Comparing malignant monocytosis across the updated WHO and ICC classifications of 2022.

ABSTRACT: The World Health Organization (WHO) classification of hematolymphoid tumors and the International Consensus Classification (ICC) of 2022 introduced major changes to the definition of chronic myelomonocytic leukemia (CMML). To assess its qualitative and quantitative implications for patient care, we started with 3311 established CMML cases (according to WHO 2017 criteria) and included 2130 oligomonocytosis cases fulfilling the new CMML diagnostic criteria. Applying both 2022 classification systems, 356 and 241 of oligomonocytosis cases were newly classified as myelodysplastic (MD)-CMML (WHO and ICC 2022, respectively), most of which were diagnosed as myelodysplastic syndrome (MDS) according to the WHO 2017 classification. Importantly, 1.5 times more oligomonocytosis cases were classified as CMML according to WHO 2022 than based on ICC, because of different diagnostic criteria. Genetic analyses of the newly classified CMML cases showed a distinct mutational profile with strong enrichment of MDS-typical alterations, resulting in a transcriptional subgroup separated from established MD and myeloproliferative CMML. Despite a different cytogenetic, molecular, immunophenotypic, and transcriptional landscape, no differences in overall survival were found between newly classified and established MD-CMML cases. To the best of our knowledge, this study represents the most comprehensive analysis of routine CMML cases to date, both in terms of clinical characterization and transcriptomic analysis, placing newly classified CMML cases on a disease continuum between MDS and previously established CMML.

The clinical and genomic landscape of patients with DDX41 variants identified during diagnostic sequencing.

Deleterious germ line variants in DDX41 are a common cause of genetic predisposition to hematologic malignancies, particularly myelodysplastic neoplasms (MDS) and acute myeloid leukemia (AML). Targeted next-generation sequencing was performed in a large cohort of sequentially recruited patients with myeloid malignancy, covering DDX41 as well as 30 other genes frequently mutated in myeloid malignancy. Whole genome transcriptome sequencing data was analyzed on a separate cohort of patients with a range of hematologic malignancies to investigate the spectrum of cancer predisposition. Altogether, 5737 patients with myeloid malignancies were studied, with 152 different DDX41 variants detected. Multiple novel variants were detected, including synonymous variants affecting splicing as demonstrated by RNA-sequencing. The presence of a somatic DDX41 variant was highly associated with DDX41 germ line variants in patients with MDS and AML, and we developed a statistical approach to incorporate the co-occurrence of a somatic DDX41 variant into germ line variant classification at a very strong level (as per the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines). Using this approach, the MDS cohort contained 108 of 2865 (3.8%) patients with germ line likely pathogenic/pathogenic (LP/P) variants, and the AML cohort 106 of 2157 (4.9%). DDX41 LP/P variants were markedly enriched in patients with AML and MDS compared with those in patients with myeloproliferative neoplasms, B-cell neoplasm, and T- or B-cell acute lymphoblastic leukemia. In summary, we have developed a framework to enhance DDX41 variant curation as well as highlighted the importance of assessment of all types of genomic variants (including synonymous and multiexon deletions) to fully detect the landscape of possible clinically relevant DDX41 variants.

Interplay of TP53 allelic state, blast count, and complex karyotype on survival of patients with AML and MDS.

Several clinical and genetic factors impact overall survival (OS) in myelodysplastic neoplasms (MDS) and acute myeloid leukemia (AML), including complex karyotype (CK), TP53 allelic state, and blast count. We analyzed the interplay of these factors by performing Cox regression analysis and by determining the frequency of TP53 single-hit (sh) and double-hit (dh) events and OS in MDS (n = 747) with <5% blasts, with ≥5% but <10% blasts, and ≥10% but <20% blasts and AML (n = 772). MDS with <5% blasts showed the best outcome, followed by with ≥5% but <10% blasts, and ≥10% but <20% blasts, and AML (median OS: 75, 54, 27, and 18 months, respectively). The same hierarchy was observed when each subgroup was divided into TP53sh, TP53dh, and without TP53 alterations (alt), revealing a dismal outcome of TP53dh in all subgroups (17, 10, 8, and 1 month[s], respectively). MDS with <5% blasts differed from the other subgroups by showing predominantly TP53sh (76% of TP53alt cases), and by an independent adverse impact of CK on OS (hazard ratio, 5.2; P < .001). The remaining subgroups displayed many similarities, with TP53dh found at high frequencies (67%, 91%, and 71%, respectively) and only TP53alt but not CK independently influencing OS, and TP53dh showing the strongest influence. When the total cohort was split based on TP53 state, only the blast count and not CK had an independent adverse impact on OS in all subgroups. Thus, TP53dh is the strongest prognostic factor, further supporting its integration into risk stratification guidelines and classification as a separate entity. However, the blast count also influences OS independent of TP53 state, whereas CK plays a minor prognostic role.

