]. NGS methods have thus now emerged as a technology of choice for deep sequencing of circulating DNA as well as for deep characterization of tumor heterogeneity but also for the analysis of hereditary predisposition. Targeted enrichment of specific loci of the human genome is a promising approach to enable sequencing-based studies of genetic variation in large populations. Droplet-based microfluidic technology represents a pertinent tool for the massively parallel enrichment of specific subsets of the human genome for targeted sequencing [184]. The described method uses a collection of premade primer pair droplets that are combined, in a microfluidic device, one at a time with picoliter droplets of fragmented genomic DNA to set up 2.5 million reactions with only 50 μL of template. DNA is loaded such that each droplet contains less than a haploid genome. The PCR reactions are thus predominately carried out on single molecules. Six samples were sequenced after enrichment by microdroplet or traditional single-plex PCR using primers targeting 435 exons from 47 genes. The different methods both generated high-quality data. Briefly, identification of the sequence variants was obtained with >99% accuracy, with high reproducibility between samples (r2 ¼ 0.9), leading to high specificity and sensitivity results. A droplet-based dPCR method based on this technology that targets up to 20,000 amplicons simultaneously in separate droplets is commercialized by Raindance Technologies (Lexington, United States). Allelic representation within the sample is preserved with the combination of single molecule reactions (where there is no competition between different alleles) and using hundreds to thousands of repeats of each reaction, even in the case of rare cancer mutations found in heterogeneous tumors [185]. Sequence enrichment using the described commercial platform has now been extended to many different cancer-related sequencing panels including hereditary predispositions in germline DNA [186–188] or the study of somatic mutations within tumors [185,189,190]. Target bisulfite sequencing has also been described for the large-scale identification DNA methylation

Droplet-Based Digital PCR

77

level [191]. The company has now developed and commercialized a new NGS target enrichment system named Thunderbolts running on the existing Raindrop® dPCR instrument. This system has been recently used in a study aiming at understanding clonal evolution of metastatic breast cancer using NGS analysis of ctDNA [192].

4. CONCLUSIONS AND FUTURE PERSPECTIVES Miniaturization coupled with the use of highly efficient microfluidic tools has allowed the development and dissemination of robust dPCR systems permitting the detection of rare sequences with high sensitivity and precision within biological samples. The use of microdroplets or microchambers has resulted in the development of technologies using partition volumes of just a few picoliters. Manipulating small volumes allows not only efficient single molecule amplifications at high-throughput but also provides the necessary saving in patients’ samples and reagent volumes. Another advantage of the technology is the possibility to perform truly quantitative experiments allowing for precise detection of target nucleic acids within samples. Recent commercial developments have made the realization and analysis of dPCR results more amenable to widespread adoption. In particular, dPCR represents an ideal tool for cancer research both for understanding fundamental questions in cancer research or for cancer patients’ clinical follow-up and treatment management. Detection of tumor markers in body effluents like blood, urine, or feces is particularly suited for patients’ followup in a minimally invasive manner. Initially dedicated to the detection of single somatic mutations within target sequences, new strategies focus on the detection of other cancer markers including, among others, miRNA expression and DNA methylation dysregulation. The advantages of dropletbased dPCR for clinical diagnostics are potentially enormous. It is anticipated that such technology could become an essential tool for personalized or precision medicine with the objective of detecting cancers in early stages and treating patients with regimens that best fit their genetic background as well as the genome of their tumor.

ACKNOWLEDGMENTS This work was supported by the Ministe`re de l’Enseignement Superieur et de la Recherche, the Universite Paris-Descartes, the Centre National de la Recherche Scientifique (CNRS), the Institut National de la Sante et de la Recherche Medicale (INSERM), the Association pour la recherche contre le cancer (ARC, no. SL220100601375), the Agence Nationale

78

G. Perkins et al.

de la Recherche (ANR Nanobiotechnologies; no. ANR-10-NANO-0002-09), the SIRIC CARPEM and the LIGUE contre le cancer (Equipe labelisee LIGUE, no EL2016.LNCC/ VaT). F.G. thanks the Fondation Servier for a fellowship within the Frontiers in Life Science PhD program (FdV). H.L. thanks the Region Ile de France for a PhD fellowship. The authors are grateful to Shufang Renault for careful reading of the manuscript.

REFERENCES [1] J.D. Brooks, Translational genomics: the challenge of developing cancer biomarkers, Genome Res. 22 (2012) 183–187. [2] C.L. Sawyers, The cancer biomarker problem, Nature 452 (2008) 548–552. [3] B.G. Blair, A. Bardelli, B.H. Park, Somatic alterations as the basis for resistance to targeted therapies, J. Pathol. 232 (2014) 244–254. [4] M. Martini, L. Vecchione, S. Siena, S. Tejpar, A. Bardelli, Targeted therapies: how personal should we go? Nat. Rev. Clin. Oncol. 9 (2012) 87–97. [5] T. Lecomte, A. Berger, F. Zinzindohoue, S. Micard, B. Landi, H. Blons, P. Beaune, P.H. Cugnenc, P. Laurent-Puig, Detection of free-circulating tumor-associated DNA in plasma of colorectal cancer patients and its association with prognosis, Int. J. Cancer 100 (2002) 542–548. [6] R. Williams, S.G. Peisajovich, O.J. Miller, S. Magdassi, D.S. Tawfik, A.D. Griffiths, Amplification of complex gene libraries by emulsion PCR, Nat. Methods 3 (2006) 545–550. [7] F. Diehl, L.A. Diaz Jr., Digital quantification of mutant DNA in cancer patients, Curr. Opin. Oncol. 19 (2007) 36–42. [8] J. Shuga, Y. Zeng, R. Novak, R.A. Mathies, P. Hainaut, M.T. Smith, Selected technologies for measuring acquired genetic damage in humans, Environ. Mol. Mutagen. 51 (2010) 851–870. [9] B. Vogelstein, K.W. Kinzler, Digital PCR, Proc. Natl. Acad. Sci. U.S.A. 96 (1999) 9236–9241. [10] K. Perez-Toralla, D. Pekin, J.F. Bartolo, F. Garlan, P. Nizard, P. Laurent-Puig, J.C. Baret, V. Taly, Digital PCR compartmentalization I. Single-molecule detection of rare mutations, Med. Sci. (Paris) 31 (2015) 84–92. [11] A. Lievre, J.B. Bachet, V. Boige, A. Cayre, D. Le Corre, E. Buc, M. Ychou, O. Bouche, B. Landi, C. Louvet, T. Andre, F. Bibeau, M.D. Diebold, P. Rougier, M. Ducreux, G. Tomasic, J.F. Emile, F. Penault-Llorca, P. Laurent-Puig, KRAS mutations as an independent prognostic factor in patients with advanced colorectal cancer treated with cetuximab, J. Clin. Oncol. 26 (2008) 374–379. [12] L. Simi, N. Pratesi, M. Vignoli, R. Sestini, F. Cianchi, R. Valanzano, S. Nobili, E. Mini, M. Pazzagli, C. Orlando, High-resolution melting analysis for rapid detection of KRAS, BRAF, and PIK3CA gene mutations in colorectal cancer, Am. J. Clin. Pathol. 130 (2008) 247–253. [13] W. Weichert, C. Schewe, A. Lehmann, C. Sers, C. Denkert, J. Budczies, A. Stenzinger, H. Joos, O. Landt, V. Heiser, C. Rocken, M. Dietel, KRAS genotyping of paraffin-embedded colorectal cancer tissue in routine diagnostics: comparison of methods and impact of histology, J. Mol. Diagn. 12 (2010) 35–42. [14] V. Taly, D. Pekin, A.E. Abed, P. Laurent-Puig, Detecting biomarkers with microdroplet technology, Trends Mol. Med. 18 (2012) 405–416. [15] O. Caen, P. Nizard, S. Garrigou, K. Perez-Toralla, E. Zonta, P. Laurent-Puig, V. Taly, Digital PCR compartmentalization II. Contribution for the quantitative detection of circulating tumor DNA, Med. Sci. (Paris) 31 (2015) 180–186. [16] M. Baker, Digital PCR hits its stride, Nat. Methods 9 (2012) 541–544.

