Quantitative Identification of Mutant Alleles Derived from
`Lung Cancer in Plasma Cell-Free DNA via Anomaly
`Detection Using Deep Sequencing Data
`Yoji Kukita1, Junji Uchida2, Shigeyuki Oba3,4, Kazumi Nishino2, Toru Kumagai2, Kazuya Taniguchi1,
`Takako Okuyama2, Fumio Imamura2, Kikuya Kato1*
`
`1 Research Institute, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka, Osaka, Japan, 2 Department of Thoracic Oncology, Osaka
`Medical Center for Cancer and Cardiovascular Diseases, Osaka, Osaka, Japan, 3 Graduate School of Informatics, Kyoto University, Kyoto, Japan, 4 PRESTO,
`JST, Uji, Kyoto, Japan
`
`Abstract
`
`The detection of rare mutants using next generation sequencing has considerable potential for diagnostic
`applications. Detecting circulating tumor DNA is the foremost application of this approach. The major obstacle to its
`use is the high read error rate of next-generation sequencers. Rather than increasing the accuracy of final
`sequences, we detected rare mutations using a semiconductor sequencer and a set of anomaly detection criteria
`based on a statistical model of the read error rate at each error position. Statistical models were deduced from
`sequence data from normal samples. We detected epidermal growth factor receptor (EGFR) mutations in the plasma
`DNA of lung cancer patients. Single-pass deep sequencing (>100,000 reads) was able to detect one activating
`mutant allele in 10,000 normal alleles. We confirmed the method using 22 prospective and 155 retrospective
`samples, mostly consisting of DNA purified from plasma. A temporal analysis suggested potential applications for
`disease management and for therapeutic decision making to select epidermal growth factor receptor tyrosine kinase
`inhibitors (EGFR-TKI).
`
`Citation: Kukita Y, Uchida J, Oba S, Nishino K, Kumagai T, et al. (2013) Quantitative Identification of Mutant Alleles Derived from Lung Cancer in Plasma
`Cell-Free DNA via Anomaly Detection Using Deep Sequencing Data. PLoS ONE 8(11): e81468. doi:10.1371/journal.pone.0081468
`Editor: Raya Khanin, Memorial Sloan Kettering Cancer Center, United States of America
`Received March 16, 2013; Accepted October 13, 2013; Published November 21, 2013
`Copyright: © 2013 Kukita et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
`unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
`Funding: This work was supported by grants from Osaka Medical Center for Cancer and Cardiovascular Diseases. The funders had no role in study
`design, data collection and analysis, decision to publish, or preparation of the manuscript.
`Competing interests: The authors have declared that no competing interests exist.
`* E-mail: katou-ki@mc.pref.osaka.jp
`
`Introduction
`
`For some molecular targeted drugs against cancer, the
`examination of genomic changes in target genes has become a
`diagnostic routine and is indispensable for treatment decisions.
`For example, the strong effects of epidermal growth factor
`receptor tyrosine kinase inhibitors (EGFR-TKIs; i.e., gefitinib
`and erlotinib) on non-small-cell lung cancer (NSCLC) are
`correlated with activating somatic mutations in EGFR [1,2].
`Patients who are administered these drugs are currently
`selected based on the presence of these activating mutations.
`The identification of the mutations is based on biopsy samples;
`the procedure is invasive and often difficult to perform. A non-
`invasive diagnostic procedure is desirable.
`Cell-free DNA in the blood consists of DNA derived from
`cancer
`tissues and has been studied
`for non-invasive
`diagnostic procedures [3]. This DNA, termed circulating tumor
`DNA (ctDNA), is rare in blood, and its detection is a technical
`challenge. A number of methods have been examined, but
`
`most of them have limitations in sensitivity and robustness.
`BEAMing (beads, emulsion, amplification and magnetics) [4] is
`most likely the most sensitive method. In BEAMing, PCR
`products amplified from a single molecule are fixed to a single
`magnetic bead using emulsion PCR. The mutation site is
`labeled with a fluorescent probe or primer extension, and the
`mutated allele is quantitatively detected by counting the
`fluorescently labeled beads. BEAMing successfully quantified
`APC and KRAS mutations in the ctDNA of colorectal cancer
`patients [5,6] and EGFR mutations in the ctDNA of lung cancer
`patients [7]. In spite of its high sensitivity and quantification
`ability, BEAMing has not gained in popularity because it is a
`laborious technology and requires oligonucleotides for each
`mutation position.
