New Pattern of Emerging Somatic Mutations in Optimal Responders Following Tyrosine Kinase Inhibitor Therapy in Chronic Myeloid Leukemia Patients

New Pattern of Emerging Somatic Mutations in Optimal Responders Following Tyrosine Kinase Inhibitor Therapy in Chronic Myeloid Leukemia Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article New Pattern of Emerging Somatic Mutations in Optimal Responders Following Tyrosine Kinase Inhibitor Therapy in Chronic Myeloid Leukemia Patients Dennis Kim, Yael Morgenstern, Maria Agustina Perusini, Gopila Gupta, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7924728/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tyrosine kinase inhibitors (TKIs) significantly improved outcomes for patients with chronic myeloid leukemia (CML), enabling optimal treatment responses and near-normal life expectancy. Despite optimal responses, clonal evolution (CE) in Philadelphia chromosome-negative (Ph-negative) cells occurs in 10–15% of optimal responders, typically detected via cytogenetics. The mutational dynamics underlying this phenomenon remain poorly understood. This study investigated mutational kinetics using targeted next-generation sequencing (NGS) of 40 leukemia-associated genes. A total of 119 serial peripheral blood samples from 51 newly diagnosed chronic-phase CML patients, with over 12 months of follow-up, were analyzed using single-molecule molecular inversion probe (smMIP)-based NGS. Remarkably, 24% of patients developed new somatic mutations during follow up, primarily in DNMT3a , TET2 and ASXL1. These mutations were absent at diagnosis and exhibited a median doubling time of 68 days. Notably, these mutations emerged despite patients maintaining deep molecular responses and without evidence for cytogenetic evolution. Our results highlight an ongoing clonal evolution in the Ph-negative clone suggesting the potential utility of longitudinal NGS every 6–7 months as an alternative to cytogenetic monitoring in CML. Health sciences/Medical research/Translational research Biological sciences/Cancer/Haematological cancer/Leukaemia/Chronic myeloid leukaemia Figures Figure 1 Figure 2 Introduction Chronic myeloid leukemia (CML) is defined by a reciprocal translocation between the Abelson murine leukemia (ABL) gene on chromosome 9 and the breakpoint cluster region (BCR) gene on chromosome 22, resulting in the formation of the Philadelphia (Ph) chromosome and the oncogenic BCR::ABL1 fusion gene 1 . The advent of tyrosine kinase inhibitors (TKIs) targeting BCR::ABL1 has revolutionized the treatment landscape of CML, dramatically improving long-term outcomes and enabling many CML patients to achieve life expectancy comparable to that of the general population 2 , 3 . TKI therapy leads to a reduction in Ph-positive leukemic clones, resulting in cytogenetic remission and subsequent re-establishment of polyclonal hematopoiesis in patients who respond optimally. However, clonal evolution (CE) involving Ph-negative cells, referred to as CE/Ph-negative, has been observed in a subset of these responders 4 . This phenomenon, characterized by the emergence of additional cytogenetic abnormalities such as trisomy 8, chromosome 7 abnormalities, and loss of the Y chromosome in Ph-negative cells, occurs in approximately 10–15% of patients who achieve a major or complete cytogenetic response (CCyR) with TKI therapy 5 – 11 . The presence of CE/Ph-negative clone generally has less prognostic significance in the absence of morphologic evidence of myelodysplasia, acute myeloid leukemia (AML), or high-risk chromosomal lesions such as monosomy 7 12,13 . Currently, the standard methodology to detect CE/Ph-neg is metaphase cytogenetic test using bone marrow specimens, while fluorescence in situ hybridization (FISH) offers an alternative but limited depending on the target of the panel used 14 . Another possibility is next-generation sequencing (NGS) but with a limitation of bulk NGS which could not differentiate between Ph-positive and Ph-negative clone. Using NGS, Schmidt et al. identified somatic mutations in both Philadelphia chromosome (Ph)-positive and Ph-negative clones, demonstrating that some of these mutations preceded the BCR::ABL1 rearrangement and may contribute to the clonal evolution of CML 15 . In a study by Kim et al., five distinct molecular patterns were identified by analyzing paired diagnostic and follow-up samples collected 6–12 months after TKI initiation 16 . These patterns encompassed persistent preleukemic mutations in epigenetic regulators (e.g., TET2 , ASXL1 ), acquired mutations associated with TKI resistance, and early clonal events detectable in both hematopoietic and T-cell compartments, suggesting a complex landscape of pre-existing and emergent mutations in CML pathogenesis. In this study, we aimed to investigate the molecular kinetic pattern in CML patients who achieved an optimal response to TKI therapy. Specifically, we performed targeted deep sequencing of 40 recurrently mutated leukemia-associated genes in paired samples obtained prior to and after TKI treatment with a follow-up duration exceeding 12 months. We hypothesized that an already known leukemia-associated mutation might exist in both Ph-negative and Ph-positive hematopoiesis that might precede the acquisition of the BCR::ABL1 fusion, reflecting early, preleukemic clonal events. Patient and methods Patient population From our biorepository at Princess Margaret Cancer Centre (Toronto, Canada) and University Hospital Brno (Czech Republic) 17 , we identified 51 cases of chronic myeloid leukemia (CML) in which both diagnostic and follow-up samples were available during tyrosine kinase inhibitor (TKI) therapy. This enabled the collection of a total of 102 serial samples for targeted deep sequencing. The study was approved by the respective institutional ethics committees, and informed consent was obtained from all patients for blood sampling. We performed sequencing and mutation profiling on paired samples collected prior to the initiation of TKI therapy and at follow-up. Clinical management and response assessment As a standard of care, diverse types of TKIs including imatinib, dasatinib and nilotinib were used for frontline treatment considering multiple factors including CML risk category (Sokal score), patient’s co-morbidities, and goal of CML therapy. BCR::ABL1 transcript quantitative Polymerase chain reaction (PCR) was tested every 3–6 months. Molecular response was reported either by the International Scale (IS) of BCR::ABL1 % or by a log scale of BCR::ABL1 transcript level, where 1%, 0.1%, 0.01%, and 0.001%, corresponding to a reduction of 2 (MR2), 3 (MR3), 4 (MR4) and 5 logs, respectively. BCR::ABL1 transcript level ≤ 1% was defined as MR2. BCR::ABL1 transcript level ≤ 0.1% was defined as major molecular response (MMR) or MR3. Optimal response was defined as BCR::ABL1 1% by 12 months of TKI therapy or thereafter for which TKI switch is advised according to ELN 2020 recommendation 18 . The response milestones of second line TKI management were similar to frontline TKI therapy. Next-generation sequencing Single molecule-tagging and molecular inversion probe (smMIP)-based sequencing methods was used to analyze the DNA extracted from the mononuclear cell fraction of peripheral blood samples collected from CML patients at initial diagnosis and during follow-up while on TKI therapy. The in-house CML-specific smMIP panel used in this study included 40 genes with 332 amplicon probes covering genes involved in epigenetic modifiers, activation signaling, myeloid transcription factor, spliceosome, tumor suppressor, cohesion, and miscellaneous functions. The limit of detection of smMIP-based panel was 0.2% variant allele frequency (VAF) 17 , 19 , 20 . Calculation of doubling time (DT) based on the variant allele frequency and time interval To evaluate kinetics of somatic mutations, we calculated