The similarity of class II HLA genotypes defines patterns of autoreactivity in idiopathic bone marrow failure disorders.

Idiopathic aplastic anemia (IAA) is a rare autoimmune bone marrow failure (BMF) disorder initiated by a human leukocyte antigen (HLA)-restricted T-cell response to unknown antigens. As in other autoimmune disorders, the predilection for certain HLA profiles seems to represent an etiologic factor; however, the structure-function patterns involved in the self-presentation in this disease remain unclear. Herein, we analyzed the molecular landscape of HLA complexes of a cohort of 300 IAA patients and almost 3000 healthy and disease controls by deeply dissecting their genotypic configurations, functional divergence, self-antigen binding capabilities, and T-cell receptor (TCR) repertoire specificities. Specifically, analysis of the evolutionary divergence of HLA genotypes (HED) showed that IAA patients carried class II HLA molecules whose antigen-binding sites were characterized by a high level of structural homology, only partially explained by specific risk allele profiles. This pattern implies reduced HLA binding capabilities, confirmed by binding analysis of hematopoietic stem cell (HSC)-derived self-peptides. IAA phenotype was associated with the enrichment in a few amino acids at specific positions within the peptide-binding groove of DRB1 molecules, affecting the interface HLA-antigen-TCR ß and potentially constituting the basis of T-cell dysfunction and autoreactivity. When analyzing associations with clinical outcomes, low HED was associated with risk of malignant progression and worse survival, underlying reduced tumor surveillance in clearing potential neoantigens derived from mechanisms of clonal hematopoiesis. Our data shed light on the immunogenetic risk associated with IAA etiology and clonal evolution and on general pathophysiological mechanisms potentially involved in other autoimmune disorders.

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.

Retrospective Study of 157 Patients with Otosclerosis to Evaluate Association Between Carhart Notch on the Preoperative Bone-Conduction Audiogram and Postoperative Hearing and Balance Evaluated by the Vestibular Disorders Activities of Daily Living Scale.

BACKGROUND A Carhart notch in the pure tone audiogram can be an indicator of stapes fixation in otosclerosis. This retrospective study of 157 patients with otosclerosis aimed to evaluate the association between the presence of a Carhart notch on the preoperative bone-conduction audiogram and postoperative hearing and balance evaluated by the Vestibular Disorders Activities of Daily Living scale. MATERIAL AND METHODS Patients with suspected otosclerosis based on medical history and audiometric tests were considered. The analysis included 157 consecutive patients who underwent surgery in the years 2016 to 2019, in whom the diagnosis of otosclerosis was confirmed during surgery. Carhart notch was defined as an impairment in the bone conduction threshold of ≥7.5 dB for 2000 Hz frequencies above the mean thresholds at higher and lower adjacent frequencies. The Vestibular Disorders Activities of Daily Living subjective scale was used in the preoperative period and 4 and 12 months after surgery. RESULTS The preoperative presence of Carhart notch and progressive sensorineural hearing loss were statistically significantly correlated with more common onset of tinnitus and then dizziness (P=0.006). Preoperative vertigo was observed in patients who had Carhart notch observed in the preoperative audiometric test. This vertigo more commonly coexisted with profound sensorineural hearing loss and minor or no improvement in average values of bone conduction after surgery (P=0.002). CONCLUSIONS Preoperative Carhart notch on audiogram and the severity of sensorineural hearing loss were associated with tinnitus and vertigo. However, preoperative Carhart notch was not associated with persistent postoperative tinnitus in patients with cochlear otosclerosis.

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.

Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes.

Myelodysplastic syndromes (MDS) are characterized by variable clinical manifestations and outcomes. Several prognostic systems relying on clinical factors and cytogenetic abnormalities have been developed to help stratify MDS patients into different risk categories of distinct prognoses and therapeutic implications. The current abundance of molecular information poses the challenges of precisely defining patients' molecular profiles and their incorporation in clinically established diagnostic and prognostic schemes. Perhaps the prognostic power of the current systems can be boosted by incorporating molecular features. Machine learning (ML) algorithms can be helpful in developing more precise prognostication models that integrate complex genomic interactions at a higher dimensional level. These techniques can potentially generate automated diagnostic and prognostic models and assist in advancing personalized therapies. This review highlights the current prognostication models used in MDS while shedding light on the latest achievements in ML-based research.

Friend or foe? The case of Wilms' Tumor 1 (WT1) mutations in acute myeloid leukemia.

Wilms tumor 1 (WT1) gene is commonly mutated in acute myeloid leukemia (AML), particularly in younger age population. The mechanism through which WT1 mutations drive leukemogenesis have not been fully elucidated; however, recent studies reported an association with the epigenetic pathway. Here, we studied the phenotypic characteristics and somatic mutational profile of 114 WT1-mutant AML patients and focused on potential WT1 gene relations to other cooperative genomic events that may impact disease prognosis. Invariant phenotypic and genomic associations of WT1 mutations in AML were uncovered and rigorously described. Our findings help improving the current understanding and definition of WT1-mutant AML patients' characteristics and clinical outcomes.

