Improved Inter-Lab Precision After CML International Scale Adoption: a 15-year history of BCR::ABL1 Proficiency Testing

Improved Inter-Lab Precision After CML International Scale Adoption: a 15-year history of BCR::ABL1 Proficiency 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 Improved Inter-Lab Precision After CML International Scale Adoption: a 15-year history of BCR::ABL1 Proficiency Testing Annette Kim, Thomas Long, Derek Oldridge, Lawrence Jennings, Yi Ding, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8921143/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Precise inter-laboratory quantitation of BCR::ABL1 RNA is critical for effective chronic myeloid leukemia (CML) disease management. Although standardized reporting on the International Scale (IS) was specifically developed to improve inter-laboratory variability in BCR::ABL1 quantitation, the clinical impact of this reporting system has not been comprehensively assessed. We analyzed 15 years of BCR::ABL1 results (2009–2023) reported by over 200 clinical laboratories (6 annual samples; >5500 results) in a College of American Pathologists proficiency testing program. Inter-laboratory precision was evaluated by standard deviations (SD) for high, intermediate, and low-transcript samples. Early rapid IS adoption (8% of labs in 2009; 86% by 2015) was correlated with a progressive decrease in inter-lab SD’s of reported BCR::ABL1 results (high sample r=-0.73, p=0.004; intermediate sample r=-0.71, p=0.022; low sample r=-0.84, p=0.002). IS implementation exceeded 99% by 2019, as inter-lab SD’s reached their nadir value (~0.2). No other assessed variable correlated with the time-dependent improvement in inter-lab reproducibility, including reference gene selection ( ABL1 versus others), RQ-PCR reagent selection, or assay type (FDA-cleared versus lab-developed). IS-based reporting is thus likely the principal driver of enhanced inter-laboratory precision in BCR::ABL1 quantitation, validating the real-world clinical relevance of adopting robust standardization in clinical molecular diagnostics. Health sciences/Diseases/Haematological diseases/Haematological cancer/Myeloproliferative disease Health sciences/Health care/Health services Chronic myeloid leukemia (CML) BCR::ABL1 measurable residual disease (MRD) monitoring International Scale (IS) inter-laboratory reproducibility quantitative PCR (RQ-PCR) proficiency testing standardization FDA-approved assays Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Key Points • Since the adoption of the International Scale (IS), inter-laboratory precision for transcript level quantification has improved. • Improved precision is correlated with use of IS and not with any specific control gene, reagent kit, or FDA-approved assay. Introduction In chronic myeloid leukemia (CML), the BCR::ABL1 fusion kinase, produced by the Philadelphia chromosome translocation [t(9;22)], serves as both an ideal therapeutic target and a specific diagnostic biomarker of post-treatment measurable residual disease (MRD). 1 Since the first approval in 2003 of a tyrosine kinase inhibitor (TKI) targeting the BCR::ABL1 oncoprotein by the Food and Drug Administration (FDA) a, the long-term survival and prognosis of this once-fatal leukemia has dramatically improved. 2 The extraordinary efficacy of these TKIs demands the use of sensitive, accurate, and precise laboratory methods to quantitatively monitor low-level MRD. Optimal TKI-based management of CML patients with the p210 fusion (created by the e13a2 or e14a2 exon breakpoints) relies on comparison to consensus milestone levels. These MRD determinations can dictate the choice and/or dose of the specific TKI drug and can serve as a key prognostic marker for predicting long-term outcomes, treatment resistance, and impending relapse. 3 – 5 Reverse transcription quantitative PCR (RQ-PCR) remains the primary clinical laboratory method for MRD monitoring, with an analytic measurement range spanning 4 or more logs. Given the importance of MRD monitoring, inter-laboratory variability in the quantitation of BCR::ABL1 RNA directly impacts patient management and clinical outcomes. 6 , 7 The non-centralized health care system in the United States (US), whereby diagnostic laboratory testing is often performed in different laboratories, depending on the patient’s employment and insurance status, further amplifies this problem. Despite well-documented clinical utility, BCR::ABL1 RQ-PCR remains a technically challenging multi-step laboratory procedure that prompted international efforts early in the TKI treatment era to create a standardized reporting unit for BCR::ABL1 RNA. 8 In 2008, these efforts resulted in the clinical and analytical validation of the international scale (IS) for the reporting of BCR::ABL1 RNA levels of the p210 fusion product. 9 IS-based reporting requires the calibration of analytically-determined transcript levels to reference standards tied to clinically proven treatment response thresholds from TKI clinical trials. 9 While the reporting paradigm is standardized through IS alignment, other steps in the BCR::ABL1 procedure remain non-standardized between laboratories, including RNA extraction, instrumentation, IS calibration methodology, automation vs manual methods, and clinical interpretation. 10 Multi-laboratory proficiency testing (PT) allows laboratories to compare the results of their multi-step testing process to gold standard accuracy standards based on the IS and, for some PT vendors, to other participants in aggregate. This PT data also informs the laboratory and clinical community of the inter-laboratory reproducibility. According to the Clinical Laboratories Improvement Act (CLIA), some form of PT is required of US-based clinical diagnostic laboratories. The College of American Pathologists (CAP) is the leading provider of standardized PT materials and has surveyed laboratories on BCR::ABL1 transcript quantitation since 2006. The resulting CAP-sponsored PT data is the most comprehensive, unbiased, real-world source of laboratory performance data for different analytes, including many that are based on quantitative PCR. 11 – 18 The purpose of this study was to evaluate whether inter-laboratory precision of quantitative BCR::ABL1 RNA reporting has improved over time, and, if so, whether that improvement is attributable to the institutional adoption of IS-based standardized calibration. This study reviews 15 years of serial data from the CAP proficiency testing survey, whereby over 200 clinical laboratories blindly tested three samples with varying BCR::ABL1 (p210) transcript levels twice per year (annual A and B mailings; 2009–2023, 30 total mailings, 90 total samples). This dataset should be representative of the performance status of BCR::ABL1 (p210) testing across the primarily US-based clinical laboratory community during the timeframe when labs were progressively implementing IS-based reporting, allowing for a comprehensive assessment of the role of IS in inter-laboratory reproducibility. Methods Survey Information Fifteen years of CAP proficiency testing data on quantitative BCR::ABL1 (p210) testing were examined, spanning the 2009A through 2023B mailings. Although originally an educational PT program, the BCR::ABL1 MRD PT survey became a graded survey in 2014, tying laboratory performance with laboratory accreditation. Laboratory performance was scored based on reported %IS and MR (log molecular response) responses, where all reported data had to fall within +/- 2 standard deviations from the mean MR value of all participants. By this definition, ~ 5% of laboratory responses would “fail” in each survey. Laboratories reporting persistent outlier results on this CAP survey risk formal lab licensure penalties under CLIA regulations, including cessation of clinical testing. Any BCR::ABL1 (p210) result that was not aligned to the IS and/or calculated using a non- ABL1 control gene could not be graded. The surveys included additional questions about the laboratory practices of the participants. For instance, each survey queries laboratories on the selection of a control gene. In addition, many surveys interrogated the origin of the PCR primers and, by the 2017B mailing, were specifically asking if the laboratory’s assay was (or was not) FDA approved (modified or unmodified), with the option to specify the non-FDA assay vendor. Specimen Information Blinded proficiency specimens were provided to participants as RNA extracted from a CML cell line diluted in BCR::ABL1 negative RNA to achieve the final dilution. Each participant received three samples per mailing, one mimicking a new CML diagnosis sample (“high” level of BCR::ABL1 transcript), and two additional samples at “intermediate” and “low” transcript levels. For the analyses described herein, these different dilutions were binned into categories of “high” (10-fold dilution), “intermediate” (1: 100, 1:1,000, and 1:5,000) or “low” (1:10,000, 1:50,000, and 1: 100,000) BCR::ABL1 RNA for each survey. Some mailings included negative control specimens not used in the analysis, and those mailings only had two (not three) levels of specimens. Specimen dilutions for each mailing are presented in Supplemental Table 1. Response Information Participant responses were expressed as absolute transcript levels on a logarithmic scale [log(ratio)] from 2009A through 2015A (“ratio” defined as p210 RNA divided by reference gene RNA), and as log (IS) ( BCR::ABL1 international scale) from 2012A through 2023B. Responses were also recorded as relative log reduction from the “diagnostic sample” (highest BCR::ABL1 sample of the blinded 3-sample mailing) from 2009A through 2018A, and as molecular response [MR, equal to log 10 (100/%IS)] from 2018B through 2023B. For several years, there was overlap of reporting mechanisms and participants could report results in a variety of formats. Where this was true, all relevant responses are included in this study. During 2009–2013, the transcript level of the “high” sample was not directly interrogated as the relative log-reduction level was zero, leaving no “high” data for those surveys. IS and MR-based reporting started in 2011B and 2015B, respectively, but only became mandatory in 2018B. Before the mandatory reporting of %IS and MR, there were periods (2011B -2018B) where a participating laboratory could report both log(ratio) and %IS, or log reduction and MR. Data Analysis Response data were excluded for log ratio values of 0, IS values of 0, or any response with a qualifier (eg. non-numeric value above or below a laboratory’s detection limit, “not detected,” or “negative” IS value). MR responses that were greater than 0.4 from an MR value calculated by CAP based upon other response data (n = 70) were removed as presumed typographical or calculation errors rather than erroneous results. A 2- pass 3-SD (standard deviation) outlier screen was applied for each measure, level, and mailing before data were summarized and analyzed. For each response measure, level, and mailing, the inter-laboratory SD was calculated and plotted against mailing date to visualize the time trend. A secondary overlaid axis displayed the percentage of participants reporting IS-aligned data. For the three time windows [early (2009-2015A), middle (2015B-2019A), and late (2019B-2023B)], a time-dependent regression line of the SD was fit, and a Pearson’s Correlation Coefficient (R) was calculated between SD and percentage of participants reporting IS at each level. Statistical tests were performed for time-dependent slopes of the fitted SD regression lines, slopes of the % reported IS regression lines, and the significance of the correlations for each time window and level. ANOVA was used to compare means between laboratories using ABL1 and any other non- ABL1 control gene. Levene’s Homogeneity of Variance test compared inter-laboratory variances 1) between ABL1 and non- ABL1 control gene utilization and 2) between FDA and non-FDA assay utilization. All analyses were completed using SAS version 9.4 (Cary, NC). Results Participant and Survey Information. Although CAP began providing the BCR::ABL1 (p210) MRD PT survey in 2006, the data from the first few years of this survey was returned to CAP in a myriad of different formats and units. This early data also preceded the 2008 publication defining the standardized IS for reporting quantitative BCR::ABL1 (p210) levels. 