Abstract
Predicting prognosis using baseline clinical and genetic features remains critical in the tyrosine-kinase-inhibitor (TKI) era of chronic myeloid leukemia (CML). We retrospectively analyzed 944 chronic phase CML (CML-CP) patients treated at a single center in China, aiming to clarify treatment responses, long-term outcomes and baseline prognostic factors in real-world settings. Analyses were performed in available patient subsets depending on endpoint-specific data completeness. The study found that female patients had higher major molecular response (MMR) than males (P = 0.004). Age ≥ 45 years was associated with lower complete cytogenetic response (CCyR; p = 0.006), overall survival (OS; p = 0.011), and CML-related survival (p = 0.010). Initial white-blood-cell counts ≥ 118 × 10⁹/L predicted inferior CCyR and MMR (both P < 0.001). Conversely, baseline haemoglobin ≥ 110 g/L independently predicted superior CCyR, MMR (both P < 0.001), event-free survival (EFS; p = 0.009), progression-free survival (PFS; p = 0.008), OS (P = 0.001), and CML-related survival (p = 0.001). Compared to patients without ACAs, high-risk additional chromosomal abnormalities (HR-ACAs) conferred worse EFS (P = 0.042), PFS (P = 0.057), OS (P = 0.010) and CML-related survival (P = 0.006). Gender and initial WBC primarily influenced TKI treatment responses, while age and initial Hgb levels influenced both TKI responses and long-term survival. Additionally, HR-ACAs significantly predicted poorer long-term survival in CML-CP patients.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12672-026-05309-1.
Keywords: Chronic myeloid leukemia, Initial hemoglobin, HR-ACAs
Introduction
Chronic myeloid leukemia (CML) is a stem-cell myeloproliferative neoplasm driven by the Philadelphia chromosome t(9;22), which joins BCR (22q11) and ABL1 (9q34) to produce the constitutively active BCR::ABL1 tyrosine kinase [1]. Since approval of the first-generation TKI imatinib (IM) in 2001, the treatment and survival of CML have changed dramatically, with survival for chronic-phase CML(CML-CP) approaching that of age-matched normal populations [2]. However, a small proportion of patients still develop resistance during TKIs treatment or even progress to blast crisis (BC), with a dismal overall survival of less than 12 months [3]. Therefore, predicting patient prognosis using baseline clinical and genetic characteristics remains crucial in the TKI era.
The Sokal [4], Hasford [5], EUTOS [6], and ELTS [7] scoring systems are widely used to predict the prognosis of patients with CML. These models incorporate variables such as age at diagnosis, spleen size, platelet count, and the percentage of peripheral blood blasts to stratify patient risk and inform clinical decision-making. However, these baseline prognostic factors are confined to general clinical characteristics and hematological parameters, without accounting for genetic or chromosome alterations. Recent studies have emphasized the prognostic significance of additional chromosomal abnormalities (ACAs) in Ph-positive cells. The ELN classifies 3q26 rearrangements, -7/7q-, + 8, 11q23 rearrangements, i(17q), + 19, additional Ph chromosome (+ Ph), and complex karyotypes as high-risk ACAs (HR-ACAs), recommending that patients harboring HR-ACAs be treated as high-risk patients [8] (a stratification that remains consistent with the updated 2025 ELN recommendations) [9].
Chinese CML patients exhibit unique ethnic features, including a notably younger age at onset compared to Western populations [10]. It remains unclear whether prognostic scoring systems derived from Western cohorts—especially those incorporating additional cytogenetic abnormalities—are fully applicable to Chinese patients in the TKI era. To address this, we performed a retrospective analysis of CML patients receiving TKI therapy at our center over the past decade, with the objective of evaluating the prognostic value of various risk factors—including additional cytogenetic abnormalities—on outcomes in Chinese CML patients.
Methods
Patients and data collection
Clinical data were collected from consecutive CML-CP patients diagnosed at our hospital between January 2011 and December 2020. Patients receiving first-line TKIs were followed regularly, with systematic documentation of treatment modifications, efficacy outcomes, and survival data. The diagnosis, disease staging, efficacy monitoring, and assessment were conducted according to the 2013 ELN recommendations [11]. First-line TKI selection was jointly decided by physicians and patients, based on drug availability, patient comorbidities, and medical insurance coverage. The data cutoff for this study was March 2022. The study was approved by the Ethics Committee of the First Affiliated Hospital of Soochow University and conducted in accordance with the Declaration of Helsinki.
Cytogenetic analysis
Cytogenetic analyses were performed on bone marrow cells using standard 24-hour unstimulated cultures. A conventional R banding assay was used for karyotypic analysis according to the International System for Human Cytogenetic Nomenclature (ISCN 2013). At least 20 metaphases were analyzed for each patient. For FISH analysis, allele-specific BCR::ABL1 dual-color dual-fusion probes (Gold Pu Jia, Beijing, China) were used according to the manufacturer’s instructions. For each patient, 300 non-overlapping interphase cells were analyzed. For ACA analysis, the following karyotype changes were excluded: abnormalities in Ph-negative cells, constitutional polymorphism inv(9) (p11q12), chromosome additions or structural rearrangements detected in fewer than two metaphases, and chromosome deletions identified in fewer than three metaphases, unless confirmed by FISH or subsequent monitoring, in which cases these were considered ACA clones.
