Voso, Maria Teresa

Myelodysplastic syndromes with isolated deletion of chromosome 5q [MDS-del(5q)] constitute a distinct biological entity traditionally associated with favorable outcomes, although up to one quarter of patients progress to acute myeloid leukemia (AML). Existing prognostic models, developed in heterogeneous MDS populations, may not adequately capture risk within this subgroup. We assembled an international cohort of 682 patients with MDS-del(5q) to evaluate the performance of the IPSS-R and IPSS-M, identify prognostic variables, and develop a disease-specific prognostic tool, the IPSS-del(5q). Most patients were classified as lower-risk by IPSS-R (94.4%) and IPSS-M (85.5%), yet both systems showed limited discriminatory ability (C-indices ≈0.5). Independent adverse prognostic factors included age ≥70 years, male sex, anemia (hemoglobin ≤10 g/dL), thrombocytopenia (platelets ≤100×10⁹/L), the presence of one additional chromosomal abnormality, ≥2 gene mutations, SF3B1 mutations, and high-risk TP53 status. Six variables were included in the IPSS-del(5q), stratifying patients into standard-risk (74.3%) and high-risk (25.7%) groups with significantly different LFS (69.2 vs. 32.0 months; p<0.01). Moreover, this model reclassified 19.1% of lower-risk IPSS-R and 14.6% of lower-risk IPSS-M patients into the high-risk IPSS-del(5q) group. However, its discriminative power remained modest, with a C-index of 0.60. Overall, this study provides the most comprehensive prognostic evaluation of MDS-del(5q) to date, demonstrates the limited discriminatory capacity of existing MDS scores in this entity, and underscores the need to develop refined disease-specific prognostic approaches for this MDS subtype.

PURPOSE Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

Abstract Background & aim: Myelodysplastic syndromes with isolated deletion of chromosome 5q (MDS-del(5q)) are associated with low-risk features and favorable prognosis. In contrast, TP53 multihit alterations, typically reflecting biallelic inactivation, are associated with high-risk features and adverse outcomes across MDS subtypes. Although current classification systems exclude MDS-del(5q) cases with TP53 multihit from low-risk categories, the prognostic significance of these alterations in this specific context remains unclear. Methods: We conducted an international, multicenter, retrospective study comparing MDS-del(5q) per 2017 WHO criteria with TP53 multihit alterations (MDS-del(5q) TP53 multihit) from 21 international institutions, to MDS with low blast counts with TP53 multihit alterations without isolated del(5q) (MDS-LB TP53 multihit) from the public data from the study of Bernard et al., Nat Med 2020. Clinical and biological data were collected at diagnosis. Genetic profiling included G-banding and NGS of myeloid genes. TP53 multihit was defined as &gt;1 TP53 mutation or a single mutation with 17p deletion, copy-neutral loss of heterozygosity (LOH), or variant allele frequency (VAF) ≥50%. Patients were stratified as lower- or higher-risk based on IPSS-R and IPSS-M scores. Lower-risk included those with IPSS-R ≤3.5 points or very-low, low, or moderate-low IPSS-M, while higher-risk included those with IPSS-R &gt;3.5 points or moderate-high, high, or very-high IPSS-M. Clinical, molecular features, overall survival (OS), and AML progression were compared between MDS-del(5q) TP53 multihit and MDS-LB TP53 multihit groups. Finally, outcomes between patients with likely biallelic TP53 multihit status, defined as harboring a single TP53 mutation accompanied by a co-occurring deletion/LOH of 17p or a TP53 VAF ≥50%, versus those considered less likely to be biallelic, thus still classified as multihit, defined as having &gt;1 TP53 mutation but a combined VAF &lt;50%, were compared in both MDS-del(5q) and MDS-LB groups. Statistical analysis was performed using R (version 4.2.2). Results: We included 113 patients with a median follow-up of 81.1 months (95% CI 74.9–NR). A total of 45 (39.8%) were