9 This study therefore evaluates the CAP PT survey data from the 2009A to the 2023B mailings, during which participant laboratories grew from 48 to 228. This study encompasses 30 surveys (two surveys, A and B, per year) with a total of 75 samples and 5,534 laboratory PT responses (Supplemental Table 1). Percent of laboratories using IS over time After the 2008 publication describing standardized BCR::ABL1 reporting on the IS, CAP began requesting IS-based PT survey results reporting for laboratories using this standardized reporting unit. 9 Utilization of IS-based reporting by CAP-participating labs (which requires calibration and quantitative quality control) increased from 8% in 2009 to 86% in 2015 (Fig. 1 ), at which point CAP mandated IS-based reporting for formal PT survey grading. By 2019B, over 99% of laboratories reported in IS. To determine if this time-dependent increase in IS-based reporting correlated with other BCR::ABL1 performance metrics, the 15 years of PT data were divided into “early” (2009-2015A), “middle” (2015B-2019A), and “late” (2019B-2023B) time windows. Inter-Laboratory Reproducibility (Precision) over time correlates with adoption of IS A major force driving standardization of BCR::ABL1 reporting on IS was the heterogeneity of testing methods between labs, limiting inter-laboratory comparability of results. To directly assess whether the primary goal of the IS standardization effort – improved inter-laboratory precision – was realized, the inter-laboratory SD of blinded CAP PT survey results for each of the 30 mailings was examined. Since precision of quantitative testing of analytes correlates with their abundance, low-level targets generally having higher imprecision, 3 distinct “levels” of BCR::ABL1 RNA per CAP mailing were defined: “high”, “intermediate”, and “low” (Supplemental Table 1). 19 Due to the non-IS results reported in the early years of this study, the inter-laboratory SDs were calculated using two log-transformed reporting units: BCR::ABL1 / control gene RNA ratio [log(ratio)] (used in 2009–2015) and/or %IS (used after 2012 for the low and intermediate samples, and after 2017 for the high-level sample). For all three BCR::ABL1 RNA levels (high, intermediate, and low), the inter-laboratory precision improved over time during the 2009-2015A “early ” time frame, demonstrated by a significant time-dependent decrease in the inter-lab SD of the reported log(ratio) BCR::ABL1 result (slope = -0.027, -0.032, -0.038 for high, intermediate, and low BCR::ABL1 samples, respectively; p < 0.001 for all three; Figs. 2 – 4 , left panels; Table 1). The percentage of labs reporting on the IS rose dramatically during this “early” period (slope = 7.68, p < 0.001, Figs. 2 – 4 , left panel; Table 1). In this early time frame, the time-dependent increase in labs using IS was significantly inversely correlated with the decrease in the inter-laboratory log-ratio SD (high sample r= -0.73, p = 0.004; intermediate sample r = -0.71, p = 0.022; low sample r= -0.84, p = 0.002; Figs. 2 – 4 ;Table 1). By 2015, at the start of the “middle” time frame (2015B-2019A), IS-based reporting had already approached its near-100% maximum (with 91.5% of laboratories reporting on IS by 2015B), and thus increased less steeply (slope = 0.89; p = 0.003) than in the previous early time frame (Figs. 2 – 4 , Table 1). The concomitant inter-laboratory SD for BCR::ABL1 log(IS) mirrored this pattern in this middle period, with a minimal decline over time toward its ultimate late period 0.2 log nadir (Figs. 2 – 4 , Table 1). Specifically, the high and intermediate level BCR::ABL1 samples showed no statistically significant decrease in log(IS) SD over this “middle” time period (slope not different from zero), and there was then no statistically significant correlation between inter-lab SD (of log(IS) values) and IS-based reporting (Figs. 2 – 3 ; Table 1). Only the low-level BCR::ABL1 sample continued to show a significantly declining inter-lab SD during this middle time period [middle time slope = -0.037 (P < 0.001), Table 1], although the SD decline was not significantly correlated with the concomitant slight increase in IS-based-reporting (Table 1). By the start of the “late” time window (2019B-2023B), the inter-laboratory precision had already reached its nadir value of ~ 0.2 log(IS) SD for all three BCR::ABL1 RNA levels, and the log(IS) SD’s did not further decrease during this time frame (slopes no longer significantly negative) (Figs. 2 – 4 , right panels). Similarly, by 2019, utilization of IS-based reporting had already plateaued at > 99%, with no further increase (slope not significantly different than zero). There was no significant statistical correlation between inter-laboratory reproducibility and IS-based reporting in this “late” time period (Table 1). Since the “early” and “middle” time periods included a mix of laboratories reporting and not reporting IS-based results, a separate analysis of only laboratories reporting on the IS between 2012 and 2019A was performed. During this period, the inter-laboratory SDs for log(IS) values trended significantly downward for the intermediate (slope = -0.010, p<.001) and low (slope = -0.012, p = 0.003) BCR::ABL1 samples (Figs. 3 and 4 , Table 1). Concomitantly, IS-based reporting significantly increased during this 2012–2019 time period (slope = 3.19; p < 0.001). [This evaluation was not performed on the high transcript sample since CAP did not collect IS results for the high transcript samples until 2017, when approximately 90% of laboratories were reporting on the IS.] In comparison, after 2019, there was no analogous downward trend in log(IS) SD values (slope not significantly less than zero), nor any significant correlation with IS-based reporting (which had plateaued). Control gene over time Since the first publication describing optimized control genes for BCR::ABL1 (p210) RQ-PCR testing, ABL1 has been identified as a control gene which does not significantly differ in expression between normal and leukemic samples at diagnosis. 20 ABL1 is one of only three control genes suitable for the IS conversion/calibration and is the most commonly used reference gene in clinical labs. 8 During this 15-year study, the utilization of ABL1 as a control gene increased from 55.9% of laboratories (2009A) to 99.6% by 2023B (Fig. 5 ). Inter-Laboratory Reproducibility (Precision) over time does not correlate to control gene utilization To assess if the time-dependent increased in ABL1 utilization as a control gene correlated with improved inter-laboratory reproducibility (precision), a homogeneity of variance comparison was performed between laboratories using ABL1 as the control gene and laboratories using any non- ABL1 control gene. To minimize the effects of IS-based calibration on this analysis, only CAP surveys prior to 2012B, when CAP began collecting IS-based reporting data, were included. These early surveys offer statistically robust comparisons, since the number of laboratories using a non- ABL1 control gene became very small thereafter. In this “early” period (2009A to 2012A), characterized by a steep time-dependent reduction in inter-laboratory SDs of BCR::ABL1 log-ratios (slopes = -0.034, -0.076, -0.072 for the high, intermediate, and low samples, respectively), there were no mailings or samples for which the inter-lab BCR::ABL1 precision significantly differed between ABL1 and non- ABL1 laboratories. Sources of primers/probes over time Participating laboratories have historically chosen from a wide range of sources for the primers and probes used in laboratory-developed BCR::ABL1 (p210) RQ-PCR tests, without dominance of a single manufacturer or reagent source (Fig. 6 ). There was no apparent change in sourcing of reagents throughout the 2009–2023 monitoring period. Commercially packaged assays that changed ownership during this time period were grouped together. Commercial assays that were approved as an in vitro diagnostic by the Food and Drug Administration (FDA) during the study period were grouped as FDA assays once they gained FDA approval (Fig. 6 , yellow: see also Fig. 7 ). The first FDA-cleared quantitative BCR::ABL1 (p210) test became available in 2016. Since 2017, CAP PT surveys specifically asked laboratories if FDA-approved methods for BCR::ABL1 quantification were used. As of the data freeze for this manuscript, three additional BCR::ABL1 assays have also attained FDA authorization. From 2017–2023, laboratories’ use of FDA-approved (unmodified or modified) BCR::ABL1 assays rose from 6% to over 30% of participants (Fig. 6 and Fig. 7 ). Inter-Laboratory Reproducibility (Precision) over time does not correlate with implementation of an FDA-approved assay Inter-laboratory precision for BCR::ABL1 RNA levels had already reached its nadir (~ 0.2 log SD) by the time FDA-approved assays became available starting in 2016, suggesting no causal relationship between these two variables. Nonetheless, a homogeneity of variance comparison between inter-laboratory precision (SD) and use of an FDA-approved assay was performed. Across all survey mailings and transcript levels (high, intermediate, and low), there was no statistically significant difference in the inter-lab BCR::ABL1 precision between FDA and non-FDA laboratories. Discussion The IS was developed with the specific goal of improving inter-laboratory reproducibility for BCR::ABL1 p210 RQ-PCR quantitation. To address whether this goal was achieved, this study evaluated over 5,534 blinded PT results from over 200 laboratories over 15 years, encompassing 30 individual semi-annual mailings and 75 blinded RNA samples. This cohort of laboratories, chosen only by their decision to participation in CAP PT in compliance with mandatory PT requirements, thus represents the broad heterogeneity of clinical molecular diagnostic laboratories in the US and is likely less enriched for laboratories with CML-specific research interests, who may have been over-represented in other published BCR::ABL1 inter-laboratory sample-sharing studies. 