`Because BEAMing and next-generation sequencers, i.e.,
`massively parallel sequencers, use the same or a very similar
`template preparation technique, it is possible to apply next-
`generation sequencers for the same purpose. There have been
`several studies on the deep sequencing of cell-free DNA [8,9].
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`These studies suggested the possibility of the approach but
`lacked critical evaluation of the detection systems. In particular,
`they did not address the problem of multiple testing, which is
`inherent to diagnostic applications.
`In this report, we established a method of detecting EGFR
`mutations in ctDNA in the peripheral blood of lung cancer
`patients using single-pass deep sequencing of amplified EGFR
`fragments. The recent development of a semiconductor
`sequencer (Ion Torrent PGM)
`[10] has addressed
`the
`shortcomings of other currently available sequencers (i.e., a
`long runtime for a single assay and high operating costs) and is
`applicable
`for diagnostic purposes. We applied anomaly
`detection [11,12] and determined a set of detection criteria
`based on a statistical model of the read error rate at each error
`position. The method quantitatively detected EGFR mutations
`in cell-free DNA at a level comparable to BEAMing, promising
`non-invasive diagnostics that complement biopsy.
`
`Results
`
`Principle of detection
`Deep sequencing of a PCR-amplified fragment containing a
`mutation site can be conducted to detect and quantitate
`mutated alleles among the vast amounts of normal alleles
`derived from host tissues. The major problem associated with
`this approach is the frequency of errors introduced during
`sequencing and PCR amplification. The key issue here is the
`setting and accurate evaluation of detection limits. When the
`frequency of a base change at a target locus is higher than a
`predetermined read error rate (RER), we may judge the
`change to be due to the presence of a mutant sequence. That
`is, anomalies
`that
`fall significantly outside of
`the RER
`distribution are regarded as mutations. The RER is defined as
`the error rate calculated from final sequence data, including
`errors in both the sequencing and PCR steps. In anomaly
`detection [11,12], as in hypothesis testing, false positives are
`controlled based on a statistical model. In our case, the
`statistical model of the RER can be constructed from sequence
`data from the target regions of a sufficient number of normal
`individuals carrying no mutations.
`If read errors occur under a probability distribution, the
`number of reads required to achieve a certain detection limit
`can be estimated. Figure 1a shows the relationship between
`the mutation detection limit, read depth, and RER at a
`significance level of p=2x10-5 for each individual detection
`without multiplicity correction, assuming that read errors occur
`following a Poisson distribution. The data illustrated in Figure
`1a are supplied in Table S1. With an increasing read depth and
`decreasing RER, the detection limit decreases. In a previous
`study by our group [7], the detection limit for rare mutant alleles
`when using BEAMing [4] was 1 in 10,000 (0.01%). Because a
`plasma DNA assay sample contains approximately 5,000
`molecules, this detection limit is reasonable. This goal can be
`achieved with 100,000 reads when the RER is below 0.01%.
`
`Read error of the EGFR target region
`For EGFR-TKI treatment, an activating EGFR mutation is
`indicative of
`treatment efficacy
`[1,2]. Patients
`to be
`
`Rare Mutation Detection Using Deep Sequencing Data
`
`administered these drugs are currently selected based on the
`presence of these activating mutations. In addition to activating
`EGFR mutations, a resistant EGFR mutation known as T790M
`appears in approximately half of patients subjected to EGFR-
`TKI treatment [13,14]. Thus, three activating mutations, i.e., a
`deletion in EGFR exon 19 and L858R and L861Q in EGFR
`exon 21, as well as the T790M resistant mutation in EGFR
`exon 20 were selected as target loci.
