Too soon to switch? Early TKI switching vs. continuous monotherapy: long-term outcomes in chronic myeloid leukemia

Background: Early switching of tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML), particularly within the first 3 months of initiation, may indicate intolerance or suboptimal response. However, the long-term clinical impact of such early modifications remains uncertain. This study compares outcomes between patients who switched early and those who remained on their initial TKI.

Methods: We used data from the TriNetX US Collaborative Network to compare matched cohorts of early switchers and long-term non-switchers for imatinib (n=349 vs. n=4,579), dasatinib (n=377 vs. n=2,849), and nilotinib (n=114 vs. n=1,308). Patients were followed for 3 and 5 years. Outcomes included mortality, cardiovascular complications, diabetes, thromboembolic events, hospitalizations, and ICU admissions. The analysis period began immediately after the TKI switch for switchers. To minimize bias and ensure correct outcome attribution, patients who had any of the outcomes before switching were excluded from both cohorts. Propensity matching and logistic regression were applied to estimate risk ratios (RRs) with 95% confidence intervals (CIs).

Results: Early switch from imatinib was associated with significantly higher 5-year mortality (24.0% vs. 11.9%; RR = 2.03, 95% CI 1.43–2.87, p < 0.001). Increased rates of heart failure were observed at both 3 years (10.8% vs. 5.4%; RR = 1.99, p = 0.015) and 5 years (12.1% vs. 7.3%; RR = 1.65, p = 0.046), alongside higher hospitalization (27.9% vs. 16.5%; p = 0.019) and ICU admissions (11.9% vs. 7.0%; p = 0.033). Dasatinib early switchers had worse 5-year mortality (21.1% vs. 14.0%; RR = 1.51, p = 0.011) and more frequent heart failure at 3 years (12.1% vs. 6.7%; RR = 1.80, p = 0.020). ICU admission was also significantly elevated at both 3 years (17.3% vs. 9.2%) and 5 years (19.9% vs. 10.3%; RR = 1.88, p = 0.002). Among nilotinib users, early switching resulted in a non-significant increase in 5-year mortality (20.2% vs. 11.5%; RR = 1.75, p = 0.074) but was linked to significantly higher ICU admissions at 5 years (19.4% vs. 9.3%; RR = 2.10, p = 0.033).

Conclusions: Early switching from first-line TKI therapy in CML is consistently associated with worse survival and greater complication rates across multiple agents. These results emphasize the need for cautious decision-making and close monitoring when considering early changes to frontline therapy.

Introduction

Chronic myeloid leukemia (CML) is a clonal myeloproliferative neoplasm defined by the BCR::ABL1 fusion oncoprotein arising from the Philadelphia chromosome translocation, and it accounts for ~15% of adult leukemias (1, 2). The advent of BCR::ABL1–targeted tyrosine kinase inhibitors (TKIs) has transformed the natural history of CML: disease-related mortality has fallen from 10–20% per year to approximately 1–2% per year, and many patients with chronic-phase CML (CP-CML) now achieve survival that approximates the general population (1, 2). This therapeutic success has shifted modern management toward optimizing initial TKI selection, monitoring early molecular milestones, and sequencing strategies that sustain depth and durability of response while minimizing toxicity and cost.

Multiple TKIs are available as first-line options for CP-CML, including imatinib (first-generation), dasatinib, nilotinib, and bosutinib (second-generation), as well as the allosteric STAMP inhibitor asciminib (3). In practice, the choice of initial therapy is individualized, integrating disease risk, comorbidities, drug–drug interactions, toxicity profiles, access and affordability, and patient preferences (4, 5). Parallel to selection, response-adapted care is standard: early molecular response (EMR) benchmarks—most prominently BCR::ABL1 ≤10% (International Scale) at roughly 3 months—inform whether to continue, dose-modify, or consider switching therapy (4–6).

Despite guideline-directed monitoring, a meaningful fraction of patients undergo changes to the initial TKI within the first months of treatment (7–9). The drivers are heterogeneous—intolerance (e.g., cytopenias, edema, pleural effusion, metabolic adverse events), perceived suboptimal response kinetics, or logistical barriers such as copay burdens or supply interruptions. The clinical impact of early switching—commonly operationalized as changing TKIs within ≤3 months of initiation—remains a central and unresolved question in routine practice. On one hand, switching may preempt entrenched resistance, mitigate cumulative toxicity, or realign therapy with patient-specific risks. On the other, premature changes can expose patients to new toxicity spectra, escalate costs, and potentially confound longitudinal management without improving hard outcomes if the switch principally reflects underlying disease biology or frailty rather than modifies it (3, 10).