19 , 21 , 22 To our knowledge, this study utilizes the largest inter-laboratory sample-sharing data set to date for BCR::ABL1 RQ-PCR testing. The major conclusion of this study is unequivocal: inter-laboratory precision has significantly improved over time with increased adoption of IS-based reporting. In particular, during the “early” years (2009–2015), with the largest year-over-year increase in IS adoption, there was a statistically significant inverse correlation between increased IS-based reporting and decreased inter-laboratory SDs of reported RQ-PCR testing results across all levels of BCR::ABL1 p210 transcript analyzed, confirming a robust time-dependent association between these 2 variables. From 2009–2015, laboratories reporting on the IS increased from 8% to 86%, while the inter-laboratory SD decreased from approximately 0.7–0.9 to 0.2 log(IS), indicating approximately 7 years to high level adoption of IS from the time of the original IS publication in 2008. 9 After 2019, however, when IS-based reporting approached 100%, there was no further improvement in inter-laboratory SD (for any BCR::ABL1 sample level), suggesting that IS-based reporting could be causally linked to improved inter-laboratory precision. In this “late” time window, inter-laboratory SD values stabilized at 0.2 log(IS), a level similar to findings from other studies restricted to laboratories using the IS. 23 When only IS-reporting laboratories before 2019 were analyzed in our data set, there was only a subtle time-dependent improvement in inter-laboratory precision. This declining SD slope for IS-only laboratories is not as steep as when non-IS results were included, as expected, potentially reflecting the gradual adjustment of laboratories to appropriate application of IS standardization. Although these data definitively show a significant correlation between IS-based reporting and improved inter-laboratory precision, causality cannot be definitively proven. Other possible explanations for the observed time-dependent decrease in inter-lab SD were considered. The increased time-dependent use of the ABL1 gene as the reference gene for RQ-PCR, despite its many drawbacks as a control gene, also increased over time from 56% of labs in 2009, to 75% percent in 2012, and to 99% in 2023. 10 However, there was no evidence of any significant effect of the use of the ABL1 reference gene (versus any non- ABL1 reference gene) on the inter-lab reproducibility of BCR::ABL1 measurements for the “early” years (2009A-2012A) when there were sufficient non- ABL1 laboratories to assess. This analysis suggests that the increased time-dependent use of the ABL1 reference gene is not a major contributor to the observed improvement in inter-laboratory precision. The convergence upon a single technical method for performing BCR::ABL1 RQ-PCR is also unlikely to account for the observed improvement in inter-laboratory precision, given the wide range of different methods/kits/reagents used for RQ-PCR throughout the study period, without any time-dependent dominance of a single method. An additional possible explanation for the improved inter-lab SDs could be the increased utilization of FDA-approved assays compared to laboratory-developed assays. While the adoption of FDA-approved BCR::ABL1 assays did rise during the 2017–2023 time-period, the inter-lab standard deviations of RQ-PCR testing results had already reached their nadir value by 2017. In addition, there was no significant difference in inter-laboratory SDs between users of laboratory-developed tests (LDTs) and FDA-approved assays. This data demonstrates that FDA-approval does not indicate improved assay performance, a conclusion also supported by other comparative assay performance studies. 12 – 14 Overall, the time-dependent improvement in inter-laboratory precision observed in this study does not correlate independently to variables such as reference gene choice, PCR primer/probe source, or use of FDA-approved kits. Instead, the improved precision strongly correlates with the increased use of IS-based reporting. This finding is clinically impactful given the consensus treatment implications for CML patients achieving critical BCR::ABL1 transcript level milestones during TKI treatment, such as early molecular response ( BCR::ABL1 < 10% IS), major molecular response ( BCR::ABL1 < 0.1% IS), deep molecular response ( BCR::ABL1 < 0.01%), or molecular relapse (1-log increase in BCR::ABL1 ). 3 , 5 This study has several limitations. First, laboratories self-reported their use of the IS, which was not independently verified and may be misclassified. Second, data capture for laboratory methods evolved over the course of the 15-year study period, leading to heterogeneous, inconsistent documentation across the survey years. Third, participation in this CAP PT program was voluntary (although participation in some form of PT is mandatory under CLIA), introducing the potential for selection bias. Fourth, laboratories could enter or exit participation in this PT survey at will, resulting in a dynamic and shifting cohort over time. Fifth, while one might speculate that CAP surveys could preferentially retain higher-performing laboratories, the substantial time-dependent increase in participants suggests that increasing numbers of newer, less experienced laboratories were also included, potentially balancing or diluting this effect. Lastly, missing data points were common due to changing laboratory participation and reporting procedures, which could impact the completeness and interpretation of certain analyses. In summary, this study provides a comprehensive 15-year analysis of quantitative BCR::ABL1 (p210) RQ-PCR proficiency testing across a broad spectrum of clinical diagnostic laboratories. This large study of blinded PT results provides strong evidence that 1) inter-laboratory precision of BCR::ABL1 results has markedly improved in the past 15 years, and 2) this improvement in inter-laboratory precision correlates with, and is likely attributable to, the progressive adoption of the IS for results reporting. This study also demonstrates that neither standardization of control gene usage (particularly adoption of ABL1 ), nor the eventual introduction of FDA-approved assays, had a significant impact on inter-laboratory precision, compared to the effect of IS implementation. By 2019, with nearly universal use of the IS by over 200 participating laboratories, inter-laboratory variance reached a nadir of 0.2 log(IS) SD. These findings underscore the critical role of IS-based reporting in ensuring consistent and reliable molecular monitoring in CML, enabling better patient care through more reproducible laboratory measurements. Declarations Funding: None. Author Contributions : A.S.K. and L.J.J. conceived the study. Data analysis was performed by A.S.K., T.L., D.A.O., P.V., C.W., and R.D.P. Manuscript was written by A.S.K., R.D.P., T.L., and P.V. Data review and manuscript editing was performed by all authors. All authors approved of the final manuscript. Conflicts of Interest: The authors declare no competing financial interests. Correspondence : Annette S. Kim University of Michigan/Michigan Medicine Department of Pathology NCRC Building 35, Room 36-1221-79 2800 Plymouth Road Ann Arbor, MI 48109-2800 E-mail: [email protected] Data sharing agreement: The data analyzed for this study are the property of the College of American Pathologists (CAP), and specific laboratory submission data is proprietary to assure privacy. Summaries of the de-identified data are presented in this manuscript. Requests for de-identified data should be addressed to the CAP. References Jabbour E, Kantarjian H. Chronic myeloid leukemia: 2025 update on diagnosis, therapy, and monitoring. Am J Hematol . 2024;99(11):2191-2212.doi:10.1002/ajh.27443 Johnson JR, Bross P, Cohen M et al. Approval summary: Imatinib mesylate capsules for treatment of adult patients with newly diagnosed philadelphia chromosome-positive chronic myelogenous leukemia in chronic phase. Clin Cancer Res . 2003;9(6):1972-1979. Chronic myeloid leukemia. NCCN Guidelines Version 1.2026 . Plymouth Meeting, PA: National Comprehensive Cancer Network; 2025. Apperley JF, Milojkovic D, Cross NCP et al. 2025 european leukemianet recommendations for the management of chronic myeloid leukemia. Leukemia . 2025;39(8):1797-1813.doi:10.1038/s41375-025-02664-w Hochhaus A, Baccarani M, Silver RT et al. European leukemianet 2020 recommendations for treating chronic myeloid leukemia. Leukemia . 2020;34(4):966-984.doi:10.1038/s41375-020-0776-2 Soverini S, Abruzzese E, Bocchia M et al. Next-generation sequencing for bcr-abl1 kinase domain mutation testing in patients with chronic myeloid leukemia: A position paper. J Hematol Oncol . 2019;12(1):131.doi:10.1186/s13045-019-0815-5 Press RD, Kamel-Reid S, Ang D. Bcr-abl1 rt-qpcr for monitoring the molecular response to tyrosine kinase inhibitors in chronic myeloid leukemia. J Mol Diagn . 2013;15(5):565-576.doi:10.1016/j.jmoldx.2013.04.007 Hughes T, Deininger M, Hochhaus A et al. Monitoring cml patients responding to treatment with tyrosine kinase inhibitors: Review and recommendations for harmonizing current methodology for detecting bcr-abl transcripts and kinase domain mutations and for expressing results. Blood . 2006;108(1):28-37.doi:10.1182/blood-2006-01-0092 Branford S, Fletcher L, Cross NC et al. Desirable performance characteristics for bcr-abl measurement on an international reporting scale to allow consistent interpretation of individual patient response and comparison of response rates between clinical trials. Blood . 2008;112(8):3330-3338.doi:10.1182/blood-2008-04-150680 Wang YL, Lee JW, Cesarman E, Jin DK, Csernus B. Molecular monitoring of chronic myelogenous leukemia: Identification of the most suitable internal control gene for real-time quantification of bcr-abl transcripts. J Mol Diagn . 2006;8(2):231-239.doi:10.2353/jmoldx.2006.040404 Merker JD, Devereaux K, Iafrate AJ et al. Proficiency testing of standardized samples shows very high interlaboratory agreement for clinical next-generation sequencing-based oncology assays. Arch Pathol Lab Med . 2019;143(4):463-471.doi:10.5858/arpa.2018-0336-CP Kim AS, Bartley AN, Bridge JA et al. Comparison of laboratory-developed tests and fda-approved assays for braf, egfr, and kras testing. JAMA Oncol . 2018;4(6):838-841.doi:10.1001/jamaoncol.2017.4021 Surrey LF, Oakley FD, Merker JD et al. Next-generation sequencing (ngs) methods show superior or equivalent performance to non-ngs methods on braf, egfr, and kras proficiency testing samples. Arch Pathol Lab Med . 2019;143(8):980-984.doi:10.5858/arpa.2018-0394-CP Moncur JT, Bartley AN, Bridge JA et al. Performance comparison of different analytic methods in proficiency testing for mutations in the braf, egfr, and kras genes: A study of the college of american pathologists molecular oncology committee. Arch Pathol Lab Med . 