Gurnari, Carmelo

Myelodysplastic syndromes with isolated deletion of chromosome 5q [MDS-del(5q)] constitute a distinct biological entity traditionally associated with favorable outcomes, although up to one quarter of patients progress to acute myeloid leukemia (AML). Existing prognostic models, developed in heterogeneous MDS populations, may not adequately capture risk within this subgroup. We assembled an international cohort of 682 patients with MDS-del(5q) to evaluate the performance of the IPSS-R and IPSS-M, identify prognostic variables, and develop a disease-specific prognostic tool, the IPSS-del(5q). Most patients were classified as lower-risk by IPSS-R (94.4%) and IPSS-M (85.5%), yet both systems showed limited discriminatory ability (C-indices ≈0.5). Independent adverse prognostic factors included age ≥70 years, male sex, anemia (hemoglobin ≤10 g/dL), thrombocytopenia (platelets ≤100×10⁹/L), the presence of one additional chromosomal abnormality, ≥2 gene mutations, SF3B1 mutations, and high-risk TP53 status. Six variables were included in the IPSS-del(5q), stratifying patients into standard-risk (74.3%) and high-risk (25.7%) groups with significantly different LFS (69.2 vs. 32.0 months; p<0.01). Moreover, this model reclassified 19.1% of lower-risk IPSS-R and 14.6% of lower-risk IPSS-M patients into the high-risk IPSS-del(5q) group. However, its discriminative power remained modest, with a C-index of 0.60. Overall, this study provides the most comprehensive prognostic evaluation of MDS-del(5q) to date, demonstrates the limited discriminatory capacity of existing MDS scores in this entity, and underscores the need to develop refined disease-specific prognostic approaches for this MDS subtype.

Objective Vacuoles, E1 enzyme, X‐linked, autoinflammatory, somatic syndrome (VEXAS) syndrome is characterized by a complex spectrum of inflammatory and hematologic manifestations. Clinical research to identify effective therapies is urgently needed but is hindered by the lack of validated outcome measures. A VEXAS‐specific disease activity index (DAI) is an essential tool for reliably capturing changes in disease activity over time, thereby supporting clinical research and informing patient care in this emerging disease. This study aimed to develop a comprehensive DAI to assess disease‐related inflammation across affected organ systems in patients with VEXAS syndrome. Methods Inflammatory manifestations of VEXAS were previously identified through a systematic literature review and expert input. A multinational expert advisory committee developed the VEXAS‐DAI using modified Delphi methodology until consensus (defined as ≥75% concurrence) was reached. Item grading was adapted from the Common Terminology Criteria for Adverse Events. Scoring criteria were developed based on 158 test cases using the Physician Global Assessment of Inflammation. Results Consensus on the VEXAS‐DAI was achieved after four Delphi rounds. The index assesses inflammatory activity at the time of evaluation across 12 domains: inflammatory rash, chondritis, periorbital, ophthalmologic, joint, pulmonary, cardiovascular, genitourinary, neurologic, oral and gastrointestinal, renal, and constitutional symptoms. The total score ranges from 0 to 40. Conclusion The VEXAS‐DAI is a novel instrument designed to quantify active inflammation over time in patients with VEXAS. Prospective validation within a clinical trial setting is ongoing. image

ABSTRACT Myelodysplastic syndrome (MDS) and chronic myelomonocytic leukemia (CMML) are clonal myeloid neoplasms shaped by genetic lesions and immune dysregulation, both of which contribute to disease progression and poor outcomes. Existing prognostic systems, such as IPSS‐R and IPSS‐M, do not incorporate immune alterations. We assessed the biological and clinical relevance of an immune dysfunction signature (IDS) across multi‐center MDS and CMML cohorts. IDS scores, derived from bulk transcriptomic data, were significantly associated with inferior leukemia‐free and overall survival. In multivariable analyses, IDS retained independent prognostic value alongside IPSS‐R, IPSS‐M, and bi‐allelic TP53 inactivation. Incorporation of IDS into existing models improved prognostic discrimination and time‐dependent predictive accuracy. Longitudinal analyses revealed that rising IDS scores paralleled disease progression and acute transformation, whereas declining scores were observed in remission. Biologically, IDS‐high cases demonstrated reduced cytotoxic T‐cell activity, expansion of regulatory T cells, enrichment of primitive progenitor signals, and increased expression of checkpoint pathway genes. These findings were validated across multiple independent MDS cohorts and consistently reproduced in CMML, where IDS also stratified risk and tracked disease evolution. Finally, integration with drug response signatures from the BeatAMLv2 cohort suggested potential therapeutic vulnerability of IDS‐high cases to multikinase and NF‐κB pathway inhibitors. These results establish IDS as a robust and dynamic biomarker in MDS and CMML, with applications in refined risk stratification, longitudinal disease monitoring, and guiding personalized therapeutic strategies targeting immune dysfunction. Trial Registration: #201709072RINC, #202109078RINB, #202207050RINB