Molecular detection
Multiplex nested PCR was used to detect 43 common leukemia-related fusion genes including BCR::ABL1 (Supplementary Table 1). Quantitative real-time PCR (qPCR) was performed using the probe method, with ABL1 as the internal control gene. BCR::ABL1 transcript levels (e13a2/e14a2) were expressed on the international scale (BCR::ABL1IS). Laboratory-specific conversion factors used were 0.41 (Jan 2011-Mar 2015), 0.96 (Apr 2015-Mar 2017), and 0.51 (from Apr 2017 onward). Direct sequencing was used to detect BCR::ABL1 kinase domain mutations.
Definition of response and assessment criteria
Complete Cytogenetic Response (CCyR) was defined as no Ph-positive cells in ≥ 20 metaphases or negative results for BCR::ABL1 by FISH in 300 non-overlapping interphase cells. Major Cytogenetic Response (MCyR) was defined as ≤ 35% Ph-positive cells (by either conventional karyotype analysis or FISH); Major Molecular Response (MMR) was defined as BCR::ABL1IS ≤0.1%. Overall Survival (OS) was defined as the time from initiation of TKI treatment to death from any cause or last follow-up. CML-related survival was defined as the time from initiation of TKI treatment to death due to CML progression or last follow-up. Progression-Free Survival (PFS) was defined as the time from initiation of TKI treatment to progression to accelerated phase (AP)/BC, death from any cause, or last follow-up. Event-Free Survival (EFS) was defined as the time from initiation of TKI treatment to any of the following events or last follow-up: loss of complete hematologic response, loss of MCyR or CCyR, progression to AP/BC, or death from any cause.
Statistical analysis
Chi-square test or Fisher’s exact test was used to analyze differences in categorical variables, while Mann-Whitney U test was used for continuous variables. The cumulative incidence of CCyR and MMR was estimated and compared using Fine-Gray test, with treatment discontinuation for any reason as a competing event. OS, CML-related survival, PFS, and EFS were calculated using the Kaplan-Meier method, with comparisons performed using log-rank tests. Competing risk model [12] was applied for multivariate analysis of cumulative CCyR and MMR, and Cox proportional hazards model was used for multivariate survival analysis. All reported P values from univariate analyses are nominal. All statistical analyses and graphs were generated using SPSS 22.0 (IBM Corp., Armonk, NY, USA), R version 3.5.1 (R Core Team, Vienna, Austria), and GraphPad Prism 9.3.0 (GraphPad Software Inc., La Jolla, CA, USA). All analyses were two-sided, and a p-value of < 0.05 was considered statistically significant.
Results
Clinical characteristics of 1382 CML-CP patients
Between January 2011 and December 2020, a total of 1,382 patients with newly diagnosed chronic-phase CML (CML-CP) were treated at our institution, including 863 males (62.4%) and 519 females (37.6%), with a median age of 45 years (range, 5–78 years). The median white blood cell count, hemoglobin, and platelet count at diagnosis were 118 × 10⁹/L, 110 g/L, and 368 × 10⁹/L, respectively. Detailed clinical and laboratory characteristics are presented in Table 1.
Table 1.
Baseline characteristics and survival outcomes of 1382 patients with chronic phase CML
| Characteristic | Value |
|---|---|
| Total patients, n | 1382 |
| Sex, n (%) | |
| Male | 863 (62.4) |
| Female | 519 (37.6) |
| Age (years), median (range) | 45 (5–78) |
| WBC (×109/L), median (range) | 118 (2.6–707.8) |
| Hemoglobin (g/L), median (range) | 110 (49–185) |
| Platelets (×109/L), median (range) | 368 (4–5312) |
| Blood blasts (%), median (range) | 1 (0–9) |
| ACAs, n (%) a No ACAs | 1228 (92.1) |
| -Yb | 13 (1.0) |
| ACAs except -Yc | 82 (6.1) |
| Normal | 11 (0.8) |
| Variant translocation, n (%)a | |
| t(9;22) | 1235 (92.6) |
| t(v;22)c | 88 (6.6) |
| Normal | 11 (0.8) |
| BCR-ABL1 transcript, n (%) | |
| e13a2/e14a2 | 1365 (98.8) |
| e1a2 | 9 (0.7) |
| e19a2 | 5 (0.4) |
| e13a3/e14a3 | 3 (0.2) |
