Little is known about the preanalytical factors that could affect the tumor mutational burden score from next‐generation sequencing‐based multiplex gene panels, a promising biomarker to predict response to immune checkpoint inhibitors. This article investigates the association.
Keywords: Mutational burden, Next‐generation sequencing, DNA quality, Immune checkpoint inhibitors
Abstract
Background.
Tumor mutational burden (TMB) measured via next‐generation sequencing (NGS)‐based gene panel is a promising biomarker for response to immune checkpoint inhibitors (ICIs) in solid tumors. However, little is known about the preanalytical factors that can affect the TMB score.
Materials and Methods.
Data of 199 patients with solid tumors who underwent multiplex NGS gene panel (OncoPrime), which was commercially provided by a Clinical Laboratory Improvement Amendments‐licensed laboratory and covered 0.78 megabase (Mb) of capture size relevant to the TMB calculation, were reviewed. Associations between the TMB score and preanalytical factors, including sample DNA quality, sample type, sampling site, and storage period, were analyzed. Clinical outcomes of patients with a high TMB score (≥10 mutations per megabase) who received anti‐programmed cell death protein 1 antibodies (n = 22) were also analyzed.
Results.
Low DNA library concentration (<5 nM), formalin‐fixed paraffin‐embedded tissue (FFPE), and the prolonged sample storage period (range, 0.9–58.1 months) correlated with a higher TMB score. After excluding low DNA library samples from the analysis, FFPE samples, but not the sample storage period, exhibited a marked correlation with a high TMB score. Of 22 patients with a high TMB score, we observed the partial response in 2 patients (9.1%).
Conclusion.
Our results indicate that the TMB score estimated via NGS‐based gene panel could be affected by the DNA library concentration and sample type. These factors could potentially increase the false‐positive and/or artifactual variant calls. As each gene panel has its own pipeline for variant calling, it is unknown whether these factors have a significant effect in other platforms.
Implications for Practice.
A high tumor mutational burden score, as estimated via next‐generation sequencing‐based gene panel testing, should be carefully interpreted as it could be affected by the DNA library concentration and sample type.
Introduction
Immune checkpoint inhibition is a novel therapeutic approach that could lead to durable antitumor effects in various tumor types [1], [2], [3]. However, most unselected patients fail to respond to immune checkpoint inhibitors (ICIs), and many researchers are trying to develop reliable biomarkers that help physicians to predict responders to ICIs.
Tumor mutational burden (TMB) is defined as the total number of mutations per coding area of a tumor genome. TMB scores greatly differ among tumor types, ranging from 0.001 to >400 mutations per megabase (Mb) [4], [5]. A large TMB range is reported within the same tumor type. Reportedly, several epidemiological factors (e.g., exposure to ultraviolet light and smoking history) or genetic factors (e.g., deficiency of mismatch repair [MMR] gene or POLE/POLD1 gene) can increase TMB [6], [7], [8]. Furthermore, the use of alkylating agents, such as temozolomide, can cause DNA damage and increase TMB [8].
Among patients with melanoma and lung cancer, those with a higher TMB score showed better response to ICIs [9], [10], [11], [12], [13], [14], [15], [16]. Pooled data from published studies have also demonstrated a positive correlation between the TMB score and the objective response rate to ICIs in various tumor types [17], [18], [19], [20].
Theoretically, whole‐exome sequencing (WES) is the best method to measure the TMB score; however, it is not commonly used in daily clinical practice because of its labor‐ and cost‐consuming procedure. Conversely, studies have established that TMB estimated by next‐generation sequencing (NGS)‐based multiplex gene panels markedly correlates with TMB measured by WES [7], [21] and is adequately precise to estimate the response to ICIs [7], [22], [23], [24]. Hence, the TMB score from multiplex gene panels is garnering considerable attention as a promising biomarker to predict the response to ICIs.
However, little is known about the preanalytical factors that could affect the TMB score estimated by this method. In this study, we investigated the association between the TMB score and preanalytical factors, including sample DNA quality, sample type (formalin‐fixed paraffin‐embedded [FFPE] or fresh frozen tissue [FF]), sampling site, and sample storage period. Additionally, we retrospectively investigated the efficacy of ICIs in patients with solid tumors who had a high TMB score.
