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Record W4283703366 · doi:10.1002/ajh.26645

Real‐life incidence of thrombotic events in leukemia patients treated with ponatinib

2022· letter· en· W4283703366 on OpenAlexaboutno aff
Dan Nichols, Elias Jabbour, Nadya Jammal, Serena Chew, Jeffrey N. Bryan, Ghayas C. Issa, Guillermo Garcia‐Manero, Koji Sasaki, Adam J. DiPippo, Hagop M. Kantarjian

Bibliographic record

VenueAmerican Journal of Hematology · 2022
Typeletter
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPonatinibMedicineIncidence (geometry)LeukemiaInternal medicineOncologyMyeloid leukemiaDasatinib

Abstract

fetched live from OpenAlex

Ponatinib is a third-generation BCR::ABL1 tyrosine kinase inhibitor (TKI) that is highly effective in treating chronic myeloid leukemia (CML) and Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph-ALL).1 It has potent activity against BCR::ABL1, including the highly-resistant T315I mutation.1 Despite this, ponatinib's use has been limited by the risk of cardiovascular (CV) events, including venous thromboembolism (VTE) and arterial occlusive events (AOE).2 The rate of CV events in clinical trials ranged 25%–50%, with the highest risk in the first 12 months of therapy and in patients with cardiovascular risk factors.3, 4 Given the risk of CV events with ponatinib, use of cardioprotective medications including aspirin and HMG-CoA reductase inhibitors (statins) has become an important consideration. Additionally, attenuated ponatinib dosing has been used to mitigate toxicity. Data has shown a reduction from 45 mg (FDA-approved starting dose) to 15 mg was associated with an approximately 33% reduction in the risk of a CV event.4 Furthermore, the recent phase II OPTIC trial has demonstrated an optimal risk-benefit in CML patients who started ponatinib at 45 mg daily and reduced to 15 mg upon achievement of response.5 Available literature on ponatinib-associated CV events has limited generalizability due to heterogeneous patient populations, and reporting in real-life patient cohorts is rare. We, therefore, aimed to determine the incidence of AOE and VTE in a real-life CML and Ph-ALL population receiving ponatinib-based therapy. We retrospectively collected data on a single-center cohort of patients ≥18 years old with CML or Ph-ALL treated with ponatinib-based therapy between March 2016 and April 2020. VTE was defined as pulmonary embolism (PE) or deep vein thrombosis; peripherally inserted central catheter-associated events were excluded. AOE was defined as myocardial infarction (MI), unstable angina, stroke, or transient ischemic attack. High-risk of a CV event was defined as ≥2 risk factors (Table 1 footnote), and low-risk was defined as ≤1 based on prior literature and guidelines.3, 4 Cardioprotective medications included aspirin and statins. The incidence of VTE and AOE was assessed as the primary endpoint. The incidence of major or minor bleeding and time to first CV event were secondary endpoints. Exposure-adjusted CV event rate was calculated as the number of CV events divided by the total treatment exposure time. For patients with events, the exposure time was the time from the first dose to event onset. A total of 165 patients with a median age of 48 years (range, 20–89 years) and diagnosis of Ph-ALL (61%) or CML (39%) were included for analysis (Table 1). Most CML patients were in the blast phase (58%). Among Ph-ALL patients, 26% initiated ponatinib at 45 mg, 63% at 30 mg, and 11% at 15 mg. For CML patients, 14% started therapy at 45 mg, 63% at 30 mg, and 22% at 15 mg. Overall, the mean ponatinib daily dose was 25 mg (range, 7.5–45 mg) and the median time on therapy was 219 days (range, 4–536 days). Most patients received ponatinib as first-line (30%) or second-line therapy (30%). Most patients used ponatinib in combination with high-intensity therapy (41%) such as the Hyper-CVAD regimen or low-intensity therapy (35%) such as blinatumomab. The median number of CV risk factors was 1 (range 0–9) and most patients (82%) had ≥1. During ponatinib therapy, 32% of patients were taking a statin and 29% were taking aspirin. The overall incidence of CV events in our study population was 10% (n = 16). The incidence of VTE and AOE was 7% (n = 11) and 3% (n = 5), respectively. The exposure-adjusted VTE rate was 11.4% and the exposure-adjusted AOE rate was 5.2%. VTEs consisted of 10 DVTs and 1 PE. AOEs consisted of 2 MIs and 3 strokes. The incidence of CV events in patients categorized as low-risk was 7% compared to 11% for high-risk. The AOE incidence was 1% for low-risk and 5% for high-risk patients. The incidence of CV events in CML patients was 11% compared to 9% in Ph-ALL patients. In the CML cohort, the CV event incidence was 22% in patients who started ponatinib at 45 mg, 10% at 30 mg, and 7% at 15 mg. For Ph-ALL patients, the CV event incidence was 15% in patients starting at 45 mg and 8% at 30 mg (none for 15 mg). Patients who experienced a CV event had a higher mean daily dose than those who did not (29 vs. 25 mg) and were on therapy for a shorter duration (195 vs. 221 days). The CV event incidence was 