PURPOSE Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

VEXAS (Vacuoles, E1 ubiquitin-activating enzyme, X-linked, Autoinflammatory, Somatic) syndrome is a systemic disorder characterized by an overlap of hematologic and inflammatory features. Most patients require chronic use of moderate-to-high doses of glucocorticoids (GCs) to maintain disease control. Data on GC-sparing therapies is limited, and there have been no prospective pharmacotherapeutic trials in VEXAS syndrome published to date. Pacritinib, an oral inhibitor of IRAK1, JAK2, and ACVR1, has emerged as a promising therapeutic option for VEXAS syndrome. The PAXIS trial is the first prospective, randomized pharmacotherapeutic study conducted in this rare and severe disease. Utilizing a novel study design and disease-specific endpoints, the trial will evaluate the efficacy and safety of two dose levels of pacritinib compared with placebo in patients with VEXAS syndrome (NCT06782373, EUCTR: 2024-516347-41-00).

Abstract Introduction The 2022 WHO/ICC classifications of myeloid neoplasms (MNs) incorporated genomics into the existing clinicopathologic framework, defining new (mono)genetic entities. However, this iterative approach resulted in a mix of pathologically and genomically-defined entities, reliant on arbitrary thresholds and historically-derived boundaries between entities. We hypothesised that integrating genomics with other features, de novo from first principles, might better incorporate modern molecular knowledge to define the biological continuum, evolutionary links and overlapping features across MN. Methods The discovery cohort comprised 6484 thoroughly annotated pts. Contemporary diagnoses included 1817 AML, 1695 MDS, 737 MPN, and 1101 MDS/MPN. Non-malignant pts (AA/CHIP/CCUS/ICUS) were included to explore beyond current neoplastic boundaries. 1407 pts had ≥1 follow-up sample/s. Validation was performed in an independent international cohort (n=2012). An unsupervised random forest algorithm was used to integrate status (and VAF) for 28 mutations, karyotypes, CBC features, sex and age (total parameters 144). Features were selected for potential to be independent of BM sampling. Proximity-based multidimensional scaling/PCA were used to visualize latent structure and identify variance-driving features. Results The model projected pts onto a continuous 3-dimensional space. We first applied clustering seeking 1st-order structure, revealing 4 major clusters (C1–C4). These were further subdivided into 58 distinct subclusters (SCs) by an unsupervised iterative clustering approach. All SCs were enriched for distinctive features, with diverse associations linking genomics to clinicopathological/outcome features. Notably, contemporary diagnoses were distributed widely throughout this landscape, with 9/58 SCs containing cases spanning multiple WHO/ICC MN categories. C1 displayed the most internal heterogeneity, with admixed AML, MDS and MDS/MPN pts. It naturally subdivided down to 4th-order subclustering. Ten SCs were dominated by splicing factor mutations. For example, SF3B1MT segregated into 3 SCs, alone or with high-VAF TET2/ASXL1 or DNMT3A mutations. A separate TET2/SRSF2 SC associated with elevated monocyte%, reflecting a discrete CMML-phenotype genomic entity. NPM1MT formed a distinct SC with FLT3-ITD, distinguished further by VAFs of FLT3, TET2, and NRAS. Current genomically-defined AML subtypes separated in the 4th subclustering. C2 encompassed classical MPN driver mutations across 6 SCs, including a distinct CALRMT group and others defined by variable JAK2 VAFs, co-mutation patterns and other CBC features: eg high-VAF SF3B1MT correlated with PB blast%, and concurrent TET2MT with higher Hb/monocyte counts. C3 was characterized by TP53 alterations, with 5 SCs differing by TP53MT burden, 17p lesions, karyotypic complexity and age. Two SCs, one marked by high TP53 VAF+complex karyotype, the other by ch17 lesions, were associated with the worst OS in the whole cohort. C4 contained most known non-malignant pts, dividing into 4 SCs: 2 largely comprising CCUS and AA, while 2 reflect overlaps/convergence between AA and MDS and MPN, respectively. Repurposing single-cell pseudotime analysis suggested multiple disease origins, with various progression trajectories converging on C3. Projecting them onto the model longitudinal samples moved along these derived paths, consistent with their known progression/remission changes, validating the model's biological relevance and predictive utility. Remarkably, the 58 SCs displayed distinct risks of clonal progression and survival. To maximize potential clinical utility we created a pan-MN prognostic score integrating these features. It performed robustly (c-index 0.84), matching/exceeding existing prognostic tools and validated in an independent dataset. Conclusion Integrating clinicopathologic and genomic data we developed ab initio a continuum model of MNs that transcends historical diagnostic boundaries, captures disease evolution and stratifies clinical outcomes. It may better reflect the core biology and natural histories of specific cases, placing more rational boundaries between discrete entities. Potential advantages extend to treatment selection/trial design (in the era of molecularly-targeted therapies) and personalised prognostication throughout each disease course. Expanded validation is needed to resolve complexity in some areas (notably in C2/C4) and validate/refine this prototype framework.