2019;143(10):1203-1211.doi:10.5858/arpa.2018-0396-CP Hayden RT, Yan X, Wick MT et al. Factors contributing to variability of quantitative viral pcr results in proficiency testing samples: A multivariate analysis. J Clin Microbiol . 2012;50(2):337-345.doi:10.1128/jcm.01287-11 Nikiforova MN, Hsi ED, Braziel RM et al. Detection of clonal igh gene rearrangements: Summary of molecular oncology surveys of the college of american pathologists. Arch Pathol Lab Med . 2007;131(2):185-189.doi:10.5858/2007-131-185-docigr Keung ES, Souers RJ, Bridge JA et al. Comparative performance of high-risk human papillomavirus rna and DNA in situ hybridization on college of american pathologists proficiency tests. Arch Pathol Lab Med . 2020;144(3):344-349.doi:10.5858/arpa.2019-0093-CP Polesky HF, Hanson MR. Human immunodeficiency virus type 1 proficiency testing. The american association of blood banks/college of american pathologists program. Arch Pathol Lab Med . 1990;114(3):268-271. Scott S, Travis D, Whitby L, Bainbridge J, Cross NCP, Barnett D. Measurement of bcr-abl1 by rt-qpcr in chronic myeloid leukaemia: Findings from an international eqa programme. Br J Haematol . 2017;177(3):414-422.doi:10.1111/bjh.14557 Beillard E, Pallisgaard N, van der Velden VH et al. Evaluation of candidate control genes for diagnosis and residual disease detection in leukemic patients using 'real-time' quantitative reverse-transcriptase polymerase chain reaction (rq-pcr) - a europe against cancer program. Leukemia . 2003;17(12):2474-2486.doi:10.1038/sj.leu.2403136 White HE, Salmon M, Albano F et al. Standardization of molecular monitoring of cml: Results and recommendations from the european treatment and outcome study. Leukemia . 2022;36(7):1834-1842.doi:10.1038/s41375-022-01607-z Griffiths M, Patton SJ, Grossi A, Clark J, Paz MF, Labourier E. Conversion, correction, and international scale standardization: Results from a multicenter external quality assessment study for bcr-abl1 testing. Arch Pathol Lab Med . 2015;139(4):522-529.doi:10.5858/arpa.2013-0754-OA Cross NC, White HE, Ernst T et al. Development and evaluation of a secondary reference panel for bcr-abl1 quantification on the international scale. Leukemia . 2016;30(9):1844-1852.doi:10.1038/leu.2016.90 Table Table 1 is available in the Supplementary Files section. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Table1.xlsx Table 1 SupplementaryMaterialsCAPMRD20260129.pdf Supplemental Table 1. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 10 Mar, 2026 Review # 2 received at journal 06 Mar, 2026 Review # 1 received at journal 25 Feb, 2026 Reviewer # 2 agreed at journal 20 Feb, 2026 Reviewer # 1 agreed at journal 20 Feb, 2026 Reviewers invited by journal 20 Feb, 2026 Editor assigned by journal 20 Feb, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 19 Feb, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8921143","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594567786,"identity":"b7c059e3-5d5f-4b57-a95b-ccba30f4ff5f","order_by":0,"name":"Annette Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACCWYogx/EAWIeKElYi4RkA9FaYLTBARiHkBbJdu7Ez4U7bOqMrx0+eIOxbZsM/+wGxgdv23BrkWbm3Sw980yahNnttGQLxrbbPBJ3DjAbzsWjRY6Zd4M0b9thoJYcMwmQFgOJBDagCF4tm3/ztv2XMJ6d/w2mhf03Pi1Ah20DmnlAwkA6hw1uCzM+LZLNvNuseduSJWfcTjO2SDgH9MuNxGbJOedwa5E4f3bzbd42O37+2ckPb3wou23PPyP54Ic3Zbi1oIIEMMnYQKz6UTAKRsEoGAU4AADpDkeN23hvzAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8699-2439","institution":"University of Michigan","correspondingAuthor":true,"prefix":"","firstName":"Annette","middleName":"","lastName":"Kim","suffix":""},{"id":594567787,"identity":"e5af720a-2bc4-4089-bae9-e72843327196","order_by":1,"name":"Thomas Long","email":"","orcid":"","institution":"College of American Pathologists","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Long","suffix":""},{"id":594567788,"identity":"e72e45ab-1039-4ee2-8864-f12177548840","order_by":2,"name":"Derek Oldridge","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Derek","middleName":"","lastName":"Oldridge","suffix":""},{"id":594567789,"identity":"1cd2cc10-d9b4-4d2c-9a27-01bfa90cadec","order_by":3,"name":"Lawrence Jennings","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"","lastName":"Jennings","suffix":""},{"id":594567790,"identity":"1af8e8b4-8c66-449d-bbad-2a7a5d4f1329","order_by":4,"name":"Yi Ding","email":"","orcid":"","institution":"Geisinger Health","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Ding","suffix":""},{"id":594567791,"identity":"e3d0d7ea-7bf8-4736-a010-3a14d7d65130","order_by":5,"name":"Patricia Vasalos","email":"","orcid":"","institution":"College of American Pathologists","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Vasalos","suffix":""},{"id":594567792,"identity":"e2ea2352-ac55-40da-8659-352c12d7eeff","order_by":6,"name":"Christopher Watt","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Watt","suffix":""},{"id":594567793,"identity":"1004382d-0885-4643-995d-567080387d2f","order_by":7,"name":"Richard Press","email":"","orcid":"https://orcid.org/0000-0002-2103-5144","institution":"Oregon Health \u0026 Science University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Press","suffix":""}],"badges":[],"createdAt":"2026-02-19 22:45:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8921143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8921143/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103444410,"identity":"10d413c7-4b38-42f7-a36f-c929aea29887","added_by":"auto","created_at":"2026-02-25 18:10:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":429807,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of laboratories reporting on the International Scale over time. X axis lists the mailing designations with the number of total participants in parentheses.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/b49b7e5495661d333e1b4b55.jpg"},{"id":103444415,"identity":"ab36d635-3eb2-4bae-9d56-7d3d67e5dda7","added_by":"auto","created_at":"2026-02-25 18:10:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":521967,"visible":true,"origin":"","legend":"\u003cp\u003eInter-laboratory standard deviation of \u003cem\u003eBCR::ABL1 \u003c/em\u003etranscript quantification reported as log(ratio) and Log(IS) over time for the high transcript sample (red and blue solid lines). The percentage of labs reporting on the International Scale is overlaid (green asterisk line; see also Figure 1). Slope and correlation coefficients are depicted for the three time windows (light blue boxes: early = 2009A – 2015A; middle = 2015B – 2019A; late = 2019B – 2023B). Abbreviations: IS – International Scale; SD – standard deviation.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/a17f9146f1982c61839f3a2c.jpg"},{"id":103444411,"identity":"3a0a6206-4c24-453a-b45d-f38e2c58840b","added_by":"auto","created_at":"2026-02-25 18:10:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":538368,"visible":true,"origin":"","legend":"\u003cp\u003eInter-laboratory standard deviation of \u003cem\u003eBCR::ABL1 \u003c/em\u003etranscript quantification reported as log(ratio) and Log(IS) over time for the intermediate transcript sample (red and blue solid lines). The percentage of labs reporting on the International Scale is overlaid (green asterisk line; see also Figure 1). Slope and correlation coefficients are depicted for the three time windows (light blue boxes: early = 2009A – 2015A; middle = 2015B – 2019A; late = 2019B – 2023B). Abbreviations: IS – International Scale; SD – standard deviation.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/3f5d55872630605d8025e39a.jpg"},{"id":103444412,"identity":"83eaf688-d827-44e8-a2f6-dcdfe506366f","added_by":"auto","created_at":"2026-02-25 18:10:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":527892,"visible":true,"origin":"","legend":"\u003cp\u003eInter-laboratory standard deviation of \u003cem\u003eBCR::ABL1 \u003c/em\u003etranscript quantification reported as log(ratio) and Log(IS) over time for the low transcript sample (red and blue solid lines). The percentage of labs reporting on the International Scale is overlaid (green asterisk line; see also Figure 1). Slope and correlation coefficients are depicted for the three time windows (light blue boxes: early = 2009A – 2015A; middle = 2015B – 2019A; late = 2019B – 2023B). Abbreviations: IS – International Scale; SD – standard deviation.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/ddf81ca1bfccf7c9f099738b.jpg"},{"id":103444417,"identity":"9fd6df91-e57c-4c10-9a39-3051f8553f3f","added_by":"auto","created_at":"2026-02-25 18:10:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":679096,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of laboratories using various control genes over time.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/5224726811ba94ca4f9dd892.jpg"},{"id":103507963,"identity":"9c4bd453-9179-4c55-8fc4-2287c5620759","added_by":"auto","created_at":"2026-02-26 13:46:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":634656,"visible":true,"origin":"","legend":"\u003cp\u003eReported source of assay reagents, primers, and/or probes.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/11c8f0e01859746bc1160eb7.jpg"},{"id":103444416,"identity":"9741653a-570e-433a-bb54-fdba4beed5fa","added_by":"auto","created_at":"2026-02-25 18:10:46","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":280818,"visible":true,"origin":"","legend":"\u003cp\u003eManufacturers of FDA approved assays.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/cb3febf6db5e88dff11fa07f.jpg"},{"id":103510028,"identity":"e93de0a9-ed18-413c-88d4-6c4d99708816","added_by":"auto","created_at":"2026-02-26 14:02:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4325766,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/498f436c-1810-489b-9b84-113f7c4fcc4c.pdf"},{"id":103444409,"identity":"e10b3bf5-6d8b-4647-8fc9-a19254b7d2c4","added_by":"auto","created_at":"2026-02-25 18:10:46","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11334,"visible":true,"origin":"","legend":"Table 1","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/91768bee5d829913792e50dd.xlsx"},{"id":103444414,"identity":"d3fc9405-e972-4cd9-a46c-e6effe664d17","added_by":"auto","created_at":"2026-02-25 18:10:46","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":84016,"visible":true,"origin":"","legend":"Supplemental Table 1.","description":"","filename":"SupplementaryMaterialsCAPMRD20260129.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8921143/v1/c99657bf1f752ca17c6fd75e.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Improved Inter-Lab Precision After CML International Scale Adoption: a 15-year history of BCR::ABL1 Proficiency Testing","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u0026bull; Since the adoption of the International Scale (IS), inter-laboratory precision for transcript level quantification has improved.\u003c/p\u003e\u003cp\u003e\u0026bull; Improved precision is correlated with use of IS and not with any specific control gene, reagent kit, or FDA-approved assay.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eIn chronic myeloid leukemia (CML), the \u003cem\u003eBCR::ABL1\u003c/em\u003e fusion kinase, produced by the Philadelphia chromosome translocation [t(9;22)], serves as both an ideal therapeutic target and a specific diagnostic biomarker of post-treatment measurable residual disease (MRD).