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To determine the survival benefit of allogeneic hematopoietic cell transplantation (allo-HCT) in chronic myelomonocytic leukemias (CMML), we assembled a retrospective cohort of CMML patients 18-70 years old diagnosed between 2000 and 2014 from an international CMML dataset (n = 730) and the EBMT registry (n = 384). The prognostic impact of allo-HCT was analyzed through univariable and multivariable time-dependent models and with a multistate model, accounting for age, sex, CMML prognostic scoring system (low or intermediate-1 grouped as lower-risk, intermediate-2 or high as higher-risk) at diagnosis, and AML transformation. In univariable analysis, lower-risk CMMLs had a 5-year overall survival (OS) of 20% with allo-HCT vs 42% without allo-HCT (P < .001). In higher-risk patients, 5-year OS was 27% with allo-HCT vs 15% without allo-HCT (P = .13). With multistate models, performing allo-HCT before AML transformation reduced OS in patients with lower-risk CMML, and a survival benefit was predicted for men with higher-risk CMML. In a multivariable analysis of lower-risk patients, performing allo-HCT before transformation to AML significantly increased the risk of death within 2 years of transplantation (hazard ratio [HR], 3.19; P < .001), with no significant change in long-term survival beyond this time point (HR, 0.98; P = .92). In higher-risk patients, allo-HCT significantly increased the risk of death in the first 2 years after transplant (HR 1.46; P = .01) but not beyond (HR, 0.60; P = .09). Performing allo-HCT before AML transformation decreases life expectancy in lower-risk patients but may be considered in higher-risk patients.

Hypomethylating agents (HMAs) are the standard of care for higher risk MDS (HR-MDS) patients. The current dogma is to begin HMA therapy in all HR-MDS patients at the time of initial diagnosis. We investigated the impact of the timing of HMA initiation among HR-MDS patients presenting with adequate blood counts to discern the possible benefit of early treatment based solely on disease risk. We identified 320 HR-MDS patients with adequate hematopoiesis who were treated with HMA. The complete response rates were 21%, 26%, 23%, and 7% respectively for patients treated within 30, 31-60, 61-90, and more than 90 days from time of diagnosis (p=.046). The median OS from the date of diagnosis was 641, 550, 979, and 806 days, respectively (p=.2). A delay in initiating HMA therapy in HR-MDS patients with adequate blood counts is not associated with worsened outcomes.

Early recognition of patients who may be at risk of developing acute symptomatic seizures would be useful. We aimed to determine whether continuous electroencephalography (cEEG) data using machine learning techniques such as neural networks and decision trees could predict seizure occurrence in hospitalized patients.This was a single center retrospective cohort analysis of cEEG data in patients aged 18-90 years who were admitted and underwent cEEG monitoring between 2010 and 2019 limited to 72 h excluding those who were seizing at the onset of recording. A total of 41,491 patients were reviewed; of these, 3874 were used to develop the static model and 1687 to develop the dynamic model (half with seizure and half without seizure in each cohort). Of these, 80% were randomly selected as derivation cohorts for each model and 20% were randomly selected as validation cohorts. Dynamic and static machine learning models (long short term memory (LSTM) and Extreme Gradient Boosting algorithm (XGBoost)) based on day-to-day dynamic EEG changes and binary static EEG features over the prior 72 h or until seizure, which ever was earlier, were used.The static model was able to predict seizure occurrence based on cEEG data with sensitivity and specificity of 0.81 and 0.59, respectively, with an AUC of 0.70. The dynamic model was able to predict seizure occurrence with sensitivity and specificity of 0.72 and 0.80, respectively, and AUC of 0.81.Machine learning models could be applied to cEEG data to predict seizure occurrence based on available cEEG data. Dynamic day-to-day EEG data are more useful in predicting seizures than binary static EEG data. These models could potentially be used to determine the need for ongoing cEEG monitoring and to prioritize resources.