Frequency and perturbations of various peripheral blood cell populations before and after eculizumab treatment in paroxysmal nocturnal hemoglobinuria.

While red blood cells (RBCs) and granulocytes have been more studied, platelets and reticulocytes are not commonly used in paroxysmal nocturnal hemoglobinuria (PNH) flow-cytometry and less is known about susceptibility to complement-mediated destruction and effects of anti-complement therapy on these populations. We performed flow-cytometry of RBCs and granulocytes in 90 PNH patients and of platelets and reticulocytes in a subgroup (N = 36), to unveil perturbations of these populations during PNH disease course before and after anti-complement treatment. We found that platelets and reticulocytes were less sensitive to complement-mediated lysis than RBCs but not as resistant as granulocytes, as shown by mean sensitive fraction (difference in a given PNH population vs. PNH granulocyte clone size). In treated patients, reticulocytes, platelets, RBCs (with differences between type II and III) and granulocytes significantly increased post-treatment, confirming the role of PNH hematopoiesis within the context of anti-complement therapy. Moreover, we found that PNH platelet clone size reflects PNH granulocyte clone size. Finally, we established correlations between sensitive fraction of PNH cell-types and thrombosis. In sum, we applied a flow-cytometry panel for investigation of PNH peripheral blood populations' perturbations before and after eculizumab treatment to explore complement-sensitivity and kinetics of these cells during the disease course.

Extended experience with a non-cytotoxic DNMT1-targeting regimen of decitabine to treat myeloid malignancies.

The nucleoside analogue decitabine can deplete the epigenetic regulator DNA methyltransferase 1 (DNMT1), an effect that occurs, and is saturated at, low concentrations/doses. A reason to pursue this molecular-targeted effect instead of the DNA damage/cytotoxicity produced with high concentrations/doses, is that non-cytotoxic DNMT1-depletion can cytoreduce even p53-null myeloid malignancies while sparing normal haematopoiesis. We thus identified minimum doses of decitabine (0·1-0·2 mg/kg) that deplete DNMT1 without off-target anti-metabolite effects/cytotoxicity, and then administered these well-tolerated doses frequently 1-2X/week to increase S-phase dependent DNMT1-depletion, and used a Myeloid Malignancy Registry to evaluate long-term outcomes in 69 patients treated this way. Consistent with the scientific rationale, treatment was well-tolerated and durable responses were produced (~40%) in genetically heterogeneous disease and the very elderly.

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.

Conversion of urodynamic pressures measured simultaneously by air-charged and water-filled catheter systems.

AIMS: The objective of this study was to compare the simultaneous responses of water-filled (WFC) and air-charged (ACC) catheters during simulated urodynamic pressures and develop an algorithm to convert peak pressures measured using an ACC to those measured by a WFC. METHODS: Examples of cough leak point pressure and valsalva leak point pressure data (n = 4) were obtained from the literature, digitized, and modified in amplitude and duration to create a set of simulated data that ranged in amplitude from 15 to 220 cm H2 O (n = 25) and duration from 0.1 to 3.0 sec (n = 25) for each original signal. Simulated pressure signals were recorded simultaneously by WFCs, ACCs, and a reference transducer in a specially designed pressure chamber. Peak pressure and time to peak pressure were calculated for each simulated pressure signal and were used to develop an algorithm to convert peak pressures recorded with ACCs to corresponding peak pressures recorded with WFCs. The algorithm was validated with additional simulated urodynamic pressure signals and additional catheters that had not been utilized to develop the algorithm. RESULTS: ACCs significantly underestimated peak pressures of more rapidly changing pressures, as in coughs, compared to those measured by WFCs. The algorithm corrected 90% of peak pressures measured by ACCs to within 5% of those measured by WFCs when simultaneously exposed to the same pressure signals. CONCLUSIONS: The developed algorithm can be used to convert rapidly changing urodynamic pressures, such as cough leak point pressure, obtained using ACC systems to corresponding values expected from WFC systems.

Clonal hematopoiesis-derived therapy-related myeloid neoplasms after autologous hematopoietic stem cell transplant for lymphoid and non-lymphoid disorders.

Therapy-related myeloid neoplasms (tMN) are complications of cytotoxic therapies. Risk of tMN is high in recipients of autologous hematopoietic stem cell transplantation (aHSCT). Acquisition of genomic mutations represents a key pathogenic driver but the origins, timing and dynamics, particularly in the context of preexisting or emergent clonal hematopoiesis (CH), have not been sufficiently clarified. We studied a cohort of 1507 patients undergoing aHSCT and a cohort of 263 patients who developed tMN without aHSCT to determine clinico-molecular features unique to post-aHSCT tMN. We show that tMN occurs in up to 2.3% of patients at median of 2.6 years post-AHSCT. Age ≥ 60 years, male sex, radiotherapy, high treatment burden ( ≥ 3 lines of chemotherapy), and graft cellularity increased the risk of tMN. Time to evolution and overall survival were shorter in post-aHSCT tMN vs. other tMN, and the earlier group's mutational pattern was enriched in PPM1D and TP53 lesions. Preexisting CH increased the risk of adverse outcomes including post-aHSCT tMN. Particularly, antecedent lesions affecting PPM1D and TP53 predicted tMN evolution post-transplant. Notably, CH-derived tMN had worse outcomes than non CH-derived tMN. As such, screening for CH before aHSCT may inform individual patients' prognostic outcomes and influence their prospective treatment plans. Presented in part as an oral abstract at the 2022 American Society of Hematology Annual Meeting, New Orleans, LA, 2022.

