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. 2022 Jul;36(7):1834-1842.
doi: 10.1038/s41375-022-01607-z. Epub 2022 May 25.

Standardization of molecular monitoring of CML: results and recommendations from the European treatment and outcome study

Helen E White  1   2 Matthew Salmon  1   2 Francesco Albano  3 Christina Søs Auður Andersen  4 Stefan Balabanov  5 Gueorgui Balatzenko  6 Gisela Barbany  7 Jean-Michel Cayuela  8 Nuno Cerveira  9 Pascale Cochaux  10 Dolors Colomer  11 Daniel Coriu  12   13 Joana Diamond  14 Christian Dietz  15 Stéphanie Dulucq  16 Marie Engvall  17 Georg N Franke  18 Egle Gineikiene-Valentine  19 Michal Gniot  20 María Teresa Gómez-Casares  21 Enrico Gottardi  22 Chloe Hayden  23 Sandrine Hayette  24 Andreas Hedblom  25 Anca Ilea  26   27 Barbara Izzo  28 Antonio Jiménez-Velasco  29 Tomas Jurcek  30   31 Veli Kairisto  32 Stephen E Langabeer  33 Thomas Lion  34 Nora Meggyesi  35 Semir Mešanović  36 Luboslav Mihok  37 Gerlinde Mitterbauer-Hohendanner  38 Sylvia Moeckel  39 Nicole Naumann  40 Olivier Nibourel  41 Elisabeth Oppliger Leibundgut  42   43 Panayiotis Panayiotidis  44 Helena Podgornik  45   46 Christiane Pott  47 Inmaculada Rapado  48   49   50 Susan J Rose  51 Vivien Schäfer  52 Tasoula Touloumenidou  53 Christopher Veigaard  54 Bianca Venniker-Punt  55 Claudia Venturi  56 Paolo Vigneri  57 Ingvild Vorkinn  58 Elizabeth Wilkinson  59 Renata Zadro  60 Magdalena Zawada  61 Hana Zizkova  62 Martin C Müller  15 Susanne Saussele  40 Thomas Ernst  52 Katerina Machova Polakova  62 Andreas Hochhaus  52 Nicholas C P Cross  63   64
Affiliations

Standardization of molecular monitoring of CML: results and recommendations from the European treatment and outcome study

Helen E White et al. Leukemia. 2022 Jul.

Abstract

Standardized monitoring of BCR::ABL1 mRNA levels is essential for the management of chronic myeloid leukemia (CML) patients. From 2016 to 2021 the European Treatment and Outcome Study for CML (EUTOS) explored the use of secondary, lyophilized cell-based BCR::ABL1 reference panels traceable to the World Health Organization primary reference material to standardize and validate local laboratory tests. Panels were used to assign and validate conversion factors (CFs) to the International Scale and assess the ability of laboratories to assess deep molecular response (DMR). The study also explored aspects of internal quality control. The percentage of EUTOS reference laboratories (n = 50) with CFs validated as optimal or satisfactory increased from 67.5% to 97.6% and 36.4% to 91.7% for ABL1 and GUSB, respectively, during the study period and 98% of laboratories were able to detect MR4.5 in most samples. Laboratories with unvalidated CFs had a higher coefficient of variation for BCR::ABL1IS and some laboratories had a limit of blank greater than zero which could affect the accurate reporting of DMR. Our study indicates that secondary reference panels can be used effectively to obtain and validate CFs in a manner equivalent to sample exchange and can also be used to monitor additional aspects of quality assurance.

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Conflict of interest statement

HW has received honoraria from Novartis. SS has received honoraria from Incyte, Novartis, Pfizer, Bristol Myers Squibb, and Roche and research support from Incyte, Novartis, and Bristol Myers Squibb. J-MC has received research support and honoraria from Incyte, and honoraria from Novartis and Cepheid. TL has received honoraria from Incyte, Novartis, Pfizer, Angelini, Bristol Myers Squibb, and research support from Incyte, Novartis, and Pfizer. PV has received honoraria from Astra-Zeneca, Eli Lilly, Gilead; GlaxoSmithKline, Novartis, Pfizer, Roche, Teva, and research support from Novartis and Pfizer. SM is employed by the MLL Munich Leukemia Laboratory GmbH, Munich. CD and MM are employed by the Institute for Hematology and Oncology (IHO GmbH), Mannheim. KMP has received honoraria from Angelini and Incyte. AH received research support from Novartis, BMS, Pfizer, and Incyte. NC has received research support and honoraria from Novartis, and honoraria from Incyte and Astellas.

Figures

Fig. 1
Fig. 1. Ability of laboratories to detect MR4.5.
Overall laboratory scores per reference gene were defined as green (detects MR4.5 in a high proportion of samples, combined score > 80%), amber (detects MR4.5 in most samples, combined score > 60%) or red (unable to detect MR4.5 in most samples, combined score < 60%). The bar charts show the number of data sets in each category for all laboratories. Several laboratories submitted data for more than one reference gene or assay and therefore the number of data sets analysed is greater than the number of participating laboratories.
Fig. 2
Fig. 2. Stability of CFs for laboratories using ABL1 as a reference gene.
CFs were calculated and provided to laboratories on an annual basis. The stability of each CF was determined as either optimal (bright green), satisfactory (green) or unvalidated (amber) by comparison with the previous year’s value using the following criteria; Optimal (+/− 1.2 fold): Old CF/New CF = 0.83–1.2, Satisfactory (+/− 1.6 fold): Old CF/New CF = 0.63–1.58 or Unvalidated: Old CF/New CF < 0.63 or >1.58. The bars charts show the number of laboratories for each category, per year for ABL1 reference gene data sets. Several laboratories submitted data for more than one assay and therefore the number of data sets analysed may be greater than the number of participating laboratories.
Fig. 3
Fig. 3. Stability of CFs for laboratories using GUSB as a reference gene.
CFs were calculated and provided to laboratories on an annual basis. The stability of each CF was determined as either optimal (bright green), satisfactory (green) or unvalidated (amber) by comparison with the previous year’s value using the following criteria; Optimal (+/− 1.2 fold): Old CF/New CF = 0.83–1.2, Satisfactory (+/− 1.6 fold): Old CF/New CF = 0.63–1.58 or Unvalidated: Old CF/New CF < 0.63 or >1.58. The bars charts show the number of laboratories for each category, per year for GUSB reference gene data sets. Several laboratories submitted data for more than one reference gene or assay and therefore the number of data sets analysed may be greater than the number of participating laboratories.
Fig. 4
Fig. 4. Use of IQC material to assess how CFs correlate with assay variability.
CVs for BCR::ABL1IS results from high and low level internal quality control material were used to assess how assay variability might correlate with CF status (optimal, 37% of laboratories who tested the internal quality control material; satisfactory, 35% of laboratories; unvalidated, 21% of laboratories). Combined variability scores for the high and low standards were assigned using the following criteria: 3 points: CV < 1st quartile, 2 points: CV between 1st quartile and median, 1 point: CV between median and 3rd quartile. 0 points: CV > 3rd quartile. The overall variability score (CbVar) was defined as the sum of the scores for the high and low level standards. The bar charts show the % of laboratories per CF status that had a combined variability scores of 6 (bright green). 4 or 5 (green), 2 or 3 (amber) or 1/0 (red).

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