Abstract Myeloid neoplasia encompasses a heterogeneous and complex molecular landscape whereby the distinct combinatorial patterns associated with clinical phenotypes are difficult to delineate. Given that genetic heterogeneity can be a consequence of the topology of the fitness landscape (peaked/non-peaked=high vs low fitness), we investigated co-mutational patterns in TP53 context as an example of hallmark genomic heterogeneity. Indeed, TP53 gene is characterized by hypomorphic/loss-of-function lesions, truncating mutations (frameshifts, stop codons), missense mutations often having a dominant-negative effect and loss of heterozygosity by chromosome 17p deletion driving clonal expansion. Clinical phenotypes of TP53 mutants are also heterogeneous. Here, we stratify TP53 mutations based on our previous published data1 whereby patients with single-hit TP53 and variant allelic frequency (VAF) &gt;50% were classified as obligatory biallelic, patients with VAF between 23-50% as probable biallelic and &lt;23% as probable monoallelic. A threshold of VAF 23% was optimal to separate monoallelic vs biallelic and was based on all-cause mortality. A tree-based model (RandomForestSRC) was used to build a survival model with VAF as a single covariate and minimum node size set to 15 to optimize the cut-off. Using this cut-off, we delineate the epistatic landscape and potential interactions among different TP53 mutational spectra and its fitness. We curated a cohort of 800 patients with TP53 mutations from our institution. In total 36% of the cases carried single hits while 64% had double hits genomic configuration. About 70% of the mutations were missense and were detected at canonical sites (R175H, Y220C, M237I, R273H, R282W, R248Q). We took into consideration 35 features and clustered our cohort of TP53 mutants using factor analysis. When we stratified the cohort based on our predetermined VAF threshold, we found that majority of the variants with high VAF, to be deemed as obligatory biallelic, co-occurred with complex karyotype. These cases were very homogeneous hence had on average a low number of additional variants, likely due to TP53 clones having reduced fitness in the presence of additional mutations. Conversely, patients without complex karyotype were mostly assigned to the monoallelic group due to low to median VAFs. Consequently, these samples had on average a higher number of additional hits, hence a heterogeneous molecular landscape. To substantiate this finding, we noticed that among monoallelic patients two characteristic pairs were observed (TP53-TET2 and TP53-SF3B1) and showed different patterns according to karyotypic associations. Indeed, both pairs in normal karyotype (TP53-TET2, 65%; TP53-SF3B1, 23%) had the highest number of additional mutations on average compared to other monoallelic hits in complex karyotype suggesting that clones resulting by these pairs were advantageous and produced higher fitness driving heterogeneity when together with other mutations. As a preliminary analysis to characterize the fitness landscape of canonical missense TP53 mutations, we investigated whether distinct TP53 mutations conferred differential clonal fitness by implementing a previously published model of stochastic hematopoietic stem cell growth based on the distribution of allelic frequencies across different age groups. Given a fixed stem cell population pool and a constant mutation rate, the distribution of allelic frequencies in the population of corresponding mutations across age can inform the fitness/selection coefficient. The model predicts that high fit mutations will expand more in the population (high VAF) depending on the time the mutation is allowed to expand (age). Based on the literature, we set the stem cell pool to 100,000 and the mutation rate to 1.4E-8.2We limited our analysis to mutations found in at least 10 individual in our cohort and to single hits due to potential epistatic interactions across multiple mutations in the same gene. Given this criterion two mutations R273H and R248Q were analyzed. Interestingly, we observed a 15% increase in fitness gain for R273H compared to R248Q. In summary, mutational heterogeneity and clonal fitness might provide clues to the diversity of TP53 mutational configurations. Our model will be further optimized for the upcoming ASH meeting by incorporating temporal sampling and increasing sample size to define differences in fitness according to mutation site.

