Clinica Chimica Acta 552 (2024) 117673 Contents lists available at ScienceDirect Clinica Chimica Acta journal homepage: www.elsevier.com/locate/cca Comparative study of droplet-digital PCR and absolute Q digital PCR for ctDNA detection in early-stage breast cancer patients Victoria Sánchez-Martín a, b, Esperanza López-López a, c, Diego Reguero-Paredes a, Ana Godoy-Ortiz a, b, c, Maria Emilia Domínguez-Recio a, c, Begoña Jiménez-Rodríguez a, b, c, Alfonso Alba-Bernal a, c, d, Maria Elena Quirós-Ortega a, c, d, María Dunia Roldán-Díaz a, c, Jesús Velasco-Suelto a, c, Noelia Linares-Valencia a, c, Alicia Garrido-Aranda a, c, d, Rocío Lavado-Valenzuela a, b, c, d, Martina Álvarez a, b, d, e, Javier Pascual a, b, c, d, Emilio Alba a, b, c, d, e, *, Iñaki Comino-Méndez a, b, c, d, * a Unidad de Gestion Clinica Intercentros de Oncologia Medica, Hospitales Universitarios Regional y Virgen de la Victoria, 29010, Malaga, Spain Centro de Investigacion Biomedica en Red de Cancer (CIBERONC - CB16/12/00481), 28029, Madrid, Spain The Biomedical Research Institute of Málaga (IBIMA-CIMES-UMA), 29010, Malaga, Spain d Andalusia-Roche Network in Precision Medical Oncology, 41092, Sevilla, Spain e University of Málaga, Faculty of Medicine, 29010 Malaga, Spain b c A R T I C L E I N F O A B S T R A C T Keywords: Circulating tumor DNA Digital PCR Liquid biopsy Breast cancer Background: Analysis of circulating tumor DNA (ctDNA) is increasingly used for clinical decision-making in oncology. However, ctDNA could represent ≤ 0.1 % of cell-free DNA in early-stage tumors and its detection requires high-sensitive techniques such as digital PCR (dPCR). Methods: In 46 samples from patients with early-stage breast cancer, we compared two leading dPCR assays for ctDNA analysis: QX200 droplet digital PCR (ddPCR) system from Bio-Rad which is the gold-standard in the field, and Absolute Q plate-based digital PCR (pdPCR) system from Thermo Fisher Scientific which has not been re­ ported before. We analyzed 5 mL of baseline plasma samples prior to any treatment. Results: Both systems displayed a comparable sensitivity with no significant differences observed in mutant allele frequency. In fact, ddPCR and pdPCR possessed a concordance > 90 % in ctDNA positivity. Nevertheless, ddPCR exhibited higher variability and a longer workflow. Finally, we explored the association between ctDNA levels and clinicopathological features. Significantly higher ctDNA levels were present in patients with a Ki67 score > 20 % or with estrogen receptor-negative or triple-negative breast cancer subtypes. Conclusion: Both ddPCR and pdPCR may constitute sensitive and reliable tools for ctDNA analysis with an adequate agreement in early-stage breast cancer patients. 1. Introduction Liquid biopsy has emerged as a minimally invasive and promising methodology in molecular oncology, offering crucial insights into various tumor components released into the bloodstream [1]. Among these components, circulating tumor DNA (ctDNA) found in the plasma of cancer patients provides a valuable source for investigating genomic aberrations that define the carcinogenic process[2]. Indeed, ctDNA has found diverse applications in cancer research, including diagnosis, pa­ tient stratification, prediction of treatment response, efficacy moni­ toring, and post-treatment surveillance for minimal residual disease (MRD) detection [3–7]. Abbreviations: BC, breast cancer; cfDNA, cell-free DNA; ctDNA, circulating tumor DNA; ddPCR, droplet digital PCR; ER, estrogen receptor; HER2, epidermal growth factor receptor 2; MAF, mutant allele frequency; PCR, pathologic complete response; pdPCR, plate-based digital PCR; PR, progesterone receptor; TNBC, triple negative breast cancer; Tm, melting temperature. * Corresponding authors at: Clinical and Traslational Cancer Research Group, IBIMA Institute, Calle Severo Ochoa, 35, Parque Tecnologico de Andalucia (PTA), Campanillas 29590 (Malaga), Spain. E-mail addresses: ealbac@uma.es (E. Alba), Inaki.comino@ibima.eu (I. Comino-Méndez). https://doi.org/10.1016/j.cca.2023.117673 Received 20 September 2023; Received in revised form 17 November 2023; Accepted 19 November 2023 Available online 23 November 2023 0009-8981/© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/). V. Sánchez-Martín et al. Clinica Chimica Acta 552 (2024) 117673 However, it is essential to note that ctDNA fractions may fluctuate based on the cancer stage, and early-stage tumors usually present ctDNA levels below 0.1 % of cell-free DNA [8]. As a result, detecting ctDNA requires highly sensitive technologies. Currently, digital