doubling time (DT) of mutations. Using the mutational VAFs from 2 time points, t1 and t2, the doubling time was calculated according to the following equation: 𝐷𝑜𝑢𝑏𝑙𝑖𝑛𝑔 𝑇𝑖𝑚𝑒 = (𝑡2 − 𝑡1) x log(2) /[log(𝑉𝐴𝐹2) − log(𝑉𝐴𝐹1)], where VAF1 and VAF2 represent mutant VAF at time point t1 and t2 respectively. Statistical analysis R studio version 2025.05.0 was used for data analysis. Categorical variables were presented as counts and percentages. Continuous variables were summarized with median and range. Failure-free survival (FFS) was defined as time from start of TKI until death, progression to accelerated phase or blast crisis, treatment failure as defined by the ELN 2020 criteria or loss of previously achieved molecular response. Categorical variables were analyzed using the Chi-square test, while continuous variables were compared using the Mann-Whitney. A p-value < 0.05 was considered statistically significant. Results Summary of the study population Targeted deep sequencing was conducted on 102 serial samples from 51 newly diagnosed chronic phase CML patients commenced TKI therapy during the period of 2001–2020. Baseline characteristics are detailed in Table 1 . The median age was 61 years (range: 17–80), with 63% (n = 32) of patients being male. Most patients (53%, n = 27) had an intermediate risk group by Sokal score, while 27% (n = 14) and 16% (n = 8) had high and low risk group, respectively. Initial treatment included imatinib in 71% (n = 36) and second-generation TKIs in 29% (n = 15) including nilotinib (n = 12) and dasatinib (n = 3). With a median follow-up duration of 987 days (range: 294–7601), 80% (n = 41) achieved an optimal response, 16% (n = 8) were non-responders, and 4% (n = 2) were not evaluable at the time of analysis. Somatic mutation profile at diagnosis prior to TKI At initial diagnosis, 14 somatic mutations were identified in 8 of 51 patients (17%). Among the 41 patients who later achieved an optimal response, 36 had no detectable mutations at baseline. The remaining 5 patients had a total of 8 baseline mutations, of which 2 mutations persisted during follow-up (in 2 patients). The most frequently mutated genes at initial diagnosis were DNMT3A (n=3, average VAF 34%), ASXL1 (n=2, average VAF 12%), and TET2 (n=3, average VAF 4%). Single mutations were also detected in TP53 (VAF 4%), BCOR (VAF 4%), FLT3 (VAF 3%), IKZF1 (VAF 7%), JAK2 (VAF 4%) and SMC1A (VAF 3%). Grouping these mutations into its biological pathways, the most frequently affected pathways were DNA methylation ( DNMT3A, TET2 ; 43%) and chromatin modification pathways ( ASXL1, BCOR ; 21%), followed by activated signaling pathways ( JAK2, FLT3; 15%), tumor suppressor genes ( TP53 ; 7%), myeloid transcription factors ( IKZF1; 7 %), and Cohesin-complex genes ( SMC1A ; 7%) (Table 2). Somatic mutation profiles at follow-up and kinetics pattern At a median follow-up of 398 days on TKI therapy across 51 patients, a total of 22 patients were found to have detectable somatic mutations at follow-up. The present cohort confirmed the 3 major patterns of somatic mutation dynamics described previously 16 : Pattern 1 (i.e. persistent mutations) was observed in 2 patients, involving DNMT3A and TET2 ; Pattern 2 (i.e. emergent mutations in a patient with suboptimal response) was identified in 1 case; Pattern 3 (i.e. clearance of mutation with variable clinical outcomes) included majority the of the mutation and cases: 11 mutations across 8 patients - five with optimal response and three with suboptimal response. The most frequently affected genes identified on follow up samples exhibiting a pattern 3 dynamics, were TET2 (n=2; 15%) and ASXL1 (n=2; 15%). Single occurrence was noted in the genes of JAK2, DNMT3A, TP53, BCOR, FLT3, IKZF1, and SMC1A . Detailed patient data for this pattern are provided in Supplementary table 1. Among the 41 patients who achieved an optimal response (MMR or deeper at 12 months post TKI initiation), 28 somatic mutations were identified in 17 patients. Of these, 5 patients (29%) patients presented with somatic mutation(s) at diagnosis, while 12 patients (71%) presented with mutations during TKI therapy that were not detected at diagnosis. One additional patient, lacking baseline data, was captured to have a somatic mutation during follow-up. New pattern of somatic mutation emergence Besides the 3 patterns of somatic mutation kinetics, which were described previously 16 , this new cohort revealed a distinct pattern of newly emerging somatic mutations (n=18) in 12 patients (24%) who achieved an optimal response to TKI therapy (range of MR depth of 3-4 log reduction). Among patients with the new pattern of mutations, the most frequently acquired mutations were DNMT3A (n=4), followed by TET2 (n=4), ASXL1 (n=3), and EZH2 (n=2). Additional mutations, JAK2, SF3B1, U2AF1, PHF6, TP53 and BRAF, were detected in one patient each. The median time to emergence of these mutations was 227 days (range: 105–7578), with a median doubling time (DT) of 68 days (range: 32–1909) (Figure 1). Since a 1-log increase corresponds to a 10-fold rise in allele frequency, this requires about 3.3 doublings (log₂10 ≈ 3.3). At roughly two months per doubling, this translates to roughly 6–7 months for the allele frequency to increase by 1 log. Clinically, this suggests that emerging mutations may expand to detectable or clinically relevant levels within half a year. A summary of the emerging mutations observed in patients with an optimal response, along with the DT for each mutation, is provided in Table 3. Baseline clinical characteristics of patients with optimal response and emergence of somatic mutations are summarized in Table 4. This emerging pattern of somatic mutations in optimal responders is not different among the patients treated with different types of TKI drugs. Of the 12 patients having somatic mutations detected with optimal response to TKI therapy, 10 patients were on imatinib, 1 on dasatinib, and 1 on nilotinib. Out of 12 patients, during their course of TKI therapy, treatment-free remission (TFR) was attempted later in 3 individuals (25%), of whom 1 remained in TFR with a follow-up of 5.7 years after initiation of TKI treatment. With a median TKI treatment duration of 1,024 days (range: 294–3,869), the clinical outcomes in the optimal responders were compared according to the emergence of somatic mutations during TKI therapy (Table 5). All mutation-positive responders (n=12) have achieved MR4 with a median 258 days (range: 168–776) to achieve MR4 response. Among these 12 patients, 10 underwent metaphase cytogenetic evaluation of bone marrow samples at a median of 224 days after initiating TKI therapy. None of the case showed any evidence of cytogenetics-based CE in Ph-negative cells. In the remaining 28 optimal responder, as described above, any emerging somatic mutations were not detected during the follow-up. Out of 28, 22 patients (79%) achieved MR4, while remaining 6 (21%) achieved MMR. Of these 28 patients, 23 underwent cytogenetic analysis during the TKI follow-up after confirmation of optimal response by PCR test. Of interest, only 1 case was found to have a CE in Ph-negative cells, characterized by –Y, which occurred 933 days after initiation of TKI therapy. Discussion The present study provides new insights into the molecular dynamics of chronic phase CML with TKI therapy, especially. In the patients responding to TKI therapy optimally. Using smMIP-based targeted sequencing at diagnosis, we observed a novel finding, the emergence of new somatic mutations in 24% of patients who responded optimally to TKI therapy. Besides 5 mutational patterns described previously 16 , 21 , this newly identified pattern was characterized by the appearance of mutations in genes such as DNMT3A, TET2, ASXL1 , and EZH2 . This distinct mutational pattern is proposed here as a sixth pattern “Emerging Mutational Clones in Optimally Responding CML