The influence of cytotoxic drugs on the immunophenotype of blast cells in paediatric B precursor acute lymphoblastic leukaemia

BACKGROUND

Flow cytometry plays is important in the diagnosis of acute lymphoblastic leukaemia (ALL) and when antigen-specific immunotherapy is indicated. We have investigated the effects of prednisolone, vincristine, daunorubicin, asparaginase and methotrexate on the antigen expression on blast cells that could influence the planning of antigen-specific therapy as well as risk-based treatment assignment.

PATIENTS AND METHODS

Patients aged ≤ 17 years with de novo B-cell ALL (B-ALL) were enrolled in the study. Blast cells were isolated and exposed in vitro to 5 individual cytotoxic drugs in logarithmically increasing concentrations. Then, the expression of CD10, CD19, CD20, CD27, CD34, CD45, CD58, CD66c and CD137 antigens was determined by quantitative flow cytometry.

RESULTS

Cytotoxic drugs caused dose-dependent or dose-independent modulation of antigen expression. Daunorubicin caused a dose-dependent down-modulation of CD10, CD19, CD34, CD45 and CD58 and an up-modulation of CD137. Vincristine caused a dose-dependent down-modulation of CD19 and CD58 and an up-modulation of CD45. Daunorubicin also caused dose-independent down-modulation of CD27 and prednisolone down-modulation of CD10, CD19, CD27, CD34 and CD58. Down-modulation of CD20 was detected only in relation to the specific dose of daunorubicin.

CONCLUSIONS

The results of the study have shown that cytotoxic drugs can alter the expression of antigens that are important for immunotherapy. Importantly, daunorubicin, prednisolone and vincristine caused down-modulation of CD19 and CD58, suggesting that these drugs are better avoided during bridging therapy prior to bispecific antibodies or CAR-T cell therapy. In addition, immunophenotypic changes on blast cells induced by different drugs could also influence risk-based treatment assignment.

Correct specification of design matrices in linear mixed effects models: tests with graphical representation

Linear mixed effects models (LMMs) are a popular and powerful tool for analysing grouped or repeated observations for numeric outcomes. LMMs consist of a fixed and a random component, which are specified in the model through their respective design matrices. Verifying the correct specification of the two design matrices is important since mis-specifying them can affect the validity and efficiency of the analysis. We show how to use empirical stochastic processes constructed from appropriately ordered and standardized residuals from the model to test whether the design matrices of the fitted LMM are correctly specified. We define two different processes: one can be used to test whether both design matrices are correctly specified, and the other can be used only to test whether the fixed effects design matrix is correctly specified. The proposed empirical stochastic processes are smoothed versions of cumulative sum processes, which have a nice graphical representation in which model mis-specification can easily be observed. The amount of smoothing can be adjusted, which facilitates visual inspection and can potentially increase the power of the tests. We propose a computationally efficient procedure for estimating p-values in which refitting of the LMM is not necessary. Its validity is shown by using theoretical results and a large Monte Carlo simulation study. The proposed methodology could be used with LMMs with multilevel or crossed random effects.