Prevalence and Therapeutic Implications of Clonal Hematopoiesis of Indeterminate Potential in Young Patients With Stroke.

BACKGROUND: Undetermined stroke etiology hampers optimal secondary prevention in a large proportion of young patients. We explored whether genetic screening for clonal hematopoiesis of indetermined potential (CHIP), a novel risk factor for stroke, could identify patients with myeloid precursor lesions or covert myeloid neoplasm requiring specific treatment. METHODS: We performed targeted sequencing on 56 genes recurrently mutated in hematologic neoplasms in a prospective cohort of patients with acute brain ischemia between 18 and 60 years. CHIP prevalence was compared with age-matched healthy controls from the Nijmegen Biomedical Study (n=1604) and the UK Biobank (n=101 678). Patients with suspicion of high-risk CHIP or myeloid neoplasm were invited for further hematologic evaluation. RESULTS: We included 248 consecutive patients (39% women) of whom 176 (71%) had cryptogenic stroke etiology. Fifty-one (21%) patients had CHIP, 3-fold more than in the general population (7.7% versus 2.6% for the Nijmegen Biomedical Study and 11.9% versus 4.1% for UK Biobank; P<0.001 for both). Patients with CHIP were older (median [interquartile range], 53 [50-59] versus 51 [41-56] years; P<0.001), had higher carotid intima-media thickness (0.68 [0.58-0.80] versus 0.59 [0.51-0.73] mm; P=0.009), and had higher burden of atherosclerosis (29.4% versus 16.7%; P=0.04). We invited 11 patients (4.4%) for further hematologic assessment, which in 7 led to the diagnosis of high-risk CHIP and in 2 to the new diagnosis of a myeloproliferative neoplasm with indication for cytoreductive therapy. CONCLUSIONS: Using genetic screening for myeloid disorders in patients with stroke of predominantly undetermined etiology, we found a 3-fold higher CHIP prevalence than in the general population. We identified high-risk CHIP and previously covert myeloproliferative neoplasms as potential stroke etiologies in 4.4% and 1% of patients, respectively. Our findings demonstrate the diagnostic and therapeutic yield of genetic screening in young patients with stroke. Future studies should investigate the role of CHIP for stroke recurrence and optimal secondary prevention.

Artificial intelligence in hematological diagnostics: Game changer or gadget?

The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based systems into routine practice to support the hematologists' decision making. Several studies have shown that AI-based models can already be used to automatically differentiate cells, reliably detect malignant cell populations, support chromosome banding analysis, and interpret clinical variants, contributing to early disease detection and prognosis. However, even the best tool can become useless if it is misapplied or the results are misinterpreted. Therefore, in order to comprehensively judge and correctly apply newly developed AI-based systems, the hematologist must have a basic understanding of the general concepts of machine learning. In this review, we provide the hematologist with a comprehensive overview of various machine learning techniques, their current implementations and approaches in different diagnostic subfields (e.g., cytogenetics, molecular genetics), and the limitations and unresolved challenges of the systems.

Molecular landscape of immune pressure and escape in aplastic anemia.

