A subset of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) show complex karyotype (CK), and these cases include a relatively high proportion of cases of therapy-related myeloid neoplasms and TP53 mutations. We aimed to evaluate the clinicopathologic features of outcome of 299 AML and MDS patients with CK. Mutations were present in 287 patients (96%) and the most common mutation detected was in TP53 gene (83%). A higher frequency of TP53 mutations was present in therapy-related cases (p=0.008) with a trend for worse overall survival (OS) in therapy-related patients as compared with de novo (p=0.08) and within the therapy-related group, the presence of TP53 mutation strongly predicted for worse outcome (p=0.0017). However, there was no difference in survival between CK patients based on categorization of AML versus MDS, (p=0.96) or presence of absence of circulating blasts ≥1% (p=0.52). TP53 mutated patients presented with older age (p=0.06) and lower hemoglobin (p=0.004) and marrow blast (p=0.02) compared to those with CK lacking TP53 mutation. Multivariable analysis identified presence of multi-hit TP53 mutation as strongest predictor of worse outcome, while neither a diagnosis of AML versus MDS nor therapy-relatedness independently influenced OS. Our findings suggest that among patients with MDS and AML, the presence of TP53 mutation (in particular multi-hit TP53 mutation) in the context of CK identifies a homogeneously aggressive disease, irrespective of the blast count at presentation or therapy-relatedness. The current classification of these cases into different disease categories artificially separates a single biologic disease entity.

Background Patients (pts) with myelodysplastic syndromes (MDS) have heterogeneous outcomes that can range from months for some pts to decades for others. Although several prognostic scoring systems have been developed to risk stratify MDS pts, survival varies even within discrete categories, which may lead to over- or under-treatment. Deficits in discriminatory power likely derive from analytic approaches or lack of incorporation of molecular data. Here, we developed a model that uses a machine learning approach to analyze genomic and clinical data to provide a personalized overall outcome that is patient-specific. Method Clinical and mutational data from MDS pts diagnosed according to 2008 WHO criteria were analyzed. The model was developed in a combined cohort from the Cleveland Clinic and Munich Leukemia Laboratory and validated in a separate cohort from the Moffitt Cancer Center. Next generation targeted deep sequencing of 40 gene mutations commonly found in myeloid malignancies was performed. Pts who underwent hematopoietic cell transplant (HCT) were censored at the time of transplant. A random survival forest (RSF) algorithm was used to build the model, in which clinical and molecular variables are randomly selected for inclusion in determining survival, thereby avoiding the shortcomings of traditional Cox step-wise regression in accounting for variable interactions. Survival prediction is thus specific to each pt's particular clinical and molecular characteristics. The accuracy of the proposed model, compared to other models, was assessed by concordance (c-) index. Results Of 2302 pts, 1471 were included in the training cohort and 831 in the validation cohort. In the training cohort, the median age was 71 years (range, 19-99), 230 pts (16%) progressed to AML, 156 (11%) had secondary/therapy-related MDS, and 130(9%) underwent HCT. Risk stratification by IPSS: 391 (27%) low, 626 (43%) intermediate-1, 280 (19%) intermediate-2, 104 (7%) high, 104 (7%) missing, and by IPSS-R: 749 (51%) very low/ low, 336 (23%) intermediate, 190 (13%) high, 92 (6%) very high, and 104 (7%) missing. Cytogenetic analysis by IPSS-R criteria: 65 (4%) very good, 1060 (72%) good, 193 (13%) intermediate, 60 (4%) poor, and 93 (6%) very poor. The most commonly mutated genes were: SF3B1 (26%), TET2 (25%), ASXL1 (20%), SRSF2 (15%), DNMT3A (12%), STAG2 (8%), RUNX1 (8%), and TP53 (8%). All clinical variables and mutations were included in the RSF algorithm. To identify the most important variables that impacted the outcome and the least number of variables that produced the best prediction, we conducted several feature extraction analyses which identified the following variables that impacted OS (ranked from the most important to the least): cytogenetic risk categories by IPSS-R, platelets, mutation number, hemoglobin, bone marrow blasts %, 2008 WHO diagnosis, WBC, age, ANC, absolute lymphocyte count (ALC), TP53, RUNX1, STAG2, ASXL1, absolute monocyte counts (AMC), SF3B1, SRSF2, RAD21, secondary vs. de novo MDS, NRAS, NPM1, TET2, and EZH2. The clinical and mutational variables can be entered into a web application that can run the trained model and provide OS and AML transformation probabilities at different time points that are specific for a pt, Figure 1. The C-index for the new model was .74 for OS and .81 for AML transformation. The new model outperformed IPSS (c-index .66, .73) and IPSS-R (.67, .73) for OS and AML transformation, respectively. The geno-clinical model outperformed mutations only (c-index .64, .72), mutations + cytogenetics (c-index .68, .74), and mutations + cytogenetics +age (c-index .69, .75) for OS and AML transformation, respectively. Addition of mutational variant allelic frequency did not significantly improve prediction accuracy. When applying the new model to the validation cohort, the c-index for OS and AML transformation were .80, and .78, respectively. Conclusion We built a personalized prediction model based on clinical and genomic data that outperformed IPSS and IPSS-R in predicting OS and AML transformation. The new model gives survival probabilities at different time points that are unique for a given pt. Incorporating clinical and mutational data outperformed a mutations only model even when cytogenetics and age were added. Disclosures Nazha: MEI: Consultancy. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Sallman:Celgene: Research Funding, Speakers Bureau. Roboz:Otsuka: Consultancy; Orsenix: Consultancy; Celgene Corporation: Consultancy; Daiichi Sankyo: Consultancy; Pfizer: Consultancy; Cellectis: Research Funding; Argenx: Consultancy; Roche/Genentech: Consultancy; Celltrion: Consultancy; Sandoz: Consultancy; Aphivena Therapeutics: Consultancy; Bayer: Consultancy; Pfizer: Consultancy; Aphivena Therapeutics: Consultancy; Eisai: Consultancy; Sandoz: Consultancy; Eisai: Consultancy; Roche/Genentech: Consultancy; AbbVie: Consultancy; Novartis: Consultancy; Janssen Pharmaceuticals: Consultancy; Bayer: Consultancy; Celltrion: Consultancy; Novartis: Consultancy; Janssen Pharmaceuticals: Consultancy; Astex Pharmaceuticals: Consultancy; Daiichi Sankyo: Consultancy; Celgene Corporation: Consultancy; Jazz Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Cellectis: Research Funding; Otsuka: Consultancy; Orsenix: Consultancy; Argenx: Consultancy; Astex Pharmaceuticals: Consultancy; AbbVie: Consultancy. List:Celgene: Research Funding. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Apellis Pharmaceuticals: Consultancy. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Sekeres:Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees.