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Since the first approval in 2003 of a tyrosine kinase inhibitor (TKI) targeting the \u003cem\u003eBCR::ABL1\u003c/em\u003e oncoprotein by the Food and Drug Administration (FDA) a, the long-term survival and prognosis of this once-fatal leukemia has dramatically improved.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The extraordinary efficacy of these TKIs demands the use of sensitive, accurate, and precise laboratory methods to quantitatively monitor low-level MRD. Optimal TKI-based management of CML patients with the p210 fusion (created by the e13a2 or e14a2 exon breakpoints) relies on comparison to consensus milestone levels. These MRD determinations can dictate the choice and/or dose of the specific TKI drug and can serve as a key prognostic marker for predicting long-term outcomes, treatment resistance, and impending relapse.\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Reverse transcription quantitative PCR (RQ-PCR) remains the primary clinical laboratory method for MRD monitoring, with an analytic measurement range spanning 4 or more logs. Given the importance of MRD monitoring, inter-laboratory variability in the quantitation of \u003cem\u003eBCR::ABL1\u003c/em\u003e RNA directly impacts patient management and clinical outcomes.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e The non-centralized health care system in the United States (US), whereby diagnostic laboratory testing is often performed in different laboratories, depending on the patient\u0026rsquo;s employment and insurance status, further amplifies this problem.\u003c/p\u003e \u003cp\u003eDespite well-documented clinical utility, \u003cem\u003eBCR::ABL1\u003c/em\u003e RQ-PCR remains a technically challenging multi-step laboratory procedure that prompted international efforts early in the TKI treatment era to create a standardized reporting unit for \u003cem\u003eBCR::ABL1\u003c/em\u003e RNA.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e In 2008, these efforts resulted in the clinical and analytical validation of the international scale (IS) for the reporting of \u003cem\u003eBCR::ABL1\u003c/em\u003e RNA levels of the p210 fusion product.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e IS-based reporting requires the calibration of analytically-determined transcript levels to reference standards tied to clinically proven treatment response thresholds from TKI clinical trials.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhile the reporting paradigm is standardized through IS alignment, other steps in the \u003cem\u003eBCR::ABL1\u003c/em\u003e procedure remain non-standardized between laboratories, including RNA extraction, instrumentation, IS calibration methodology, automation \u003cem\u003evs\u003c/em\u003e manual methods, and clinical interpretation.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Multi-laboratory proficiency testing (PT) allows laboratories to compare the results of their multi-step testing process to gold standard accuracy standards based on the IS and, for some PT vendors, to other participants in aggregate. This PT data also informs the laboratory and clinical community of the inter-laboratory reproducibility. According to the Clinical Laboratories Improvement Act (CLIA), some form of PT is required of US-based clinical diagnostic laboratories. The College of American Pathologists (CAP) is the leading provider of standardized PT materials and has surveyed laboratories on \u003cem\u003eBCR::ABL1\u003c/em\u003e transcript quantitation since 2006. The resulting CAP-sponsored PT data is the most comprehensive, unbiased, real-world source of laboratory performance data for different analytes, including many that are based on quantitative PCR.\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe purpose of this study was to evaluate whether inter-laboratory precision of quantitative \u003cem\u003eBCR::ABL1\u003c/em\u003e RNA reporting has improved over time, and, if so, whether that improvement is attributable to the institutional adoption of IS-based standardized calibration. This study reviews 15 years of serial data from the CAP proficiency testing survey, whereby over 200 clinical laboratories blindly tested three samples with varying \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) transcript levels twice per year (annual A and B mailings; 2009\u0026ndash;2023, 30 total mailings, 90 total samples). This dataset should be representative of the performance status of \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) testing across the primarily US-based clinical laboratory community during the timeframe when labs were progressively implementing IS-based reporting, allowing for a comprehensive assessment of the role of IS in inter-laboratory reproducibility.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSurvey Information\u003c/h2\u003e \u003cp\u003eFifteen years of CAP proficiency testing data on quantitative \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) testing were examined, spanning the 2009A through 2023B mailings. Although originally an educational PT program, the \u003cem\u003eBCR::ABL1\u003c/em\u003e MRD PT survey became a graded survey in 2014, tying laboratory performance with laboratory accreditation. Laboratory performance was scored based on reported %IS and MR (log molecular response) responses, where all reported data had to fall within +/- 2 standard deviations from the mean MR value of all participants. By this definition, ~\u0026thinsp;5% of laboratory responses would \u0026ldquo;fail\u0026rdquo; in each survey. Laboratories reporting persistent outlier results on this CAP survey risk formal lab licensure penalties under CLIA regulations, including cessation of clinical testing. Any \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) result that was not aligned to the IS and/or calculated using a non-\u003cem\u003eABL1\u003c/em\u003e control gene could not be graded.\u003c/p\u003e \u003cp\u003eThe surveys included additional questions about the laboratory practices of the participants. For instance, each survey queries laboratories on the selection of a control gene. In addition, many surveys interrogated the origin of the PCR primers and, by the 2017B mailing, were specifically asking if the laboratory\u0026rsquo;s assay was (or was not) FDA approved (modified or unmodified), with the option to specify the non-FDA assay vendor.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpecimen Information\u003c/h3\u003e\n\u003cp\u003eBlinded proficiency specimens were provided to participants as RNA extracted from a CML cell line diluted in \u003cem\u003eBCR::ABL1\u003c/em\u003e negative RNA to achieve the final dilution. Each participant received three samples per mailing, one mimicking a new CML diagnosis sample (\u0026ldquo;high\u0026rdquo; level of \u003cem\u003eBCR::ABL1\u003c/em\u003e transcript), and two additional samples at \u0026ldquo;intermediate\u0026rdquo; and \u0026ldquo;low\u0026rdquo; transcript levels. For the analyses described herein, these different dilutions were binned into categories of \u0026ldquo;high\u0026rdquo; (10-fold dilution), \u0026ldquo;intermediate\u0026rdquo; (1: 100, 1:1,000, and 1:5,000) or \u0026ldquo;low\u0026rdquo; (1:10,000, 1:50,000, and 1: 100,000) \u003cem\u003eBCR::ABL1\u003c/em\u003e RNA for each survey. Some mailings included negative control specimens not used in the analysis, and those mailings only had two (not three) levels of specimens. Specimen dilutions for each mailing are presented in Supplemental Table\u0026nbsp;1.\u003c/p\u003e\n\u003ch3\u003eResponse Information\u003c/h3\u003e\n\u003cp\u003eParticipant responses were expressed as absolute transcript levels on a logarithmic scale [log(ratio)] from 2009A through 2015A (\u0026ldquo;ratio\u0026rdquo; defined as p210 RNA divided by reference gene RNA), and as log (IS) (\u003cem\u003eBCR::ABL1\u003c/em\u003e international scale) from 2012A through 2023B. Responses were also recorded as relative log reduction from the \u0026ldquo;diagnostic sample\u0026rdquo; (highest \u003cem\u003eBCR::ABL1\u003c/em\u003e sample of the blinded 3-sample mailing) from 2009A through 2018A, and as molecular response [MR, equal to log\u003csub\u003e10\u003c/sub\u003e(100/%IS)] from 2018B through 2023B. For several years, there was overlap of reporting mechanisms and participants could report results in a variety of formats. Where this was true, all relevant responses are included in this study. During 2009\u0026ndash;2013, the transcript level of the \u0026ldquo;high\u0026rdquo; sample was not directly interrogated as the relative log-reduction level was zero, leaving no \u0026ldquo;high\u0026rdquo; data for those surveys. IS and MR-based reporting started in 2011B and 2015B, respectively, but only became mandatory in 2018B. Before the mandatory reporting of %IS and MR, there were periods (2011B -2018B) where a participating laboratory could report both log(ratio) and %IS, or log reduction and MR.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eResponse data were excluded for log ratio values of 0, IS values of 0, or any response with a qualifier (eg. non-numeric value above or below a laboratory\u0026rsquo;s detection limit, \u0026ldquo;not detected,\u0026rdquo; or \u0026ldquo;negative\u0026rdquo; IS value). MR responses that were greater than 0.4 from an MR value calculated by CAP based upon other response data (n\u0026thinsp;=\u0026thinsp;70) were removed as presumed typographical or calculation errors rather than erroneous results. A 2- pass 3-SD (standard deviation) outlier screen was applied for each measure, level, and mailing before data were summarized and analyzed.\u003c/p\u003e \u003cp\u003eFor each response measure, level, and mailing, the inter-laboratory SD was calculated and plotted against mailing date to visualize the time trend. A secondary overlaid axis displayed the percentage of participants reporting IS-aligned data. For the three time windows [early (2009-2015A), middle (2015B-2019A), and late (2019B-2023B)], a time-dependent regression line of the SD was fit, and a Pearson\u0026rsquo;s Correlation Coefficient (R) was calculated between SD and percentage of participants reporting IS at each level. Statistical tests were performed for time-dependent slopes of the fitted SD regression lines, slopes of the % reported IS regression lines, and the significance of the correlations for each time window and level. ANOVA was used to compare means between laboratories using \u003cem\u003eABL1\u003c/em\u003e and any other non-\u003cem\u003eABL1\u003c/em\u003e control gene. Levene\u0026rsquo;s Homogeneity of Variance test compared inter-laboratory variances 1) between \u003cem\u003eABL1\u003c/em\u003e and non-\u003cem\u003eABL1\u003c/em\u003e control gene utilization and 2) between FDA and non-FDA assay utilization. All analyses were completed using SAS version 9.4 (Cary, NC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003eParticipant and Survey Information.