`We determined the RERs in a 169 base region around the
`target loci consisting by performing deep sequencing of DNA
`samples from normal individuals. We used an Ion Torrent PGM
`[10] sequencer for this work. Single-pass sequencing was
`performed, and the number of reads ranged from 44,400 to
`373,000, averaging 162,000. We employed three types of DNA
`samples: 19 plasma DNA samples with amounts comparable to
`patients’ samples, 16 leucocyte (white blood cell, WBC) DNA
`samples with amounts that were 10 or 50 times the size of a
`patient’s sample, and 13 WBC DNA samples with amounts that
`were one-tenth the size of a patient’s sample. We divided
`substitution errors
`into
`four patterns, corresponding
`to
`conversion to A, C, G, or T. Thus, there were 507 possible
`types of substitutions (169 base positions x 3 patterns) in the
`target region. A substitution RER is graphically shown in Figure
`1b, excluding the conversion from G to A at position 2,361 due
`to a frequent SNP. The substitution RERs are not uniform, nor
`are they independent from each other, and high RERs are
`associated with specific base positions. In addition, one
`substitution pattern is dominant at each base position. An
`insertion/deletion RER is graphically shown in Figure 1c. We
`did not distinguish between deletion and insertion errors, as
`insertions are often recognized as deletions and vice versa by
`the sequence alignment software. The insertion/deletion RER
`is generally higher than the substitution RER. A tendency
`similar to that of substitution is observed, in that high insertion/
`deletion RERs are associated with specific base positions.
`Figure 1d presents the distribution of the RERs. There were
`substantial differences between the substitution and insertion/
`deletion RERs. In 410 out of the possible 506 types of
`substitution (81.0%), the RER was lower than 0.01%. In
`contrast, out of the 169 types of insertions/deletion, the RER
`was lower than 0.01% in only 79 (46.7%). These results agreed
`with previously reported observations from the PGM platform
`[15]. The data illustrated in Figures 1b and 1c are supplied in
`Tables S2 and S3, respectively.
`Due to high insertion/deletion read errors, we employed a
`specific method to detect the exon 19 deletion mutations. We
`prepared eight
`template exon 19
`sequences with
`representative deletions and screened the deletion sequences
`by matching them with the template sequences. This method
`was quite effective for screening out read errors; no sequences
`with deletion read errors were found among the 48 samples
`tested.
`
`Statistical models of read error rates and criteria for
`anomaly detection
`We then examined statistical models of read error. In a
`Poisson distribution model, the average and variance of the
`number of incidences are expected to be the same and are
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`Figure 1. Read error of Ion Torrent PGM in the EGFR target region. a, Relationship between the read error rate, read depth,
`and detection limit for mutations when the significance level is p=2x10-5. Horizontal axis, read depth; vertical axis, detection limit (%).
`From top to bottom, each line indicates a read error rate (RER) of 1%, 0.2%, 0.05%, or 0.01%. b, Three-dimensional representation
`of substitution RER. x-axis, base positions of EGFR exons 19–21. From left to right, the arrowheads indicate the positions of
`T790M, L858R, and L861Q. y-axis, 48 DNA samples from normal individuals. From front to back, conversions to A (green), C
`(yellow), G (magenta), or T (blue) are aligned for each sample. z-axis, RER (%). c, Three-dimensional representation of the
`insertion/deletion error. x-axis, base positions of EGFR exons 19–21. The bar indicates the position of the exon 19 deletion. y-axis,
`48 DNA samples from normal individuals. Blue, plasma DNA; light blue, WBC DNA (large amount); dark blue, WBC DNA (small
`amount). z-axis, RER (%). d, Distribution of the RER. White column, substitution error; gray column, insertion/deletion error.
`Horizontal axis, range of RER (%); vertical axis, incidence (%).
