PS968 GENETIC LANDSCAPE OF FLT3‐MUTATED ACUTE MYELOID LEUKEMIA (AML) PATIENTS TREATED WITHIN THE RATIFY TRIAL: CALGB 10603 (ALLIANCE)
Bibliographic record
Abstract
Background: Recently, the oral multi‐targeted FLT3 inhibitor midostaurin (M) was approved in combination with standard chemotherapy for treatment of FLT3 mutated ( FLT3 mut ) AML. In the international RATIFY (NCT00651261) trial, M significantly improved overall (OS) and event‐free survival (EFS) compared to placebo, thus defining a new standard of care in this AML subset (Stone RM et al. NEJM 2017). Aims: To comprehensively delineate the mutational profile of FLT3 mut AML patients (pts) treated within the RATIFY trial and to correlate genetic findings with clinical outcome. Methods: Targeted sequencing was performed of 262 candidate genes including 20 kinases targeted by M (M kinome, MK) using SureSelectXT (Agilent). Diagnostic specimens were available from 475 (66%) of 717 FLT3 mut pts. Sequencing was carried out on a HiSeq (Illumina). FLT3 mutation (mut) status was available for all 475 pts [tyrosine kinase domain (TKD: 24%); internal tandem duplication (ITD: 76%)]; cytogenetic data were present for 371 pts (78%). Results: Overall, 1813 mut were identified (median coverage: 978x) with an average of 3.8 mut per pt ( FLT3 strata; TKD: 3.9; ITD: 3.8). Mut ≥5% were found for NPM1 (61%), DNMT3A (39%), WT1 (21%), TET2 (12%), RUNX1 (11%), NRAS (11%), PTPN11 (9%), ASXL1 (8%), IDH1 (8%), IDH2 (7%; #R140 only), and SMC1A (6%). In contrast, MK mut were rare events (7% in total) with KIT (2%), MAP3K11 (1%), and NTRK3 (1%) being most frequently mutated. First, we categorized all pts to the recently defined 11 genomic AML classes (Papaemmanuil E et al. NEJM 2016). The majority of cases fell into two classes, namely the NPM1 (N; 60%), and the chromatin‐spliceosome (C‐S; 15%) classes. Core‐binding factor (CBF) AML [t(8;21) and inv(16)] accounted for 4% only; in 13% no class‐defining lesion was identified (no class, NC). In terms of clinical outcome, CBF and N classes showed better response to induction therapy than C‐S and NC (Complete remission (CR) rate: 83%, 70%, 60%, 38%; p = .0005), which translated into significantly superior OS ( p < .0001) and EFS ( p < .0001) for CBF and N compared to C‐S and NC. When stratified according to treatment arm, addition of M resulted in improved OS in N, C‐S, as well as NC, irrespective of the genetic background. Next, we categorized pts according to the 2017 European LeukemiaNet (ELN) genetic risk groups (Döhner H et al. Blood 2017). CR rate was inferior within the adverse (ADV) and intermediate (INT) as compared to the favorable (FAV) risk group (52%, 62%, 74%; p = .002) translating into significantly shorter EFS ( p < .0001) and OS ( p < .0001) in INT and ADV than FAV. Of note, M showed consistent beneficial effects across all ELN risk groups. When comparing pts with MK gene mut vs. pts with wildtype MK, no differences in outcome were observed. Within the group of MK mut pts there was no benefit for M; however, the results have to be interpreted cautiously due to the small sample size. Summary/Conclusion: In this comprehensive targeted sequencing approach of 475 FLT3 mut AML pts treated within the RATIFY trial, remarkable differences in the underlying AML genomic classes were found. Predominant classes were N, C‐S, CBF, showing significant differences with regard to response to treatment and survival. Of note, treatment with M showed beneficial effects across all genomic AML classes. In addition, M was associated with a better outcome in all 2017 ELN risk categories, demonstrating a consistent benefit of M across various genetic AML subgroups. Support: U10CA180821, ‐ U10CA180882, U24CA196171; Novartis; https://acknowledgments.alliancefound.org
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".