PCR (dPCR) stands as the gold-standard approach due to its exceptional sensitivity, experimental simplicity, and favorable price-to-sample ratio [9].More­ over, next-generation sequencing (NGS) based procedures allow for a comprehensive description of mutational profiles, but NGS remains relatively expensive compared to other methods [10]. Several studies have utilized Bio-Rad’s QX droplet digital PCR systems to analyze ctDNA in early-stage breast cancer (BC). These investigations have demon­ strated the potential of ctDNA tracking in predicting future relapse, monitoring treatment response, and assessing clinical outcomes [11–14]. In the present work, we aimed to conduct a side-by-side evaluation of two leading dPCR assays for ctDNA detection: the QX200 droplet digital PCR system from Bio-Rad, which is the most used system to date, and the Applied Biosystems QuantStudio Absolute Q digital PCR system from Thermo Fisher Scientific which has not been previously reported in the scientific literature. We tested 46 samples from patients with early-stage BC, and we analyzed their baseline plasma samples prior to any treat­ ment separately with both dPCR systems. By comparing the perfor­ mance and sensitivity of these two dPCR systems, we aim to provide valuable insights into their potential applications for ctDNA detection in early-stage BC. This study aims to have a significant impact on ctDNAbased diagnostics and monitoring in clinical settings, ultimately lead­ ing to improved patient management and the development of person­ alized treatment strategies. libraries were prepared using SureSelect Human All Exon v6 (Agilent, 5190–8863) with probes capturing the whole exome, and sequenced on a DNB-seq platform (BGI Genomics, China). Quality control of WES data was performed using fastQC (v0.11.9), followed by trimming and quality filtering using Trim Galore (v0.6.7). Pre-processed reads were mapped to the GRCh38 reference genome by BWA-mem (v0.7.17). Data correction for technical biases and somatic mutation calling were performed according to GATK’s best practices (htt ps://software.broadinstitute.org/gatk/best-practices). The resulting aligned SAM files were sorted by coordinate using Picard Sortsam (Picard v2.26.10), and converted to BAM format with Samtools (v1.9). Picard Mark Duplicates was run to mark duplicated reads from each BAM file. GATK Base Recalibrator and Apply BQSR (GATK v4.2.2.0) were used for base quality score recalibration. Somatic variants analysis for each tumor sample was performed by GATK Mutect2 in matched normal mode including a custom panel of normal non-cancer variations which was previously built and a germline variant annotation file for the GRCh38 reference genome obtained from the GATK resource bundle. Reads counting summaries were obtained using GATK Get Pileup Summaries and passed to GATK Caculate Contamination for contami­ nation calculation. The reported variants were filtered to get true so­ matic mutations using Filter Mutect Calls. Somatic variants were annotated by ANNOVAR (v20200608) with custom made databases for COSMIC v95 and TCGA mutation data retrieved from GDC data portal [15]. After identifying somatic mutations, custom TaqMan™ SNP Geno­ typing Assays with FAM fluorophore mutant probes and VIC fluorophore wild-type probes were designed and ordered (Thermo Fisher Scientific). Finally, every tracking somatic mutation was re-validated using tumor and germline DNA by droplet digital PCR (see below), optimizing the annealing temperature for each assay (Table 1). 2. Materials and methods 2.1. Patients and samples 2.4. DNA extraction, quantification and sample preparation In this prospective study, we enrolled a cohort of 23 patients starting in 2020diagnosed with early-stage BC. Prior to cancer diagnosis, plasma samples were collected just before any treatment. Here, we tested 2 samples per patient with a total of 46 samples analyzed. The patients were recruited, and samples were collected at Hospitals Virgen de la Victoria and Regional of Malaga, Spain. All patients provided informed consent, and the study followed the principles of the Helsinki Declara­ tion and received approval from the local Ethical Committee. Human rights were protected. Cell-free DNA (cfDNA) was obtained from plasma samples using the QIAamp Circulating Nucleic Acid Kit (Qiagen, 55114) following the manufacturer’s instructions. Specifically, 5 mL of plasma were extracted for use with the two dPCR platforms. The elution of cfDNA was per­ formed using 100 µL of AVE buffer, and the samples were subsequently