MDS-del(5q) TP53 multihit, and 68 (60.2%) MDS-LB TP53 multihit. Compared to MDS-LB TP53 multihit, patients with MDS-del(5q) TP53 multihit had significantly higher platelet counts (209 ×10⁹/L vs. 77 ×10⁹/L) and were more frequently classified as lower-risk by both IPSS-R (75.4% vs. 9.8%) and IPSS-M (90.2% vs. 24.6%). The median number of mutations per patient, excluding TP53, was 1 in both groups, and only SF3B1 mutations were significantly more frequent in the MDS-del(5q) TP53 multihit group compared to MDS-LB TP53 multihit (20.5% vs. 2.9%; p&lt;0.01). Regarding survival, MDS-del(5q) TP53 multihit patients showed an improved OS compared to MDS-LB TP53 multihit (median OS of 57.0 months (95%CI, 43.0–112.0) vs 14.0 months (95%CI, 8.6–19.0); p&lt;0.01). Moreover, while the risk of progression to AML at 48 months were comparable between groups (34.9% for MDS-del(5q) TP53 multihit and 33.0% for MDS-LB TP53 multihit; p=0.8), a significant difference was observed in the timing of AML progression: median time to AML evolution of 31.7 months (IQR 23.4–55.3) in the MDS-del(5q) TP53 multihit group and 7.2 months (IQR 4.1–12.9) in the MDS-LB TP53 multihit group (p&lt;0.01). Notably, no significant differences in outcomes were found between TP53 multihit and most likely biallelic cases in both MDS-del(5q) and MDS-LB cohorts. The impact of complex karyotype (CK) in MDS-LB TP53 multihit was also explored. MDS-LB TP53 multihitwith CK showed a significantly worse OS compared to MDS-LB TP53 multihitwithout CK (median OS of 10.0 months (95% CI 7.5–17.0) vs 40.0 months (95% CI 14.0–NA); respectively; p&lt;0.001. However, the incidence of AML and median time to AML evolution were comparable between both groups. Finally, OS between MDS-LB TP53 multihit patients without CK and the MDS-del(5q) TP53 multihit cohort was compared. Median OS was significantly shorter in the former group (median of 40 months) compared to the latter (median of 61 months) (p &lt;0.01) Conclusions: These findings underscore that, in contrast to other TP53-multihit MDS subtypes, MDS-del(5q) with TP53 multihit alterations constitute a clinically distinct entity with better outcomes, thereby warranting reconsideration of its place in current classification systems.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P&lt;.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P&lt;.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P&lt;.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P&lt;.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Abstract The results from the RATIFY trial (ClinicalTrials.gov: NCT00651261; CALGB 10603) showed that midostaurin combined with standard chemotherapy significantly improved outcomes in patients with FMS-like tyrosine kinase 3 (FLT3)–mutated acute myeloid leukemia (AML), compared with placebo. In this post hoc subgroup analysis from the trial, we evaluated the impact of midostaurin in 163 patients with FLT3-tyrosine kinase domain (TKD) mutations. At a median follow-up of 60.7 months (95% CI, 55.0-70.8), the 5-year event-free survival (EFS) rate was significantly higher in patients treated with midostaurin than in those treated with placebo (45.2% vs 30.1%; P = .044). A trend toward improved disease-free survival was also observed with midostaurin (67.3% vs 53.4%; P = .089), whereas overall survival (OS) was similar in the 2 groups. Patients with AML and NPM1mut/FLT3-TKDmut or core binding factor (CBF)–rearranged/FLT3-TKDmut genotypes had significantly prolonged OS with or without censoring at hematopoietic cell transplantation (HCT), compared with NPM1WT/CBF-negative AMLs. The multivariable model for OS and EFS adjusted for allogeneic HCT in first complete remission as a time-dependent covariable, revealed NPM1 mutations and CBF rearrangements as significant favorable factors. These data show that NPM1 mutations or CBF rearrangements identify favorable prognostic groups in patients with FLT3-TKD AMLs, independent of other factors, also in the context of midostaurin treatment.