Idiopathic aplastic anemia (IAA) pathophysiology is dominated by autoreactivity of human leukocyte antigen (HLA)-restricted T-cells against antigens presented by hematopoietic stem and progenitor cells (HSPCs). Expansion of PIGA and HLA class I mutant HSPCs have been linked to immune evasion from T-cell mediated pressures. We hypothesized that in analogy with antitumor immunity, the pathophysiological cascade of immune escape in IAA is initiated by immunoediting pressures and culminates with mechanisms of clonal evolution characterized by hits in immune recognition and response genes. To that end, we studied the genetic and transcriptomic make-up of the antigen presentation complexes in a large cohort of patients with IAA and paroxysmal nocturnal hemoglobinuria (PNH) by using single-cell RNA, high throughput DNA sequencing and single nucleotide polymorphism (SNP)-array platforms. At disease onset, HSPCs displayed activation of selected HLA class I and II-restricted mechanisms, without extensive inhibition of immune checkpoint apparatus. Using a newly implemented bioinformatic framework we found that not only class I but also class II genes were often impaired by acquisition of genetic aberrations. We also demonstrated the presence of novel somatic alterations in immune genes possibly contributing to the evasion from the autoimmune T-cells. In contrast, these hits were absent in myeloid neoplasia. These aberrations were not mutually exclusive with PNH and did not correlate with the accumulation of myeloid-driver hits. Our findings shed light on the mechanisms of immune activation and escape in IAA and define alternative modes of clonal hematopoiesis.

Clonal evolution in chronic lymphocytic leukemia is associated with an unmutated IGHV status and frequently leads to a combination of loss of TP53 and TP53 mutation.

BACKGROUND: Chromosomal abnormalities and gene mutations determine the prognosis of patients with chronic lymphocytic leukemia (CLL). Genetic lesions can be acquired by clonal evolution (CE) likely correlating with clinical progression. METHODS AND RESULTS: Samples of 169 CLL patients were analyzed for cytogenetic clonal evolution (CCE) and CE affecting the genes TP53 and SF3B1. Moreover, the mutational status of IGHV and the clinical outcome was evaluated. CCE was observed in 35% of CLL patients. The most frequently gained cytogenetic aberration was a deletion of TP53. Acquired TP53 deletion was more frequent in patients with SF3B1 mutations compared to those without (19% vs. 7%). CCE showed a tendency to occur more frequently in patients with an aberrant karyotype at first investigation than in patients with a normal karyotype. In 73% of patients with CCE (p = 0.002) and 92% of patients with CE affecting the genes TP53 and SF3B1 (p < 0.001) an unmutated IGHV status was present. CCE and CE affecting the genes TP53 and SF3B1 were significantly associated with each other (p < 0.001). In 7% of patients, CE resulted in the co-occurrence of TP53 deletion and TP53 mutation resulting in a significantly shorter overall survival. CONCLUSIONS: The most frequently gained cytogenetic aberration during CCE was a deletion of TP53, which was associated with SF3B1 mutations. Moreover, CCE was associated with an unmutated IGHV status. Our results indicate the importance of re-evaluation of the TP53 status during the course of the disease to ensure correct treatment guidance.

Aberrant somatic hypermutation of CCND1 generates non-coding drivers of mantle cell lymphomagenesis.

Aberrant somatic hypermutation (aSHM) can target proto-oncogenes and drive oncogenesis. In mantle cell lymphoma (MCL), CCND1 is targeted by aSHM in the non-nodal subtype (nnMCL), giving rise to exon1 encoded mutant proteins like E36K, Y44D, and C47S that contribute to lymphomagenesis by virtue of their increased protein stability and nuclear localization. However, the vast majority of somatic variants generated by aSHM are found in the first intron of CCND1 but their significance for mantle cell lymphomagenesis is unknown. We performed whole-genome and whole-transcriptome sequencing in 84 MCL patients to explore the contribution of non-coding somatic variants created by aSHM to lymphomagenesis. We show that non-coding variants are enriched in a MCL specific manner in transcription factor-binding sites, that non-coding variants are associated with increased CCND1 mRNA expression, and that coding variants in the first exon of CCND1 are more often synonymous or cause benign amino acid changes than in other types of lymphomas carrying a t(11;14) translocation. Therefore, the increased frequency of somatic variants due to aSHM might be a consequence of selection pressure manifested at the transcriptional level rather than being a mere mechanistic consequence of misguided activation-induced cytidine deaminase (AID) activity.

Indeterminate and oncogenic potential: CHIP vs CHOP mutations in AML with NPM1 alteration.