Materials and Methods
Patients
Data of 213 consecutive patients with histopathologically confirmed solid tumors who underwent NGS‐based multiplex gene panel (OncoPrime) at Kyoto University Hospital between April 2015 and April 2018 were analyzed. The major indications for this assay were rare tumors, cancers of unknown primary site, and cancers refractory to standard chemotherapy. The use of both clinical and genomic data for the research followed the tenets of the World Medical Association's Declaration of Helsinki and was approved by the Ethics Committee of the Graduate School of Medicine, Kyoto University (approval number: G692).
NGS‐based multiplex gene testing
OncoPrime is a hybrid capture‐based NGS gene panel assay with a total capture size of 1.33 Mb, covering the entire coding region of 215 genes and the rearrangement of 17 selected genes with clinical or preclinical relevance; this panel is commercially available, and >10 institutions use this panel in daily clinical practice in Japan. NGS is performed in a Clinical Laboratory Improvement Amendment (CLIA)‐certified laboratory using Illumina HiSeq 2500 (EA Genomics; Morrisville, NC), as previously described [25]. Supplemental online Table 1 presents a gene list. Of note, microsatellite instability (MSI) status cannot be evaluated in this panel. The minimum/standard input DNA quantity for creating libraries was 150 ng per sample, and the median depth of coverage in the sequencing assay was >3,000.
Assessment of Tumor Mutational Burden
VarPROWL is used for all OncoPrime tests [26]. Briefly, VarPROWL builds a statistical model at runtime, adaptively adjusting for the current data set. Each read direction is modeled independently using a beta‐binomial assumption, and logistic regression is used to estimate sequencing error rates using various informative metrics. In addition, variant quality is independently characterized using another set of metrics, modeled using germline variant positions. The software also accounts for mappability or uniqueness of the reference genome to represent false positives arising from poor mapping. A position is considered as variant if the following two conditions are fulfilled: the likelihood of observing the alternate allele is much higher than the error rate at that position for variants with allele frequency <30% (p < .001) and the alternate allele is supported by forward and reverse strands in similar proportions (strand bias <0.30). Only bases with the base quality of ≥20 coming from reads of mapping quality ≥20 were used for variant calling analyses.
In this study, the TMB score was evaluated as follows (supplemental online Fig. 1). Step 1: All base substitutions (excluding silent mutations) and indels in the coding region of the targeted genes were initially counted. Step 2: All nonreference alleles that appeared in >1% of the population (high minor allele frequency) were removed, as these are likely germline events. Step 3: All nonreference alleles with allele frequencies <4% and >95% were removed because our limit of detection was 4%, and allele of >95% frequency is most likely germline, as the sample material has at least 20% tumor content. We used public databases 1000 Genomes (http://www.1000genomes.org/), ExAC (http://exac.broadinstitute.org/), and ESP6500 (http://evs.gs.washington.edu/EVS/) at this step and accessed at the date when the sequencing results of each assay were reported. Step 4: The remaining number of mutations was divided by the size of the coding region of the targeted genes (0.78 Mb).
Based on the TMB score, patients were divided into the following two groups: low TMB (<10 mutations per Mb) and high TMB (≥10 mutations per Mb), as described previously [7], [8], [16], [17], [24]. After excluding 14 patients who failed the NGS assay, TMB scores obtained from 199 patients were available for this analysis.
Assessment of Tumor DNA Library Concentration
The tumor DNA library was quantified using KAPA Library Quantification Kits (PN KK4824), following the manufacturer's instructions. In brief, the tumor DNA library concentration was calculated as the size‐adjusted concentration (nM) using the following formula:
Tumor DNA library concentration = Average concentration × (Size of the DNA standard, bp / Average fragment size of the library, bp)
We obtained the calculated concentration of the 1:1600 dilution of the library, as determined by real‐time polymerase chain reaction, in relation to the concentrations of DNA standards. Next, using the Agilent TapeStation system (Santa Clara, CA), we performed a size adjustment calculation to account for the difference in size between the average fragment length of the library and the DNA standard. Finally, we calculated the concentration of the undiluted library by taking account of the relevant dilution factor.
Treatment Using Immune Checkpoint Inhibitors
The molecular tumor board (MTB) discussed the use of anti‐programmed cell death protein 1 (PD‐1) antibodies for patients with a high TMB score (≥ 10 mutations per Mb), and treating physicians offered this treatment to the patient if the MTB judged its use as reasonable. A total of 22 patients with a high TMB score agreed to undergo treatment with anti‐PD‐1 antibodies; the off‐label use of the anti‐PD‐1 antibodies was approved by the Ethics Committee of the Graduate School of Medicine, Kyoto University. Because the originally approved dose and schedule of nivolumab for melanoma was 2 mg/kg administered every 3 weeks in Japan, we adopted the same dosage and schedule for 21 patients; one patient was treated with pembrolizumab (2 mg/kg every 3 weeks). Clinical data were retrieved using a prospective cohort database system (CyberOncology; Cyber Laboratory Inc., Tokyo, Japan) and electronic medical records.