| First line TKIs, n (%)d | |
| Imatinib | 748 (79.2) |
| Nilotinib | 153 (16.2) |
| Dasatinib | 26 (2.8) |
| Flumatinib | 17 (1.8) |
| Survivald Follow-up (months), median (range) | 62.5 (3.5–133.5) |
| 5-year CCyR (95% CI) | 93.4% (93.4–93.4%) |
| 2-year MMR (95% CI) | 89.5% (89.4–89.5%) |
| 5-year EFS (95% CI) | 88.6% (86.4–90.8%) |
| 5-year PFS (95% CI) | 93.1% (91.4–94.9%) |
| 5-year OS (95% CI) | 95.0% (93.5–96.6%) |
| 5-year CML related OS (95% CI) | 95.3% (93.8–96.8%) |
WBC, white blood cell; ACAs, additional chromosomal abnormalities; TKIs, tyrosine kinase inhibitors; MMR, major molecular response; EFS, event free survival; PFS, progression free survival; OS, overall survival; CCyR, complete cytogenetic response
a Among 1382 patients, 1334 (96.5%) had evaluable karyotype results
b (-Y) was detected in 1.5% of male patients
c 8 patients were t(v;22) with ACAs
d Among 1382 patients, 944 (68.3%) had treatment follow-up information; among these, 923 had evaluable baseline karyotypes, including 66 with ACAs other than -Y
Chromosomal karyotype analysis was successfully conducted in 1,334 patients (96.5%). Among these, 1,235 (92.6%) exhibited classic Philadelphia chromosome t(9;22), 88 (6.6%) had variant Ph chromosome t(v;22), and 11 (0.8%) had a normal karyotype (Table 1, Supplementary Fig. 1). Thirteen patients (1.0%) had -Y chromosomal abnormality (accounting for 1.5% of male patients), and 82 (6.1%) patients had ACAs other than -Y in Ph-positive cells (8 patients with variant Ph chromosome) (Supplementary Fig. 2). Based on prior studies [13–19] and ELN recommendations [8] certain types of ACAs have clearly adverse effects on CML prognosis, therefore these ACAs were defined as high-risk ACAs (HR-ACAs), including + Ph, -7/7q-, + 8, i(17q), + 19, +21, complex karyotype, and karyotypes involving 3q26 rearrangement and 11q23 rearrangement. All additional ACAs other than HR-ACAs were classified as “other ACAs”. Among the 82 patients with ACAs other than -Y, 39 (47.6%) had HR-ACAs, while 43 (52.4%) had other ACAs (Table 1, Supplementary Fig. 2). The BCR::ABL1 fusion gene was detected in all patients, with e13a2/e14a2 transcripts identified in 1,365 patients (98.8%) and rare types (e1a2, e19a2, e13a3/e14a3) identified in 17 patients (1.2%).
Overall treatment outcomes of CML-CP patients
Of the 1,382 patients, follow-up data on treatment outcomes were available for 944 (68.3%). All patients received first-line TKI therapy, including imatinib (748, 79.2%), nilotinib (153, 16.2%), dasatinib (26, 2.8%), and flumatinib (17, 1.8%). The median follow-up duration was 62.5 months (range, 3.5-133.5 months). The 5-year cumulative rates were as follows: CCyR, 93.4%; MMR, 89.5%; EFS, 88.6%; PFS, 93.1%; OS, 95.0%; and CML-related survival, 95.3% (Table 1). Sokal and ELTS prognostic scores were available for 213 patients, with outcome analyses detailed in Supplementary Figs. 3 and 4.
Impact of general clinical features, hematological parameters, and first-line TKI type
For continuous variables such as age, white blood cell count (WBC), hemoglobin (Hgb), and platelet count (PLT), time-dependent ROC curves were initially analyzed to determine optimal cut-off values for cumulative CCyR, cumulative MMR, EFS, PFS, and OS. As all variables yielded area under the curve (AUC) values less than 0.7 and the optimal cut-offs varied by endpoint, we used the median value in the overall cohort for group comparisons.
Female patients achieved a higher cumulative MMR compared to male patients (90.8% vs. 88.7%, P = 0.004), while gender showed no significant association with cumulative CCyR or any survival outcomes (Supplementary Fig. 5). Patients aged ≥ 45 years had lower cumulative CCyR (92.1% vs. 94.8%, P = 0.006), OS (93.3% vs. 96.7%, P = 0.011), and CML-related survival (93.5% vs. 97.0%, P = 0.010) compared to those < 45 years; while MMR, EFS and PFS did not differ significantly between the two age groups (Table 2, Supplementary Fig. 6).
Table 2.