The controversy is compounded by several pragmatic considerations. First, many early adverse effects are transient and manageable with dose holds, reductions, and supportive care, making “switch avoidance” a reasonable strategy when toxicity is expected to abate without jeopardizing long-term goals (5). Second, EMR measurements at 3 months are prognostically informative but can be influenced by assay variability, adherence fluctuations, and interlaboratory reporting; a single early data point may not capture true trajectory, and some patients ultimately “catch up” by 6 months with continued therapy (4, 5). Third, while kinase-domain mutations or unequivocal intolerance justify switching, real-world pathways to adjudicate “suboptimal response” versus noise are less standardized than in trials, inviting variation in clinical thresholds for early change (7–9). Collectively, these factors raise the possibility that early switching is as much a marker of baseline risk as it is a modifier of outcomes.

Prior reviews synthesize the dramatic survival gains with TKIs and emphasize milestone-based decision algorithms, yet they acknowledge uncertainty at the margins—particularly in the earliest treatment window where clinicians and patients face the tension between acting early versus allowing time for stabilization (1, 2, 5). Earlier commentaries on the “early switch” question suggested potential risks of unnecessary changes—including cumulative toxicity and cost—if made before confirming persistent milestone failure or exhausting supportive measures; however, conclusions have been limited by small series, selection bias, and heterogeneous definitions of “early.”5 Contemporary expert perspectives also underscore the need to distinguish true resistance, relapse, and intolerance, and to tailor sequencing without compromising the possibility of deep molecular responses or future treatment-free remission (1, 2).

Real-world evidence at scale is well positioned to inform this debate. Large electronic health record networks can capture practice-pattern variation and long-term outcomes across diverse settings, but they must contend with indication bias, immortal-time bias, and contamination by events occurring before a switch decision is operationalized. Addressing these threats to inference requires explicit cohort definitions, exclusion of pre-switch outcomes when attributing risk to post-switch exposure, and propensity methods to balance measured confounding. In addition, agent-specific analyses are important because toxicity profiles, adherence behavior, and switching rationales differ across imatinib, dasatinib, and nilotinib, potentially producing nonuniform risk signals.

Rationale and novelty: Building on these gaps, we leveraged a large U.S. multi-institutional network to evaluate the long-term consequences of early TKI switching (≤3 months from initiation) compared with continuous monotherapy in CP-CML. Our approach applies two safeguards intended to sharpen attribution: (i) exclusion of outcomes that occurred before the switch event to mitigate reverse causation and immortal-time bias, and (ii) propensity score matching to improve balance on measured baseline characteristics. By analyzing imatinib, dasatinib, and nilotinib cohorts separately, we provide pragmatic, class-specific estimates that complement guideline frameworks with real-world performance signals relevant to frontline selection, early monitoring, and sequencing.

Objective: To compare 3- and 5-year all-cause mortality and major complications between early switchers and non-switchers within imatinib, dasatinib, and nilotinib user cohorts in CP-CML, using propensity-matched analyses that exclude pre-switch events to minimize bias.

Methods

Study design and setting

We conducted a retrospective, propensity score–matched cohort study using de-identified electronic health record (EHR) data from the TriNetX US Collaborative Network. The study period spanned January 1, 2000 through December 31, 2022, with follow-up accrued to the longest available time horizon within the network for each patient. Summary provided as Figure 1.

Figure 1

Figure 1. Cohort diagram.

Data source (TriNetX)

TriNetX is a federated network of participating health systems that harmonizes encounter-level EHR data (demographics, diagnoses, procedures, medications, vitals, and selected laboratory results) using standard terminologies (e.g., ICD-9/10, CPT, LOINC, RxNorm). Only de-identified, aggregate counts and summary statistics are returned to end users; site-level data quality checks and centralized mapping ensure semantic alignment. Because TriNetX reflects routine clinical documentation, missingness is not imputed and represents real-world data capture. Platform analytics support cohort building, outcome definitions, and propensity score matching with standardized diagnostics (e.g., absolute standardized mean differences [SMDs]) (11).

Cohort eligibility and exposure definitions

We identified adults (≥18 y) with a diagnosis of chronic myeloid leukemia (CML) who initiated first-line tyrosine kinase inhibitor (TKI) therapy with imatinib, dasatinib, or nilotinib during the study window.

● Index therapy (line 1): first observed prescription/administration of imatinib, dasatinib, or nilotinib after CML diagnosis (index date).

● Early switchers: patients who changed the index TKI within 90 days of initiation (switch date = exposure anchor).

● Non-switchers: patients who continued the index TKI beyond 90 days without change (reference anchor defined below).

Key exclusions

To reduce immortal-time and reverse-causality biases, we excluded patients who had any study outcome recorded before the exposure anchor (for switchers) or its aligned reference time (for non-switchers): all-cause mortality, heart failure (HF), thromboembolic events, diabetes, cardiovascular complications, all-cause hospitalization, or ICU admission. Patients with missing age or sex were excluded. Where exact dates were coarsened, standard TriNetX anchoring rules were applied.

Time-at-risk anchoring

For switchers, follow-up began immediately after the switch date. For non-switchers, we assigned a pseudo-switch reference time equal to the median switch time observed among switchers within the same TKI stratum (or the individual-level matched switch time in the post-match analysis), and follow-up began immediately after that reference time. Patients were followed to the earliest of outcome, death, disenrollment/no further encounters, or the analytic horizon (3 or 5 years).