Statistical Analysis
The relationship between the TMB score and preanalytical factors, including the sample DNA quality, sample type (FFPE or FF), sampling site, and sample storage period were analyzed. The association between the TMB score and the effect of radiation therapy was also investigated. Using the nonparametric Mann‐Whitney U test, we examined differences in TMB between the two groups. Furthermore, the Fisher's exact test was used to compare proportions between the two groups, and correlations were examined by the Spearman correlation method. In this study, all reported p values are two‐sided, and p values of <.05 are considered statistically significant.
Responses of patients who received treatment with anti‐PD‐1 antibodies were assessed at the physician's discretion based on the RECIST version 1.1. Overall response rate (ORR; the proportion of patients with partial or complete response to therapy), progression‐free survival (PFS; the duration from the initiation of anti‐PD‐1 therapy until disease progression or patient's death from any cause), and overall survival (OS; the duration from the initiation of anti‐PD‐1 therapy until the patient's death) were calculated. Patients were censored on the day of their last follow‐up visit for PFS and OS if they had not progressed or died, respectively. The data cutoff date was April 30th, 2018. All data were analyzed using IBM SPSS Statistics for Windows, version 20 (IBM Corp., Armonk, NY).
Analysis of Mutation Calls Associated with TMB Elevation
To investigate potential factors related to the increased TMB, we integrated all variant call data and filtered mutations within population database such as 1000 Genomes and compared the distribution of allele frequency and depth of detected mutations between samples with very high TMB (>50 mutations per Mb) and the other samples. Analysis was performed by R version 3.5.3 and Rstudio version 1.2.1335 (RStudio Inc., Boston, MA), with the following packages “tidyverse,” “stringr,” “extrafont,” “ggplot2,” and “data.table.”
Results
Patient Characteristics
Patient characteristics are summarized in Table 1. The median age was 58.6 years (range, 8–82). Most patients (86.5%) had solid tumors refractory to standard chemotherapy, whereas the remaining had cancers of unknown primary site (9.0%) or rare tumors (4.5%). The most common solid tumor types tested were pancreatic (n = 36; 18%), followed by colorectal (n = 25; 12.6%) and biliary tract cancers (n = 20; 10%). Most samples were extracted from FFPE sections (n = 158; 79.4%) and were archived within 5 years from the date of sequencing (n = 195; 97.5%). The median TMB was 10.4 mutations per Mb, and 52% of the samples showed a high TMB (≥10 mutations per Mb). The median variant allele frequency was 0.0515 for samples with high TMB and 0.0642 for the other samples.
Table 1. Patient characteristics.
The number of cases with mutations related to deficient mismatch repair are shown in the parentheses.
Abbreviations: FF, fresh frozen tissue; FFPE, formalin‐fixed paraffin‐embedded tissue; TMB, tumor mutational burden.
In this cohort, eight patients harbored mutations related to MMR deficiency [PMS2 (n = 6), MLH1 (n = 1), and MSH2 (n = 1)]. Of these, 50% of patients had TMB score ≥10 mutations per Mb. No patient exhibited POLE/POLD1‐mutated tumors.
Association Between the TMB Score and Sample Characteristics
First, we tested the association between the TMB score and the concentration of the DNA library, which reflects the quality of the extracted DNA. As shown in Figure 1A, the TMB score was significantly higher when the concentration of the DNA library was <5 nM (p < .01). In contrast, all 41 cases with a TMB score >50 mutations per Mb had a DNA library concentration of <5 nM (Fig. 1B). Because the TMB score greatly fluctuated in the left end of the plot in Figure 1A, we selected 5 nM as the cutoff value for a low DNA library concentration.
Figure 1.
The TMB score was higher in samples that yielded a DNA library concentration of <5 nM. (A): The TMB score was higher in samples that yielded a DNA library concentration of <5 nM. The vertical axis (log‐scaled) shows the number of mutations per megabase. The medians, interquartile ranges, and minimum and maximum values are shown in boxplots. (B): All samples with a TMB score >50 mutations per Mb were classified as having a low DNA library concentration. The medians, interquartile ranges, and minimum and maximum values are shown in boxplots. n = 199, *p < .001.
