Comprehensive Molecular Profiling of FLT3-Mutated Acute Myeloid Leukemia (AML) Patients Treated within the Ratify Trial (Alliance C10603)
Bibliographic record
Abstract
Abstract Background: Recently, the oral multitargeted small molecule FLT3 inhibitor midostaurin (M) was approved for treatment of FLT3-mutated AML in combination with standard chemotherapy. In the international RATIFY (NCT00651261) trial, addition of M led to superior overall and event-free survival compared to placebo, thus defining a new standard of care in this AML subset (Stone RM et al. NEJM 2017). Although not powered for subgroup analyses, M showed consistent effects across all FLT3 mutation strata [tyrosine kinase domain (TKD); internal tandem duplication (ITD) with low (0.05-0.7; ITDlow) or high (>0.7; ITDhigh) allelic ratio] suggesting significant off-target activity beyond FLT3 inhibition. Aim: We aimed to comprehensively profile the mutational landscape of FLT3 mutated (FLT3mut) AML in a large, well characterized cohort of patients (pts) treated within the RATIFY trial using a high-throughput targeted sequencing (HTS) approach. Methods: HTS was performed on the entire coding region of 262 genes involved in hematologic malignancies including 20 genes that encode kinases targeted by M (M kinome, MK). Pretreatment peripheral blood (PB; 14%) or bone marrow (BM; 86%) specimens were available from 475 (66%) of 717 FLT3mut AML RATIFY pts. Libraries were prepared using SureSelectXT custom solutions (Agilent). Paired-end sequencing was carried out on a HiSeq platform (Illumina). FLT3 mutation (mut) status was available for all pts [TKD: 24%; ITD: 76% (ITDlow: 45%; ITDhigh:31%)], and cytogenetic data for 454 pts (96%). Results: An average sequencing depth of 978x was obtained for all pts. In sum, 1815 mut (missense: 49%; indels: 40%; nonsense: 7%; other: 3%) were identified with a mean of 3.8 mut per pt (FLT3 strata; TKD: 4; ITDlow: 4; ITDhigh: 3.6).Overall, recurrent mut (>5% of all pts) were found in 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, TP53 (1%) and biallelic CEPBA (1%) mut were rare events. This was also true for aberrations of the MK (7% in total) with KIT (2%), MAP3K11 (1%), and NTRK3 (1%) being most frequently mutated. When stratified according to FLT3mut type, mut in NRAS (24% vs 7%, p<.0001), SMC1A (10% vs 4%, p=.02), and KIT (4% vs 1%, p=.02) occurred significantly more often in TKD than ITD groups, respectively, whereas WT1 (13% vs 24%, p=.018) was more frequently co-mutated in the ITD group. In general, pts in the TKD group had significantly more mut in genes encoding for cohesin (TKD: 29% vs ITD: 16%, p=.004) and signaling (TKD: 40% vs ITD: 24%, p=.001) proteins compared to ITD pts, who had significantly more mut in transcription genes (TKD: 37% vs ITD: 48%, p=.03). Based on the mut and cytogenetic data, we next sought to assign all FLT3mut pts to the 11 recently defined molecular AML classes (Papaemmanuil E et al. NEJM 2016). The majority fell into two classes, namely the NPM1 (N; 62%) and the chromatin-spliceosome (CS; 15%) classes, underscoring the significance of FLT3mut as the driver in these particular genomic classes. Other class-defining lesions were rare or absent in this cohort [inv(16): 2%; t(8;21): 2%; t(11q23;x): 2%; t(6;9): 1%, TP53-aneuploidy: 1%; CEBPAbiallelic: 1%; IDH2R172: 0%]. In 14% of all pts categorization was not possible (no or >1 class-defining lesion), emphasizing the need for further refinement of this classification. When focusing on these two groups, N and CS had comparable FLT3mut patterns (TKD: 24% vs 21%; ITDlow: 44% vs 45%; ITDhigh: 32% vs 33%), whereas N more frequently correlated with a normal karyotype (N: 91% vs CS: 63%). With respect to clinical characteristics, no differences between N and CS in terms of age, white blood cells, platelets, PB and BM blasts, as well as history of MDS were observed. Conclusion: In this comprehensive sequencing approach, we could further delineate the molecular pattern of FLT3mut AML. Here, FLT3-ITD and -TKD groups exhibited remarkable differences in cooperating pathways, highlighting distinct biologic features in the leukemogenesis of FLT3mut AML. Overall, mut of MK genes were rare events, not fully explaining the complexity of M off-target effects. Understanding the underlying disease mechanism will potentially provide useful information on prognosis and prediction of response to M. Further analyses including correlation with clinical outcome are ongoing. Support: U10CA180821, U10CA180861, U10CA180882, U24CA196171 Disclosures Bullinger: Janssen: Speakers Bureau; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Speakers Bureau; Pfizer: Speakers Bureau; Sanofi: Research Funding, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Bayer Oncology: Research Funding. Gathmann:Novartis: Employment. Larson:Ariad/Takeda: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BristolMyers Squibb: Consultancy, Research Funding. Medeiros:Genentech: Employment; Celgene: Consultancy, Research Funding. Tallman:ADC Therapeutics: Research Funding; AROG: Research Funding; BioSight: Other: Advisory board; Orsenix: Other: Advisory board; AbbVie: Research Funding; Daiichi-Sankyo: Other: Advisory board; Cellerant: Research Funding. Tiecke:Novartis: Employment. Pallaud:Novartis: Employment. Ehninger:Cellex Gesellschaft fuer Zellgewinnung mbH: Employment, Equity Ownership; GEMoaB Monoclonals GmbH: Employment, Equity Ownership; Bayer: Research Funding. Ganser:Novartis: Membership on an entity's Board of Directors or advisory committees. Stone:Otsuka: Consultancy; Jazz: Consultancy; Cornerstone: Consultancy; Fujifilm: Consultancy; Arog: Consultancy, Research Funding; Pfizer: Consultancy; Sumitomo: Consultancy; Novartis: Consultancy, Research Funding; Ono: Consultancy; Orsenix: Consultancy; Merck: Consultancy; Argenx: Other: Data and Safety Monitoring Board; AbbVie: Consultancy; Agios: Consultancy, Research Funding; Amgen: Consultancy; Astellas: Consultancy; Celgene: Consultancy, Other: Data and Safety Monitoring Board, Steering Committee. Thiede:AgenDix: Other: Ownership; Novartis: Honoraria, Research Funding. Döhner:AROG Pharmaceuticals: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; AROG Pharmaceuticals: Research Funding; Pfizer: Research Funding; Bristol Myers Squibb: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Celator: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Sunesis: Consultancy, Honoraria, Research Funding; Astellas: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Jazz: Consultancy, Honoraria; Pfizer: Research Funding; Seattle Genetics: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Celator: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Agios: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Sunesis: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Jazz: Consultancy, Honoraria.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".