Abstract
Objective:
This study aimed to investigate the correlation between additional cytogenetic abnormalities (ACAs) at diagnosis and their clinical consequences in 337 Chinese chronic myeloid leukemia (CML) patients.
Design:
Retrospective observational cohort study.
Methods:
Response criteria were applied according to the European LeukemiaNet. Event-free survival (EFS) and progression-free survival (PFS) were analyzed. Independent predictors of PFS were assessed using Cox regression analysis.
Results:
At diagnosis, ACAs were identified in 41 patients (12.2%), with 24 exhibiting high-risk ACAs. Patients with high-risk ACAs showed significantly lower molecular response rates than those with low-risk ACAs or non-ACAs. Furthermore, patients with high-risk ACAs demonstrated diminished EFS (45.8% vs 76.5% vs 78.0%, respectively, p = 0.03) and PFS (54.2% vs 94.1% vs 93.9%, p < 0.001) compared with those in the other groups. In the multivariate analysis, both the EUTOS long-term survival (ELTS) score and high-risk ACAs at diagnosis emerged as independent prognostic factors influencing the cumulative major molecular response (MMR) rate and PFS. Moreover, when stratified according to high-risk ACAs and ELTS score, patients with high-risk ACAs alongside an intermediate/high ELTS score exhibited reduced MMR (p < 0.001) and inferior PFS rates (p = 0.0014).
Conclusion:
These findings underscore the importance of integrating cytogenetics-based risk assessments into CML management.
Keywords: additional cytogenetic aberrations, at diagnosis, chronic myeloid leukemia, clinical response, prognosis
Introduction
Chronic myeloid leukemia (CML) is an uncommon hematological malignancy characterized by the fusion of the breakpoint cluster region (BCR) and Abelson (ABL) genes, resulting in the Philadelphia (Ph) chromosome t(9;22) (q34;q11).1 –3 In 80%–90% of newly diagnosed CML patients in the chronic phase (CP), the primary cytogenetic abnormality detected is standard or variant translocation (5%–10%), with the remaining cases displaying additional cytogenetic abnormalities (ACAs).4,5 While ACAs are relatively infrequent in CP patients, this proportion progressively escalates with disease progression, peaking at 30% in the accelerated phase (AP) 6 and ranging from 60% to 80% in blast crisis (BC).7,8
The 2020 European LeukemiaNet (ELN) guidelines 9 on CML explicitly pinpoint specific anomalies such as 3q26, monosomy 7/7q- (-7/7q-), trisomy 8 (+8), 11q23, i(17)(q10), trisomy 19 (+19), additional Philadelphia chromosome (+Ph), and complex karyotypes as high-risk ACAs. The identification of any high-risk ACAs during treatment at any phase indicates disease progression, reflects treatment inefficacy, and necessitates a reassessment of the treatment approach. 10 Nevertheless, the precise implications of ACAs detected at the initial diagnosis are insufficiently defined. In a recent study involving CML-CP patients, five high-risk ACAs were noted along with the four major types (+8, isochromosome 17q, +Ph, and +19, while excluding loss of Y): +21, 3q26.2 rearrangements, -7/7q-, 11q23 rearrangements, and complex karyotypes. The presence of these ACAs often preceded an elevation in the blast percentage, indicating impending disease progression. 11 However, another study highlighted that the prognostic impact of +8 or +Ph was not apparent when they occurred as sole ACAs, and adverse outcomes emerged only when these anomalies coincided with other concurrent ACAs. 12 This discrepancy may be attributed to the rarity of high-risk ACAs at diagnosis, which are found in less than 3% of de novo CML-CP patients.4,13 Consequently, earlier studies on patients with high-risk ACAs had relatively small sample sizes, necessitating validation of their results. This study aimed to assess the relationship between ACAs at diagnosis and the clinical implications in 337 Chinese CML patients.
Patients and methods
Reporting guidelines
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines 14 were used in this study to ensure the quality and transparency of the reported data (Supplemental File).