`doi: 10.1371/journal.pone.0081468.g001
`
`determined by the intensity parameter lambda. Here, instead of
`using the RER, the read error incidence was presented as the
`incidence in 100,000 reads, and its average and variance at
`each base position were calculated. The relationships between
`the average and variance are shown in Figure 2a and Figure
`S1 in File S1 for the substitution and insertion/deletion read
`errors, respectively. In both cases, the variance becomes
`greater than the average in a considerable proportion of the
`cases. In these cases, application of the Poisson distribution
`would lead to increased numbers of false positives. This
`phenomenon, termed “overdispersion”, is common in biological
`
`studies, and in such cases, a negative binomial distribution is
`applied [16]. Overdispersion is due to fluctuations of the
`intensity parameter, and it is rational to assume that the
`intensity parameter follows a gamma distribution. Under this
`scenario, the incidence number theoretically follows a negative
`binomial distribution. In Figure 2b, the increase in the threshold
`for substitution
`from a Poisson
`to a negative binomial
`distribution is plotted against the variance/average ratio of the
`read error for the substitution types whose variance/average
`ratio
`ranged
`from 1
`to 2. When
`the
`ratio exceeded
`approximately 1.2-1.4, there were substantial increases in
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`Figure 2. Characteristics of the mutation detection system. a, Relationship between the average and variance of the
`substitution error presented as the number per 100,000 reads. Horizontal axis, average; vertical axis, variance. The red line
`indicates where the average and variance are equal. b, Difference between thresholds calculated according to a negative binomial
`distribution and a Poisson distribution. The threshold is the minimum number of base changes in 100,000 reads meeting the level of
`statistical significance (p-0.01). Horizontal axis, variance/average ratio of the substitution read error; vertical axis, difference
`between thresholds. The types of substitutions whose variance/average ratio ranged from 1 to 2 are plotted. c, Accuracy of
`quantitation. Each data point represents the average of three assays. Horizontal axis, fraction of mutant alleles in artificial products;
`vertical axis, fraction of mutant alleles estimated from deep sequencing. d, Reproducibility of quantitation. Horizontal axis, base
`change rate in the first trial; vertical axis, base change rate in the second trial.
`doi: 10.1371/journal.pone.0081468.g002
`
`threshold. Thus, we constructed our statistical model of each
`substitution under the following criteria.
`1 When the average read error in 100,000 reads was less than
`1, a Poisson distribution with λ set to 1 was applied (169 types
`of substitutions).
`2 When the average was greater than 1 and the variance/
`average ratio of the read error was less than 1.2, a Poisson
`distribution was applied (15 types of substitutions).
`
`3 When the average was greater than 1 and the variance/
`average ratio of the read error was greater than 1.2, a negative
`binomial distribution was applied (323 types of substitutions).
`
`The exon 19 deletion and L858R belonged to the first
`category, while the L861Q and T790M mutation sites belonged
`to the second and the third categories, respectively. The
`detection limits for the exon 19 deletion and the L858R, L861Q,
`and T790M substitution mutations at a significance level of
`p=2x10-5 were less than 0.01% and less than 0.01%, 0.01%,
`and 0.05%, respectively. In the following analysis, we used
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`for each single
`threshold
`the significance
`p=2x10-5 as
`detection, without considering a multiplicity correction,
`expecting one false positive in 50,000 samples.
`The outline of the method is 1) amplification of EGFR
`fragments with exon-specific primers from plasma DNA; 2)
`deep sequencing of EGFR fragments with PGM (>100,000
`reads / fragment), combining the PCR products; 3) matching
`the output sequences with EGFR template sequences; 4)
`detection of deletions and substitutions, and conversion of
`number of events into that in 100,000 reads; and 5) evaluation
`of the base changes with the anomaly detection criteria. In
`anomaly detection, the base changes are judged as mutations,
`when the number of events in 100,000 reads is equal to or
`exceeds the threshold value (exon 19 deletion, 7; L858R, 7;
`L861Q, 12; T790M, 60). A schematic representation is shown
`in Figure S2 in File S1.
`
`Quantitativity and reproducibility
`First, we examined the method’s quantification ability. We
`prepared test samples including various fractions of PCR
`products of mutated EGFR fragments. There was a very good
`linearity (r=0.998) between the inoculated amounts of the PCR
`products and the observed mutant-to-normal allele ratios
`deduced from deep sequencing (Figure 2c). We then examined
`the reproducibility of the method using plasma samples from
`lung cancer patients whose primary lesions were confirmed to
`carry activating mutations. The fractions of the mutant alleles
`measured in two trials are plotted in Figure 2d. A high
`concordance (r=0.989) was observed, except in samples that
`contained small amounts of the mutant alleles, corresponding
`to an approximately 0.3% fraction of the alleles present or less.
`In these cases, the initial phase of PCR amplification was likely
`to be unsuccessful due to the low numbers of mutant
`templates, estimated at 15 copies or less. Thus, the limit of
`quantitation was approximately 0.3%.
`
`Validation with samples from lung cancer patients
`We further evaluated our method using lung cancer biopsy
`specimens, sampling plasma DNA and the primary lesion
`simultaneously as part of a prospective study. The results for
`the samples from 22 patients showed 86% concordance (95%
`confidence interval, 66 - 95), 78% (44 - 93) sensitivity, and 92%
`(66 - 98) specificity, setting the tissue biopsy as the standard.
