BCR-ABL1 Doubling-Times and Halving-Times May Predict CML Response to Tyrosine Kinase Inhibitors

Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy

Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.

Measurable residual disease in chronic myeloid leukemia

Chronic myeloid leukemia is characterized by a single genetic abnormality resulting in a fusion gene whose mRNA product is easily detected and quantified by reverse-transcriptase polymerase chain reaction analysis. Measuring residual disease was originally introduced to identify patients relapsing after allogeneic stem cell transplantation but rapidly adopted to quantify responses to tyrosine kinase inhibitors. Real-time quantitative polymerase chain reaction is now an essential tool for the management of patients and is used to influence treatment decisions. In this review we track this development including the international collaboration to standardize results, discuss the integration of molecular monitoring with other factors that affect patients' management, and describe emerging technology. Four case histories describe varying scenarios in which the accurate measurement of residual disease identified patients at risk of disease progression and allowed appropriate investigations and timely clinical intervention.

Leukemia: Reduction Ratio and Halving Time of BCR: : ABL1 IS Transcript Levels

Objective:

Achieving an early molecular response (EMR) is crucial for improving the prognosis of patients with chronic myeloid leukemia (CML). The halving time (HT) and reduction ratio (RR) of BCR::ABL1 transcript levels have recently emerged as additional prognostic indexes besides the BCR::ABL1 International Scale (IS). We aimed to investigate the prognostic role of BCR::ABL1 transcript levels, HT, and RR on molecular response kinetics at 3 months in patients with newly diagnosed chronic-phase (CP)-CML.

Materials and Methods:

Forty patients with CP-CML who received first-line imatinib treatment were included in this study. BCR::ABL1 transcript levels and molecular responses at baseline and at 3, 6, 12, and 24 months of treatment were evaluated retrospectively. Major molecular response (MMR) at 12 months and event-free survival (EFS) were determined as primary endpoints and the effects of treatment kinetics on these parameters were examined.

Results:

Of the 40 patients, BCR::ABL1 IS was ≤10% at 3 months in 72.5%, representing EMR. The rate of event occurrence was 45.5% in patients with BCR::ABL1 IS of >10%, whereas it was 6.9% in those with BCR::ABL1 IS of ≤10% (p=0.004). MMR was detected in 62.1% of the patients with EMR and in 9.1% of those without EMR (p=0.003). The cut-off value for achieving MMR was 24 days for HT and 0.04 for RR. Deep molecular response (DMR) at 24 months was associated with HT of ≤24 days and RR of ≤0.04. EFS was found to be significantly better in the group with BCR::ABL1 IS of ≤10% and HT of ≤24 days (p=0.001) and in the group with BCR::ABL1 IS of ≤10% and RR of ≤0.04 (p=0.007) compared to others.

Conclusion:

Our findings revealed that MMR could be predicted via EMR as well as by HT and RR. Additionally, HT of ≤24 days and RR of ≤0.04 were more important thanBCR::ABL1 IS of ≤10% in achieving DMR at 24 months, and the combination of BCR::ABL1 IS of ≤10% with both HT of ≤24 days and RR of ≤0.04 has the best predictive value for EFS.

Impact of Different Cell Counting Methods in Molecular Monitoring of Chronic Myeloid Leukemia Patients

Background: Detection of BCR-ABL1 transcript level via real-time quantitative-polymerase-chain reaction (Q-PCR) is a clinical routine for disease monitoring, assessing Tyrosine Kinase Inhibitor therapy efficacy and predicting long-term response in chronic myeloid leukemia (CML) patients. For valid Q-PCR results, each stage of the laboratory procedures need be optimized, including the cell-counting method that represents a critical step in obtaining g an appropriate amount of RNA and reliable Q-PCR results. Traditionally, manual or automated methods are used for the detection and enumeration of white blood cells (WBCs). Here, we compared the performance of the manual counting measurement to the flow cytometry (FC)-based automatic counting assay employing CytoFLEX platform. Methods: We tested five different types of measurements: one manual hemocytometer-based count and four FC-based automatic cell-counting methods, including absolute, based on beads, based on 7-amino actinomycin D, combining and associating beads and 7AAD. The recovery efficiency for each counting method was established considering the quality and quantity of total RNA isolated and the Q-PCR results in matched samples from 90 adults with CML. Results: Our analyses showed no consistent bias between the different types of measurements, with comparable number of WBCs counted for each type of measurement. Similarly, we observed a 100% concordance in the amount of RNA extracted and in the Q-PCR cycle threshold values for both BCR-ABL1 and ABL1 gene transcripts in matched counted specimens from all the investigated groups. Overall, we show that FC-based automatic absolute cell counting has comparable performance to manual measurements and allows accurate cell counts without the use of expensive beads or the addition of the time-consuming intercalator 7AAD. Conclusions: This automatic method can replace the more laborious manual workflow, especially when high-throughput isolations from blood of CML patients are needed.

BCR-ABL1 (p210) Transcript Kinetics

Context.—

Delta checks are a powerful technique for monitoring clinical assays in many disciplines but have not been routinely used in molecular testing.

Objective.—

To determine if the biologically determined kinetics of BCR-ABL1's rise and fall could allow the development of a delta check in BCR-ABL1 testing.

Design.—

Nine years of BCR-ABL1 p210 results were evaluated and patients with 3 or more results were selected for inclusion. The kinetics of these percentages of international standard values were plotted against time along with the median and the 90th and 95th percentile lines. A Monte Carlo simulation of a batch mix-up was performed for 6 months of data to determine the efficacy of the proposed cutoff.