Patients
Patients with CML were retrospectively enrolled at the Union Hospital, Tongji Medical College, Huazhong University of Science and Technology between January 2011 and December 2022. Eligibility criteria included a diagnosis of CML-CP treated with tyrosine kinase inhibitors (TKIs), age ⩾18 years, and good medication adherence. Patients were excluded if they presented with AP or BC at diagnosis, showed poor compliance, or had incomplete data. The study was approved by the Institutional Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (Wuhan, China; [2024] 0136) and was conducted in accordance with the Declaration of Helsinki guidelines. Written informed consent was obtained from all participants before commencement of the study. Clinical information, demographics, medications, and laboratory results were collected retrospectively during regular outpatient follow-up visits.
Response assessment
CML-CP was characterized as blasts <15%, with no extramedullary involvement. AP was indicated by blasts ⩾15%, blasts + promyelocytes ⩾30%, basophils ⩾20%, and platelets <100 × 109/L, not related to therapy, or cytogenetic clonal evolution, while BC consisted of blasts ⩾30% or extramedullary disease, in accordance with ELN recommendations. The patients’ clinical response to treatment was monitored using cytogenetic (karyotype and/or fluorescence in situ hybridization) and molecular tests (quantitative reverse transcriptase polymerase chain reaction) to gauge BCR::ABL1 mRNA levels on an international scale (IS). High-risk ACAs were identified according to the ELN guideline (+8, i(17q), +Ph, +19, +21, 3q26.2, -7/7q-, 11q23.2, and complex karyotype present in Ph-positive cells), with other ACAs in Ph-positive cells categorized as low-risk ACAs. 9 The complete cytogenetic response (CCyR) denoted 0% Ph metaphase cells in the bone marrow, whereas the major cytogenetic response (MCyR) was described as 0%–35% Ph metaphase cells. A major molecular response (MMR) indicated BCR-ABL1IS expression ⩽0.1%, and a deep molecular response (DMR) was defined by a BCR::ABL1IS ⩽0.01%.
Event-free survival (EFS) was calculated from treatment initiation until encountering any of the following subsequent events during therapy: loss of complete hematologic remission, loss of MCyR, loss of MMR, progression to AP or BC, or mortality from any cause during the study period. Failure-free survival (FFS) was calculated from the onset of treatment until the occurrence of any events outlined under the EFS, and treatment discontinuation for reasons other than treatment-free remission (TFR). These reasons include switching to alternative TKIs because of suboptimal responses, toxicity, or financial constraints. Progression-free survival (PFS) was tracked from the treatment outset to the occurrence of progression to AP or BC during therapy or death during the study.
Statistical analysis
Categorical variables were delineated according to frequencies and percentages. For normally distributed continuous data, mean ± standard deviation was utilized; otherwise, median and interquartile range were presented. Categorical variables were compared using Pearson’s Chi-square or Fisher’s exact test, while continuous variables were evaluated using the Kruskal–Wallis test. Survival analysis involved Kaplan–Meier estimates, with subgroup comparisons conducted using the log-rank test. To examine the independent factors influencing PFS and MMR, missing EUTOS long-term survival (ELTS) scores were excluded. Independent predictors of PFS were assessed using Cox regression analysis. Statistical analyses were conducted using IBM SPSS Statistics (version 25.0; IBM Corp., Armonk, NY, USA), with p value <0.05 considered statistically significant.
Results
Patient characteristics
During the study period, 740 CML patients sought treatment at our hospital. The exclusion criteria were as follows: 21 patients initially diagnosed with AP or BC, 203 lacking chromosomal information at diagnosis, 113 with incomplete data, and 66 lost to follow-up. Ultimately, 337 patients were included, of whom 208 (61.7%) were male. The flow diagram is shown in Figure 1. ACAs were detected in 41 patients (12.2%) at diagnosis, with 24 exhibiting high-risk ACAs (7.1%; Table 1). Predominant high-risk ACAs were +8 (n = 9) and +Ph (n = 8). Patients with high-risk ACAs had higher ELTS scores at the time of diagnosis (p = 0.043). Patients without ACAs at diagnosis tended to continue first-line TKI treatment (p < 0.001). Baseline characteristics, including age at diagnosis, sex, and use of second-generation TKIs as first-line treatment, were similar across the subgroups. More detailed basic information on the entire study population is presented in Table S1.
Figure 1.
The flow diagram.
Table 1.