stored at − 20 ◦ C. Germline DNA from PBMCs was used as a negative control for each patient to ensure the reliability of the results and control for false pos­ itives and clonal hematopoiesis of indeterminate potential (CHIP) events. DNA from PBMCs was extracted using the QIAamp DNA Blood Mini Kit (Qiagen, 51104) following the manufacturer’s protocol, and the samples were subsequently stored at − 20 ◦ C until use. DNA quantification was performed using the RNAse P assay (Ther­ moFisher Scientific) [11]. For each patient sample, the entire cfDNA amount and an equivalent amount of DNA from PBMCs were used as input in the subsequent an­ alyses. Preparations of cfDNA were dried down at 45 ◦ C using the Eppendorf™ Concentrator plus (Thermo Fisher Scientific) and subse­ quently resuspended in 7 µL of nuclease-free water (Canvax Biotech, E0320). The corresponding amount of germline DNA from PBMCs was also dried down and resuspended in the same manner. 2.2. Blood sample processing Blood samples from the study participants were collected in citrate blood bags and processed within 2 h following venipuncture. The plasma supernatant was isolated by centrifugation for 10 min at 3,000 rpm at room temperature and subsequently stored at − 80 ◦ C until the extraction of cell-free DNA. For the isolation of PBMCs, a density gradient centrifugation method was employed using Lymphoprep (Stem Cell Technologies, 07801) following the manufacturer’s instructions. The isolated PBMCs were stored at − 196 ◦ C until further use. 2.3. Whole exome sequencing The identification of somatic mutations for each patient was accomplished through whole exome sequencing (WES) of both formalinfixed paraffin-embedded (FFPE) or fresh frozen (FF) tumor tissue and peripheral blood mononuclear cells (PBMCs). For tumor tissue DNA extraction, the RecoverAll™ Total Nucleic Acid Isolation Kit (Thermo Fisher Scientific, AM1975) was used following the manufacturer’s in­ structions. Germline DNA from PBMCs was isolated using the QIAamp DNA Blood Mini Kit (Qiagen, 51104) following the manufacturer’s protocol. To quantify DNA, the RNAse P assay (Thermo Fisher Scientific) was employed, following a previously published protocol [11]. WES 2.5. Droplet digital PCR Samples were screened using the customTaqMan™ SNP Genotyping Assays with FAM fluorophore mutant probes and VIC fluorophore wildtype probes.The screening was conducted using the optimal conditions that had been previously optimized.Droplet digital PCR (ddPCR) was performed on a QX200 Droplet Digital PCR System (Bio-Rad).The cfDNA samples were divided into 7 independent PCR reactions. Each PCR re­ action consisted of 1X ddPCR Supermix for probes (Bio-Rad, 1863024), 1X custom TaqMan ddPCR assays (ThermoFisher Scientific), and 1 µL of 2 V. Sánchez-Martín et al. Clinica Chimica Acta 552 (2024) 117673 Table 1 Somatic mutations tracked and ctDNA detection results. This table presents the results for the analysis of 23 plasma samples from early-stage BC patients. Using Whole Exome Sequencing (WES), one truncal somatic mutation was identified per patient. TaqMan assays were custom-designed to cover these mutations, and the respective melting temperature (Tm) was optimized. The total FAM + VIC- compartments indicate the number of droplets or micro-chambers containing ctDNA, detected by ddPCR or pdPCR, respectively. The last column displays the Mutant Allele Frequency (MAF) values (%). Patient sample BC-008 BC-009 BC-010 BC-014 BC-015 BC-016 BC-017 BC-021 BC-026 BC-031 BC-033 BC-035 BC-037 BC-041 BC-046 BC-053 BC-059 BC-060 BC-061 BC-064 BC-069 BC-082 BC-085 Somatic mutation tracked in ctDNA Gene Mutation TP53 TCP11L2 DIDO1 PIK3CA PIK3CA TP53 FI3AI TSPAN32 ERBB2 ADAM29 PIK3CA TMEM205 IPO8 TP53 TP53 TP53 TP53 PIK3CA TP53 HIPR1 GSAP TP53 TP53 R248Q E269X R187H E545K E545K C238Y V297I F37F V777L G414E E545K A183T R53Q R249S R213X E221X R213X H1047Y R248Q R252Q F209F G266R R273C Total FAM+VIC- compartments Tm 60 ◦ C 60 ◦ C 60 ◦ C 64 ◦ C 64 ◦ C 62 ◦ C 60 ◦ C 64 ◦ C 60 ◦ C 64 ◦ C 64 ◦ C 64 ◦ C 60 ◦ C 64 ◦ C 60 ◦ C 60 ◦ C 60 ◦ C 60 ◦ C 60 ◦ C 60 ◦ C 60 ◦ C 60 ◦ C 60 ◦ C MAF (%) ddPCR pdPCR ddPCR pdPCR 2 1 2 183 1 34 1 11 0 1 1 0 3 0 512 124 13 28 87 14 0 0 12 2 0 1 272 0 0 0 23 0 1 1 0 9 0 558 188 9 47 153 28 1 1 21 0.012 0.003 0.007 0.647 0.008 0.317 0.011 0.143 0.000 0.006 0.002 0.000 0.025 0.000 6.120 1.739 0.147 0.197 0.412 0.211 0.000 0.000 0.122 0.008 0.000 0.003 0.902 0.000 0.000 0.000 0.135 0.000 0.003 0.002 0.000 0.045 0.000 5.131 1.506 0.096 0.224 0.483 0.346 0.013 0.002 0.158 ddPCR, droplet digital PCR; MAF, mutant allele