Droplet-Based Digital PCR

79

[17] P.J. Sykes, S.H. Neoh, M.J. Brisco, E. Hughes, J. Condon, A.A. Morley, Quantitation of targets for PCR by use of limiting dilution, Biotechniques 13 (1992) 444–449. [18] P. Simmonds, P. Balfe, J.F. Peutherer, C.A. Ludlam, J.O. Bishop, A.J. Brown, Human immunodeficiency virus-infected individuals contain provirus in small numbers of peripheral mononuclear cells and at low copy numbers, J. Virol. 64 (1990) 864–872. [19] Y. Liu, A.M. Hernandez, D. Shibata, G.A. Cortopassi, BCL2 translocation frequency rises with age in humans, Proc. Natl. Acad. Sci. U.S.A. 91 (1994) 8910–8914. [20] M. Trippler, K.H. Meyer zum Buschenfelde, G. Gerken, HBV viral load within subpopulations of peripheral blood mononuclear cells in HBV infection using limiting dilution PCR, J. Virol. Methods 78 (1999) 129–147. [21] A.G. Rodrigo, P.C. Goracke, K. Rowhanian, J.I. Mullins, Quantitation of target molecules from polymerase chain reaction-based limiting dilution assays, AIDS Res. Hum. Retroviruses 13 (1997) 737–742. [22] G. Traverso, A. Shuber, L. Olsson, B. Levin, C. Johnson, S.R. Hamilton, K. Boynton, K.W. Kinzler, B. Vogelstein, Detection of proximal colorectal cancers through analysis of faecal DNA, Lancet 359 (2002) 403–404. [23] G. Pohl, M. Shih Ie, Principle and applications of digital PCR, Expert Rev. Mol. Diagn. 4 (2004) 41–47. [24] I.M. Shih, W. Zhou, S.N. Goodman, C. Lengauer, K.W. Kinzler, B. Vogelstein, Evidence that genetic instability occurs at an early stage of colorectal tumorigenesis, Cancer Res. 61 (2001) 818–822. [25] W. Zhou, G. Galizia, E. Lieto, S.N. Goodman, K.E. Romans, K.W. Kinzler, B. Vogelstein, M.A. Choti, E.A. Montgomery, Counting alleles reveals a connection between chromosome 18q loss and vascular invasion, Nat. Biotechnol. 19 (2001) 78–81. [26] W. Zhou, S.N. Goodman, G. Galizia, E. Lieto, F. Ferraraccio, C. Pignatelli, C.A. Purdie, J. Piris, R. Morris, D.J. Harrison, P.B. Paty, A. Culliford, K.E. Romans, E.A. Montgomery, M.A. Choti, K.W. Kinzler, B. Vogelstein, Counting alleles to predict recurrence of early-stage colorectal cancers, Lancet 359 (2002) 219–225. [27] G. Singer, R.J. Kurman, H.W. Chang, S.K. Cho, M. Shih Ie, Diverse tumorigenic pathways in ovarian serous carcinoma, Am. J. Pathol. 160 (2002) 1223–1228. [28] G. Singer, R. Oldt 3rd, Y. Cohen, B.G. Wang, D. Sidransky, R.J. Kurman, M. Shih Ie, Mutations in BRAF and KRAS characterize the development of low-grade ovarian serous carcinoma, J. Natl. Cancer Inst. 95 (2003) 484–486. [29] I.M. Shih, T.L. Wang, G. Traverso, K. Romans, S.R. Hamilton, S. Ben-Sasson, K.W. Kinzler, B. Vogelstein, Top-down morphogenesis of colorectal tumors, Proc. Natl. Acad. Sci. U.S.A. 98 (2001) 2640–2645. [30] H. Yan, Z. Dobbie, S.B. Gruber, S. Markowitz, K. Romans, F.M. Giardiello, K.W. Kinzler, B. Vogelstein, Small changes in expression affect predisposition to tumorigenesis, Nat. Genet. 30 (2002) 25–26. [31] H.W. Chang, S.M. Lee, S.N. Goodman, G. Singer, S.K. Cho, L.J. Sokoll, F.J. Montz, R. Roden, Z. Zhang, D.W. Chan, R.J. Kurman, M. Shih Ie, Assessment of plasma DNA levels, allelic imbalance, and CA 125 as diagnostic tests for cancer, J. Natl. Cancer Inst. 94 (2002) 1697–1703. [32] H.W. Chang, S.Z. Ali, S.K. Cho, R.J. Kurman, M. Shih Ie, Detection of allelic imbalance in ascitic supernatant by digital single nucleotide polymorphism analysis, Clin. Cancer Res. 8 (2002) 2580–2585. [33] R.J. Davies, R. Miller, N. Coleman, Colorectal cancer screening: prospects for molecular stool analysis, Nat. Rev. Cancer 5 (2005) 199–209. [34] L. Benhaim, K. Maley, D. Le Corre, H. Blons, V. Taly, F. Bibeau, J.-F. Emile, P. Laurent-Puig, KRAS mutation detection trap, J. Clin. Oncol. 29 (2011) e208–e209. [35] D. Santini, S. Galluzzo, L. Gaeta, A. Zoccoli, E. Riva, A. Ruzzo, B. Vincenzi, F. Graziano, F. Loupakis, A. Falcone, A. Onetti Muda, G. Tonini, Should oncologists

80

[36] [37] [38] [39] [40] [41] [42]

[43] [44] [45] [46] [47] [48] [49]

[50]

[51] [52]

[53]

G. Perkins et al.

be aware in their clinical practice of K-ras molecular analysis? J. Clin. Oncol. 29 (2011) e206–e207. J.C. Baret, V. Taly, M. Ryckelynck, C.A. Merten, A.D. Griffiths, Droplets and emulsions: very high-throughput screening in biology, Med. Sci. (Paris) 25 (2009) 627–632. A. Dove, Drug screening—beyond the bottleneck, Nat. Biotechnol. 17 (1999) 859–863. J.-C. Baret, V. Taly, Tiny droplets for high-throughput cell-based assays, in: M.-C. Potier, N. Bontoux (Eds.), Unravelling More of Cell Genomics: Let’s Use the Micro and Nanotools!, RCS Publication, London, 2010, pp. 261–284. C. Zhang, D. Xing, Single-molecule DNA amplification and analysis using microfluidics, Chem. Rev. 110 (2010) 4910–4947. T. Morrison, J. Hurley, J. Garcia, K. Yoder, A. Katz, D. Roberts, J. Cho, T. Kanigan, S.E. Ilyin, D. Horowitz, J.M. Dixon, C.J. Brenan, Nanoliter high throughput quantitative PCR, Nucleic Acids Res. 34 (2006). e123. F. Shen, W.B. Du, E.K. Davydova, M.A. Karymov, J. Pandey, R.F. Ismagilov, Nanoliter multiplex PCR arrays on a SlipChip, Anal. Chem. 82 (2010) 4606–4612. J.H. Leamon, W.L. Lee, K.R. Tartaro, J.R. Lanza, G.J. Sarkis, A.D. deWinter, J. Berka, M. Weiner, J.M. Rothberg, K.L. Lohman, A massively parallel PicoTiterPlate based platform for discrete picoliter-scale polymerase chain reactions, Electrophoresis 24 (2003) 3769–3777. B.T. Kelly, J.C. Baret, V. Taly, A.D. Griffiths, Miniaturizing chemistry and biology in microdroplets, Chem. Commun. (Camb.) 14 (2007) 1773–1788. V. Taly, B.T. Kelly, A.D. Griffiths, Droplets as microreactors for high-throughput biology, Chembiochem 8 (2007) 263–272. H.V. Chetverina, A.B. Chetverin, Cloning of RNA molecules in vitro, Nucleic Acids Res. 21 (1993) 2349–2353. R.D. Mitra, G.M. Church, In situ localized amplification and contact replication of many individual DNA molecules, Nucleic Acids Res. 27 (1999). e34. T. Thorsen, S.J. Maerkl, S.R. Quake, Microfluidic large-scale integration, Science 298 (2002) 580–584. L. Warren, D. Bryder, I.L. Weissman, S.R. Quake, Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR, Proc. Natl. Acad. Sci. U.S.A. 103 (2006) 17807–17812. T.K. Yung, K.C. Chan, T.S. Mok, J. Tong, K.F. To, Y.M. Lo, Single-molecule detection of epidermal growth factor receptor mutations in plasma by microfluidics digital PCR in non-small cell lung cancer patients, Clin. Cancer Res. 15 (2009) 2076–2084. P. Dalerba, T. Kalisky, D. Sahoo, P.S. Rajendran, M.E. Rothenberg, A.A. Leyrat, S. Sim, J. Okamoto, D.M. Johnston, D. Qian, M. Zabala, J. Bueno, N.F. Neff, J. Wang, A.A. Shelton, B. Visser, S. Hisamori, Y. Shimono, M. van de Wetering, H. Clevers, M.F. Clarke, S.R. Quake, Single-cell dissection of transcriptional heterogeneity in human colon tumors, Nat. Biotechnol. 29 (2011) 1120–1127. M. Ennen, C. Keime, D. Kobi, G. Mengus, D. Lipsker, C. Thibault-Carpentier, I. Davidson, Single-cell gene expression signatures reveal melanoma cell heterogeneity, Oncogene 34 (2015) 3251–3263. L.A. Henriquez-Hernandez, A. Valenciano, E. Herrera-Ramos, M. Lloret, A. Riveros-Perez, P.C. Lara, High-throughput genotyping system as a robust and useful tool in oncology: experience from a single institution, Biologicals 41 (2013) 424–429. J. Hudson, E. Duncavage, A. Tamburrino, P. Salerno, L. Xi, M. Raffeld, J. Moley, R.D. Chernock, Overexpression of miR-10a and miR-375 and downregulation of YAP1 in medullary thyroid carcinoma, Exp. Mol. Pathol. 95 (2013) 62–67.