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAlthough CAP began providing the \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) MRD PT survey in 2006, the data from the first few years of this survey was returned to CAP in a myriad of different formats and units. This early data also preceded the 2008 publication defining the standardized IS for reporting quantitative \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) levels.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e This study therefore evaluates the CAP PT survey data from the 2009A to the 2023B mailings, during which participant laboratories grew from 48 to 228. This study encompasses 30 surveys (two surveys, A and B, per year) with a total of 75 samples and 5,534 laboratory PT responses (Supplemental Table\u0026nbsp;1).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePercent of laboratories using IS over time\u003c/h2\u003e \u003cp\u003eAfter the 2008 publication describing standardized \u003cem\u003eBCR::ABL1\u003c/em\u003e reporting on the IS, CAP began requesting IS-based PT survey results reporting for laboratories using this standardized reporting unit.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Utilization of IS-based reporting by CAP-participating labs (which requires calibration and quantitative quality control) increased from 8% in 2009 to 86% in 2015 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), at which point CAP mandated IS-based reporting for formal PT survey grading. By 2019B, over 99% of laboratories reported in IS. To determine if this time-dependent increase in IS-based reporting correlated with other \u003cem\u003eBCR::ABL1\u003c/em\u003e performance metrics, the 15 years of PT data were divided into \u0026ldquo;early\u0026rdquo; (2009-2015A), \u0026ldquo;middle\u0026rdquo; (2015B-2019A), and \u0026ldquo;late\u0026rdquo; (2019B-2023B) time windows.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInter-Laboratory Reproducibility (Precision) over time correlates with adoption of IS\u003c/h3\u003e\n\u003cp\u003eA major force driving standardization of \u003cem\u003eBCR::ABL1\u003c/em\u003e reporting on IS was the heterogeneity of testing methods between labs, limiting inter-laboratory comparability of results. To directly assess whether the primary goal of the IS standardization effort \u0026ndash; improved inter-laboratory precision \u0026ndash; was realized, the inter-laboratory SD of blinded CAP PT survey results for each of the 30 mailings was examined. Since precision of quantitative testing of analytes correlates with their abundance, low-level targets generally having higher imprecision, 3 distinct \u0026ldquo;levels\u0026rdquo; of \u003cem\u003eBCR::ABL1\u003c/em\u003e RNA per CAP mailing were defined: \u0026ldquo;high\u0026rdquo;, \u0026ldquo;intermediate\u0026rdquo;, and \u0026ldquo;low\u0026rdquo; (Supplemental Table\u0026nbsp;1).\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Due to the non-IS results reported in the early years of this study, the inter-laboratory SDs were calculated using two log-transformed reporting units: \u003cem\u003eBCR::ABL1\u003c/em\u003e / control gene RNA ratio [log(ratio)] (used in 2009\u0026ndash;2015) and/or %IS (used after 2012 for the low and intermediate samples, and after 2017 for the high-level sample).\u003c/p\u003e \u003cp\u003eFor all three \u003cem\u003eBCR::ABL1\u003c/em\u003e RNA levels (high, intermediate, and low), the inter-laboratory precision improved over time during the 2009-2015A \u0026ldquo;early \u0026rdquo; time frame, demonstrated by a significant time-dependent decrease in the inter-lab SD of the reported log(ratio) \u003cem\u003eBCR::ABL1\u003c/em\u003e result (slope = -0.027, -0.032, -0.038 for high, intermediate, and low \u003cem\u003eBCR::ABL1\u003c/em\u003e samples, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all three; Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, left panels; Table\u0026nbsp;1). The percentage of labs reporting on the IS rose dramatically during this \u0026ldquo;early\u0026rdquo; period (slope\u0026thinsp;=\u0026thinsp;7.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, left panel; Table\u0026nbsp;1). In this early time frame, the time-dependent increase in labs using IS was significantly inversely correlated with the decrease in the inter-laboratory log-ratio SD (high sample r= -0.73, p\u0026thinsp;=\u0026thinsp;0.004; intermediate sample r = -0.71, p\u0026thinsp;=\u0026thinsp;0.022; low sample r= -0.84, p\u0026thinsp;=\u0026thinsp;0.002; Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e;Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy 2015, at the start of the \u0026ldquo;middle\u0026rdquo; time frame (2015B-2019A), IS-based reporting had already approached its near-100% maximum (with 91.5% of laboratories reporting on IS by 2015B), and thus increased less steeply (slope\u0026thinsp;=\u0026thinsp;0.89; p\u0026thinsp;=\u0026thinsp;0.003) than in the previous early time frame (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;1). The concomitant inter-laboratory SD for \u003cem\u003eBCR::ABL1\u003c/em\u003e log(IS) mirrored this pattern in this middle period, with a minimal decline over time toward its ultimate late period 0.2 log nadir (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;1). Specifically, the high and intermediate level \u003cem\u003eBCR::ABL1\u003c/em\u003e samples showed no statistically significant decrease in log(IS) SD over this \u0026ldquo;middle\u0026rdquo; time period (slope not different from zero), and there was then no statistically significant correlation between inter-lab SD (of log(IS) values) and IS-based reporting (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;1). Only the low-level \u003cem\u003eBCR::ABL1\u003c/em\u003e sample continued to show a significantly declining inter-lab SD during this middle time period [middle time slope = -0.037 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Table\u0026nbsp;1], although the SD decline was not significantly correlated with the concomitant slight increase in IS-based-reporting (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy the start of the \u0026ldquo;late\u0026rdquo; time window (2019B-2023B), the inter-laboratory precision had already reached its nadir value of ~\u0026thinsp;0.2 log(IS) SD for all three \u003cem\u003eBCR::ABL1\u003c/em\u003e RNA levels, and the log(IS) SD\u0026rsquo;s did not further decrease during this time frame (slopes no longer significantly negative) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, right panels). Similarly, by 2019, utilization of IS-based reporting had already plateaued at \u0026gt;\u0026thinsp;99%, with no further increase (slope not significantly different than zero). There was no significant statistical correlation between inter-laboratory reproducibility and IS-based reporting in this \u0026ldquo;late\u0026rdquo; time period (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eSince the \u0026ldquo;early\u0026rdquo; and \u0026ldquo;middle\u0026rdquo; time periods included a mix of laboratories reporting and not reporting IS-based results, a separate analysis of only laboratories reporting on the IS between 2012 and 2019A was performed. During this period, the inter-laboratory SDs for log(IS) values trended significantly downward for the intermediate (slope = -0.010, p\u0026lt;.001) and low (slope = -0.012, p\u0026thinsp;=\u0026thinsp;0.003) \u003cem\u003eBCR::ABL1\u003c/em\u003e samples (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;1). Concomitantly, IS-based reporting significantly increased during this 2012\u0026ndash;2019 time period (slope\u0026thinsp;=\u0026thinsp;3.19; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). [This evaluation was not performed on the high transcript sample since CAP did not collect IS results for the high transcript samples until 2017, when approximately 90% of laboratories were reporting on the IS.] In comparison, after 2019, there was no analogous downward trend in log(IS) SD values (slope not significantly less than zero), nor any significant correlation with IS-based reporting (which had plateaued).\u003c/p\u003e\n\u003ch3\u003eControl gene over time\u003c/h3\u003e\n\u003cp\u003eSince the first publication describing optimized control genes for \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) RQ-PCR testing, \u003cem\u003eABL1\u003c/em\u003e has been identified as a control gene which does not significantly differ in expression between normal and leukemic samples at diagnosis.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eABL1\u003c/em\u003e is one of only three control genes suitable for the IS conversion/calibration and is the most commonly used reference gene in clinical labs.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e During this 15-year study, the utilization of \u003cem\u003eABL1\u003c/em\u003e as a control gene increased from 55.9% of laboratories (2009A) to 99.6% by 2023B (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInter-Laboratory Reproducibility (Precision) over time does not correlate to control gene utilization\u003c/h2\u003e \u003cp\u003eTo assess if the time-dependent increased in \u003cem\u003eABL1\u003c/em\u003e utilization as a control gene correlated with improved inter-laboratory reproducibility (precision), a homogeneity of variance comparison was performed between laboratories using \u003cem\u003eABL1\u003c/em\u003e as the control gene and laboratories using any non-\u003cem\u003eABL1\u003c/em\u003e control gene. To minimize the effects of IS-based calibration on this analysis, only CAP surveys prior to 2012B, when CAP began collecting IS-based reporting data, were included. These early surveys offer statistically robust comparisons, since the number of laboratories using a non-\u003cem\u003eABL1\u003c/em\u003e control gene became very small thereafter. In this \u0026ldquo;early\u0026rdquo; period (2009A to 2012A), characterized by a steep time-dependent reduction in inter-laboratory SDs of \u003cem\u003eBCR::ABL1\u003c/em\u003e log-ratios (slopes = -0.034, -0.076, -0.072 for the high, intermediate, and low samples, respectively), there were no mailings or samples for which the inter-lab BCR::ABL1 precision significantly differed between \u003cem\u003eABL1\u003c/em\u003e and non-\u003cem\u003eABL1\u003c/em\u003e laboratories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSources of primers/probes over time\u003c/h2\u003e \u003cp\u003eParticipating laboratories have historically chosen from a wide range of sources for the primers and probes used in laboratory-developed \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) RQ-PCR tests, without dominance of a single manufacturer or reagent source (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). There was no apparent change in sourcing of reagents throughout the 2009\u0026ndash;2023 monitoring period. Commercially packaged assays that changed ownership during this time period were grouped together. Commercial assays that were approved as an in vitro diagnostic by the Food and Drug Administration (FDA) during the study period were grouped as FDA assays once they gained FDA approval (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, yellow: see also Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first FDA-cleared quantitative \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) test became available in 2016. Since 2017, CAP PT surveys specifically asked laboratories if FDA-approved methods for \u003cem\u003eBCR::ABL1\u003c/em\u003e quantification were used. As of the data freeze for this manuscript, three additional \u003cem\u003eBCR::ABL1\u003c/em\u003e assays have also attained FDA authorization. From 2017\u0026ndash;2023, laboratories\u0026rsquo; use of FDA-approved (unmodified or modified) \u003cem\u003eBCR::ABL1\u003c/em\u003e assays rose from 6% to over 30% of participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInter-Laboratory Reproducibility (Precision) over time does not correlate with implementation of an FDA-approved assay\u003c/h2\u003e \u003cp\u003eInter-laboratory precision for \u003cem\u003eBCR::ABL1\u003c/em\u003e RNA levels had already reached its nadir (~\u0026thinsp;0.2 log SD) by the time FDA-approved assays became available starting in 2016, suggesting no causal relationship between these two variables. Nonetheless, a homogeneity of variance comparison between inter-laboratory precision (SD) and use of an FDA-approved assay was performed. Across all survey mailings and transcript levels (high, intermediate, and low), there was no statistically significant difference in the inter-lab BCR::ABL1 precision between FDA and non-FDA laboratories.