8% in patients who were dose-reduced versus 10% in those who were not. There were five minor bleeding events (none in patients receiving aspirin) and no major bleeding events. Median time to CV event was 95 days (range 24–319) for VTE and 312 days (range 112–543) for AOE. Median survival was 1.5 years for patients who experienced a CV event and 2.3 years for those who did not. Of the 5 patients who experienced an AOE, 2 started ponatinib at 45 mg, 2 at 30 mg, and 1 at 15 mg. At the time of AOE, 4 patients were on 30 mg and 1 on 15 mg. Only one patient (Ph-ALL) was receiving ponatinib as part of a high-intensity regimen, and 3 patients (all CML) were using it as 4th line therapy or beyond. Four of 5 patients who had an AOE were classified as high-risk for a CV event. Their most common risk factors were hypertension, tobacco use, obesity, and hyperlipidemia. No event-related death occurred. The AOE incidence of 3% seen in our cohort of leukemia patients receiving ponatinib is lower than reported in the literature. Results of the PACE trial showed a 14% cumulative incidence of grade 3–4 AOE in CML and Ph-ALL patients on ponatinib with a starting dose of 45 mg.4 The OPTIC trial, in which CML patients were evenly assigned to starting doses of 45, 30, and 15 mg, reports a 5% incidence of grade 3–4 AOE.5 Like the OPTIC trial, we found an increased incidence of AOE at higher starting doses of ponatinib. Our lower incidence of AOE may be related to a larger portion of patients starting on lower doses, with a median starting dose of 30 mg (Table 1). We also demonstrated a higher incidence of AOE in high-risk versus low-risk patients. Patients with ≥2 cardiovascular risk factors had a 5% AOE incidence compared to 1% in low-risk patients. The PACE trial similarly found patients with ≥2 risk factors had the highest risk for AOE occurrence.4 Lastly, the median time to AOE (10.2 months) in our patient population is similar to the PACE trial which showed a median onset of 13.4 months.4 Ponatinib is reserved for patients who fail therapy with at least two TKIs or harbor a T315I mutation; its frontline use has been hampered by cardiotoxicity. However, dose optimization and risk reduction strategies may expand the use of this effective therapy, as was demonstrated in a study combining ponatinib with high-intensity chemotherapy for Ph-ALL.6 We present a real-life cohort of patients with a lower incidence of CV events on ponatinib therapy, likely owing to reduced doses and the use of cardioprotective medications. Limitations of this study include its single-centered, retrospective design and inability to account for all confounding factors. In conclusion, the CV event incidence of 10% and AOE incidence of 3% reported in our real-life cohort of leukemia patients taking ponatinib were lower than prior reports. Given the considerable risk of thrombosis, dose optimization and preventative strategies should be considered as part of the management of patients on ponatinib. E Dan Nichols, Nadya Jammal, Serena Chew, and Jeffrey Bryan contributed to the overall design, performed research, collected, analyzed, and interpreted data, and prepared and wrote the manuscript; Ghayas Issa, Guillermo Garcia-Manero, Koji Sasaki, and Hagop Kantarjian contributed to the overall design, performed research, and interpreted data; Adam DiPippo contributed to the overall design, performed research, collected, analyzed, and interpreted data, prepared and wrote the manuscript, and supervised the study; Elias Jabbour contributed to the overall design, performed research, interpreted data, and supervised the study; and all authors critically reviewed and approved the final version of the manuscript. The authors thank the leukemia patients and their caregivers for allowing us to partner with them in their health care. Drs Nichols, Jammal, Chew, Bryan, Garcia-Manero, and DiPippo have no disclosures to report. Dr Kantarjian has received research grants from AbbVie, Amgen, Ascentage, BMS, Daiichi-Sankyo, Immunogen, Jazz, Novartis, and Pfizer; and honoraria from AbbVie, Amgen, Aptitude Health, Ascentage, Astellas Health, AstraZeneca, Ipsen Biopharmaceuticals, KAHR Medical Ltd, NOVA Research, Novartis, Pfizer, Precision BioSciences, and Taiho Pharma Canada. Dr Jabbour has received research grants from Amgen, AbbVie, Spectrum, BMS, Takeda, Pfizer, Adaptive, and Genentech. Dr Sasaki is a member of Pfizer's and Daiichi-Sankyo's Board of Directors or advisory committees and has received research grants from and is a consultant for Novartis. Dr Issa has received research grants from Celgene, Kura Oncology, Syndax, and Novartis and has received consultancy fees from Novartis and Kura Oncology. The data that support the findings of this study are available from the corresponding author upon reasonable request. The data that support the findings of this study are available from the corresponding author upon reasonable request.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.267
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations8
Published2022
Admission routes1
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