Droplet-Based Digital PCR

81

[54] F. Shen, W. Du, J.E. Kreutz, A. Fok, R.F. Ismagilov, Digital PCR on a SlipChip, Lab Chip 10 (2010) 2666–2672. [55] D.S. Tawfik, A.D. Griffiths, Man-made cell-like compartments for molecular evolution, Nat. Biotechnol. 16 (1998) 652–656. [56] J. Lederberg, A simple method for isolating individual microbes, J. Bacteriol. 68 (1954) 258–259. [57] B. Rotman, Measurement of activity of single molecules of beta-D-galactosidase, Proc. Natl. Acad. Sci. U.S.A. 47 (1961) 1981–1991. [58] M.T. Guo, A. Rotem, J.A. Heyman, D.A. Weitz, Droplet microfluidics for highthroughput biological assays, Lab Chip 12 (2012) 2146–2155. [59] A.B. Theberge, F. Courtois, Y. Schaerli, M. Fischlechner, C. Abell, F. Hollfelder, W.T. Huck, Microdroplets in microfluidics: an evolving platform for discoveries in chemistry and biology, Angew. Chem. Int. Ed. Engl. 49 (2010) 5846–5868. [60] (a) S. Vyawahare, A.D. Griffiths, C.A. Merten, Miniaturization and parallelization of biological and chemical assays in microfluidic devices, Chem. Biol. 17 (2010) 1052–1065. (b) R. Dangla, S.C. Kayi, C.N. Baroud, Droplet microfluidics driven by gradients of confinement, Proc Natl Acad Sci USA 110 (2013) 853–858. (c) J. Madic, A. Zocevic, V. Senlis, E. Fradet, B. Andre, S. Muller, R. Dangla, and M.E. Droniou, Three-color crystal digital PCR, Biomol. Detect. Quantif. 2016, in press, http://dx.doi.org/10.1016/j.bdq.2016.10.002. [61] M. Margulies, M. Egholm, W.E. Altman, S. Attiya, J.S. Bader, L.A. Bemben, J. Berka, M.S. Braverman, Y.J. Chen, Z. Chen, S.B. Dewell, L. Du, J.M. Fierro, X.V. Gomes, B.C. Godwin, W. He, S. Helgesen, C.H. Ho, G.P. Irzyk, S.C. Jando, M.L. Alenquer, T.P. Jarvie, K.B. Jirage, J.B. Kim, J.R. Knight, J.R. Lanza, J.H. Leamon, S.M. Lefkowitz, M. Lei, J. Li, K.L. Lohman, H. Lu, V.B. Makhijani, K.E. McDade, M.P. McKenna, E.W. Myers, E. Nickerson, J.R. Nobile, R. Plant, B.P. Puc, M.T. Ronan, G.T. Roth, G.J. Sarkis, J.F. Simons, J.W. Simpson, M. Srinivasan, K.R. Tartaro, A. Tomasz, K.A. Vogt, G.A. Volkmer, S.H. Wang, Y. Wang, M.P. Weiner, P. Yu, R.F. Begley, J.M. Rothberg, Genome sequencing in microfabricated high-density picolitre reactors, Nature 437 (2005) 376–380. [62] J. Shendure, G.J. Porreca, N.B. Reppas, X. Lin, J.P. McCutcheon, A.M. Rosenbaum, M.D. Wang, K. Zhang, R.D. Mitra, G.M. Church, Accurate multiplex polony sequencing of an evolved bacterial genome, Science 309 (2005) 1728–1732. [63] J.M. Rothberg, W. Hinz, T.M. Rearick, J. Schultz, W. Mileski, M. Davey, J.H. Leamon, K. Johnson, M.J. Milgrew, M. Edwards, J. Hoon, J.F. Simons, D. Marran, J.W. Myers, J.F. Davidson, A. Branting, J.R. Nobile, B.P. Puc, D. Light, T.A. Clark, M. Huber, J.T. Branciforte, I.B. Stoner, S.E. Cawley, M. Lyons, Y. Fu, N. Homer, M. Sedova, X. Miao, B. Reed, J. Sabina, E. Feierstein, M. Schorn, M. Alanjary, E. Dimalanta, D. Dressman, R. Kasinskas, T. Sokolsky, J.A. Fidanza, E. Namsaraev, K.J. McKernan, A. Williams, G.T. Roth, J. Bustillo, An integrated semiconductor device enabling non-optical genome sequencing, Nature 475 (2011) 348–352. [64] E.H. Turner, S.B. Ng, D.A. Nickerson, J. Shendure, Methods for genomic partitioning, Annu. Rev. Genomics Hum. Genet. 10 (2009) 263–284. [65] D. Dressman, H. Yan, G. Traverso, K.W. Kinzler, B. Vogelstein, Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations, Proc. Natl. Acad. Sci. U.S.A. 100 (2003) 8817–8822. [66] M. Li, F. Diehl, D. Dressman, B. Vogelstein, K.W. Kinzler, BEAMing up for detection and quantification of rare sequence variants, Nat. Methods 3 (2006) 95–97. [67] F. Diehl, M. Li, D. Dressman, Y. He, D. Shen, S. Szabo, L.A. Diaz Jr., S.N. Goodman, K.A. David, H. Juhl, K.W. Kinzler, B. Vogelstein, Detection and quantification of mutations in the plasma of patients with colorectal tumors, Proc. Natl. Acad. Sci. U.S.A. 102 (2005) 16368–16373.

82

G. Perkins et al.

[68] F. Diehl, K. Schmidt, M.A. Choti, K. Romans, S. Goodman, M. Li, K. Thornton, N. Agrawal, L. Sokoll, S.A. Szabo, K.W. Kinzler, B. Vogelstein, L.A. Diaz Jr., Circulating mutant DNA to assess tumor dynamics, Nat. Med. 14 (2008) 985–990. [69] F. Diehl, K. Schmidt, K.H. Durkee, K.J. Moore, S.N. Goodman, A.P. Shuber, K.W. Kinzler, B. Vogelstein, Analysis of mutations in DNA isolated from plasma and stool of colorectal cancer patients, Gastroenterology 135 (2008) 489–498. [70] C. Bettegowda, M. Sausen, R.J. Leary, I. Kinde, Y. Wang, N. Agrawal, B.R. Bartlett, H. Wang, B. Luber, R.M. Alani, E.S. Antonarakis, N.S. Azad, A. Bardelli, H. Brem, J.L. Cameron, C.C. Lee, L.A. Fecher, G.L. Gallia, P. Gibbs, D. Le, R.L. Giuntoli, M. Goggins, M.D. Hogarty, M. Holdhoff, S.M. Hong, Y. Jiao, H.H. Juhl, J.J. Kim, G. Siravegna, D.A. Laheru, C. Lauricella, M. Lim, E.J. Lipson, S.K. Marie, G.J. Netto, K.S. Oliner, A. Olivi, L. Olsson, G.J. Riggins, A. SartoreBianchi, K. Schmidt, M. Shih l, S.M. Oba-Shinjo, S. Siena, D. Theodorescu, J. Tie, T.T. Harkins, S. Veronese, T.L. Wang, J.D. Weingart, C.L. Wolfgang, L.D. Wood, D. Xing, R.H. Hruban, J. Wu, P.J. Allen, C.M. Schmidt, M.A. Choti, V.E. Velculescu, K.W. Kinzler, B. Vogelstein, N. Papadopoulos, L.A. Diaz Jr., Detection of circulating tumor DNA in early- and late-stage human malignancies, Sci. Transl. Med. 6 (2014) 224ra224. [71] F. Janku, P. Angenendt, A.M. Tsimberidou, S. Fu, A. Naing, G.S. Falchook, D.S. Hong, V.R. Holley, G. Cabrilo, J.J. Wheler, S.A. Piha-Paul, R.G. Zinner, A.Y. Bedikian, M.J. Overman, B.K. Kee, K.B. Kim, E.S. Kopetz, R. Luthra, F. Diehl, F. Meric-Bernstam, R. Kurzrock, Actionable mutations in plasma cell-free DNA in patients with advanced cancers referred for experimental targeted therapies, Oncotarget 6 (2015) 12809–12821. [72] M.J. Higgins, D. Jelovac, E. Barnathan, B. Blair, S.A. Slater, P. Powers, J. Zorzi, S.C. Jeter, G. Oliver, J.H. Fetting, L.A. Emens, C. Riley, V. Stearns, F. Diehl, P. Angenendt, P. Huang, L.M. Cope, P. Argani, K. Murphy, K.E. Bachman, J. Greshock, A.C. Wolff, B.H. Park, Detection of tumor PIK3CA status in metastatic breast cancer using peripheral blood, Clin. Cancer Res. 18 (2012) 3462–3469. [73] S. Misale, R. Yaeger, S. Hobor, E. Scala, M. Janakiraman, D. Liska, E. Valtorta, R. Schiavo, M. Buscarino, G. Siravegna, K. Bencardino, A. Cercek, C.T. Chen, S. Veronese, C. Zanon, A. Sartore-Bianchi, M. Gambacorta, M. Gallicchio, E. Vakiani, V. Boscaro, E. Medico, M. Weiser, S. Siena, F. Di Nicolantonio, D. Solit, A. Bardelli, Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer, Nature 486 (2012) 532–536. [74] G. Siravegna, B. Mussolin, M. Buscarino, G. Corti, A. Cassingena, G. Crisafulli, A. Ponzetti, C. Cremolini, A. Amatu, C. Lauricella, S. Lamba, S. Hobor, A. Avallone, E. Valtorta, G. Rospo, E. Medico, V. Motta, C. Antoniotti, F. Tatangelo, B. Bellosillo, S. Veronese, A. Budillon, C. Montagut, P. Racca, S. Marsoni, A. Falcone, R.B. Corcoran, F. Di Nicolantonio, F. Loupakis, S. Siena, A. Sartore-Bianchi, A. Bardelli, Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients, Nat. Med. 21 (2015) 795–801. [75] K. Taniguchi, J. Uchida, K. Nishino, T. Kumagai, T. Okuyama, J. Okami, M. Higashiyama, K. Kodama, F. Imamura, K. Kato, Quantitative detection of EGFR mutations in circulating tumor DNA derived from lung adenocarcinomas, Clin. Cancer Res. 17 (2011) 7808–7815. [76] M. Li, W.D. Chen, N. Papadopoulos, S.N. Goodman, N.C. Bjerregaard, S. Laurberg, B. Levin, H. Juhl, N. Arber, H. Moinova, K. Durkee, K. Schmidt, Y. He, F. Diehl, V.E. Velculescu, S. Zhou, L.A. Diaz Jr., K.W. Kinzler, S.D. Markowitz, B. Vogelstein, Sensitive digital quantification of DNA methylation in clinical samples, Nat. Biotechnol. 27 (2009) 858–863.

