Serial monitoring of circulating tumor DNA in patients with primary breast cancer for detection of occult metastatic disease
2015; Springer Nature; Volume: 7; Issue: 8 Linguagem: Inglês
10.15252/emmm.201404913
ISSN1757-4684
AutoresEleonor Olsson, Christof Winter, Anthony M. George, Yilun Chen, Jillian Howlin, Man‐Hung Eric Tang, Malin Dahlgren, Ralph Schulz, Dorthe Grabau, Danielle van Westen, Mårten Fernö, Christian Ingvar, Carsten Rose, Pär‐Ola Bendahl, Lisa Rydén, Åke Borg, Sofia K. Gruvberger-Saal, Helena Jernström, Lao H. Saal,
Tópico(s)Lung Cancer Treatments and Mutations
ResumoResearch Article18 May 2015Open Access Serial monitoring of circulating tumor DNA in patients with primary breast cancer for detection of occult metastatic disease Eleonor Olsson Eleonor Olsson Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Christof Winter Christof Winter Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Anthony George Anthony George Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Yilun Chen Yilun Chen Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Jillian Howlin Jillian Howlin Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Man-Hung Eric Tang Man-Hung Eric Tang Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Malin Dahlgren Malin Dahlgren Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Ralph Schulz Ralph Schulz Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden SCIBLU Genomics, Department of Clinical Sciences, Lund University, Lund, Sweden Search for more papers by this author Dorthe Grabau Dorthe Grabau Department of Pathology, Skåne University Hospital, Lund, Sweden Search for more papers by this author Danielle van Westen Danielle van Westen Department of Radiology, Skåne University Hospital, Lund, Sweden Search for more papers by this author Mårten Fernö Mårten Fernö Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Christian Ingvar Christian Ingvar Department of Surgery, Lund University and Skåne University Hospital, Lund, Sweden Search for more papers by this author Carsten Rose Carsten Rose Lund University Cancer Center, Lund, Sweden Department of Immunotechnology, Lund University, Lund, Sweden CREATE Health Strategic Centre for Translational Cancer Research, Lund University, Lund, Sweden Search for more papers by this author Pär-Ola Bendahl Pär-Ola Bendahl Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Lisa Rydén Lisa Rydén Lund University Cancer Center, Lund, Sweden Department of Surgery, Lund University and Skåne University Hospital, Lund, Sweden Search for more papers by this author Åke Borg Åke Borg Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden SCIBLU Genomics, Department of Clinical Sciences, Lund University, Lund, Sweden CREATE Health Strategic Centre for Translational Cancer Research, Lund University, Lund, Sweden Search for more papers by this author Sofia K Gruvberger-Saal Sofia K Gruvberger-Saal Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Helena Jernström Helena Jernström Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Lao H Saal Corresponding Author Lao H Saal Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden CREATE Health Strategic Centre for Translational Cancer Research, Lund University, Lund, Sweden Search for more papers by this author Eleonor Olsson Eleonor Olsson Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Christof Winter Christof Winter Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Anthony George Anthony George Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Yilun Chen Yilun Chen Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Jillian Howlin Jillian Howlin Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Man-Hung Eric Tang Man-Hung Eric Tang Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Malin Dahlgren Malin Dahlgren Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Ralph Schulz Ralph Schulz Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden SCIBLU Genomics, Department of Clinical Sciences, Lund University, Lund, Sweden Search for more papers by this author Dorthe Grabau Dorthe Grabau Department of Pathology, Skåne University Hospital, Lund, Sweden Search for more papers by this author Danielle van Westen Danielle van Westen Department of Radiology, Skåne University Hospital, Lund, Sweden Search for more papers by this author Mårten Fernö Mårten Fernö Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Christian Ingvar Christian