Role of allogeneic transplantation in chronic myelomonocytic leukemia: an international collaborative analysis.

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.

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.

Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

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.

The ABNL-MARRO 001 study: a phase 1-2 study of randomly allocated active myeloid target compound combinations in MDS/MPN overlap syndromes.

BACKGROUND: Myelodysplastic/myeloproliferative neoplasms (MDS/MPN) comprise several rare hematologic malignancies with shared concomitant dysplastic and proliferative clinicopathologic features of bone marrow failure and propensity of acute leukemic transformation, and have significant impact on patient quality of life. The only approved disease-modifying therapies for any of the MDS/MPN are DNA methyltransferase inhibitors (DNMTi) for patients with dysplastic CMML, and still, outcomes are generally poor, making this an important area of unmet clinical need. Due to both the rarity and the heterogeneous nature of MDS/MPN, they have been challenging to study in dedicated prospective studies. Thus, refining first-line treatment strategies has been difficult, and optimal salvage treatments following DNMTi failure have also not been rigorously studied. ABNL-MARRO (A Basket study of Novel therapy for untreated MDS/MPN and Relapsed/Refractory Overlap Syndromes) is an international cooperation that leverages the expertise of the MDS/MPN International Working Group (IWG) and provides the framework for collaborative studies to advance treatment of MDS/MPN and to explore clinical and pathologic markers of disease severity, prognosis, and treatment response. METHODS: ABNL MARRO 001 (AM-001) is an open label, randomly allocated phase 1/2 study that will test novel treatment combinations in MDS/MPNs, beginning with the novel targeted agent itacitinib, a selective JAK1 inhibitor, combined with ASTX727, a fixed dose oral combination of the DNMTi decitabine and the cytidine deaminase inhibitor cedazuridine to improve decitabine bioavailability. DISCUSSION: Beyond the primary objectives of the study to evaluate the safety and efficacy of novel treatment combinations in MDS/MPN, the study will (i) Establish the ABNL MARRO infrastructure for future prospective studies, (ii) Forge innovative scientific research that will improve our understanding of pathogenetic mechanisms of disease, and (iii) Inform the clinical application of diagnostic criteria, risk stratification and prognostication tools, as well as response assessments in this heterogeneous patient population. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov on August 19, 2019 (Registration No. NCT04061421).

Personalized model to predict seizures based on dynamic and static continuous EEG monitoring data.

BACKGROUND/OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

A Personalized Prediction Model for Outcomes after Allogeneic Hematopoietic Cell Transplant in Patients with Myelodysplastic Syndromes.

Allogeneic hematopoietic stem cell transplantation (HCT) remains the only potentially curative option for myelodysplastic syndromes (MDS). Mortality after HCT is high, with deaths related to relapse or transplant-related complications. Thus, identifying patients who may or may not benefit from HCT is clinically important. We identified 1514 patients with MDS enrolled in the Center for International Blood and Marrow Transplant Research Registry and had their peripheral blood samples sequenced for the presence of 129 commonly mutated genes in myeloid malignancies. A random survival forest algorithm was used to build the model, and the accuracy of the proposed model was assessed by concordance index. The median age of the entire cohort was 59 years. The most commonly mutated genes were ASXL1(20%), TP53 (19%), DNMT3A (15%), and TET2 (12%). The algorithm identified the following variables prior to HCT that impacted overall survival: age, TP53 mutations, absolute neutrophils count, cytogenetics per International Prognostic Scoring System-Revised, Karnofsky performance status, conditioning regimen, donor age, WBC count, hemoglobin, diagnosis of therapy-related MDS, peripheral blast percentage, mutations in RAS pathway, JAK2 mutation, number of mutations/sample, ZRSR2, and CUX1 mutations. Different variables impacted the risk of relapse post-transplant. The new model can provide survival probability at different time points that are specific (personalized) for a given patient based on the clinical and mutational variables that are listed above. The outcomes' probability at different time points may aid physicians and patients in their decision regarding HCT.

Large granular lymphocytic leukaemia after solid organ and haematopoietic stem cell transplantation.

T-cell large granular lymphocytic leukaemia (T-LGLL) is a chronic clonal lymphoproliferative disorder of cytotoxic T lymphocytes which commonly occurs in older patients and is often associated with autoimmune diseases. Among 246 patients with T-LGLL seen at our institution over the last 10 years, we encountered 15 cases following solid organ or haematopoietic stem cell transplantation. Here, we studied the clinical characterization of these cases and compared them to de novo T-LGLL. This experience represented a clear picture of the intricate nature of the disease manifestation and the complexities of several immune mechanisms triggering the clonal expansion.