Droplet-Based Digital PCR

83

[77] F. Diehl, M. Li, Y. He, K.W. Kinzler, B. Vogelstein, D. Dressman, BEAMing: singlemolecule PCR on microparticles in water-in-oil emulsions, Nat. Methods 3 (2006) 551–559. [78] G.M. Whitesides, The origins and the future of microfluidics, Nature 442 (2006) 368–373. [79] D. Eicher, C.A. Merten, Microfluidic devices for diagnostic applications, Expert Rev. Mol. Diagn. 11 (2011) 505–519. [80] O.J. Dressler, R.M. Maceiczyk, S.I. Chang, A.J. deMello, Droplet-based microfluidics: enabling impact on drug discovery, J. Biomol. Screen. 19 (2014) 483–496. [81] S.Y. Teh, R. Lin, L.H. Hung, A.P. Lee, Droplet microfluidics, Lab Chip 8 (2008) 198–220. [82] J.C. Baret, Surfactants in droplet-based microfluidics, Lab Chip 12 (2012) 422–433. [83] T.B. Morrison, J.J. Weis, C.T. Wittwer, Quantification of low-copy transcripts by continuous SYBR Green I monitoring during amplification, Biotechniques 24 (1998) 954–958. 960,962. [84] P.M. Holland, R.D. Abramson, R. Watson, D.H. Gelfand, Detection of specific polymerase chain reaction product by utilizing the 5’——3’ exonuclease activity of Thermus aquaticus DNA polymerase, Proc. Natl. Acad. Sci. U.S.A. 88 (1991) 7276–7280. [85] Y. Schaerli, F. Hollfelder, The potential of microfluidic water-in-oil droplets in experimental biology, Mol. Biosyst. 5 (2009) 1392–1404. [86] H. Zec, D.J. Shin, T.H. Wang, Novel droplet platforms for the detection of disease biomarkers, Expert Rev. Mol. Diagn. 14 (2014) 787–801. [87] M.G. Pollack, V.K. Pamula, V. Srinivasan, A.E. Eckhardt, Applications of electrowetting-based digital microfluidics in clinical diagnostics, Expert Rev. Mol. Diagn. 11 (2011) 393–407. [88] M.M. Kiss, L. Ortoleva-Donnelly, N.R. Beer, J. Warner, C.G. Bailey, B.W. Colston, J.M. Rothberg, D.R. Link, J.H. Leamon, High-throughput quantitative polymerase chain reaction in picoliter droplets, Anal. Chem. 80 (2008) 8975–8981. [89] Y. Schaerli, R.C. Wootton, T. Robinson, V. Stein, C. Dunsby, M.A. Neil, P.M. French, A.J. Demello, C. Abell, F. Hollfelder, Continuous-flow polymerase chain reaction of single-copy DNA in microfluidic microdroplets, Anal. Chem. 81 (2009) 302–306. [90] N.R. Beer, B.J. Hindson, E.K. Wheeler, S.B. Hall, K.A. Rose, I.M. Kennedy, B.W. Colston, On-chip, real-time, single-copy polymerase chain reaction in picoliter droplets, Anal. Chem. 79 (2007) 8471–8475. [91] N.R. Beer, E.K. Wheeler, L. Lee-Houghton, N. Watkins, S. Nasarabadi, N. Hebert, P. Leung, D.W. Arnold, C.G. Bailey, B.W. Colston, On-chip single-copy real-time reverse-transcription PCR in isolated picoliter droplets, Anal. Chem. 80 (2008) 1854–1858. [92] A.C. Hatch, J.S. Fisher, A.R. Tovar, A.T. Hsieh, R. Lin, S.L. Pentoney, D.L. Yang, A.P. Lee, 1-Million droplet array with wide-field fluorescence imaging for digital PCR, Lab Chip 11 (2011) 3838–3845. [93] A.C. Hatch, J.S. Fisher, S.L. Pentoney, D.L. Yang, A.P. Lee, Tunable 3D droplet selfassembly for ultra-high-density digital micro-reactor arrays, Lab Chip 11 (2011) 2509–2517. [94] D. Pekin, Y. Skhiri, J.C. Baret, D. Le Corre, L. Mazutis, C. Ben Salem, F. Millot, A. El Harrak, J.B. Hutchison, J.W. Larson, D.R. Link, P. Laurent-Puig, A.D. Griffiths, V. Taly, Quantitative and sensitive detection of rare mutations using droplet-based microfluidics, Lab Chip 11 (2011) 2156–2166. [95] Q. Zhong, S. Bhattacharya, S. Kotsopoulos, J. Olson, V. Taly, A.D. Griffiths, D.R. Link, J.W. Larson, Multiplex digital PCR: breaking the one target per color barrier of quantitative PCR, Lab Chip 11 (2011) 2167–2174.

84

G. Perkins et al.

[96] M. Nakano, J. Komatsu, S. Matsuura, K. Takashima, S. Katsura, A. Mizuno, Singlemolecule PCR using water-in-oil emulsion, J. Biotechnol. 102 (2003) 117–124. [97] M. Curcio, J. Roeraade, Continuous segmented-flow polymerase chain reaction for high-throughput miniaturized DNA amplification, Anal. Chem. 75 (2003) 1–7. [98] K.D. Dorfman, M. Chabert, J.H. Codarbox, G. Rousseau, P. de Cremoux, J.L. Viovy, Contamination free continuous flow microfluidic polymerase chain reaction for quantitative and clinical applications, Anal. Chem. 77 (2005) 3700–3704. [99] B.J. Hindson, K.D. Ness, D.A. Masquelier, P. Belgrader, N.J. Heredia, A.J. Makarewicz, I.J. Bright, M.Y. Lucero, A.L. Hiddessen, T.C. Legler, T.K. Kitano, M.R. Hodel, J.F. Petersen, P.W. Wyatt, E.R. Steenblock, P.H. Shah, L.J. Bousse, C.B. Troup, J.C. Mellen, D.K. Wittmann, N.G. Erndt, T.H. Cauley, R.T. Koehler, A.P. So, S. Dube, K.A. Rose, L. Montesclaros, S. Wang, D.P. Stumbo, S.P. Hodges, S. Romine, F.P. Milanovich, H.E. White, J.F. Regan, G.A. Karlin-Neumann, C.M. Hindson, S. Saxonov, B.W. Colston, High-throughput droplet digital PCR system for absolute quantitation of DNA copy number, Anal. Chem. 83 (2011) 8604–8610. [100] V. Taly, D. Pekin, L. Benhaim, S.K. Kotsopoulos, D. Le Corre, X. Li, I. Atochin, D.R. Link, A.D. Griffiths, K. Pallier, H. Blons, O. Bouche, B. Landi, J.B. Hutchison, P. Laurent-Puig, Multiplex picodroplet digital PCR to detect KRAS mutations in circulating DNA from the plasma of colorectal cancer patients, Clin. Chem. 59 (2013) 1722–1731. [101] A. Didelot, S.K. Kotsopoulos, A. Lupo, D. Pekin, X. Li, I. Atochin, P. Srinivasan, Q. Zhong, J. Olson, D.R. Link, P. Laurent-Puig, H. Blons, J.B. Hutchison, V. Taly, Multiplex picoliter-droplet digital PCR for quantitative assessment of DNA integrity in clinical samples, Clin. Chem. 59 (2013) 815–823. [102] P. Laurent-Puig, D. Pekin, C. Normand, S.K. Kotsopoulos, P. Nizard, K. PerezToralla, R. Rowell, J. Olson, P. Srinivasan, D. Le Corre, T. Hor, Z. El Harrak, X. Li, D.R. Link, O. Bouche, J.F. Emile, B. Landi, V. Boige, J.B. Hutchison, V. Taly, Clinical relevance of KRAS-mutated subclones detected with picodroplet digital PCR in advanced colorectal cancer treated with anti-EGFR therapy, Clin. Cancer Res. 21 (2015) 1087–1097. [103] R.H. Sedlak, J. Kuypers, K.R. Jerome, A multiplexed droplet digital PCR assay performs better than qPCR on inhibition prone samples, Diagn. Microbiol. Infect. Dis. 80 (2014) 285–286. [104] G.P. McDermott, D. Do, C.M. Litterst, D. Maar, C.M. Hindson, E.R. Steenblock, T.C. Legler, Y. Jouvenot, S.H. Marrs, A. Bemis, P. Shah, J. Wong, S. Wang, D. Sally, L. Javier, T. Dinio, C. Han, T.P. Brackbill, S.P. Hodges, Y. Ling, N. Klitgord, G.J. Carman, J.R. Berman, R.T. Koehler, A.L. Hiddessen, P. Walse, L. Bousse, S. Tzonev, E. Hefner, B.J. Hindson, T.H. Cauly, K. Hamby, V.P. Patel, J.F. Regan, P.W. Wyatt, G.A. Karlin-Neumann, D.P. Stumbo, A.J. Lowe, Multiplexed target detection using DNA-binding dye chemistry in droplet digital PCR, Anal. Chem. 85 (2013) 11619–11627. [105] L. Miotke, B.T. Lau, R.T. Rumma, H.P. Ji, High sensitivity detection and quantitation of DNA copy number and single nucleotide variants with single color droplet digital PCR, Anal. Chem. 86 (2014) 2618–2624. [106] J. Li, G.M. Makrigiorgos, COLD-PCR: a new platform for highly improved mutation detection in cancer and genetic testing, Biochem. Soc. Trans. 37 (2009) 427–432. [107] J. Li, C.A. Milbury, C. Li, G.M. Makrigiorgos, Two-round coamplification at lower denaturation temperature-PCR (COLD-PCR)-based sanger sequencing identifies a novel spectrum of low-level mutations in lung adenocarcinoma, Hum. Mutat. 30 (2009) 1583–1590.

