Importance of EQA/PT for the detection of genetic variants in comprehensive cancer genome testing

Importance of EQA/PT for the detection of genetic variants in comprehensive cancer genome testing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Importance of EQA/PT for the detection of genetic variants in comprehensive cancer genome testing Kazuyuki Matsushita, Takayuki Ishige, Kousuke Watanabe, Toshiaki Akahane, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5189991/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Comprehensive genomic profiling (CGP) is increasingly used as a clinical laboratory test and being applied to cancer treatment; however, standardization and external quality assessments (EQA) have not been fully developed. This study performed cost-effective EQA and proficiency tests (PT) for CGP testing among multiple institutions those belong to the EQA working group of Japan Association for Clinical Laboratory Science (JACLS). This study revealed that preanalytical processes, such as derived nucleic acids (NA) extraction from formalin fixed paraffine embedded (FFPE) samples, are critical. First, EQA with extracted DNA from cell lines showed a detection rate of 100% (9 out of 9) in KRAS (c.38G > A; p.G12D), PIK3CA (p.H1047R), and B-Raf proto-oncogene, serine/threonine kinase ( BRAF ) (c.1799T > A; p.V600E) in cases of > 10% variant allele frequency (VAF). However, BRAF (c.1799T > A; p.V600E) detection decreased to 67% (6 out of 9) for a VAF of 4.9%. Second, when DNA was extracted from FFPE samples, pathogenic variants or companion diagnostics were detected in all 10 participating laboratories. Each variant had < 20% VAFs on average (8.1–19.1%) and wide variability among laboratories was observed (relative standard deviation, 13–60%). Nonetheless, BRAF (c.1798_1799delinsAA; p.V600K) of 8.1% VAF, EGFR (c.2235_2249del; p.E746_A750del) of 9.7% VAF, and EGFR (c.2254_2277del; p.S752_I759del) of 9.8% VAF were detected with 70% (7/10), 70% (7/10), and 60% (6/10) probability, respectively. Therefore, 10% VAF in pre-analytic processing for DNA extraction from FFPE is critical for variant detection in CGP analysis. Further, incorrect results were reported in case independent variant calling of BRAF; c.1798_1799delinsAA (p.V600K) was interpreted as c.1798G > A, and c.1799T > A was on the other allele. In conclusion, the EQA/PT among 10 institutes with common samples revealed the importance of VAF in pre-analysis and helped us understand the significance of the pipeline and common pitfalls usually ignored by the internal quality control in a single institute. Biological sciences/Biochemistry Biological sciences/Biological techniques Biological sciences/Biotechnology Biological sciences/Cancer Health sciences/Medical research External quality assessment (EQA) Proficiency testing (PT) genetic variants comprehensive cancer genome testing NGS Figures Figure 1 Figure 2 Introduction In genomic medicine, clinical tests have a wide range of roles, applications and expectations. Liquid biopsies, CGP testing for hematopoietic tumors, whole exon analysis for intractable and rare diseases, whole genome analysis, etc. are rapidly expanding techniques in actual clinical practice 1 – 6 . To achieve precise medicine, EQA/PT for genetic-related testing in clinical settings is necessary 7 – 10 . Genetic-related tests in clinical laboratories include three categories: nucleic acid (NA) testing from pathogenic microorganisms, human DNA from somatic and germline cells. EQA/PT is required for the assessment of a laboratory’s NGS analysis before approval in all testing laboratories 11 . In this study, we aggregated and analyzed the results from various institutions and examined differences in results based on NGS equipment and reagents as well as on the detection rate for each genetic mutation. Further, we evaluated the characteristics depending on method and NGS platform. This allowed us to learn about the current state of cancer gene panel testing in Japan and allowed each participating facility to discuss the results. This study focused performed the alternative cost-effective EQA/PT on human DNA from somatic cells in 10 laboratory institutions with DNA extracted from cancer cell lines and commercially available FFPEs of simulated tumor (T)/non-tumor (N) matched-pair samples for standardization of gene-related tests such as CDx in the clinic. Furthermore, gene determination may differ depending on the analysis pipeline used, oversight when multiple variants exist in the same gene, and the limit of detection (LOD), VAF with a detection rate of 95%, may differ depending on the VAF setting at the facility. The present results aim to assist in recognizing possible pitfalls in CGP testing and in the standardization of NGS analysis. Together, EQA/PT procedures in laboratories will promote high-quality genomic medicine. Results EQA/PT design and protocol Representative CGP tests consist of three steps: pre-analytic (sampling and NA extraction), analytic (library preparation and DNA sequencing), and post-analytic (Fig. 1 A) 12 , 13 . Therefore, this study designed two independent EQA/PT (Fig. 1 A, B). With respect to the EQA, CGP testing methodology was compared among five university hospital laboratories and five company-laboratories in Japan (Table S1 ). NA was extracted from several cancer cell lines and its quality, yield, as well as the detection rate of CDx genes at each facility were examined and compared for a 1st EQA in 2022. The 2nd EQA was performed in 2023; the department of laboratory medicine in Chiba University Hospital delivered simulated paired matched T/N FFPE samples, and each institute (Laboratories A–C, E–G, I, and J) extracted DNA/RNA and performed the following steps on their own (Fig. 1 B). First, to compare analysis and post-analysis, EQA/PT was performed with delivered NAs of cancer cell lines. There was no difference in the detection frequency of pathogenic variants among all facilities, and the LOD, VAF with a detection rate of 95%, was approximately 6–6.1% for NAs of cancer cell lines and 10.8–10.9% for FFPE samples. Next, EQA/PT was performed from NAs extraction from FFPE as a pre-analysis process by simulated paired tumor and non-tumor (T/N) of formalin fixed paraffin embedded (FFPE) specimens. The limit of detection (LOD) of pathogenic gene variants was 6 to 6.1% VAF in cancer cells For the 1st EQA, five genomic DNA samples of cancer cell lines (1 reference and 4 test samples; Table S2) were prepared by the EQA organizer (Laboratory D in Chiba University) and delivered to the other nine laboratories (Laboratories A–I; Table S1 ). Analytic process in the participated laboratories included library preparation, DNA sequencing, and bioinformatics analysis (Fig. 1 A). NGS system, library preparation procedures, reagents for examination, reference human genome version (GRCh37/hg19 or GRCh38/hg38), and sensitivities of SNV/Indel (Table S1 ). In addition, gene name, type of variants in human genome variation society nomenclature, VAF%, and %RSD were calculated (Table 1A). For example, pathogenic variant of cell No.2, % VAF, mean, % RSD and %Detection (N) of KRAS : c.38G > A (p.G12D) were 11.7%, 11%, and 100% (9/9), respectively (Table 1A). In four test samples, the CaCO2 mixed with K562 (ratio of 3:1) sample had no variants in BRAF, EGFR, KRAS, NRAS , and PIK3CA genes (Table S2). In case of BRAF : c.1799T > A (p.V600E), % VAF, mean, % RSD and %Detection (N) were 4.9%, 16%, and 67% (6/9) in cell No.3 whereas 12.5%, 8%, and 100% (9/9) in cell No.4. In BRAF : c.1799T > A (p.V600E) in cell No3, that 3 out of 9 were not reported this variant, however, these 3 institutes confirmed the detection of this variant in Binary Alignment/Map (BAM) files. These results indicated that 5% VAF is the limit of detection (LOD) for the participating laboratories. DNA extraction from FFPE is critical for the VAF of CDx genes Laboratory D in Chiba University delivered simulated paired matched Tumor (T)/non-tumor (N) FFPE samples to each institute (Laboratories A-C, E-G, I, and J) and they extracted DNA by their own procedures (Fig. 1 B). For the 2nd EQA, sufficient DNA yields were obtained from T/N matched-pair samples by NA extraction. These simulated matched-pair samples were essential for the correct evaluation of NCC Oncopanel™ which using the matched-pair analysis technique, allowing the same procedures to be operated as for FFPE human clinical specimens during the post-analysis process and ensuring the EQA could be performed accurately. Regarding comprehensive genomic testing using NGS, detailed EQA/PT data for the curation of analytical processes is summarized in Table 1B. FFPE samples had more comprehensive hotspot variants in AKT, BRAF, EGFR, KIT, KRAS, NRAS , and PIK3CA (Table 1B). Most pathogenic or companion variants were accurately detected in the 10 participating laboratories. Each variant had < 20% VAFs on average (8.1–19.1%) and there was wide variability among labs (%RSD