Prognostic impact of CEBPA mutational subgroups in adult AML.

Despite recent refinements in the diagnostic and prognostic assessment of CEBPA mutations in AML, several questions remain open, i.e. implications of different types of basic region leucin zipper (bZIP) mutations, the role of co-mutations and the allelic state. Using pooled primary data analysis on 1010 CEBPA-mutant adult AML patients, a comparison was performed taking into account the type of mutation (bZIP: either typical in-frame insertion/deletion (InDel) mutations (bZIPInDel), frameshift InDel or nonsense mutations inducing translational stop (bZIPSTOP) or single base-pair missense alterations (bZIPms), and transcription activation domain (TAD) mutations) and the allelic state (single (smCEBPA) vs. double mutant (dmCEBPA)). Only bZIPInDel patients had significantly higher rates of complete remission and longer relapse free and overall survival (OS) compared with all other CEBPA-mutant subgroups. Moreover, co-mutations in bZIPInDel patients (e.g. GATA2, FLT3, WT1 as well as ELN2022 adverse risk aberrations) had no independent impact on OS, whereas in non-bZIPInDel patients, grouping according to ELN2022 recommendations added significant prognostic information. In conclusion, these results demonstrate bZIPInDel mutations to be the major independent determinant of outcome in CEBPA-mutant AML, thereby refining current classifications according to WHO (including all dmCEBPA and smCEBPA bZIP) as well as ELN2022 and ICC recommendations (including CEBPA bZIPms).

VEXAS syndrome: complete molecular remission after hypomethylating therapy.

The VEXAS syndrome, a genetically defined autoimmune disease, associated with various hematological neoplasms has been attracting growing attention since its initial description in 2020. While various therapeutic strategies have been explored in case studies, the optimal treatment strategy is still under investigation and allogeneic cell transplantation is considered the only curative treatment. Here, we describe 2 patients who achieved complete molecular remission of the underlying UBA1 mutant clone outside the context of allogeneic HCT. Both patients received treatment with the hypomethylating agent azacitidine, and deep molecular remission triggered treatment de-escalation and even cessation with sustained molecular remission in one of them. Prospective studies are necessary to clarify which VEXAS patients will benefit most from hypomethylating therapy and to understand the variability in the response to different treatment strategies.

Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles.

BACKGROUND: Increasingly large and complex biomedical data sets challenge conventional hypothesis-driven analytical approaches, however, data-driven unsupervised learning can detect inherent patterns in such data sets. METHODS: While unsupervised analysis in the medical literature commonly only utilizes a single clustering algorithm for a given data set, we developed a large-scale model with 605 different combinations of target dimensionalities as well as transformation and clustering algorithms and subsequent meta-clustering of individual results. With this model, we investigated a large cohort of 1383 patients from 59 centers in Germany with newly diagnosed acute myeloid leukemia for whom 212 clinical, laboratory, cytogenetic and molecular genetic parameters were available. RESULTS: Unsupervised learning identifies four distinct patient clusters, and statistical analysis shows significant differences in rate of complete remissions, event-free, relapse-free and overall survival between the four clusters. In comparison to the standard-of-care hypothesis-driven European Leukemia Net (ELN2017) risk stratification model, we find all three ELN2017 risk categories being represented in all four clusters in varying proportions indicating unappreciated complexity of AML biology in current established risk stratification models. Further, by using assigned clusters as labels we subsequently train a supervised model to validate cluster assignments on a large external multicenter cohort of 664 intensively treated AML patients. CONCLUSIONS: Dynamic data-driven models are likely more suitable for risk stratification in the context of increasingly complex medical data than rigid hypothesis-driven models to allow for a more personalized treatment allocation and gain novel insights into disease biology.

There are various ways in which clinicians can predict the risk of disease progression in patients with leukemia, helping them to treat the patients accordingly. However, these approaches are usually designed by human experts and might not fully capture the complexity of a patient's disease. Here, with a large cohort of patients with acute myeloid leukemia, we design an unsupervised machine learning model ­ a type of computer model that learns from patterns in data without human input­to separate these patients into subgroups according to risk. We identify four distinct groups which differ with regards to patient genetics, laboratory values, and clinical characteristics. These groups have differences in response to treatment and patient survival, and we validate our findings in another dataset. Our approach might help clinicians to better predict outcomes in patients with leukemia and make decisions on treatment.