The NCCN Guidelines for Myelodysplastic Syndromes (MDS) provide recommendations for the evaluation, diagnosis, and management of patients with MDS based on a review of clinical evidence that has led to important advances in treatment or has yielded new information on biologic factors that may have prognostic significance in MDS. The multidisciplinary panel of MDS experts meets on an annual basis to update the recommendations. These NCCN Guidelines Insights focus on some of the updates for the 2022 version of the NCCN Guidelines, which include treatment recommendations both for lower-risk and higher-risk MDS, emerging therapies, supportive care recommendations, and genetic familial high-risk assessment for hereditary myeloid malignancy predisposition syndromes.

The classic Philadelphia chromosome–negative myeloproliferative neoplasms (MPN) consist of myelofibrosis, polycythemia vera, and essential thrombocythemia and are a heterogeneous group of clonal blood disorders characterized by an overproduction of blood cells. The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for MPN were developed as a result of meetings convened by a multidisciplinary panel with expertise in MPN, with the goal of providing recommendations for the management of MPN in adults. The Guidelines include recommendations for the diagnostic workup, risk stratification, treatment, and supportive care strategies for the management of myelofibrosis, polycythemia vera, and essential thrombocythemia. Assessment of symptoms at baseline and monitoring of symptom status during the course of treatment is recommended for all patients. This article focuses on the recommendations as outlined in the NCCN Guidelines for the diagnosis of MPN and the risk stratification, management, and supportive care relevant to MF.

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Background:Patients (pts) with lower risk myelodysplastic syndromes (LR-MDS) and anemia experience severe fatigue that negatively impacts overall functioning and daily life. Fatigue can also be commonly reported with treatments for LR-MDS, the goals of which are to minimize transfusions and improve pt-reported outcomes (PRO). In the IMerge Phase 3 (Ph3) study (NCT02598661), imetelstat, a first-in-class telomerase inhibitor, demonstrated statistically significant and meaningfully improved 8-and 24-wk transfusion independence (TI) rates, durable TI, and increased hemoglobin levels compared with placebo in heavily red blood cell (RBC) transfusion-dependent (TD) non-del(5q) LR-MDS pts who were ineligible/relapsed/refractory to ESA and naive to lenalidomide/hypomethylating agents. We also evaluated pt reported fatigue (rate of deterioration/improvement) during treatment with imetelstat or placebo.

Mutations in IDH genes occur frequently in acute myeloid leukemia (AML) and other human cancers to generate the oncometabolite R-2HG. Allosteric inhibition of mutant IDH suppresses R-2HG production in a subset of AML patients; however, acquired resistance emerges as a new challenge and the underlying mechanisms remain incompletely understood. Here we establish isogenic leukemia cells containing common IDH oncogenic mutations by CRISPR base editing. By mutational scanning of IDH single-amino acid variants in base-edited cells, we describe a repertoire of IDH second-site mutations responsible for therapy resistance through disabling uncompetitive enzyme inhibition. Recurrent mutations at NADPH binding sites within IDH heterodimers act in cis or trans to prevent the formation of stable enzyme-inhibitor complexes, restore R-2HG production in the presence of inhibitors, and drive therapy resistance in IDH-mutant AML cells and patients. We therefore uncover a new class of pathogenic mutations and mechanisms for acquired resistance to targeted cancer therapies.

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