Kelly A. Soltysiak

Background: There are limited data on the clonal mechanisms underlying leukemogenesis, prognostic factors, and optimal therapy for atypical chronic myeloid leukemia (aCML).Methods: The authors evaluated the clinicopathologic features, outcomes, and responses to therapy of 65 patients with aCML. The median age was 67 years (range, 46‐89 years).Results: The most frequently mutated genes included ASXL1 (83%), SRSF2 (68%), and SETBP1 (58%). Mutations in SETBP1, SRSF2, TET2, and GATA2 appeared at variant allele frequencies (VAFs) greater than 40%, whereas other RAS pathway mutations were more likely to appear at low VAFs. The acquisition of new, previously undetectable mutations at transformation was observed in 63% of the evaluable patients, with the most common involving signaling pathway mutations. Hypomethylating agents (HMAs) were associated with the highest response rates but with a short duration of response (median, 2.7 months). Therapy with ruxolitinib was not associated with clinically significant responses as a single agent or in combination with an HMA. Allogeneic stem cell transplantation was the only therapy associated with improved outcomes (hazard ratio, 0.144; 95% CI, 0.035‐0.593; P = .007). Age, platelet counts, bone marrow blast percentages, and serum lactate dehydrogenase (LDH) levels were independent predictors of survival and were integrated in a multivariable model that allowed the prediction of 1‐ and 3‐year survival.Conclusions: aCML is characterized by high frequencies of ASXL1, SRSF2, and SETBP1 mutations and is associated with a high risk of acute myeloid leukemia transformation. Response and survival outcomes with current therapies remain poor. The incorporation of age, platelet counts, bone marrow blast percentages, and LDH levels can allow survival prediction, and allogeneic stem cell transplantation should be considered for all eligible patients.

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 < .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.