Outcomes

Outcomes were ascertained from diagnosis/procedure codes and vital status where available, and evaluated at 3-year and 5-year horizons:

● Primary outcome: all-cause mortality at 5 years.

● Secondary outcomes: all-cause mortality at 3 years; heart failure, ICU admission, all-cause hospitalization, and major complications (e.g., cerebrovascular events, thromboembolism) at 3 and 5 years. Definitions followed common real-world evidence conventions and prior CML/TKI literature; outcome definitions were held constant across strata.

Covariates

Baseline (pre-anchor) covariates included demographics (age, sex, race), disease duration proxies (time from CML diagnosis to index), comorbidities (e.g., cardiovascular disease, diabetes, hypertension, obesity, chronic kidney disease, chronic lung disease), prior hospital/ICU utilization, and (where available) selected labs/vitals. Covariates were captured during a look-back window up to one day before the exposure anchor (switch or pseudo-switch).

Propensity score modeling and matching

Within each TKI stratum (imatinib, dasatinib, nilotinib), we estimated the propensity to early-switch using logistic regression including all covariates above (main effects). We performed 1:1 greedy nearest-neighbor matching without replacement using a caliper of 0 (12). standard deviations of the logit of the propensity score. Covariate balance was assessed by absolute SMD, with SMD <0 (13). prespecified as acceptable residual imbalance. We report pre- and post-match SMDs and p-values for transparency, while interpreting treatment effect estimates primarily under SMD thresholds.

Statistical analysis

For matched cohorts, we estimated risk ratios (RRs) with 95% confidence intervals (CIs) for each outcome at 3 and 5 years using log-binomial or Poisson models with robust variance where appropriate; two-sided p < 0 (8). denoted statistical significance. In matched analyses, standard errors were clustered on matched pairs. Descriptive statistics are presented as mean (SD) or % as appropriate. Missing data were analyzed as observed (no imputation), consistent with TriNetX conventions and real-world data guidance. Analyses followed commonly used TriNetX pipelines for outcome analytics and matched cohort estimation.

Basic cohort characteristics (summary)

Cohort counts (pre- and post-match):

● Imatinib: early switchers n=349 vs non-switchers n=4,579 before matching; n=349 vs n=349 after 1:1 matching.

● Dasatinib: n=377 vs n=2,849 before; n=377 vs n=377 after matching.

● Nilotinib: n=114 vs n=1,308 before; n=114 vs n=114 after matching.

Results

Cohorts and baseline characteristics

After propensity score matching, the analytic cohorts comprised imatinib (early switch ≤3 months, n=349; non-switchers, n=4,579), dasatinib (n=377 vs n=2,849), and nilotinib (n=114 vs n=1,308). Post-match distributions of age, sex, and race were well balanced within each TKI stratum. Baseline characteristics after matching are summarized in Table 1, and full before/after balance panels are presented in Supplementary Tables S1–S3.

Table 1

Table 1. Baseline characteristics after propensity score matching by TKI (switchers vs non-switchers).

Clinical outcomes

Imatinib—At 5 years, all-cause mortality was higher among early switchers compared with non-switchers (24.0% vs 11.9%; RR 2.03, 95% CI 1.43–2.87; p<0.001). Heart failure was higher at 3 years (10.8% vs 5.4%; RR 1.99, 95% CI 1.14–3.48; p=0.015) and 5 years (12.1% vs 7.3%; RR 1.65, 95% CI 1.01–2.71; p=0.046). Hospitalizations (27.9% vs 16.5%; RR 1.69, 95% CI 1.09–2.62; p=0.019) and ICU admissions (11.9% vs 7.0%; RR 1.70, 95% CI 1.04–2.78; p=0.033) were also higher.

Dasatinib—Early switching was associated with higher 5-year mortality (21.1% vs 14.0%; RR 1.51, 95% CI 1.10–2.08; p=0.011). Heart failure at 3 years was higher (12.1% vs 6.7%; RR 1.80, 95% CI 1.09–2.97; p=0.020). ICU utilization was greater at 3 years (17.3% vs 9.2%; RR 1.88, 95% CI 1.28–2.75) and 5 years (19.9% vs 10.3%; RR 1.88, 95% CI 1.27–2.79; p=0.002).

Nilotinib—A numerical increase in 5-year mortality did not reach significance (20.2% vs 11.5%; RR 1.75, 95% CI 0.95–3.23; p=0.074), while ICU admissions were significantly higher at 5 years (19.4% vs 9.3%; RR 2.10, 95% CI 1.07–4.11; p=0.033).

A consolidated summary of absolute risks and relative risks is provided in Table 2. Figure 2 displays a forest plot of risk ratios across outcomes and TKIs. Figure 3 depicts Kaplan–Meier survival curves for the imatinib stratum using survival probabilities that reproduce the 5-year estimates. Figure 4 illustrates cohort assembly.

