Effective Menin inhibitor-based combinations against AML with MLL rearrangement or NPM1 mutation (NPM1c)

Treatment with Menin inhibitor (MI) disrupts the interaction between Menin and MLL1 or MLL1-fusion protein (FP), inhibits HOXA9/MEIS1, induces differentiation and loss of survival of AML harboring MLL1 re-arrangement (r) and FP, or expressing mutant (mt)-NPM1. Following MI treatment, although clinical responses are common, the majority of patients with AML with MLL1-r or mt-NPM1 succumb to their disease. Pre-clinical studies presented here demonstrate that genetic knockout or degradation of Menin or treatment with the MI SNDX-50469 reduces MLL1/MLL1-FP targets, associated with MI-induced differentiation and loss of viability. MI treatment also attenuates BCL2 and CDK6 levels. Co-treatment with SNDX-50469 and BCL2 inhibitor (venetoclax), or CDK6 inhibitor (abemaciclib) induces synergistic lethality in cell lines and patient-derived AML cells harboring MLL1-r or mtNPM1. Combined therapy with SNDX-5613 and venetoclax exerts superior in vivo efficacy in a cell line or PD AML cell xenografts harboring MLL1-r or mt-NPM1. Synergy with the MI-based combinations is preserved against MLL1-r AML cells expressing FLT3 mutation, also CRISPR-edited to introduce mtTP53. These findings highlight the promise of clinically testing these MI-based combinations against AML harboring MLL1-r or mtNPM1

Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia

Although genomic alterations drive the pathogenesis of acute myeloid leukemia (AML), traditional classifications are largely based on morphology, and prototypic genetic founder lesions define only a small proportion of AML patients. The historical subdivision of primary/de novo AML and secondary AML has shown to variably correlate with genetic patterns. The combinatorial complexity and heterogeneity of AML genomic architecture may have thus far precluded genomic-based subclassification to identify distinct molecularly defined subtypes more reflective of shared pathogenesis. We integrated cytogenetic and gene sequencing data from a multicenter cohort of 6788 AML patients that were analyzed using standard and machine learning methods to generate a novel AML molecular subclassification with biologic correlates corresponding to underlying pathogenesis. Standard supervised analyses resulted in modest cross-validation accuracy when attempting to use molecular patterns to predict traditional pathomorphologic AML classifications. We performed unsupervised analysis by applying the Bayesian latent class method that identified 4 unique genomic clusters of distinct prognoses. Invariant genomic features driving each cluster were extracted and resulted in 97% cross-validation accuracy when used for genomic subclassification. Subclasses of AML defined by molecular signatures overlapped current pathomorphologic and clinically defined AML subtypes. We internally and externally validated our results and share an open-access molecular classification scheme for AML patients. Although the heterogeneity inherent in the genomic changes across nearly 7000 AML patients was too vast for traditional prediction methods, machine learning methods allowed for the definition of novel genomic AML subclasses, indicating that traditional pathomorphologic definitions may be less reflective of overlapping pathogenesis

Genomic context and TP53 allele frequency define clinical outcomes in TP53-mutated myelodysplastic syndromes

TP53 mutations are associated with adverse outcomes and shorter response to hypomethylating agents (HMAs) in myelodysplastic syndrome (MDS). Limited data have evaluated the impact of the type, number, and patterns of TP53 mutations in response outcomes and prognosis of MDS. We evaluated the clinicopathologic characteristics, outcomes, and response to therapy of 261 patients with MDS and TP53 mutations. Median age was 68 years (range, 18-80 years). A total of 217 patients (83%) had a complex karyotype. TP53 mutations were detected at a median variant allele frequency (VAF) of 0.39 (range, 0.01-0.94). TP53 deletion was associated with lower overall response rate (ORR) (odds ratio, 0.3; P = .021), and lower TP53 VAF correlated with higher ORR to HMAs. Increase in TP53 VAF at the time of transformation was observed in 13 patients (61%), and previously undetectable mutations were observed in 15 patients (65%). TP53 VAF was associated with worse prognosis (hazard ratio, 1.02 per 1% VAF increase; 95% confidence interval, 1.01-1.03; P \u3c .001). Integration of TP53 VAF and karyotypic complexity identified prognostic subgroups within TP53-mutant MDS. We developed a multivariable model for overall survival that included the revised International Prognostic Scoring System (IPSS-R) categories and TP53 VAF. Total score for each patient was calculated as follows: VAF TP53 + 13 × IPSS-R blast score + 16 × IPSS-R cytogenetic score + 28 × IPSS-R hemoglobin score + 46 × IPSS-R platelet score. Use of this model identified 4 prognostic subgroups with median survival times of not reached, 42.2, 21.9, and 9.2 months. These data suggest that outcomes of patients with TP53-mutated MDS are heterogeneous and that transformation may be driven not only by TP53 but also by other factors

Superior efficacy of co-targeting GFI1/KDM1A and BRD4 against AML and post-MPN secondary AML cells.

There is an unmet need to overcome nongenetic therapy-resistance to improve outcomes in AML, especially post-myeloproliferative neoplasm (MPN) secondary (s) AML. Studies presented describe effects of genetic knockout, degradation or small molecule targeted-inhibition of GFI1/LSD1 on active enhancers, altering gene-expressions and inducing differentiation and lethality in AML and (MPN) sAML cells. A protein domain-focused CRISPR screen in LSD1 (KDM1A) inhibitor (i) treated AML cells, identified BRD4, MOZ, HDAC3 and DOT1L among the codependencies. Our findings demonstrate that co-targeting LSD1 and one of these co-dependencies exerted synergistic in vitro lethality in AML and post-MPN sAML cells. Co-treatment with LSD1i and the JAKi ruxolitinib was also synergistically lethal against post-MPN sAML cells. LSD1i pre-treatment induced GFI1, PU.1 and CEBPα but depleted c-Myc, overcoming nongenetic resistance to ruxolitinib, or to BETi in post-MPN sAML cells. Co-treatment with LSD1i and BETi or ruxolitinib exerted superior in vivo efficacy against post-MPN sAML cells. These findings highlight LSD1i-based combinations that merit testing for clinical efficacy, especially to overcome nongenetic therapy-resistance in AML and post-MPN sAML