Somatic gene mutation patterns and burden influence outcomes with enasidenib in relapsed/refractory IDH2-mutated AML
Bibliographic record
Abstract
Limited treatment options are available for patients with relapsed/refractory acute myeloid leukemia (R/R AML). We recently reported results from the phase 3 IDHENTIFY trial (NCT02577406) showing improved response rates and event-free survival with enasidenib monotherapy compared with conventional care regimens (CCR) in heavily pretreated, older patients with late-stage R/R AML bearing IDH2 mutations. Here we investigated the prognostic impact of mutational burden and different co-mutation patterns at study entry within the predominant IDH2 variant subclasses, IDH2-R140 and IDH2-R172. The prognostic relevance of these variants is well documented in newly diagnosed AML, but data are lacking in R/R AML. In this large R/R AML patient cohort, targeted next-generation sequencing at baseline (screening) revealed distinct co-mutation patterns and mutational burden between subgroups bearing different IDH2 variants: variant IDH2-R140 was associated with greater mutational burden and was enriched predominantly with poor-risk mutations, including FLT3, RUNX1, and NRAS, while variant IDH2-R172 was associated with lower mutational burden and was preferentially co-mutated with DNMT3A. In multivariable analyses, RAS and RTK pathway mutations were significantly associated with decreased overall survival, after adjusting for treatment arm, IDH2 variant, and mutational burden. Importantly, enasidenib-mediated survival benefit was more pronounced in patients with IDH2-R172 variants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".