ranged 13–60%). Variants with low detection rate (< 80%) among labs were BRAF : c.1798_1799delinsAA (p.V600K), EGFR : c.2235_2249del (p.E746_A750del), and EGFR : c.2254_2277del (p.S752_I759del), all with < 10% VAFs. Notably, two laboratories reported BRAF V600M but not V600K. Incorrect results were probably reported because the independent variant calling of BRAF; c.1798_1799delinsAA (p.V600K) was interpreted as c.1798G > A and c.1799T > A on the other allele (Figure S1 A–C). Thus, these results were affected not only by sensitivity but also by variant call accuracy. Correlation analysis of VAFs showed good correlation between laboratories where the same instrument and reagents were used: the Spearman’s rho of Lab A and B was 0.75 (instrument, Thermo; method, amplicon); Lab F and G was 0.87 (instrument, Illumina; method, capture; Fig. 2 , Table S1 ). As compared with amplicon and capture methods, no significant differences were observed in variant frequency (Figure S2). The 11% VAF affected the detection rate of gene variants depending on DNA extraction procedures from FFPE The probit regression analysis showed that 10.8–10.9% VAF was required for achieving a 95% detection rate, LOD, among laboratories (Figure S3B). The relationship and difference between % VAF and detection rate is indicated in 1st EQA and 2nd EQA (Fig. 1 B, Table 1). In the 1st EQA with DNA from cancer cell lies, 100% detection was achieved for VAF > 5–10% (Table 2A). However, in the 2nd EQA: the detection rate was 100% for VAF > 15%. These results indicated that VAF detection significantly relates to DNA quality and amount. Furthermore, cDNA library preparation for NGS analysis affected VAF depending on genetic variants. In case of 1st EQA, there was no significant difference in VAF in terms of genetic variants between amplicon (Labs A–E) or capture (Labs F–J) methods for library preparation (Table S1 ). However, in the 2nd EQA, the VAF of deletion variants differed between amplicon and capture methods in certain sequences (Fig. S2). Based on our findings, we hypothesize that in case of deletion variants, the PCR efficacy will increase; however, capture efficacy will decrease depending on the deleted sequence. Further research is needed to corroborate this hypothesis. Therefore, pre-analytic DNA’s preparation for cDNA library was critical for the standardization of CGP tests. In ordinal clinical testing in the clinical laboratories whether in the hospital or companies, once we have established the initial analytic pipelines, if there are no problems with validation tests, we will continue testing without making any changes. Therefore, based on the results obtained from this study, we hope that conducting EQA/PT will provide an opportunity to review the pipeline to deal with pitfalls that are usually not noticed. In this project, we used nucleic acids extracted from cell lines and simulated T/N pair specimens to blindly examine inter-laboratory differences in companion diagnostics (CDx) genes’ detection (Fig. 1 A, B)such as AKT1, BRAF, EGFR, KIT, KRAS, NRAS, and PIK3CA (Table 1). Read depth (variant caller) affected the interpretation of BRAF V600 variants Precise detection of BRAF V600 pathogenic variants is important for molecular target therapy for solid tumors 14 . In case of BRAF : c.1798_1799delinsAA (p.V600K), % VAF, mean, % RSD and %Detection (N) were 8.1%, 18%, and 70% (7/10). Similarly, EGFR : c.2235_2249del (p.E746_A750del), % VAF, mean, % RSD and %Detection (N) were 9.7%, 28%, and 70% (7/10) and c.2254_2277del (p.S752_I759del) were 9.8%, 60%, and 60% (6/10) (Table 2). Significantly, the TT variant of BRAF V600K exists in the same allele but could be incorrectly determined as V600M (Fig. 2 A). The %VAF of each genetic change, equipment used, library preparation method, human reference genomes, and DNA sequencing reagents information are summarized in Figure S1 C. The difference in the judgment of BRAF V600E may be due to the different Variant Callers used (GATK and Verscan; Fig. S1 C). Additionally, if variants are detected within the same gene, it is difficult to detect them both simultaneously. The reason why the difficulty to discriminate the allele of the variant is that the pipelines created were different. Therefore, it is important to determine which is the most suitable pipeline (Fig. S1 C). Integrative Genomics Viewer (IGV) of BAM files were beneficial to reduce miscalling DNA sequences (Fig. S1 A). It was observed that genetic mutations used in companion diagnostics ( KRAS : c.34G > T (p.G12C) and NRAS : c.181C > A (p.Q61K) etc.) were detected by all ten participated institutes even though % VAF was less than 10% (Table 1). The reason of this discrepancy is that when multiple variants exist within the same gene, it may not be possible to detect them at the same time if their bases are close together. Due to the reference material used in this investigation, we included pathogenic variants in artificially closer locations. It is important not to overlook clinically important pathogenic variants and CDx in clinical examinations. However, it is not realistic for clinical testing to seek 100% accuracy through repeated checks, so it is important to share the best protocols within limited medical resources. If CGP testing will be first start up in the facility, EQA/PT will be able to notice pitfalls as performed in this project. Since it is not easy to modify a pipeline once created, it is important to perform EQA/PT in advance. In particular, objective evaluation should be conducted blindly. Furthermore, in the future, it would be advantageous in terms of cost if ethical issues could be resolved and EQA/PT using human clinical specimens could be implemented even on a small scale. The requirements of ISO 15189 require evaluation of the suitability of services provided by external parties (including those like this survey), so we believe it would be ideal to be able to choose from a variety of programs. It was surprising to see that the variation in VAF was large. I think it is suitable for evaluating the extent to which it is possible to detect alleles with a relatively low allele frequency of 5%. Regarding the variation in VAF, it is necessary to equalize the read amount (depth) of the sequences. Together, in case of high-quality DNA such as extracts of cell lines, % VAF of EQA/PT needs to 5–10%, however, low quality DNA specimens as extracts of FFPE, % VAF of EQA/PT needs to 15%. Correlation matrix among laboratories The analysis and post-analysis processes in the participating facilities included different library preparation methods, DNA sequence reagents (Manufacture), DNA sequencer (Instrument), Human Reference Genome, and Sensitivity (LOD; Table S1 ). We performed a comparison of genetic variants and %VAF for CDx testing at each facility (Table S3). Then, we created a correlation matrix between facilities, which indicated that the analysis and post-analysis influenced the detection results(Fig. 2 ). Accordingly, it was considered important for each facility to understand the characteristics of its own method when participating in EQA when conducting NGS analysis. Discussion In clinical practice, it is important that CDx are efficiently detected to provide the appropriate treatment 15 , 16 . So, accuracy control is important in cancer genome analysis, and EQA/PT is necessary for this purpose 17 . In this study, an EQA/PT among 10 clinical laboratories indicated that preanalytical processes, especially NA extraction, are critical for the quality of CGP tests. The content of this EQA closely resembles the requirements of ISO 15189. Genetic changes can be confirmed using an Integrative Genomics Viewer (IGV) by careful visual observation, but it is difficult to know how to modify the pipeline for those that cannot be detected in this pipeline. In addition, there were more differences in VAF among facilities than expected (Figure S2). This variability may be due to differences in capture or PCR efficiency, or in other experimental aspects, as well as differences in analysis pipelines. The degree of error likely depends on the specific variant to be detected 18 , partially due to methodological differences in the preparation of cDNA libraries. Furthermore, capture efficiency differs depending on the genomic region. While digital PCR is highly quantitative, it is unclear whether it can be considered as a standard. One of the advantages of this EQA study is that, using reference samples with known allele frequencies allows to assess the systematic bias in VAF across methods. The reliability of these reference samples needs to be ensured, as their VAF have been confirmed using both digital PCR and NGS. Therefore, it is very meaningful to confirm the difference from the actual allele frequency obtained using a standard substance with a known correct allele frequency, as in this EQA. In summary, if the number of repetitive EQA among laboratories is performed, it will tell us the degree of