Patients” as illustrated in Fig. 2 . The mutations spanned diverse functional categories, including DNA methylation, chromatin modification, signaling pathways, tumor suppressors, transcription factors, and spliceosome components, highlighting the clonal competition between CML and non-CML cell populations under TKI pressure. These mutations exhibited variable doubling times (32–1909 days), suggesting ongoing clonal evolution in Ph-negative cells after clearance of Ph-positive cells with TKI therapy which was captured by NGS assay even in the absence of detectable cytogenetic lesions by conventional methods. From cancer cell hierarchy perspective, several patterns were reported including clonal divergence (accumulation of distinct genetic alternations) 22 – 24 , clonal competition (different clones residing but competing less fit clones) 25 , clonal expansion (rapid proliferation of a clone having survival advantages) 26 , 27 , clonal sweep and switch (clone with significantly fitness advantage over the population) 28 . Our findings indicate that during TKI therapy, the growth of Ph-negative, non-CML clones should be carefully considered when interpreting NGS results. Notably, the median DT of somatic mutations arising in these Ph-negative clones was 68 days, substantially faster than the rates typically reported for clonal hematopoiesis-associated mutations 29 . This observation suggests that the CML bone marrow microenvironment, even in patients achieving an optimal response to TKI treatment, may harbor unique signaling mechanisms that promote accelerated proliferation of such clones. Accordingly, the present result questions the paradigm that an optimal molecular response necessarily reflects molecular stability. The emergence of new somatic mutations in optimally responding patients suggests that clonal switch and repopulation may continue in Ph-negative non-CML hematopoietic cells, after control of Ph-positive CML clone with TKI therapy. This aligns with findings by Schmidt et al., which demonstrated BCR::ABL1 -independent mutations in Ph-negative clones during TKI therapy, including DNMT3A, EZH2, RUNX1, TET2 , and TP53 15 . It also identified overlapping mutations in both Ph-positive and Ph-negative compartments, supporting the concept of early clonal hematopoiesis contributing to CML pathogenesis and evolution 16 , 30 . Our findings also raise the question of whether peripheral blood-based NGS testing could complement or even replace bone marrow cytogenetics for monitoring clonal evolution, particularly in Ph-negative clones. NGS offers a non-invasive, sensitive, and scalable approach for tracking molecular changes over time and may provide earlier insights into repopulation dynamics of CML vs non-CML cells than conventional methods. Practically, it can be implemented into the current clinical practice for those patients achieving optimal molecular response which is confirmed by the BCR::ABL1 qPCR. Based on the doubling time of about 60 days for those emerging mutations, we propose every 6–7 months frequency for NGS testing to monitor those emerging mutations which could represent repopulation of CE in Ph-negative clone. Similar to clonal hematopoiesis (CH), the enrichment of somatic mutations in epigenetic regulators suggests that dysregulation of chromatin and methylation pathways may contribute to the development of Ph-negative clones, even after controlling CML disease effectively with TKI therapy and achieving sustained molecular response. Further studies are required to clarify how these mutations interact with BCR::ABL1 signaling and impact long-term outcomes such as success in TFR. This study is limited by its relatively small sample size and single-center design. Larger, multicenter studies are needed to validate the proposed sixth mutational pattern and to assess its potential prognostic significance, particularly in determining whether mutation dynamics can predict successful TFR or relapse. Additionally, single-cell sequencing studies are warranted to investigate whether these emerging mutational clones are associated with cytogenetically identified CE/Ph-negative clones. In summary, the current study suggests a new sixth pattern of mutational dynamics showing emerging somatic mutations in optimal responders to TKI therapy, which could be from expansion of Ph- clone. We also propose longitudinal NGS monitoring in CML patients every 6 months, which could offer valuable insights into the clonal evolution of the Ph-negative clone, complementing metaphase cytogenetic testing. Declarations Competing Interests None Author Contributions YM, MAP and DK conceptualized and designed the study. YM, MAP, GG, JM, SA and DK conducted data analysis. YM and DK wrote the manuscript. DZ, IJ, AK, TJ, AK and JM provided CML patient samples for the study. All authors reviewed the manuscript. Acknowledgements The present study was supported by Leukemia & Lymphoma Society of Canada and by Cancer Research Society, and by Princess Margaret Cancer Foundation in part. References Rowley JD. A New Consistent Chromosomal Abnormality in Chronic Myelogenous Leukaemia identified by Quinacrine Fluorescence and Giemsa Staining. Nature . 1973;243(5405):290–293. doi: 10.1038/243290a0 Bower H, Björkholm M, Dickman PW, Höglund M, Lambert PC, Andersson TML. Life Expectancy of Patients With Chronic Myeloid Leukemia Approaches the Life Expectancy of the General Population. J Clin Oncol Off J Am Soc Clin Oncol . 2016;34(24):2851–2857. doi: 10.1200/JCO.2015.66.2866 Hochhaus A, Larson RA, Guilhot F, et al. Long-Term Outcomes of Imatinib Treatment for Chronic Myeloid Leukemia. 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Baseline characteristics of the study population (n=51 patients) Parameters No. of patients (%) Age, median (range) >60 y/o 40-60 y/o <40 y/o 61(17-80) 26 (51) 19 (37) 6 (12) Sex, male (%) 32 (63) Chronic phase (%) 51 (100) Cytogenetics Normal Not evaluable ACA* Not available 39 (76.5) 6 (12) 4 (8) 2 (4) Sokal risk score Low Intermediate High Unknown 14 (27) 27 (53) 9 (18) 1 (2) First line TKI Imatinib Nilotinib Dasatinib 36 (71) 12(24) 3 (6) Response to TKI Optimal response Suboptimal response Not evaluable 41 (80%) 8 (16%) 2 (4%) *Abbreviations: ACA additional cytogenetic abnormalities, TKI tyrosine kinase inhibitor * Complex cytogenetics (n=3), i(17) (n=1) Table 2 . List of biological pathways with mutated genes at initial diagnosis Biological pathways Genes Frequency (%) DNA-methylation–related genes DNMT3A, TET2 43% Chromatin-modifying genes ASXL1, BCOR 21% Activated signaling genes JAK2, FLT3 15% Tumor-suppressor genes TP53 7% Myeloid transcription-factor genes IKZF1 7% Cohesion-complex genes SMC1A 7% Table 3 . Summary of emerg ing mutations in patients with optimal response Case No. Response to TKI Gene T1 T2 VAF1 VAF2 DT (days) 1 Optimal TET2 9 3717 ND 0.004 1,909 2 Optimal TET2 0 205 ND 0.058 43 2 Optimal DNMT3A 0 205 ND 0.027 35 3 Optimal ASXL1 0 203 ND 0.208 264 4 Optimal BRAF 0 355 ND 0.029 73 4 Optimal JAK2 0 180 ND 0.319 39 4 Optimal SF3B1 0 180 ND 0.008 84 4 Optimal TET2 0 180 ND 0.026 62 5 Optimal EZH2 0 777 ND 0.019 182 6* Optimal* PHF6 0 242 ND 0.009 77 7 Optimal ASXL1 0 195 ND 0.030 40 7 Optimal DNMT3A 0 195 ND 0.011 56 8 Optimal U2AF1 0 396 ND 0.034 74 9 Optimal EZH2 0 105 ND 0.012 29 10 Optimal DNMT3A 31 227 ND 0.070 32 11 Optimal DNMT3A -12 2288 ND 0.093 352 11 Optimal TP53 -12 2288 ND 0.018 555 12** Optimal ASXL1 873 7578 ND 0.201 877 13 Optimal TET2 0 2408 ND 0.008 791 T1, time of initial analysis (days from start of TKI); T2, time of second analysis (days from start of TKI); VAF1, variant allele frequency at T1; VAF2, variant allele frequency at T2; ND, not detected. *The patient has 2 different mutations having different mutational patterns – pattern 1 (persistent mutation in DNMT3A ) and a novel pattern (emergence of new mutations) ** The patient has no baseline sample prior