Droplet-Based Digital PCR

85

[108] J. Li, L. Wang, H. Mamon, M.H. Kulke, R. Berbeco, G.M. Makrigiorgos, Replacing PCR with COLD-PCR enriches variant DNA sequences and redefines the sensitivity of genetic testing, Nat. Med. 14 (2008) 579–584. [109] E. Castellanos-Rizaldos, C. Paweletz, C. Song, G.R. Oxnard, H. Mamon, P.A. Janne, G.M. Makrigiorgos, Enhanced ratio of signals enables digital mutation scanning for rare allele detection, J. Mol. Diagn. 17 (2015) 284–292. [110] J.F. Huggett, C.A. Foy, V. Benes, K. Emslie, J.A. Garson, R. Haynes, J. Hellemans, M. Kubista, R.D. Mueller, T. Nolan, M.W. Pfaffl, G.L. Shipley, J. Vandesompele, C.T. Wittwer, S.A. Bustin, The digital MIQE guidelines: minimum information for publication of quantitative digital PCR experiments, Clin. Chem. 59 (2013) 892–902. [111] C.A. Milbury, Q. Zhong, J. Lin, M. Williams, J. Olson, D.R. Link, B. Hutchison, Determining lower limits of detection of digital PCR assays for cancer-related gene mutations, BDQ 1 (2014) 8–22. [112] R.H. Sedlak, K.R. Jerome, Viral diagnostics in the era of digital polymerase chain reaction, Diagn. Microbiol. Infect. Dis. 75 (2013) 1–4. [113] J.F. Huggett, S. Cowen, C.A. Foy, Considerations for digital PCR as an accurate molecular diagnostic tool, Clin. Chem. 61 (2015) 79–88. [114] J.F. Huggett, A. Whale, Digital PCR as a novel technology and its potential implications for molecular diagnostics, Clin. Chem. 59 (2013) 1691–1693. [115] I. Hudecova, Digital PCR analysis of circulating nucleic acids, Clin. Biochem. 48 (2015) 948–956. [116] G.J. Liao, A.M. Gronowski, Z. Zhao, Non-invasive prenatal testing using cell-free fetal DNA in maternal circulation, Clin. Chim. Acta 428 (2014) 44–50. [117] R. Hall Sedlak, K.R. Jerome, The potential advantages of digital PCR for clinical virology diagnostics, Expert Rev. Mol. Diagn. 14 (2014) 501–507. [118] W. Brugger, M. Thomas, EGFR-TKI resistant non-small cell lung cancer (NSCLC): new developments and implications for future treatment, Lung Cancer 77 (2012) 2–8. [119] C. Ma, S. Wei, Y. Song, T790M and acquired resistance of EGFR TKI: a literature review of clinical reports, J. Thorac. Dis. 3 (2011) 10–18. [120] J.A. Engelman, K. Zejnullahu, T. Mitsudomi, Y. Song, C. Hyland, J.O. Park, N. Lindeman, C.M. Gale, X. Zhao, J. Christensen, T. Kosaka, A.J. Holmes, A.M. Rogers, F. Cappuzzo, T. Mok, C. Lee, B.E. Johnson, L.C. Cantley, P.A. Janne, MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling, Science 316 (2007) 1039–1043. [121] S. Kobayashi, T.J. Boggon, T. Dayaram, P.A. Janne, O. Kocher, M. Meyerson, B.E. Johnson, M.J. Eck, D.G. Tenen, B. Halmos, EGFR mutation and resistance of non-small-cell lung cancer to gefitinib, N. Engl. J. Med. 352 (2005) 786–792. [122] A.B. Turke, K. Zejnullahu, Y.L. Wu, Y. Song, D. Dias-Santagata, E. Lifshits, L. Toschi, A. Rogers, T. Mok, L. Sequist, N.I. Lindeman, C. Murphy, S. Akhavanfard, B.Y. Yeap, Y. Xiao, M. Capelletti, A.J. Iafrate, C. Lee, J.G. Christensen, J.A. Engelman, P.A. Janne, Preexistence and clonal selection of MET amplification in EGFR mutant NSCLC, Cancer Cell 17 (2010) 77–88. [123] H. Li, H. Hu, R. Wang, Y. Pan, L. Wang, Y. Li, Y. Zhang, T. Ye, Y. Zhang, B. Li, L. Shen, Y. Sun, H. Chen, Primary concomitant EGFR T790M mutation predicted worse prognosis in non-small cell lung cancer patients, Onco. Targets Ther. 7 (2014) 513–524. [124] H.A. Yu, M.E. Arcila, M.D. Hellmann, M.G. Kris, M. Ladanyi, G.J. Riely, Poor response to erlotinib in patients with tumors containing baseline EGFR T790M mutations found by routine clinical molecular testing, Ann. Oncol. 25 (2014) 423–428. [125] Y. Fujita, K. Suda, H. Kimura, K. Matsumoto, T. Arao, T. Nagai, N. Saijo, Y. Yatabe, T. Mitsudomi, K. Nishio, Highly sensitive detection of EGFR T790M mutation using

86

[126]

[127]

[128]

[129] [130]

[131]

