Unstable major molecular response as a trigger for next generation sequencing‐based <i>BCR::ABL1</i> mutation testing in chronic myeloid leukemia
Authors: Adela Benesova, Sara De Santis, Vaclava Polivkova, Pavla Pecherkova, Jitka Krizkova, Pavla Suchankova, Cecilia Monaldi, Hana Klamova, Dana Srbova, Hana Zizkova, Andreas Hochhaus, Simona Soverini, Katerina Machova Polakova
Journal: American Journal of Hematology (2024)

In chronic myeloid leukemia (CML) patients treated with tyrosine kinase inhibitors (TKIs), molecular response (MR) milestones have been identified that harbor prognostic significance. Major molecular response (MMR)—defined as 3-log reduction in BCR::ABL1 transcript levels from the standardized baseline on the International Scale (IS)—was the very first of such MR milestones to be recognized: It emerged at the time of the IRIS trial as a “safe haven” protecting from loss of response and progression.1 According to the ELN recommendations, achievement of MMR after 12 months of TKI treatment defines an optimal response whereas failure to achieve MMR or loss of MMR represents a warning, requiring the therapeutic strategy to be carefully evaluated for continuation or change.2 Emerging TKI-resistant point mutations in the kinase domain (KD) of BCR::ABL1 may underlie warning responses in a proportion of patients, tilting the balance toward treatment change. Previous studies have interestingly shown that a peculiar kinetics of BCR::ABL1 transcripts fluctuating around 0.1% IS (“unstable MMR”) may be observed during TKI therapy in some patients and have suggested it may be an early indicator for BCR::ABL1 KD mutation testing.3 However, further data are needed to support inclusion of unstable MMR among the indications for mutation testing in clinical recommendations. The aim of this collaborative EUTOS (European Treatment and Outcome Study for CML) study was to take advantage of next generation sequencing (NGS) to investigate the role of BCR::ABL1 KD mutations in CML patients with unstable MMR on TKI therapy. In order to assess the clinical relevance of low frequency variants and the value of NGS-based analysis in this setting, we established a dedicated bioinformatic pipeline including an in-house developed software using a statistically derived dynamic threshold for mutation calling, which enabled us to maximize discrimination between true variants and sequencing errors. Importantly, we showed that therapy changes in patients with confirmed BCR::ABL1 mutations during unstable MMR resulted in higher probability of deep molecular response (DMR) achievement. A total of 91 CML patients with unstable MMR on TKI therapy were analyzed by NGS, with unstable MMR defined as BCR::ABL1 transcript levels in the range of 0.1% IS ±0.16 (the uncertainty of the measurement) for a minimum of 6 months. Two groups were identified according to molecular response trends (Table S1). Group A (n = 15/91) included patients on TKI therapy (1st line, n = 13; 2nd line, n = 1; 5th line, n = 1) displaying unstable MMR after an initial DMR. In regard to ELN recommendation, all patients were classified as warning.2 Group B (n = 76/91) consisted of patients (1st line, n = 59 [8/59 patients maintained an unstable MMR also during subsequent lines of therapy]; 2nd and/or subsequent line, n = 17) who had achieved no better response than unstable MMR. According to ELN recommendations, these patients have optimal response even when BCR::ABL1 levels fluctuate around the level of 0.1% IS. Detailed characteristics of the patients are summarized in Table S2. Follow-up samples were also analyzed. Altogether, 159 unstable MMR samples were analyzed by NGS (median, two samples per patient; range, 1–11) during the course of unstable MMR period. Sample preparation, BCR::ABL1 transcript quantification, NGS analysis, Limit of Blank (LoB), Limit of Detection (LoD), and Limit of Quantification (LoQ) estimation and statistical analysis are described in detail in Supplementary data including Tables S3–S5, Figures S1–S3. Having optimized a bioinformatic pipeline minimizing NGS errors at low mutation frequencies (Supplementary data; Figure S4), we investigated the rate and kinetics of BCR::ABL1 KD variants in patients with unstable MMR. We chose to perform the analysis in duplicate (starting from reverse transcription of RNA to cDNA) to assess reproducibility of mutation results at lower transcript levels, and we checked for mutation kinetics in follow-up samples. Variant frequencies (VAFs) of variants detected in both duplicates were similar and we provided their mean. We found that underlying BCR::ABL1 KD mutations can be detected both in patients who display unstable MMR after an initial DMR (group A) and in patients with persistent unstable MMR as their best response (group B)—although mutations were more frequently observed in the former than in the latter group. The median VAF in both duplicates was 80% (range 4.6%–100%) in group A. In group B the median of VAF in both duplicates was 9.1% (range 4.4%–30%). Mutations were indeed found in 9/15 (60%) as against 12/76 (16%) patients, respectively (Figure 1); in 7/15 (47%) and 6/76 (8%) patients, the mutations identified could be recognized as poorly sensitive to the administered TKI based on IC50 data or literature reports. However, 3/9 mutations in group A and 7/12 mutations (one at high level) in group B were detected in only one of the two replicates. Except for two mutations (F311L and M244V), all low-level mutations (below 20% VAF) and also one high-level mutation detected in only one replicate were not confirmed in follow-up samples, despite no change in therapy. One possible explanation is that these mutations might have been artifacts. Even when using an optimized pipeline of NGS data analysis, in case of a single assessment some low-level variants may happen to pass the LoQ reaching the statistically significant VAF for calling. In contrast, variants passing this level in both duplicates are highly likely to be true mutations. However, it cannot be excluded that at low BCR::ABL1 transcripts and low mutation frequency, true mutations may happen to be picked in one replicate but not in another. Nevertheless, the fact that they disappeared in subsequent samples indicates that such few mutant molecules would have no clinical relevance anyway.4 To assess the frequency of low-level variants in relation to BCR::ABL1 transcript levels, 499 samples from 151 patients with transcript levels in the range of 0.1%–1% IS and 287 samples from 143 patients with transcript levels >1% IS routinely analyzed by NGS between 2017 and 2021 were compared. In addition, samples from 30 healthy donors were used for estimation of NGS error rates at each nucleotide position of the KD. Frequency of detected clinically relevant mutations did not differ between the samples with BCR::ABL1 transcript level >1% IS and with 0.1%–1% IS. However, low-level variants were significantly more frequent in samples with BCR::ABL1 transcript levels 0.1%–1% IS. The results are described in detail in Supplementary data including Tables S6 and S7. To evaluate the clinical benefit of mutational analysis during unstable MMR, the probability of DMR achievement was compared between patients with unchanged therapy and with therapy change (TKI switch or TKI dose increase) (Figure S5). In group A, the therapy change increased the probability of DMR re-achievement regardless of whether the patients had no BCR::ABL1 mutations (n = 3) or had confirmed mutations (n = 5). In contrast, DMR was not re-achieved in two patients with mutations and no therapy change. A decreased probability of DMR re-achievement was found also for patients without mutations and no therapy change (n = 5). The differences were not significant due to low number of patients. Similar observations were found for group B: a higher probability of DMR achievement was observed in patients who changed the therapy, both in case of confirmed mutations (n = 5) and in case of no mutations (n = 31). The probability of DMR was slightly lower for patient without mutations and without therapy change (n = 38), but was not significantly different compared to patients with therapy change (Figure S5). In conclusion, TKI switch or dose increase in patients with detected mutations during unstable MMR led to higher probability of DMR achievement compared to no therapy change. Based on these results, we propose unstable MMR as a novel trigger for BCR::ABL1 KD mutation analysis. Loss of DMR and BCR::ABL1 increase to the levels of unstable MMR is most probability associated with the development of TKI-resistant mutations; thus, once 0.1% IS BCR::ABL1 transcript level is reached, samples should undergo NGS analysis. For patients whose response to TKI therapy fluctuates around MMR with no further improvement, the likelihood of an underlying mutation is much lower, and NGS analysis might be performed 1–2 times per year during unstable MMR, and promptly in case of BCR::ABL1 transcript increase above the uncertainty of the measurement. The reasons of achievement of unstable MMR as the best response may be more often related to patient compliance, lower doses of TKIs, or clonal hematopoiesis. The loss of deep MR represents a risk factor of BCR::ABL1 resistant mutation development. Patients, whose measurable residual disease fluctuates at levels of MMR, but not deeper, are in lower risk of TKI-resistant mutation acquisition. We suggest the use of our novel bioinformatic tool (freely available at nextdom.uhkt.cz) specifically optimized for BCR::ABL1 KD mutation calling and calibrated on the calculation of error rates all over the KD of the BCR::ABL1 transcript. Based on our observations, mutation testing in unstable MMR samples should be done in duplicate. Moreover, whenever total BCR::ABL1 levels are in the range 0.1%–1% IS, dynamics of low-level mutations (below 20% VAF) should be checked in at least one subsequent sample before treatment change. AB performed mutational analysis, established the bioinformatical pipeline, evaluated data, drafted methodological part of the manuscript, and created Tables and Figures of the paper; SdS and CM