Precision Medicine in Myelodysplastic Syndromes and Leukemias: Lessons from Sequential Mutations.

Precision medicine can be simply defined as the identification of personalized treatment that matches patient-specific clinical and genomic characteristics. Since the completion of the Human Genome Project in 2003, significant advances have been made in our understanding of the genetic makeup of diseases, especially cancers. The identification of somatic mutations that can drive cancer has led to the development of therapies that specifically target the abnormal proteins derived from these mutations. This has led to a paradigm shift in our treatment methodology. Although some success has been achieved in targeting some genetic abnormalities, several challenges and limitations exist when applying precision-medicine concepts in leukemia and myelodysplastic syndromes. We review the current understanding of genomics in myelodysplastic syndromes (MDS) and leukemias and the limitations of precision-medicine concepts in MDS.

Impact of germline CTC1 alterations on telomere length in acquired bone marrow failure.

Compound heterozygous germline mutations in CTC1 gene have been found in patients with atypical dyskeratosis congenita (DC), whereas heterozygous carriers are unaffected. Through screening of a large cohort of adult patients with acquired bone marrow failure syndromes, in addition to a DC case, we have also found extremely rare or novel heterozygous deleterious germline variants of CTC1 in patients with aplastic anaemia (AA; n = 5), paroxysmal nocturnal haemoglobinuria (PNH; n = 3) and myelodysplastic syndrome (MDS; n = 2). A compound heterozygous case of AA showed clonal evolution. Our results suggest that some of the inherited CTC1 variants may represent predisposition factors for acquired bone marrow failure.

Prognosis of patients with intermediate risk IPSS-R myelodysplastic syndrome indicates variable outcomes and need for models beyond IPSS-R.

The International Prognostic Scoring System-Revised (IPSS-R) is one standard for myelodysplastic syndrome (MDS) risk stratification. It divides patients into five categories including an intermediate subset (IPSS-R int-risk). Outcomes and clinical interventions for patients with IPSS-R int-risk are not well defined. We performed an analysis of outcomes of this group of patients. Out of 3167 patients, a total of 298 were identified with IPSS-R int-risk MDS and retrospectively analyzed to assess characteristics affecting outcomes. Cox proportional hazard models for overall survival (OS) were performed to identify statistically significant clinical factors that influence survival. Age of 66 years or greater, peripheral blood blasts of 2% or more, and history of red blood cell (RBC) transfusion were significantly associated with inferior survival. Based on these features, MDS patients with IPSS-R int-risk were classified into two prognostic risk groups for analysis, an int-favorable group and an int-adverse group, and had significantly divergent outcomes. Sequential prognostication was validated using two independent datasets comprising over 700 IPSS-R int-risk patients. The difference in median survival between int-favorable and int-adverse patients was 3.7 years in the test cohort, and 1.8 and 2.0 years in the two validation cohorts. These results confirm significantly variable outcomes of patients with IPSS-R int-risk and need for different prognostic systems.

Molecular features of early onset adult myelodysplastic syndrome.

Myelodysplastic syndromes are typically diseases of older adults. Patients in whom the onset is early may have distinct molecular and clinical features or reflect a demographic continuum. The identification of differences between "early onset" patients and those diagnosed at a traditional age has the potential to advance understanding of the pathogenesis of myelodysplasia and may lead to formation of distinct morphological subcategories. We studied a cohort of 634 patients with various subcategories of myelodysplastic syndrome and secondary acute myeloid leukemia, stratifying them based on age at presentation and clinical parameters. We then characterized molecular abnormalities detected by next-generation deep sequencing of 60 genes that are commonly mutated in myeloid malignancies. The number of mutations increased linearly with age and on average, patients >50 years of age had more mutations. TET2, SRSF2, and DNMT3A were more commonly mutated in patients >50 years old compared to patients ≤50 years old. In general, patients >50 years of age also had more mutations in spliceosomal, epigenetic modifier, and RAS gene families. Although there are age-related differences in molecular features among patients with myelodysplasia, most notably in the incidence of SRSF2 mutations, our results suggest that patients ≤50 years old belong to a disease continuum with a distinct pattern of early onset ancestral events.

Where to Turn for Second-Line Cytoreduction After Hydroxyurea in Polycythemia Vera?