Demographic and clinical characteristics of patients with chronic myeloid leukemia.
| Variable | Non-ACAs (N = 296) |
Low-risk ACAs (N = 17) |
High-risk ACAs (N = 24) |
p Value |
|---|---|---|---|---|
| Sex (male), n (%) | 180 (60.8) | 14 (82.4) | 14 (58.3) | 0.194 |
| Age at diagnosis (years), median (IQR) | 38 (29–50) | 50 (38–52) | 42 (30–53) | 0.122 |
| TKI therapy duration (years; IQR) | 4.18 (2.48–7.84) | 5.75 (2.68–12.86) | 4.35 (2.39–9.05) | 0.232 |
| Comorbidity, n (%) | ||||
| Hypertension Diabetes |
21 (7.1) 10 (3.4) |
3 (15.6) 0 |
4 (16.7) 0 |
0.094 0.490 |
| Coronary heart disease | 9 (3.0) | 0 | 1 (4.2) | 0.724 |
| Hepatitis B | 14 (4.7) | 0 | 1 (4.2) | 0.654 |
| Thyroid diseases | 8 (2.7) | 0 | 0 | 0.567 |
| Others | 21 (7.1) | 1 (5.9) | 2 (8.3) | |
| Sokal score, n (%) | 0.165 | |||
| Low | 128 (43.2) | 7 (41.2) | 7 (29.2) | |
| Intermediate | 67 (22.6) | 2 (11.8) | 8 (33.3) | |
| High | 32 (10.8) | 2 (11.8) | 6 (25.0) | |
| Unknown | 69 (23.3) | 6 (35.2) | 3 (12.5) | |
| ELTS score, n (%) | 0.043 | |||
| Low | 160 (54.1) | 7 (41.2) | 10 (41.7) | |
| Intermediate | 47 (15.9) | 3 (15.6) | 5 (20.8) | |
| High | 20 (6.8) | 1 (5.9) | 6 (25.0) | |
| Unknown | 69 (23.3) | 6 (35.2) | 3 (12.5) | |
| Line of TKIs, n (%) | <0.001 | |||
| 1st line | 169 (57.1) | 6 (35.2) | 8 (33.3) | |
| 2nd line 3rd line Successive line |
87 (29.4) 32 (10.8) 8 (2.7) |
5 (29.4) 2 (11.8) 4 (23.5) |
12 (50.0) 1 (4.2) 3 (12.5) |
|
| First-line TKIs treatment, n (%) | 0.543 | |||
| Imatinib | 187 (63.2) | 13 (76.5) | 13 (54.2) | |
| Dasatinib | 39 (13.2) | 3 (15.6) | 5 (20.8) | |
| Nilotinib | 26 (8.8) | 1 (5.9) | 3 (12.5) | |
| Flumatinib | 44 (14.8) | 0 | 3 (12.5) | |
| First-line TKIs treatment, n (%) | 0.345 | |||
| Imatinib | 187 (63.2) | 13 (76.5) | 13 (54.2) | |
| 2G-TKIs | 109 (36.8) | 4 (23.5) | 11 (45.8) | |
2G, second-generation; ACAs, additional cytogenetic aberrations; ELTS, EUTOS long-term survival score; IQR, interquartile range; TKI, tyrosine kinase inhibitor.
The relationship between ACAs at diagnosis and clinical response
Attainment of CCyR, MMR, and DMR was associated with the presence of ACAs at the time of diagnosis (Table 2). Within 6 months, patients with high-risk ACAs exhibited significantly lower CCyR rates than those with low-risk ACAs and non-ACAs (37.5% vs 52.9% vs 63.2%, respectively, p = 0.037). Moreover, at the 12-month mark, the MMR rate in patients with high-risk ACAs was notably lower than that in patients with low-risk ACAs and non-ACAs (20.8% vs 41.2% vs 48.3%, respectively, p = 0.032). However, no significant disparities were observed across the groups concerning DMR by the 24-month milestone. Furthermore, during a median follow-up of 4.2 years, patients with high-risk ACAs demonstrated considerably diminished rates of CCyR (54.2% vs 76.5% vs 93.2%, p < 0.001), MMR (45.8% vs 58.8% vs 82.1%, p < 0.001), and DMR (37.5% vs 52.9% vs 62.8%, p = 0.041) than those with low-risk ACAs and non-ACAs. Patients with low-risk ACAs exhibited lower response rates for certain key efficacy endpoints, particularly the cumulative incidence of CCyR/MMR, than non-ACA patients. The cumulative clinical response incidence over time is depicted in Figure S1, illustrating a correlation between the cumulative CCyR (p = 0.054), MMR (p < 0.001), and DMR (p = 0.052) and ACAs at diagnosis. In addition, independent predictors of cumulative MMR rate included ELTS score (intermediate risk: hazard ratio (HR) = 4.159; 95% confidence interval (CI), 1.388–14.713, p = 0.012; high risk: HR = 7.470; 95% CI, 1.709–32.661, p = 0.008), high-risk ACAs at diagnosis (HR = 4.290; 95% CI, 1.711–10.755, p = 0.002), and duration of treatment with TKIs (HR = 0.820; 95% CI, 0.697–0.964, p = 0.016; Table S2).