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe IS was developed with the specific goal of improving inter-laboratory reproducibility for \u003cem\u003eBCR::ABL1\u003c/em\u003e p210 RQ-PCR quantitation. To address whether this goal was achieved, this study evaluated over 5,534 blinded PT results from over 200 laboratories over 15 years, encompassing 30 individual semi-annual mailings and 75 blinded RNA samples. This cohort of laboratories, chosen only by their decision to participation in CAP PT in compliance with mandatory PT requirements, thus represents the broad heterogeneity of clinical molecular diagnostic laboratories in the US and is likely less enriched for laboratories with CML-specific research interests, who may have been over-represented in other published \u003cem\u003eBCR::ABL1\u003c/em\u003e inter-laboratory sample-sharing studies.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e To our knowledge, this study utilizes the largest inter-laboratory sample-sharing data set to date for \u003cem\u003eBCR::ABL1\u003c/em\u003e RQ-PCR testing. The major conclusion of this study is unequivocal: inter-laboratory precision has significantly improved over time with increased adoption of IS-based reporting. In particular, during the \u0026ldquo;early\u0026rdquo; years (2009\u0026ndash;2015), with the largest year-over-year increase in IS adoption, there was a statistically significant inverse correlation between increased IS-based reporting and decreased inter-laboratory SDs of reported RQ-PCR testing results across all levels of \u003cem\u003eBCR::ABL1\u003c/em\u003e p210 transcript analyzed, confirming a robust time-dependent association between these 2 variables.\u003c/p\u003e \u003cp\u003eFrom 2009\u0026ndash;2015, laboratories reporting on the IS increased from 8% to 86%, while the inter-laboratory SD decreased from approximately 0.7\u0026ndash;0.9 to 0.2 log(IS), indicating approximately 7 years to high level adoption of IS from the time of the original IS publication in 2008.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e After 2019, however, when IS-based reporting approached 100%, there was no further improvement in inter-laboratory SD (for any \u003cem\u003eBCR::ABL1\u003c/em\u003e sample level), suggesting that IS-based reporting could be causally linked to improved inter-laboratory precision. In this \u0026ldquo;late\u0026rdquo; time window, inter-laboratory SD values stabilized at 0.2 log(IS), a level similar to findings from other studies restricted to laboratories using the IS.\u003csup\u003e23\u003c/sup\u003e When only IS-reporting laboratories before 2019 were analyzed in our data set, there was only a subtle time-dependent improvement in inter-laboratory precision. This declining SD slope for IS-only laboratories is not as steep as when non-IS results were included, as expected, potentially reflecting the gradual adjustment of laboratories to appropriate application of IS standardization.\u003c/p\u003e \u003cp\u003eAlthough these data definitively show a significant correlation between IS-based reporting and improved inter-laboratory precision, causality cannot be definitively proven. Other possible explanations for the observed time-dependent decrease in inter-lab SD were considered. The increased time-dependent use of the \u003cem\u003eABL1\u003c/em\u003e gene as the reference gene for RQ-PCR, despite its many drawbacks as a control gene, also increased over time from 56% of labs in 2009, to 75% percent in 2012, and to 99% in 2023.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e However, there was no evidence of any significant effect of the use of the \u003cem\u003eABL1\u003c/em\u003e reference gene (versus any non-\u003cem\u003eABL1\u003c/em\u003e reference gene) on the inter-lab reproducibility of \u003cem\u003eBCR::ABL1\u003c/em\u003e measurements for the \u0026ldquo;early\u0026rdquo; years (2009A-2012A) when there were sufficient non-\u003cem\u003eABL1\u003c/em\u003e laboratories to assess. This analysis suggests that the increased time-dependent use of the \u003cem\u003eABL1\u003c/em\u003e reference gene is not a major contributor to the observed improvement in inter-laboratory precision.\u003c/p\u003e \u003cp\u003eThe convergence upon a single technical method for performing \u003cem\u003eBCR::ABL1\u003c/em\u003e RQ-PCR is also unlikely to account for the observed improvement in inter-laboratory precision, given the wide range of different methods/kits/reagents used for RQ-PCR throughout the study period, without any time-dependent dominance of a single method. An additional possible explanation for the improved inter-lab SDs could be the increased utilization of FDA-approved assays compared to laboratory-developed assays. While the adoption of FDA-approved \u003cem\u003eBCR::ABL1\u003c/em\u003e assays did rise during the 2017\u0026ndash;2023 time-period, the inter-lab standard deviations of RQ-PCR testing results had already reached their nadir value by 2017. In addition, there was no significant difference in inter-laboratory SDs between users of laboratory-developed tests (LDTs) and FDA-approved assays. This data demonstrates that FDA-approval does not indicate improved assay performance, a conclusion also supported by other comparative assay performance studies.\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOverall, the time-dependent improvement in inter-laboratory precision observed in this study does not correlate independently to variables such as reference gene choice, PCR primer/probe source, or use of FDA-approved kits. Instead, the improved precision strongly correlates with the increased use of IS-based reporting. This finding is clinically impactful given the consensus treatment implications for CML patients achieving critical \u003cem\u003eBCR::ABL1\u003c/em\u003e transcript level milestones during TKI treatment, such as early molecular response (\u003cem\u003eBCR::ABL1\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10% IS), major molecular response (\u003cem\u003eBCR::ABL1\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1% IS), deep molecular response (\u003cem\u003eBCR::ABL1\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01%), or molecular relapse (1-log increase in \u003cem\u003eBCR::ABL1\u003c/em\u003e).\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, laboratories self-reported their use of the IS, which was not independently verified and may be misclassified. Second, data capture for laboratory methods evolved over the course of the 15-year study period, leading to heterogeneous, inconsistent documentation across the survey years. Third, participation in this CAP PT program was voluntary (although participation in some form of PT is mandatory under CLIA), introducing the potential for selection bias. Fourth, laboratories could enter or exit participation in this PT survey at will, resulting in a dynamic and shifting cohort over time. Fifth, while one might speculate that CAP surveys could preferentially retain higher-performing laboratories, the substantial time-dependent increase in participants suggests that increasing numbers of newer, less experienced laboratories were also included, potentially balancing or diluting this effect. Lastly, missing data points were common due to changing laboratory participation and reporting procedures, which could impact the completeness and interpretation of certain analyses.\u003c/p\u003e \u003cp\u003eIn summary, this study provides a comprehensive 15-year analysis of quantitative \u003cem\u003eBCR::ABL1\u003c/em\u003e (p210) RQ-PCR proficiency testing across a broad spectrum of clinical diagnostic laboratories. This large study of blinded PT results provides strong evidence that 1) inter-laboratory precision of \u003cem\u003eBCR::ABL1\u003c/em\u003e results has markedly improved in the past 15 years, and 2) this improvement in inter-laboratory precision correlates with, and is likely attributable to, the progressive adoption of the IS for results reporting. This study also demonstrates that neither standardization of control gene usage (particularly adoption of \u003cem\u003eABL1\u003c/em\u003e), nor the eventual introduction of FDA-approved assays, had a significant impact on inter-laboratory precision, compared to the effect of IS implementation. By 2019, with nearly universal use of the IS by over 200 participating laboratories, inter-laboratory variance reached a nadir of 0.2 log(IS) SD. These findings underscore the critical role of IS-based reporting in ensuring consistent and reliable molecular monitoring in CML, enabling better patient care through more reproducible laboratory measurements.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA.S.K. and L.J.J. conceived the study. Data analysis was performed by A.S.K., T.L., D.A.O., P.V., C.W., and R.D.P. \u0026nbsp;Manuscript was written by A.S.K., R.D.P., T.L., and P.V. Data review and manuscript editing was performed by all authors. All authors approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnnette S. Kim\u003c/p\u003e\n\u003cp\u003eUniversity of Michigan/Michigan Medicine\u003c/p\u003e\n\u003cp\u003eDepartment of Pathology\u003c/p\u003e\n\u003cp\u003eNCRC Building 35, Room 36-1221-79\u003c/p\u003e\n\u003cp\u003e2800 Plymouth Road\u003c/p\u003e\n\u003cp\u003eAnn Arbor, MI 48109-2800 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing agreement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed for this study are the property of the College of American Pathologists (CAP), and specific laboratory submission data is proprietary to assure privacy. Summaries of the de-identified data are presented in this manuscript. Requests for de-identified data should be addressed to the CAP. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJabbour E, Kantarjian H. Chronic myeloid leukemia: 2025 update on diagnosis, therapy, and monitoring. \u003cem\u003eAm J Hematol\u003c/em\u003e. 2024;99(11):2191-2212.doi:10.1002/ajh.27443\u003c/li\u003e\n\u003cli\u003eJohnson JR, Bross P, Cohen M et al. Approval summary: Imatinib mesylate capsules for treatment of adult patients with newly diagnosed philadelphia chromosome-positive chronic myelogenous leukemia in chronic phase. \u003cem\u003eClin Cancer Res\u003c/em\u003e. 2003;9(6):1972-1979.\u003c/li\u003e\n\u003cli\u003eChronic myeloid leukemia. \u003cem\u003eNCCN Guidelines Version 1.2026\u003c/em\u003e. Plymouth Meeting, PA: National Comprehensive Cancer Network; 2025.\u003c/li\u003e\n\u003cli\u003eApperley JF, Milojkovic D, Cross NCP et al. 2025 european leukemianet recommendations for the management of chronic myeloid leukemia. \u003cem\u003eLeukemia\u003c/em\u003e. 2025;39(8):1797-1813.doi:10.1038/s41375-025-02664-w\u003c/li\u003e\n\u003cli\u003eHochhaus A, Baccarani M, Silver RT et al. European leukemianet 2020 recommendations for treating chronic myeloid leukemia. \u003cem\u003eLeukemia\u003c/em\u003e. 2020;34(4):966-984.doi:10.1038/s41375-020-0776-2\u003c/li\u003e\n\u003cli\u003eSoverini S, Abruzzese E, Bocchia M et al. Next-generation sequencing for bcr-abl1 kinase domain mutation testing in patients with chronic myeloid leukemia: A position paper. \u003cem\u003eJ Hematol Oncol\u003c/em\u003e. 2019;12(1):131.doi:10.1186/s13045-019-0815-5\u003c/li\u003e\n\u003cli\u003ePress RD, Kamel-Reid S, Ang D. Bcr-abl1 rt-qpcr for monitoring the molecular response to tyrosine kinase inhibitors in chronic myeloid leukemia. \u003cem\u003eJ Mol Diagn\u003c/em\u003e. 2013;15(5):565-576.doi:10.1016/j.jmoldx.2013.04.007\u003c/li\u003e\n\u003cli\u003eHughes T, Deininger M, Hochhaus A et al. Monitoring cml patients responding to treatment with tyrosine kinase inhibitors: Review and recommendations for harmonizing current methodology for detecting bcr-abl transcripts and kinase domain mutations and for expressing results. \u003cem\u003eBlood\u003c/em\u003e. 2006;108(1):28-37.doi:10.1182/blood-2006-01-0092\u003c/li\u003e\n\u003cli\u003eBranford S, Fletcher L, Cross NC et al. Desirable performance characteristics for bcr-abl measurement on an international reporting scale to allow consistent interpretation of individual patient response and comparison of response rates between clinical trials. \u003cem\u003eBlood\u003c/em\u003e. 2008;112(8):3330-3338.doi:10.1182/blood-2008-04-150680\u003c/li\u003e\n\u003cli\u003eWang YL, Lee JW, Cesarman E, Jin DK, Csernus B. Molecular monitoring of chronic myelogenous leukemia: Identification of the most suitable internal control gene for real-time quantification of bcr-abl transcripts. \u003cem\u003eJ Mol Diagn\u003c/em\u003e. 2006;8(2):231-239.doi:10.2353/jmoldx.2006.040404\u003c/li\u003e\n\u003cli\u003eMerker JD, Devereaux K, Iafrate AJ et al. Proficiency testing of standardized samples shows very high interlaboratory agreement for clinical next-generation sequencing-based oncology assays. \u003cem\u003eArch Pathol Lab Med\u003c/em\u003e. 2019;143(4):463-471.doi:10.5858/arpa.2018-0336-CP\u003c/li\u003e\n\u003cli\u003eKim AS, Bartley AN, Bridge JA et al. Comparison of laboratory-developed tests and fda-approved assays for braf, egfr, and kras testing. \u003cem\u003eJAMA Oncol\u003c/em\u003e. 2018;4(6):838-841.doi:10.1001/jamaoncol.2017.4021\u003c/li\u003e\n\u003cli\u003eSurrey LF, Oakley FD, Merker JD et al. Next-generation sequencing (ngs) methods show superior or equivalent performance to non-ngs methods on braf, egfr, and kras proficiency testing samples. \u003cem\u003eArch Pathol Lab Med\u003c/em\u003e. 2019;143(8):980-984.doi:10.5858/arpa.2018-0394-CP\u003c/li\u003e\n\u003cli\u003eMoncur JT, Bartley AN, Bridge JA et al. Performance comparison of different analytic methods in proficiency testing for mutations in the braf, egfr, and kras genes: A study of the college of american pathologists molecular oncology committee. \u003cem\u003eArch Pathol Lab Med\u003c/em\u003e. 2019;143(10):1203-1211.doi:10.5858/arpa.2018-0396-CP\u003c/li\u003e\n\u003cli\u003eHayden RT, Yan X, Wick MT et al. Factors contributing to variability of quantitative viral pcr results in proficiency testing samples: A multivariate analysis. \u003cem\u003eJ Clin Microbiol\u003c/em\u003e. 2012;50(2):337-345.doi:10.1128/jcm.01287-11\u003c/li\u003e\n\u003cli\u003eNikiforova MN, Hsi ED, Braziel RM et al. Detection of clonal igh gene rearrangements: Summary of molecular oncology surveys of the college of american pathologists. \u003cem\u003eArch Pathol Lab Med\u003c/em\u003e. 2007;131(2):185-189.doi:10.5858/2007-131-185-docigr\u003c/li\u003e\n\u003cli\u003eKeung ES, Souers RJ, Bridge JA et al. Comparative performance of high-risk human papillomavirus rna and DNA in situ hybridization on college of american pathologists proficiency tests. \u003cem\u003eArch Pathol Lab Med\u003c/em\u003e. 2020;144(3):344-349.doi:10.5858/arpa.2019-0093-CP\u003c/li\u003e\n\u003cli\u003ePolesky HF, Hanson MR. Human immunodeficiency virus type 1 proficiency testing. The american association of blood banks/college of american pathologists program. \u003cem\u003eArch Pathol Lab Med\u003c/em\u003e. 1990;114(3):268-271.\u003c/li\u003e\n\u003cli\u003eScott S, Travis D, Whitby L, Bainbridge J, Cross NCP, Barnett D. Measurement of bcr-abl1 by rt-qpcr in chronic myeloid leukaemia: Findings from an international eqa programme. \u003cem\u003eBr J Haematol\u003c/em\u003e. 2017;177(3):414-422.doi:10.1111/bjh.14557\u003c/li\u003e\n\u003cli\u003eBeillard E, Pallisgaard N, van der Velden VH et al. Evaluation of candidate control genes for diagnosis and residual disease detection in leukemic patients using \u0026apos;real-time\u0026apos; quantitative reverse-transcriptase polymerase chain reaction (rq-pcr) - a europe against cancer program. \u003cem\u003eLeukemia\u003c/em\u003e. 2003;17(12):2474-2486.doi:10.1038/sj.leu.2403136\u003c/li\u003e\n\u003cli\u003eWhite HE, Salmon M, Albano F et al. Standardization of molecular monitoring of cml: Results and recommendations from the european treatment and outcome study. \u003cem\u003eLeukemia\u003c/em\u003e. 2022;36(7):1834-1842.doi:10.1038/s41375-022-01607-z\u003c/li\u003e\n\u003cli\u003eGriffiths M, Patton SJ, Grossi A, Clark J, Paz MF, Labourier E. Conversion, correction, and international scale standardization: Results from a multicenter external quality assessment study for bcr-abl1 testing. \u003cem\u003eArch Pathol Lab Med\u003c/em\u003e. 2015;139(4):522-529.doi:10.5858/arpa.2013-0754-OA\u003c/li\u003e\n\u003cli\u003eCross NC, White HE, Ernst T et al. Development and evaluation of a secondary reference panel for bcr-abl1 quantification on the international scale. \u003cem\u003eLeukemia\u003c/em\u003e. 2016;30(9):1844-1852.doi:10.1038/leu.2016.90\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"leukemia","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"leu","sideBox":"Learn more about [Leukemia](http://www.nature.com/leu/)","snPcode":"41375","submissionUrl":"https://mts-leu.nature.com/cgi-bin/main.plex","title":"Leukemia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Chronic myeloid leukemia (CML), BCR::ABL1, measurable residual disease (MRD) monitoring, International Scale (IS), inter-laboratory reproducibility, quantitative PCR (RQ-PCR), proficiency testing, standardization, FDA-approved assays","lastPublishedDoi":"10.21203/rs.3.rs-8921143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8921143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrecise inter-laboratory quantitation of \u003cem\u003eBCR::ABL1\u003c/em\u003eRNA is critical for effective chronic myeloid leukemia (CML) disease management. Although standardized reporting on the International Scale (IS) was specifically developed to improve inter-laboratory variability in BCR::ABL1 quantitation, the clinical impact of this reporting system has not been comprehensively assessed. We analyzed 15 years of \u003cem\u003eBCR::ABL1\u003c/em\u003e results (2009–2023) reported by over 200 clinical laboratories (6 annual samples; \u0026gt;5500 results) in a College of American Pathologists proficiency testing program. Inter-laboratory precision was evaluated by standard deviations (SD) for high, intermediate, and low-transcript samples. Early rapid IS adoption (8% of labs in 2009; 86% by 2015) was correlated with a progressive decrease in inter-lab SD’s of reported \u003cem\u003eBCR::ABL1\u003c/em\u003e results (high sample r=-0.73, p=0.004; intermediate sample r=-0.71, p=0.022; low sample r=-0.84, p=0.002). IS implementation exceeded 99% by 2019, as inter-lab SD’s reached their nadir value (~0.2). No other assessed variable correlated with the time-dependent improvement in inter-lab reproducibility, including reference gene selection (\u003cem\u003eABL1\u003c/em\u003e versus others), RQ-PCR reagent selection, or assay type (FDA-cleared versus lab-developed). IS-based reporting is thus likely the principal driver of enhanced inter-laboratory precision in \u003cem\u003eBCR::ABL1\u003c/em\u003equantitation, validating the real-world clinical relevance of adopting robust standardization in clinical molecular diagnostics.\u003c/p\u003e","manuscriptTitle":"Improved Inter-Lab Precision After CML International Scale Adoption: a 15-year history of BCR::ABL1 Proficiency Testing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 18:10:41","doi":"10.21203/rs.3.rs-8921143/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-03-10T15:21:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-03-06T21:04:00+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-26T00:43:39+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-20T21:43:52+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-20T19:49:07+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-02-20T16:27:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-20T16:15:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-20T16:15:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Leukemia","date":"2026-02-19T22:40:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"leukemia","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"leu","sideBox":"Learn more about [Leukemia](http://www.nature.com/leu/)","snPcode":"41375","submissionUrl":"https://mts-leu.nature.com/cgi-bin/main.plex","title":"Leukemia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"15b72d0f-5994-4c15-93ec-bdc753d27748","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63274340,"name":"Health sciences/Diseases/Haematological diseases/Haematological cancer/Myeloproliferative disease"},{"id":63274341,"name":"Health sciences/Health care/Health services"}],"tags":[],"updatedAt":"2026-05-06T01:55:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 18:10:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8921143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8921143","identity":"rs-8921143","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}