Ingvar Department of Surgery, Lund University and Skåne University Hospital, Lund, Sweden Search for more papers by this author Carsten Rose Carsten Rose Lund University Cancer Center, Lund, Sweden Department of Immunotechnology, Lund University, Lund, Sweden CREATE Health Strategic Centre for Translational Cancer Research, Lund University, Lund, Sweden Search for more papers by this author Pär-Ola Bendahl Pär-Ola Bendahl Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Lisa Rydén Lisa Rydén Lund University Cancer Center, Lund, Sweden Department of Surgery, Lund University and Skåne University Hospital, Lund, Sweden Search for more papers by this author Åke Borg Åke Borg Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden SCIBLU Genomics, Department of Clinical Sciences, Lund University, Lund, Sweden CREATE Health Strategic Centre for Translational Cancer Research, Lund University, Lund, Sweden Search for more papers by this author Sofia K Gruvberger-Saal Sofia K Gruvberger-Saal Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Helena Jernström Helena Jernström Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden Search for more papers by this author Lao H Saal Corresponding Author Lao H Saal Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden Lund University Cancer Center, Lund, Sweden CREATE Health Strategic Centre for Translational Cancer Research, Lund University, Lund, Sweden Search for more papers by this author Author Information Eleonor Olsson1,2,‡, Christof Winter1,2,‡, Anthony George1,2, Yilun Chen1,2, Jillian Howlin1,2, Man-Hung Eric Tang1,2, Malin Dahlgren1,2, Ralph Schulz1,2,3, Dorthe Grabau4, Danielle Westen5, Mårten Fernö1,2, Christian Ingvar6, Carsten Rose2,7,8, Pär-Ola Bendahl1,2, Lisa Rydén2,6, Åke Borg1,2,3,8, Sofia K Gruvberger-Saal1,2, Helena Jernström1,2 and Lao H Saal 1,2,8 1Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden 2Lund University Cancer Center, Lund, Sweden 3SCIBLU Genomics, Department of Clinical Sciences, Lund University, Lund, Sweden 4Department of Pathology, Skåne University Hospital, Lund, Sweden 5Department of Radiology, Skåne University Hospital, Lund, Sweden 6Department of Surgery, Lund University and Skåne University Hospital, Lund, Sweden 7Department of Immunotechnology, Lund University, Lund, Sweden 8CREATE Health Strategic Centre for Translational Cancer Research, Lund University, Lund, Sweden ‡These authors contributed equally to this article and are listed alphabetically *Corresponding author. Tel: +46 46 2220365; Fax: +46 46 147327; E-mail: [email protected]; Twitter: @LaoSaal EMBO Mol Med (2015)7:1034-1047https://doi.org/10.15252/emmm.201404913 See also: TM af Hällström et al (August 2015) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Metastatic breast cancer is usually diagnosed after becoming symptomatic, at which point it is rarely curable. Cell-free circulating tumor DNA (ctDNA) contains tumor-specific chromosomal rearrangements that may be interrogated in blood plasma. We evaluated serial monitoring of ctDNA for earlier detection of metastasis in a retrospective study of 20 patients diagnosed with primary breast cancer and long follow-up. Using an approach combining low-coverage whole-genome sequencing of primary tumors and quantification of tumor-specific rearrangements in plasma by droplet digital PCR, we identify for the first time that ctDNA monitoring is highly accurate for postsurgical discrimination between patients with (93%) and without (100%) eventual clinically detected recurrence. ctDNA-based detection preceded clinical detection of metastasis in 86% of patients with an average lead time of 11 months (range 0–37 months), whereas patients with long-term disease-free survival had undetectable ctDNA postoperatively. ctDNA quantity was predictive of poor survival. These findings establish the rationale for larger validation studies in early breast cancer to evaluate ctDNA as a monitoring tool for early metastasis detection, therapy modification, and to aid in avoidance of overtreatment. Synopsis Serial measurement of circulating tumor DNA (ctDNA) is shown to be a robust and accurate occult metastatic disease biomarker in patients diagnosed with primary breast cancer. Measured ctDNA levels are a quantitative risk factor for poor outcomes. A combination of low-coverage whole-genome sequencing with personalized droplet digital PCR, analytical methods, and a bioinformatics