UBTF tandem duplications are rare but recurrent alterations in adult AML and associated with younger age, myelodysplasia, and inferior outcome.

Tandem-duplication mutations of the UBTF gene (UBTF-TDs) coding for the upstream binding transcription factor have recently been described in pediatric patients with acute myeloid leukemia (AML) and were found to be associated with particular genetics (trisomy 8 (+8), FLT3-internal tandem duplications (FLT3-ITD), WT1-mutations) and inferior outcome. Due to limited knowledge on UBTF-TDs in adult AML, we screened 4247 newly diagnosed adult AML and higher-risk myelodysplastic syndrome (MDS) patients using high-resolution fragment analysis. UBTF-TDs were overall rare (n = 52/4247; 1.2%), but significantly enriched in younger patients (median age 41 years) and associated with MDS-related morphology as well as significantly lower hemoglobin and platelet levels. Patients with UBTF-TDs had significantly higher rates of +8 (34% vs. 9%), WT1 (52% vs. 7%) and FLT3-ITD (50% vs. 20.8%) co-mutations, whereas UBTF-TDs were mutually exclusive with several class-defining lesions such as mutant NPM1, in-frame CEBPAbZIP mutations as well as t(8;21). Based on the high-variant allele frequency found and the fact that all relapsed patients analyzed (n = 5) retained the UBTF-TD mutation, UBTF-TDs represent early clonal events and are stable over the disease course. In univariate analysis, UBTF-TDs did not represent a significant factor for overall or relapse-free survival in the entire cohort. However, in patients under 50 years of age, who represent the majority of UBTF-mutant patients, UBTF-TDs were an independent prognostic factor for inferior event-free (EFS), relapse-free (RFS) and overall survival (OS), which was confirmed by multivariable analyses including established risk factors such as age and ELN2022 genetic risk groups (EFS [HR: 2.20; 95% CI 1.52-3.17, p < 0.001], RFS [HR: 1.59; 95% CI 1.02-2.46, p = 0.039] and OS [HR: 1.64; 95% CI 1.08-2.49, p = 0.020]). In summary, UBTF-TDs appear to represent a novel class-defining lesion not only in pediatric AML but also younger adults and are associated with myelodysplasia and inferior outcome in these patients.

Deciphering the genetic basis of immune thrombocytopenia: current evidence for genetic predisposition in adult ITP.

Immune thrombocytopenia (ITP) is the consequence of a complex, still incompletely understood immunological dysregulation. Proposed mechanisms include autoantibody-induced platelet destruction, impaired platelet production as well as abnormalities in T-cell immunity, such as T helper cells (Th1) polarization, a high proportion of Th17 cells, and a reduced number of regulatory T cells. Although the etiology of ITP is incompletely understood and considered multifactorial in most cases, genetic variants are thought to play a key role in susceptibility to ITP, especially in persistent or chronic ITP. Efforts are currently underway to uncover possible predisposing genetic factors for the development of ITP. Single-nucleotide polymorphisms and copy number variations have been identified in several immune-related genes, such as cytokine genes, Fcγ receptor genes or T-cell costimulation genes, and have been associated with patients' susceptibility to ITP. However, because of the clinical heterogeneity and low incidence of ITP it remains challenging to perform genetic analyses with sufficiently large sample size within informative patient populations, highlighting the need for collection of well-annotated biomaterials in clinical trials or registry projects. Another significant challenge is to go beyond performing association studies alone and to establish genotype-phenotype associations, thus proving causality between a genetic alteration and ITP pathogenesis. This review summarizes our current knowledge on genetic alterations identified as potential predisposing factors for the development of ITP in adults, thereby addressing signaling pathways considered critical for ITP pathogenesis.

Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning.