Abstract Patients with acute myeloid leukemia (AML) harboring FLT3 internal tandem duplications (ITDs) have poor outcomes, in particular AML with a high (≥0.5) mutant/wild-type allelic ratio (AR). The 2017 European LeukemiaNet (ELN) recommendations defined 4 distinct FLT3-ITD genotypes based on the ITD AR and the NPM1 mutational status. In this retrospective exploratory study, we investigated the prognostic and predictive impact of the NPM1/FLT3-ITD genotypes categorized according to the 2017 ELN risk groups in patients randomized within the RATIFY trial, which evaluated the addition of midostaurin to standard chemotherapy. The 4 NPM1/FLT3-ITD genotypes differed significantly with regard to clinical and concurrent genetic features. Complete ELN risk categorization could be done in 318 of 549 trial patients with FLT3-ITD AML. Significant factors for response after 1 or 2 induction cycles were ELN risk group and white blood cell (WBC) counts; treatment with midostaurin had no influence. Overall survival (OS) differed significantly among ELN risk groups, with estimated 5-year OS probabilities of 0.63, 0.43, and 0.33 for favorable-, intermediate-, and adverse-risk groups, respectively (P &lt; .001). A multivariate Cox model for OS using allogeneic hematopoietic cell transplantation (HCT) in first complete remission as a time-dependent variable revealed treatment with midostaurin, allogeneic HCT, ELN favorable-risk group, and lower WBC counts as significant favorable factors. In this model, there was a consistent beneficial effect of midostaurin across ELN risk groups.

Abstract Introduction: Mutations localized in the tyrosine kinase domain activation loop of FLT3 (FLT3-TKD), representing point mutations in codon D835/I836 and rarely deletions of codon I836, induce constitutive tyrosine phosphorylation and activation of the receptor tyrosine kinase similarly to FLT3 internal tandem duplication (ITD) mutations. However, the prognostic role of FLT3-TKD in AML, particularly in the presence of NPM1 mutations, is not well established. The phase 3 RATIFY trial [NCT00651261; Stone et al. N Engl J Med. 2017] showed that in combination with standard chemotherapy, midostaurin (PKC412) improved survival outcomes across all 3 FLT3 stratification subgroups (ITD high allelic ratio [≥ 0.7], ITD low allelic ratio [< 0.7], and TKD) vs placebo in patients with newly diagnosed FLT3-mutated AML. Here, we evaluated the prognostic impact of FLT3-TKD and NPM1 mutations in a post hoc analysis from the RATIFY trial. Methods: In RATIFY, newly diagnosed patients with AML 18-60 years old were randomly assigned to receive midostaurin or placebo together with standard induction and consolidation therapy followed by 12 28-day cycles of maintenance therapy with midostaurin or placebo. FLT3-TKD mutation was detected by PCR and capillary electrophoresis at 9 reference laboratories. Patients were categorized as NPM1 mutated (mut) or NPM1 wild-type (WT) using PCR. Efficacy outcomes included complete remission (CR), overall survival (OS), event-free survival (EFS) and disease-free survival (DFS). EFS and DFS analyses were performed considering CR within a 60-day window. P values presented have not been adjusted for multiplicity. Results: Of the total randomized 162 FLT3-TKD patients, 134 with available NPM1 data had consented for exploratory analysis and thus were included in this study (see Table for subgroup distribution). Overall, 47.8% of patients were male, and the median age was 49 years (95% CI, 45.5-51.1 years). The median white blood cell (WBC) count was higher in patients with NPM1-mut than in patients with NPM1-WT (34.1 vs 15.5 × 109/L, P = .0011). CR rates (during the first 60 days) were higher in patients with FLT3-TKD/NPM1-mut vs FLT3-TKD/NPM1-WT (66% vs 53%); however, this was driven by the higher rate of CR in the midostaurin arm (76% NPM1-mut vs 44% NPM1-WT) rather than the placebo arm (53% NPM1-mut vs 60% NPM1-WT). The overall CR rate (regardless of NPM1 genotype) was 64% for midostaurin and 56% for placebo in FLT3-TKD patients. The prognostic effect of the NPM1 mutation concurrent with FLT3-TKD was seen for all endpoints consistently with hazard ratios (HRs) around 0.50 or lower (Figures 1 and 2 and Table). Overall (regardless of treatment) OS, EFS, and