Treatment decisions in primary myelofibrosis (PMF) are guided by numerous prognostic systems. Patient-specific comorbidities have influence on treatment-related survival and are considered in clinical contexts but have not been routinely incorporated into current prognostic models. We hypothesized that patient-specific comorbidities would inform prognosis and could be incorporated into a quantitative score. All patients with PMF or secondary myelofibrosis with available DNA and comprehensive electronic health record (EHR) data treated at Vanderbilt University Medical Center between 1995 and 2016 were identified within Vanderbilt’s Synthetic Derivative and BioVU Biobank. We recapitulated established PMF risk scores (eg, Dynamic International Prognostic Scoring System [DIPSS], DIPSS plus, Genetics-Based Prognostic Scoring System, Mutation-Enhanced International Prognostic Scoring System 70+) and comorbidities through EHR chart extraction and next-generation sequencing on biobanked peripheral blood DNA. The impact of comorbidities was assessed via DIPSS-adjusted overall survival using Bonferroni correction. Comorbidities associated with inferior survival include renal failure/dysfunction (hazard ratio [HR], 4.3; 95% confidence interval [95% CI], 2.1-8.9; P = .0001), intracranial hemorrhage (HR, 28.7; 95% CI, 7.0-116.8; P = 2.83e-06), invasive fungal infection (HR, 41.2; 95% CI, 7.2-235.2; P = 2.90e-05), and chronic encephalopathy (HR, 15.1; 95% CI, 3.8-59.4; P = .0001). The extended DIPSS model including all 4 significant comorbidities showed a significantly higher discriminating power (C-index 0.81; 95% CI, 0.78-0.84) than the original DIPSS model (C-index 0.73; 95% CI, 0.70-0.77). In summary, we repurposed an institutional biobank to identify and risk-classify an uncommon hematologic malignancy by established (eg, DIPSS) and other clinical and pathologic factors (eg, comorbidities) in an unbiased fashion. The inclusion of comorbidities into risk evaluation may augment prognostic capability of future genetics-based scoring systems.

Importance Matched sibling donors (MSDs) are preferred for allogeneic hematopoietic cell transplantation (allo-HCT) in myelodysplastic syndrome even if they are older. However, whether older MSDs or younger human leukocyte antigen-matched unrelated donors (MUDs) are associated with better outcomes remains unclear. Objective To investigate whether allo-HCT for myelodysplastic syndrome using younger MUDs would be associated with improved disease-free survival and less relapse compared with older MSDs. Design, Setting, and Participants This retrospective cohort study assessed data reported to the Center for International Blood and Marrow Transplant Research database from 1761 adults 50 years or older with myelodysplastic syndrome who underwent allo-HCT using an older MSD or younger MUD between January 1, 2011, and December 31, 2017, with a median follow-up of 48 months. Data analysis was performed from January 8, 2019, to December 30, 2020. Interventions/Exposures Allo-HCT from an older MSD (donor age ���50 years) or a younger MUD (donor age ���35 years). Main Outcomes and Measures The primary outcome was disease-free survival. Secondary outcomes were overall survival, relapse, nonrelapse mortality, acute graft-vs-host disease (GVHD), chronic GVHD, and GVHD-free relapse-free survival. Results Of 1761 patients (1162 [66%] male; median [range] age, 64.9 [50.2-77.6] years in the MSD cohort and 66.5 [50.4-80.9] years in MUD cohort), 646 underwent allo-HCT with an older MSD and 1115 with a younger MUD. In multivariable analysis, the rate of disease-free survival was significantly lower in allo-HCTs with older MSDs compared with younger MUDs (hazard ratio [HR], 1.17; 95% CI, 1.02-1.34; P���=���.02), whereas the difference in overall survival rate of allo-HCT with younger MUDs vs older MSDs was not statistically significant (HR, 1.13; 95% CI, 0.98-1.29; P���=���.07). Allo-HCT with older MSDs was associated with significantly higher relapse (HR, 1.62; 95% CI, 1.32-1.97; P���

Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features, but the complexity of morphologic and genetic changes makes clear associations challenging. This article interrogates novel clinical subtypes of MDS using a machine-learning technique devised to identify patterns of cooccurrence among morphologic features and genomic events. We sequenced 1079 MDS patients and analyzed bone marrow morphologic alterations and other clinical features. A total of 1929 somatic mutations were identified. Five distinct morphologic profiles with unique clinical characteristics were defined. Seventy-seven percent of higher-risk patients clustered in profile 1. All lower-risk (LR) patients clustered into the remaining 4 profiles: profile 2 was characterized by pancytopenia, profile 3 by monocytosis, profile 4 by elevated megakaryocytes, and profile 5 by erythroid dysplasia. These profiles could also separate patients with different prognoses. LR MDS patients were classified into 8 genetic signatures (eg, signature A had TET2 mutations, signature B had both TET2 and SRSF2 mutations, and signature G had SF3B1 mutations), demonstrating association with specific morphologic profiles. Six morphologic profiles/genetic signature associations were confirmed in a separate analysis of an independent cohort. Our study demonstrates that nonrandom or even pathognomonic relationships between morphology and genotype to define clinical features can be identified. This is the first comprehensive implementation of machine-learning algorithms to elucidate potential intrinsic interdependencies among genetic lesions, morphologies, and clinical prognostic in attributes of MDS.