Abstract Chronic idiopathic neutropenia (CIN) is characterized by a persistently reduced absolute neutrophil count, typically &lt;1.5×10⁹/L for ≥3 months, without identifiable infectious, nutritional, autoimmune, clonal, congenital, or drug-related causes. It most commonly affects adult women, carries a variable risk of infection, and is frequently managed expectantly in the absence of severe or recurrent infections. An index case of CIN with irregular menses and normalization of counts during pregnancy prompted an institution-wide review of adult neutropenia. U[p front we excluded cases of known congenital and drug-induced neutropenia. Among 238 presumed CIN patients, 88 had large granular lymphocytic (LGL) leukemia–associated neutropenia, 63 had autoimmune neutropenia defined by the presence of anti-neutrophil antibodies, and 87 met criteria for idiopathic neutropenia. Within the presumptive CIN group, extended blood antigen screening identified 12 patients with a Duffy-null phenotype, who were excluded from further idiopathic analysis (final n=75). This included 10 African American, 1 Caucasian, and 1 Asian patient. Extended blood-group profiling (Kidd, Colton, Lutheran, Dombrock, LW, ABO) showed no significant deviation from population frequencies. In the remaining non-Duffy CIN patients, remarkably 54/75 were female with a mean age of 45±16 years. At presentation, mean WBC was 2.84±0.58×10⁹/L and ANC was 0.92±0.39×10⁹/L, Hgb was 13.5±1.2g/dL and platelets were 229±83×10⁹/L. Antinuclear antibodies were assessed in 73 patients, with 63 negative and 10 positive, predominantly speckled (66%) but rheumatologic consultation did not yield systemic autoimmune diagnoses. Hypothyroidism was noted in two CIN cases. Irregular menses were reported in 50% of female CIN patients with documented histories, and among those, 80% exhibited higher or normal neutrophil counts during gestation. Physiologically, pregnancy is a progesterone-high state associated with neutrophilia and physiologic leukocytosis; in addition, progesterone can dampen cytotoxic T-cell activity and inflammatory signaling while supporting marrow niche function. Furthermore, progesterone binds PR on marrow stromal cells and can upregulate CSF3 (G-CSF) and possibly other hematopoietic growth factors, thus regulating granulopoiesis. By contrast, anovulatory or irregular cycles often reflect luteal progesterone deficiency, which may predispose susceptible patients to lower ANC prompting us to formulate a theory that excess of CIN in females may be due to a progesterone-responsive CIN subtype. From our cohort, there were no patients on progesterone-supplement to evaluate if there would be responsiveness secondary to its utilization. To further support this theory we turned to metanalytic resources. TriNetX1allowed us search and identify women receiving exogenous progesterone versus a control receiving non-hormonal contraceptives and assign to them a corresponding ANC. When we compared ANC in 1:1 greedy propensity-score matching to control for covariates (demographics, BMI, and disease confounders e.g., malignancy). balance between comparator groups was confirmed by standardized mean differences &lt;0.10. After matching (n=18,561 per arm), the exogenous progesterone group had a significantly higher neutrophil count compared to non-hormonal contraception (1.9±3.7 vs 1.5±1.7; t=3.5, p=.0004). The cohort age range was similar to our patient population 44 ±19. Our observations motivate prospective evaluation of progesterone-high states as potential modifiers of ANC in otherwise idiopathic cases and particularly refractory cases Cycle-synchronized CBC with paired progesterone and estradiol measurements, together with controlled progesterone augmentation trials, can quantify ANC changes and clinical outcomes in this subset. We thank TriNetX and all participants for their contribution, without whom this research would not have been possible. ReferencesRobertson SA, Mayrhofer G, Seamark RF. Ovarian steroid hormones regulate granulocyte-macrophage colony-stimulating factor synthesis by uterine epithelial cells in the mouse. Biol Reprod. 1996 Jan;54(1):183-96. doi: 10.1095/biolreprod54.1.183. PMID: 8838016.