UNLABELLED: The goals of therapy in patients with polycythemia vera (PV) are to improve disease-related symptoms, prevent the incidence or recurrence of thrombosis, and possibly delay or prevent the transformation into myelofibrosis or acute myeloid leukemia (AML). Cytoreductive therapies have been used in older patients and those with a history of thrombosis to achieve these goals. Hydroxyurea (HU) remains the first-line cytoreductive choice; however, up to one in four patients treated with HU over time will develop resistance or intolerance to HU. More importantly, patients who fail HU have a 5.6-fold increase in mortality and a 6.8-fold increase risk of transformation to myelofibrosis or AML; therefore, alternative therapies are needed for these patients. Interferon-α has been used in PV and has shown significant activity in achieving hematologic responses and decreasing JAK2 V617F mutation allele burden. JAK inhibition has also been investigated and recently garnered regulatory approval for this indication. In this review, we will discuss the current treatment options that are available for patients after HU and the novel therapies that are currently under investigation. IMPLICATIONS FOR PRACTICE: The outcomes of PV patients who fail or who are intolerant of hydroxyurea are poor. Although pegylated interferon can be considered in younger patients, currently, ruxolitinib is the only U.S. Food and Drug Administration-approved agent in this setting, representing a viable option, leading to hematocrit control and a reduction in spleen size and constitutional symptoms. Although a small number of patients will achieve a molecular response with continuous treatment, the implications of such response on the clinical outcomes are still unknown. Patients whose disease is not adequately controlled with ruxolitinib, or who lose their response, can be treated with low-dose busulfan or pipobroman; however, they should be encouraged to participate in trials with novel therapies.

Interactions and relevance of blast percentage and treatment strategy among younger and older patients with acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS).

Acute myeloid leukemia (AML) is defined as ≥20% myeloblasts, representing a change from original guidelines where ≤30% blasts were considered as myelodysplastic syndromes (MDS), and 20-29% blasts classified as refractory anemia with excess blasts in transformation (RAEB-T). Whether the diagnostic bone marrow blast percentage has current value with regards to patient prognostication or identification of optimal treatment strategies is unclear. We retrospectively studied 1652 treatment-naïve adults with MDS or AML and ≥10% blasts from January 2000 to April 2014. Patients with 20-29% blasts were more similar to MDS patients in terms of advanced age, increased frequency of poor-risk cytogenetics, lower WBC count, and less frequent NPM1 and FLT3-ITD mutations. Median overall survival of MDS and RAEB-T were similar, 16.0 and 16.0 months, compared to 13.5 months for AML with ≥30% blasts (P = 0.045). Multivariate analysis showed inferior survival with increased age (HR 1.81 age 60-69, HR 2.68 age ≥70, P < 0.0005); poor-risk cytogenetics (HR 2.25, P < 0.0005); therapy-related disease (HR 1.44, P < 0.0005); and markers of proliferative disease including WBC ≥25 × 10(9) /L (HR 1.35, P = 0.0003), elevated LDH count (HR 1.24, P = 0.0015), and peripheral blasts (HR 1.25, P = 0.004). Among younger patients (≤60 years), intensive AML-type therapy resulted in similar outcomes regardless of blast percentage, suggesting this to be optimal therapy in this context. Among older patients (≥70 years), patients with 20-29% blasts had similar outcomes to patients with <20% blasts, and better than those with ≥30% blasts. In addition, among older patients, epigenetic therapy provided at least equivalent outcome to intensive chemotherapy.

Fibrogenesis in Primary Myelofibrosis: Diagnostic, Clinical, and Therapeutic Implications.

Primary myelofibrosis is a stem cell-derived clonal malignancy characterized by unchecked proliferation of myeloid cells, resulting in bone marrow fibrosis, osteosclerosis, and pathologic angiogenesis. Bone marrow fibrosis (BMF) plays a central role in the pathophysiology of the disease. This review describes current issues regarding BMF in primary myelofibrosis, including the pathophysiology and impact of abnormal deposition of excess collagen and reticulin fibers in bone marrow spaces, the modified Bauermeister and the European Consensus grading systems of BMF, and the prognostic impact of BMF on the overall outcome of patients with primary myelofibrosis. The impact of novel therapeutic strategies, including JAK-STAT inhibitors and allogeneic stem cell transplant, on BMF is discussed.

Molecular Testing in Myelodysplastic Syndromes for the Practicing Oncologist: Will the Progress Fulfill the Promise?