Here we investigate single codon SF3B1 mutations (K700E, K666, H662) to identify whether discrete amino acid substitutions may influence clinical phenotypes, survival and response to treatments in ...

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.

FF-10501-01 potently inhibits inosine-5-monophosphate dehydrogenase (IMPDH), inducing anti-proliferative and pro-apoptotic effects in acute myeloid leukemia (AML) human cell lines resistant to hypomethylating agents. In this Phase 1/2a study, Phase 1 enrolled 38 patients with relapsed/refractory AML (

Hospital systems, payers, and regulators have focused on reducing length of stay (LOS) and early readmission, with uncertain benefit. Interpretable machine learning (ML) may assist in transparently identifying the risk of important outcomes. We conducted a retrospective cohort study of hospitalizations at a tertiary academic medical center and its branches from January 2011 to May 2018. A consecutive sample of all hospitalizations in the study period were included. Algorithms were trained on medical, sociodemographic, and institutional variables to predict readmission, length of stay (LOS), and death within 48–72 h. Prediction performance was measured by area under the receiver operator characteristic curve (AUC), Brier score loss (BSL), which measures how well predicted probability matches observed probability, and other metrics. Interpretations were generated using multiple feature extraction algorithms. The study cohort included 1,485,880 hospitalizations for 708,089 unique patients (median age of 59 years, first and third quartiles (QI) [39, 73]; 55.6% female; 71% white). There were 211,022 30-day readmissions for an overall readmission rate of 14% (for patients ≥65 years: 16%). Median LOS, including observation and labor and delivery patients, was 2.94 days (QI [1.67, 5.34]), or, if these patients are excluded, 3.71 days (QI [2.15, 6.51]). Predictive performance was as follows: 30-day readmission (AUC 0.76/BSL 0.11); LOS > 5 days (AUC 0.84/BSL 0.15); death within 48–72 h (AUC 0.91/BSL 0.001). Explanatory diagrams showed factors that impacted each prediction.

Artificial intelligence (AI), and in particular its subcategory machine learning, is finding an increasing number of applications in medicine, driven in large part by an abundance of data and powerful, accessible tools that have made AI accessible to a larger circle of investigators. AI has been employed in the analysis of hematopathological, radiographic, laboratory, genomic, pharmacological, and chemical data to better inform diagnosis, prognosis, treatment planning, and foundational knowledge related to benign and malignant hematology. As more widespread implementation of clinical AI nears, attention has also turned to the effects this will have on other areas in medicine. AI offers many promising tools to clinicians broadly, and specifically in the practice of hematology. Ongoing research into its various applications will likely result in an increasing utilization of AI by a broader swath of clinicians.

Myelodysplastic syndromes (MDSs) are potentially devastating monoclonal deviations of hematopoiesis that lead to bone marrow dysplasia and variable cytopenias. Predicting severity of disease progression and likelihood to undergo acute myeloid leukemia transformation is the basis of treatment strategy. Some patients belong to a low-risk cohort best managed with conservative supportive care, whereas others are included in a high-risk cohort that requires decisive therapy with hematopoietic cell transplantation or hypomethylating agent administration. Risk scoring systems for MDS prognostication were traditionally based on karyotype characteristics and clinical factors readily available from chart review, and validation was typically conducted on de novo MDS patients. However, retrospective analysis found a large subset of patients incorrectly risk-stratified. In this review, the most commonly used scoring systems are evaluated, and pitfalls therein are identified. Emerging technologies such as personal genomics and machine learning are then explored for efficacy in MDS risk modeling. Barriers to clinical adoption of artificial intelligence-derived models are discussed, with focus on approaches meant to increase model interpretability and clinical relevance. Finally, a guiding set of recommendations is proposed for best designing an accurate and universally applicable prognostic model for MDS, which is supported by more than 20 years of observation of traditional scoring system performance, as well as modern efforts in creating hybrid genomic-clinical scoring systems.