to starting TKI Table 4. Baseline characteristics of optimal responders with emerging somatic mutations Case No Treatment BCR::ABL1 level at last follow up Time to MR4 (days) TFR attempt Outcome FFS (days) 1 Imatinib MR4 761 no switch to dasatinib 3,869 2 Imatinib MR4 261 no imatinib 1,843 3 Nilotinib MR4 715 yes nilotinib, sustained TFR 1,481 6 Imatinib MR4 168 no imatinib 840 8 Imatinib MR4 188 no switch to dasatinib 1,140 16 Dasatinib MR4 168 no dasatinib 908 21 Imatinib MR4 255 no imatinib 703 24 Imatinib MR4 252 no imatinib 637 34 Imatinib MR4 280 no imatinib 364 35 Imatinib MR4 182 no imatinib 294 39 Imatinib MR4 575 yes MMR loss, switch to bosutinib 3,172 42 Imatinib MR4 776 yes MMR loss, switch to dasatinib and then bosutinib 3,355 *Abbreviations: MR4 – molecular response of 4 log reduction which is equivalent to 0.01% international scale; TFR – treatment free remission, FFS – failure free survival, MMR – major molecular response which is equivalent to 0.1% international scale Table 5. Clinical characteristics of optimal responders with and without emerging somatic mutations No of pts (%) Optimal responder with emergence of new somatic mutations (n=12) Optimal responders without acquisition of somatic mutations (n=28) p-value Age, yr (median) 65 59 0.45 Sex, male 33 70 0.07 FFS, days (median) 1,024 (range 294-3,869) 930 (range 350-5,317) 0.58 No. of patients TFR attempted 3 1 0.07 No of the patients succeeded TFR 1 out of 3 1 out of 1 0.99 No. of patients achieving MR4 (%) 12 (100) 20 (71) 0.09 Time to MR4, days (median) 258 days (range 168–776) 461 (range 168-797) 0.15 CE in Ph-negative cells 0 (out of 10 patients tested) 1 (out of 23 patients tested) 0.99 * Abbreviations: FFS, failure free survival; TFR, treatment free remission; MR4, molecular response of 4 log reduction which is equivalent to 0.01% international scale; CE, clonal evolution Additional Declarations There is NO conflict of interest to disclose. Supplementary Files MorgensternetalSuppTable1.docx Supplementary Table 1 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7924728","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":534042147,"identity":"c96d1253-f6f3-4d2d-a771-d2b0ea455588","order_by":0,"name":"Dennis 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1","display":"","copyAsset":false,"role":"figure","size":33386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDoubling time of emerging mutations. \u003c/strong\u003eDoubling time of emerging mutation in patients with optimal response as calculated based on VAF in sequential samples\u003c/p\u003e","description":"","filename":"MorgensternetalFig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7924728/v1/d9817cc64c0914afe66696b8.jpg"},{"id":95226550,"identity":"0492e66e-d650-47b4-b794-e8700f2c93d4","added_by":"auto","created_at":"2025-11-05 16:31:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSomatic mutations in Ph-negative clones in optimally responding CML Patients\u003c/strong\u003e. CML patients achieving an optimal response with TKI therapy show emergence of Ph-negative subclones harboring somatic mutations detected via NGS.\u003c/p\u003e","description":"","filename":"MorgensternetalFig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7924728/v1/b0d28c5bdea695f86d325c13.jpg"},{"id":97664649,"identity":"4a491eef-7dc4-474c-acb6-79933cf0e05c","added_by":"auto","created_at":"2025-12-08 09:12:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1383761,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7924728/v1/f5733123-29b6-4c79-8322-f51c6f3aff7e.pdf"},{"id":95171608,"identity":"55c34757-0ab4-4826-829d-b066a72fd293","added_by":"auto","created_at":"2025-11-05 06:35:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25597,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1\u003c/p\u003e","description":"","filename":"MorgensternetalSuppTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7924728/v1/18734988fa96ea6ca8a1fccf.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"New Pattern of Emerging Somatic Mutations in Optimal Responders Following Tyrosine Kinase Inhibitor Therapy in Chronic Myeloid Leukemia Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic myeloid leukemia (CML) is defined by a reciprocal translocation between the Abelson murine leukemia (ABL) gene on chromosome 9 and the breakpoint cluster region (BCR) gene on chromosome 22, resulting in the formation of the Philadelphia (Ph) chromosome and the oncogenic \u003cem\u003eBCR::ABL1\u003c/em\u003e fusion gene\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The advent of tyrosine kinase inhibitors (TKIs) targeting \u003cem\u003eBCR::ABL1\u003c/em\u003e has revolutionized the treatment landscape of CML, dramatically improving long-term outcomes and enabling many CML patients to achieve life expectancy comparable to that of the general population\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTKI therapy leads to a reduction in Ph-positive leukemic clones, resulting in cytogenetic remission and subsequent re-establishment of polyclonal hematopoiesis in patients who respond optimally. However, clonal evolution (CE) involving Ph-negative cells, referred to as CE/Ph-negative, has been observed in a subset of these responders\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This phenomenon, characterized by the emergence of additional cytogenetic abnormalities such as trisomy 8, chromosome 7 abnormalities, and loss of the Y chromosome in Ph-negative cells, occurs in approximately 10\u0026ndash;15% of patients who achieve a major or complete cytogenetic response (CCyR) with TKI therapy\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The presence of CE/Ph-negative clone generally has less prognostic significance in the absence of morphologic evidence of myelodysplasia, acute myeloid leukemia (AML), or high-risk chromosomal lesions such as monosomy 7\u003csup\u003e12,13\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, the standard methodology to detect CE/Ph-neg is metaphase cytogenetic test using bone marrow specimens, while fluorescence in situ hybridization (FISH) offers an alternative but limited depending on the target of the panel used\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Another possibility is next-generation sequencing (NGS) but with a limitation of bulk NGS which could not differentiate between Ph-positive and Ph-negative clone. Using NGS, Schmidt et al. identified somatic mutations in both Philadelphia chromosome (Ph)-positive and Ph-negative clones, demonstrating that some of these mutations preceded the \u003cem\u003eBCR::ABL1\u003c/em\u003e rearrangement and may contribute to the clonal evolution of CML\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In a study by Kim et al., five distinct molecular patterns were identified by analyzing paired diagnostic and follow-up samples collected 6\u0026ndash;12 months after TKI initiation\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These patterns encompassed persistent preleukemic mutations in epigenetic regulators (e.g., \u003cem\u003eTET2\u003c/em\u003e, \u003cem\u003eASXL1\u003c/em\u003e), acquired mutations associated with TKI resistance, and early clonal events detectable in both hematopoietic and T-cell compartments, suggesting a complex landscape of pre-existing and emergent mutations in CML pathogenesis.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to investigate the molecular kinetic pattern in CML patients who achieved an optimal response to TKI therapy. Specifically, we performed targeted deep sequencing of 40 recurrently mutated leukemia-associated genes in paired samples obtained prior to and after TKI treatment with a follow-up duration exceeding 12 months. We hypothesized that an already known leukemia-associated mutation might exist in both Ph-negative and Ph-positive hematopoiesis that might precede the acquisition of the \u003cem\u003eBCR::ABL1\u003c/em\u003e fusion, reflecting early, preleukemic clonal events.