Table 2

Table 2. Comparative outcomes of early switching vs non-switching (propensity-matched cohorts).

Figure 2

Figure 2. Forest plot of risk ratios (95% CI) for outcomes by TKI.

Figure 3

Figure 3. Kaplan–Meier survival (imatinib), based on reported 5-year survival probabilities (76 (14).% vs 88 (12).%).

Figure 4

Figure 4. Cohort assembly diagram.

To improve interpretability of the outcome data, we also summarize the 5-year absolute event rates as grouped bar charts by TKI and early-switch status (Figure 5), while retaining Table 2 to provide complete numerical detail.

Figure 5

Figure 5. Bar charts of 5-year absolute event rates (all-cause mortality and major adverse events) by TKI and early-switch vs non-switch status.

Discussion

Principal findings and real-world relevance

In this large, propensity-matched, real-world cohort drawn from the TriNetX US Collaborative Network, early switching of frontline TKI (≤3 months) was associated with higher long-term risks (including mortality and major complications) compared with continued therapy on the initial agent, despite balance on measured covariates after matching (13, 15–18).

These results align with the broader real-world observation that the need to switch early often reflects intolerance or emerging resistance—clinical states that portend inferior molecular trajectories and more complicated courses in practice (19–24).

Together, these data reinforce that “routine” early switching should not be reflexive in the absence of guideline-defined failure or unmanageable toxicity (3, 7, 8, 25).

Concordance with prior observational cohorts (including SIMPLICITY)

Our observations are consistent with the SIMPLICITY program and other multicenter registries showing that intolerance is the predominant driver of early TKI change and that patients who remain on their initial TKI are more likely to achieve key cytogenetic/molecular milestones, with similar short-term survival but more favorable complication profiles over time (19, 20, 23, 24).

In SIMPLICITY’s European cohort, non-switchers more frequently attained CCyR and MMR by year 3, supporting the concept that treatment continuity facilitates timely depth of response when clinically feasible (20).

Additional real-world datasets (Italian Network/CML Campus, UK TARGET, Swiss series) further suggest that durable outcomes are achievable across TKI choices provided patients can persist on therapy with adequate monitoring and supportive care (22–24).

Comparative efficacy–toxicity trade-offs across TKIs

Randomized and pooled analyses show that second-/third-generation TKIs induce faster and deeper molecular responses vs. imatinib but have not translated into overall survival advantages in the frontline setting, while increasing specific toxicities (e.g., cardiovascular, pleural, hepatic) (26–28).

These trade-offs imply that an early switch intended to “accelerate” molecular kinetics may expose certain patients to higher adverse-event burdens without proven survival gain, particularly when the trigger is intolerance rather than bona fide resistance; our findings are consistent with this risk–benefit tension in routine care (26–28).

From a kinetic standpoint, second-generation TKIs achieve early and deep molecular responses more quickly than imatinib, but long-term randomized and pooled data show little or no overall survival advantage when milestone-driven switching is applied (23, 26). At the same time, generic imatinib is substantially less expensive than patented 2GTKIs, and health-economic analyses consistently find that “imatinib-first” strategies remain the most cost-effective frontline option in many health systems, particularly for older or comorbid patients (1, 2, 10, 26, 27). Nilotinib and other 2GTKIs also carry a time-dependent risk of arterial occlusive and other vascular events that continues to accrue with prolonged exposure, often beyond 5–8 years of therapy (26). Because our median follow-up was approximately 5 years, our study likely underestimates the full lifetime burden of nilotinib-associated vascular toxicity and the extent to which this toxicity might offset any earlier molecular advantages of switching.

Biological and clinical explanations for the early-switch signal

Patients who switch early likely represent a biologically and clinically enriched risk subset—those with higher comorbidity burden, frailty, pharmacogenomic variability, or early suboptimal response (including occult kinase domain mutations or adverse ELTS/Sokal risk)—all factors associated with inferior outcomes independent of the specific switch decision (3, 7, 29–33).

In routine practice, baseline TKI selection itself encodes aspects of clinical risk: older patients, those with substantial cardiovascular comorbidity, and those with presumed low- or intermediate-risk CML are more often started on imatinib, whereas younger patients and those perceived to have higher-risk disease or a greater need for rapid cytoreduction are preferentially offered 2GTKIs (1, 2, 5, 8). In our TriNetX cohorts, this pattern was reflected in the unmatched characteristics, with imatinib recipients being older and more comorbid, and early switchers tending to have a heavier comorbidity burden than non-switchers within each TKI stratum. Our propensity-score models incorporated age, sex, comorbid conditions (including cardiovascular disease), and available baseline laboratory parameters to minimize these imbalances, but residual confounding by unmeasured disease-specific risk (e.g., Sokal or ELTS scores) cannot be excluded.

Moreover, rapid exposure to multiple TKIs in succession can compound organ-specific toxicities, drug–drug interactions, and monitoring complexity, increasing the probability of treatment interruptions and cumulative harm that may offset any theoretical efficacy gains from a more potent agent (26, 27, 31, 32).