In AML patients, recurrent mutations were shown to persist in remission, however, only some have a prognostic value and persistent mutations might therefore reflect a re-established premalignant state or truly active disease causing relapse. We aimed to dissect the nature of co-mutations in NPM1 mutated AML where the detection of NPM1 transcripts allows highly specific and sensitive detection of complete molecular remission (CMR). We analysed 150 consecutive patients who achieved CMR following intensive treatment by next generation sequencing on paired samples at diagnosis, CMR and relapse (38/150 patients). Patients with persistence or the acquisition of non-DTA (DNMT3A, TET2, ASXL1) mutations at CMR (23/150 patients, 15%) have a significantly worse prognosis (EFS HR = 2.7, p = 0.003; OS HR = 3.6, p = 0.012). Based on clonal evolution analysis of diagnostic, CMR and relapse samples, we redefine pre-malignant mutations and include IDH1, IDH2 and SRSF2 with the DTA genes in this newly defined group. Only the persistence or acquisition of CHOP-like (clonal hematopoiesis of oncogenic potential) mutations was significantly associated with an inferior outcome (EFS HR = 4.5, p = 0.0002; OS HR = 5.5, p = 0.002). Moreover, the detection of CHOP-like mutations at relapse was detrimental (HR = 4.5, p = 0.01). We confirmed these findings in a second independent whole genome sequencing cohort.

How artificial intelligence might disrupt diagnostics in hematology in the near future.

Artificial intelligence (AI) is about to make itself indispensable in the health care sector. Examples of successful applications or promising approaches range from the application of pattern recognition software to pre-process and analyze digital medical images, to deep learning algorithms for subtype or disease classification, and digital twin technology and in silico clinical trials. Moreover, machine-learning techniques are used to identify patterns and anomalies in electronic health records and to perform ad-hoc evaluations of gathered data from wearable health tracking devices for deep longitudinal phenotyping. In the last years, substantial progress has been made in automated image classification, reaching even superhuman level in some instances. Despite the increasing awareness of the importance of the genetic context, the diagnosis in hematology is still mainly based on the evaluation of the phenotype. Either by the analysis of microscopic images of cells in cytomorphology or by the analysis of cell populations in bidimensional plots obtained by flow cytometry. Here, AI algorithms not only spot details that might escape the human eye, but might also identify entirely new ways of interpreting these images. With the introduction of high-throughput next-generation sequencing in molecular genetics, the amount of available information is increasing exponentially, priming the field for the application of machine learning approaches. The goal of all the approaches is to allow personalized and informed interventions, to enhance treatment success, to improve the timeliness and accuracy of diagnoses, and to minimize technically induced misclassifications. The potential of AI-based applications is virtually endless but where do we stand in hematology and how far can we go?

Complex landscape of alternative splicing in myeloid neoplasms.

Myeloid neoplasms are characterized by frequent mutations in at least seven components of the spliceosome that have distinct roles in the process of pre-mRNA splicing. Hotspot mutations in SF3B1, SRSF2, U2AF1 and loss of function mutations in ZRSR2 have revealed widely different aberrant splicing signatures with little overlap. However, previous studies lacked the power necessary to identify commonly mis-spliced transcripts in heterogeneous patient cohorts. By performing RNA-Seq on bone marrow samples from 1258 myeloid neoplasm patients and 63 healthy bone marrow donors, we identified transcripts frequently mis-spliced by mutated splicing factors (SF), rare SF mutations with common alternative splicing (AS) signatures, and SF-dependent neojunctions. We characterized 17,300 dysregulated AS events using a pipeline designed to predict the impact of mis-splicing on protein function. Meta-splicing analysis revealed a pattern of reduced levels of retained introns among disease samples that was exacerbated in patients with splicing factor mutations. These introns share characteristics with "detained introns," a class of introns that have been shown to promote differentiation by detaining pro-proliferative transcripts in the nucleus. In this study, we have functionally characterized 17,300 targets of mis-splicing by the SF mutations, identifying a common pathway by which AS may promote maintenance of a proliferative state.

Molecular characterization of a second myeloid neoplasm developing after treatment for acute myeloid leukemia.