pipeline was developed for quantification of ctDNA in blood plasma samples collected during clinical follow-up of patients with primary (non-metastatic) breast cancer. ctDNA analysis can discriminate patients with eventual metastasis from those with long-term disease-free survival with 93% sensitivity and 100% specificity (ROC area 0.98, P = 0.001). ctDNA-based detection of occult metastatic disease preceded clinical detection for 86% of patients by an average 11 months and in some cases by 3 years. No ctDNA could be detected at any time-point after surgery for patients with long-term disease-free survival. The level of ctDNA was a quantitative risk factor for clinical metastatic disease (logistic regression odds ratio 2.1 for each doubling of ctDNA levels, P = 0.02) and death (odds ratio 1.3 per ctDNA doubling, P = 0.04). Introduction Breast cancer is the most common malignancy and leading cause of cancer-related death in women worldwide; once the tumor has metastasized, it is essentially an incurable disease (Jemal et al, 2011). The difficulty in curing metastatic breast cancer may be in part because metastatic spread is usually detected only after the deposit has grown large enough to be palpable, cause overt clinical symptoms, or be identified by imaging. In patients with primary (non-metastatic) breast cancer at diagnosis, the risk of subsequent metastatic relapse is greatest within 2 years after primary surgery (Cheng et al, 2012). However, an estimated 50% of recurrences are diagnosed > 5 years after surgery (Early Breast Cancer Trialists' Collaborative Group, 2005), indicating that occult metastatic dissemination can have a protracted subclinical period. Earlier detection of metastatic breast cancer may be clinically beneficial. A reasonable assumption is that identification of recurrent disease at the earliest moment will allow for initiation of auxiliary therapies against a nominal tumor burden that has accumulated fewer oncogenic events. So far, this assumption has been tested without success, most likely because modalities and biomarkers that lack sufficient sensitivity and/or specificity have been utilized thus far (Lippman & Osborne, 2013). For example, whereas circulating tumor cells (CTCs) may carry additional prognostic information in primary breast cancer (Lucci et al, 2012; Rack et al, 2014), available evidence does not support the use of imaging, serum protein markers, and CTCs for routine monitoring after primary surgery (Khatcheressian et al, 2013; Theriault et al, 2013). At the same time, many breast cancer patients are likely being overtreated; that is, they may in fact be cured by locoregional treatment and unnecessarily enduring the side effects of systemic therapies. For these reasons, improved surveillance methods to determine occult tumor burden (or lack thereof) in the primary breast cancer setting are still highly desirable (Lippman & Osborne, 2013). Clinical monitoring of minimal residual disease is routinely performed in several hematological malignancies with known pathognomonic chromosomal rearrangements, for example by serial quantification of TEL-AML1 or BCR-ABL fusion-gene chromosomal translocations in acute lymphoblastic leukemia and chronic myelogenous leukemia, respectively (Dolken, 2001). In cancer patients, tumor-derived DNA (termed cell-free circulating tumor DNA; ctDNA) can be found in the blood circulation and usually comprises a small fraction of the total circulating DNA (Jung et al, 2010). Circulating DNA is rapidly degraded into short fragments, and the quantity of ctDNA appears to be related to tumor progression (Stroun et al, 1989; Diehl et al, 2008; Yung et al, 2009; Jung et al, 2010; Leary et al, 2010; McBride et al, 2010; Diaz et al, 2012; Dawson et al, 2013; Murtaza et al, 2013; Bettegowda et al, 2014; Newman et al, 2014). Therefore, ctDNA "liquid biopsy" analysis is an attractive biomarker for noninvasive monitoring of tumor growth, response, and spread (McDermott et al, 2011). Until recently, assays for ctDNA have been infeasible for most solid cancers due to a paucity of recurrent mutations for interrogation as well as the practical and economical hurdles of enumerating tumor-specific aberrations on a per-patient basis. Advances in deep-sequencing technology now enable comprehensive cataloguing of tumor-specific (somatic) chromosomal rearrangements and mutations at an ever-decreasing cost (Meyerson et al, 2010). Recent studies have shown that breast cancer genomes may harbor from a few to several hundred rearrangements and mutations per tumor (Shah et al, 2009; Stephens et al, 2009, 2012; Banerji et al, 2012; Cancer Genome Atlas Network, 2012; Ellis et al, 2012; Nik-Zainal et al, 2012). In contrast to somatic point mutations, in which the identical mutation can be present across many tumors, tumor types, and individuals (for example PIK3CA hot-spot mutations), chromosomal rearrangements are inherently highly tumor specific and can serve as unique genetic "fingerprints" of an individual tumor (Leary et al, 2012). Serial measurement of ctDNA using various methods has shown encouraging results for several solid cancer types (Diehl et al, 2008; Yung et al, 2009; Leary et al, 2010; McBride et al, 2010; Diaz et al, 2012; Misale et al, 2012; Newman et al, 2014), and in the metastatic breast cancer setting, measurement of ctDNA dynamics compares favorably to the serum protein marker CA 15-3 and CTCs (Dawson et al, 2013). Here, we tested in patients with primary breast cancer and long-term follow-up the hypothesis that monitoring of tumor-specific chromosomal rearrangements in cell-free circulating DNA can detect occult metastatic disease following primary surgery and serve as a sensitive, specific, and thus potentially clinically useful noninvasive biomarker in the adjuvant setting (Fig 1). Figure 1. Analysis of personalized ctDNA biomarkers in primary breast cancer Patient flow diagram indicating patient selection criteria. EM = eventual metastasis; DF = long-term disease-free. Study schema. For 20 women with primary breast cancer, patient- and tumor-specific chromosomal rearrangements were determined through whole-genome sequencing of 21 tumor tissue specimens (one patient had bilateral tumors). Genomic fusion sequences were bioinformatically reconstructed, and selected rearrangements were validated as somatic. Personalized droplet digital PCR assays were used to quantify rearranged DNA sequences in the cell-free circulating DNA isolated from 93 patient blood plasma samples taken serially during the clinical course. ctDNA results were then compared to clinical endpoints. Download figure Download PowerPoint Results Enumeration of tumor-specific chromosomal rearrangements Twenty patients enrolled in the Breast Cancer and Blood Study (BC Blood, Sweden) (Borgquist et al, 2013), an ongoing prospective study at Lund University since 2002, were included in the present investigation for retrospective analysis of ctDNA (Fig 1A). Six patients had long-term disease-free survival (9.2 years median follow-up; termed DF patients), and 14 had eventual diagnosis of clinical metastasis from 1.2 to 5.1 years after primary surgery (termed eventual metastatic [EM] patients) (Table 1). For each patient, a sample of the primary tumor, a normal tissue sample, and 3–6 blood plasma samples that were collected during the clinical course were available. First, to identify tumor-associated chromosomal rearrangements that could serve as biomarkers, whole-genome sequencing (WGS) was performed on DNA isolated from 21 primary breast tumors (patient EM6 had bilateral primary breast cancers). On average, 93 million DNA fragments were sequenced per tumor (range 54–160 million), yielding a mean genome sequence coverage of 5.3-fold (range 1.8–12.9) and mean physical coverage of 15.6 (range 9.2–28.2) (Supplementary Table S1). We developed an analysis pipeline incorporating our SplitSeq computational method to identify inter- and intra-chromosomal rearrangements using an approach that scanned for paired sequence reads where the two reads aligned to discordant positions in the human genome, or individual reads in a read pair that contained juxtaposed sequences from two disparate genomic regions. Chromosomal rearrangements supported by two or more sequenced fragments could be detected in all primary tumors, and on average, 92 rearrangements were identified per tumor (range 21–305) (Fig 2, Supplementary Fig S1 and Supplementary Tables S1 and S2). There was no significant difference in sequence coverage or frequencies of chromosomal rearrangements detected between EM patients and DF patients (Mann–Whitney test), and the numbers of detected rearrangements for these 21 cases are similar to other studies of primary breast tumors (Stephens et al, 2009; Banerji et al, 2012; Nik-Zainal et al, 