DFS estimates at 3 years were 73% vs 52%, 48% vs 25%, and 74% vs 47%, respectively, in patients with FLT3-TKD/NPM1-mut vs FLT3-TKD/NPM1-WT. Whereas the HRs for midostaurin vs placebo were 0.73 for both OS and EFS, the impact of treatment on outcomes varied between the individual NPM1/TKD subgroups and was not consistently observed when endpoints were censored at stem cell transplant (SCT) (Table). It should be noted that the number of patients in each subgroup was small and therefore the HRs with 95% CIs should be interpreted with caution. Multivariate analyses in these FLT3-TKD patients revealed that NPM1 genotype was an independent prognostic factor for OS, EFS and DFS (2-sided P < .05), whereas study drug, age, sex, WBC at baseline and SCT (no/yes) did not reach this level of significance in the Cox model. Conclusions: This post hoc analysis of the FLT3-TKD patient subset in the RATIFY trial showed the high prognostic value of NPM1 mutational status. Whereas midostaurin showed an overall benefit in the FLT3-TKD patients for OS, EFS, CR and DFS, the impact of treatment on outcome varied between the individual NPM1 subgroups within these FLT3-TKD patients and was not consistently observed.Further analyses using additional endpoints and additional multivariate analyses are planned. Support: U10CA180821, U10CA180882, U10CA180820, U10CA180791, U10CA180888, U10CA180863, (CCSRI) #704970, U24CA196171; ClinicalTrials.gov Identifier: NCT00651261 Disclosures Voso: Celgene: Research Funding, Speakers Bureau. Larson:Ariad/Takeda: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BristolMyers Squibb: Consultancy, Research Funding. Heuser:Janssen: Consultancy; Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; StemLine Therapeutics: Consultancy; Astellas: Research Funding; BergenBio: Research Funding; Karyopharm: Research Funding; Bayer Pharma AG: Consultancy, Research Funding; Tetralogic: Research Funding; Sunesis: Research Funding; Daiichi Sankyo: Research Funding. Wei:Novartis: Honoraria, Other: Advisory committee, Research Funding, Speakers Bureau; Pfizer: Honoraria, Other: Advisory committee; Amgen: Honoraria, Other: Advisory committee, Research Funding; Abbvie: Honoraria, Other: Advisory board, Research Funding, Speakers Bureau; Servier: Consultancy, Honoraria, Other: Advisory committee, Research Funding; Celgene: Honoraria, Other: Advisory committee, Research Funding. Brandwein:Lundbeck: Consultancy; Celgene: Consultancy; Pfizer: Consultancy; Novartis: Consultancy; Boehringer Ingelheim: Consultancy, Research Funding. de Witte:Novartis: Research Funding; Amgen: Consultancy, Research Funding; Celgene: Honoraria, Research Funding. Medeiros:Celgene: Consultancy, Research Funding; Genentech: Employment. Tallman:Cellerant: Research Funding; Orsenix: Other: Advisory board; BioSight: Other: Advisory board; ADC Therapeutics: Research Funding; AROG: Research Funding; AbbVie: Research Funding; Daiichi-Sankyo: Other: Advisory board. Schlenk:Pfizer: Research Funding, Speakers Bureau. Ganser:Novartis: Membership on an entity's Board of Directors or advisory committees. Cheng:Novartis: Employment. Gathmann:Novartis: Employment. Tiecke:Novartis: Employment. Thiede:AgenDix: Other: Ownership; Novartis: Honoraria, Research Funding. Döhner:AbbVie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; Celator: Consultancy, Honoraria; Sunesis: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Sunesis: Consultancy, Honoraria, Research Funding; Astellas: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Celator: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Agios: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Jazz: Consultancy, Honoraria; Pfizer: Research Funding; Pfizer: Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Stone:Otsuka: Consultancy; Argenx: Other: Data and Safety Monitoring Board; Amgen: Consultancy; Agios: Consultancy, Research Funding; Orsenix: Consultancy; Ono: Consultancy; Novartis: Consultancy, Research Funding; Astellas: Consultancy; Arog: Consultancy, Research Funding; Merck: Consultancy; Cornerstone: Consultancy; Fujifilm: Consultancy; Jazz: Consultancy; Celgene: Consultancy, Other: Data and Safety Monitoring Board, Steering Committee; Pfizer: Consultancy; Sumitomo: Consultancy; AbbVie: Consultancy.