The term machine learning (ML) is emerging more often in the medical literature. There are successful clinical applications of ML with the specialties of ophthalmology and radiology leading the way. For example, in ophthalmology the diagnosis of diabetic retinopathy and retinopathy of prematurity,1 and in radiology the diagnosis of stroke or cancers from digital images is promising.2 This success is expected to expand into other medical disciplines including general paediatrics and its subspecialties. Therefore, healthcare practitioners and researchers are most likely to benefit from getting familiar with ML terminology in order to stay up to date and better understand the ML research methodology and its applications in medicine. This brief review is intended to be an introduction to ML and its associated terminology, and to review selected studies with ML models from the paediatric literature. Predictive models are mathematical functions that take input variable(s), process the input variable(s) and produce an output. The mathematical definition of processing the input variable(s) to produce an output is an algorithm. Describing the relation between the variables in a dataset can be feasible by linear equations in some datasets. For instance, linear regression models aim to fit a linear line that relates input variable(s) to output variable(s). However, when there are a large number of variables, or their relation to the output variable is too complex, or non-linear to be described by linear lines, or even there is no obvious output variable to be predicted in the dataset, then the classic linear regression models fail. In such cases, a different approach is needed. This alternative strategy is called ML. In ML, algorithms are developed to discover unknown complex relations between …

PURPOSE Patients with myelodysplastic syndromes (MDS) have a survival that can range from months to decades. Prognostic systems that incorporate advanced analytics of clinical, pathologic, and molecular data have the potential to more accurately and dynamically predict survival in patients receiving various therapies. METHODS A total of 1,471 MDS patients with comprehensively annotated clinical and molecular data were included in a training cohort and analyzed using machine learning techniques. A random survival algorithm was used to build a prognostic model, which was then validated in external cohorts. The accuracy of the proposed model, compared with other established models, was assessed using a concordance (c)index. RESULTS The median age for the training cohort was 71 years. Commonly mutated genes included SF3B1, TET2, and ASXL1. The algorithm identified chromosomal karyotype, platelet, hemoglobin levels, bone marrow blast percentage, age, other clinical variables, seven discrete gene mutations, and mutation number as having prognostic impact on overall and leukemia-free survivals. The model was validated in an independent external cohort of 465 patients, a cohort of patients with MDS treated in a prospective clinical trial, a cohort of patients with paired samples at different time points during the disease course, and a cohort of patients who underwent hematopoietic stem-cell transplantation. CONCLUSION A personalized prediction model on the basis of clinical and genomic data outperformed established prognostic models in MDS. The new model was dynamic, predicting survival and leukemia transformation probabilities at different time points that are unique for a given patient, and can upstage and downstage patients into more appropriate risk categories.

Natural killer (NK) cells are regulated by killer immunoglobulin-like receptor (KIR) interactions with human leukocyte antigen class I ligands. Various models of NK cell alloreactivity have been associated with outcomes after allogeneic hematopoietic cell transplant (alloHCT), but results have varied widely. We hypothesized that somatic mutations in acute myeloid leukemia (AML) in the context of KIR profiles may further refine their association with transplant outcomes. In this single-center, retrospective, observational study, 81 AML patients who underwent matched-related donor alloHCT were included. Post-HCT outcomes were assessed based on mutational status and KIR profiles with the Kaplan–Meier method and log-rank test. On multivariable analysis those with any somatic mutations and C1/C2 heterozygosity had less acute graft-versus-host disease (GvHD) (hazard ratio [HR], 0.32; 95% confidence interval [CI], 0.14-0.75; P = .009), more relapse (HR, 3.02; 95% CI, 1.30-7.01; P = .010), inferior relapse-free survival (RFS; HR, 2.22; 95% CI, 1.17-4.20; P = .014), and overall survival (OS; HR, 2.21; 95% CI, 1.17-4.20; P = .015), whereas those with a missing KIR ligand had superior RFS (HR, 0.53; 95% CI, 0.30-0.94; P = .031). The presence of a somatic mutation and donor haplotype A was also associated with less acute GvHD (HR, 0.38; 95% CI, 0.16-0.92; P = .032), more relapse (HR, 2.72; 95% CI, 1.13-6.52; P = .025), inferior RFS (HR, 2.11; 95% CI, 1.07-4.14; P = .030), and OS (HR, 2.20; 95% CI, 1.11-4.38; P = .024). Enhanced NK cell alloreactivity from more KIR activating signals (donor B haplotype) and fewer inhibitory signals (recipient missing KIR ligand or C1 or C2 homozygosity) may help mitigate the adverse prognosis associated with some AML somatic mutations. These results may have implications for improving patient risk stratification prior to transplant and optimizing donor selection.

