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Record W4389229583 · doi:10.1182/blood-2023-190293

Propensity Score Matching Comparing Asciminib <i>Versus</i> Ponatinib in Chronic Myeloid Leukemia Patients Who Failed Prior Tyrosine Kinase Inhibitor Therapy

2023· article· en· W4389229583 on OpenAlexaffabout
María Agustina Perusini, Camille Kockerols, Daniela Žáčková, Franck E. Nicolini, Fausto Castagnetti, Carolina Pavlovsky, Massimo Breccia, Delphine Réa, Carmen Fava, Mary Lynn Savoie, Christophe Bouvier, Petra Čičátková, Julien Bollard, Swe Mar Linn, Gopila Gupta, Emilia Scalzulli, Jiřı́ Mayer, Peter E. Westerweel, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsFoothills Medical CentrePrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineBosutinibPonatinibInternal medicineDiscontinuationPropensity score matchingOncologyImatinibClinical endpointMyeloid leukemiaAdverse effectClinical trialDasatinib

Abstract

fetched live from OpenAlex

Background ASCEMBL trial has demonstrated superior efficacy of Asciminib (ASC) over Bosutinib in terms of molecular response, and event-free survival. Furthermore, ASC could reduce discontinuation rates due to lower occurrences of adverse events (7% vs 25%). However, it remains to be established how ASC would compare to Ponatinib (PON), which is frequently used in chronic myeloid leukemia (CML) practice for patients (pts) failing multiple lines of tyrosine kinase inhibitor (TKI) therapy. No prospective study has been performed or is ongoing comparing ASC vs PON, but alternatively, observational data can provide valuable insight on this topic. The propensity score is the probability of therapy assignment conditional on observed baseline characteristics. We performed a propensity score matching (PSM) analysis to balance the variables affecting the treatment choice among ASC vs PON therapy. The present study aimed to compare the therapeutic efficacy of ASC to PON in CML pts who failed prior TKIs concerning molecular response, failure-free survival (FFS), progression, and overall survival (OS). Patients and methods A total of 426 CML pts who had been treated with ASC or PON in 6 countries (Canada, Netherlands, Czech Republic, France, Argentine, and Italy) were included. In this preliminary report, data from 394 pts were analyzed. Primary endpoint was FFS at 1 year which was defined as the interval from TKI therapy start until treatment failure or death. Treatment outcomes were assessed for molecular response and long-term outcomes such as MR2 (BCR::ABL1 <1%IS or molecular response 2 log or deeper), MMR (BCR::ABL1 <0.1%IS or molecular response 3 log or deeper), MR4 (BCR::ABL1 <0.01%IS or molecular response 4 log or deeper), FFS, Progression, and OS. These parameters were evaluated in the overall population (n=394), resistant (n=254), or intolerant subgroups (n=126), T315I mutated (n=89) or non-T315I mutated subgroups (n=304), those having a past history of cardiovascular disease (CVD) (n=89) or not (n=304). For propensity score calculation, the following pre-treatment variables were selected using a binary logistic regression model: age, history of cardiovascular disease (CVD), disease phase, T315I mutation, and reason for failure to prior TKI line. 160 pts (i.e. 80 case-control pairs) were finally extracted through the PSM process within 0.2 of caliper difference. Results Significant differences were observed in various factors between ASC vs PON group, such as age (p<0.01), past history of CVD (p<0.01), previous TKI lines (p<0.01), presence of mutations (p=0.034), and reason for failure to prior TKI line (p<0.01). These differences ceased to exist when PSM was applied (Table 1). In the overall population, at 12 months, FFS was 46.7%, 95%CI [40.0-52.0%], MR2 62.6% [57.1-67.6%], MMR 44.7% [39.2-50.1%], MR4 28.6% [23.9-33.6%]), and OS 88.1% [84.1-91.1%]. In the overall population, PON group showed a lower FFS than ASC group (HR of PON 1.61, 95%CI [1.14-2.28], p=0.006), but, this difference was no significant when limited to the PSM selected pts. Figure 1 illustrates FFS in Asciminib vs Ponatinib-treated patients. In a subgroup of pts without T315I mutation, those on ASC showed higher FFS (HR of PON 1.82, [1.21-2.71], p=0.003), higher MR2 rate (HR of PON 0.69, [0.511-0.94], p=0.020), and higher MMR rate (HR of PON 0.677, [0.47-0.97], p=0.035). Additionally, in a subgroup of pts with a history of CVD those on ASC showed higher FFS (HR of PON 2.21, [1.19-4.10], p=0.011). Also, in pts in the chronic phase, ASC group showed higher FFS (HR of PON 1.77, [1.18-2.65], p=0.005), MR2 rate (HR of PON 0.73, [0.55-0.97], p=0.032), and MMR rate (HR of PON 0.71, [0.52-0.99], p=0.047). In the PSM balanced population, only MR2 rate was found to be significantly higher in favor of ASC (HR of PON 0.6, [0.39-0.93], p=0.022), while no significant differences were observed in all the other endpoints of interest. Conclusion From our current preliminary results, it can be established that ASC has at least equal efficacy to PON regarding FFS, MMR, MR4, progression, and OS which was confirmed in a PSM cohort. These findings contribute valuable insights to the understanding of ASC and PON treatments in pts with specific disease characteristics and may have implications for personalized therapy decisions in the absence of clinical trials comparing these drugs. Our aim is to expand our PSM cohort.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.273
Teacher spread0.228 · 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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Citations3
Published2023
Admission routes2
Has abstractyes

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