Real-World Evaluation of Treatment Patterns and Clinical Outcomes Among Patients with Chronic Myeloid Leukemia in Chronic Phase (CML-CP) Treated with Asciminib in US Clinical Practice
Bibliographic record
Abstract
Introduction: Tyrosine kinase inhibitors (TKIs) are the mainstay of treatment for CML-CP. Asciminib, a first-in-class inhibitor to Specifically Target the ABL Myristoyl Pocket (STAMP), was FDA-approved on 10/29/2021 for the treatment of adult patients with Philadelphia chromosome-positive (Ph+) CML-CP, previously treated with two or more TKIs or Ph+ CML-CP with the T315I mutation. This study described treatment patterns and real-world clinical outcomes of asciminib in the US. Methods: Data through 11/30/2022 from adult patients with Ph+ CML-CP who initiated asciminib were obtained from the Flatiron Health oncology electronic health record (EHR)-derived de-identified database. Patients who initiated asciminib after ≥2 previous TKIs, without a stem-cell transplant prior to asciminib, and without the T315I mutation, were included in the overall cohort. Time-to-treatment discontinuation and molecular response (MR; time-to-BCR::ABL1 ≤0.1% and time-to-BCR::ABL1 ≤1%, separately) were evaluated from asciminib initiation (index date) using Kaplan-Meier analyses overall, and by the number of TKIs used pre-index (2 previous TKIs, and ≥3 previous TKIs). Patients were required to have ≥1 MR test post-index to be included in the MR assessment; a sensitivity analysis was conducted excluding patients with BCR::ABL1 ≤0.1% or better (≤1% or better) for time-to-BCR::ABL1 ≤0.1% (time-to-BCR:ABL1 ≤1%), as evaluated in the ASCEMBL trial. Results: Overall,97 patients with Ph+ CML-CP initiated asciminib (median age: 63 years, 50.5% female, 64.9% White, 84.8% ECOG 0-1, 61.9% from community-based practices, median follow-up of 6 months post-index). Other malignancies (19.6%), chronic pulmonary disease (18.6%), renal disease (18.6%), and congestive heart failure (16.5%) were the most prevalent pre-index comorbidities. The distribution of last MR in the 3 months pre-index was: 24.7% BCR::ABL1 >10%, 18.6% BCR::ABL1 ≤10% and >1%, 12.4% BCR::ABL1 ≤1% and >0.1%, 12.4% BCR::ABL1 ≤0.1% and >0.01%, and 8.2% BCR::ABL1 ≤0.01% (23.7% not tested/documented). Forty-six patients (47.4%) initiated asciminib after 2 previous TKIs, 24 (24.7%) after 3 TKIs, and the remainder after ≥4 previous TKIs (27.8%). Most patients (78.4%) were prescribed 80 mg (40.2% once daily, 38.1% 40 mg twice a day) at treatment initiation. Use of TKIs prior to asciminib included dasatinib (85.6%), imatinib (63.9%), bosutinib (61.9%), nilotinib (53.6%), and ponatinib (15.5%). Treatment sequences among patients who initiated asciminib after 2 previous TKIs (n=46) are presented in Figure 1. Overall (n=97), persistence rates were 85.7% by 12-weeks (2 previous TKIs: 89.8%; ≥3 previous TKIs: 82.5%) and 78.1% by 24-weeks (2 previous TKIs: 81.8%; ≥3 previous TKIs: 75.3%) post-index. Among patients with ≥1 MR test post-index (n=70), 31.3% (2 previous TKIs: 43.3%; ≥3 previous TKIs: 21.6%) and 49.7% (2 previous TKIs: 71.5%; ≥3 previous TKIs: 33.4%) achieved or maintained BCR::ABL1 ≤0.1% by 12- and 24-weeks, respectively. In addition, 51.3% (2 previous TKIs: 68.7%; ≥3 previous TKIs: 37.2%) and 64.2% (2 previous TKIs: 79.7%; ≥3 previous TKIs: 51.4%) achieved or maintained BCR::ABL1 ≤1% by 12- and 24-weeks, respectively. For patients without BCR::ABL1 ≤0.1% prior to asciminib (n=52; Figure 2), 14.6% (2 previous TKIs: 20.6%; ≥3 previous TKIs: 10.4%) and 32.9% (2 previous TKIs: 56.7%; ≥3 previous TKIs: 17.9%) achieved BCR::ABL1 ≤0.1% by 12- and 24-weeks, respectively, with a median time of 30.7 weeks (2 previous TKIs: 23.0 weeks; ≥3 previous TKIs: 38.1 weeks). For patients without BCR::ABL1 ≤1% prior to asciminib (n=43), 35.6% (2 previous TKIs: 61.9%; ≥3 previous TKIs: 21.9%) and 46.3% (2 previous TKIs: 61.9%; ≥3 previous TKIs: 37.2%) achieved BCR::ABL1 ≤1% by 12- and 24-weeks, respectively, with a median time of 24.6 weeks (2 previous TKIs: 11.3 weeks; ≥3 previous TKIs: 26.9 weeks). Conclusion: This is the first real-world study describing treatment patterns and clinical outcomes among patients with Ph+ CML-CP treated with asciminib in the US. The findings of real-world treatment efficacy based on achieving BCR::ABL1 ≤0.1% were consistent with the ASCEMBL trial. Patients who initiated asciminib after 2 previous TKIs had higher persistence and treatment response rates than those initiated on asciminib after ≥3 previous TKIs. Further studies are warranted to continuously demonstrate the effectiveness of asciminib in a real-world setting.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".