Abbreviation: TMB, tumor mutational burden.
Regarding sample type, the TMB score was significantly higher in the FFPE samples than in the FF samples (median 11.7 vs. 5.2 mutations per Mb, p < .01). Because the proportion of the samples with a DNA library concentration of <5 nM was also higher in the FFPE samples than in the FF samples (39.2% vs. 5.0%, p < .01), we limited the analysis to 134 samples with library DNA concentrations of ≥5 nM. The difference was smaller but still statistically significant under this condition (median 9.1 vs. 5.8 mutations per Mb, p < .05; Fig. 2A).
Figure 2.
TMB scores in different sample types and sampling sites. (A): The TMB score was higher in the FFPE samples than in the FF samples. The medians, interquartile ranges, and minimum and maximum values are shown in boxplots. The vertical axis (log‐scaled) shows the number of mutations per megabase. *p < .05, **p < .01. (B): The TMB score did not differ between samples obtained from primary and metastatic sites. The medians, interquartile ranges, and minimum and maximum values are shown in boxplots.
Abbreviations: FF, fresh frozen; FFPE, formalin‐fixed paraffin‐embedded; TMB, tumor mutational burden.
The TMB score did not differ between the tumor sampling sites, with a median TMB score of 11.7 mutations per Mb in 117 primary sites compared with a score of 9.1 mutations per Mb in 82 metastatic sites. This result remained the same even when we repeated the analysis with the 134 samples that yielded ≥5 nM of library DNA (Fig. 2B).
Next, we analyzed the association between the TMB score and the sample storage period, which was calculated from the date of sampling via biopsy or surgery to the date when the samples were submitted for NGS. Four samples had been archived >5 years prior to NGS assay, and all of those yielded DNA library concentrations of <5 nM (range; 1.17–3.26). After excluding these four samples as outliers, a positive correlation was observed between the TMB score and the sample storage period (r = 0.39, p < .001, n = 195; Fig. 3A). However, if we limited the analysis to 134 samples with library DNA concentrations of ≥5 nM, statistical significance disappeared (r = 0.14, p = .10; Fig. 3B).
Figure 3.
Association between the TMB score and sample storage period. (A): The TMB score was positively correlated with the storage period when all 195 samples were analyzed (r = 0.39, p < .001). (B): No correlation existed between TMB and sample storage period in 134 samples with library DNA concentrations ≥5 nM. The vertical axis (log‐scaled) shows the number of mutations per megabase.
Abbreviation: TMB, tumor mutational burden.
Eight esophageal cancer samples with DNA library concentrations of ≥5 nM were analyzed to investigate the effect of radiation therapy on the TMB score. Three samples had been obtained from lesions within the previous radiation field; their TMB scores were significantly higher compared with samples from the nonradiation field (median 15.6 vs. 5.2 mutations per Mb, p = .02; Fig. 4A).
Figure 4.
Effect of radiation therapy or chemotherapy on the TMB score. (A): The TMB score was higher in esophageal cancer samples that were previously exposed to radiation (n = 8). The medians, interquartile ranges, and minimum and maximum values are shown in boxplots. *p < .05. (B): There was no association between the number of chemotherapy regimens and TMB score.
Abbreviation: TMB, tumor mutational burden.
As the use of anticancer drugs, such as temozolomide, could cause DNA damage and increase TMB [8], we investigated the correlation between TMB and the prior number of chemotherapy regimens. In contrast to our expectation, TMB was not affected by the number of chemotherapy regimens (Fig. 4B). Only one patient had a history of temozolomide chemotherapy, but TMB of this patient was not significant (5.19 mutations per Mb).
Efficacy of Anti‐PD‐1 Antibodies on Patients with a High TMB Score
Supplemental online Table 2 summarizes the characteristics of the 22 patients. We included two patients with mutations related to MMR deficiency‐related genes: patient 6 (PMS2, breast cancer) and patient 7 (MLH1, pancreatic cancer). The median patient age was 65 years (range, 36–83), and the median TMB score was 36.3 mutations per Mb (range, 10.4–4,443.2). Nivolumab was administered in 21 patients, and pembrolizumab was administered in one patient. The median PFS was 2.5 months (range, 0.4–19.7), whereas the median OS was 4.3 months (range, 0.6–29.8).