Univariate analysis of treatment response and long-term survival in CML-CP patients
| Variates | 5-year CCyR (95% CI) |
P | 5-year MMR (95% CI) |
P | 5-year EFS (95% CI) |
P | 5-year PFS (95% CI) |
P | 5-year OS (95% CI) |
P | 5-year CML related Survival (95% CI) |
P | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sex, n | 928 | 0.705 | 906 | 0.004 | 918 | 0.280 | 934 | 0.910 | 944 | 0.750 | 944 | 0.420 | |
| Male |
93.3% (93.2%-93.3%) |
88.7% (88.6%-88.7%) |
87.8% (85.0%-90.8%) |
93.3% (91.2%-95.5%) |
95.4% (93.6%-97.3%) |
95.8% (94.1%-97.6%) |
|||||||
| Female |
93.5% (93.4%-93.5%) |
90.8% (90.7%-90.8%) |
89.8% (86.3%-93.4%) |
92.8% (89.8%-95.8%) |
94.4% (91.7%-97.1%) |
94.4% (91.7%-97.1%) |
|||||||
| Age at Dx (y), n | 928 | 0.006 | 906 | 0.320 | 918 | 0.330 | 934 | 0.100 | 944 | 0.011 | 944 | 0.010 | |
| < 45 |
94.8% (94.7%-94.8%) |
91.4% (91.3%-91.4%) |
89.9% (87.1%-92.9%) |
94.9% (92.8%-97.0%) |
96.7% (95.0%-98.5%) |
97.0% (95.3%-98.7%) |
|||||||
| ≥ 45 |
92.1% (92.0%-92.2%) |
87.5% (87.4%-87.6%) |
87.1% (83.8%-90.6%) |
91.4% (88.6%-94.2%) |
93.3% (90.7%-95.8%) |
93.5% (91.0%-96.0%) |
|||||||
| WBC (×109/L) at Dx, n | 734 | < 0.001 | 715 | < 0.001 | 723 | 0.660 | 741 | 0.100 | 748 | 0.450 | 748 | 0.510 | |
| < 118 |
96.7% (96.7%-96.8%) |
92.6% (92.6%-92.7%) |
88.9% (85.4%-92.6%) |
95.1% (92.8%-97.5%) |
95.5% (93.1%-98.0%) |
95.8% (93.4%-98.2%) |
|||||||
| ≥ 118 |
90.5% (90.5%-90.6%) |
86.9% (86.9%-87.0%) |
88.3% (84.9%-91.8%) |
91.5% (88.6%-94.5%) |
94.0% (91.4%-96.6%) |
94.3% (91.8%-96.8%) |
|||||||
| Hgb (g/L) at Dx, n | 696 | < 0.001 | 679 | < 0.001 | 684 | 0.009 | 703 | 0.008 | 709 | 0.001 | 709 | 0.001 | |
| < 110 |
91.6% (91.5%-91.7%) |
87.3% (87.2%-87.4%) |
85.7% (81.8%-89.7%) |
90.2% (86.9%-93.6%) |
91.5% (88.3%-94.8%) |
91.9% (88.7%-95.1%) |
|||||||
| ≥ 110 |
95.6% (95.6%-95.7%) |
92.9% (92.8%-93.0%) |
91.9% (88.8%-95.1%) |
96.0% (93.8%-98.2%) |
97.9% (96.3%-99.6%) |
98.2% (96.7%-99.8%) |
|||||||
| PLT (×109/L) at Dx, n | 712 | 0.718 | 696 | 0.029 | 701 | 0.680 | 719 | 0.570 | 726 | 0.580 | 726 | 0.680 | |
| < 368 |
94.8% (94.8%-94.8%) |
89.4% (89.3%-89.5%) |
88.2% (84.7%-91.8%) |
93.0% (90.3%-95.7%) |
94.5% (92.0%-97.1%) |
95.2% (92.8%-97.6%) |
|||||||
| ≥ 368 |
93.2% (93.2%-93.3%) |
91.4% (91.2%-91.5%) |
89.4% (85.9%-93.0%) |
93.2% (90.4%-96.1%) |
94.8% (92.3%-97.4%) |
94.8% (92.3%-97.4%) |
|||||||
| First line TKIs, n | 928 | < 0.001 | 906 | < 0.001 | 918 | 0.250 | 934 | 0.060 | 944 | 0.150 | 944 | 0.110 | |
| IM |
92.8% (92.8%-92.9%) |
88.6% (88.6%-88.7%) |
87.6% (85.0%-90.2%) |
92.3% (90.3%-94.4%) |
94.5% (92.7%-96.3%) |
94.8% (93.0%-96.5%) |
|||||||
| 2GTKI |
95.3% (95.2–95.3%) |
92.9% (92.8%-93.0%) |
92.9% (89.1%-96.9%) |
96.5% (93.8%-99.8%) |
97.6% (95.3%-100%) |
97.6% (95.3%-100%) |
|||||||
A baseline WBC ≥ 118 × 10⁹/L was associated with reduced CCyR (90.5% vs. 96.7%, P < 0.001) and MMR (86.9% vs. 92.6%, P < 0.001), without affecting survival outcomes (Table 2, Supplementary Fig. 7). Higher baseline Hgb (≥ 110 g/L) correlated with superior CCyR (95.6% vs. 91.6%, P < 0.001), MMR (92.9% vs. 87.3%, P < 0.001) and all four survival endpoints (EFS 91.9% vs. 85.7%, P = 0.009; PFS 96.0% vs. 90.2%, P = 0.008; OS 97.9% vs. 91.5%, P = 0.001; CML-related survival 98.2% vs. 91.9%, P = 0.001) (Fig. 1). Platelet count influenced only MMR, with PLT ≥ 368 × 10⁹/L associated with a slightly higher MMR (91.4% vs. 89.4%, P = 0.029), but had no effect on other outcomes (Table 2, Supplementary Fig. 8).