frequency; pdPCR, plate-based digital PCR; Tm, melting temperature. cell-free DNA in a total volume of 20 µL. For germline DNA from PBMCs, 5U HindIII-HF (New England Biolabs, R3104) was added to fragment genomic DNA. Droplets were generated using the Auto droplet generator (Bio-Rad) following the manufacturer’s instructions. The assays were conducted on 96-well plates with the following thermal cycling condi­ tions in a C1000 Touch™ thermal cycler (Bio-Rad): 95 ◦ C for 10 min, 40 cycles of 94 ◦ C for 30 sec and a specific annealing temperature for 60 sec, 98 ◦ C for 10 min. The temperature ramp increment was 2 ◦ C/sec for all steps. The plates were read on the Bio-Rad QX-200 droplet reader (BioRad). Data analysis was performed using QuantaSoft v1.7 software (BioRad). Threshold gating was manually set for each patient in the previous validation phase and maintained consistently for all plasma samples from the same patient. Positive results required at least 2 droplets on the mutant channel, and at least one non-template negative control was run with every assay. samples from the same patient. Positive results required at least 2 microchambers on the mutant channel, and at least one non-template control was run with every assay. 2.7. Data analyses The experiment was deemed invalid if positivity was detected in any of the non-template controls. The number of FAM-positive (mutant) droplets or micro-chambers and the mean of mutant copies per micro­ liter were directly obtained from the QX200 Droplet Digital PCR System (Bio-Rad) or the Applied Biosystems QuantStudio Absolute Q Digital PCR System (ThermoFisher Scientific), respectively. The mutant copies per microliter were then transformed into mutant copies per eluate using the following formula: Mutant copies per eluate = Mutant copies per μL × μL of mix 2.6. Plate-based digital PCR × number of reactions assayed The plate-based digital PCR (pdPCR) assays were conducted under the same conditions as ddPCR, with respect to TaqMan probes and cycling conditions as previously described. Input samples were parti­ tioned into 7 independent PCR reactions, as the ddPCR setup. The Applied Biosystems QuantStudio Absolute Q Digital PCR System (ThermoFisher Scientific) was used for the pdPCR experiments. Each pdPCR reaction consisted of 1X dPCR Master Mix (ThermoFisher Sci­ entific, A52490), 1X custom TaqMan ddPCR assays (ThermoFisher Sci­ entific), and 1 µL of cell-free DNA in a total volume of 9 µL. For germline DNA from PBMCs, 5U HindIII-HF (New England Biolabs, R3104) was added to fragment genomic DNA. The PCR reactions were loaded onto MAP16 plates (ThermoFisher Scientific, A52688), and then, 15 µL of Isolation Buffer (ThermoFisher Scientific, A52730) was transferred to wells with the PCR mix. All the necessary steps for dPCR, including compartmentalizing, thermal cycling, and data acquisition, were per­ formed on a single instrument, the Applied Biosystems QuantStudio Absolute Q Digital PCR System (ThermoFisher Scientific). Threshold gating was set individually and manually for each in the previous vali­ dation phase patient sample but consistently maintained for all plasma Where the volume of the mix was 20 µL in the case of the Bio-Rad system and 9 µL for the Absolute Q. Regardless of the platform, the assays were partitioned into 7 reactions. The mutant copies per mL of plasma were calculated as follows: Mutant copies per mL of plasma = Mutant copies per eluate/mL of plasma assayed Where the volume of plasma employed was 5 mL in all the assays. Finally, mutant allele frequency (MAF) was calculated as follows: MAF (%) = Mutant copies per μL/Wild − type copies per μL × 100 Where both the values of mutant and wild-type copies per µL were directly obtained from the corresponding platform. 2.8. Statistical analyses Statistical analyses and graphical representations were performed using R (v4.2.2) and GraphPad Prism 8. The agreement between 3 V. Sánchez-Martín et al. Clinica Chimica Acta 552 (2024) 117673 platforms was assessed using the Cohen К statistic, and correlation was calculated using the Spearman ρ coefficient. Differences between plat­ forms were evaluated using the Mann-Whitney-Wilcoxon test, and analysis of variances was conducted with the Fisher test. For all tests, pvalues below 0.05 were considered statistically significant and denoted as follows: *p < 0.05; **p < 0.01, and ***p < 0.001. For the sliding window analysis, all samples with a positive call from either platform were included. The samples were ranked from the highest to the lowest mutant allele frequency (MAF), considering