systematic error that occurs with a particular test method 19 . In addition, traceability is also important for company samples to see whether the allele frequency is truly correct, so that it is considered highly reliable 20 . Future research is needed to determine the extent to which this difference is based on the methods used. It is necessary to consider what to evaluate, including the effects of differences in depth and software (variant caller). The true answer is unknown in clinical human samples; therefore, it is necessary to consider what should be evaluated as a correct answer in an actual sample. For example, TMB and microsatellite instability were not included in the data requested for submission this time, it is important to look at them as well. Currently, artificial samples such as commercially available standard materials spiked with fragments with artificially introduced genetic mutations, or commercially available products subjected to various types of gene editing have been used to perform QA 21 . This is advantageous because it allows systematically introducing genetic mutations in advance and the ability to produce it in large quantities. However, samples produced in this way do not ensure compatibility depending on the measurement method (commutability) and have different properties from the samples used for routine analysis 20 . An ideal EQA program uses patient samples to ensure commutability. Ideally, a DNA sample from the FFPE or blood would be best. By doing this, it is desirable to eliminate the influence of FFPE location variations and NA extraction methods and investigate the process of NGS analysis only. However, at present, there seems to be no consensus on the use of human specimens for EQA outside of clinical research 22 , 23 . It is desirable that human specimens become available for EQA/PT in the future. Timely EQA and reviews of the analysis pipeline, including post-analysis 24 could improve testing quality and future advance genomic medicine. Further, sharing information on libraries for of analysis and quality indicators of sequence data (basic concept for ensuring the quality and accuracy of cancer gene panel tests) could improve test performance. Clinicians are confused not only by the difference in whether the reference sequence used this time is hg19 or hg38, but also by the variant form of the gene and the information in different databases 25 , 26 . There was a possibility that the situation would become easier. To overcome these discrepancies, it is necessary to align the notation method with MANE 27 in the future. Materials and methods Participants Ten clinical laboratories of five university hospitals (Chiba University, University of Tokyo, Kagoshima University, Mie university, and Fijita medical college) and five commercial companies in Japan (SRL, BML, RIKEN-Tsukiji, RIKEN-Kawasaki, and Sysmex Co. Ldt.) participated in this study (Table S1 ). Five laboratories used the amplicon method and the rest used the capture method for DNA detection. Manufactures of NGS sequencers were Thermo for four labs, Qiagen for one, and Agilent for five. DNA extraction from cell lines DNA samples were extracted from one leukemia cell line (K562) and four colorectal cancer cell lines (Caco2, HCT116, HT29, and RKO). DNA concentrations were adjusted to 25 ng/µL using a fluorometric assay. Then, EQA/PT samples were prepared by mixing the K562 cell line, used as baseline for matched pair analysis, and each colorectal cancer cell line at a 3:1 ratio (Fig. 2 ). Microsatellite instability and EGFR, KRAS, NRAS, BRAF, PIC3CA , and TP53 variants were examined as target genes of CDx. K562, human leukemia cell line, was used for standard that has no pathogenic variants in CDx genes. Sample preparation was performed by mixing cell lines with mutations. We conducted a simulated matched pair study in which “DNA derived from a cell line without the target gene mutation” was derived from normal cells, and “DNA derived from a cell line with the target gene mutation” was derived from tumor cells. Samples 1–4 were prepared at laboratory D and delivered to other EQA participated laboratories. Sample cell No. 1: No mutations detected in EGFR, KRAS, NRAS, BRAF, PIK3CA. Microsatellite status, stable. Sample cell No. 2: KRAS p.G13D mutation detected at approximately 12.5% and PIK3CA p.H1047R detected at approximately 12.5%. Microsatellite status, high. Sample cell No. 3: BRAF p.V600E mutation detected at approximately 6%. Microsatellite status, stable. Sample cell No. 4: BRAF p.V600E mutation detected at approximately 16.75%, and PIK3CA p.H1047R detected at approximately 12.5%. Microsatellite status, high. Evaluation genes, relatively common genes in solid tumors with variant hotspots: EGFR exons 18–21 KRAS codons 12, 13, 59, 61, 117, 146 BRAF codon 600 PIK3CA codons 542, 545, 1047 FFPE specimens Two specimens, Seraseq Compromised FFPE Tumor DNA Reference Material (0710–1492) and Seraseq Compromised FFPE WT (DNA/RNA) Reference Material (0710–1710), were purchased from SeraCare Life Sciences, MA, USA. EQA evaluation protocol Detected variants and their frequencies (% Variant allele frequency) were examined in a mutually blinded manner using each facility’s detection method. The criteria for determining whether a sample was appropriate when there is no match were: a match rate between facilities ≥ 80% was considered suitable, and < 80% inappropriate. Samples and PT evaluation methods for matched paired T/N FFPE samples FFPE reference cells carrying synthetic DNA constructs manufactured to more closely mimic the quality of patient tissue. Using commercially available reference materials (Seraseq® Compromised FFPE Tumor DNA RM, 0710–1492 and Compromised FFPE WT RM, 0710–1710; LGC Clinical Diagnostics; Teddington, Middlesex, TW11 0LY, UK) containing cells from GM24385 cell line with or without synthetic DNA constructs containing tumor variants, formalin-fixed and paraffin-embedded (FFPE) following a protocol mimicking the quality of patient tissue specimens. LGC Clinical Diagnostics have provided contrived samples for clinical genomics ring trials, EQAs and proficiency exercises worldwide, including GenQA and country-specific schemes. This time’s matched pair samples simulate "cell line-DNA, RNA)" with normal tissue-derived FFPE, and "cell line-derived nucleic acids (DNA, RNA) + multiple synthetic genes (spiked in)". Tumor tissue shall be FFPE. Since the artificial FFPE sample used in this study is a transparent pellet smaller than a typical clinical specimen, care had to be taken to ensure that the sample was not lost during extraction. DNA analysis (RNA analysis optional) were performed at all facilities. The test was to be conducted using typical methods (EQA/PT). NA extraction was performed using the method of each facility, and the results were read together on a later date. Statistical analysis Statistical analysis (probit and correlation analyses) was performed using R (version). Declarations Acknowledgments The authors appreciate the assistance provided by all participating staff members and laboratories. This EQA/PT was performed as clinical laboratory examination of the EQA working group of Japan Society for Clinical Laboratory Automation (JSCLA). We would like to thank Enago (https://www.enago.jp/advanced-editing) for English language editing. The authors thank to Drs Kaname Nakatani (Mie University), Shuji Tohda (Tokyo medical and Dental University), Eizaburo Sueoka (Saga University), Kaname Niida (Kanazawa Medical University), and Hirotaka Matsi (National Cancer Center Hospital) and Ms. Satoko Nakajo (SRL) for their professional advice and suggestions as the member of EQA working group of JACLS. We also thank Ms. Mitsuko Takarada, Manager, International Sales, LGC Clinical Diagnostics, Inc. (910 Clopper Road, Suite 150 South Building, Gaithersburg, MD 20878 USA) as a sample provider. Author contributions K.M., T.I., K.N., K.W., T.A. A.T., M.Y., T.H., Y.O., and M.H. contributed to the study design and conducted this project. M.Y., M.I. and M.T. prepared the samples for distribution. K.M., T.I., K.N., K.W., T.A. A.T., M.Y., T.H., Y.O., M.H., M.Y., N.A., K.S., N.H., H.S., Y.T., F.A., and M.T. analyzed the samples. K.M. and T.I. wrote the manuscript and all authors reviewed and approved the final version of the manuscript. 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Diagnostics (Basel) . 13 (4), 629. 10.3390/diagnostics13040629 (2023). PMID: 36832117; PMCID: PMC9955861. Fujiki, R., Ikeda, M., Ohara, O. & Short DNA Probes Developed for Sample Tracking and Quality Assurance in Gene Panel Testing. J. Mol. Diagn. 21 (6), 1079–1094. 10.1016/j.jmoldx.2019.07.003 (2019). Epub 2019 Aug 22. PMID: 31445212. Coppola, L. et al. Biobanking in health care: evolution and future directions. J. Transl Med. 17 (1), 172. 10.1186/s12967-019-1922-3 (2019). PMID: 31118074; PMCID: PMC6532145. Gronowski, A. M., Budelier, M. M. & Campbell, S. M. Ethics for Laboratory Medicine. Clin. Chem. 65 (12), 1497–1507. 