Fig. 1.
Impact of initial hemoglobin on treatment response and survival in chronic-phase CML (CML-CP). The panels compare outcomes between patients with baseline hemoglobin (Hgb) < 110 g/L (blue lines) and ≥ 110 g/L (red lines). Cumulative incidence of A complete cytogenetic response (CCyR) and B major molecular response (MMR) were estimated using the Fine-Gray competing risk model, with treatment discontinuation as a competing event. Kaplan-Meier analysis was used to estimate C event-free survival, D progression-free survival, E overall survival, and F CML-related survival. Differences between groups were assessed using the log-rank test
Second-generation TKIs (2GTKIs, nilotinib, dasatinib, and flumatinib) were associated with significantly higher cumulative CCyR and MMR compared to imatinib (both P < 0.001). The advantage was more pronounced during early phases of treatment, at 1 year (CCyR 85.6% vs. 69.0%; MMR 60.0% vs. 40.3%), 2 years (CCyR 93.3% vs. 84.3%; MMR 81.2% vs. 66.3%) and 5 years (CCyR 95.3% vs. 92.8%; MMR 92.9% vs. 88.6%; all P < 0.001). In contrast, EFS, PFS, OS and CML-related survival remained comparable between the two classes (Table 2, Supplementary Fig. 9).
Impact of chromosomal karyotype
Baseline karyotype was available for 923 of the 944 followed patients (97.8%). Isolated -Y was identified in 9 patients (1.0%). As this abnormality occurs exclusively in male patients, is age-related, and has been shown to have no prognostic impact on CML [13, 20], these cases were pooled with patients without ACAs (no ACAs). Among the 923 evaluable karyotypes, 844 patients (91.4%) exhibited no ACAs, 4 (0.4%) displayed a normal karyotype, and 66 (7.2%) harbored ACAs other than -Y. Among these 66 patients, 30 were classified as HR-ACAs and 36 as other ACAs.
Compared with patients without ACAs, the HR-ACA group showed lower cumulative CCyR (89.8% vs. 93.7%, P = 0.355) and MMR (82.7% vs. 90.0%, P = 0.445), although these differences were not statistically significant. Long-term outcomes were poorer in the HR-ACA group, with 5-year EFS of 77.8% vs. 89.3% (P = 0.042), OS of 92.0% vs. 95.4% (P = 0.010), and CML-related survival of 92.0% vs. 95.6% (P = 0.006), along with a trend toward inferior PFS of 88.1% vs. 93.5% (P = 0.057). In contrast, other ACAs did not significantly influence CCyR (90.6% vs. 93.7%, P = 0.359), MMR (81.2% vs. 90.0%, P = 0.255) or any survival endpoint (all P > 0.05) (Table 3; Fig. 2). Direct comparison was also performed between the HR-ACA and other ACA subgroups. This analysis revealed a trend toward poorer OS and CML-related survival in patients with HR-ACAs (both P = 0.084). However, the differences in CCyR (P = 0.980), MMR (P = 0.898), EFS (P = 0.290), and PFS (P = 0.200) did not reach statistical significance, likely limited by the small sample sizes within these specific sub-cohorts (30 HR-ACAs vs. 36 other ACAs) (Supplementary Fig. 10).
Table 3.
Univariate analysis of the impact of cytogenetic abnormalities on treatment response and long-term survival in CML-CP Patients
| Variates | 5-year CCyR (95% CI) |
P | 5-year MMR (95% CI) |
P | 5-year EFS (95% CI) |
P | 5-year PFS (95% CI) |
P | 5-year OS (95% CI) |
P | 5-year CML related Survival (95% CI) |
P | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ACAs, n | 907 | 885 | 897 | 914 | 923 | 923 | |||||||
| No ACAs |
93.7% (93.7%-93.8%) |
90.0% (90.0%-90.1%) |
89.3% (87.1%-91.6%) |
93.5% (91.7%-95.3%) |
95.4% (93.8%-96.9%) |
95.6% (94.1%-97.2%) |
|||||||
| All ACAs |
89.4% (88.5%-90.3%) |
0.199* |
81.1% (80.0%-82.3%) |
0.178* |
83.4% (73.9%-94.2%) |
0.068* |
91.6% (84.7%-98.9%) |
0.240* |
95.0% (89.5%-100%) |
0.200* |
95.0% (89.5%-100%) |
0.130* | |
| Other ACAs |
90.6% (88.6%-92.6%) |
0.359* |
81.2% (78.6%-83.9%) |
0.255* |
87.6% (77.0%-99.8%) |
0.460* |
94.2% (86.7%-100%) |
0.950* |
97.1% (91.8%-100%) |
0.630* |
97.1% (91.8%-100%) |
0.730* | |
| HR ACAs |
89.8% (87.2%-92.3%) |
0.355* |
82.7% (79.8%-85.5%) |
0.445* |
77.8% (61.9%-98.0%) |
0.042* |
88.1% (76.3%-100%) |
0.057* |
92.0% (81.9%-100%) |
0.010* |
92.0% (81.9%-100%) |
0.006* | |
| Variant Ph, n | 838 | 0.051 | 818 | 0.121 | 828 | 0.048 | 845 | 0.014 | 853 | 0.510 | 853 | 0.390 | |
| (9;22) |
94.3% (94.3%-94.4%) |
90.4% (90.4%-90.5%) |
89.8% (87.6%-91.4%) |
94.1% (92.3%-95.9%) |
95.4% (93.8%-97.0%) |
95.7% (94.1%-97.3%) |
|||||||
| t(v;22) |
84.0% (83.5%-84.5%) |
85.5% (84.9%-86.1%) |
82.0% (72.2%-93.1%) |
84.8% (75.4%-95.4%) |
94.9% (89.5%-100%) |
94.9% (89.5%-100%) |
|||||||
CCyR, complete cytogenetic response; MMR, major molecular response; EFS, event free survival; PFS, progression free survival. OS, over survival; ACAs, additional chromosomal abnormalities; CI, confidence interval; HR ACAs, high risk ACAs; *Comparison to the patients without ACAs
Fig. 2.