the largest MAF value obtained from both technologies for each sample. A sliding window of 5 patients was established, starting from the samples with the highest MAF. Within each sliding window, the median MAF and the percentage of concordant samples between platforms were calcu­ lated and plotted. For each new window, the sample with the highest MAF was removed, and the sample with the next highest MAF was included. These calculations were repeated for each window until the last window containing the samples with the lowest MAF. MAF in either method (Spearman ρ = -0.06, p-value > 0.05 for ddPCR and Spearman ρ = 0.07, p-value > 0.05 for pdPCR) (Fig. 2C). Interest­ ingly, the number of compartments analyzed was significantly higher in pdPCR than ddPCR (Wilcoxon W = 529, p-value < 0.001), and the comparison of variances showed a significantly higher variability in ddPCR compared to pdPCR (Fisher F = 0.0008, p-value < 0.001). 3.2. Concordance of ctDNA detection between ddPCR and pdPCR We compared ctDNA positivity between ddPCR and pdPCR in 23 patients with early-stage BC. The results showed that ddPCR detected ctDNA in 56.5 % (13/23) of patients, while pdPCR detected ctDNA in 47.8 % (11/23) (Fig. 3A). Despite this slight difference in ctDNA detection rates, there was close agreement between the two platforms, with a Cohen κ value of 0.83 (95 % CI, 0.60 – 1.00). Only 8.7 % (2/23) of cases showed discordant ctDNA positivity. In particular, discordance occurred in BC-010 and BC-016 samples, which were ctDNA positive by ddPCR but ctDNA negative by pdPCR (Fig. 3B). Composite allele fraction of MAF between the two methodologies was tightly correlated with a Spearman ρ = 0.76 (p-value < 0.0001) (Fig. 3C). Analyzing patients with a positive result from at least one platform using a sliding window of 5 patients from high to low MAF, we observed that the percentage of concordant cases varied with the MAF of windows (Fig. 3D). Total concordance was reported for windows with MAFs ≤ 0.147 % and MAFs ≥ 0.902 %, while the percentage of concordant cases was 80 % for windows with MAFs in the range 0.158 % − 0.403 %. No sharp dete­ rioration in concordance was observed in any of the analyzed windows. 3. Results 3.1. Comparing droplet and Plate-Based digital PCR for ctDNA analysis A total of 23 patients with early-stage BC participated in this ctDNA assessment using digital PCR (dPCR). Particularly, 2 samples per patient were tested with a total of 46 samples analyzed. Cell-free DNA (cfDNA) was isolated from 5 mL of plasma, quantified and subjected to dPCR for ctDNA detection. We utilized two different platforms for analysis: the QX200 droplet digital PCR system (ddPCR) and the Applied Biosystems QuantStudio Absolute Q digital PCR system (pdPCR) (Fig. 1A). ddPCR is based on water–oil emulsion technology, creating nanoliter-sized droplets that act as partitions to separate DNA mole­ cules. On the other hand, pdPCR relies on microfluidics to compart­ mentalize DNA molecules into micro-chambers. Both platforms require compartmentalization, PCR amplification, and data acquisition to identify mutant DNA molecules in a sample. In terms of experimental steps and time-to-perform comparison, ddPCR involves a droplet generator for compartmentalization, a thermal cycler for PCR amplifi­ cation, and a droplet reader for data acquisition. In contrast, pdPCR with Absolute Q allows all necessary steps to be conducted on a single in­ strument, making the workflow faster. In fact, pdPCR takes ~ 3 h to complete the full protocol, while ddPCR requires ~ 4 h. (Fig. 1B). In this comparative analysis, we studied baseline plasma samples from 23 patients with early-stage BC using the two different method­ ologies. Whole-exome sequencing of tumor and germline DNA was performed to identify somatic mutations, from which one truncal mu­ tation per patient was selected (Table 1). Custom TaqMan™ SNP Gen­ otyping Assays with FAM and VIC fluorophore probes for mutant and wild-type alleles, respectively, were designed and optimized (Table 1). In our cohort, both ddPCR and pdPCR detected a minimum of 1 mutant FAM-positive VIC-negative allele, with a maximum of 512 and 518 FAM-positive VIC-negative droplets and microchambers respec­ tively (Table 1). The determination of mutant allele frequency (MAF) showed similar results for both platforms (Fig. 2A), with a minimum MAF of 0.002 % in both methods for the BC-033 plasma sample (Table 1). The mean MAF was 0.440 % (range 0 – 6.120) in ddPCR and 0.394 % (range 0– 5.131) in pdPCR, and there were no statistical dif­ ferences between the