\u003c/p\u003e"},{"header":"Patient and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient population\u003c/h2\u003e\u003cp\u003eFrom our biorepository at Princess Margaret Cancer Centre (Toronto, Canada) and University Hospital Brno (Czech Republic)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, we identified 51 cases of chronic myeloid leukemia (CML) in which both diagnostic and follow-up samples were available during tyrosine kinase inhibitor (TKI) therapy. This enabled the collection of a total of 102 serial samples for targeted deep sequencing. The study was approved by the respective institutional ethics committees, and informed consent was obtained from all patients for blood sampling. We performed sequencing and mutation profiling on paired samples collected prior to the initiation of TKI therapy and at follow-up.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical management and response assessment\u003c/h3\u003e\n\u003cp\u003eAs a standard of care, diverse types of TKIs including imatinib, dasatinib and nilotinib were used for frontline treatment considering multiple factors including CML risk category (Sokal score), patient\u0026rsquo;s co-morbidities, and goal of CML therapy. \u003cem\u003eBCR::ABL1\u003c/em\u003e transcript quantitative Polymerase chain reaction (PCR) was tested every 3\u0026ndash;6 months. Molecular response was reported either by the International Scale (IS) of \u003cem\u003eBCR::ABL1\u003c/em\u003e% or by a log scale of \u003cem\u003eBCR::ABL1\u003c/em\u003e transcript level, where 1%, 0.1%, 0.01%, and 0.001%, corresponding to a reduction of 2 (MR2), 3 (MR3), 4 (MR4) and 5 logs, respectively. \u003cem\u003eBCR::ABL1\u003c/em\u003e transcript level\u0026thinsp;\u0026le;\u0026thinsp;1% was defined as MR2. \u003cem\u003eBCR::ABL1\u003c/em\u003e transcript level\u0026thinsp;\u0026le;\u0026thinsp;0.1% was defined as major molecular response (MMR) or MR3. Optimal response was defined as BCR::ABL1\u0026thinsp;\u0026lt;\u0026thinsp;0.1% at 12 months of TKI treatment and thereafter. TKI failure was defined as BCR::ABL1\u0026thinsp;\u0026gt;\u0026thinsp;1% by 12 months of TKI therapy or thereafter for which TKI switch is advised according to ELN 2020 recommendation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The response milestones of second line TKI management were similar to frontline TKI therapy.\u003c/p\u003e\n\u003ch3\u003eNext-generation sequencing\u003c/h3\u003e\n\u003cp\u003eSingle molecule-tagging and molecular inversion probe (smMIP)-based sequencing methods was used to analyze the DNA extracted from the mononuclear cell fraction of peripheral blood samples collected from CML patients at initial diagnosis and during follow-up while on TKI therapy. The in-house CML-specific smMIP panel used in this study included 40 genes with 332 amplicon probes covering genes involved in epigenetic modifiers, activation signaling, myeloid transcription factor, spliceosome, tumor suppressor, cohesion, and miscellaneous functions. The limit of detection of smMIP-based panel was 0.2% variant allele frequency (VAF)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCalculation of doubling time (DT) based on the variant allele frequency and time interval\u003c/h3\u003e\n\u003cp\u003eTo evaluate kinetics of somatic mutations, we calculated doubling time (DT) of mutations. Using the mutational VAFs from 2 time points, t1 and t2, the doubling time was calculated according to the following equation: \u0026#119863;\u0026#119900;\u0026#119906;\u0026#119887;\u0026#119897;\u0026#119894;\u0026#119899;\u0026#119892; \u0026#119879;\u0026#119894;\u0026#119898;\u0026#119890; = (\u0026#119905;2 \u0026minus; \u0026#119905;1) x log(2) /[log(\u0026#119881;\u0026#119860;\u0026#119865;2)\u0026thinsp;\u0026minus;\u0026thinsp;log(\u0026#119881;\u0026#119860;\u0026#119865;1)], where VAF1 and VAF2 represent mutant VAF at time point t1 and t2 respectively.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eR studio version 2025.05.0 was used for data analysis. Categorical variables were presented as counts and percentages. Continuous variables were summarized with median and range. Failure-free survival (FFS) was defined as time from start of TKI until death, progression to accelerated phase or blast crisis, treatment failure as defined by the ELN 2020 criteria or loss of previously achieved molecular response. Categorical variables were analyzed using the Chi-square test, while continuous variables were compared using the Mann-Whitney. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eSummary of the study population\u003c/h2\u003e\n \u003cp\u003eTargeted deep sequencing was conducted on 102 serial samples from 51 newly diagnosed chronic phase CML patients commenced TKI therapy during the period of 2001\u0026ndash;2020. Baseline characteristics are detailed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 61 years (range: 17\u0026ndash;80), with 63% (n\u0026thinsp;=\u0026thinsp;32) of patients being male. Most patients (53%, n\u0026thinsp;=\u0026thinsp;27) had an intermediate risk group by Sokal score, while 27% (n\u0026thinsp;=\u0026thinsp;14) and 16% (n\u0026thinsp;=\u0026thinsp;8) had high and low risk group, respectively. Initial treatment included imatinib in 71% (n\u0026thinsp;=\u0026thinsp;36) and second-generation TKIs in 29% (n\u0026thinsp;=\u0026thinsp;15) including nilotinib (n\u0026thinsp;=\u0026thinsp;12) and dasatinib (n\u0026thinsp;=\u0026thinsp;3). With a median follow-up duration of 987 days (range: 294\u0026ndash;7601), 80% (n\u0026thinsp;=\u0026thinsp;41) achieved an optimal response, 16% (n\u0026thinsp;=\u0026thinsp;8) were non-responders, and 4% (n\u0026thinsp;=\u0026thinsp;2) were not evaluable at the time of analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSomatic mutation profile at diagnosis prior to TKI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt initial diagnosis, 14 somatic mutations were identified in 8 of 51 patients (17%). Among the 41 patients who later achieved an optimal response, 36 had no detectable mutations at baseline. The remaining 5 patients had a total of 8 baseline mutations, of which 2 mutations persisted during follow-up (in 2 patients). The most frequently mutated genes at initial diagnosis were \u003cem\u003eDNMT3A\u003c/em\u003e (n=3, average VAF 34%), \u003cem\u003eASXL1\u0026nbsp;\u003c/em\u003e(n=2, average VAF 12%), and \u003cem\u003eTET2\u0026nbsp;\u003c/em\u003e(n=3, average VAF 4%). Single mutations were also detected in \u003cem\u003eTP53\u003c/em\u003e (VAF 4%), \u003cem\u003eBCOR\u003c/em\u003e (VAF 4%), \u003cem\u003eFLT3\u003c/em\u003e (VAF 3%), \u003cem\u003eIKZF1\u003c/em\u003e (VAF 7%), \u003cem\u003eJAK2\u003c/em\u003e (VAF 4%) and \u003cem\u003eSMC1A\u003c/em\u003e (VAF 3%). Grouping these mutations into its biological pathways, the most frequently affected pathways were DNA methylation (\u003cem\u003eDNMT3A, TET2\u003c/em\u003e; 43%) and chromatin modification pathways (\u003cem\u003eASXL1, BCOR\u003c/em\u003e; 21%), followed by activated signaling pathways (\u003cem\u003eJAK2, FLT3;\u003c/em\u003e15%), tumor suppressor genes (\u003cem\u003eTP53\u003c/em\u003e; 7%), myeloid transcription factors (\u003cem\u003eIKZF1; 7\u003c/em\u003e%), and Cohesin-complex genes (\u003cem\u003eSMC1A\u003c/em\u003e; 7%) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSomatic mutation profiles at follow-up and kinetics pattern\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt a median follow-up of 398 days on TKI therapy across 51 patients, a total of 22 patients were found to have detectable somatic mutations at follow-up. The present cohort confirmed the 3 major patterns of somatic mutation dynamics described previously\u003csup\u003e16\u003c/sup\u003e: Pattern 1 (i.e. persistent mutations) was observed in 2 patients, involving \u003cem\u003eDNMT3A\u0026nbsp;\u003c/em\u003eand \u003cem\u003eTET2\u003c/em\u003e; Pattern 2 (i.e. emergent mutations in a patient with suboptimal response) was identified in 1 case; Pattern 3 (i.e. clearance of mutation with variable clinical outcomes) included majority the of the mutation and cases: 11 mutations across 8 patients - five with optimal response and three with suboptimal response. The most frequently affected genes identified on follow up samples exhibiting a pattern 3 dynamics, were \u003cem\u003eTET2\u003c/em\u003e (n=2; 15%) and \u003cem\u003eASXL1\u003c/em\u003e (n=2; 15%). Single occurrence was noted in the genes of \u003cem\u003eJAK2, DNMT3A, TP53, BCOR, FLT3, IKZF1,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eSMC1A\u003c/em\u003e. Detailed patient data for this pattern are provided in Supplementary table 1.