Achievement of complete remission signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. We used nine machine learning (ML) models to predict complete remission and 2-year overall survival in a large multicenter cohort of 1,383 AML patients who received intensive induction therapy. Clinical, laboratory, cytogenetic and molecular genetic data were incorporated and our results were validated on an external multicenter cohort. Our ML models autonomously selected predictive features including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, and U2AF1, t(8;21), inv(16)/t(16;16), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin concentration at initial diagnosis were statistically significant markers predictive of complete remission, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), double-mutated CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, and TP53 mutations, age, white blood cell count, peripheral blast count, serum lactate dehydrogenase level and hemoglobin concentration at initial diagnosis as well as extramedullary manifestations were predictive for 2-year overall survival. For prediction of complete remission and 2-year overall survival areas under the receiver operating characteristic curves ranged between 0.77-0.86 and between 0.63-0.74, respectively in our test set, and between 0.71-0.80 and 0.65-0.75 in the external validation cohort. We demonstrated the feasibility of ML for risk stratification in AML as a model disease for hematologic neoplasms, using a scalable and reusable ML framework. Our study illustrates the clinical applicability of ML as a decision support system in hematology.

Analysis of Subset Chimerism for MRD-Detection and Pre-Emptive Treatment in AML.

Allogeneic hematopoietic stem cell transplantation (alloHCT) represents the only potentially curative treatment in high-risk AML patients, but up to 40% of patients suffer from relapse after alloHCT. Treatment of overt relapse poses a major therapeutic challenge and long-term disease control is achieved only in a minority of patients. In order to avoid post-allograft relapse, maintenance as well as pre-emptive therapy strategies based on MRD-detection have been used. A prerequisite for the implementation of pre-emptive therapy is the accurate identification of patients at risk for imminent relapse. Detection of measurable residual disease (MRD) represents an effective tool for early relapse prediction in the post-transplant setting. However, using established MRD methods such as multicolor flow cytometry or quantitative PCR, sensitive MRD monitoring is only applicable in about half of the patients with AML and advanced MDS undergoing alloHCT. Donor chimerism analysis, in particular when performed on enriched leukemic stem and progenitor cells, e.g. CD34+ cells, is a sensitive method and has emerged as an alternative option in the post alloHCT setting. In this review, we will focus on the current strategies for lineage specific chimerism analysis, results of pre-emptive treatment using this technology as well as future developments in this field.

CEBPA mutations in 4708 patients with acute myeloid leukemia: differential impact of bZIP and TAD mutations on outcome.

Biallelic mutations of the CEBPA gene (CEBPAbi) define a distinct entity associated with favorable prognosis; however, the role of monoallelic mutations (CEBPAsm) is poorly understood. We retrospectively analyzed 4708 adults with acute myeloid leukemia (AML) who had been recruited into the Study Alliance Leukemia trials, to investigate the prognostic impact of CEBPAsm. CEBPA mutations were identified in 240 patients (5.1%): 131 CEBPAbi and 109 CEBPAsm (60 affecting the N-terminal transactivation domains [CEBPAsmTAD] and 49 the C-terminal DNA-binding or basic leucine zipper region [CEBPAsmbZIP]). Interestingly, patients carrying CEBPAbi or CEBPAsmbZIP shared several clinical factors: they were significantly younger (median, 46 and 50 years, respectively) and had higher white blood cell (WBC) counts at diagnosis (median, 23.7 × 109/L and 35.7 × 109/L) than patients with CEBPAsmTAD (median age, 63 years, median WBC 13.1 × 109/L; P < .001). Co-mutations were similar in both groups: GATA2 mutations (35.1% CEBPAbi; 36.7% CEBPAsmbZIP vs 6.7% CEBPAsmTAD; P < .001) or NPM1 mutations (3.1% CEBPAbi; 8.2% CEBPAsmbZIP vs 38.3% CEBPAsmTAD; P < .001). CEBPAbi and CEBPAsmbZIP, but not CEBPAsmTAD were associated with significantly improved overall (OS; median 103 and 63 vs 13 months) and event-free survival (EFS; median, 20.7 and 17.1 months vs 5.7 months), in univariate and multivariable analyses. Additional analyses revealed that the clinical and molecular features as well as the favorable survival were confined to patients with in-frame mutations in bZIP (CEBPAbZIP-inf). When patients were classified according to CEBPAbZIP-inf and CEBPAother (including CEBPAsmTAD and non-CEBPAbZIP-inf), only patients bearing CEBPAbZIP-inf showed superior complete remission rates and the longest median OS and EFS, arguing for a previously undefined prognostic role of this type of mutation.