G. Perkins et al.

colony hybridization predicts favorable prognosis of patients with lung cancer harboring activating EGFR mutation, J. Thorac. Oncol. 7 (2012) 1640–1644. R. Rosell, M.A. Molina, C. Costa, S. Simonetti, A. Gimenez-Capitan, J. BertranAlamillo, C. Mayo, T. Moran, P. Mendez, F. Cardenal, D. Isla, M. Provencio, M. Cobo, A. Insa, R. Garcia-Campelo, N. Reguart, M. Majem, S. Viteri, E. Carcereny, R. Porta, B. Massuti, C. Queralt, I. de Aguirre, J.M. Sanchez, M. Sanchez-Ronco, J.L. Mate, A. Ariza, S. Benlloch, J.J. Sanchez, T.G. Bivona, C.L. Sawyers, M. Taron, Pretreatment EGFR T790M mutation and BRCA1 mRNA expression in erlotinib-treated advanced non-small-cell lung cancer patients with EGFR mutations, Clin. Cancer Res. 17 (2011) 1160–1168. M. Watanabe, T. Kawaguchi, S.I. Isa, M. Ando, A. Tamiya, A. Kubo, H. Saka, S. Takeo, H. Adachi, T. Tagawa, S. Kakegawa, M. Yamashita, K. Kataoka, Y. Ichinose, Y. Takeuchi, K. Sakamoto, A. Matsumura, Y. Koh, Ultra-sensitive detection of the pretreatment EGFR T790M mutation in non-small cell lung cancer patients with an EGFR-activating mutation using droplet digital PCR, Clin. Cancer Res. 21 (2015) 3552–3560. A.A. Tone, M.K. McConechy, W. Yang, J. Ding, S. Yip, E. Kong, K.K. Wong, D.M. Gershenson, H. Mackay, S. Shah, B. Gilks, A.V. Tinker, B. Clarke, J.N. McAlpine, D. Huntsman, Intratumoral heterogeneity in a minority of ovarian low-grade serous carcinomas, BMC Cancer 14 (2014) 982. M.C. Ravnan, M.S. Matalka, Vemurafenib in patients with BRAF V600E mutationpositive advanced melanoma, Clin. Ther. 34 (2012) 1474–1486. P.B. Chapman, A. Hauschild, C. Robert, J.B. Haanen, P. Ascierto, J. Larkin, R. Dummer, C. Garbe, A. Testori, M. Maio, D. Hogg, P. Lorigan, C. Lebbe, T. Jouary, D. Schadendorf, A. Ribas, S.J. O’Day, J.A. Sosman, J.M. Kirkwood, A.M.M. Eggermont, B. Dreno, K. Nolop, J. Li, B. Nelson, J. Hou, R.J. Lee, K.T. Flaherty, G.A. McArthur, B.-S. Group, F. Boyle, B. Brady, M. Eastgate, A. Guminski, R. Kefford, M. Millward, P. Parente, M. Wu, V. Atkinson, P. Hersey, F. Couture, E. McWhirter, W. Miller, T. Petrella, M. Smylie, R. Wong, D. Cupissol, J.J. Grob, P. Joly, L. Mortier, J.P. Ortonne, L. Thomas, C. Berking, A. Enk, A. Gesierich, R. Gutzmer, C. Hafner, R. Herbst, R. Kaufmann, C. Loquai, C. Mauch, P. Mohr, S. Sell, J. Simon, R. Stadler, A. Stein, M. Lotem, I. Ron, J. Schachter, F. Cognetti, M. Del Vecchio, M. Guida, P. Queirolo, S. Siena, G. Hospers, A. van den Eertwegh, C. Barrow, R. Isaacs, C. Jackson, M. Jameson, M. McCrystal, J. Hansson, L. Lundgren, I. Ljuslinder, G. Wagenius, T. Walz, O. Michelien, E. Brown, D. Chao, P. Corrie, J. Evans, M. Harries, M. Middleton, P. Nathan, C. Ottensmeier, P. Patel, R. Plummer, J. Wagstaff, V. Wolstenholme, W. Akerley, F. Collichio, R. Conry, L. Cranmer, B. Curti, L. Flaherty, J. Hainsworth, D. Lawson, G. Linette, T. Logan, K. Margolin, A. Pavlick, L. Spitler, R. Gonzalez, T. Hutson, S. Moschos, L. Schuchter, B. Helms, F. Bossierre, S. Cheng, V. Deo, S. Harrison, A. Joe, A. Kohler, V. Kucera, J. Lawrence, A. Muehlig, S. Nanni, J. Parmi, M. Rothe, K. Tchernakov, K. Trunzer, G. Bollag, Improved survival with vemurafenib in melanoma with BRAF V600E mutation, N. Engl. J. Med. 364 (2011) 2507–2516. G.A. McArthur, P.B. Chapman, C. Robert, J. Larkin, J.B. Haanen, R. Dummer, A. Ribas, D. Hogg, O. Hamid, P.A. Ascierto, C. Garbe, A. Testori, M. Maio, P. Lorigan, C. Lebbe, T. Jouary, D. Schadendorf, S.J. O’Day, J.M. Kirkwood, A.M. Eggermont, B. Dreno, J.A. Sosman, K.T. Flaherty, M. Yin, I. Caro, S. Cheng, K. Trunzer, A. Hauschild, Safety and efficacy of vemurafenib in BRAF(V600E) and BRAF(V600K) mutation-positive melanoma (BRIM-3): extended follow-up of a phase 3, randomised, open-label study, Lancet Oncol. 15 (2014) 323–332.

Droplet-Based Digital PCR

87

[132] P.-J. Lamy, F. Castan, N. Lozano, C. Montelion, P. Audran, F. Bibeau, S. Roques, F. Montels, A.-C. Laberenne, Next-generation genotyping by digital PCR to detect and quantify the BRAF V600E mutation in melanoma biopsies, J. Mol. Diagn. 17 (2015) 366–373. [133] D.J. Eastburn, A. Sciambi, A.R. Abate, Ultrahigh-throughput Mammalian single-cell reverse-transcriptase polymerase chain reaction in microfluidic drops, Anal. Chem. 85 (2013) 8016–8021. [134] J.-C. Baret, O.J. Miller, V. Taly, M. Ryckelynck, A. El-Harrak, L. Frenz, C. Rick, M.L. Samuels, J.B. Hutchison, J.J. Agresti, D.R. Link, D.A. Weitz, A.D. Griffiths, Fluorescence-activated droplet sorting (FADS): efficient microfluidic cell sorting based on enzymatic activity, Lab Chip 9 (2009) 1850–1858. [135] D.J. Eastburn, A. Sciambi, A.R. Abate, Identification and genetic analysis of cancer cells with PCR-activated cell sorting, Nucleic Acids Res. 42 (2014). e128. [136] E. Crowley, F. Di Nicolantonio, F. Loupakis, A. Bardelli, Liquid biopsy: monitoring cancer-genetics in the blood, Nat. Rev. Clin. Oncol. 10 (2013) 472–484. [137] M. van der Vaart, P.J. Pretorius, Is the role of circulating DNA as a biomarker of cancer being prematurely overrated? Clin. Biochem. 43 (2010) 26–36. [138] E. Gormally, E. Caboux, P. Vineis, P. Hainaut, Circulating free DNA in plasma or serum as biomarker of carcinogenesis: practical aspects and biological significance, Mutat. Res. 635 (2007) 105–117. [139] S. Jahr, H. Hentze, S. Englisch, D. Hardt, F.O. Fackelmayer, R.D. Hesch, R. Knippers, DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells, Cancer Res. 61 (2001) 1659–1665. [140] H. Schwarzenbach, D.S. Hoon, K. Pantel, Cell-free nucleic acids as biomarkers in cancer patients, Nat. Rev. Cancer 11 (2011) 426–437. [141] A. Bardelli, S. Corso, A. Bertotti, S. Hobor, E. Valtorta, G. Siravegna, A. SartoreBianchi, E. Scala, A. Cassingena, D. Zecchin, M. Apicella, G. Migliardi, F. Galimi, C. Lauricella, C. Zanon, T. Perera, S. Veronese, G. Corti, A. Amatu, M. Gambacorta, L.A. Diaz Jr., M. Sausen, V.E. Velculescu, P. Comoglio, L. Trusolino, F. Di Nicolantonio, S. Giordano, S. Siena, Amplification of the MET receptor drives resistance to anti-EGFR therapies in colorectal cancer, Cancer Discov. 3 (2013) 658–673. [142] D.L. Robertson, G.F. Joyce, Selection in vitro of an RNA enzyme that specifically cleaves single-stranded DNA, Nature 344 (1990) 467–468. [143] P.L. Bedard, A.R. Hansen, M.J. Ratain, L.L. Siu, Tumour heterogeneity in the clinic, Nature 501 (2013) 355–364. [144] M. Gerlinger, A.J. Rowan, S. Horswell, J. Larkin, D. Endesfelder, E. Gronroos, P. Martinez, N. Matthews, A. Stewart, P. Tarpey, I. Varela, B. Phillimore, S. Begum, N.Q. McDonald, A. Butler, D. Jones, K. Raine, C. Latimer, C.R. Santos, M. Nohadani, A.C. Eklund, B. Spencer-Dene, G. Clark, L. Pickering, G. Stamp, M. Gore, Z. Szallasi, J. Downward, P.A. Futreal, C. Swanton, Intratumor heterogeneity and branched evolution revealed by multiregion sequencing, N. Engl. J. Med. 366 (2012) 883–892. [145] G.R. Oxnard, C.P. Paweletz, Y. Kuang, S.L. Mach, A. O’Connell, M.M. Messineo, J.J. Luke, M. Butaney, P. Kirschmeier, D.M. Jackman, P.A. Janne, Noninvasive detection of response and resistance in EGFR-mutant lung cancer using quantitative nextgeneration genotyping of cell-free plasma DNA, Clin. Cancer Res. 20 (2014) 1698–1705. [146] S.J. Dawson, D.W. Tsui, M. Murtaza, H. Biggs, O.M. Rueda, S.F. Chin, M.J. Dunning, D. Gale, T. Forshew, B. Mahler-Araujo, S. Rajan, S. Humphray, J. Becq, D. Halsall, M. Wallis, D. Bentley, C. Caldas, N. Rosenfeld, Analysis of circulating tumor DNA to monitor metastatic breast cancer, N. Engl. J. Med. 368 (2013) 1199–1209.