\u003c/p\u003e\n\u003cp\u003eAmong the 41 patients who achieved an optimal response (MMR or deeper at 12 months post TKI initiation), 28 somatic mutations were identified in 17 patients. Of these, 5 patients (29%) patients presented with somatic mutation(s) at diagnosis, while 12 patients (71%) presented with mutations during TKI therapy that were not detected at diagnosis. One additional patient, lacking baseline data, was captured to have a somatic mutation during follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNew pattern of somatic mutation emergence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBesides the 3 patterns of somatic mutation kinetics, which were described previously\u003csup\u003e16\u003c/sup\u003e, this new cohort revealed a distinct pattern of newly emerging somatic mutations (n=18) in 12 patients (24%) who achieved an optimal response to TKI therapy (range of MR depth of 3-4 log reduction). Among patients with the new pattern of mutations, the most frequently acquired mutations were \u003cem\u003eDNMT3A\u003c/em\u003e (n=4), followed by \u003cem\u003eTET2\u003c/em\u003e (n=4), \u003cem\u003eASXL1\u003c/em\u003e (n=3), and \u003cem\u003eEZH2\u003c/em\u003e (n=2). Additional mutations, \u003cem\u003eJAK2, SF3B1, U2AF1, PHF6, TP53\u003c/em\u003e and \u003cem\u003eBRAF,\u003c/em\u003e were detected in one patient each. The median time to emergence of these mutations was 227 days (range: 105\u0026ndash;7578), with a median doubling time (DT) of 68 days (range: 32\u0026ndash;1909) (Figure 1). Since a 1-log increase corresponds to a 10-fold rise in allele frequency, this requires about 3.3 doublings (log₂10 \u0026asymp; 3.3). At roughly two months per doubling, this translates to roughly 6\u0026ndash;7 months for the allele frequency to increase by 1 log. Clinically, this suggests that emerging mutations may expand to detectable or clinically relevant levels within half a year. A summary of the emerging mutations observed in patients with an optimal response, along with the DT for each mutation, is provided in Table 3. Baseline clinical characteristics of patients with optimal response and emergence of somatic mutations are summarized in Table 4.\u003c/p\u003e\n\u003cp\u003eThis emerging pattern of somatic mutations in optimal responders is not different among the patients treated with different types of TKI drugs. Of the 12 patients having somatic mutations detected with optimal response to TKI therapy, 10 patients were on imatinib, 1 on dasatinib, and 1 on nilotinib. Out of 12 patients, during their course of TKI therapy, treatment-free remission (TFR) was attempted later in 3 individuals (25%), of whom 1 remained in TFR with a follow-up of 5.7 years after initiation of TKI treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith a median TKI treatment duration of 1,024 days (range: 294\u0026ndash;3,869), the clinical outcomes in the optimal responders were compared according to the emergence of somatic mutations during TKI therapy (Table 5).\u0026nbsp;All mutation-positive responders (n=12) have achieved MR4 with a median 258 days (range: 168\u0026ndash;776) to achieve MR4 response. Among these 12 patients, 10 underwent metaphase cytogenetic evaluation of bone marrow samples at a median of 224 days after initiating TKI therapy. None of the case showed any evidence of cytogenetics-based CE in\u0026nbsp;Ph-negative cells.\u003c/p\u003e\n\u003cp\u003eIn the remaining 28 optimal responder, as described above, any emerging somatic mutations were not detected during the follow-up. Out of 28, 22 patients (79%) achieved MR4, while remaining 6 (21%) achieved MMR. Of these 28 patients, 23 underwent cytogenetic analysis during the TKI follow-up after confirmation of optimal response by PCR test. Of interest, only 1 case was found to have a CE in Ph-negative cells, characterized by \u0026ndash;Y, which occurred 933 days after initiation of TKI therapy.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides new insights into the molecular dynamics of chronic phase CML with TKI therapy, especially. In the patients responding to TKI therapy optimally. Using smMIP-based targeted sequencing at diagnosis, we observed a novel finding, the emergence of new somatic mutations in 24% of patients who responded optimally to TKI therapy. Besides 5 mutational patterns described previously\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, this newly identified pattern was characterized by the appearance of mutations in genes such as \u003cem\u003eDNMT3A, TET2, ASXL1\u003c/em\u003e, and \u003cem\u003eEZH2\u003c/em\u003e. This distinct mutational pattern is proposed here as a sixth pattern \u0026ldquo;Emerging Mutational Clones in Optimally Responding CML Patients\u0026rdquo; as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The mutations spanned diverse functional categories, including DNA methylation, chromatin modification, signaling pathways, tumor suppressors, transcription factors, and spliceosome components, highlighting the clonal competition between CML and non-CML cell populations under TKI pressure. These mutations exhibited variable doubling times (32\u0026ndash;1909 days), suggesting ongoing clonal evolution in Ph-negative cells after clearance of Ph-positive cells with TKI therapy which was captured by NGS assay even in the absence of detectable cytogenetic lesions by conventional methods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom cancer cell hierarchy perspective, several patterns were reported including clonal divergence (accumulation of distinct genetic alternations)\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, clonal competition (different clones residing but competing less fit clones)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, clonal expansion (rapid proliferation of a clone having survival advantages)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, clonal sweep and switch (clone with significantly fitness advantage over the population)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Our findings indicate that during TKI therapy, the growth of Ph-negative, non-CML clones should be carefully considered when interpreting NGS results. Notably, the median DT of somatic mutations arising in these Ph-negative clones was 68 days, substantially faster than the rates typically reported for clonal hematopoiesis-associated mutations\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This observation suggests that the CML bone marrow microenvironment, even in patients achieving an optimal response to TKI treatment, may harbor unique signaling mechanisms that promote accelerated proliferation of such clones. Accordingly, the present result questions the paradigm that an optimal molecular response necessarily reflects molecular stability.