Table 2.
Clinical responses over time.
| Variables | Non-ACAs (N = 296) |
Low-risk ACAs (N = 17) |
High-risk ACAs (N = 24) |
p Value |
|---|---|---|---|---|
| CCyR at 6 months | 187 (63.2) a | 9 (52.9)a,b | 9 (37.5) b | 0.037 |
| MMR at 1 year | 143 (48.3) a | 7 (41.2)a,b | 5 (20.8) b | 0.032 |
| MMR at 2 years | 200 (67.6) a | 8 (47.1)a,b | 10 (41.7) b | 0.011 |
| DMR at 2 years | 119 (40.2) a | 8 (47.1) a | 10 (41.7) a | 0.850 |
| Cumulative incidence of CCyR | 276 (93.2) a | 13 (76.5) b | 13 (54.2) b | <0.001 |
| Cumulative incidence of MMR | 243 (82.1) a | 10 (58.8) b | 11 (45.8) b | <0.001 |
| Cumulative incidence of DMR | 186 (62.8) a | 9 (52.9)a,b | 9 (37.5) b | 0.041 |
The results of pairwise comparisons among groups are indicated using superscript letters. Groups sharing a common superscript letter indicate no statistically significant difference between them, while groups bearing differing superscript letters denote a statistically significant difference.
ACAs, additional cytogenetic aberrations; CCyR, complete cytogenetic response; DMR, deep molecular response; MMR, major molecular response.
The results indicated a numerical advantage of frontline second-generation TKI therapy in achieving early molecular responses in patients with high-risk ACAs (Table S3): CCyR at 6 months (54.5% vs 23.1%) and MMR at 1 year (45.5% vs 0%). However, these differences were not significant after adjusting for multiple comparisons. However, cumulative response rates were ultimately comparable between the two treatment groups. Among the 24 patients identified with high-risk ACAs, 16 received subsequent lines of therapy, including 4 who received third-line or later treatments. Of these, two achieved CCyR, one attained MMR, four reached DMR, five transformed to BC, and two died—one attributed to COVID-19 and one resulting from the BC. To specifically isolate the impact of the treatment lines, we analyzed the clinical response rates only in patients who received initial TKI therapy (Table S4). These analyses confirmed that high-risk ACAs were significantly associated with a reduced probability of cumulatively achieving the CCyR (25.0% vs 54.1%, p = 0.008) and MMR (25.0% vs 50.7%, p = 0.029) during frontline treatment.
Table 3.
Multivariate analyses of influencing factors of progression-free survival.
| Variables a | HR (95% CI) | p Value |
|---|---|---|
| Age at diagnosis | 1.028 (0.995–1.061) | 0.1 |
| Sokal (low-risk reference) | 0.435 | |
| Intermediate risk | 0.658 (0.191–2.263) | 0.506 |
| High risk | 0.349 (0.069–1.781) | 0.206 |
| ELTS score (low-risk reference) | 0.015 | |
| Intermediate risk | 4.159 (1.388–14.713) | 0.012 |
| High risk | 7.470 (1.709–32.661) | 0.008 |
| ACAs (non-ACAs reference) | 0.006 | |
| Low-risk ACAs | 0.679 (0.088–5.248) | 0.710 |
| High-risk ACAs | 4.290 (1.711–10.755) | 0.002 |
| TKIs therapy duration | 0.820 (0.697–0.964) | 0.016 |
We excluded 78 patients with missing ELTS scores, resulting in the inclusion of 259 patients for analysis.