88

G. Perkins et al.

[147] H. Gevensleben, I. Garcia-Murillas, M.K. Graeser, G. Schiavon, P. Osin, M. Parton, I.E. Smith, A. Ashworth, N.C. Turner, Noninvasive detection of HER2 amplification with plasma DNA digital PCR, Clin. Cancer Res. 19 (2013) 3276–3284. [148] T. Reinert, L.V. Scholer, R. Thomsen, H. Tobiasen, S. Vang, I. Nordentoft, P. Lamy, A.S. Kannerup, F.V. Mortensen, K. Stribolt, S. Hamilton-Dutoit, H.J. Nielsen, S. Laurberg, N. Pallisgaard, J.S. Pedersen, T.F. Orntoft, C.L. Andersen, Analysis of circulating tumour DNA to monitor disease burden following colorectal cancer surgery, Gut 65 (2015) 625–634. [149] S. Chang-Hao Tsao, J. Weiss, C. Hudson, C. Christophi, J. Cebon, A. Behren, A. Dobrovic, Monitoring response to therapy in melanoma by quantifying circulating tumour DNA with droplet digital PCR for BRAF and NRAS mutations, Sci. Rep. 5 (2015) 11198. [150] M.F. Sanmamed, S. Fernandez-Landazuri, C. Rodriguez, R. Zarate, M.D. Lozano, L. Zubiri, J.L. Perez-Gracia, S. Martin-Algarra, A. Gonzalez, Quantitative cell-free circulating BRAFV600E mutation analysis by use of droplet digital PCR in the followup of patients with melanoma being treated with BRAF inhibitors, Clin. Chem. 61 (2015) 297–304. [151] A.L. Reid, J.B. Freeman, M. Millward, M. Ziman, E.S. Gray, Detection of BRAFV600E and V600K in melanoma circulating tumour cells by droplet digital PCR, Clin. Biochem. 48 (2015) 999–1002. [152] M. Versluis, M.J. de Lange, S.I. van Pelt, C.A. Ruivenkamp, W.G. Kroes, J. Cao, M.J. Jager, G.P. Luyten, P.A. van der Velden, Digital PCR validates 8q dosage as prognostic tool in uveal melanoma, PLoS One 10 (2015). e0116371. [153] H. Davies, G.R. Bignell, C. Cox, P. Stephens, S. Edkins, S. Clegg, J. Teague, H. Woffendin, M.J. Garnett, W. Bottomley, N. Davis, E. Dicks, R. Ewing, Y. Floyd, K. Gray, S. Hall, R. Hawes, J. Hughes, V. Kosmidou, A. Menzies, C. Mould, A. Parker, C. Stevens, S. Watt, S. Hooper, R. Wilson, H. Jayatilake, B.A. Gusterson, C. Cooper, J. Shipley, D. Hargrave, K. Pritchard-Jones, N. Maitland, G. ChenevixTrench, G.J. Riggins, D.D. Bigner, G. Palmieri, A. Cossu, A. Flanagan, A. Nicholson, J.W. Ho, S.Y. Leung, S.T. Yuen, B.L. Weber, H.F. Seigler, T.L. Darrow, H. Paterson, R. Marais, C.J. Marshall, R. Wooster, M.R. Stratton, P.A. Futreal, Mutations of the BRAF gene in human cancer, Nature 417 (2002) 949–954. [154] E.S. Gray, H. Rizos, A.L. Reid, S.C. Boyd, M.R. Pereira, J. Lo, V. Tembe, J. Freeman, J.H. Lee, R.A. Scolyer, K. Siew, C. Lomma, A. Cooper, M.A. Khattak, T.M. Meniawy, G.V. Long, M.S. Carlino, M. Millward, M. Ziman, Circulating tumor DNA to monitor treatment response and detect acquired resistance in patients with metastatic melanoma, Oncotarget 6 (2015) 42008–42018. [155] J.A. Beaver, D. Jelovac, S. Balukrishna, R.L. Cochran, S. Croessmann, D.J. Zabransky, H.Y. Wong, P. Valda Toro, J. Cidado, B.G. Blair, D. Chu, T. Burns, M.J. Higgins, V. Stearns, L. Jacobs, M. Habibi, J. Lange, P.J. Hurley, J. Lauring, D.A. VanDenBerg, J. Kessler, S. Jeter, M.L. Samuels, D. Maar, L. Cope, A. Cimino-Mathews, P. Argani, A.C. Wolff, B.H. Park, Detection of cancer DNA in plasma of patients with early-stage breast cancer, Clin. Cancer Res. 20 (2014) 2643–2650. [156] E. Olsson, C. Winter, A. George, Y. Chen, J. Howlin, M.H. Tang, M. Dahlgren, R. Schulz, D. Grabau, D. van Westen, M. Ferno, C. Ingvar, C. Rose, P.O. Bendahl, L. Ryden, A. Borg, S.K. Gruvberger-Saal, H. Jernstrom, L.H. Saal, Serial monitoring of circulating tumor DNA in patients with primary breast cancer for detection of occult metastatic disease, EMBO Mol. Med. 7 (2015) 1034–1047. [157] J.F. Emile, E.L. Diamond, Z. Helias-Rodzewicz, F. Cohen-Aubart, F. Charlotte, D.M. Hyman, E. Kim, R. Rampal, M. Patel, C. Ganzel, S. Aumann, G. Faucher, C. Le Gall, K. Leroy, M. Colombat, J.E. Kahn, S. Trad, P. Nizard, J. Donadieu,

Droplet-Based Digital PCR

[158]

[159]

[160]

[161]

[162] [163] [164] [165] [166] [167]

[168]

[169]

[170] [171]

89

V. Taly, Z. Amoura, O. Abdel-Wahab, J. Haroche, Recurrent RAS and PIK3CA mutations in Erdheim-Chester disease, Blood 124 (2014) 3016–3019. B. Hervier, J. Haroche, L. Arnaud, F. Charlotte, J. Donadieu, A. Neel, F. Lifermann, C. Villabona, B. Graffin, O. Hermine, A. Rigolet, C. Roubille, E. Hachulla, T. Carmoi, M. Bezier, V. Meignin, M. Conrad, L. Marie, E. Kostrzewa, J.M. Michot, S. Barete, V. Taly, K. Cury, J.F. Emile, Z. Amoura, G. French, Histiocytoses Study, Association of both Langerhans cell histiocytosis and ErdheimChester disease linked to the BRAFV600E mutation, Blood 124 (2014) 1119–1126. D.M. Hyman, E.L. Diamond, C.R.T. Vibat, L. Hassaine, J.C. Poole, M. Patel, V.R. Holley, G. Cabrilo, T.T. Lu, M.E. Arcila, Y.R. Chung, R. Rampal, M.E. Lacouture, N. Rosen, F. Meric-Bernstam, J. Baselga, R. Kurzrock, M.G. Erlander, F. Janku, O. Abdel-Wahab, Prospective blinded study of BRAFV600E mutation detection in cell-free DNA of patients with systemic histiocytic disorders, Cancer Discov. 5 (2015) 64–71. F. Janku, C.R. Vibat, K. Kosco, V.R. Holley, G. Cabrilo, F. Meric-Bernstam, V.M. Stepanek, P.P. Lin, L. Leppin, L. Hassaine, J.C. Poole, R. Kurzrock, M.G. Erlander, BRAF V600E mutations in urine and plasma cell-free DNA from patients with Erdheim-Chester disease, Oncotarget 5 (2014) 3607–3610. E.L. Kwak, L.G. Ahronian, G. Siravegna, B. Mussolin, J.T. Godfrey, J.W. Clark, L.S. Blaszkowsky, D.P. Ryan, J.K. Lennerz, A.J. Iafrate, A. Bardelli, T.S. Hong, R.B. Corcoran, Molecular heterogeneity and receptor coamplification drive resistance to targeted therapy in MET-amplified esophagogastric cancer, Cancer Discov. 5 (2015) 1271–1281. C.M. Croce, Causes and consequences of microRNA dysregulation in cancer, Nat. Rev. Genet. 10 (2009) 704–714. V. Ambros, microRNAs: tiny regulators with great potential, Cell 107 (2001) 823–826. H. Corte, G. Manceau, H. Blons, P. Laurent-Puig, MicroRNA and colorectal cancer, Dig. Liver Dis. 44 (2012) 195–200. A.W. Wark, H.J. Lee, R.M. Corn, Multiplexed detection methods for profiling microRNA expression in biological samples, Angew. Chem. Int. Ed. Engl. 47 (2008) 644–652. K. Zen, C.Y. Zhang, Circulating MicroRNAs: a novel class of biomarkers to diagnose and monitor human cancers, Med. Res. Rev. 32 (2012) 326–348. E. Miotto, E. Saccenti, L. Lupini, E. Callegari, M. Negrini, M. Ferracin, Quantification of circulating miRNAs by droplet digital PCR: comparison of EvaGreen- and TaqMan-based chemistries, Cancer Epidemiol. Biomarkers Prev. 23 (2014) 2638–2642. A. Mangolini, M. Ferracin, M.V. Zanzi, E. Saccenti, S.O. Ebnaof, V.V. Poma, J.M. Sanz, A. Passaro, M. Pedriali, A. Frassoldati, P. Querzoli, S. Sabbioni, P. Carcoforo, A. Hollingsworth, M. Negrini, Diagnostic and prognostic microRNAs in the serum of breast cancer patients measured by droplet digital PCR, Biomark. Res. 3 (2015) 12. M. Ferracin, L. Lupini, I. Salamon, E. Saccenti, M.V. Zanzi, A. Rocchi, L. Da Ros, B. Zagatti, G. Musa, C. Bassi, A. Mangolini, G. Cavallesco, A. Frassoldati, S. Volpato, P. Carcoforo, A.B. Hollingsworth, M. Negrini, Absolute quantification of cell-free microRNAs in cancer patients, Oncotarget 6 (2015) 14545–14555. N. Li, J. Ma, M.A. Guarnera, H. Fang, L. Cai, F. Jiang, Digital PCR quantification of miRNAs in sputum for diagnosis of lung cancer, J. Cancer Res. Clin. Oncol. 140 (2014) 145–150. J. Ma, N. Li, M. Guarnera, F. Jiang, Quantification of plasma miRNAs by digital PCR for cancer diagnosis, Biomark. Insights 8 (2013) 127–136.