Impact of Additional Cytogenetic Abnormalities (ACAs) on Treatment Response and Survival in CML-CP Patients. The panels illustrate prognostic differences comparing patients with no ACAs to those with other ACAs, high-risk ACAs (HR-ACAs), or all ACAs combined. Cumulative incidence of complete cytogenetic response (CCyR) A–C and major molecular response (MMR) D–F were estimated using the Fine-Gray competing risk model. Kaplan-Meier analysis was used to estimate event-free survival G–I, progression-free survival J–M, overall survival N–P, and CML-related survival Q–S. Blue indicates no ACAs; green indicates other ACAs; purple, all ACAs; red, HR-ACAs. Differences between groups were assessed using the log-rank test
In recent years, there has been a paucity of large-scale studies investigating the impact of variant Ph chromosomes on clinical outcomes in CML-CP patients beyond initial ACAs, and the results remain incompletely elucidated. Among the 944 patients with follow-up data in this study, variant Ph chromosomes were detected in 69 patients (7.3%), including 6 cases with concomitant ACAs. To eliminate potential confounding effects from ACAs, these 6 patients were excluded from the univariate analysis examining the impact of variant Ph chromosomes on CML-CP outcomes. Compared to classic Ph, variant Ph was associated with a trend toward lower 5-year CCyR (84.0% vs. 94.3%, P = 0.051) and MMR (85.5% vs. 90.4%, P = 0.121), and significantly inferior EFS (82.0% vs. 89.8%, P = 0.048) and PFS (84.8% vs. 94.1%, P = 0.014). However, OS (94.9% vs. 95.4%, P = 0.510) and CML-related survival (94.9% vs. 95.7%, P = 0.390) were comparable between groups. (Table 3; Fig. 3).
Fig. 3.
Impact of Variant Philadelphia Chromosome on Treatment Response and Survival in CML-CP Patients. The panels compare outcomes between patients harboring the classic Philadelphia chromosome t(9;22) (blue lines) and those with variant translocations t(v;22) (red lines). Cumulative incidence of A complete cytogenetic response (CCyR) and B major molecular response (MMR) were estimated using the Fine-Gray competing risk model. Kaplan-Meier analysis was used to estimate C event-free survival, D progression-free survival, E overall survival, and F CML-related survival. Differences between groups were assessed using the log-rank test
Multivariate analysis of CML-CP patient outcomes
Covariates with P < 0.20 in univariate analysis—along with age and first-line TKI type—were entered into the multivariate models. In the multivariate analysis of CCyR, older age (≥ 45 years) was identified as an adverse prognostic factor (HR 0.705; 95% CI, 0.596–0.836; P < 0.001), while the presence of a variant Ph chromosome t(v;22) showed a trend toward unfavorable outcome (HR 0.721; 95% CI, 0.517–1.007; P = 0.055). Conversely, higher baseline hemoglobin (≥ 110 g/L) and initial treatment with 2GTKIs significantly favored achieving CCyR (HR 1.371; 95% CI, 1.153–1.630; P < 0.001, and HR 1.639; 95% CI, 1.309–2.053; P < 0.001, respectively). Initial WBC count did not independently influence cumulative CCyR (Supplementary Table 2). For cumulative MMR, older age (≥ 45 years; HR 0.740; 95% CI, 0.614–0.890; P = 0.001) and higher initial WBC count (≥ 118 × 10^9/L; HR 0.726; 95% CI, 0.594–0.889; P = 0.002) were recognized as negative predictors. Male patients were less likely to achieve MMR compared to females (HR 0.825; 95% CI, 0.684–0.996; P = 0.045). Higher Hgb levels (≥ 110 g/L; HR 1.388; 95% CI, 1.145–1.682; P < 0.001) and first-line treatment with 2GTKI (HR 1.449; 95% CI, 1.141–1.841; P = 0.002) were independently associated with a higher likelihood of achieving MMR. Baseline platelet count and variant Ph chromosome status were not independent predictors of MMR (Supplementary Table 3).