Results.—

The median kinetics showed a 1-log drop of the percentage of international standard in 90 days, with less than 5% of cases showing faster than a 2-log drop in 90 days, and less than 2.5% showing a faster than 3-log drop in 90 days (extrapolated to 1 log in 30 days). The Monte Carlo simulation of a batch mix-up showed that an average batch mix-up of 23 samples could routinely be flagged by this cutoff, albeit with wide variance.

Conclusions.—

These results suggest that using a drop in the percentage of international standard of greater than 1 log in 30 days can be a useful trigger in implementing a delta-check system for this molecular test.

Making Treatment-Free Remission (TFR) Easier in Chronic Myeloid Leukemia: Fact-Checking and Practical Management Tools

In chronic-phase chronic myeloid leukemia (CML), tyrosine kinase inhibitors (TKIs) are the standard of care, and treatment-free remission (TFR) following the achievement of a stable deep molecular response (DMR) has become, alongside survival, a primary goal for virtually all patients. The GIMEMA CML working party recently suggested that the possibility of achieving TFR cannot be denied to any patient, and proposed specific treatment policies according to the patient's age and risk. However, other international recommendations (including 2020 ELN recommendations) are more focused on survival and provide less detailed suggestions on how to choose first and subsequent lines of treatment. Consequently, some grey areas remain. After literature review, a panel of Italian experts discussed the following controversial issues: (1) early prediction of DMR and TFR: female sex, non-high disease risk score, e14a2 transcript and early MR achievement have been associated with stable DMR, but the lack of these criteria is not sufficient to exclude any patient from TFR; (2) criteria for first and subsequent line therapy choice: a number of patient and drug characteristics have been proposed to make a personalized decision; (3) monitoring of residual disease after discontinuation: after the first 6 months, the frequency of molecular tests can be reduced based on MR4.5 persistence and short turnaround time; (4) prognosis of TFR: therapy and DMR duration are important to predict TFR; although immunological control of CML plays a role, no immunological predictive phenotype is currently available. This guidance is intended as a practical tool to support physicians in decision making.

Early BCR-ABL1 kinetics are predictive of subsequent achievement of treatment-free remission in chronic myeloid leukemia

With treatment-free remission (TFR) rapidly becoming the ultimate goal of therapy in chronic myeloid leukemia (CML), there is a need to develop strategies to maximize sustained TFR by improving our understanding of its key determinants. Chronic-phase CML patients attempting TFR were evaluated to identify the impact of multiple variables on the probability of sustained TFR. Early molecular response dynamics were included as a predictive variable, assessed by calculating the patient-specific halving time of BCR-ABL1 after commencing tyrosine kinase inhibitor (TKI) therapy. Overall, 115 patients attempted TFR and had ≥12 months of follow-up. The probability of sustained TFR, defined as remaining in major molecular response off TKI therapy for 12 months, was 55%. The time taken for the BCR-ABL1 value to halve was the strongest independent predictor of sustained TFR: 80% in patients with a halving time of <9.35 days (first quartile) compared with only 4% if the halving time was >21.85 days (last quartile) (P < .001). The e14a2 BCR-ABL1 transcript type and duration of TKI exposure before attempting TFR were also independent predictors of sustained TFR. However, the BCR-ABL1 value measured at 3 months of TKI was not an independent predictor of sustained TFR. A more rapid initial BCR-ABL1 decline after commencing TKI also correlated with an increased likelihood of achieving TFR eligibility. The association between sustained TFR and the time taken for BCR-ABL1 to halve after commencing TKI was validated using an independent dataset. These data support the critical importance of the initial kinetics of BCR-ABL1 decline for long-term outcomes.

Optimal Response in a Patient With CML Expressing BCR-ABL1 E6A2 Fusion Transcript With Nilotinib Therapy: A Case Report

BACKGROUND/AIM

The Philadelphia chromosome is considered the hallmark of chronic myeloid leukemia (CML). However, although most patients with CML are diagnosed with the e13a2 or e14a2 breakpoint cluster region (BCR)-Abelson 1 (ABL1) fusion transcripts, about 5% of them carry rare BCR-ABL1 fusion transcripts, such as e19a2, e8a2, e13a3, e14a3, e1a3 and e6a2. In particular, the e6a2 fusion transcript has been associated with clinically aggressive disease frequently presenting in accelerated or blast crisis phases; there is limited evidence on the efficacy of front-line second-generation tyrosine kinase inhibitors for this genotype.

CASE REPORT

We describe a case of atypical BCR-ABL1 e6a2 fusion transcript in a 46-year-old woman with CML.

RESULTS

The use of primers recognizing more distant exons from the common BCR-ABL1 breakpoint region correctly identified the atypical BCR-ABL1 e16a2 fusion transcript. Treatment with second-generation tyrosine kinase inhibitor nilotinib was effective in this patient expressing the atypical e6a2 BCR-ABL1 fusion transcript.

Overlooking the obvious? On the potential of treatment alterations to predict patient-specific therapy response

Prognostic or therapeutic classification of diseases is often based on clinical or genetic characteristics at diagnosis or response landmarks determined at a certain time point of treatment. On the other hand, there are more and more means, such as molecular markers and sensor data, that allow for quantification of disease or therapeutic parameters over time. Although a general value of time-resolved disease monitoring is widely accepted, the full potential of using the available information on disease and treatment dynamics in the context of outcome prediction or individualized treatment optimization still seems to be, at least partially, overlooked. Within this Perspective, we summarize the conceptual idea of using dynamic information to obtain a better understanding of complex pathophysiological processes within their particular "host environment," which also allows us to intrinsically map patient-specific heterogeneity. Specifically, we discuss to which extent treatment alterations can provide additional information to understand a patient's individual condition and use this information to further adapt the therapeutic strategy. This conceptual discussion is illustrated by using examples from myeloid leukemias to which we recently applied this concept using statistical and mathematical modeling.