10.1373/clinchem.2019.306670 (2019). Epub 2019 Aug 21. PMID: 31434657. Badrick, T., Punyalack, W. & Graham, P. Commutability and traceability in EQA programs. Clin. Biochem. 56 , 102–104. 10.1016/j.clinbiochem.2018.04.018 (2018). Epub 2018 Apr 20. PMID: 29684367. Pan, B. et al. Similarities and differences between variants called with human reference genome HG19 or HG38. BMC Bioinformatics. ;20(Suppl 2):101. doi: (2019). 10.1186/s12859-019-2620-0 . Erratum in: BMC Bioinformatics. 2019;20(1):252. doi: 10.1186/s12859-019-2776-7. PMID: 30871461; PMCID: PMC6419332. Hadar, N. et al. VARista: a free web platform for streamlined whole-genome variant analysis across T2T, hg38, and hg19. Hum. Genet. 143 (5), 695–701. 10.1007/s00439-024-02671-4 (2024). Epub 2024 Apr 12. PMID: 38607411. Matched Annotation from NCBI and EMBL-EBI (MANE). https://www.ncbi.nlm.nih.gov/refseq/MANE/ https://crisp-bio.blog.jp/archives/29037381.html Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1..pdf Table 1. Required conditions of EQA summary of the two times EQA in 2022 and 2023. Supplementdata.pdf Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviews received at journal 15 Nov, 2024 Reviewers agreed at journal 15 Nov, 2024 Reviews received at journal 22 Oct, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviewers invited by journal 05 Oct, 2024 Editor assigned by journal 05 Oct, 2024 Editor invited by journal 04 Oct, 2024 Submission checks completed at journal 04 Oct, 2024 First submitted to journal 01 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5189991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":380889034,"identity":"a2cad320-54df-4b3b-85e9-1816eefbf3be","order_by":0,"name":"Kazuyuki Matsushita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACCQhlww8kGA+AmAbMPGAhZjxaGBsYGNIkgQTDgQMkaDmMpIWBB7/DJKcdfv7wZ9t5Cf72MwaHP7YxyJuz8x78wNjGwG6OQ4u0dJphM2/bbQmJMzkGBw62MRjubOZLlgBqYbZswK5FTjrBsJmx7XYdww0esJYEg8M8BtJ/gVoMDuDSkv6x8WfbOQl5JC3GPxjxaJGWzjFs4G07IGGApMVMAp8Wydk5hbN5ziVLGJ5JKzhw5pyE4YbDfGkWDOckcPpF4nb6ho8/yuwk5I4f3vigosxG3uD82cM3GMpsknGFGCpgZJOAm5VsQJQWhj8Iph2RWkbBKBgFo2D4AwAGl1mBl3tyLgAAAABJRU5ErkJggg==","orcid":"","institution":"Chiba University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Kazuyuki","middleName":"","lastName":"Matsushita","suffix":""},{"id":380889035,"identity":"ae2eb8aa-5667-48e2-98d8-1b11b6f3e310","order_by":1,"name":"Takayuki Ishige","email":"","orcid":"","institution":"Chiba University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Takayuki","middleName":"","lastName":"Ishige","suffix":""},{"id":380889036,"identity":"28b12905-aef8-4479-9806-922a449ea0fb","order_by":2,"name":"Kousuke Watanabe","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Kousuke","middleName":"","lastName":"Watanabe","suffix":""},{"id":380889037,"identity":"ad9c7c96-11e8-430b-9bee-dd93e345848b","order_by":3,"name":"Toshiaki Akahane","email":"","orcid":"","institution":"Kagoshima University","correspondingAuthor":false,"prefix":"","firstName":"Toshiaki","middleName":"","lastName":"Akahane","suffix":""},{"id":380889038,"identity":"c7c3bd98-27f9-4570-855a-d10ea648f1ac","order_by":4,"name":"Akihide Tanimoto","email":"","orcid":"","institution":"Kagoshima University Graduate School of Medical and Dental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Akihide","middleName":"","lastName":"Tanimoto","suffix":""},{"id":380889039,"identity":"99d53b15-7e47-45fa-a941-b7214f77ece3","order_by":5,"name":"Michiko Yoshimoto","email":"","orcid":"","institution":"Sysmex (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Michiko","middleName":"","lastName":"Yoshimoto","suffix":""},{"id":380889040,"identity":"166a7b8a-a39c-4e8c-a65e-b8df0d0e1879","order_by":6,"name":"Munekazu Yamakuchi","email":"","orcid":"","institution":"Kagoshima University Graduate School of Medical and Dental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Munekazu","middleName":"","lastName":"Yamakuchi","suffix":""},{"id":380889041,"identity":"3ca88ec9-39a2-468d-b80b-1aaa46575074","order_by":7,"name":"Teruto Hashiguchi","email":"","orcid":"","institution":"Kagoshima University Graduate School of Medical and Dental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Teruto","middleName":"","lastName":"Hashiguchi","suffix":""},{"id":380889042,"identity":"cc279094-5887-4342-a582-934cf8c00b0b","order_by":8,"name":"Yoshinaga Okugawa","email":"","orcid":"","institution":"Mie University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yoshinaga","middleName":"","lastName":"Okugawa","suffix":""},{"id":380889043,"identity":"17a8d976-9456-4cb7-9d86-3f4eb31be283","order_by":9,"name":"Makoto Ikejiri","email":"","orcid":"","institution":"Mie University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Makoto","middleName":"","lastName":"Ikejiri","suffix":""},{"id":380889044,"identity":"55a9a5a4-6780-4378-a4d7-cdad42320b11","order_by":10,"name":"Toshikazu Yamaguchi","email":"","orcid":"","institution":"BML, Inc","correspondingAuthor":false,"prefix":"","firstName":"Toshikazu","middleName":"","lastName":"Yamaguchi","suffix":""},{"id":380889045,"identity":"4b01d6c2-e4b9-4cd8-b4b6-e4d1d6cabacc","order_by":11,"name":"Tadashi Yamasaki","email":"","orcid":"","institution":"BML, Inc","correspondingAuthor":false,"prefix":"","firstName":"Tadashi","middleName":"","lastName":"Yamasaki","suffix":""},{"id":380889046,"identity":"37c366da-9a00-4957-bcc5-0040a08a97f4","order_by":12,"name":"Mayu Takeda","email":"","orcid":"","institution":"Fujita Health University","correspondingAuthor":false,"prefix":"","firstName":"Mayu","middleName":"","lastName":"Takeda","suffix":""},{"id":380889047,"identity":"d5e23ddc-799c-4d6f-9650-42493a4fff5b","order_by":13,"name":"Masaaki Hibi","email":"","orcid":"","institution":"SRL (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Masaaki","middleName":"","lastName":"Hibi","suffix":""},{"id":380889048,"identity":"786480c7-c68c-4a69-9f3e-2bffb6a0474d","order_by":14,"name":"Naoki Akiyama","email":"","orcid":"","institution":"Sysmex (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Naoki","middleName":"","lastName":"Akiyama","suffix":""},{"id":380889049,"identity":"ec69e54d-6a86-4019-b981-48da6ccc87fa","order_by":15,"name":"Kaho Shimizu","email":"","orcid":"","institution":"Sysmex (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Kaho","middleName":"","lastName":"Shimizu","suffix":""},{"id":380889050,"identity":"0621397d-83c1-48cd-9063-9dcfb8f46d31","order_by":16,"name":"Naonori Hashimoto","email":"","orcid":"","institution":"Sysmex (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Naonori","middleName":"","lastName":"Hashimoto","suffix":""},{"id":380889051,"identity":"7b632955-ef0e-4339-bd16-b1388bcb6310","order_by":17,"name":"Hiroko Sato","email":"","orcid":"","institution":"Riken Genesis Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Hiroko","middleName":"","lastName":"Sato","suffix":""},{"id":380889052,"identity":"f56de4a6-aa7a-44d4-85f8-6deb3fc129f0","order_by":18,"name":"Yoshinori Tanaka","email":"","orcid":"","institution":"Riken Genesis Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Yoshinori","middleName":"","lastName":"Tanaka","suffix":""},{"id":380889053,"identity":"feefe7ec-8098-4a73-b300-2d26a89a386e","order_by":19,"name":"Fumie Amari","email":"","orcid":"","institution":"Riken Genesis Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Fumie","middleName":"","lastName":"Amari","suffix":""},{"id":380889054,"identity":"d4d3718b-0316-4d86-bf88-dbc6669a2c7b","order_by":20,"name":"EQA working group of Japan Association for Clinical Laboratory Science (JACLS)","email":"","orcid":"","institution":"EQA working group of Japan Association for Clinical Laboratory Science (JACLS)","correspondingAuthor":false,"prefix":"","firstName":"EQA","middleName":"working group of Japan Association for Clinical Laboratory Science","lastName":"(JACLS)","suffix":""}],"badges":[],"createdAt":"2024-10-02 00:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5189991/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5189991/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-84714-4","type":"published","date":"2025-01-07T15:56:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71478331,"identity":"c71e8f06-4d9e-4337-bb24-0610866705dd","added_by":"auto","created_at":"2024-12-16 05:32:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1858455,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Representative CGP tests indicated as pre-analytic (sampling and nucleic acids (DNA/RNA) extraction), analytic (Library preparation and DNA sequencing), and post-analytic (Bioinformatics). Validation, IQC, and EQA are required for all processes. For the 1\u003csup\u003est\u003c/sup\u003e EQA, NA (DNA/RNA) extraction (pre-analysis) was carried out at laboratory D and DNA/RNA samples were delivered to participated laboratories (Laboratories A-C, E-G, I, and J). (\u003cstrong\u003eB\u003c/strong\u003e). For the 2\u003csup\u003end\u003c/sup\u003e EQA, laboratory D delivered simulated paired matched T/N FFPE samples and each institute (Laboratories A-C, E-G, I, and J) extracted DNA/RNA and performed the rest of the process.