Therapy-related myeloid neoplasms (tMN) following successful treatment of acute myeloid leukemia (AML) are rare and poorly characterized. To evaluate the presence of a common ancestral clone, we performed whole-exome sequencing of 25 patients at AML diagnosis, tMN diagnosis (tMDS: 13; tAML: 12), and matched remission samples, identifying 607 mutations affecting 504 different genes (46 recurrently mutated). Number of mutations was higher in tAML vs. tMDS cases (median 19 vs 13 mutations, p = 0.05). Focusing on 24 genes commonly mutated in hematological malignancies, 19/25 (76%) patients were found to share mutations between AML and tMN, mostly affecting epigenetic modifiers (21/32; 66%), splicing factors (6/32; 19%), and chromatin modifiers (3/32; 9%). Analysis of remission samples identified 13 persisting mutations in 10/22 patients, affecting DNMT3A (n = 6), TET2 (n = 5), IDH1 and SRSF2 (n = 1, each). Comparison of cytogenetics revealed that 9/12 patients with a normal karyotype (NK) in AML harbored aberrations in tMN, four aberrant AML cases presented with NK in tMN, four other patients showed unrelated cytogenetic aberrations. Our study provides novel insights into the pathogenesis of tMN, hypothesizing the presence of a common ancestral clone in AML and tMN. Mutations mostly affected epigenetic modifiers, which have previously been linked to clonal hematopoiesis.

Molecular characterization of AML with RUNX1-RUNX1T1 at diagnosis and relapse reveals net loss of co-mutations.

AML with RUNX1-RUNX1T1 fusion is a WHO entity with a favorable outcome following intensive chemotherapy. The absence of RUNX1-RUNX1T1 transcripts in remission defines complete molecular response and correlates with a superior survival. However, a significant proportion of patients still relapses and defining molecular risk factors that identify patients at diagnosis or at molecular remission that are at risk of relapse could help tailor treatment strategies for those high risk patients. Here, we analyze a cohort of 94 patients that reach a molecular remission (MR) following intensive treatment and identify 21 patients that relapse despite achieving MR. Using targeted sequencing of 63 genes implicated in hematologic malignancies we show that at diagnosis patients who relapse following MR have a higher burden of co-mutated genes than patients that do not relapse (median = 2 vs median = 0; P = 0.0156). This resulted in a relapse free survival rate of 65% vs 86% at 2 years, respectively (≥1 co-mutation vs no co-mutation, P = 0.02) with a trend for inferior overall survival (n.s.). Applying sensitive sequencing to reassess mutations at relapse in paired samples of 17/21 patients we demonstrate a net loss of co-mutations at relapse: median 2 (range 0-5) vs 1 (0-4) at diagnosis and relapse (P = 0.048). At relapse more patients had no detected co-mutation compared to diagnosis (47% vs 17%, P = 0.034). Co-mutations at diagnosis, therefore, might represent a general susceptibility of the AML clone to acquire mutations and the true nature of 2nd hit mutations that drive leukemia has to be defined for AML with RUNX1-RUNX1T1 fusion.

Dark-matter matters: Discriminating subtle blood cancers using the darkest DNA.

The confluence of deep sequencing and powerful machine learning is providing an unprecedented peek at the darkest of the dark genomic matter, the non-coding genomic regions lacking any functional annotation. While deep sequencing uncovers rare tumor variants, the heterogeneity of the disease confounds the best of machine learning (ML) algorithms. Here we set out to answer if the dark-matter of the genome encompass signals that can distinguish the fine subtypes of disease that are otherwise genomically indistinguishable. We introduce a novel stochastic regularization, ReVeaL, that empowers ML to discriminate subtle cancer subtypes even from the same 'cell of origin'. Analogous to heritability, implicitly defined on whole genome, we use predictability (F1 score) definable on portions of the genome. In an effort to distinguish cancer subtypes using dark-matter DNA, we applied ReVeaL to a new WGS dataset from 727 patient samples with seven forms of hematological cancers and assessed the predictivity over several genomic regions including genic, non-dark, non-coding, non-genic, and dark. ReVeaL enabled improved discrimination of cancer subtypes for all segments of the genome. The non-genic, non-coding and dark-matter had the highest F1 scores, with dark-matter having the highest level of predictability. Based on ReVeaL's predictability of different genomic regions, dark-matter contains enough signal to significantly discriminate fine subtypes of disease. Hence, the agglomeration of rare variants, even in the hitherto unannotated and ill-understood regions of the genome, may play a substantial role in the disease etiology and deserve much more attention.

NPM1 mutated AML can relapse with wild-type NPM1: persistent clonal hematopoiesis can drive relapse.