\u003c/p\u003e\u003cp\u003eThe emergence of new somatic mutations in optimally responding patients suggests that clonal switch and repopulation may continue in Ph-negative non-CML hematopoietic cells, after control of Ph-positive CML clone with TKI therapy. This aligns with findings by Schmidt et al., which demonstrated \u003cem\u003eBCR::ABL1\u003c/em\u003e-independent mutations in Ph-negative clones during TKI therapy, including \u003cem\u003eDNMT3A, EZH2, RUNX1, TET2\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003e. It also identified overlapping mutations in both Ph-positive and Ph-negative compartments, supporting the concept of early clonal hematopoiesis contributing to CML pathogenesis and evolution\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur findings also raise the question of whether peripheral blood-based NGS testing could complement or even replace bone marrow cytogenetics for monitoring clonal evolution, particularly in Ph-negative clones. NGS offers a non-invasive, sensitive, and scalable approach for tracking molecular changes over time and may provide earlier insights into repopulation dynamics of CML vs non-CML cells than conventional methods. Practically, it can be implemented into the current clinical practice for those patients achieving optimal molecular response which is confirmed by the \u003cem\u003eBCR::ABL1\u003c/em\u003e qPCR. Based on the doubling time of about 60 days for those emerging mutations, we propose every 6\u0026ndash;7 months frequency for NGS testing to monitor those emerging mutations which could represent repopulation of CE in Ph-negative clone.\u003c/p\u003e\u003cp\u003eSimilar to clonal hematopoiesis (CH), the enrichment of somatic mutations in epigenetic regulators suggests that dysregulation of chromatin and methylation pathways may contribute to the development of Ph-negative clones, even after controlling CML disease effectively with TKI therapy and achieving sustained molecular response. Further studies are required to clarify how these mutations interact with BCR::ABL1 signaling and impact long-term outcomes such as success in TFR.\u003c/p\u003e\u003cp\u003eThis study is limited by its relatively small sample size and single-center design. Larger, multicenter studies are needed to validate the proposed sixth mutational pattern and to assess its potential prognostic significance, particularly in determining whether mutation dynamics can predict successful TFR or relapse. Additionally, single-cell sequencing studies are warranted to investigate whether these emerging mutational clones are associated with cytogenetically identified CE/Ph-negative clones. In summary, the current study suggests a new sixth pattern of mutational dynamics showing emerging somatic mutations in optimal responders to TKI therapy, which could be from expansion of Ph- clone. We also propose longitudinal NGS monitoring in CML patients every 6 months, which could offer valuable insights into the clonal evolution of the Ph-negative clone, complementing metaphase cytogenetic testing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eYM, MAP and DK conceptualized and designed the study. YM, MAP, GG, JM, SA and DK conducted data analysis. YM and DK wrote the manuscript. DZ, IJ, AK, TJ, AK and JM provided CML patient samples for the study. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe present study was supported by Leukemia \u0026amp; Lymphoma Society of Canada and by Cancer Research Society, and by Princess Margaret Cancer Foundation in part.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRowley JD. 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Published online 4 September 2025. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12185-025-04046-5\u003c/span\u003e\u003cspan address=\"10.1007/s12185-025-04046-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of the study population (n=51 patients)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"378\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of patients (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAge, median (range)\u003c/p\u003e\n \u003cp\u003e\u0026gt;60 y/o\u003c/p\u003e\n \u003cp\u003e40-60 y/o\u003c/p\u003e\n \u003cp\u003e\u0026lt;40 y/o\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e61(17-80)\u003c/p\u003e\n \u003cp\u003e26 (51)\u003c/p\u003e\n \u003cp\u003e19 (37)\u003c/p\u003e\n \u003cp\u003e6 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSex, male (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e32 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eChronic phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e51 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCytogenetics\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eNot evaluable\u003c/p\u003e\n \u003cp\u003eACA*\u003c/p\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39 (76.5)\u003c/p\u003e\n \u003cp\u003e6 (12)\u003c/p\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003cp\u003e2 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSokal risk score\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (27)\u003c/p\u003e\n \u003cp\u003e27 (53)\u003c/p\u003e\n \u003cp\u003e9 (18)\u003c/p\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst line TKI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003cp\u003eNilotinib\u003c/p\u003e\n \u003cp\u003eDasatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36 (71)\u003c/p\u003e\n \u003cp\u003e12(24)\u003c/p\u003e\n \u003cp\u003e3 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse to TKI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOptimal response\u003c/p\u003e\n \u003cp\u003eSuboptimal response\u003c/p\u003e\n \u003cp\u003eNot evaluable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41 (80%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (16%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Abbreviations: ACA additional cytogenetic abnormalities, TKI tyrosine kinase inhibitor\u003c/p\u003e\n\u003cp\u003e* Complex cytogenetics (n=3), i(17) (n=1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eList of biological pathways with mutated genes at initial diagnosis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiological pathways\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDNA-methylation\u0026ndash;related genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eDNMT3A, TET2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eChromatin-modifying genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eASXL1, BCOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e21%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eActivated signaling genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eJAK2, FLT3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eTumor-suppressor genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eMyeloid transcription-factor genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eIKZF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eCohesion-complex genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003eSMC1A\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Summary of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eemerg\u003c/strong\u003e\u003cstrong\u003eing\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;mutations in patients with optimal response\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse to TKI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAF1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAF2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDT (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eTET2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1,909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eTET2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eDNMT3A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eASXL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eBRAF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eJAK2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eSF3B1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eTET2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eEZH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003ePHF6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eASXL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eDNMT3A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eU2AF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eEZH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eDNMT3A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eDNMT3A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eASXL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e7578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cem\u003eTET2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eT1, time of initial analysis (days from start of TKI); T2, time of second analysis (days from start of TKI); VAF1, variant allele frequency at T1; VAF2, variant allele frequency at T2; ND, not detected.