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5189991/v1/2b6f0ae5b6860e6b099e84ce.jpg"},{"id":71478330,"identity":"42d7a97e-529f-4d93-a23e-98389a81e9dd","added_by":"auto","created_at":"2024-12-16 05:32:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1340414,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix. Analysis and post-analysis of participating facilities using different procedures for CGP panel testing. A correlation matrix between facilities based on these results suggested that the analysis and post-analysis influenced the results.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5189991/v1/8a0a5b885dd1b18960c4b42e.jpg"},{"id":73693709,"identity":"c7dc9675-fd5a-456e-841a-0b2c1fa2c23e","added_by":"auto","created_at":"2025-01-13 15:59:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8314070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5189991/v1/d20e6c61-45e5-470b-b29d-a15b4073525b.pdf"},{"id":71478334,"identity":"f1026b95-2492-411e-84b4-7a71022b3594","added_by":"auto","created_at":"2024-12-16 05:32:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":570960,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1. Required conditions of EQA summary of the two times EQA in 2022 and 2023.\u003c/p\u003e","description":"","filename":"Table1..pdf","url":"https://assets-eu.researchsquare.com/files/rs-5189991/v1/a8067649b455997154d4ad69.pdf"},{"id":71479381,"identity":"695e54c8-c77d-4669-9b4f-d1c0ce4cd767","added_by":"auto","created_at":"2024-12-16 05:40:21","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":947711,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementdata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5189991/v1/f88804b1493b6f472166bdb6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Importance of EQA/PT for the detection of genetic variants in comprehensive cancer genome testing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn genomic medicine, clinical tests have a wide range of roles, applications and expectations. Liquid biopsies, CGP testing for hematopoietic tumors, whole exon analysis for intractable and rare diseases, whole genome analysis, etc. are rapidly expanding techniques in actual clinical practice\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. To achieve precise medicine, EQA/PT for genetic-related testing in clinical settings is necessary\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Genetic-related tests in clinical laboratories include three categories: nucleic acid (NA) testing from pathogenic microorganisms, human DNA from somatic and germline cells. EQA/PT is required for the assessment of a laboratory\u0026rsquo;s NGS analysis before approval in all testing laboratories\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In this study, we aggregated and analyzed the results from various institutions and examined differences in results based on NGS equipment and reagents as well as on the detection rate for each genetic mutation. Further, we evaluated the characteristics depending on method and NGS platform. This allowed us to learn about the current state of cancer gene panel testing in Japan and allowed each participating facility to discuss the results. This study focused performed the alternative cost-effective EQA/PT on human DNA from somatic cells in 10 laboratory institutions with DNA extracted from cancer cell lines and commercially available FFPEs of simulated tumor (T)/non-tumor (N) matched-pair samples for standardization of gene-related tests such as CDx in the clinic. Furthermore, gene determination may differ depending on the analysis pipeline used, oversight when multiple variants exist in the same gene, and the limit of detection (LOD), VAF with a detection rate of 95%, may differ depending on the VAF setting at the facility. The present results aim to assist in recognizing possible pitfalls in CGP testing and in the standardization of NGS analysis. Together, EQA/PT procedures in laboratories will promote high-quality genomic medicine.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEQA/PT design and protocol\u003c/h2\u003e \u003cp\u003eRepresentative CGP tests consist of three steps: pre-analytic (sampling and NA extraction), analytic (library preparation and DNA sequencing), and post-analytic (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Therefore, this study designed two independent EQA/PT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). With respect to the EQA, CGP testing methodology was compared among five university hospital laboratories and five company-laboratories in Japan (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). NA was extracted from several cancer cell lines and its quality, yield, as well as the detection rate of CDx genes at each facility were examined and compared for a 1st EQA in 2022. The 2nd EQA was performed in 2023; the department of laboratory medicine in Chiba University Hospital delivered simulated paired matched T/N FFPE samples, and each institute (Laboratories A\u0026ndash;C, E\u0026ndash;G, I, and J) extracted DNA/RNA and performed the following steps on their own (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). First, to compare analysis and post-analysis, EQA/PT was performed with delivered NAs of cancer cell lines. There was no difference in the detection frequency of pathogenic variants among all facilities, and the LOD, VAF with a detection rate of 95%, was approximately 6\u0026ndash;6.1% for NAs of cancer cell lines and 10.8\u0026ndash;10.9% for FFPE samples. Next, EQA/PT was performed from NAs extraction from FFPE as a pre-analysis process by simulated paired tumor and non-tumor (T/N) of formalin fixed paraffin embedded (FFPE) specimens.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe limit of detection (LOD) of pathogenic gene variants was 6 to 6.1% VAF in cancer cells\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor the 1st EQA, five genomic DNA samples of cancer cell lines (1 reference and 4 test samples; Table S2) were prepared by the EQA organizer (Laboratory D in Chiba University) and delivered to the other nine laboratories (Laboratories A\u0026ndash;I; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Analytic process in the participated laboratories included library preparation, DNA sequencing, and bioinformatics analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). NGS system, library preparation procedures, reagents for examination, reference human genome version (GRCh37/hg19 or GRCh38/hg38), and sensitivities of SNV/Indel (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In addition, gene name, type of variants in human genome variation society nomenclature, VAF%, and %RSD were calculated (Table\u0026nbsp;1A). For example, pathogenic variant of cell No.2, % VAF, mean, % RSD and %Detection (N) of \u003cem\u003eKRAS\u003c/em\u003e: c.38G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.G12D) were 11.7%, 11%, and 100% (9/9), respectively (Table\u0026nbsp;1A). In four test samples, the CaCO2 mixed with K562 (ratio of 3:1) sample had no variants in \u003cem\u003eBRAF, EGFR, KRAS, NRAS\u003c/em\u003e, and \u003cem\u003ePIK3CA\u003c/em\u003e genes (Table S2). In case of \u003cem\u003eBRAF\u003c/em\u003e: c.1799T\u0026thinsp;\u0026gt;\u0026thinsp;A (p.V600E), % VAF, mean, % RSD and %Detection (N) were 4.9%, 16%, and 67% (6/9) in cell No.3 whereas 12.5%, 8%, and 100% (9/9) in cell No.4. In \u003cem\u003eBRAF\u003c/em\u003e: c.1799T\u0026thinsp;\u0026gt;\u0026thinsp;A (p.V600E) in cell No3, that 3 out of 9 were not reported this variant, however, these 3 institutes confirmed the detection of this variant in Binary Alignment/Map (BAM) files. These results indicated that 5% VAF is the limit of detection (LOD) for the participating laboratories.