performed mutational analysis; VP participated on mutational analysis; PP developed NextDOM; JK participated on mutational analysis; PS converted NextDOM to open access tool and performed statistical analysis; HK evaluated and provided clinical data; DS evaluated and provided clinical data; HZ was responsible for BCR::ABL1 transcript level monitoring; AH supervised the work and the project; SS coordinated the study, interpreted results, and revised the paper; KMP designed the study, interpreted results, and wrote the paper. This work was funded by Project Grant NU21-07-00225 from the Czech Health Research Council, by the European Treatment and Outcome study (EUTOS) for CML, by MH CZ – DRO (IHBT – 00023736), and by Ministerstvo Školství, Mládež a Tělovýchovy (MEYS CZ) (BBMRI.cz no. LM2023033). Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA CZ ID:90140) supported by MEYS CZ. The authors are grateful to the patients for providing their samples for this study. AH, KMP, and SS received support by Novartis through the European Treatment and Outcome Study (EUTOS) for CML. The data that support the findings of this study are available from the corresponding author upon reasonable request. Data S1. Supporting Information FIGURE S1. Scheme of LoQ calculation based on sequenced ABL1 kinase domain of 30 healthy donors. (A) After data normalization the data were divided into individual nucleotide changes and using the Poisson distribution the limits “of specific nucleotide change” were counted; (B) After data normalization the number of reads of each position were processed with Poisson distribution to obtain the limits “of each nucleotide position.” FIGURE S2. The threshold data set cover the range of amino acids A28-D504. The amplicons prepared for sequencing are delineated with full arrow: for the patient sample (PT—upper part) it is 1493 bp, for the healthy donor (HD) it is 1429 bp. The threshold data set was generated for the region 272–1704 (1433 bp), that is, A28-D504 (ref. seq. NM_005157) and is depicted with dashed arrow. FIGURE S3. The limits of quantification of the most frequently detected TKI-resistant mutations (n = 29). Red bars show the most frequently used uniform thresholds levels in published NGS studies—1% and 3%. The T315I mutation is highlighted with red color. FIGURE S4. The NextDOM pipeline. (A) Scheme depicts how the software NextDOM utilizes the data generated in software NextGENe, furthermore the data are normalized and evaluated using either LoD or LoQ data set. As a final outcome, the statistically significant variants are provided (p-value ≤.05). (B) The interphase of NextDOM comprises of windows for paths to files and of instructions for using either LoD or LoQ data set. FIGURE S5. Cumulative achievement of DMR of patients with therapy change and with unchanged therapy in relation of mutation detection during unstable MMR. (A) Analysis of patients from Group A. (B) Analysis of patients from Group B. Blue curve—patients with therapy change during unstable MMR with no mutation detected; dashed blue curve—patients with therapy change during unstable MMR with mutation detected; red curve—patients no therapy changes during unstable MMR with no mutation detected; dashed red—patients no therapy changes during unstable MMR with mutation detected. TABLE S1. Characteristics of CML patients with unMMR during TKI therapy. Group A includes patients who displayed unMMR after an initial DMR. Group B includes patients who never achieved any better response than unMMR. TABLE S2. Patient characteristics. Group A includes patients (n = 15) on TKI therapy who had initially achieved deep molecular response, but BCR::ABL1 transcripts had subsequently increased to the levels of unMMR. Group B consists of patients (n = 76) who never achieved better response to TKIs than unMMR TABLE S3. The LoQ and thresholds of significant mutation detection. TABLE S4. The values of LoD and LoQ for each specific nucleotide change. (A) Thirty samples of healthy donors were sequenced and for each type of nucleotide change the LoDs were calculated. (B) The values of LoQs were set to fivefold the LoDs. TABLE S5. Limits of quantification of each nucleotide position. LoQs were calculated for 1248 positions of the KD, and for each position they were compared to the value of LoQ of the specific nucleotide changes. Then the higher value was chosen for the final threshold data set. The second column shows the total number of each individual nucleotide in the 1248 bp region. The third and the fifth columns show the number of nucleotides with higher LoQ for each nucleotide positions for transitions and for transversion, respectively. TABLE S6. Frequency of low- and high-level variants detected by NGS in samples with BCR::ABL1 below or above 1% IS. TABLE S7. ddASO PCR analysis of low-level variants. The table presents the results of ddASO PCR analysis of five low-level variants, which were detected by NGS only in one duplicate, in five samples. Only in one case the variant was confirmed by ddASO PCR. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.