Regarding long-term survival outcomes, initial Hgb emerged as a critical prognostic factor. Higher Hgb levels (≥ 110 g/L) were independently associated with improved EFS (HR 0.58; 95% CI, 0.36–0.94; P = 0.028) and PFS (HR 0.50; 95% CI, 0.25–0.99; P = 0.048). HR-ACAs demonstrated a tendency towards poorer PFS (HR 2.89; 95% CI, 0.88–9.55; P = 0.082), although this did not achieve statistical significance. Older age (≥ 45 years) negatively impacted OS (HR 2.11; 95% CI, 1.02–4.39; P = 0.045), whereas higher Hgb was a protective factor (HR 0.33; 95% CI, 0.15–0.73; P = 0.006). Similarly, for CML-related survival, older age demonstrated borderline significance as an adverse factor (HR 2.11; 95% CI, 0.98–4.55; P = 0.056), and higher Hgb remained significantly favorable (HR 0.31; 95% CI, 0.133–0.73; P = 0.007). Neither HR-ACAs nor initial treatment with 2GTKI independently influenced OS or CML-related survival (Fig. 4).
Fig. 4.
Multivariate analysis of long-term survival in CML-CP patients. Forest plots show hazard ratios (HRs) and 95% confidence intervals for factors associated with A event-free survival, B progression-free survival, C overall survival, and D CML-related survival. Black squares represent the HR point estimates, and horizontal lines represent the 95% confidence intervals
Discussion
In this study, we retrospectively analyzed the treatment responses and long-term survival outcomes of CML-CP patients who received first-line TKI therapy at our center over the past decade. Our results showed that patients achieved a 5-year cumulative CCyR of 93.4%, cumulative MMR of 89.5%, EFS of 88.6%, PFS of 93.1%, OS of 95.0%, and CML-related survival of 95.3%. These results are consistent with major clinical studies both domestically and internationally [21–24], and slightly superior to international reports, while similar to domestic reports [24], possibly due to the younger onset age observed in Chinese CML populations [10].
When analyzing general clinical characteristics and hematological parameters, age at diagnosis emerged as an independent predictor of TKI treatment response and survival outcomes in CML-CP patients. Older age was associated with reduced CCyR, MMR, OS, and CML-related survival, consistent with previous studies [25, 26]. Another prominent prognostic indicator was initial Hgb value. Higher baseline Hgb levels independently predicted superior CCyR, MMR, EFS, PFS, OS, and CML-related survival. Although baseline Hgb is not a variable in either Sokal or ELTS scoring systems, previous studies have found associations between lower initial Hgb and adverse treatment responses and prognoses [27, 28]. Recently, a large-sample study from China also found that patients with lower initial Hgb were more likely to experience imatinib treatment failure [29]. Therefore, patients presenting with anemia may need intensified monitoring strategies. Additionally, gender and initial WBC were independent factors affecting TKI response, with females more likely to achieve MMR and patients with higher initial WBC less likely to achieve MMR. Previous reports have similarly documented better molecular responses and prognosis in female CML patients [30, 31], possibly related to medication adherence and/or gender-specific biological characteristics [32]. While high initial WBC count is a recognized adverse prognostic factor in acute leukemia, its prognostic significance in CML has received less attention. Qin et al. reported that high initial WBC was an independent adverse factor for achieving MR [4, 5, 33], and our findings are similar. Mechanistically, we hypothesize that lower initial Hgb and higher initial WBC may reflect a longer, undetected disease duration. Due to genomic instability in CML clones [34, 35], longer disease duration may lead to accumulation of more genetic abnormalities, potentially resulting in drug resistance. Future genomic studies in patients with these baseline characteristics may further elucidate these mechanisms, potentially guiding tailored interventions.
Regarding cytogenetic abnormalities, ACAs are considered to result from genomic instability induced by the BCR::ABL1 fusion gene and may lead to disease progression [34–36]. Different types of ACAs may exert varying effects. Due to the limited numbers of individual ACAs, early studies categorized ACAs into “major route” (including + 8, i(17q), + 19, and + Ph) and “minor route” ACAs (comprising all other ACAs excluding “major route” ACAs) based on their frequency of occurrence. Although this classification was determined solely by frequency, prognostic analysis revealed that “major route” ACAs present at diagnosis adversely affected CML patient prognosis, while the impact of “minor route” ACAs was less apparent [13, 20]. As data accumulated, the prognostic significance of individual ACAs (including “minor route” ACAs) has been gradually elucidated. 3q26.2, 11q23 rearrangements, and − 7/7q- were associated with poor CML prognosis [14, 16, 17]; while -Y abnormality showed no significant impact on CML prognosis [13, 37]; patients with isolated + 8 had poorer OS than those without ACAs, but better than those with ACAs other than + 837. Gong et al. proposed a BC progression risk classification based on each ACA, with high-risk group including 3q26.2 rearrangement, -7/7q-, i(17q), intermediate-2 risk group comprising complex karyotypes without high-risk ACAs, and intermediate-1 risk group including + 8, +Ph, and other single ACAs [38]. A recent study confirmed that high-risk ACAs, including − 7/7q, + 8, +Ph, i(17q), + 17, +19, + 21, 3q26.2 rearrangement, 11q23 rearrangement, and complex karyotype, indicated increased mortality risk even when blast cells were low in CML patients [15].