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction from FFPE is critical for the VAF of CDx genes\u003c/h3\u003e\n\u003cp\u003eLaboratory D in Chiba University delivered simulated paired matched Tumor (T)/non-tumor (N) FFPE samples to each institute (Laboratories A-C, E-G, I, and J) and they extracted DNA by their own procedures (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For the 2nd EQA, sufficient DNA yields were obtained from T/N matched-pair samples by NA extraction. These simulated matched-pair samples were essential for the correct evaluation of NCC Oncopanel\u0026trade; which using the matched-pair analysis technique, allowing the same procedures to be operated as for FFPE human clinical specimens during the post-analysis process and ensuring the EQA could be performed accurately. Regarding comprehensive genomic testing using NGS, detailed EQA/PT data for the curation of analytical processes is summarized in Table\u0026nbsp;1B. FFPE samples had more comprehensive hotspot variants in \u003cem\u003eAKT, BRAF, EGFR, KIT, KRAS, NRAS\u003c/em\u003e, and \u003cem\u003ePIK3CA\u003c/em\u003e (Table\u0026nbsp;1B). Most pathogenic or companion variants were accurately detected in the 10 participating laboratories. Each variant had\u0026thinsp;\u0026lt;\u0026thinsp;20% VAFs on average (8.1\u0026ndash;19.1%) and there was wide variability among labs (%RSD ranged 13\u0026ndash;60%). Variants with low detection rate (\u0026lt;\u0026thinsp;80%) among labs were \u003cem\u003eBRAF\u003c/em\u003e: c.1798_1799delinsAA (p.V600K), \u003cem\u003eEGFR\u003c/em\u003e: c.2235_2249del (p.E746_A750del), and \u003cem\u003eEGFR\u003c/em\u003e: c.2254_2277del (p.S752_I759del), all with \u0026lt;\u0026thinsp;10% VAFs. Notably, two laboratories reported \u003cem\u003eBRAF\u003c/em\u003e V600M but not V600K. Incorrect results were probably reported because the independent variant calling of \u003cem\u003eBRAF;\u003c/em\u003e c.1798_1799delinsAA (p.V600K) was interpreted as c.1798G\u0026thinsp;\u0026gt;\u0026thinsp;A and c.1799T\u0026thinsp;\u0026gt;\u0026thinsp;A on the other allele (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u0026ndash;C). Thus, these results were affected not only by sensitivity but also by variant call accuracy. Correlation analysis of VAFs showed good correlation between laboratories where the same instrument and reagents were used: the Spearman\u0026rsquo;s rho of Lab A and B was 0.75 (instrument, Thermo; method, amplicon); Lab F and G was 0.87 (instrument, Illumina; method, capture; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). As compared with amplicon and capture methods, no significant differences were observed in variant frequency (Figure S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe 11% VAF affected the detection rate of gene variants depending on DNA extraction procedures from FFPE\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe probit regression analysis showed that 10.8\u0026ndash;10.9% VAF was required for achieving a 95% detection rate, LOD, among laboratories (Figure S3B). The relationship and difference between % VAF and detection rate is indicated in 1st EQA and 2nd EQA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Table\u0026nbsp;1). In the 1st EQA with DNA from cancer cell lies, 100% detection was achieved for VAF\u0026thinsp;\u0026gt;\u0026thinsp;5\u0026ndash;10% (Table\u0026nbsp;2A). However, in the 2nd EQA: the detection rate was 100% for VAF\u0026thinsp;\u0026gt;\u0026thinsp;15%. These results indicated that VAF detection significantly relates to DNA quality and amount. Furthermore, cDNA library preparation for NGS analysis affected VAF depending on genetic variants. In case of 1st EQA, there was no significant difference in VAF in terms of genetic variants between amplicon (Labs A\u0026ndash;E) or capture (Labs F\u0026ndash;J) methods for library preparation (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, in the 2nd EQA, the VAF of deletion variants differed between amplicon and capture methods in certain sequences (Fig. S2). Based on our findings, we hypothesize that in case of deletion variants, the PCR efficacy will increase; however, capture efficacy will decrease depending on the deleted sequence. Further research is needed to corroborate this hypothesis. Therefore, pre-analytic DNA\u0026rsquo;s preparation for cDNA library was critical for the standardization of CGP tests.\u003c/p\u003e \u003cp\u003eIn ordinal clinical testing in the clinical laboratories whether in the hospital or companies, once we have established the initial analytic pipelines, if there are no problems with validation tests, we will continue testing without making any changes. Therefore, based on the results obtained from this study, we hope that conducting EQA/PT will provide an opportunity to review the pipeline to deal with pitfalls that are usually not noticed. In this project, we used nucleic acids extracted from cell lines and simulated T/N pair specimens to blindly examine inter-laboratory differences in companion diagnostics (CDx) genes\u0026rsquo; detection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B)such as \u003cem\u003eAKT1, BRAF, EGFR, KIT, KRAS, NRAS, and PIK3CA\u003c/em\u003e (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRead depth (variant caller) affected the interpretation of\u003c/b\u003e \u003cb\u003eBRAF\u003c/b\u003e \u003cb\u003eV600 variants\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePrecise detection of \u003cem\u003eBRAF\u003c/em\u003e V600 pathogenic variants is important for molecular target therapy for solid tumors\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In case of \u003cem\u003eBRAF\u003c/em\u003e: c.1798_1799delinsAA (p.V600K), % VAF, mean, % RSD and %Detection (N) were 8.1%, 18%, and 70% (7/10). Similarly, \u003cem\u003eEGFR\u003c/em\u003e: c.2235_2249del (p.E746_A750del), % VAF, mean, % RSD and %Detection (N) were 9.7%, 28%, and 70% (7/10) and c.2254_2277del (p.S752_I759del) were 9.8%, 60%, and 60% (6/10) (Table\u0026nbsp;2). Significantly, the TT variant of \u003cem\u003eBRAF\u003c/em\u003e V600K exists in the same allele but could be incorrectly determined as V600M (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The %VAF of each genetic change, equipment used, library preparation method, human reference genomes, and DNA sequencing reagents information are summarized in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC. The difference in the judgment of \u003cem\u003eBRAF\u003c/em\u003e V600E may be due to the different Variant Callers used (GATK and Verscan; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Additionally, if variants are detected within the same gene, it is difficult to detect them both simultaneously. The reason why the difficulty to discriminate the allele of the variant is that the pipelines created were different. Therefore, it is important to determine which is the most suitable pipeline (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Integrative Genomics Viewer (IGV) of BAM files were beneficial to reduce miscalling DNA sequences (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). It was observed that genetic mutations used in companion diagnostics (\u003cem\u003eKRAS\u003c/em\u003e: c.34G\u0026thinsp;\u0026gt;\u0026thinsp;T (p.G12C) and \u003cem\u003eNRAS\u003c/em\u003e: c.181C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Q61K) etc.) were detected by all ten participated institutes even though % VAF was less than 10% (Table\u0026nbsp;1). The reason of this discrepancy is that when multiple variants exist within the same gene, it may not be possible to detect them at the same time if their bases are close together. Due to the reference material used in this investigation, we included pathogenic variants in artificially closer locations. It is important not to overlook clinically important pathogenic variants and CDx in clinical examinations. However, it is not realistic for clinical testing to seek 100% accuracy through repeated checks, so it is important to share the best protocols within limited medical resources.\u003c/p\u003e \u003cp\u003eIf CGP testing will be first start up in the facility, EQA/PT will be able to notice pitfalls as performed in this project. Since it is not easy to modify a pipeline once created, it is important to perform EQA/PT in advance. In particular, objective evaluation should be conducted blindly. Furthermore, in the future, it would be advantageous in terms of cost if ethical issues could be resolved and EQA/PT using human clinical specimens could be implemented even on a small scale. The requirements of ISO 15189 require evaluation of the suitability of services provided by external parties (including those like this survey), so we believe it would be ideal to be able to choose from a variety of programs. It was surprising to see that the variation in VAF was large. I think it is suitable for evaluating the extent to which it is possible to detect alleles with a relatively low allele frequency of 5%. Regarding the variation in VAF, it is necessary to equalize the read amount (depth) of the sequences. Together, in case of high-quality DNA such as extracts of cell lines, % VAF of EQA/PT needs to 5\u0026ndash;10%, however, low quality DNA specimens as extracts of FFPE, % VAF of EQA/PT needs to 15%.