UNLABELLED: Myelodysplastic syndromes (MDS) are heterogeneous hematopoietic neoplasms that are driven by somatically acquired genetic mutations and epigenetic alterations. Accurate risk stratification is essential for delivery of risk-adaptive therapeutic interventions. The current prognostic tools sum the impact of clinical, pathologic, and laboratory parameters. Newer technologies with next-generation targeted deep sequencing and whole-genome and -exome sequencing have identified several recurrent mutations that play a vital role in the pathophysiology of MDS and the impact of these genetic changes on disease phenotype. Equally important, well-annotated databases of MDS patients with paired clinicopathologic and genetic data have enabled better understanding of the independent prognostic impact of several molecular mutations on important clinical endpoints such as overall survival and probability of leukemic progression. Cumulative evidence suggests that genomic data can also be used clinically to aid with the diagnosis, prognosis, prediction of response to specific therapies, and the development of novel and rationally targeted therapies. However, the optimal use of this mutational profiling remains a work in progress and currently there is no standard set of genes or techniques that are recommended for routine use in the clinic. In this review, we discuss the genomic revolution and its impact on our understanding of MDS biology and risk stratification. We also discuss the current role and the challenges of the application of genetic mutational data into daily clinical practice and how future research could help improve the prognostication precision and specific therapy selection for patients with MDS. IMPLICATIONS FOR PRACTICE: Heterogeneity in clinical outcomes of MDS is partly related to interpatient variability of recurrent somatic mutations that drive disease phenotype and progression. Although clinical risk stratification tools have functioned well in prognostication for patients with MDS, their ability to predict clinical benefits of specific MDS therapies is limited. Molecular testing shows promise in aiding diagnosis, risk stratification, and therapy-specific benefit prediction for MDS patients. Nonetheless, logistical issues related to assay performance standardization, validation, interpretation, and development of guidelines for how to use the results to inform clinical decisions are yet to be resolved.

Early responses predict better outcomes in patients with newly diagnosed chronic myeloid leukemia: results with four tyrosine kinase inhibitor modalities.

Early responses to tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML)-chronic phase (CP) are associated with improved outcome. We analyzed the impact of such a response on outcomes among patients treated with 4 TKI modalities as frontline therapy in CML-CP. A total of 483 patients who received 400 or 800 mg imatinib, nilotinib, or dasatinib were analyzed. The median follow-up was 72 mo. Landmark analysis at 3 mo by molecular response showed that the cumulative proportions of 3-y event-free survival (EFS) for 3-mo BCR-ABL levels was 95% for those with ≤1%, 98% for >1% to 10%, and 61% for those with >10% (P = .001). The corresponding values by cytogenetic responses were 97% if Ph+ = 0%, 89% if Ph+ = 1% to 35%, and 81% if Ph+ >35% (P = .001). Cytogenetic response at 3 mo significantly discriminated for 3-y overall survival (OS): 98%, 96%, and 92%, respectively (P = .01). In multivariate analysis, young patients, high Sokal index, and treatment with imatinib 400 significantly predicted for poor (>35%) cytogenetic response at 3 mo. Early responses are predictive for EFS and failure-free survival and to a lesser extent OS, regardless of the treatment modality, although therapies other than standard-dose imatinib result in higher rates of deep early responses.

Measuring the symptom burden associated with the treatment of chronic myeloid leukemia.

We developed a module of the MD Anderson Symptom Inventory (MDASI) for patients with chronic myeloid leukemia (CML). To develop the MDASI-CML, we identified CML-specific symptoms from qualitative interviews with 35 patients. A list of candidate symptoms was reduced by a panel of patients, caregivers, and clinicians to the 13 core MDASI symptom items and 6 CML-specific items; these items were subsequently administered to 30 patients. Cognitive debriefing confirmed that the items were clear, relevant, and easy to use. One additional CML-specific symptom item was added, for a total of 7. The refined MDASI-CML was administered to 152 patients once every 2 weeks for 1 year. The content, concurrent, known-group, and construct validity of the MDASI-CML were evaluated. The internal consistency and test-retest reliabilities of the module were adequate. Longitudinal analysis showed relatively stable symptom severity scores over time. The most severe symptoms were fatigue, drowsiness, disturbed sleep, muscle soreness and cramping, and trouble remembering things. Approximately one-third of the patients who completed the MDASI-CML reported persistent moderate-to-severe symptoms. The MDASI-CML is a valid and reliable symptom assessment instrument that can be used in clinical studies of symptom status in patients with CML.