2012). Table 1. Patient and tumor characteristics Patient ID Age at primary diagnosis (years) Tumor size (mm) Lymph node status (positive/total) Distant metastasis at diagnosis ER status PR status HER2 status Nottingham Histological Grade Time to recurrence (months) Time to last follow-up or deathb (months) EM1 42 33 0/2 No Positive Positive Negative 3 20.0 29.4b EM2 57 28 1/17 No Positive Positive Negative 2 40.0 55.2b EM3 78 20 6/17 No Positive Positive Negative 3 16.1 17.9b EM4 34 28 0/2 No Positive Positive Negative 2 31.8 99.0 EM5 61 12 0/5 No Positive Positive Negative 1 48.8 97.1b EM6 62 Right: 28 2/13 No Positive Positive Negative 2 61.3 87.3 Left: 55 1/12 Positive Positive Negative 3 EM7 55 22 0/2 No Positive Negative Amplified 2 18.9 59.3b EM8 67 22 2/12 No Positive Negative Negative 2 13.9 33.2b EM9 50 18 0/1 No Positive Positive Negative 3 36.0 54.7b EM10 64 45 1/14 No Positive Negative Negativea 2 17.7 33.2b EM11 59 20 16/18 No Positive Positive Negativea 3 13.9 32.5b EM12 53 37 4/10 No Positive Positive Negative 2 16.2 33.5b EM13 69 25 0/4 No Positive Negative Negativea 2 43.9 58.7b EM14 47 19 1/18 No Negative Positive Amplified 2 20.0 46.7b DF1 58 15 0/2 No Positive Positive Negative 3 108.7 DF2 37 20 0/3 No Positive Positive Negative 3 111.7 DF3 56 19 0/1 No Positive Positive Negativea 2 109.9 DF4 46 13 0/1 No Negative Negative Negativea 2 110.4 DF5 54 15 0/2 No Positive Positive Negativea 3 109.5 DF6 58 18 0/2 No Positive Negative Negativea 2 113.2 All patients analyzed are women. a Clinical HER2 analysis not performed. HER2 status determined from gene copy number derived from whole-genome sequencing results. b Time from primary diagnosis to death. Figure 2. Identification of chromosomal rearrangements and personalized assay design Low-coverage whole-genome sequencing of the primary tumor was used to enumerate chromosomal rearrangements. Shown are results for patient DF1, with inter- and intra-chromosomal rearrangements plotted as a Circos diagram (Krzywinski et al, 2009). Chromosomes 1–22 and X are ordered in the outer circle. From the outside, concentrically, are plotted the DNA copy number estimations from the whole-genome sequencing data and the chromosome ideograms. The orange intra-chromosomal and blue inter-chromosomal arcs in the center indicate chromosomal rearrangements supported by two or more paired-end reads. Circos diagram for patient EM11. Plots for all patient tumors are shown in Supplementary Fig S1. One example rearrangement from patient EM11, indicated in red in (B), with identification of the exact fusion sequence between chromosomes 8p22 and 11q14.1. Aligned sequencing reads are highlighted in blue when its read pair aligns concordantly on the same chromosome or in light green if its read pair aligns on another chromosome. Within each sequencing read, nucleotide bases with exact match to the reference sequence (shown in the middle with yellow shading) are not printed. Mismatching bases are shown in blue if matching to 11q14.1 and green if matching to 8p22. At the bottom, the personalized dual-labeled probe and primers designed for this validated rearrangement are illustrated. F denotes the fluorescent molecule and Q the two quenching molecules. Download figure Download PowerPoint Selection and validation of rearrangements To account for possible intra-tumoral heterogeneity, and since it is not possible to know a priori which rearrangements in the primary tumor will be part of derivative metastatic clone(s), candidate rearrangements were selected such that a range of apparent copy number states (in other words, a range of number of supporting reads) were represented for each patient tumor. Our strategy was to design assays for ~10 rearrangements per primary tumor and select additional rearrangements in the event of assay failure or validation as not somatic. In summary, for each of the 237 selected candidate rearrangements, one assay was designed and tested by conventional PCR across the breakpoint junction in tumor and normal DNA from the same patient. Of 197 informative assays (83%; 7–17 per tumor), 167 (85%) were confirmed to be somatic by PCR (Supplementary Tables S3 and S4). Of these, due to limitations on the available plasma volumes and our desire to perform replicate analyses, four to six rearrangements per tumor were selected (again to reflect a variety of copy number states) and the corresponding probe was