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Record W3096217495 · doi:10.1182/blood-2020-137589

Prognostic Impact of a Modified European LeukemiaNet (ELN) Genetic Risk Stratification in Predicting Outcomes for Adults with Acute Myeloid Leukemia (AML) Undergoing Allogeneic Hematopoietic Stem Cell Transplantation (HCT). a Center for International Blood and Marrow Transplant Research (CIBMTR) Analysis for the CIBMTR Acute Leukemia Writing Committee

2020· article· en· W3096217495 on OpenAlexaff
Antonio Jiménez, Trent Wang, Marcos de Lima, Krishna V. Komanduri, Partow Kebriaei, Mark R. Litzow, Vijaya Raj Bhatt, Frédéric Baron, Ayman Saad, Nandita Khera, Joseph Maakaron, Hemant S. Murthy, Edward A. Copelan, Zachariah DeFilipp, Christopher Bredeson, Rodrigo Martino, Maxwell M. Krem, Taiga Nishihori, Mei‐Jie Zhang, Daniel J. Weisdorf, Karen Chen, Wael Saber

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineInternal medicineCumulative incidenceTransplantationHazard ratioUnivariate analysisOncologyHematopoietic stem cell transplantationClinical endpointProportional hazards modelGraft-versus-host diseaseMultivariate analysisClinical trialConfidence interval