Positioning within contemporary guidelines and practice

NCCN and ELN endorse molecular response milestones at 3 and 6 months, with action thresholds that distinguish “warning” from “failure,” emphasizing that management of mild–moderate toxicities should prioritize dose adjustment or brief interruption before switching whenever feasible (3, 8, 25, 34–36).

Our real-world signal—worse long-term outcomes among early switchers—supports these frameworks by cautioning against pre-emptive switching before the 3-month assessment unless toxicity is severe and non-reversible, or clear resistance biology is present (3, 7, 8, 25, 29, 30, 36, 37).

Defining “early” switching and implications for interpretation

We defined early switching as any change within ≤3 months of initiation, a pragmatic window broader than guideline triggers that anchor decision-making to the 3-month molecular check (with earlier changes generally reserved for severe intolerance) (3, 25, 29).

Consequently, our early-switch cohort is inherently heterogeneous (intolerance, adherence issues, pharmacokinetics, early suboptimal response), which likely contributes to the adverse outcomes observed and should be considered when extrapolating these findings to guideline-directed switching after formal milestone assessment (3, 25, 38).

Emerging agents and future directions (asciminib as an example)

Asciminib has demonstrated higher 48-week MMR rates with favorable tolerability vs. investigator-selected TKIs in frontline CML, suggesting that safer first-line options may reduce intolerance-driven switching and its downstream risks observed in legacy practice patterns (39, 40).

As the therapeutic portfolio evolves, the calculus around early modification may shift—yet the core principles of milestone-guided decisions, toxicity mitigation before switching, and individualized agent selection by comorbidity/mutation profile remain central (3, 7, 8, 29, 30, 37, 39).

Methodological strengths and limitations of this TriNetX analysis

Strengths include large scale, multi-health-system capture, prespecified outcomes, exclusion of patients with outcome events prior to the switch index, and propensity matching to reduce measured confounding (13, 15–18).

Nonetheless, residual confounding by indication is unavoidable (e.g., undocumented reasons for switching, adherence, dose intensity), molecular landmarks are incompletely captured, misclassification is possible in EHR-derived data, and informative censoring/competing risks may bias effect estimates even after matching (13, 15–18, 38).

Time-varying confounding (e.g., evolving toxicity, disease kinetics) complicates causal interpretation; future target-trial emulations with marginal structural models and richer molecular/PRO inputs could further strengthen inferences (13, 15–18, 38).

A further limitation of this study is that established prognostic scores such as Sokal and ELTS could not be calculated within the TriNetX US Collaborative Network, as several key variables required for their computation are unavailable or incompletely captured. As a result, we were unable to stratify “early switchers” and “non–early switchers” by these risk categories, which may have limited our ability to fully account for baseline disease risk and to explain some of the observed differences in outcomes between groups (4, 33). Although individual-level data needed to compute Sokal, EUTOS, or ELTS scores (e.g., spleen size, peripheral blast percentage, detailed cytogenetics) were not consistently available in TriNetX, several components of these scores—such as age and platelet counts—were indirectly accounted for through our matching strategy (4, 33). Nonetheless, systematic differences in baseline CML risk between frontline imatinib and 2GTKIs, or between early-switchers and non-switchers within a given TKI, may partially contribute to the outcome differences observed.

Finally, mortality in this analysis reflects all-cause death as captured in the aggregated electronic health record rather than adjudicated CML-related mortality. For many patients with chronic-phase CML on long-term TKI therapy, deaths are now more frequently due to competing comorbidities—particularly cardiovascular disease, secondary malignancies, and serious infections—than to leukemic progression itself (1, 2, 5, 31). We could not reliably assign a cause of death or directly link individual deaths to specific toxicities such as arterial occlusive events or pleural effusions, which limits mechanistic interpretation of the modest mortality differences between early-switch and non-switch cohorts.

Clinical implications

For most patients, an initial strategy of optimizing the current TKI—addressing adherence, drug interactions, supportive care, and dose modification—before switching aligns with both our real-world signal and contemporary guidance (3, 7, 8, 25, 36, 37). The 2025 European LeukemiaNet recommendations similarly move away from a purely milestone-driven mandate to switch TKIs at the first sign of suboptimal response and instead emphasize individualized decisions that integrate disease risk, comorbidities, toxicity, cost, and patient preferences (34). Our observation that patients who remain on their initial TKI when early response goals are met have outcomes at least as good as, and often better than, early switchers provides real-world support for this more nuanced, patient-centered approach.

Switching should be prioritized for guideline-defined failure, persistent and severe intolerance despite optimization, or resistance with actionable mutations, with agent choice tailored to comorbid risks (e.g., vascular risk with nilotinib/ponatinib, pleural-pulmonary risk with dasatinib) and patient goals (e.g., TFR vs. durable disease control) (3, 7, 26, 29, 30, 37). Consistent with this guidance, judicious dose reduction or supportive management of chronic low-grade adverse events may often be preferable to reflexive TKI substitution, particularly in older or comorbid patients in whom imatinib remains highly effective and cost-effective (1, 2, 10, 26, 27, 34).

