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Record W4415464815 · doi:10.1186/s40364-025-00827-6

High IL1R1 expression predicts poor survival and benefit from stem cell transplant in intermediate-risk acute myeloid leukemia from the Leucegene cohort

2025· article· en· W4415464815 on OpenAlexafffund
Guillaume Richard‐Carpentier, François Béliveau, Sandrine Lacoste, Banafsheh Khakipoor, Véronique Lisi, Michael Vladovsky, Miriam Marquis, Jean-François Spinella, Patrick Gendron, Sébastien Lemieux, Vincent‐Philippe Lavallée, Guy Sauvageau, Josée Hébert

Bibliographic record

VenueBiomarker Research · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and CancerCentre Hospitalier Universitaire Sainte-JustineHôpital Maisonneuve-RosemontPrincess Margaret Cancer CentreUniversity of Toronto
FundersAlliance de recherche numérique du CanadaGovernment of CanadaGénome QuébecFonds de Recherche du Québec - SantéGenome Canada
KeywordsMyeloid leukemiaStem cellCohortHematopoietic stem cell transplantationTransplantationMyeloidHaematopoiesis

Abstract

fetched live from OpenAlex

Abstract Background There is an unmet clinical need to identify patients with acute myeloid leukemia and intermediate-risk cytogenetics who benefit from allogeneic hematopoietic stem cell transplantation in first remission, especially among those without FLT3 -ITD mutation. Methods We analyzed transcriptomic data from the Leucegene cohort composed of 316 patients with acute myeloid leukemia and intermediate-risk cytogenetics who have been treated with intensive chemotherapy. We evaluated associations between gene expression and overall survival or relapse-free survival and we analyzed the interaction between gene expression and allogeneic hematopoietic stem cell transplantation to identify biomarkers that predict the benefit of stem cell transplantation in this subgroup of patients. Results We identified high IL1R1 expression ( IL1R1 high ) as a prognostic and predictive marker in the Leucegene cohort. IL1R1 high (≥ 2.0 transcripts per million) was associated with older age, monocytic differentiation, higher frequency of FLT3 -ITD and RUNX1 mutations and lower frequency of IDH1 / 2 and bZIP CEBPA mutations. Patients with IL1R1 high had lower 5-year overall survival (10% vs 38%, p < 0.01), and higher 5-year cumulative incidence of relapse (76% vs 59%, p < 0.01) than those with low IL1R1 expression. IL1R1 high was independently associated with overall survival in multivariable analyses including age, white blood cell count at diagnosis and NPM1 , FLT3 -ITD, bZIP CEBPA , RUNX1 , ASXL1 and DNMT3A mutations (HR 1.78, p < 0.01). Importantly, in landmark analysis, hematopoietic stem cell transplantation in first remission significantly improved 5-year overall survival in patients with IL1R1 high (67% vs 27%, HR 0.33, p < 0.01), but not in patients with IL1R1 low (62% vs 54%, HR 0.72, p = 0.31), especially among those without FLT3 -ITD mutation (48% vs 50%, HR 0.93, p = 0.85). In patients who proceeded to allogeneic hematopoietic stem cell transplantation, the 5-year overall survival was 60% in patients with IL1R1 high compared to 56% in patients with IL1R1 low confirming that the worse prognosis associated with high expression of IL1R1 was abrogated by stem cell transplantation. Conclusion IL1R1 expression is a candidate marker to identify patients with intermediate-risk cytogenetics acute myeloid leukemia at high risk of relapse who benefit from allogeneic hematopoietic stem cell transplantation in first remission.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.307
Teacher spread0.279 · 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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Citations1
Published2025
Admission routes2
Has abstractyes

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