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P430: MOLECULAR CHARACTERIZATION OF CLINICAL RESPONSE IN NEWLY-DIAGNOSED ACUTE MYELOID LEUKEMIA PATIENTS TREATED WITH IVOSIDENIB + AZACITIDINE COMPARED TO PLACEBO + AZACITIDINE

2022· article· en· W4283397932 on OpenAlexaff
S. de Botton, S. Choe, D. Marchione, P. Montesinos, C. Recher, S. Vives Polo, E. Zarzycka, Jacky H Wang, G. Bertani, M. Heuser, R. Calado, A. Schuh, S.-P. Yeh, Jia-Jun Hui, S. Pandya, D. Gianolio, S. Daigle, C. DiNardo, H. Dohner

Bibliographic record

VenueHemaSphere · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsIDH1Isocitrate dehydrogenaseMyeloid leukemiaMedicinePeripheral blood mononuclear cellInternal medicineBone marrowAzacitidinePlaceboOncologyGastroenterologyMutationBiologyPathologyGenetics

Abstract

fetched live from OpenAlex

Background: Acute myeloid leukemia (AML) is a disease with a dynamic mutational landscape; 6–10% of patients (pts) have somatic mutations in isocitrate dehydrogenase 1 (IDH1), which can drive oncogenesis. Ivosidenib (IVO) is a potent oral targeted inhibitor of mutant IDH1 (mIDH1). IVO 500 mg QD + azacitidine (AZA) 75 mg/m2 SC or IV for 7 days in 28-day cycles was shown to significantly improve event-free survival (HR=0.33 [95% CI 0.16, 0.69], p=0.0011), median overall survival (24.0 vs 7.9 months), and complete remission + partial hematologic recovery rates (CR/CRh; 52.8% vs 17.6%) vs placebo + AZA in the double-blind phase 3 AGILE study (NCT03173248) in pts with newly diagnosed IDH1-mutated AML (ND-AML). Aims: To assess the impact of IVO+AZA on IDH1-mutation clearance (IDH1-MC) and baseline co-mutation analysis from AGILE. Methods: Genomic DNA from bone marrow mononuclear cells (BMMCs) or peripheral blood mononuclear cells (PBMCs), and/or bone marrow aspirate (BMA) were used for molecular studies. IDH1-MC analysis on BMMCs was performed by BEAMing digital PCR (limit of detection 0.02%-0.04%). BMA, BMMCs and PBMCs were utilized for co-mutational analysis by next-generation sequencing, ACE Extended Cancer Panel (detection limit 2%). All patients gave written informed consent. Results: 146 pts were randomized: 72 to IVO+AZA; 74 to placebo+AZA. Median (range) baseline mIDH1 variant allele frequency in BMMCs was 36.7% (3.1–50.5%) in the IVO+AZA arm and 35.5% (3.0–48.6%) in the placebo+AZA arm. Updated IDH1-MC data (October 2021) from 47 IVO+AZA and 32 placebo+AZA treated pts with at least 1 on-treatment sample demonstrated IDH1-MC in 21/35 (60%) IVO+AZA pts achieving CR/CRh vs 4/11 (36%) placebo+AZA pts. In CR/CRh pts with time points available after IDH1-MC, suppression of the mIDH1 was durable and IDH1-MC maintained in all subsequent samples in 17/17 (100%) IVO+AZA treated pts and 1/3 (33%) placebo+AZA pts. Further analysis of baseline co-mutations on 120 pts (IVO+AZA: n=58; placebo+AZA: n=62) showed that DNMT3A, SRSF2, and RUNX1 were the most frequent in both treatment arms. Importantly, comparison of CR/CRh and non CR/CRh responses by cohort did not identify any single gene or pathway associated with an inferior outcome in IVO+AZA pts compared to placebo+AZA pts (p<0.05, Fisher’s Exact test). Several genes (DNMT3A, RUNX1, SRSF2, STAG2) and pathways (Differentiation, Epigenetics, Splicing) were associated with improved outcomes with IVO+AZA, including the RTK pathway, which was previously reported to be associated with primary resistance to IVO monotherapy. Further analysis of patient subgroups, including R132 variants (i.e., R132C vs R132S), will be presented. Summary/Conclusion: These data suggest that improved clinical outcomes with IVO+AZA are associated with sustained clearance of the mIDH1 clone including pts with disease that harbor mutations implicated in resistance to IVO monotherapy (e.g., with RTK pathway mutations).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.311
Teacher spread0.288 · 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 teacher head, not a consensus.

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
Published2022
Admission routes1
Has abstractyes

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