Correction: Standardisation and consensus guidelines for minimal residual disease assessment in Philadelphia-positive acute lymphoblastic leukemia (Ph+ALL) by real-time quantitative reverse transcriptase PCR of e1a2 BCR-ABL1.

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Standardisation and consensus guidelines for minimal residual disease assessment in Philadelphia-positive acute lymphoblastic leukemia (Ph + ALL) by real-time quantitative reverse transcriptase PCR of e1a2 BCR-ABL1.

Minimal residual disease (MRD) is a powerful prognostic factor in acute lymphoblastic leukemia (ALL) and is used for patient stratification and treatment decisions, but its precise role in Philadelphia chromosome positive ALL is less clear. This uncertainty results largely from methodological differences relating to the use of real-time quantitative PCR (qRT-PCR) to measure BCR-ABL1 transcript levels for MRD analysis. We here describe the first results by the EURO-MRD consortium on standardization of qRT-PCR for the e1a2 BCR-ABL1 transcript in Ph + ALL, designed to overcome the lack of standardisation of laboratory procedures and data interpretation. Standardised use of EAC primer/probe sets and of centrally prepared plasmid standards had the greatest impact on reducing interlaboratory variability. In QC1 the proportion of analyses with BCR-ABL1/ABL1 ratios within half a log difference were 40/67 (60%) and 52/67 (78%) at 10-3 and 36/67 (53%) and 53/67 (79%) at 10-4BCR-ABL1/ABL1. Standardized RNA extraction, cDNA synthesis and cycler platforms did not improve results further, whereas stringent application of technical criteria for assay quality and uniform criteria for data interpretation and reporting were essential. We provide detailed laboratory recommendations for the standardized MRD analysis in routine diagnostic settings and in multicenter clinical trials for Ph + ALL.

Regression-based reference limits: determination of sufficient sample size.

Regression analysis is the method of choice for the production of covariate-dependent reference limits. There are currently no recommendations on what sample size should be used when regression-based reference limits and confidence intervals are calculated. In this study we used Monte Carlo simulation to study a reference sample group of 374 age-dependent hemoglobin values. From this sample, 5000 random subsamples, with replacement, were constructed with 10-220 observations per sample. Regression analysis was used to estimate age-dependent 95% reference intervals for hemoglobin concentrations and erythrocyte counts. The maximum difference between mean values of the root mean square error and original values for hemoglobin was 0.05 g/L when the sample size was > or = 60. The parameter estimators and width of reference intervals changed negligibly from the values calculated from the original sample regardless of what sample size was used. SDs and CVs for these factors changed rapidly up to a sample size of 30; after that changes were smaller. The largest and smallest absolute differences in root mean square error and width of reference interval between sample values and values calculated from the original sample were also evaluated. As expected, differences were largest in small sample sizes, and as sample size increased differences decreased. To obtain appropriate reference limits and confidence intervals, we propose the following scheme: (a) check whether the assumptions of regression analysis can be fulfilled with/without transformation of data; (b) check that the value of v, which describes how the covariate value is situated in relation to both the mean value and the spread of the covariate values, does not exceed 0.1 at minimum and maximum covariate positions; and (c) if steps 1 and 2 can be accepted, the reference limits with confidence intervals can be produced by regression analysis, and the minimum acceptable sample size will be approximately 70.