\u003c/p\u003e\n\u003ch3\u003eCorrelation matrix among laboratories\u003c/h3\u003e\n\u003cp\u003eThe analysis and post-analysis processes in the participating facilities included different library preparation methods, DNA sequence reagents (Manufacture), DNA sequencer (Instrument), Human Reference Genome, and Sensitivity (LOD; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We performed a comparison of genetic variants and %VAF for CDx testing at each facility (Table S3). Then, we created a correlation matrix between facilities, which indicated that the analysis and post-analysis influenced the detection results(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Accordingly, it was considered important for each facility to understand the characteristics of its own method when participating in EQA when conducting NGS analysis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn clinical practice, it is important that CDx are efficiently detected to provide the appropriate treatment\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. So, accuracy control is important in cancer genome analysis, and EQA/PT is necessary for this purpose\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In this study, an EQA/PT among 10 clinical laboratories indicated that preanalytical processes, especially NA extraction, are critical for the quality of CGP tests. The content of this EQA closely resembles the requirements of ISO 15189. Genetic changes can be confirmed using an Integrative Genomics Viewer (IGV) by careful visual observation, but it is difficult to know how to modify the pipeline for those that cannot be detected in this pipeline. In addition, there were more differences in VAF among facilities than expected (Figure S2). This variability may be due to differences in capture or PCR efficiency, or in other experimental aspects, as well as differences in analysis pipelines. The degree of error likely depends on the specific variant to be detected\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, partially due to methodological differences in the preparation of cDNA libraries. Furthermore, capture efficiency differs depending on the genomic region. While digital PCR is highly quantitative, it is unclear whether it can be considered as a standard. One of the advantages of this EQA study is that, using reference samples with known allele frequencies allows to assess the systematic bias in VAF across methods. The reliability of these reference samples needs to be ensured, as their VAF have been confirmed using both digital PCR and NGS. Therefore, it is very meaningful to confirm the difference from the actual allele frequency obtained using a standard substance with a known correct allele frequency, as in this EQA. In summary, if the number of repetitive EQA among laboratories is performed, it will tell us the degree of systematic error that occurs with a particular test method\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In addition, traceability is also important for company samples to see whether the allele frequency is truly correct, so that it is considered highly reliable\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Future research is needed to determine the extent to which this difference is based on the methods used.\u003c/p\u003e \u003cp\u003eIt is necessary to consider what to evaluate, including the effects of differences in depth and software (variant caller). The true answer is unknown in clinical human samples; therefore, it is necessary to consider what should be evaluated as a correct answer in an actual sample. For example, TMB and microsatellite instability were not included in the data requested for submission this time, it is important to look at them as well.\u003c/p\u003e \u003cp\u003eCurrently, artificial samples such as commercially available standard materials spiked with fragments with artificially introduced genetic mutations, or commercially available products subjected to various types of gene editing have been used to perform QA\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This is advantageous because it allows systematically introducing genetic mutations in advance and the ability to produce it in large quantities. However, samples produced in this way do not ensure compatibility depending on the measurement method (commutability) and have different properties from the samples used for routine analysis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. An ideal EQA program uses patient samples to ensure commutability. Ideally, a DNA sample from the FFPE or blood would be best. By doing this, it is desirable to eliminate the influence of FFPE location variations and NA extraction methods and investigate the process of NGS analysis only. However, at present, there seems to be no consensus on the use of human specimens for EQA outside of clinical research\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. It is desirable that human specimens become available for EQA/PT in the future.\u003c/p\u003e \u003cp\u003eTimely EQA and reviews of the analysis pipeline, including post-analysis\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e could improve testing quality and future advance genomic medicine. Further, sharing information on libraries for of analysis and quality indicators of sequence data (basic concept for ensuring the quality and accuracy of cancer gene panel tests) could improve test performance. Clinicians are confused not only by the difference in whether the reference sequence used this time is hg19 or hg38, but also by the variant form of the gene and the information in different databases\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. There was a possibility that the situation would become easier. To overcome these discrepancies, it is necessary to align the notation method with MANE\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e in the future.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eTen clinical laboratories of five university hospitals (Chiba University, University of Tokyo, Kagoshima University, Mie university, and Fijita medical college) and five commercial companies in Japan (SRL, BML, RIKEN-Tsukiji, RIKEN-Kawasaki, and Sysmex Co. Ldt.) participated in this study (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Five laboratories used the amplicon method and the rest used the capture method for DNA detection. Manufactures of NGS sequencers were Thermo for four labs, Qiagen for one, and Agilent for five.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction from cell lines\u003c/h3\u003e\n\u003cp\u003eDNA samples were extracted from one leukemia cell line (K562) and four colorectal cancer cell lines (Caco2, HCT116, HT29, and RKO). DNA concentrations were adjusted to 25 ng/\u0026micro;L using a fluorometric assay. Then, EQA/PT samples were prepared by mixing the K562 cell line, used as baseline for matched pair analysis, and each colorectal cancer cell line at a 3:1 ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Microsatellite instability and \u003cem\u003eEGFR, KRAS, NRAS, BRAF, PIC3CA\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e variants were examined as target genes of CDx. K562, human leukemia cell line, was used for standard that has no pathogenic variants in CDx genes. Sample preparation was performed by mixing cell lines with mutations. We conducted a simulated matched pair study in which \u0026ldquo;DNA derived from a cell line without the target gene mutation\u0026rdquo; was derived from normal cells, and \u0026ldquo;DNA derived from a cell line with the target gene mutation\u0026rdquo; was derived from tumor cells.\u003c/p\u003e \u003cp\u003eSamples 1\u0026ndash;4 were prepared at laboratory D and delivered to other EQA participated laboratories.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSample cell No. 1: No mutations detected in \u003cem\u003eEGFR, KRAS, NRAS, BRAF, PIK3CA.\u003c/em\u003e Microsatellite status, stable.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSample cell No. 2: \u003cem\u003eKRAS\u003c/em\u003e p.G13D mutation detected at approximately 12.5% and \u003cem\u003ePIK3CA\u003c/em\u003e p.H1047R detected at approximately 12.5%. Microsatellite status, high.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSample cell No. 3: \u003cem\u003eBRAF\u003c/em\u003e p.V600E mutation detected at approximately 6%. Microsatellite status, stable.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSample cell No. 4: \u003cem\u003eBRAF\u003c/em\u003e p.V600E mutation detected at approximately 16.75%, and \u003cem\u003ePIK3CA\u003c/em\u003e p.H1047R detected at approximately 12.5%. Microsatellite status, high.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eEvaluation genes, relatively common genes in solid tumors with variant hotspots:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eEGFR\u003c/em\u003e exons 18\u0026ndash;21\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eKRAS\u003c/em\u003e codons 12, 13, 59, 61, 117, 146\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eBRAF\u003c/em\u003e codon 600\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePIK3CA\u003c/em\u003e codons 542, 545, 1047\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eFFPE specimens\u003c/h3\u003e\n\u003cp\u003eTwo specimens, Seraseq Compromised FFPE Tumor DNA Reference Material (0710\u0026ndash;1492) and Seraseq Compromised FFPE WT (DNA/RNA) Reference Material (0710\u0026ndash;1710), were purchased from SeraCare Life Sciences, MA, USA.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEQA evaluation protocol\u003c/h2\u003e \u003cp\u003eDetected variants and their frequencies (% Variant allele frequency) were examined in a mutually blinded manner using each facility\u0026rsquo;s detection method.