Ultimately, disciplined milestone-based management and judicious switching can minimize avoidable harm while preserving long-term survival gains achieved in the TKI era (3, 7, 8, 26).

Conclusions

Early switching of frontline TKI within ≤3 months was associated with higher long-term risks—including mortality and major complications—compared with continued monotherapy, a signal consistent with multi-country real-world cohorts (e.g., SIMPLICITY, Italian Network/CML Campus, UK TARGET, Swiss series) and complementary evidence from pooled and randomized analyses on efficacy–toxicity trade-offs across TKIs (19–24, 26–28).

This real-world pattern underscores the novelty and clinical importance of timing: modifying therapy too soon may compound toxicity and destabilize response kinetics without a proven survival gain in the aggregate (26–28).

Clinically, our findings support guideline-concordant, milestone-based care: verify adherence and drug interactions, optimize the current TKI (dose modification/brief interruption) for manageable toxicities, and reserve switching for guideline-defined failure, persistent severe intolerance, or resistance with actionable mutations—tailoring second-line choice to comorbid risk and patient goals (3, 8, 25, 29, 30, 34–37).

The observed risk likely reflects a mix of patient frailty, adverse biology (e.g., higher ELTS/Sokal risk or kinase-domain mutations), and cumulative/overlapping toxicities and interactions that accrue when multiple TKIs are used in rapid succession (3, 7, 26, 27, 29–33).

Future work should refine when and for whom early modification improves outcomes—using target-trial emulations and richer molecular/PRO data—and clarify how safer frontline options (e.g., asciminib) might lessen intolerance-driven early switches and their downstream harms in routine practice (13, 15–18, 38–40).

Until such data mature, a disciplined, milestone-guided strategy with judicious switching remains the most evidence-aligned path to maximize durable benefit and minimize avoidable harm for patients with chronic-phase CML (3, 8, 25, 34–36).

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Author contributions

MM: Conceptualization, Data curation, Methodology, Resources, Writing – original draft, Writing – review & editing. CU: Methodology, Writing – original draft, Writing – review & editing. EO: Formal analysis, Writing – review & editing. AT: Supervision, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Acknowledgments

Publication made possible in part by support from the Thomas Jefferson University Open Access Fund.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. We used a generative AI assistant to streamline prose for clarity and grammar, renumber in-text citations, reorder references and standardize section headings. The tool did not generate scientific results, analyze data, design the study, or draft conclusions. All outputs were independently checked, edited, and approved by the authors, who remain responsible for the content. No identifiable patient information or proprietary data were uploaded.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frhem.2025.1730554/full#supplementary-material

References

1. Jabbour E and Kantarjian H. Chronic myeloid leukemia: A review. JAMA. (2025) 333:1618–29. doi: 10.1001/jama.2025.0220

PubMed Abstract | Crossref Full Text | Google Scholar

2. Jabbour E and Kantarjian H. Chronic myeloid leukemia: 2022 update on diagnosis, therapy, and monitoring. Am J Hematol. (2022) 97:1236–56. doi: 10.1002/ajh.26642

PubMed Abstract | Crossref Full Text | Google Scholar

3. Sweet K and Pinilla-Ibarz J. Early switch in tyrosine kinase inhibitor therapy for patients with chronic myeloid leukemia: an emerging clinical question. Crit Rev Oncol Hematol. (2016) 103:99–108. doi: 10.1016/j.critrevonc.2016.05.009

PubMed Abstract | Crossref Full Text | Google Scholar

4. National Comprehensive Cancer Network. Chronic Myeloid Leukemia (NCCN Clinical Practice Guidelines in Oncology). Version 2025. Available online at: https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1416 (Accessed October 3, 2025).

Google Scholar

5. Senapati J, Sasaki K, Issa GC, Lipton JH, Radich JP, and Jabbour E. Management of chronic myeloid leukemia in 2023—Common ground and common sense. Blood Cancer J. (2023) 13:58. doi: 10.1038/s41408-023-00823-9

PubMed Abstract | Crossref Full Text | Google Scholar

6. O’Brien S, Radich JP, Abboud CN, Akhtari M, Altman JK, and Berman E. Chronic myelogenous leukemia, version 1.2014. J Natl Compr Canc Netw. (2013) 11:1327–40. doi: 10.6004/jnccn.2013.0157

PubMed Abstract | Crossref Full Text | Google Scholar

7. Soverini S and Castagnetti F. How I evaluate and treat resistance and relapse in CML. Blood. (2025). doi: 10.1182/blood.2024026511

PubMed Abstract | Crossref Full Text | Google Scholar

8. Haddad FG and Kantarjian H. Navigating the management of chronic phase CML in the era of generic BCR::ABL1 tyrosine kinase inhibitors. J Natl Compr Canc Netw. (2024) 22:e237116. doi: 10.6004/jnccn.2023.7116