Tumor response assessment revealed that partial response (PR) was observed in two patients (9.1%) and stable disease (SD) was observed in five (22.7%; Fig. 5).
Figure 5.
Progression‐free survival and survival time after disease progression in patients who harbored high TMB scores and received anti‐programmed cell death protein 1 antibodies. The TMB score (mutations per Mb) of each patient are shown in the parenthesis.
Abbreviations: NE, not evaluable; PD, progressive disease; PR, partial response; SD, stable disease; TMB, tumor mutational burden.
Discussion
The TMB can be theoretically determined via whole‐exome sequencing; however, this method is not commonly used in daily clinical practice, as it is a labor‐ and cost‐consuming procedure. Previous studies have shown that the TMB can be inferred by analyzing a multiplex NGS panel comprising several hundred genes (170–468 genes) covering approximately 0.52–1.2 Mb of the coding genome [7], [27], [28], [29], [30]. Our NGS panel (OncoPrime) covers 0.78 Mb of the entire coding region of 215 genes, which, in terms of gene number and the span of the coding region, is comparable to the multiplex NGS panels mentioned above.
In this study, we set 10 mutations per Mb as the cutoff value for a high TMB score based on the published data [7], [31]. Recent clinical trials (CheckMate 227 and 568) also supported the utility of this cutoff value for selecting responders to ICIs, irrespective of the PD‐L1 expression levels of tumors in lung cancer [16], [24]. Furthermore, a segmental linear regression analysis conducted using the data of mutation burdens from 81,337 patients revealed that 10 mutations per Mb is the threshold for a high TMB score [8]. In the future, the establishment of the standard cutoff value for a high TMB score is eagerly awaited.
This study primarily aimed to investigate whether any preanalytical factors could affect the TMB score. The quality of the extracted tumor DNA was found to be the most relevant factor for evaluating the validity of the TMB score measured via NGS‐based multiplex gene panel. In this study, the median TMB score was significantly higher when the concentration of the DNA library was <5 nM. It is well known that poor DNA quality could increase the incidence of false‐positive mutation calls [32], [33].
The tumor DNA quality was also influenced by the sample type (FFPE or FF). In the current study, 95% (39/41) of the FF samples yielded a DNA library concentration of >5 nM, whereas only 60.4% (97/159) of the FFPE samples satisfied this condition. Even after limiting the analysis to the 134 samples with DNA library concentrations of ≥5 nM, the TMB score was significantly higher in the FFPE samples (median, 9.1 vs. 5.8 mutations per Mb; p < .05). These observations corroborated with those reported in previous studies demonstrating that DNA degradation is more evident in FFPE samples than in FF samples owing to the fragmentation and chemical modification of DNA [34], [35], [36], [37], [38]. The sample storage period also reportedly influences DNA degradation in FFPE samples [39], [40], [41]. In the current study, the sample storage period was inversely correlated with the DNA library concentration (r = ‐0.4, p < .01; supplemental online Fig. 2), and all FFPE samples archived >5 years before sequencing yielded DNA library concentrations of <5 nM. Although the time in formalin before processing into paraffin‐embedded blocks could also affect the TMB score, information about this parameter was not available in our cohort.
Consistent with the impact of low DNA library concentration on the TMB score, we observed a high degree of variability in TMB estimates in samples with low DNA yield (Fig. 1A). Further analysis revealed an increase of mutations with a low‐frequency allele in very high TMB samples (TMB >50 mutations per Mb) compared with others (supplemental online Fig. 3). Perhaps, a majority of these calls on low‐frequency allele derived from false‐positive and/or artifactual variant calls. The depth of coverage was similar in the samples with very high TMB and other samples (supplemental online Fig. 4).
The possibility of false‐positive results was also suggested from the estimated percentage of samples that were expected to have a high TMB score based on particular tumor types in our cohort. Based on a previous study regarding the fraction of high TMB in various types of cancer (>20 mutations per Mb by definition), the expected percentage of samples with high TMB score was 5.2% [7], whereas it was 29.1% in our report. However, when we only counted the samples with library DNA concentrations of ≥5 nM, the percentage was 4.5% (9/199), which was comparable to the expected value.
Although our sample size was limited, our data indicated that the history of radiation therapy also affects the TMB score. The TMB score was significantly higher in the samples obtained from esophageal cancers exposed to radiation (Fig. 4). Because radiation can induce DNA damage, our results are reasonable and should be further confirmed in a larger cohort.