Abstract

fetched live from OpenAlex

Background: Allogeneic HCT continues to be the optimal consolidation strategy for many patients with AML. Cytogenetic and molecular abnormalities are known to influence post-transplant outcomes. We tested the prognostic ability of a modified (mELN) classification system based on available CIBMTR genetic data, to predict post-transplant outcomes. Methods: Adult patients with a diagnosis of AML in first complete remission (CR1) with available pre-transplant cytogenetic and molecular mutational data, receiving a first allogeneic HCT from 2013-2017, were included. Patients were stratified according to mELN genetic classification in three distinct groups: favorable (Fav), intermediate (IM) and adverse (Adv). Clinical outcomes following HCT were compared among groups after adjusting for significant patient, disease, and transplant-related variables. The primary endpoint was disease free survival (DFS). Secondary endpoints were overall survival (OS), non-relapse mortality (NRM), cumulative incidence of relapse, acute GVHD, and chronic GVHD. Cox proportional hazard models were used to compare endpoints among mELN risk groups, age groups and genetic subsets within the adverse-risk group. Results: Demographic characteristics are summarized in Table 1. 2289 patients (Fav, n=181; IM n=1185; and Adv n=923) met the inclusion criteria. Median follow-up for survivors was 35 months. Importantly, 41% of transplant recipients (n=936) were >60 years, 76% (n=1743) had de novo AML and 48% (n=1111) received a myeloablative conditioning regimen. Univariate analysis (UVA) demonstrated significant differences in 2-year OS (Fav: 67.7%, IM: 64.9% and Adv: 53.9%; p<0.001); DFS (Fav: 57.8%, IM: 55.5% and Adv: 45.3; p<0.001) and relapse (Fav: 28%, IM: 27.5% and Adv: 37.5%; p<0.001) There were no significant differences in NRM (p=0.467) or the incidence of acute (p=0.423) and chronic GVHD (p=0.442) among mELN groups. Initial multivariate analysis (MVA) of mELN risk groups indicated that there was no significant difference in clinical outcomes between the Fav and IM risk groups. Thus, these groups were combined for subsequent analyses. Adv risk (vs. Fav/IM) led to significantly worse OS (HR 1.39 [1.24-1.57] p=<0.001), DFS (HR 1.32 [1.18-1.48] p=<0.001), and relapse (HR 1.42 [1.23-1.63] p=<0.001) (Table 2). This mELN classification effectively stratified both younger (<60 y/o) and older (>60 y/o) patients for OS (Adv vs. Fav/IM HR for <60: 1.43 [1.21-1.69] p<0.001; >60: 1.44 [1.21-1.72] p<0.001), DFS (Adv HR for <60: 1.31 [1.12-1.53] p<0.001; >60: 1.41 [1.19-1.66] p<0.001) and relapse (Adv HR for <60: 1.44 [1.20-1.74] p<0.001, >60: 1.42 [1.15-1.76] p=0.001). NRM was higher for older patients (>60 y/o) in both the Fav/IM (HR 1.40 [1.10-1.78] p=0.007) and Adv-risk cohorts (HR 1.59 [1.18-2.13] p=0.002). Genetic subset comparisons within the adverse-risk group showed that patients carrying monosomy 5, del(5q) or monosomy 7 had inferior 2-year OS (42.6%, p<0.001) and DFS (35.2%, p<0.001), as well as higher rates of relapse (45%, p= 0.002) when compared to other patients within the Adv-risk cohort (OS 60%, DFS 50.8%, relapse 33.5%). Conclusion: Stratification using mELN criteria resulted in clear prognostic separation of OS, DFS and relapse in this large cohort of AML patients undergoing allogeneic HCT. While Fav and IM groups had similar OS, DFS and relapse rates; patients in the Adv risk group had the highest risk of relapse and inferior DFS/OS. However, the majority of patients in all cohorts had favorable outcomes for HCT in CR1. Our findings confirm the value of a combined genetic prognostic model in the AML HCT setting and justify the use of this stratification system in future HCT trials. Correlation of genetic subtypes with other important transplant variables, such as conditioning intensity and pre-transplant MRD status deserves further evaluation. There remains a subset of Adv-risk patients for which post-transplant outcomes continue to be poor, even when transplanted in CR1. Novel peri-transplant pre-emptive/therapeutic strategies are urgently needed for this high-risk cohort. Disclosures de Lima: Celgene: Research Funding; Pfizer: Other: Personal fees, advisory board, Research Funding; BMS: Other: Personal Fees, advisory board; Incyte: Other: Personal Fees, advisory board; Kadmon: Other: Personal Fees, Advisory board. Komanduri:Kiadis: Consultancy; Takeda: Consultancy; Celgene: Consultancy; Atara: Consultancy, Membership on an entity's Board of Directors or advisory committees; Adaptimmune: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kebriaei:Ziopharm: Other: Research Support; Jazz: Consultancy; Kite: Other: Served on advisory board; Amgen: Other: Research Support; Novartis: Other: Served on advisory board; Pfizer: Other: Served on advisory board. Bhatt:National Marrow Donor Program: Research Funding; Rigel Pharmaceuticals: Other; Jazz: Research Funding; Agios: Other: Personal Fees; Incyte: Other: Personal Fees, Research Funding; Takeda: Other: Personal Fees; Partner Therapeutics: Other: Personal Fees; Pfizer: Other, Research Funding; CSL Behring: Other; Tolero Pharmaceuticals: Research Funding; Oncoceutics: Other: Drug support for a trial; Partnership for health analytic research, LLC: Other: Personal Fees; Omeros: Other: Personal Fees; Abbvie: Other: Personal Fees, Research Funding. Saad:Orcabio: Other: research support; Kadmon: Other: research support; Amgen: Other: research support; Incyte Pharmaceuticals: Other: Personal Fees; Magenta Therapeutics: Other: Personal Fees. Copelan:Amgen: Membership on an entity's Board of Directors or advisory committees. Defilipp:Incyte: Research Funding; Regimmune: Research Funding; Syndax Pharmaceuticals: Consultancy. Nishihori:Karyopharm: Other: Research support to institution; Novartis: Other: Research support to institution. Weisdorf:Incyte: Research Funding; FATE Therapeutics: Consultancy.

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)0.003
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.029
GPT teacher head0.297
Teacher spread0.267 · 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
Published2020
Admission routes1
Has abstractyes

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