Beyond survival in chronic myeloid leukemia: A systematic review of secondary malignancies in the era of tyrosine kinase inhibitors
Bibliographic record
Abstract
Abstract Background: The introduction of tyrosine kinase inhibitors (TKIs) has markedly extended survival in chronic myeloid leukemia (CML), shifting clinical priorities toward long-term safety and late treatment effects, including the emergence of secondary malignancies (SMs). Reports on this risk are mixed, with varying conclusions regarding the frequency and spectrum of SMs in TKI-treated CML patients. This systematic review aimed to synthesize the existing evidence on the incidence, types, and timing of SMs in patients with chronic-phase CML treated with TKIs. Methods: We conducted a systematic review following PRISMA guidelines (PROSPERO ID: CRD420251067085). A comprehensive literature search was performed through May 2025 across PubMed, Embase, Medline, and Web of Science, using Medical Subject Headings (MeSH) terms. Eligible studies were those reporting the incidence of SMs in adults with chronic-phase CML treated with TKIs, including imatinib, dasatinib, nilotinib, bosutinib, ponatinib, and asciminib. Studies were excluded if they involved patients with advanced CML phases or received concomitant chemotherapy. Case reports, grey literature, or non-English studies were also excluded. Two independent reviewers performed the screening, data extraction, and quality assessment using the Newcastle-Ottawa Scale, National Institutes of Health (NIH), and Cochrane tools, as appropriate. Data synthesis was descriptive of the SM types and ranges of incidence due to clinical and methodological heterogeneity. Results: A total of 408 records were identified [EMBASE: 199; PubMed: 81; Medline: 77; Web of Science: 51]. After title and abstract screening, 154 duplicates and 84 irrelevant records were excluded. Following full-text screening, 156 studies were excluded for ineligible populations (n=48), absence of outcomes of interest (n=45), lack of SM data reporting (n=24), inappropriate design [case reports/series (n=19), grey literature (n=16)], and non-English language (n=4). Fourteen studies were included in the final analysis (13 retrospective cohorts, 1 prospective RCT), representing 42,781 chronic-phase CML patients treated with TKI. The studies were geographically diverse, with overlapping use of multiple TKIs (n=7). Imatinib was the most commonly reported (n=12), followed by dasatinib (n=8) and nilotinib (n=7). Data on bosutinib (n=2) and ponatinib (n=1) were limited, while asciminib was not reported in any study. Due to heterogeneity in reporting, pooling of incidence rates was precluded by inconsistencies in exposure duration and sequencing of therapies. Reported SM incidence ranged from 3.1% to 7.5% in single-institution cohorts, while large population-based studies reported proportions between 4.2% and 5.6%. Standardized incidence ratios (SIRs) ranged from 0.6 to 2.45 with a median time to SM onset of 2.4–6.0 years post-TKI initiation. The most commonly reported SMs affected the gastrointestinal (GI), genitourinary (GU), and respiratory (RSP) systems. In the largest study (Sasaki et al., n=13,276; 597 SMs), GI, GU, and RSP cancers accounted for 21.6%, 31.2%, and 15.4% of SMs, respectively. Across studies, the most frequent tumor types were prostate (13.3%–21.6%), colorectal (6.1%–10.4%), and lung (9.0%–15.1%) cancers. Hematologic SMs, particularly non-Hodgkin lymphoma (NHL), were reported in 4%–16.5% of SM cases; Miranda et al. observed an elevated NHL risk (SIR 3.33 in men, 4.29 in women). Endocrine, skin, and breast cancers were also frequent, comprising 9%–47.5%of reported SMs. SM-related mortality ranged from 14% to 56%. The development of an SM was associated with a poor prognosis. No consistent association was found between TKI generation, treatment duration, or line of therapy and SM risk. Most studies did not report additional malignancy risk factors and were of moderate to high quality based on NOS, NIH, or Cochrane risk assessment tools. Conclusion: This review identified a notable incidence of SMs, particularly involving prostate, colorectal, lung, and lymphoid cancers. While some variability in cancer types was observed across studies, no consistent association could be established between TKI generation or treatment duration and SMs risk. SM development was associated with poor prognosis and increased mortality, highlighting the need for proactive surveillance. Large-scale prospective studies are needed to clarify causality, define long-term safety, and inform survivorship strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".