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Record W4310107771 · doi:10.1182/blood-2022-167581

Risk Factor Analysis for Second Treatment-Free Remission Failure from the Canadian TKI Discontinuation (TRAD) Trial in CML Patients: Treatment-Free Remission Accomplished By Dasatinib

2022· article· en· W4310107771 on OpenAlexaffabout
María Agustina Perusini, Eshetu G. Atenafu, Donna L. Forrest, Bence-Bruckler Isabelle, Lynn Savoie, Mary‐Margaret Keating, Lambert Busque, Robert Delage, Anargyros Xenocostas, Elena Liew, Pierre Laneuville, Kristjan Paulson, Tracy Stockley, Jeffrey H. Lipton, Brian Leber, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsMcMaster UniversityMcGill UniversityLondon Health Sciences CentreUniversity of ManitobaCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHôpital de l'Enfant-JésusHôpital Maisonneuve-RosemontNova Scotia Health AuthorityPrincess Margaret Cancer CentreUniversity of CalgaryAlberta Health ServicesUniversity of Alberta HospitalOttawa HospitalUniversity Health NetworkUniversity of TorontoAlberta Hospital EdmontonUniversity of British Columbia
Fundersnot available
KeywordsDiscontinuationDasatinibMedicineInternal medicineComplete remissionOncologySurgeryImatinibChemotherapyMyeloid leukemia

Abstract

fetched live from OpenAlex

Background The Canadian TKI discontinuation (TRAD) trial (NCT02268370) has evaluated whether Dasatinib (DA) rechallenge can sustain a second treatment-free remission (TFR2) after failure of Imatinib (IM) discontinuation (DISC) for the first TFR (TFR1) attempt. We previously reported that: 1) The 12-month molecular relapse-free survival (mRFS) rate for TFR1 is 58.0%; 2) Re-challenge with DA following TFR1 failure after IM DISC restores deep molecular response (MR) quickly; 3) 12 months DA rechallenge does not significantly improve TFR2 rate (EHA 2022). The clinical risk factors associated with TRF2 failure are evaluated in this study. Patients and Methods This prospective study (BMS CA180-543, NCT#02268370) had 3 phases: 1) IM discontinuation for TFR1, 2) DA rechallenge, and 3) DA discontinuation for TFR2. Key inclusion criteria included: 1) CML in chronic phase, 2) minimum duration of IM therapy 3 years, 3) minimum duration of MR4.5 or deeper over 2 years. Molecular relapse was defined as an increase BCR::ABL qPCR >MR4 on 2 consecutive occasions, or a single increase in BCR::ABL qPCR >MR3. DA treatment was started at 100mg daily after molecular relapse was confirmed, and continued for at least 12 months after achieving ≥MR4 until TFR2 is attempted. Molecular relapse-free survival (RFS) after DA discontinuation was calculated and analyzed according to the risk factors. Results With a median follow-up duration of 27.5 months (range 1.8-51), in the TFR2 phase, 35 pts stopped DA for a TFR2. Only 3 of 35 pts (8.6%), maintained the MR at last follow-up, the remaining 32 lost the MR within a median 3.65 months. The actuarial mRFS rate at 6 and 12 months was 22.9% (95% CI, 10.8-37.6%) and 10.0% (2.7-23.1%). Potential risk factors for TFR2 from three different phases were evaluated, and 5 were found to be associated with TFR2 failure in univariate analysis (table 1): 1. In the DA rechallenge phase, failure to achieve MMR in less than 1 month with DA rechallenge increased the risk of TFR2 failure, in comparison to those who achieved MMR in less than a month (HR 4.656, p=0.0002). 2. Prior to DA DISC phase, any detectable BCR::ABL1 qPCR level between MR4 to MR5.4 showed an increased risk of TFR2 failure, compared to undetectable BCR::ABL1 qPCR level below MR5.5 (HR 4.04, p=0.0001). 3. Among the variables during the IM DISC phase: 4. Molecular relapse pattern: in comparison to the loss of MR4 only, those who lost MMR had an increased risk of TFR2 failure (HR 4.163, p= 0.001). 5. Time to loss of MR within 2.8 months after IM DISC increased the risk of TFR2 failure, in comparison who lost MR beyond 2.8 months (HR 4.717, p=0.0002). 6. Shorter doubling time (DT) at 2 months below 12.75 days and above 0 increased the risk of TFR2 failure, compared to DT at 2 months above 12.75 days or below 0 (HR 654, p=0.002) Multivariate analysis was performed to identify independent risk factors, identifying 3 clinical factors predictive of TFR2 failure: 1) Failure to achieve MMR in less than 1 month following DA rechallenge (p=0.007, HR 9.542 [1.819-50]); 2) DT below 12.75 days at 2 months after IM DISC (p=0.015, HR 4.368 [1.334-14.30]); and 3) any detectable BCR::ABL1 transcript level between MR4 and MR5.4 before DA DISC (p=0.047, HR 2.759 [1.014-7.513]). A risk score model was generated incorporating these 3 clinical risk factors. A score of 1 was assigned to each one of these risk factors. The group with a score of 0 vs 1-3 was labeled as low vs high-risk, respectively. The low-risk group (n=7) showed 85.7% and 34.3% of TFR2 rate at 6 and 12 months, while the high-risk group (n=24) showed 7.4% and 3.7% of TFR2 rate at 6 and 12 months, (p=0.0003; HR 6.518 [2.219-19.95] for high-risk group vs low risk; Figure 1). Conclusion As we have previously reported, DA treatment rarely achieves a sustained TFR2 after failing the first IM DISC attempt. However, our current result suggests that TFR2 attempt can be successful in a selected group of patients if: 1) Rapid reduction of initial molecular kinetics is achieved within 1 month of DA rechallenge, 2) an undetectable BCR::ABL1 qPCR level is reached before TFR2 attempt 3) longer doubling time at 2 months after IM DISC is noted. The proposed risk model can be used as a predictive tool for TFR2 failure. Further studies to improve TFR2 rate should investigate the addition of new therapies and consider the use of innovative tools with higher precision and sensitivity to quantify the MR. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

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)0.003
metaresearch head score (Gemma)0.003
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 score0.983
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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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Citations0
Published2022
Admission routes2
Has abstractyes

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