ACAs, additional cytogenetic aberrations; CI, confidence interval; ELTS score, EUTOS long-term survival score; HR, hazard ratio; TKIs, tyrosine kinase inhibitors.
The relationship between ACAs at diagnosis and prognosis
During a median follow-up of 4.2 years, nine (2.7%) deaths were observed. Among these, four deaths were attributed to non-CML comorbidities (two due to COVID-19, one due to bladder cancer, and one due to breast cancer), whereas only five (1.5%) deaths resulted from blast transformation. Thirteen patients progressed to BC (8 myeloid BC and 5 lymphoid BC), while 14 advanced to AP. Of the 13 patients who progressed to BC, 3 died. Patients with high-risk ACAs exhibited lower EFS (45.8% vs 76.5% vs 78.0%, p = 0.03, respectively, Figure 2(a)) and PFS (54.2% vs 94.1% vs 93.9%, p < 0.001, Figure 2(b)) than those with low-risk ACAs and non-ACAs. However, no significant difference was observed in FFS (29.1% vs 35.3% vs 52.4%, p = 0.29, Figure 2(c)) among these groups.
Figure 2.
The relationship between the presence of additional cytogenetic aberrations at diagnosis and clinical outcomes. (a) EFS. (b). FFS. (c) PFS.
EFS, event-free survival; FFS, failure-free survival; PFS, progression-free survival.
In univariable analysis, factors such as age at diagnosis, Sokal score, ELTS score, TKIs therapy duration, and presence of ACAs at diagnosis were found to predict PFS (Table S5). The results of multivariate analysis revealed significant associations between the ELTS score (intermediate risk: HR = 4.159; 95% CI, 1.388–14.713, p = 0.012; high risk: HR = 7.470; 95% CI, 1.709–32.661, p = 0.008), high-risk ACAs at diagnosis (HR = 4.290; 95% CI, 1.711–10.755, p = 0.002), and duration of TKIs treatment (HR = 0.820; 95% CI, 0.697–0.964, p = 0.016) and PFS (Table 3).
Combining ACAs and ELTS to predict MMR and PFS
Given that ACAs and ELTS scores were identified as crucial independent predictors of PFS and MMR, this study evaluated the combined impact of these variables on predicting PFS and MMR. The intermediate and high-risk scores exhibited similar trends across all assessed outcomes, prompting their combination to enhance the sample size. Upon further stratification by high-risk ACAs and ELTS score at diagnosis, patients with high-risk ACAs in conjunction with an intermediate or high ELTS score showed a diminished MMR rate (low ELTS + non-ACAs vs Int/high ELTS + non-ACAs vs low ELTS + high-risk ACAs vs Int/high ELTS + high-risk ACAs = 81.8% vs 55.2% vs 50.0% vs 45.4%, p < 0.001, respectively, Figure 3(a)) and lower PFS rates (94.3% vs 86.6% vs 80.0% vs 18.2%, p = 0.0014, respectively, Figure 3(b)).
Figure 3.
Major molecular response and progression-free survival based on the presence of high-risk additional cytogenetic aberrations at diagnosis and the EUTOS long-term survival score. (a) Major molecular response. (b) Progression-free survival.