Background While histo- and cytomorphological examinations are central to the diagnosis of myelodysplastic syndromes (MDS), significant inter-observer variability exists. The diagnosis can be challenging in pancytopenic patients (pts) without evidence of dysplasia and is contingent on observer expertise. We developed and externally validated a geno-clinical model that uses mutational data and peripheral blood counts/clinical variables to distinguish MDS from other myeloid malignancies. Methods Clinical and genomic data, including commercially available next-generation sequencing panels, were obtained for patients (pts) treated at the Cleveland Clinic (CC; 652 pts), Munich Leukemia Laboratory (MLL; 1509 pts), and the University of Pavia in Italy (UP, 536 pts). All patients had carried a diagnosis of MDS, chronic myelomonocytic leukemia (CMML), MDS/myeloproliferative neoplasm overlap (MDS/MPN), myeloproliferative neoplasm (MPN; either polycythemia vera, essential thrombocythemia, or myelofibrosis), clonal cytopenia of undetermined significance (CCUS), or idiopathic cytopenia of undetermined significance (ICUS). All diagnoses were established with bone marrow aspiration and according to World Health Organization 2017 criteria. The training cohort included data from CC and UP and randomly divided into learner (80%) and test (20%) cohorts. The final model was independently validated in the MLL cohort. A machine learning algorithm was used to build the model; multiple extraction algorithms were used to extract genomic/clinical variables on both the cohort and individual levels. Performance was evaluated according to the area under the curve of the receiver operating characteristic (ROC-AUC) and accuracy matrices. Results Among the 2697 pts included from all sites, the median age was 70 years [36 - 86]. Median hemoglobin (Hb) was 10.4g/dl [6.9 - 15.7], median platelet count (PLT) was 132 k/dL [14 - 722], median WBC count was 5.3 k/dL [1.4 - 49.9], median ANC was 2.8 k/dL [0.3 - 27.7], median monocyte count was 0.3 k/dL [0 - 9.9], and median lymphocyte count (ALC) was 1.1 k/dL [0.1 - 5.4], and median peripheral blast percentage 0% [0 - 8]. The most commonly mutated genes in all patients were (list top 5 genes) and among pts with MDS were SF3B1 (27%), TET2 (25%), ASXL1 (19%), SRSF2 (16%), and DNMT3A (11%); among patients with MDS-MPN/CMML, the most commonly mutated genes were MDS-MPN/CMML (TET2 46%, ASXL1 34%, SRSF2 29%, RUNX1 13%, CBL 12%) ; among patients with MPNs, the most commonly mutated genes were (JAK2 64%, ASXL1 27%, TET2 14%, DNMT3A 8%, U2AF1 7%); among patients with CCUS the most commonly mutated genes were (TET2 41%, DNMT3A 27%, ASXL1 19%, SRSF2 17%, ZRSR2 10%). The most important features for model predictions (ranked from the most to the least important) included: number of mutations detected/sample, peripheral blast percentage, AMC, JAK2 status, Hb, basophil count, age, eosinophil count, ALC, WBC, EZH2 mutation status, ANC, mutation status of KRAS and SF3B1, platelets, and gender. The final model achieved an average AUROC of 0.95 (95% CI 0.93-0.96) when applied to the test cohort and 0.93 (95% CI 0.91 - 0.94) when it was applied to the MLL cohort. The model also provides individual-level explanations for predictions, providing top differential diagnoses and individual-level explanations of how features influence a putative diagnosis (Figure 1b). Conclusions We developed and externally validated a highly accurate and interpretable model that can distinguish MDS from other myeloid malignancies using clinical and mutational data from a large international cohort. The model can provide personalized interpretations of its outcome and can aid physicians and hematopathologists in recognizing MDS with high accuracy when encountering pts with pancytopenia and with a suspected diagnosis of MDS. Disclosures Sekeres: Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mukherjee:Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Honoraria; Bristol Myers Squib: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Aplastic Anemia and MDS International Foundation: Honoraria; Celgene/Acceleron: Membership on an entity's Board of Directors or advisory committees; EUSA Pharma: Consultancy. Gerds:Sierra Oncology: Research Funding; Imago Biosciences: Research Funding; Apexx Oncology: Consultancy; Celgene: Consultancy, Research Funding; Incyte Corporation: Consultancy, Research Funding; Roche/Genentech: Research Funding; CTI Biopharma: Consultancy, Research Funding; AstraZeneca/MedImmune: Consultancy; Gilead Sciences: Research Funding; Pfizer: Research Funding. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria. Nazha:Jazz: Research Funding; Incyte: Speakers Bureau; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee.