A multimodal analysis of genomic and RNA splicing features in myeloid malignancies.

RNA splicing dysfunctions are more widespread than what is believed by only estimating the effects resulting by splicing factor mutations (SFMT) in myeloid neoplasia (MN). The genetic complexity of MN is amenable to machine learning (ML) strategies. We applied an integrative ML approach to identify co-varying features by combining genomic lesions (mutations, deletions, and copy number), exon-inclusion ratio as measure of RNA splicing (percent spliced in, PSI), and gene expression (GE) of 1,258 MN and 63 normal controls. We identified 15 clusters based on mutations, GE, and PSI. Different PSI levels were present at various extents regardless of SFMT suggesting that changes in RNA splicing were not strictly related to SFMT. Combination of PSI and GE further distinguished the features and identified PSI similarities and differences, common pathways, and expression signatures across clusters. Thus, multimodal features can resolve the complex architecture of MN and help identifying convergent molecular and transcriptomic pathways amenable to therapies.

Molecular patterns identify distinct subclasses of myeloid neoplasia.

Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource ( https://drmz.shinyapps.io/mds_latent ).

A Focus on Intermediate-Risk Acute Myeloid Leukemia: Sub-Classification Updates and Therapeutic Challenges.

Acute myeloid leukemia (AML) represents a heterogeneous group of hematopoietic neoplasms deriving from the abnormal proliferation of myeloid progenitors in the bone marrow. Patients with AML may have highly variable outcomes, which are generally dictated by individual clinical and genomic characteristics. As such, the European LeukemiaNet 2017 and 2022 guidelines categorize newly diagnosed AML into favorable-, intermediate-, and adverse-risk groups, based on their molecular and cytogenetic profiles. Nevertheless, the intermediate-risk category remains poorly defined, as many patients fall into this group as a result of their exclusion from the other two. Moreover, further genomic data with potential prognostic and therapeutic influences continue to emerge, though they are yet to be integrated into the diagnostic and prognostic models of AML. This review highlights the latest therapeutic advances and challenges that warrant refining the prognostic classification of intermediate-risk AML.

Rare germline alterations of myeloperoxidase predispose to myeloid neoplasms.

Myeloperoxidase (MPO) gene alterations with variable clinical penetrance have been found in hereditary MPO deficiency, but their leukemia association in patients and carriers has not been established. Germline MPO alterations were found to be significantly enriched in myeloid neoplasms: 28 pathogenic/likely pathogenic variants were identified in 100 patients. The most common alterations were c.2031-2 A > C, R569W, M519fs* and Y173C accounting for about half of the cases. While functional experiments showed that the marrow stem cell pool of Mpo-/- mice was not increased, using competitive repopulation demonstrated that Mpo-/- grafts gained growth advantage over MPO wild type cells. This finding also correlated with increased clonogenic potential after serial replating in the setting of H2O2-induced oxidative stress. Furthermore, we demonstrated that H2O2-induced DNA damage and activation of error-prone DNA repair may result in secondary genetic damage potentially predisposing to leukemia leukemic evolution. In conclusion, our study for the first time demonstrates that germline MPO variants may constitute risk alleles for MN evolution.

TET2 mutations as a part of DNA dioxygenase deficiency in myelodysplastic syndromes.

Decrease in DNA dioxygenase activity generated by TET2 gene family is crucial in myelodysplastic syndromes (MDS). The general downregulation of 5-hydroxymethylcytosine (5-hmC) argues for a role of DNA demethylation in MDS beyond TET2 mutations, which albeit frequent, do not convey any prognostic significance. We investigated TETs expression to identify factors which can modulate the impact of mutations and thus 5-hmC levels on clinical phenotypes and prognosis of MDS patients. DNA/RNA-sequencing and 5-hmC data were collected from 1665 patients with MDS and 91 controls. Irrespective of mutations, a significant fraction of MDS patients exhibited lower TET2 expression, whereas 5-hmC levels were not uniformly decreased. In searching for factors explaining compensatory mechanisms, we discovered that TET3 was upregulated in MDS and inversely correlated with TET2 expression in wild-type cases. Although TET2 was reduced across all age groups, TET3 levels were increased in a likely feedback mechanism induced by TET2 dysfunction. This inverse relationship of TET2 and TET3 expression also corresponded to the expression of L-2-hydroxyglutarate dehydrogenase, involved in agonist/antagonist substrate metabolism. Importantly, elevated TET3 levels influenced the clinical phenotype of TET2 deficiency whereby the lack of compensation by TET3 (low TET3 expression) was associated with poor outcomes of TET2 mutant carriers.