Discussion
ACAs in Ph-positive CML patients signify an escalation in genetic instability and can act as predictive markers for the emergence of malignant characteristics.15,16 Their prognostic importance is evident when they arise during the disease course, indicating progression toward advanced stages of CML, development of resistance, and treatment failure. 17 However, the prognostic value of ACAs at diagnosis in CML-CP patients remains controversial. Alhuraiji et al. 18 reported that the presence of ACAs at diagnosis did not impact long-term survival outcomes or necessitate alterations in treatment strategies for CML patients undergoing TKI therapy. Conversely, Ratajczak et al. 19 suggested that the presence of ACAs/Ph-positive at diagnosis or their occurrence during therapy is clinically significant, not only concerning the risk of BC transformation but also regarding treatment response. Kockerols et al. 20 observed that high-risk ACAs at diagnosis were associated with poorer responses, an increased likelihood of disease progression, and CML-related mortality, whereas patients with low-risk ACAs demonstrated PFS rates similar to those of other CML-CP patients. Our results support the recently proposed risk classification for ACAs. Patients identified with high-risk ACAs at diagnosis exhibited diminished responses and a significantly increased risk of disease progression, whereas those with low-risk ACAs displayed PFS rates comparable to those of their CML-CP counterparts. In addition, frontline second-generation TKI therapy has a numerical advantage in achieving early molecular responses. Although these differences did not reach statistical significance after adjusting for multiple comparisons, likely because of the limited sample size, their potential clinical relevance merits consideration, as early DMR is strongly associated with long-term survival outcomes. Conversely, cumulative response rates were ultimately comparable between the two treatment groups. This convergence may be attributed to subsequent treatment crossovers, as some patients initially treated with imatinib but experiencing treatment failure may have achieved salvage by switching to a second-generation TKI. Nevertheless, suboptimal early responses may increase the risk of disease progression and necessitate further therapeutic interventions, potentially leading to an increased healthcare burden. Overall, these findings suggest that initiating treatment with a second-generation TKI in patients with high-risk ACAs facilitates a more rapid attainment of a molecular response, which may reduce the risk of early disease progression. While cumulative response rates converge over time, achieving a deep early response remains critical for long-term prognosis and for determining eligibility for potential TFR attempts. Furthermore, even when exclusively considering standard frontline TKI therapy, patients with high-risk ACAs exhibited significantly reduced success rates in attaining cumulative CCyR and MMR rates compared with those without ACAs or with low-risk ACAs. These findings robustly demonstrate that high-risk ACAs constitute an independent factor that directly contributes to a suboptimal response to first-line TKI therapy.
The presence of ACAs does not correlate with Sokal or ELTS scores, suggesting that ACAs may provide additional information beyond fundamental demographic and diagnostic hematological data. The amalgamation of ACAs with the ELTS score into a composite metric could potentially bolster their predictive ability for disease progression. A study involving 210 patients from the TIDEL-II trial who were treated with imatinib as first-line therapy revealed through multivariable analysis that the simultaneous presence of additional genetic abnormalities (AGAs, including cancer-gene variants and Ph-associated rearrangements) and the ELTS score independently led to lower molecular response rates and heightened treatment failure rates. Despite proactive treatment strategies, patients with AGAs undergoing initial imatinib therapy exhibit reduced response rates. 21 In this investigation, high-risk ACAs identified at diagnosis continued to autonomously predict MMR and PFS in a multivariable regression model that included the ELTS score, which is consistent with prior research.
Therefore, the presence of ACAs in patients during the CP disease diagnosis serves as a cautionary indicator necessitating heightened surveillance of this patient cohort. It is advised that these patients should receive second-generation TKIs as first-line therapy and undergo more frequent monitoring. In case of treatment failure, prompt changes to the treatment plan or intensification of therapy are necessary because high-risk ACAs ultimately have a negative impact on treatment response and disease progression. Furthermore, according to the ELN guidelines, patients showing atypical translocations, rare, or unusual BCR::ABL1 transcripts should undergo cytogenetic testing. In cases of treatment failure or disease progression, it is advisable to conduct cytogenetic analysis to exclude ACAs. With advancements in detection technologies, there is increasing recognition of the impact of cancer-related genetic variations on risk stratification across different hematologic malignancies. Genetic profiling has become integral to diagnosis, prognostication, and treatment protocols. Unlike myelodysplastic syndromes such as IPSS and IPSS-R for acute myeloid leukemia, no standardized system classifying aberrations based on progression risk has been established for ACA/Ph-positive with consistent prognostic value. Investigating the intricacies of Ph-associated rearrangements may offer a new perspective as predictors of adverse outcomes in CML.
