Tumor protein p53 (TP53) is the most frequently mutated gene in cancer 1,2. In patients with myelodysplastic syndromes (MDS), TP53 mutations are associated with high-risk disease 3,4 , rapid transformation to acute myeloid leukemia (AML) 5 , resistance to conventional therapies 6-8 and dismal outcomes 9. Consistent with the tumor-suppressive role of TP53, patients harbor both mono-and biallelic mutations 10. However, the biological and clinical implications of TP53 allelic state have not been fully investigated in MDS or any other cancer type. We analyzed 3,324 patients with MDS for TP53 mutations and allelic imbalances and delineated two subsets of patients with distinct phenotypes and outcomes. One-third of TP53-mutated patients had monoallelic mutations whereas two-thirds had multiple hits (multi-hit) consistent with biallelic targeting. Established associations with complex karyotype, few co-occurring mutations, high-risk presentation and poor outcomes were specific to multi-hit patients only. TP53 multi-hit state predicted risk of death and leukemic transformation independently of the Revised International Prognostic Scoring System (IPSS-R) 11. Surprisingly, monoallelic patients did not differ from TP53 wild-type patients in outcomes and response to therapy. This study shows that consideration of TP53 allelic state is critical for diagnostic and prognostic precision in MDS as well as in future correlative studies of treatment response. In collaboration with the International Working Group for Prognosis in MDS (Supplementary Table 1), we assembled a cohort of 3,324 peridiagnostic and treatment-naive patients with MDS or closely related myeloid neoplasms (Extended Data Fig. 1 and Supplementary Fig. 1). Genetic profiling included conventional G-banding analyses (CBA) and tumor-only, capture-based, next-generation sequencing (NGS) of a panel of genes recurrently mutated in MDS, as well as genome-wide copy number probes. Allele-specific copy number profiles were generated from NGS data using the CNACS algorithm 7 (see Methods and Code availability). An additional 1,120 samples derived from the Japanese MDS consortium (Extended Data Fig. 2) were used as a validation cohort. To study the effect of TP53 allelic state on genome stability, clinical presentation, outcome and response to therapy, we performed a detailed characterization of alterations at the TP53 locus. First, we assessed genome-wide allelic imbalances in the cohort of 3,324 patients, to include arm-level or focal (~3 Mb) ploidy alterations and regions of copy-neutral loss of heterozygosity (cnLOH) (Extended Data Fig. 3, Supplementary Figs. 2-4 and Methods).

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Key Points A risk-adapted, MRD-driven transplant strategy is a feasible approach for the treatment of younger adults with AML. Pretransplant MRD positivity should not contraindicate delivery of an allogeneic stem cell transplant.

PURPOSE Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations ( SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia–like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.