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Record W4405038355 · doi:10.1182/blood-2024-211761

Multicenter Retrospective Analysis on the Clinical Benefit of Post-Transplant Tyrosine Kinase Inhibitor (TKI) Maintenance Therapy in Ph-Positive Acute Lymphoblastic Leukemia Suggests Improved GvHD-Free, Relapse Free Survival and Non-Relapse Mortality, but Not the Risk of Relapse

2024· article· en· W4405038355 on OpenAlexaff
Eshrak Al‐Shaibani, Yu Cai, Xianmin Song, Mohsen Al Zahrani, Mohamed Elemary, Muhned Alhumaid, Hanan Alkhaldi, Ayman Saad, Francesca Biavasco, Robert Zeiser, Joon Ho Moon, Sang Kyun Sohn, Jonas Mattsson, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsSaskatchewan Cancer AgencyUniversity of SaskatchewanPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineInternal medicineMaintenance therapyOncologyTyrosine-kinase inhibitorAcute lymphocytic leukemiaRetrospective cohort studyHematologyLymphoblastic LeukemiaProgression-free survivalOverall survivalLeukemiaImmunologyChemotherapyCancer

Abstract

fetched live from OpenAlex

Introduction: Post-transplant maintenance therapy with tyrosine kinase inhibitor (TKI) is very common practice in the management of Philadelphia positive acute lymphoblastic leukemia (Ph+ALL) following allogeneic hematopoietic stem cell transplantation (HCT) with an expectation to reduce the risk of relapse. However, it is still not clearly demonstrated whether TKI brings clinical benefit to the patients (pts). Conflicting retrospective results were reported while data from a randomized controlled trial is lacking. The present study evaluated the benefit of TKI in 245 pts from 7 international HCT centers with respect to for overall (OS) and relapse-free survival (RFS), non-relapse mortality (NRM), cumulative incidence of relapse (CIR), GvHD-free, relapse-free survival (GRFS). Patients and Method: We have retrospectively reviewed 245 pts with Ph+ ALL who underwent their first HCT in complete remission (CR) from 2000 till 2024. The OS, RFS and GRFS were calculated using the Kaplan-Meier method and analyzed by a log-rank test, while the CIR, NRM and chronic GvHD (cGvHD) incidence were calculated considering competing events and analyzed using the Fine-Gray model. Time-dependent analysis using Cox or Fine-gray model was conducted using time to TKI starts as a time-dependent covariate, and compared the outcomes between the TKI vs no-TKI groups using Mantel-Byar test which will avoid immortal bias. Results: Out of 245 pts, 126 (51.4%) received TKI. ABL1 kinase-domain mutation (KDM) was detected in 18 pts prior to HCT: T315I (n=8) and others (n=10), of whom 12 pts received TKI. The median time to start TKI after HCT was 3 months (range: 0.5-115 months) with imatinib (n=33), dasatinib (n=53), ponatinib (n=31), or other TKIs (n=7). The median duration of TKI was 23 months (0-163 months). Between the TKI and no-TKI groups, the characteristics were very similar in terms of age (p=0.17), sex (p=0.78), BCR-ABL qPCR level prior to HCT (p=1.0), and the presence of ABL kinase mutation (p=0.18). However, a significant difference was observed with respect to the remission status prior to HCT, with a higher proportion of TKI group received HCT in second CR or beyond (26.6% vs 12.4%, p=0.012). With a median follow-up duration of 42 months among survivors, 101 (41.2%) deaths and 52 (21%) relapses were observed out of 245 pts, resulting in 63.4% of OS, 55.2% of RFS, 61.3% of GRFS, 15.9% of CIR, 24.8% of NRM and 33.0% of cGvHD rates at 3 years. In comparison to non-TKI group, the TKI group showed less frequent occurrence of deaths (27.0% vs 56.3%, p<0.001), NRM (8.7% vs 47.1%, p<0.001), cGvHD (22.4% vs 38.7%, p=0.008), but a more frequent occurrence of relapse (27.8% vs 14.3%, p=0.012). However, because this analysis did not take account of TKI start day as a time-dependent covariate, we have conducted time-dependent analysis subsequently. Of interest, the time-dependent analysis showed trends of favouring TKI use for OS (HR, 0.8, P=0.33), RFS (HR, 0.73, P=0.14), and the significantly favourable impact of TKI use toward GRFS (HR, 0.74, P<0.0001), NRM (HR, 0.37, P= 0.003), and cGVHD (HR, 0.58, P= 0.04). However, it did not affect CIR (HR, 1.674, P= 0.07). Multivariable analysis did not demonstrate the TKI as an independent factor for OS, RFS or CIR. Of surprise, TKI use was confirmed as an independently favourable prognostic factor for GRFS (HR 0.565, p=0.012) and for NRM (HR 0.338, p=0.001). Out of 126 pts who received TKI, TKI was discontinued in 51 (41%) pts, of whom 22 pts completed the planned treatment, while 29 pts discontinued due to either relapse (n=12), TKI-related toxicity (n=14), and death (n=3). In the TKI group, 17 pts relapsed of whom 11 pts relapsed during TKI therapy and 6 pts relapsed after TKI stopped due to toxicity. The incidence of relapse after TKI starts was 22.8%, 33.4% and 33.4% at 3, 4 and 5 years, showing a plateau after 4 years. The current study strongly suggested a significant risk of relapse in the patients having ABL1 KDM, both T315I mutation and others. While only 11 (9.6%) out of 227 pts without ABL1 KDM relapsed, 6 (50%) out of 18 pts carrying ABL1 KDM had relapsed (p=0.001). Conclusion: The present results again can not confirm TKI's benefit with respect to relapse risk while it strongly suggests TKI's benefit by improving GRFS and reducing the risk of NRM. The patients with ABL1 KDM prior to HCT carry the highest risk of relapse even with TKI, implying additional intervention would be required on top of TKI-based maintenance.

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)0.002
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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
Published2024
Admission routes1
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