In 22 patients with high TMB scores who were treated with anti‐PD‐1 antibodies, the overall response rate (ORR) was 9.1%, and the disease control rate was 31.8%, with three patients having survived for >16 months. In our cohort, the ORR was lower than that reported by Goodman et al. (ORR, 26% in tumors with TMB ≥6 mutations per Mb) [19]. Of 15 nonresponders in this cohort, 11 (73.3%) exhibited a low DNA library concentration, indicating that high TMB could be attributable to false‐positive and/or artifactual variant calls. In line with this idea, if we limited the same analysis to 10 cases with library DNA concentration ≥5 nM, the ORR was 20% and the DCR was 60%, which corroborated published data (Fig. 5).
Our study has several limitations. First, as this was a retrospective analysis, we could not assess the correlation between the TMB score and other parameters of the DNA quality (e.g., Q‐value, ΔCt), that could be monitored at an earlier step. Second, our NGS assay could not ascertain the microsatellite instability high (MSI‐H) status, and we could not assess its effect on the TMB score. Reportedly, MMR‐deficient tumors are relatively common in cancer of the endometrium, stomach, small intestine, colon and rectum, cervix, prostate, bile duct, and liver; however, even in these cancer types, the proportion of MSI‐H tumor comprises <4% of stage IV disease [42]. Hence, we consider that the number of latent MSI‐H tumor in our cohort is relatively limited, and its effect on the TMB score will be negligible in this study.
Finally, our current observations might be only applicable to specific assays. The comparison with another CLIA‐certified gene panel (440 target genes) using 15 samples revealed that the TMB scores were not significantly different between the two panels (Wilcoxon signed‐rank test, p = .12; supplemental online Table 3). However, we did not compare the OncoPrime‐based TMB against other reference methods, such as whole‐exome‐sequencing‐based TMB. As each approach has its own pipeline for variant calling and filtering, the involvement of preanalytical factors demonstrated in this study should be assessed in other platforms.
Conclusion
Our results suggest that the TMB score estimated via NGS‐based gene panel can be affected by the DNA library concentration and sample type. Thus, a high TMB score derived from samples with poor quality DNA should be carefully interpreted.
See http://www.TheOncologist.com for supplemental material available online.
Acknowledgments
We thank E. Sasaki and K. Ashida for their excellent technical assistance and secretarial help. We also deeply thank all patients, clinicians, and medical staff who participated in this research.
Author Contributions
Study concept and design: Pham Nguyen Quy, Masashi Kanai
Provision of study material or patients: Masashi Kanai, Tadayuki Kou, Tomohiro Kondo, Junichi Matsubara, Yoshihiro Yamamoto, Kyoichi Takaori, Toshihiko Masui, Yuji Eso, Hiroshi Seno, Shigemi Matsumoto, and Manabu Muto
Collection and/or assembly of data: Pham Nguyen Quy, Masashi Kanai, Tadayuki Kou, Tomohiro Kondo
Data analysis and interpretation: Pham Nguyen Quy, Masashi Kanai, Sachiko Minamiguchi, Akinori Hiroshima, Tomohiro Sakuma, Hiroaki Mochizuki, Keita Fukuyama, Mayumi Kamada, Masahiko Nakatsui
Manuscript writing: Pham Nguyen Quy, Masashi Kanai
Final approval of manuscript: Tadayuki Kou, Tomohiro Kondo, Yoshihiro Yamamoto, Junichi Matsubara, Akinori Hiroshima, Hiroaki Mochizuki, Tomohiro Sakuma, Mayumi Kamada, Masahiko Nakatsui, Yuji Eso, Hiroshi Seno, Toshihiko Masui, Kyoichi Takaori, Sachiko Minamiguchi, Shigemi Matsumoto, Manabu Muto
Disclosures
Hiroaki Mochizuki: Mitsui Knowledge Industry Co., Ltd. (E); Tomohiro Sakuma: Mitsui Knowledge Industry (E); Manabu Muto: Mitsui Knowledge Inc, Sysmex, Riken Genetics (RF). The other authors indicated no financial relationships.
(C/A) Consulting/advisory relationship; (RF) Research funding; (E) Employment; (ET) Expert testimony; (H) Honoraria received; (OI) Ownership interests; (IP) Intellectual property rights/inventor/patent holder; (SAB) Scientific advisory board
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