Myelodysplastic syndromes (MDS) arise in older adults through stepwise acquisitions of multiple somatic mutations. Here, analyzing 1809 MDS patients, we infer clonal architecture by using a stringent, the single-cell sequencing validated PyClone bioanalytic pipeline, and assess the position of the mutations within the clonal architecture. All 3,971 mutations are grouped based on their rank in the deduced clonal hierarchy (dominant and secondary). We evaluated how they affect the resultant morphology, progression, survival and response to therapies. Mutations of SF3B1, U2AF1, and TP53 are more likely to be dominant, those of ASXL1, CBL, and KRAS are secondary. Among distinct combinations of dominant/secondary mutations we identified 37 significant relationships, of which 12 affect clinical phenotypes, 5 cooperatively associate with poor prognosis. They also predict response to hypomethylating therapies. The clonal hierarchy has distinct ranking and the resultant invariant combinations of dominant/secondary mutations yield novel insights into the specific clinical phenotype of MDS., Stepwise acquisition of mutations gives rise to myelodysplastic syndrome (MDS) in older adults. Here, the authors infer the clonal hierarchy of 1809 MDS patients, revealing insights into the evolution of dominant/secondary mutations and how these impact clinical phenotypes like leukemic progression and therapy response.

Somatic mutations of the CUT-like homeobox 1 ( CUX1 ) gene ( CUX1 MT ) can be found in myeloid neoplasms (MNs), in particular, in myelodysplastic syndromes (MDSs). The CUX1 locus is also deleted in 3 of 4 MN cases with −7/del(7q). A cohort of 1480 MN patients was used to characterize clinical features and clonal hierarchy associated with CUX1 MT and CUX1 deletions ( CUX1 DEL ) and to analyze their functional consequences in vitro. CUX1 MT were present in 4% of chronic MNs. CUX1 DEL were preferentially found in advanced cases (6%). Most MDS and acute myeloid leukemia (AML) patients with −7/del(7q) and up to 15% of MDS patients and 5% of AML patients diploid for the CUX1 locus exhibited downmodulated CUX1 expression. In 75% of mutant cases, CUX1 MT were heterozygous, whereas microdeletions and homozygous and compound-heterozygous mutations were less common. CUX MT/DEL were associated with worse survival compared with CUX1 WT . Within the clonal hierarchy, 1 of 3 CUX1 MT served as founder events often followed by secondary BCOR and ASXL1 subclonal hits, whereas TET2 was the most common ancestral lesion, followed by subclonal CUX1 MT . Comet assay of patients’ bone marrow progenitor cells and leukemic cell lines performed in various experimental conditions revealed that frameshift mutations, hemizygous deletions, or experimental CUX1 knockdown decrease the repair of oxidized bases. These functional findings may explain why samples with either CUX1 MT or low CUX1 expression coincided with significantly higher numbers of somatic hits by whole-exome sequencing. Our findings implicate the DNA repair dysfunction resulting from CUX1 lesions in the pathogenesis of MNs, in which they lead to a mutator phenotype.

