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Record W2982913861 · doi:10.1182/blood-2019-129990

A 9-Gene Expression Signature Identifies Patients with Core-Binding Factor Acute Myeloid Leukemia at High Risk of Relapse after Achieving Complete Remission

2019· article· en· W2982913861 on OpenAlexaff
Guillaume Richard‐Carpentier, Miriam Marquis, François Béliveau, Guy Sauvageau, Josée Hébert

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsInstitute for Research in Immunology and CancerUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsOncologyInternal medicineCytarabineProportional hazards modelHazard ratioMyeloid leukemiaMedicineUnivariate analysisGene signatureHematologyChemotherapy regimenChemotherapyBiologyGene expressionGeneMultivariate analysisConfidence intervalGenetics

Abstract

fetched live from OpenAlex

Background: Core-binding factor acute myeloid leukemias (CBF-AML) are characterized by inv(16)/t(16;16)(p13.1;q22) or t(8;21)(q22;q22.1) and encompass a distinct subgroup of AML with a favorable prognosis. This subgroup represents approximately 15% of newly diagnosed adult AML and is more frequent in young adults. With intensive chemotherapy, nearly all patients with CBF-AML achieve complete remission (CR) and the long-term overall survival (OS) is 75 to 85%. However, up to 40% of adult patients will relapse after achieving CR and some patients may die of progressive disease. Early identification of patients with higher risk disease would help to select patients who may benefit from targeted or higher intensity therapeutic approaches to reduce their risk of relapse. The aim of this study was to develop a gene expression signature to predict at time of diagnosis the risk of relapse in patients with CBF-AML. Methods: We analyzed the RNA sequencing (RNA-Seq) data of 44 diagnostic specimens from patients with de novo CBF-AML treated with intensive chemotherapy (7+3 regimen followed by high-dose cytarabine consolidations). We performed logarithmic transformation and standardization of RNA-Seq data normalized in RPKM and excluded genes with very low expression (< 1 RPKM in all specimens). We selected the genes which were significantly associated with relapse-free survival (RFS) in univariate Cox proportional hazard (CPH) models with a significance level of < 0.05. In this subset of genes, we fitted a CPH regression model with the LASSO algorithm using L1 penalty regularization on coefficients to minimize the 10-fold cross-validation partial likelihood deviance. We derived a score from the genes selected by the LASSO algorithm based on the sum of each gene expression weighted by their regression coefficients. The prognostic impact of the score was evaluated in CPH regression models for RFS and OS. We also assessed the prognostic impact of individual genes included in the score after dichotomizing them into binary variables. Results: Our cohort of CBF-AML included 28 patients with inv(16)/t(16;16) and 16 patients with t(8;21). Characteristics of the patients at diagnosis are summarized in table 1. The median age at diagnosis was 42.5 year-old (range, 17-75). Patients with inv(16)/t(16;16) had a higher white blood cell count at diagnosis (median 42.8 vs 18.7, p < 0.01). Rates of CR were 100% (16/16 pts) and 89% (25/28 pts) for patients with t(8;21) and inv(16)/t(16;16), respectively. The 3 pts with inv(16)/t(16;16) who did not achieve CR were older than 60 year-old and died during induction of chemotherapy-related complications. With a median follow-up of 6.8 years, 28 patients remain alive in remission, 12 patients relapsed and 1 patient died in CR. The 5-year OS and RFS are 72% (95% confidence interval [CI], 60 - 87%) and 67% (95% CI, 54 - 84%), respectively. Among 24 586 genes for which RNA-Seq data was available, 506 genes (2.06%) were significantly associated with RFS in univariate analyses. Nine genes were selected by the LASSO algorithm and were used for calculation of the CBF-score: CPSF6, DUSP2, H3F3A, NAB2, NOTCH3, PAG1, RASGEF1A, THRAP3, TRIM24. The CBF-score as continuous variable was significantly associated with RFS (p < 0.001) and OS (p = 0.003), and remained significant for both outcomes when adjusted for age. When the CBF-score was dichotomized on the median, none of the patients with a low score relapsed whereas 12/21 (57.1%) patients with a high score relapsed. The estimated 5-year RFS rates were 100% (95% CI, 100 - 100%) and 37% (95% CI, 20 - 65%) in patients with a low and high CBF-score, respectively (p < 0.001) (Figure 1A-1B). When each gene of the score were individually dichotomized, all except NOTCH3 were significantly associated with worse RFS. For any additional adverse single-gene expression signature, the hazard ratio for RFS was 2.17 (p < 0.001). In patients with 4 or more adverse single-gene signatures, the 5-year RFS was 25% (95% CI, 11 - 58%) versus 95% (95% CI, 86 - 100%) in patients with 3 or less single-gene signatures (Figure 1D). Conclusion: We developed in silico a 9-gene expression signature that predicts with high accuracy patients with CBF-AML at high risk of relapse (high CBF-score) and patients who are likely to be cured with standard chemotherapy (low CBF-score). This prognostic signature is currently being validated in independent internal and external validation cohorts. Disclosures Sauvageau: ExCellThera: Consultancy, Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.239
Teacher spread0.229 · 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
Published2019
Admission routes1
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