This study had several limitations. First, the total number of patients with ACAs was limited to 17 with low-risk ACAs and 24 with high-risk ACAs. Consequently, subgroup analyses, particularly those examining the influence of specific TKIs or treatment lines, were constrained by the small sample sizes within each category. This limitation increases the risk of both Type I and Type II statistical errors and reduces the precision of our findings. Further validation using larger prospective cohorts is necessary to confirm these results. Second, the outcome analysis primarily focused on molecular responses and PFS. The observation of only a small number of death events (n = 9) during the follow-up period, compounded by competing risks (with four deaths attributed to non-CML causes), hindered the meaningful evaluation of overall survival. Longer-term follow-up in this cohort, along with larger studies, is needed to definitively assess the impact of baseline ACAs on long-term survival. In addition, by design, this study exclusively focused on prognostically significant ACAs as defined by current guidelines, such as those from the ELN. The potential impact of complex variant translocations at diagnosis on disease outcomes was not investigated, representing a distinct area that warrants future research. The principal focus of this investigation was the influence of ACAs on clinical efficacy and prognosis. A dedicated analysis of the association between ACAs and treatment-related adverse events was not undertaken and merits further exploration in subsequent studies.
Conclusion
This study validates the discriminatory power of the recently proposed risk-stratified categorization of ACAs. Patients diagnosed with high-risk ACAs demonstrated reduced molecular responses and a significantly increased risk of disease progression. In contrast, the outcomes for patients with low-risk ACAs were comparable with those of patients without ACAs. Notably, combining high-risk ACA status with the ELTS score improves predictive accuracy for progression risk, emphasizing the necessity for intensified management strategies, including frontline second-generation TKIs and rigorous molecular monitoring of these patients. Future research that elucidates the biological and prognostic significance of specific ACA subtypes and novel genetic alterations will enhance risk assessment and inform therapeutic decision-making for CML.
Supplemental Material
Supplemental material, sj-docx-1-tam-10.1177_17588359251370504 for Impact of additional cytogenetic aberrations at diagnosis of chronic phase chronic myeloid leukemia by Fang Cheng, Zheng Cui, Qiang Li, Jundong Tong and Weiming Li in Therapeutic Advances in Medical Oncology
Supplemental material, sj-docx-2-tam-10.1177_17588359251370504 for Impact of additional cytogenetic aberrations at diagnosis of chronic phase chronic myeloid leukemia by Fang Cheng, Zheng Cui, Qiang Li, Jundong Tong and Weiming Li in Therapeutic Advances in Medical Oncology
Acknowledgments
None.
Footnotes
ORCID iD: Fang Cheng 
https://orcid.org/0000-0003-2257-6847
Supplemental material: Supplemental material for this article is available online.
Contributor Information
Fang Cheng, Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, China.
Zheng Cui, Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, China.
Qiang Li, Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, China.
Jundong Tong, Department of Information and Data Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China.
Weiming Li, Department of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China.
Declarations
Ethics approval and consent to participate: This study has been approved by the Institutional Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (Wuhan, China; [2024] 0136). Written informed consent was obtained from all the participants prior to the study.
Consent for publication: Not applicable.
Author contributions: Fang Cheng: Data curation; Formal analysis; Funding acquisition; Writing – original draft; Writing – review & editing.
Zheng Cui: Data curation; Writing – original draft; Writing – review & editing.
Qiang Li: Data curation; Writing – review & editing.
Jundong Tong: Data curation; Validation; Writing – original draft; Writing – review & editing.
Weiming Li: Conceptualization; Project administration; Supervision; Writing – original draft; Writing – review & editing.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Hubei Chen Xiaoping Science and Technology Development Foundation Youth Special Funding Initiative (CXPJJH124001-2445), and the Chinese Pharmaceutical Association Hospital Pharmacy Department (No. CPA-Z05-ZC-2023002).
The authors declare that there is no conflict of interest.
Availability of data and materials: All data relevant to the study are included in the article or uploaded as Supplemental Information.
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Supplementary Materials
Supplemental material, sj-docx-1-tam-10.1177_17588359251370504 for Impact of additional cytogenetic aberrations at diagnosis of chronic phase chronic myeloid leukemia by Fang Cheng, Zheng Cui, Qiang Li, Jundong Tong and Weiming Li in Therapeutic Advances in Medical Oncology
Supplemental material, sj-docx-2-tam-10.1177_17588359251370504 for Impact of additional cytogenetic aberrations at diagnosis of chronic phase chronic myeloid leukemia by Fang Cheng, Zheng Cui, Qiang Li, Jundong Tong and Weiming Li in Therapeutic Advances in Medical Oncology