Purpose: The prognosis of patients with relapsed/refractory (R/R) acute myeloid leukemia (AML) remains poor, and novel therapies are needed. The proteasome pathway represents a potential therapeutic target. A phase I trial of the second-generation proteasome inhibitor ixazomib in combination with MEC (mitoxantrone, etoposide, and cytarabine) was conducted in patients with R/R AML. Patients and Methods: Dose escalation of ixazomib was performed using a standard 3 × 3 design. Gene-expression profiling was performed on pretreatment and posttreatment bone marrow or blood samples. Results: The maximum tolerated dose of ixazomib in combination with MEC was 1.0 mg. The dose limiting toxicity was thrombocytopenia. Despite a poor risk population, the response rate [complete remission (CR)/CR with incomplete count recovery (CRi)] was encouraging at 53%. Gene-expression analysis identified two genes, IFI30 (γ-interferon inducible lysosomal thiol reductase) and RORα (retinoic orphan receptor A), which were significantly differentially expressed between responding and resistant patients and could classify CR. Conclusions: These results are encouraging, but a randomized trial is needed to address whether the addition of ixazomib to MEC improves outcome. Gene-expression profiling also helped us identify predictors of response and potentially novel therapeutic targets.

Somatic TET2 mutations ( TET2 MT ) are frequent in myeloid neoplasia (MN), particularly chronic myelomonocytic leukemia (CMML). TET2 MT includes mostly loss-of-function/hypomorphic hits. Impaired TET2 activity skews differentiation of hematopoietic stem cells toward proliferating myeloid precursors. This study was prompted by the observation of frequent biallelic TET2 gene inactivations ( biTET2 i ) in CMML. We speculated that biTET2 i might be associated with distinct clinicohematological features. We analyzed TET2 MT in 1045 patients with MN. Of 82 biTET2 i cases, 66 were biTET2 MT , 13 were hemizygous TET2 MT , and 3 were homozygous TET2 MT (uniparental disomy); the remaining patients (denoted biTET2 − hereafter) were either monoallelic TET2 MT (n = 96) or wild-type TET2 (n = 823). Truncation mutations were found in 83% of biTET2 i vs 65% of biTET2 − cases ( P = .02). TET2 hits were founder lesions in 72% of biTET2 i vs 38% of biTET2 − cases ( P biTET2 i , significantly concurrent hits included SRSF2 MT (33%; P KRAS / NRAS MT (16%; P = .03) as compared with biTET2 − . When the first TET2 hit was ancestral in biTET2 i , the most common subsequent hits affected a second TET2 MT , followed by SRSF2 MT , ASXL1 MT , RAS MT , and DNMT3A MT . BiTET2 i patients without any monocytosis showed an absence of S RSF2 MT . BiTET2 i patients were older and had monocytosis, CMML, normal karyotypes, and lower-risk disease compared with biTET2 − patients. Hence, while a second TET2 hit occurred frequently, biTET2 i did not portend faster progression but rather determined monocytic differentiation, consistent with its prevalence in CMML. Additionally, biTET2 i showed lower odds of cytopenias and marrow blasts (≥5%) and higher odds of myeloid dysplasia and marrow hypercellularity. Thus, biTET2 i might represent an auxiliary assessment tool in MN.

Next generation sequencing (NGS) has become an important tool to inform disease risk for myeloid malignancies, however data remains conflicting regarding the significance of individual mutations. We performed targeted NGS on 112 patients with AML, and 80 with MDS, who underwent allogeneic hematopoietic cell transplantation. The most common mutations in AML were TET2 (14.7%), FLT3 (12.9%), DNMT3A (12.1%), and RUNX1 (7.8%). Complex cytogenetics (HR 2.82, P = .017) and disease status (CR) (HR 2.58, P .001) was significantly associated with worse RFS. No individual mutation, nor variant allelic frequency (VAF), was found to be prognostic, except mutations in the RNA-splicing pathway, (HR 2.09, P = .023). Within the MDS cohort, most common mutations were ASXL1 (12.5%), SRSF2 (12%), TET2 (8.8%), and TP53 (8.8%). Complex cytogenetics (HR 5.01, P .001), and presence of U2AF1 (HR 3.60, P = .019), was associated with worse RFS. Analysis of VAF found that TP53 and EZH2 mutations with allelic frequencies of33% were associated with poor RFS (HR 3.57, P = .017; and HR 6.57, P = .003; respectively). Molecular profiling is increasingly important in the care of patients with AML and MDS. Further studies are needed to understand the molecular complexities, including the significance of clonal burden, to better inform care decisions.