\u003c/p\u003e\n\u003cp\u003e*The patient has 2 different mutations having different mutational patterns \u0026ndash; pattern 1 (persistent mutation in \u003cem\u003eDNMT3A\u003c/em\u003e) and a\u0026nbsp;novel pattern (emergence of new mutations)\u003c/p\u003e\n\u003cp\u003e** The patient has no baseline sample prior to starting TKI\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Baseline characteristics of optimal responders with emerging somatic mutations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"803\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBCR::ABL1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;level\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;at last follow up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime to MR4 (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFR attempt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFFS (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eswitch to dasatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3,869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eimatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1,843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNilotinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003enilotinib, sustained TFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1,481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eimatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eswitch to dasatinib \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1,140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDasatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003edasatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eimatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eimatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eimatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eimatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eMMR loss, switch to bosutinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3,172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eMMR loss, switch to dasatinib and then bosutinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3,355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Abbreviations: MR4 \u0026ndash; molecular response of 4 log reduction which is equivalent to 0.01% international scale; TFR \u0026ndash; treatment free remission, FFS \u0026ndash; failure free survival, MMR \u0026ndash; major molecular response which is equivalent to 0.1% international scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Clinical characteristics of optimal responders with and without emerging somatic mutations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"765\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003epts\u0026nbsp;(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOptimal responder with emergence of new somatic mutations (n=12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOptimal responders without acquisition of somatic mutations (n=28)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eAge, yr (median)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eSex, male\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eFFS, days (median)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e1,024 (range 294-3,869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e930 (range 350-5,317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eNo. of patients TFR attempted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eNo of the patients succeeded TFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e1 out of 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e1 out of 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eNo. of patients achieving MR4 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e12 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e20 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eTime to MR4, days (median)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e258 days (range\u0026nbsp;168\u0026ndash;776)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e461 (range 168-797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eCE in Ph-negative cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e0 (out of 10 patients tested)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e1 (out of 23 patients tested)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Abbreviations: FFS, failure free survival; TFR, treatment free remission; MR4, molecular response of 4 log reduction which is equivalent to 0.01% international scale; CE, clonal evolution\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7924728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7924728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTyrosine kinase inhibitors (TKIs) significantly improved outcomes for patients with chronic myeloid leukemia (CML), enabling optimal treatment responses and near-normal life expectancy. Despite optimal responses, clonal evolution (CE) in Philadelphia chromosome-negative (Ph-negative) cells occurs in 10\u0026ndash;15% of optimal responders, typically detected via cytogenetics. The mutational dynamics underlying this phenomenon remain poorly understood. This study investigated mutational kinetics using targeted next-generation sequencing (NGS) of 40 leukemia-associated genes. A total of 119 serial peripheral blood samples from 51 newly diagnosed chronic-phase CML patients, with over 12 months of follow-up, were analyzed using single-molecule molecular inversion probe (smMIP)-based NGS. Remarkably, 24% of patients developed new somatic mutations during follow up, primarily in \u003cem\u003eDNMT3a\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e and \u003cem\u003eASXL1.\u003c/em\u003e These mutations were absent at diagnosis and exhibited a median doubling time of 68 days. Notably, these mutations emerged despite patients maintaining deep molecular responses and without evidence for cytogenetic evolution. Our results highlight an ongoing clonal evolution in the Ph-negative clone suggesting the potential utility of longitudinal NGS every 6\u0026ndash;7 months as an alternative to cytogenetic monitoring in CML.\u003c/p\u003e","manuscriptTitle":"New Pattern of Emerging Somatic Mutations in Optimal Responders Following Tyrosine Kinase Inhibitor Therapy in Chronic Myeloid Leukemia Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 06:35:19","doi":"10.21203/rs.3.rs-7924728/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef30fe3f-04e4-42fc-ab3b-3b0458e596d8","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56784158,"name":"Health sciences/Medical research/Translational research"},{"id":56784159,"name":"Biological sciences/Cancer/Haematological cancer/Leukaemia/Chronic myeloid leukaemia"}],"tags":[],"updatedAt":"2025-12-03T16:45:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 06:35:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7924728","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7924728","identity":"rs-7924728","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}