platforms (Wilcoxon W = 289, p-value > 0.05). Next, we explored the potential role of input cfDNA for ddPCR and pdPCR and its relation to MAF. The amount of input cfDNA was not correlated with MAF in either platform, with a Spearman ρ = -0.12 (pvalue > 0.05) for ddPCR and a Spearman ρ = -0.30 (p-value > 0.05) for pdPCR (Fig. 2B). Moreover, there were no significant differences in cfDNA input between the two platforms (Wilcoxon W = 258, p-value > 0.05). We further assessed the total number of compartments analyzed in both methodologies and its potential association with MAF. The number of droplets in ddPCR or micro-chambers in pdPCR did not correlate with 3.3. Association between ctDNA levels and clinicopathological characteristics In this study, we explored the correlation between ctDNA levels and various clinicopathological features in early-stage BC patients. In particular, the mean of MAF obtained from both ddPCR and pdPCR platforms was considered. The clinicopathological characteristics analyzed included tumor size, affected lymph nodes, tumor grade, Ki67 score, estrogen receptor (ER), progesterone receptor (PR) status and epidermal growth factor receptor 2 (HER2) status, triple-negative breast cancer (TNBC) subtype, and pathologic complete response (PCR) after neoadjuvant chemotherapy (Fig. 4).The results showed that ctDNA levels were not significantly influenced by tumor size, affected lymph nodes, tumor grade, PR status, HER2 status, or PCR. However, we observed significantly higher ctDNA levels in patients with ER-negative, TNBC subtype and those with a Ki67 score above 20 %. In this prospective study, patients are currently under clinical followup, and thus far, two of them have experienced relapse (BC-016 and BC046). Notably, the patient BC-046, who underwent neoadjuvant chemotherapy, exhibited the highest MAF (6.12 %, as determined by ddPCR) among the positive samples. When compared to the calculated median of all positive samples (ddPCR values), this represented a sub­ stantial 50.16-fold change (see Fig. 2 and Table 1). It is crucial to emphasize that the other relapsed patient (BC-016) demonstrated ctDNA positivity at baseline through ddPCR but not pdPCR (see Fig. 2 and Fig. 3). Additionally, it is noteworthy that the time-to-relapse from the baseline sample extraction was shorter for the patient with the highest ctDNA levels (BC-046), at 1.43 years, compared to the other patient (BC-016), who experienced relapse after 2.08 years. 4. Discussion To date, ddPCR has been exploited to detect ctDNA in early-stage BC with high sensitivity [12–14,16–18].In fact, ddPCR remains as the gold standard methodology for ctDNA analysis not only in BC, but also in other types of cancer [19]. In this regard, a recent study compared the performance of the ddPCR technology with a different novel array-based digital PCR system for detecting specific mutations in lung and 4 V. Sánchez-Martín et al. Clinica Chimica Acta 552 (2024) 117673 Fig. 1. Comparison of ddPCR and pdPCR for ctDNA analysis. A) Illustrative depiction of the experimental workflow employed in this comparative study. Beginning with 5 mL of plasma, cfDNA extraction, quantification, and subsequent application for either ddPCR (with an anticipated analysis of 20,000 droplets per reaction) or pdPCR (with an expected analysis of 20,000 micro-chambers per reaction).B) Schematic presentation of the experimental protocol involving ddPCR and pdPCR, encompassing the subsequent stages: formulation and loading of reaction mix, compartmentalization, PCR amplification, data capture, and data analysis. cfDNA, cellfree DNA; ddPCR, droplet digital PCR; pdPCR, plate-based digital PCR. 5 V. Sánchez-Martín et al. Clinica Chimica Acta 552 (2024) 117673 Fig. 2. Analysis of sensitivity and variability for ctDNA detection between ddPCR and pdPCR. A) Representation of the values of MAF (%) per patient that were obtained both by ddPCR and pdPCR, rendering information about sensitivity. B) Graphical presentation of MAF (%) values versus the total cfDNA quantity extracted from each plasma sample, assessing the influence of input cfDNA on MAF (%) values for each platform (ddPCR on the left, pdPCR on the right).C) Depiction of MAF (%) values against the total count of compartments examined per plasma sample, aimed at assessing the influence of partitions on the resulting MAF (%) values using both platforms (ddPCR on the left, pdPCR on the right). Positive outcomes were ascertained when a minimum of 2 compartments were identified on the mutant channel. ddPCR, droplet digital PCR; pdPCR, plate-based