synthesized for droplet digital PCR (ddPCR) analysis of patient plasma samples. Probe assay success rate was high, with 113 of 122 (93%) validating for ddPCR (Supplementary Table S4). Optimization of droplet digital PCR In ddPCR, the PCR with input DNA and target sequence-specific fluorescent probe and primers is partitioned into thousands of nanoliter-sized reaction droplets. Following thermocycling, successful amplification of the target cleaves the fluorescent molecule from the specific probe, thereby unquenching the fluorophore (Fig 2C). Each droplet is read as either containing amplifiable target sequence (positive fluorescence above a threshold) or not, yielding a binary (digital) readout. Because the distribution of zero, one, two, or more amplifiable targets into droplets is a random process, the fraction of positive droplets to total droplets can be Poisson-corrected to derive a highly quantitative estimate of the number of amplifiable molecules that were present in the input sample (Hindson et al, 2011). We optimized a ddPCR method for measurement of circulating DNA that employs a universal touchdown PCR thermocycling protocol for increased specificity. For quantification of tumor-specific rearrangements, we determined our ddPCR method to be highly linear over at least 3 orders of magnitude and able to discriminate somatic mutant rearranged sequences down to 0.01% tumor DNA content (one rearranged sequence per 10,000 wild-type sequences) (Fig 3A and B). Importantly, zero tumor-specific rearrangements were detected by our method in over 2.7 million negative control DNA droplets analysed, corresponding to > 200 control ddPCR reactions that in total interrogated more than 2.5 million normal haploid genome equivalents (i.e. zero rate of false-positive signals). Figure 3. Performance of droplet digital PCR (ddPCR) method Dilution series for two tumor-specific rearrangements, patient EM13-del(15)(q26.3q26.3) and patient EM1-t(13;13)(q12.3;q13.2), starting with input of 20 ng of the respective patient's primary tumor DNA in each ddPCR, and diluting twofold in the series as indicated (x-axis). Experiments were performed in duplicate. Linear regression lines are plotted in black, and goodness of fit statistics (R2) were calculated. Observed percentages by ddPCR of a tumor-specific chromosomal rearrangement, patient DF1-t(10;14)(p14;q22.3), in admixtures of tumor and normal DNA of varying amounts from 50% down to 0.01% tumor DNA content (total DNA input fixed at 200 ng). Concentrations of the tumor-specific rearrangement and the control region in chromosome 2p14 were used in the calculations for amounts of tumor and total DNA, respectively. The black diagonal dashed line indicates the ideal correlation line (y = x). The R2 was calculated for the linear regression line (not plotted). All axes are on log scales. Correlation between whole-genome sequencing (WGS) rearrangement copy number estimates and the number of copies in 40 ng primary tumor DNA as measured by ddPCR. Axes on log2 scales. The R2 was calculated for the linear regression line (drawn in red). Download figure Download PowerPoint Quantification of ctDNA in serial plasma samples Circulating cell-free DNA was isolated from 93 plasma samples for the 20 patients. The number of fragments of each tumor-specific chromosomal rearrangement was quantified in the circulating DNA by ddPCR. Each tumor-specific rearrangement assay was run in duplicate and included positive (primary tumor DNA) and negative (matched normal DNA) controls, and on average, 25,704 (SD 2,320) droplets were analyzed per assay per plasma sample. As expected, the relative copy numbers of rearrangements were well correlated between the WGS analysis and ddPCR analysis of primary tumor DNA (R2 = 0.65; Fig 3C). A ddPCR assay targeting a non-rearranged normal region of chromosome 2p14, which rarely undergoes copy number alteration in breast cancer (Jonsson et al, 2010), was used to estimate total circulating DNA (both tumor and normal cell derived). The average number of amplifiable 2p14 control region fragments was 1,908 copies/ml plasma (range 280–8,960) (Supplementary Fig S2 and Supplementary Table S5). There was no significant difference in the number of 2p14 control region fragments per ml plasma between EM and DF patients within the preoperative time-points nor when comparing across all time-points (Mann–Whitney U-test). Tum
Referência(s)