Based on previous multiple studies [13–19] and ELN recommendations [8], HR-ACAs in this study included + Ph, -7/7q-, + 8, i(17q), + 19, +21, complex karyotype, and karyotypes involving 3q26 and 11q23 rearrangements. Among newly diagnosed CML-CP patients, 6.1% had ACAs excluding -Y, with approximately half (47.6%) being high-risk ACAs. our study confirmed that HR-ACAs significantly influenced long-term survival, consistent with prior research [13, 15, 20]. Patients with HR-ACAs experienced reduced EFS, PFS, OS, and CML-related survival compared to those without ACAs. In contrast, overall ACAs without risk stratification did not demonstrate prognostic significance, aligning with previous literature [39]. Therefore, we speculate that the emergence of high-risk ACAs may indicate CML progression to a stage irreversible by TKIs, suggesting that more aggressive treatment measures should be considered for these patients, while patients with other non-high-risk ACAs may still benefit from TKI therapy.
Interestingly, patients with variant Ph chromosomes exhibited somewhat lower EFS and PFS compared to those with classic Ph chromosomes, a divergence from earlier reports primarily assessing imatinib treatment responses [13, 40]. By excluding cases with concurrent ACAs, our study minimized confounding factors, potentially revealing subtle prognostic differences that warrant further investigation in larger cohorts.
Given the retrospective nature of our study, inherent limitations include missing data such as incomplete Sokal and ELTS scores, variability in patient compliance, and inconsistent treatment adjustments during disease progression. Furthermore, the small number of cases with HR-ACAs and variant Ph chromosomes limited the statistical robustness of subgroup analyses. Specifically, while a direct statistical comparison between HR-ACAs and other ACAs revealed trends toward poorer survival in the HR-ACA group, it lacked sufficient power to achieve statistical significance due to the limited sample sizes. The prognostic impact of HR-ACAs relative to other ACAs requires validation in larger cohorts. Besides, because multiple response and survival endpoints were evaluated without adjusting for multiple testing, there is a potential for type I error inflation, meaning borderline significant findings should be interpreted with caution.
In conclusion, our findings highlight the potential prognostic significance of baseline Hgb and HR-ACAs in patients with CML-CP. Future prospective, multicenter, registry-based studies with rigorous monitoring are warranted to validate these observations. Such studies could facilitate the integration of baseline Hgb and HR-ACAs into existing prognostic models or aid the development of new risk stratification tools.
Supplementary Information
Below is the link to the electronic supplementary material.
Author contributions
QYW, XYZ, YCJ and YYZ: contribution of patients, acquisition of data, analysis and interpretation of data. SNC and MXX: Design of study, acquisition of funding contribution of patients, interpretation of data, supervision of the study, and revision of the manuscript. ZZ, XFY, QW, JLP, LY and JNC: contribution of patients and supervision of the study. QYW and XYZ wrote the paper. YCJ performed the additional statistical analyses during revision, prepared the revised figures, contributed to the point-by-point response to reviewers, and critically revised the manuscript. All authors have read and approved the final manuscript.
Funding
This work was supported by grants from the the National Natural Science Foundation of China (82200150).
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. No DNA/RNA sequencing, microarray, protein sequence, macromolecular structure, or other omics datasets were generated or analysed in this study. The data analysed in this retrospective study consist of de-identified clinical, cytogenetic, treatment-response, and survival data. Because these data are linked to individual patient-level clinical information, they are not publicly deposited due to privacy and ethical restrictions. De-identified data may be made available from the corresponding author upon reasonable request and with appropriate institutional approval.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of the First Affiliated Hospital of Soochow University (Approval Number: 2017169). Written informed consent to participate was obtained from all participants prior to data collection and analysis.
Consent for publication
not applicable. No individual person’s data (including images, videos, or other identifying details) are included in this manuscript.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Qingyuan Wang,Yucheng Jiang, Xiangyu Zhao and Yiyan Zhu are Co-first Authors.
Contributor Information
Suning Chen, Email: chensuning@suda.edu.cn.
Mengxing Xue, Email: xuemengxing@suda.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. No DNA/RNA sequencing, microarray, protein sequence, macromolecular structure, or other omics datasets were generated or analysed in this study. The data analysed in this retrospective study consist of de-identified clinical, cytogenetic, treatment-response, and survival data. Because these data are linked to individual patient-level clinical information, they are not publicly deposited due to privacy and ethical restrictions. De-identified data may be made available from the corresponding author upon reasonable request and with appropriate institutional approval.