PubMed Abstract | Crossref Full Text | Google Scholar

9. Kantarjian HM, Welch MA, and Jabbour E. Revisiting six established practices in the treatment of chronic myeloid leukaemia. Lancet Haematol. (2023) 10:e860–4. doi: 10.1016/S2352-3026(23)00164-3

PubMed Abstract | Crossref Full Text | Google Scholar

10. Padula WV, Larson RA, Jamieson BD, Apperley JF, Hehlmann R, and Baccarani M. Cost-effectiveness of tyrosine kinase inhibitor treatment strategies for chronic myeloid leukemia in chronic phase after generic entry of imatinib. J Natl Cancer Inst. (2016) 108:djw003. doi: 10.1093/jnci/djw003

PubMed Abstract | Crossref Full Text | Google Scholar

11. Talviste G, Leinsalu M, Ross P, and Viigimaa M. Lipid-lowering treatment gaps in patients after acute myocardial infarction: using global database TriNetX. Med (Kaunas). (2024) 60:1433. doi: 10.3390/medicina60091433

PubMed Abstract | Crossref Full Text | Google Scholar

12. Cross NCP, Ernst T, Branford S, Cayuela JM, Deininger MW, and Fabarius A. European LeukemiaNet laboratory recommendations for the diagnosis and management of chronic myeloid leukemia. Leukemia. (2023) 37:2150–67. doi: 10.1038/s41375-023-02048-y

PubMed Abstract | Crossref Full Text | Google Scholar

13. Passamonti F, Corrao G, Castellani G, Mora B, Maggioni D, and Gale RP. The future of research in hematology: integration of conventional studies with real-world data and artificial intelligence. Blood Rev. (2022) 54:100914. doi: 10.1016/j.blre.2021.100914

PubMed Abstract | Crossref Full Text | Google Scholar

14. Passamonti F, Corrao G, Castellani G, Mora B, Maggioni D, and Della Porta MG. Using real-world evidence in haematology. Best Pract Res Clin Haematol. (2024) 37:101536. doi: 10.1016/j.beha.2024.101536

PubMed Abstract | Crossref Full Text | Google Scholar

15. Barrette E, Crown WH, Hanisch M, Alfonso-Cristancho R, Sheth S, and Gautier L. ISPOR special interest group report on real-world data analyses. Value Health. (2025) 28:979. doi: 10.1016/j.jval.2025.02.017

PubMed Abstract | Crossref Full Text | Google Scholar

16. Kantarjian H, Breccia M, Haddad FG, Hehlmann R, Issa GC, and Malhotra H. Management of chronic myeloid leukemia in 2025. Cancer. (2025) 131:e35953. doi: 10.1002/cncr.35953

PubMed Abstract | Crossref Full Text | Google Scholar

17. Hochhaus A, Wang J, Kim DW, Kim DDH, Mayer J, and Goh YT. Asciminib in newly diagnosed chronic myeloid leukemia. N Engl J Med. (2024) 391:885–98. doi: 10.1056/NEJMoa2400858

PubMed Abstract | Crossref Full Text | Google Scholar

18. Hehlmann R, Cortes JE, Zyczynski T, Gambacorti-Passerini C, Goldberg SL, and Mauro MJ. SIMPLICITY: interruptions, discontinuations and switching. Am J Hematol. (2019) 94:46–54. doi: 10.1002/ajh.25306

PubMed Abstract | Crossref Full Text | Google Scholar

19. Ma CE, Ghosh S, Leyshon C, Blosser S, Dersch-Mills D, and Jupp J. Outcomes after switching from 1L 2G TKI. Leuk Res. (2021) 111:106674. doi: 10.1016/j.leukres.2021.106674

PubMed Abstract | Crossref Full Text | Google Scholar

20. Giai V, Rosso T, Castagnetti F, Pregno P, Bonifacio M, and Capodanno I. Italian network/CML campus. Cancer. (2025) 131:e35963. doi: 10.1002/cncr.35963

PubMed Abstract | Crossref Full Text | Google Scholar

21. Feng X, Liu C, Zhang X, Wang Z, Wu Z, and Wang Z. Swiss real-world evidence. BMC Cancer. (2022) 22:1192. doi: 10.1186/s12885-022-10241-y

PubMed Abstract | Crossref Full Text | Google Scholar

22. Milojkovic D, Cross NCP, Ali S, Byrne J, Campbell G, and Dignan F. UK TARGET CML. Br J Haematol. (2021) 192:62–74. doi: 10.1111/bjh.16733

PubMed Abstract | Crossref Full Text | Google Scholar

23. Vener C, Banzi R, Ambrogi F, Ferrero A, Saglio G, and Pravettoni G. Imatinib vs 2G/3G TKIs: meta-analysis. Blood Adv. (2020) 4:2723–35. doi: 10.1182/bloodadvances.2019001329