`These results are promising with respect to the development of
`a diagnostic tool to complement lung cancer biopsy.
`We then analyzed a total of 155 samples: 144 samples from
`plasma, eight from cerebrospinal fluid, and one each from
`urine, pleural effusion, and bronchial alveolar lavage. As for
`plasma samples, two or more samples were obtained from 32
`patients at different time points of the disease courses. All of
`the obtained data are shown in Table S4. Clinical data of the
`patients including stage, histology, treatment, and status of
`resistance to EGFR-TKI are also listed in this Table. Among
`the 33 patients associated with a primary lesion containing the
`exon 19 deletion, this mutation was found in at least one of the
`plasma samples from 24 patients (72.7%). Of the 23 patients
`for which the primary lesions exhibited the L858R or L861Q
`substitutions, these mutations were found in at least one of the
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`Rare Mutation Detection Using Deep Sequencing Data
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`plasma samples from 18 patients (78.2%). A double mutation
`(simultaneous detection of the exon 19 deletion and L858R)
`was observed
`in 12 plasma samples, although double
`mutations are not frequent in biopsy samples. Discrepancies
`between the activation mutation types identified in biopsy and
`plasma DNA samples were observed in five plasma samples.
`T790M was found in 13 out of 57 plasma samples (22.8%)
`from patients with EGFR-TKI resistance, and in 7 out of 87
`plasma samples (8.0%) without EGFR-TKI resistance.
`
`Temporal changes of EGFR mutation levels during the
`disease course
`A considerable number of samples were collected from the
`same patient at different time points in the disease course.
`Temporal changes of EGFR mutation levels in plasma DNA
`from patients with three or more samples are schematically
`shown in Figure 3. Due to the relatively short sampling period,
`samples were obtained from only part of the disease course in
`most cases. We focused on two transitions: transition due to
`EGFR-TKI treatment initiation and that after acquiring EGFR-
`TKI resistance. Data before the treatment initiation was
`obtained in six cases. A significant decrease in the activation of
`mutation levels with the treatment was seen in all cases
`(p=1.7x10-4). Clearance of ctDNA by the treatment initiation is a
`general phenomenon.
`Data were obtained both before and after acquiring EGFR-
`TKI resistance in seven cases. After acquiring resistance, the
`activation of mutation level was increased in five patients (218,
`226, 259, 61, 66), decreased in one patient (44), and increased
`with delay in another patient (178). Increase of activation of
`mutations may correlate with disease progression. Despite the
`clear correlation between T790M and the EGFR-TKI-resistance
`status in the above validation study, dynamics of T790M during
`the disease course was not as clear as that of activation of
`mutations; T790M often appeared before acquiring resistance.
`Three patients are described in more detail. Patient 226 was
`treated with gefitinib as first line chemotherapy. The gefitinib
`treatment was stopped several times due to adverse effects. A
`radiological response (partial response, PR) was observed
`from month 1 to month 9, and disease progression was
`observed in month 10. Prior to gefitinib treatment, the fraction
`of the mutant allele was very high (>50%), but after only one
`week of this treatment, the fraction of the mutant allele
`decreased to 0.3%, prior to any radiological changes (Figure
`S3a in File S1). T790M appeared at 10 months when disease
`progression began. Patient 243 also exhibited a skewed
`decrease in the mutant allele fraction at the initiation of gefitinib
`treatment (Figure S3b in File S1). This patient was treated with
`surgery and adjuvant chemotherapy (CDDP plus VNR)
`previously, and
`then subjected
`to gefitinib. Patient 41
`presented with progression of neoplastic meningitis, and was
`subjected to combined erlotinib-pemetrexed therapy. Previous
`treatments were CDDP plus gemicitabine, gefitinib, and
`erlotinib. A minor radiological response was observed from
`months one
`to
`four, and disease progression occurred
`subsequently. There was a skewed decrease in the mutant
`allele fraction at the beginning of the therapy, and the increase
`upon disease progression was only slight (Figure S3c in File
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`Figure 3. Temporal changes of EGFR mutations in plasma DNA from patients with three or more samples. Each dot
`represents a time point of sampling. The diagram is not precise representation of time scale, and only the order of dots is valid
`information. Figures represent EGFR mutations in 10,000 sequence reads: black, exon 19 deletion; blue, L858R; red, T790M. Only
`figures exceeding the thresholds are shown. “Mutation type” indicates that in the biopsy samples.