90

G. Perkins et al.

[172] P. Wang, F. Jing, G. Li, Z. Wu, Z. Cheng, J. Zhang, H. Zhang, C. Jia, Q. Jin, H. Mao, J. Zhao, Absolute quantification of lung cancer related microRNA by droplet digital PCR, Biosens. Bioelectron. 74 (2015) 836–842. [173] K. Zhang, D.-K. Kang, M.M. Ali, L. Liu, L. Labanieh, M. Lu, H. Riazifar, T.N. Nguyen, J.A. Zell, M.A. Digman, E. Gratton, J. Li, W. Zhao, Digital quantification of miRNA directly in plasma using integrated comprehensive droplet digital detection, Lab Chip 15 (2015) 4217–4226. [174] S. Jia, D. Zocco, M.L. Samuels, M.F. Chou, R. Chammas, J. Skog, N. Zarovni, F. Momen-Heravi, W.P. Kuo, Emerging technologies in extracellular vesicle-based molecular diagnostics, Expert Rev. Mol. Diagn. 14 (2014) 307–321. [175] W.W. Chen, L. Balaj, L.M. Liau, M.L. Samuels, S.K. Kotsopoulos, C.A. Maguire, L. Loguidice, H. Soto, M. Garrett, L.D. Zhu, S. Sivaraman, C. Chen, E.T. Wong, B.S. Carter, F.H. Hochberg, X.O. Breakefield, J. Skog, BEAMing and droplet digital PCR analysis of mutant IDH1 mRNA in glioma patient serum and cerebrospinal fluid extracellular vesicles, Mol. Ther. Nucleic Acids 2 (2013). e109. [176] D.A. Armstrong, B.B. Green, J.D. Seigne, A.R. Schned, C.J. Marsit, MicroRNA molecular profiling from matched tumor and bio-fluids in bladder cancer, Mol. Cancer 14 (2015) 194. [177] T. Wittenberger, S. Sleigh, D. Reisel, M. Zikan, B. Wahl, M. Alunni-Fabbroni, A. Jones, I. Evans, J. Koch, T. Paprotka, H. Lempiainen, T. Rujan, B. Rack, D. Cibula, M. Widschwendter, DNA methylation markers for early detection of women’s cancer: promise and challenges, Epigenomics 6 (2014) 311–327. [178] L. Barault, A. Amatu, F.E. Bleeker, C. Moutinho, C. Falcomata, V. Fiano, A. Cassingena, G. Siravegna, M. Milione, P. Cassoni, F. De Braud, R. Ruda, R. Soffietti, T. Venesio, A. Bardelli, P. Wesseling, P. de Witt Hamer, F. Pietrantonio, S. Siena, M. Esteller, A. Sartore-Bianchi, F. Di Nicolantonio, Digital PCR quantification of MGMT methylation refines prediction of clinical benefit from alkylating agents in glioblastoma and metastatic colorectal cancer, Ann. Oncol. 26 (2015) 1994–1999. [179] E. Zonta, P. Nizard, V. Taly, Assessment of DNA integrity, applications for cancer research, in: G. Makowski (Ed.), Advances in Clinical Chemistry, vol. 70, Elsevier, Amsterdam, 2015, pp. 197–246. [180] Using EvaGreen® Dye on the RainDrop® Digital PCR System to Quantify NGS Libraries, in: RainDance Technologies, 2015. [181] H. Alakus, M.L. Babicky, P. Ghosh, S. Yost, K. Jepsen, Y. Dai, A. Arias, M.L. Samuels, E.S. Mose, R.B. Schwab, M.R. Peterson, A.M. Lowy, K.A. Frazer, O. Harismendy, Genome-wide mutational landscape of mucinous carcinomatosis peritonei of appendiceal origin, Genome Med. 6 (2014) 43. [182] I. Kinde, J. Wu, N. Papadopoulos, K.W. Kinzler, B. Vogelstein, Detection and quantification of rare mutations with massively parallel sequencing, Proc. Natl. Acad. Sci. U.S.A. 108 (2011) 9530–9535. [183] A.M. Newman, S.V. Bratman, J. To, J.F. Wynne, N.C. Eclov, L.A. Modlin, C.L. Liu, J.W. Neal, H.A. Wakelee, R.E. Merritt, J.B. Shrager, B.W. Loo Jr., A.A. Alizadeh, M. Diehn, An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage, Nat. Med. 20 (2014) 548–554. [184] R. Tewhey, J.B. Warner, M. Nakano, B. Libby, M. Medkova, P.H. David, S.K. Kotsopoulos, M.L. Samuels, J.B. Hutchison, J.W. Larson, E.J. Topol, M.P. Weiner, O. Harismendy, J. Olson, D.R. Link, K.A. Frazer, Microdroplet-based PCR enrichment for large-scale targeted sequencing, Nat. Biotechnol. 27 (2009) 1025–1031. [185] O. Harismendy, R.B. Schwab, L. Bao, J. Olson, S. Rozenzhak, S.K. Kotsopoulos, S. Pond, B. Crain, M.S. Chee, K. Messer, D.R. Link, K.A. Frazer, Detection of

Droplet-Based Digital PCR

[186] [187]

[188]

[189] [190]

[191] [192]

91

low prevalence somatic mutations in solid tumors with ultra-deep targeted sequencing, Genome Biol. 12 (2011) R124. L.E. Minion, J.S. Dolinsky, D.M. Chase, C.L. Dunlop, E.C. Chao, B.J. Monk, Hereditary predisposition to ovarian cancer, looking beyond BRCA1/BRCA2, Gynecol. Oncol. 137 (2015) 86–92. N. Tung, C. Battelli, B. Allen, R. Kaldate, S. Bhatnagar, K. Bowles, K. Timms, J.E. Garber, C. Herold, L. Ellisen, J. Krejdovsky, K. DeLeonardis, K. Sedgwick, K. Soltis, B. Roa, R.J. Wenstrup, A.R. Hartman, Frequency of mutations in individuals with breast cancer referred for BRCA1 and BRCA2 testing using next-generation sequencing with a 25-gene panel, Cancer 121 (2015) 25–33. H.K. Chong, T. Wang, H.M. Lu, S. Seidler, H. Lu, S. Keiles, E.C. Chao, A.J. Stuenkel, X. Li, A.M. Elliott, The validation and clinical implementation of BRCAplus: a comprehensive high-risk breast cancer diagnostic assay, PLoS One 9 (2014). e97408. J.D. Holbrook, J.S. Parker, K.T. Gallagher, W.S. Halsey, A.M. Hughes, V.J. Weigman, P.F. Lebowitz, R. Kumar, Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine, J. Transl. Med. 9 (2011) 119. C.M. Robbins, W.A. Tembe, A. Baker, S. Sinari, T.Y. Moses, S. BeckstromSternberg, J. Beckstrom-Sternberg, M. Barrett, J. Long, A. Chinnaiyan, J. Lowey, E. Suh, J.V. Pearson, D.W. Craig, D.B. Agus, K.J. Pienta, J.D. Carpten, Copy number and targeted mutational analysis reveals novel somatic events in metastatic prostate tumors, Genome Res. 21 (2011) 47–55. D.S. Paul, P. Guilhamon, A. Karpathakis, L.M. Butcher, C. Thirlwell, A. Feber, S. Beck, Assessment of RainDrop BS-seq as a method for large-scale, targeted bisulfite sequencing, Epigenetics 9 (2014) 678–684. M. Murtaza, S.J. Dawson, K. Pogrebniak, O.M. Rueda, E. Provenzano, J. Grant, S.F. Chin, D.W. Tsui, F. Marass, D. Gale, H.R. Ali, P. Shah, T. Contente-Cuomo, H. Farahani, K. Shumansky, Z. Kingsbury, S. Humphray, D. Bentley, S.P. Shah, M. Wallis, N. Rosenfeld, C. Caldas, Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer, Nat. Commun. 6 (2015) 8760.