Acute myeloid leukemia (AML) with NPM1 mutation (NPM1 mut) defines a World Health Organization entity. Absence of minimal residual disease (MRD) following induction chemotherapy is associated with an excellent prognosis. Data are conflicting on NPM1 mut AML relapsing with wild-type NPM1 (NPM1 wt ). We analyzed 104 paired samples of NPM1 mut AML patients with relapse and identified 14/104 that relapsed with NPM1 wt AML. Blood counts at diagnosis differed significantly between patients with NPM1 mut and NPM1 wt relapse (median white blood cell count, 30 vs 3 × 109/L, P = .008; platelet count, 66 vs 128 × 109/l, P = .018). NPM1 mut relapse occurred significantly earlier than NPM1 wt relapse (14 vs 43 months, P = .004). At diagnosis, FLT3-ITD were more frequent in patients with NPM1 mut relapse (P = .029), whereas DNMT3A mutations were more frequent in patients with NPM1 wt relapse (P = .035). Sequencing analysis of paired samples at diagnosis, molecular remission, and NPM1 wt relapse identified cooccurring mutations that persist from diagnosis throughout remission and at relapse, suggestive of a preexisting clonal hematopoiesis. We provide evidence that AML relapsing with NPM1 wt is a distinct disease and that initial leukemia and relapse potentially arise from a premalignant clonal hematopoiesis.

Identification of prognostic parameters in CLL with no abnormalities detected by chromosome banding and FISH analyses.

Chronic Lymphocytic Leukaemia (CLL) is a heterogeneous disease with a clinical course dependent on cytogenetic features. However, in 15-20% of cases both chromosome banding and fluorescence in situ hybridisation analyses do not show any kind of abnormality. With the aim to identify dependable molecular prognostic factors in this subgroup, we performed a comprehensive analysis on 171 patients including genomic arrays (comparative genomic hybridisation and single nucleotide polymorphism), immunoglobulin heavy chain variable region genes (IGHV) status, flow cytometry and targeted sequencing. Genomic arrays detected 73 aberrations in 39 patients (23%). Most frequently, patients had 1 aberration (25/171; 15%), while 14 patients (8%) had at least 2 aberrations. IGHV status was unmutated in 53/171 (31%) patients. SF3B1 was the most frequently mutated gene (26/171 patients; 15%), followed by NOTCH1 (15/171; 9%). At univariate analysis, an adverse impact on time to treatment (TTT) was evident for SF3B1 mutations, higher white blood cell count, higher CLL cells percentage by flow cytometry, CD38 positivity, IGHV unmutated status and at least 2 genomic array abnormalities. Of these, SF3B1 mutations, CLL cells percentage, IGHV unmutated status and number of genomic array aberrations maintained their impact in multivariate analysis. In conclusion, by integrating genomic and molecular data, we identified patients at higher risk for treatment need.

Selective inhibition of BCL-2 is a promising target in patients with high-risk myelodysplastic syndromes and adverse mutational profile.

Somatic mutations in genes such as ASXL1, RUNX1, TP53 or EZH2 adversely affect the outcome of patients with myelodysplastic syndromes (MDS). Since selective BCL-2 inhibition is a promising treatment strategy in hematologic malignancies, we tested the therapeutic impact of ABT-199 on MDS patient samples bearing an adverse mutational profile. By gene expression, we found that the level of pro-apoptotic BIM significantly decreased during MDS disease progression in line with an acquired resistance to cell death. Supporting the potential for ABT-199 treatment in MDS, high-risk MDS patient samples specifically underwent cell death in response to ABT-199 even when harbouring mutations in ASXL1, RUNX1, TP53 or EZH2. ABT-199 effectively targeted the stem- and progenitor compartment in advanced MDS harbouring mutations in ASXL1, RUNX1, TP53 or EZH2 and even proved effective in patients harbouring more than one of the defined high-risk mutations. Moreover, we utilized the protein abundance of BCL-2 family members in primary patient samples using flow cytometry as a biomarker to predict ABT-199 treatment response. Our data demonstrate that ABT-199 effectively induces apoptosis in progenitors of high-risk MDS/sAML despite the presence of adverse genetic mutations supporting the notion that pro-apoptotic intervention will hold broad therapeutic potential in high-risk MDS patients with poor prognosis.