PubMed Abstract | Crossref Full Text | Google Scholar

24. Dahlén T, Edgren G, Ljungman P, Flygt H, Richter J, and Olsson-Strömberg U. Nationwide register study. Am J Hematol. (2022) 97:421–30. doi: 10.1002/ajh.26463

PubMed Abstract | Crossref Full Text | Google Scholar

25. Hermans SJF, van der Maas NG, van Norden Y, Dinmohamed AG, Berkx E, and Huijgens PC. Externally controlled studies using real-world data in patients with hematological cancers: A systematic review. JAMA Oncol. (2024) 10:1426–36. doi: 10.1001/jamaoncol.2024.3466

PubMed Abstract | Crossref Full Text | Google Scholar

26. Douxfils J, Haguet H, Mullier F, Chatelain C, Graux C, and Dogné JM. TKIs & CV events: meta-analysis. JAMA Oncol. (2016) 2:625–32. doi: 10.1001/jamaoncol.2015.5932

PubMed Abstract | Crossref Full Text | Google Scholar

27. Belleudi V, Trotta F, Vecchi S, Amato L, Addis A, and Davoli M. Drug switchability: scoping review. J Clin Epidemiol. (2018) 101:5–16. doi: 10.1016/j.jclinepi.2018.05.003

PubMed Abstract | Crossref Full Text | Google Scholar

28. Breccia M, Molica M, Colafigli G, Massaro F, and Alimena G. Early molecular response & Halving time. Leuk Res. (2016) 48:20–5. doi: 10.1016/j.leukres.2016.06.010

PubMed Abstract | Crossref Full Text | Google Scholar

29. Kronick O, Chen X, Mehra N, Varmeziar A, Fisher R, and Kartchner D. Hematologic AEs with TKIs. Cancers (Basel). (2023) 15:4354. doi: 10.3390/cancers15174354

PubMed Abstract | Crossref Full Text | Google Scholar

30. Kumar V, Singh P, Gupta SK, Ali V, and Verma M. Transport and metabolism of TKIs. Mol Cell Biochem. (2022) 477:1261–79. doi: 10.1007/s11010-022-04376-6

PubMed Abstract | Crossref Full Text | Google Scholar

31. Shanmuganathan N, Hiwase DK, and Ross DM. Assessing risk & Monitoring. Leuk Lymphoma. (2017) 58:2799–810. doi: 10.1080/10428194.2017.1312377

PubMed Abstract | Crossref Full Text | Google Scholar

32. Morita K and Sasaki K. Current status and novel strategy of CML. Int J Hematol. (2021) 113:624–31. doi: 10.1007/s12185-021-03127-5

PubMed Abstract | Crossref Full Text | Google Scholar

33. Pfirrmann M, Clark RE, Prejzner W, Lauseker M, Baccarani M, Saussele S, et al. The EUTOS long-term survival (ELTS) score is superior to the Sokal score for predicting survival in chronic myeloid leukemia. Leukemia. (2020) 34:2138–49. doi: 10.1038/s41375-020-0931-9

PubMed Abstract | Crossref Full Text | Google Scholar

34. Apperley JF, Milojkovic D, Cross NCP, Hjorth-Hansen H, Hochhaus A, and Kantarjian H. 2025 European LeukemiaNet recommendations for the management of chronic myeloid leukemia. Leukemia. (2025) 39:1797–813. doi: 10.1038/s41375-025-02664-w

PubMed Abstract | Crossref Full Text | Google Scholar

35. García-Gutiérrez V, Breccia M, Jabbour E, Mauro M, and Cortes JE. Clinician perspective on CP-CML. J Hematol Oncol. (2022) 15:90. doi: 10.1186/s13045-022-01309-0

PubMed Abstract | Crossref Full Text | Google Scholar

36. Quintás-Cardama A and Jabbour EJ. Early switch to nilotinib/dasatinib. Leuk Res. (2013) 37:487–95. doi: 10.1016/j.leukres.2013.01.006

PubMed Abstract | Crossref Full Text | Google Scholar

37. Soverini S. Resistance mutations in CML. Hematol ASH Educ Program. (2023) 2023:469–75. doi: 10.1182/hematology.2023000447

PubMed Abstract | Crossref Full Text | Google Scholar

38. Akard LP and Bixby D. Baseline factors and early response. Leuk Lymphoma. (2016) 57:1002–14. doi: 10.3109/10428194.2015.1122779

PubMed Abstract | Crossref Full Text | Google Scholar

39. Derman BA, Belli AJ, Battiwalla M, Hamadani M, Kansagra A, and Lazarus HM. Reality check: real-world evidence to support therapeutic development in hematologic Malignancies. Blood Rev. (2022) 53:100913. doi: 10.1016/j.blre.2021.100913

PubMed Abstract | Crossref Full Text | Google Scholar

40. Gambacorti-Passerini C, Chen C, Davis C, Sen GP, Guyan C, and Hehlmann R. 3-year european SIMPLICITY data. Eur J Haematol. (2021) 106:82–9. doi: 10.1111/ejh.13524

PubMed Abstract | Crossref Full Text | Google Scholar