Background:Mutations localized in the tyrosine kinase domain activation loop of FLT3 (FLT3‐TKD), induce constitutive tyrosine phosphorylation, and activation of the receptor tyrosine kinase similarly to FLT3 internal tandem duplication (ITD) mutations. However, the prognostic role of FLT3‐TKD in AML is not well established. In the phase 3 RATIFY trial [CALGB 10603/Alliance; NCT00651261; Stone et al. N Engl J Med. 2017] midostaurin, in combination with standard chemotherapy, improved survival outcomes across all three FLT3 stratification subgroups (ITD high allelic ratio (AR) [≥ 0.7], ITD low AR [< 0.7] and TKD) vs placebo.Aims:Here, a post hoc analysis was used to establish the efficacy of midostaurin in patients (pts) in the RATIFY trial stratified by FLT3‐TKD AR and NPM1 mutational status.Methods:In RATIFY, newly diagnosed pts with AML 18–60 years (y) old were randomly assigned to receive midostaurin or placebo together with standard induction and consolidation therapy followed by twelve 28‐day cycles of maintenance therapy with midostaurin or placebo. FLT3‐TKD mutation was detected by PCR and capillary electrophoresis at nine reference laboratories. The FLT3‐TKD AR was calculated as the ratio between the areas under the curve of mutant to wild‐type allele. Pts were categorized as NPM1 mutated (mut) or NPM1 wild‐type (WT) using PCR. Efficacy outcomes included complete remission (CR), overall survival (OS), event‐free survival (EFS) and disease‐free survival (DFS). EFS and DFS analyses were performed considering CR within a 60‐day window. P values presented were not adjusted for multiplicity.Results:Of 163 FLT3‐TKD pts, 134 with available NPM1 data had consented for exploratory analysis and were included in this study. Overall, 58.9% of pts had NPM1‐mut, 47.8% were male and the median age was 49 y (range: 19.3–59.9 y). The median white blood cell (WBC) count was higher in pts with NPM1‐mut than in pts with NPM1‐WT (34.1 vs 15.5 × 109 /L). The median FLT3‐TKD AR was 0.4 (range: 0–2.7). Overall CR rate (regardless of NPM1 genotype) was 64% for midostaurin and 56% for placebo in FLT3‐TKD pts. Pts with FLT3‐TKD AR ≥ 0.4 treated with midostaurin showed a trend towards improved OS and reduced cumulative incidence of relapse (CIR) (Figure). In FLT3‐TKD AR ≥ 0.4 pts, the presence of a NPM1 mutation had a significant prognostic effect for all endpoints consistently with hazard ratios (HRs) around 0.50 or lower. Regardless of treatment OS, EFS, and DFS estimates at 3 y were 73% vs 52%, 48% vs 25%, and 74% vs 47%, respectively, for the 45 pts with FLT3‐TKD/NPM1‐mut vs the 25 pts with FLT3‐TKD/NPM1‐WT. It should be noted that the number of pts in each subgroup was small. Multivariate analyses in these FLT3‐TKD pts revealed that NPM1 genotype was an independent prognostic factor for OS and EFS (2‐sided P < 0.05), whereas study drug, age, sex, TKD AR, WBC at baseline and stem cell transplantation (SCT) (no/yes) did not reach this level of significance in the Cox model.Summary/Conclusion:This post hoc analysis of the subset of FLT3‐TKD pts in the RATIFY trial showed the high prognostic value of NPM1 mutational status. Midostaurin showed an overall benefit in the FLT3‐TKD pts for OS, EFS, CR and DFS, with the impact of treatment on outcome for NPM1‐mut pts being greatest in those with higher FLT3‐TKD allelic burden. U10CA180821, U10CA180882, U24CA196171, U10CA180861; Novartis; htt∗∗∗ps://acknowledgments.alliancefound.orgimage