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`S1). It should be noted that the respose of ctDNA to EGFR-TKI
`treatment initiation was rapid in all three cases (patient 229,
`one week; 243, two weeks; 41, one month).
`
`Mutation detection in the entire target region
`We explored
`the possibility of
`identifying substitution
`mutations in the entire target EGFR region corresponding to
`503 types of substitutions, excluding L858R, L861Q, and
`T790M. Because the significance level was set at p=2x10-5 for
`each detection, false positives were expected to appear once
`in 100 samples. In reality, a median of three substitutions were
`found per sample. The distribution of
`the number of
`substitutions per sample is shown in Figure 4a. Based on the
`experience gained from the biopsy samples, most of these
`substitutions were likely to be false positives. A considerable
`fraction of the different types of substitutions presented no false
`positives (56.2%, Figure 4b), and the statistical models were of
`practical use with these types of substitutions. For others, the
`parameter estimation from the data from 48 normal individuals
`was not sufficiently conservative for the exclusion of false
`positives.
`
`Discussion
`
`Rare mutation detection of target loci through the deep
`sequencing of plasma cell-free DNA has a comparable
`sensitivity to BEAMing. The specificity is also acceptable
`because the EGFR mutation types in biopsy and plasma
`samples exhibited a high concordance. Thus, rare mutation
`detection with deep sequencing has now reached a sufficient
`level to proceed to confirmation through a prospective study.
`The method could be applied to a limited number of target loci
`at any base position; using the pair-end method or sequencing
`from the opposite direction would increase the accuracy of high
`error rate positions, increasing sensitivity and specificity to
`acceptable levels.
`However, it is difficult to extend mutation detection to a larger
`region. The incidence of false positives is not acceptable for
`diagnostic applications. Parameter estimation with increased
`numbers of normal samples and/or more conservative
`estimation methods, such as Bayesian
`inference, might
`decrease false positives. We used mutation-free DNA from
`normal individuals for the survey of read error, but mutation
`detection was performed with plasma DNA from lung cancer
`patients. A possible cause of the inadequate thresholds may be
`the difference
`in DNA quality. The recent discovery of
`artifactual mutations introduced during experimental processes
`[17] suggests the possibility of still undiscovered causes of
`artifacts using plasma samples.
`Our procedure is optimized for our objectives and social
`environment, but there is room for technical improvement. In
`addition to the paired-end method [9], methods to produce
`error-free sequences through the repeated sequencing of
`templates from a single molecule [18,19] might be applicable to
`enhance accuracy. We employed small amounts of plasma
`DNA for PCR amplification due to the ethical standards of our
`hospital and relevant regional hospitals. However, in a different
`social environment, using an increased amount of plasma DNA
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`Rare Mutation Detection Using Deep Sequencing Data
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`Figure 4. Substitutions introduced in output sequences of
`the 155 retrospective samples. a, Distribution of the number
`of different types of substitutions judged as mutations per
`sample. Horizontal axis, number of the types of substitutions;
`vertical axis, number of samples. b, Distribution of the number
`of samples with a substitution type judged as a mutation.
`Horizontal axis, the number of samples with a substitution type
`judged as a mutation; vertical axis, number of the types of
`substitutions.
`doi: 10.1371/journal.pone.0081468.g004
`
`may improve the reproducibility of the detection of low-level
`mutations.
`In addition to being applied for the non-invasive diagnosis of
`EGFR mutations, as shown in the above temporal analyses,
`this method is also informative for elucidating the dynamics of
`mutant alleles during the course of the disease. In particular, it
`should be noted that a skewed decrease in the mutant allele
`fraction preceded radiological changes, which will likely be
`useful for the prediction of drug efficacy.
`Biopsies of advanced cases and repeated biopsies are
`technically demanding, and replacement with a non-invasive
`method would be beneficial. In this context, monitoring T790M
`with our method would have substantial benefits for patient
`management. For example, detecting the T790M mutation in
`blood samples would be useful for patient selection for
`treatment with new EGFR-TKIs for lung cancers that are
`resistant to gefitinib and erlotinib [20].