digital PCR. 6 V. Sánchez-Martín et al. Clinica Chimica Acta 552 (2024) 117673 Fig. 3. Concordance of ctDNA detection between ddPCR and pdPCR. A) Contingency table for positive and negative ctDNA detection per patient sample using ddPCR and pdPCR. B) 2D plots from ddPCR and pdPCR discordant samples (BC-010 and BC-016). C) Composite allele fraction of the agreement on MAF between ddPCR and pdPCR. D) Sliding window analysis of patients with positive ctDNA detection in at least 1 platform, showing variation in the percentage of concordant cases with median MAF of the window (n = 5 in every sliding window). ddPCR, droplet digital PCR; pdPCR, plate-based digital PCR. colorectal cancer patients. The authors revealed weak concordance be­ tween both platforms, particularly for EGFR or RAS mutations, espe­ cially in detecting ctDNA mutations with very low mutant allele frequency (MAF). The array-based system demonstrated better sensi­ tivity in this context [20]. However, to the best of our knowledge, there are no previous studies assaying and comparing ctDNA detection using the novel Absolute Q pdPCR platform. The present study aimed to compare two digital PCR platforms for the analysis of ctDNA in early-stage BC patients. Our results showed that both ddPCR and pdPCR displayed a comparable sensitivity for ctDNA detection, with no significant differences observed in MAF between the two plat­ forms. It is important to note here that the amplification conditions were optimized for ddPCR and turned out to be optimal also for pdPCR. How­ ever, ddPCR exhibited higher variability in the number of compartments analyzed compared to pdPCR. This finding suggests that pdPCR may provide a more consistent and reproducible measurement of ctDNA levels in blood samples. Regarding ctDNA positivity, we found a high level of concordance between ddPCR and pdPCR, with only a small percentage of cases showing discordance. Importantly, the discordant cases exhibited very low MAF values, close to the minimum limit of detection for both platforms. This observation highlights the importance of considering the lower limit of detection when interpreting ctDNA results in clinical practice, particularly in cases with low tumor burden. In such scenarios, repeating the testing enhances overall sensitivity and is recommended to prevent false-negative or false-positive results [21]. In addition to technical comparisons, we explored the association between ctDNA levels and various clinicopathological characteristics. We observed that ctDNA levels were not significantly influenced by tumor size, affected lymph nodes, tumor grade, PR and HER2 status, or pathologic complete response after neoadjuvant chemotherapy in our sample cohort. However, intriguingly, we found distinct associations between ctDNA levels and HR status as well as the Ki67 score, a marker of cell proliferation.These findings partially overlap with results from 7 V. Sánchez-Martín et al. Clinica Chimica Acta 552 (2024) 117673 Fig. 4. Association of ctDNA levels with clinicopathological characteristics. Plots representing the statistical analysis between mean ctDNA MAFs from ddPCR and pdPCR platforms and A) tumor size, B) affected lymph nodes (according to TNM scale) C) differentiation grade, D) Ki67 score, E) ER status, F) PR status, G) HER2 status, H) TNBC subtype and I) PCR. ddPCR, droplet digital PCR; ER, estrogen receptor; HER2, epidermal growth factor receptor 2; PCR, pathologic complete response; pdPCR, plate-based digital PCR; PR, progesterone receptor; TNBC, triple negative breast cancer. previous studies, reinforcing the notion that baseline ctDNA analysis offers insights into clinical features in early-stage BC patients [3]. For example, the presence of elevated ctDNA levels has been notably linked to specific BC subtypes, such as triple-negative breast cancer (TNBC), a particularly aggressive subtype [22]. Aligning with our findings, Mag­ banua et al. reported higher ctDNA positivity rates in TNBC patients both before and during neoadjuvant chemotherapy [23] a finding that was also observed in other studies [24,25]. In terms of the ER status, existing studies have shown that HR status can predict the detectability of ctDNA in blood; however, these analyses did not delve into ER status specifically [26]. However, our findings align with previous in­ vestigations in this regard [24,25]. Turning to the Ki67 score, divergent findings are evident in the literature. Similar to our outcomes, baseline ctDNA detection has been significantly associated with a Ki67 index exceeding 20 % in early-stage BC