\u003c/p\u003e \u003cp\u003eThe criteria for determining whether a sample was appropriate when there is no match were: a match rate between facilities\u0026thinsp;\u0026ge;\u0026thinsp;80% was considered suitable, and \u0026lt;\u0026thinsp;80% inappropriate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSamples and PT evaluation methods for matched paired T/N FFPE samples\u003c/h2\u003e \u003cp\u003eFFPE reference cells carrying synthetic DNA constructs manufactured to more closely mimic the quality of patient tissue. Using commercially available reference materials (Seraseq\u0026reg; Compromised FFPE Tumor DNA RM, 0710\u0026ndash;1492 and Compromised FFPE WT RM, 0710\u0026ndash;1710; LGC Clinical Diagnostics; Teddington, Middlesex, TW11 0LY, UK) containing cells from GM24385 cell line with or without synthetic DNA constructs containing tumor variants, formalin-fixed and paraffin-embedded (FFPE) following a protocol mimicking the quality of patient tissue specimens.\u003c/p\u003e \u003cp\u003eLGC Clinical Diagnostics have provided contrived samples for clinical genomics ring trials, EQAs and proficiency exercises worldwide, including GenQA and country-specific schemes. This time\u0026rsquo;s matched pair samples simulate \"cell line-DNA, RNA)\" with normal tissue-derived FFPE, and \"cell line-derived nucleic acids (DNA, RNA)\u0026thinsp;+\u0026thinsp;multiple synthetic genes (spiked in)\". Tumor tissue shall be FFPE. Since the artificial FFPE sample used in this study is a transparent pellet smaller than a typical clinical specimen, care had to be taken to ensure that the sample was not lost during extraction. DNA analysis (RNA analysis optional) were performed at all facilities. The test was to be conducted using typical methods (EQA/PT). NA extraction was performed using the method of each facility, and the results were read together on a later date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis (probit and correlation analyses) was performed using R (version).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the assistance provided by all participating staff members and laboratories. This EQA/PT was performed as clinical laboratory examination of the EQA working group of Japan Society for Clinical Laboratory Automation (JSCLA). We would like to thank Enago (https://www.enago.jp/advanced-editing) for English language editing. The authors thank to Drs Kaname Nakatani (Mie University), Shuji Tohda (Tokyo medical and Dental University), Eizaburo Sueoka (Saga University), Kaname Niida (Kanazawa Medical University), and Hirotaka Matsi (National Cancer Center Hospital) and Ms. Satoko Nakajo (SRL) for their professional advice and suggestions as the member of EQA working group of JACLS. We also thank Ms. Mitsuko Takarada, Manager, International Sales, LGC Clinical Diagnostics, Inc. (910 Clopper Road, Suite 150 South Building, Gaithersburg, MD 20878 USA) as a sample provider.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.M., T.I., K.N., K.W., T.A. A.T., M.Y., T.H., Y.O., and M.H. contributed to the study design and conducted this project. M.Y., M.I. and M.T. prepared the samples for distribution. K.M., T.I., K.N., K.W., T.A. A.T., M.Y., T.H., Y.O., M.H., M.Y., N.A., K.S., N.H., H.S., Y.T., F.A., and M.T. analyzed the samples. K.M. and T.I. wrote the manuscript and all authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants have nothing to declare in COI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u0026nbsp;\u003c/strong\u003eand requests for materials should be addressed to K.M.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cp\u003e\u003cb\u003eData Availability\u003c/p\u003e \u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePetersen, B. S., Fredrich, B., Hoeppner, M. P., Ellinghaus, D. \u0026amp; Franke, A. 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PMID: 38607411.\u003c/span\u003e\u003c/li\u003e \u003cli\u003eMatched Annotation from NCBI and EMBL-EBI (MANE). https://www.ncbi.nlm.nih.gov/refseq/MANE/ https://crisp-bio.blog.jp/archives/29037381.html\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"External quality assessment (EQA), Proficiency testing (PT), genetic variants, comprehensive cancer genome testing, NGS","lastPublishedDoi":"10.21203/rs.3.rs-5189991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5189991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComprehensive genomic profiling (CGP) is increasingly used as a clinical laboratory test and being applied to cancer treatment; however, standardization and external quality assessments (EQA) have not been fully developed. This study performed cost-effective EQA and proficiency tests (PT) for CGP testing among multiple institutions those belong to the EQA working group of Japan Association for Clinical Laboratory Science (JACLS). This study revealed that preanalytical processes, such as derived nucleic acids (NA) extraction from formalin fixed paraffine embedded (FFPE) samples, are critical. First, EQA with extracted DNA from cell lines showed a detection rate of 100% (9 out of 9) in \u003cem\u003eKRAS\u003c/em\u003e (c.38G\u0026thinsp;\u0026gt;\u0026thinsp;A; p.G12D), \u003cem\u003ePIK3CA\u003c/em\u003e (p.H1047R), and B-Raf proto-oncogene, serine/threonine kinase (\u003cem\u003eBRAF\u003c/em\u003e) (c.1799T\u0026thinsp;\u0026gt;\u0026thinsp;A; p.V600E) in cases of \u0026gt;\u0026thinsp;10% variant allele frequency (VAF). However, \u003cem\u003eBRAF\u003c/em\u003e (c.1799T\u0026thinsp;\u0026gt;\u0026thinsp;A; p.V600E) detection decreased to 67% (6 out of 9) for a VAF of 4.9%. Second, when DNA was extracted from FFPE samples, pathogenic variants or companion diagnostics were detected in all 10 participating laboratories. Each variant had\u0026thinsp;\u0026lt;\u0026thinsp;20% VAFs on average (8.1\u0026ndash;19.1%) and wide variability among laboratories was observed (relative standard deviation, 13\u0026ndash;60%). Nonetheless, \u003cem\u003eBRAF\u003c/em\u003e (c.1798_1799delinsAA; p.V600K) of 8.1% VAF, \u003cem\u003eEGFR\u003c/em\u003e (c.2235_2249del; p.E746_A750del) of 9.7% VAF, and \u003cem\u003eEGFR\u003c/em\u003e (c.2254_2277del; p.S752_I759del) of 9.8% VAF were detected with 70% (7/10), 70% (7/10), and 60% (6/10) probability, respectively. Therefore, 10% VAF in pre-analytic processing for DNA extraction from FFPE is critical for variant detection in CGP analysis. Further, incorrect results were reported in case independent variant calling of \u003cem\u003eBRAF;\u003c/em\u003e c.1798_1799delinsAA (p.V600K) was interpreted as c.1798G\u0026thinsp;\u0026gt;\u0026thinsp;A, and c.1799T\u0026thinsp;\u0026gt;\u0026thinsp;A was on the other allele. In conclusion, the EQA/PT among 10 institutes with common samples revealed the importance of VAF in pre-analysis and helped us understand the significance of the pipeline and common pitfalls usually ignored by the internal quality control in a single institute.\u003c/p\u003e","manuscriptTitle":"Importance of EQA/PT for the detection of genetic variants in comprehensive cancer genome testing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 05:32:16","doi":"10.21203/rs.3.rs-5189991/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-21T10:57:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-15T08:41:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96824767288128208143939160396069260166","date":"2024-11-15T08:29:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-22T18:53:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128230090539232435989271033425369911879","date":"2024-10-07T13:14:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-05T10:05:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-05T09:55:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-04T09:23:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-04T04:28:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-02T00:14:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c9e22c5-8231-4d9b-a59d-fa8f86203cf2","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40564572,"name":"Biological sciences/Biochemistry"},{"id":40564573,"name":"Biological sciences/Biological techniques"},{"id":40564574,"name":"Biological sciences/Biotechnology"},{"id":40564575,"name":"Biological sciences/Cancer"},{"id":40564576,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-01-13T15:58:12+00:00","versionOfRecord":{"articleIdentity":"rs-5189991","link":"https://doi.org/10.1038/s41598-024-84714-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-07 15:56:49","publishedOnDateReadable":"January 7th, 2025"},"versionCreatedAt":"2024-12-16 05:32:16","video":"","vorDoi":"10.1038/s41598-024-84714-4","vorDoiUrl":"https://doi.org/10.1038/s41598-024-84714-4","workflowStages":[]},"version":"v1","identity":"rs-5189991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5189991","identity":"rs-5189991","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}