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`Recent two studies suggest other possibilities of ctDNA
`analysis. Dawson et al. followed the dynamics of ctDNA in
`metastatic breast cancer patients using mutations in TP53
`and/or PIK3CA, and found its merit for monitoring disease
`progression [21]. Use of common mutations may enable its
`application to a wide variety of tumors. On the contrary, our
`research focus is more specific, i.e., mutation detection for
`therapeutic decision making, although our method can also be
`applied for their purpose. Murtaza et al. performed exome
`sequencing using plasma DNA from cancer patients [22],
`Analysis of cancer genomes at any stage of the disease course
`might uncover genetic changes leading to disease progression
`or drug resistance. Analysis of ctDNA will have a profound
`value in scientific and diagnostic aspects of cancer research.
`
`Materials and Methods
`
`Patient characteristics
`Patients with activating EGFR mutations in tumor tissues
`were recruited at Osaka Medical Center for Cancer and
`Cardiovascular Diseases. Pleural fluid, cerebrospinal fluid
`and/or urine samples were collected from some patients. In all
`of the patients, activating EGFR mutations were found in
`biopsy samples using the PNA-LNA PCR clamp method [23].
`The response to therapy and disease progression were mainly
`evaluated from radiological data based on the RECIST criteria
`[24].
`
`DNA extraction from liquid samples
`Plasma was prepared via centrifugation of 4-5 ml of EDTA-
`treated blood at 800 g for 10 min at room temperature. The
`plasma was transferred to a fresh tube and re-centrifuged at
`15,100 g for 10 min at room temperature. After centrifugation,
`the upper plasma was transferred to a fresh tube. Pleural fluid
`and urine samples were centrifuged at 800 g for 10 min at
`room temperature, and the supernatants were transferred to
`fresh tubes. Centrifuged liquid samples were frozen at -80 °C
`until DNA extraction. Cerebrospinal fluid was frozen without
`centrifugation. DNA was extracted from 1.5–2.0 ml of a liquid
`sample (or 5 ml of urine) using the QIAamp circulating nucleic
`acid kit
`(Qiagen, Hilden, Germany) according
`to
`the
`manufacturer’s
`instructions. The DNA concentration was
`determined by measuring the copy number of LINE-1 [25] or
`using the Qubit ssDNA Assay Kit (Life Technologies, Carlsbad,
`CA, USA).
`
`Amplicon library construction and deep sequencing
`Sequencing
`library construction.
` To amplify
`target
`regions of the EGFR gene, PCR primer pairs were designed
`with Primer3 (http://frodo.wi.mit.edu/). Primer pairs have 5-nt
`indexes (to discriminate individuals) and adaptor sequences for
`semiconductor-sequencing. Positions of PCR-target regions
`and primer sequences are shown
`in Table S5. PCR
`amplification was conducted in a 50 µl reaction mixture
`containing plasma DNA obtained from 300 µl of plasma (10 ng
`or more), 20 pmol of each primers and 1 unit of KOD -Plus-
`DNA polymerase (Toyobo, Osaka, Japan). To analyze the read
`
`Rare Mutation Detection Using Deep Sequencing Data
`
`error, we used genomic DNA from plasma or leukocytes from
`healthy individuals as a PCR template. The cycling profile was
`as follows: 2 min at 94°C for initial denaturation, followed by 40
`cycles of 15 sec at 94°C for denaturation, 30 sec at 55°C for
`annealing, and 50 sec at 68 °C for extension. The products
`were purified using the QIAquick 96 PCR Purification Kit
`(Qiagen) or the MinElute PCR Purification Kit (Qiagen), and the
`DNA concentration was determined using the Quant-iT™
`PicoGreen® dsDNA Assay Kit (Life Technologies) or an
`ND-1000 Spectrophotometer
`(NanoDrop Technologies,
`Montchanin, DE, USA). Subsequently, we mixed equal
`volumes of the purified PCR products and diluted them to
`create a template for emulsion PCR. We mixed 12 and 24
`types of PCR products for use with Ion 316 and Ion 318
`semiconductor chips, respectively.
`Semiconductor sequencing.
`template
` Sequencing
`preparation (emulsion PCR and beads-enrichment)
`from
`sequencing libraries was carried out using an Ion OneTouch
`Template Kit (Life Technologies) and Ion OneTouch system
`(Ion