patients [27]. In contrast, a distinct study failed to establish a substantial connection between baseline ctDNA positivity and the Ki67 index in early-stage BC [25]. Regarding disease relapse, two patients, BC-016 and BC-046, have experienced recurrence to date. Intriguingly, the patient with the highest ctDNA levels, BC-046, relapsed earlier and belonged to the TNBC subtype, consistent with findings from prior studies [23,24]. This observation suggests the possibility that patient BC-046 may have an undetectable but active micro metastatic site. Therefore, these results once again empha­ sized the significance of baseline ctDNA measurements in predicting 8 V. Sánchez-Martín et al. Clinica Chimica Acta 552 (2024) 117673 disease prognosis among early-stage BC patients. Overall, our findings underscore the potential of ctDNA analysis to propel personalized medicine forward, offering a non-invasive avenue to gather critical disease-related insights. Authors’ contribution VS-M performed the comparative experiments and data analysis. ELL carried out the bioinformatic and statistical analyses. DR-P performed the comparative experiments. AG-O, MED-R, and BJ-R recruited the patients. AA-B, MEQ-O, MDR-D, JV-S, and NL-V processed the blood samples. AG-A, RL-V, and MA managed the project and resources. VS-M and IC-M wrote the manuscript. JP and EA reviewed the manuscript. ICM conceptualized and supervised the project, and reviewed and edited the manuscript. All authors read and approved the final manuscript 5. Conclusions Our study demonstrates the utility of the novel Absolute Q pdPCR platform as a sensitive and reliable tool for ctDNA analysis in early-stage cancers. Compared to ddPCR (Bio-Rad), both platforms exhibited a high level of concordance in ctDNA detection. Absolute Q pdPCR surpassed ddPCR in terms of reproducibility with a higher and less variable number of compartments analyzed. In addition, Absolute Q pdPCR allowed to perform all the required steps in a single instrument, short­ ening the workflow. Finally, ctDNA detection performed by both plat­ forms revealed associations with clinicopathological characteristics providing valuable insights into the clinical implications of ctDNA analysis in cancer management. Ethics approval and consent. This study included a cohort of 23 patients diagnosed with earlystage breast cancer at Hospitals Virgen de la Victoria and Regional of Malaga, Spain. Prior to participation, all patients provided informed consent, and the study adhered to the principles of the Helsinki Decla­ ration. Ethical approval was obtained from the local Ethical Committee. References [1] A. Alba-Bernal, et al., Challenges and achievements of liquid biopsy technologies employed in early breast cancer, EBioMedicine 62 (2020.), 103100. [2] J.C.M. Wan, et al., Liquid biopsies come of age: towards implementation of circulating tumour DNA, Nat. Rev. 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CRediT authorship contribution statement Victoria Sánchez-Martín: Data curation, Formal analysis, Investi­ gation, Methodology, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Esperanza LópezLópez: Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Visualization. Diego Reguero-Paredes: Investi­ gation, Methodology, Validation. Ana Godoy-Ortiz: Investigation, Methodology, Resources. Maria Emilia Domínguez-Recio: Investiga­ tion, Methodology, Resources. Begoña Jiménez-Rodríguez: Investi­ gation, Methodology. Alfonso Alba-Bernal: Investigation, Methodology. Maria Elena Quirós-Ortega: Investigation, Methodol­ ogy, Resources. María Dunia Roldán-Díaz: Investigation, Methodol­ ogy. Jesús Velasco-Suelto: Investigation, Methodology. Noelia Linares-Valencia: Investigation, Methodology. Alicia Garrido-Ara­ nda: Investigation, Methodology. Rocío Lavado-Valenzuela: Investi­ gation, Methodology. Martina Álvarez: Investigation, Methodology, Resources. Javier Pascual: Investigation, Methodology, Writing – re­ view & editing. Emilio Alba: Conceptualization, Funding acquisition, Investigation, Resources, Supervision, Writing – original draft, Writing – review & editing. Iñaki Comino-Méndez: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Method­ ology, Project administration, Resources, Software, Supervision, Vali­ dation, Visualization, Writing – original draft, Writing – review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability All data generated or analysed during this study are included in this published article. Acknowledgements We would like to express our gratitude to all women who partici­ pated in this study. 9