Abstract Background: Higher-risk myelodysplastic syndromes/neoplasms (HR-MDS) and secondary acute myeloid leukemia (sAML) share overlapping molecular features but both remain clinically diverse with poor prognosis. Machine learning (ML)-based genomic clustering offers an opportunity to redefine disease subgroups beyond traditional clinical prognostic schemes or pathologic classifications. Prognostic scoring systems emphasize outcomes while pathologic classifications morphology resulting in inclusion of patients (pts) with different genetic background into same outcome or morphologic subgroup. We compared our ML-unsupervised clustering (UC) model named molecular nosology1 with the taxonomy model2 to evaluate differences in cluster architecture and clinical relevance in HR-MDS and sAML. Methods We analyzed HR-MDS (≥10% blasts) and sAML pts with available molecular data at diagnosis from a well-annotated cohort. Our molecular nosology model used UC via a binary latent-factor model to generate molecular clusters (MCs). The taxonomy model assigned pts to predefined genomic classes. We compared MC size, genomic enrichment, and overall survival (OS) across diagnostic categories. Concordance was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Purity and entropy were calculated to assess how concentrated (purity) or dispersed (entropy) each taxonomy group was across MCs. Results Among 898 pts, 367 (41%) had HR-MDS and 531 (59%) had sAML. Median age was 71 (IQR 65–77) years. Compared to sAML, HR-MDS pts had similar age (71 vs. 72, p=0.556) but higher hemoglobin (9.9 vs. 9.3, p&lt;0.001) and platelet counts (91 vs. 50, p&lt;0.001). Normal karyotype was more common in HR-MDS (55% vs. 42%, p&lt;0.001). TET2 (24%), ASXL1 (24%), RUNX1 (23%), and SRSF2 (22%) were the most frequently mutated genes. Using our nosology model, we identified 14 MCs.1 The most frequent were MC2 (normal karyotype) at 22% and MC13 (TP53-complex) at 17%, followed by MC1 (+8/ASXL1/TET2/RUNX1, 12%), MC6 (SRSF2/RAS-pathway, 9%), and MC9 and MC14 (each 8%). Less common clusters included MC12 (5%) and MC3–MC5 (2–3%). For comparison, the taxonomy model included 18 genomic groups: TP53-complex (20%), mNOS (13%), bi-TET2, IDH-STAG2, and no event (each 10%), CCUS-like, SETBP1/-7, and AML-like (each 6%), SRSF2 (4%), del(5q), BCOR/L1, EZH2-ASXL1, and SF3B1 (each 3%), and five rare subgroups (DDX41, der(1;7), U2AF1157, U2AF134, ZRSR2; all &lt;1%). Our MCs aligned closely with several taxonomy groups: 82% of TP53-complex cases were assigned to MC13, 99% of no event cases mapped to MC2, and 97% of del(5q) cases mapped to MC8. Bi-TET2 cases were distributed across MC6 and MC12 (both TET2-enriched); SETBP1/-7 to MC11; and EZH2-ASXL1 cases to MC1 and MC12. In contrast, mNOS, AML-like, and CCUS-like groups were genomically heterogeneous and spanned multiple MCs. ARI and NMI values were 0.391 and 0.458, indicating moderate concordance. Purity was highest in no event (0.989), del(5q) (0.967), and TP53-complex (0.823), and lowest in IDH-STAG2 (0.247), CCUS-like (0.241), and AML-like (0.347), all with high entropy (&gt;2.5), reflecting genomic diversity not captured by taxonomy groups alone. Median OS for the entire cohort was 17 mo (95%CI: 14–20). HR-MDS pts had longer OS vs. sAML (26 vs. 12 mo, p&lt;0.001). OS did not differ significantly between HR-MDS and sAML within most taxonomy groups or MCs, except for MC3 (NA vs. 11 mo, p=0.014), MC6 (111 vs. 15 mo, p&lt;0.001), and MC9 (18 vs. 9 mo, p=0.027) for the nosology model and SF3B1 (129 vs. 23 mo, p=0.033), del(5q) (33 vs. 6 mo, p=0.022), bi-TET2 (25 vs. 8 mo, p=0.019), mNOS (34 vs. 9 mo, p&lt;0.001) for taxonomy. Conclusions Some molecular taxonomy groups showed strong concordance with our nosology-defined clusters, which served as the gold standard for genomically distinct subsets (e.g., TP53-complex, del(5q), and no event), irrespective of diagnosis or blast percentage which may present the stage of the disease. In contrast, taxonomy-defined categories such as CCUS-like, AML-like, and mNOS were more genetically diverse and less aligned with specific clusters in the nosology model. Median OS within most clusters was not significantly influenced by blast count, underscoring the potential of molecular-driven clustering to reveal underlying disease biology not captured by morphology and to refine classification in MDS/AML and sAML with few exceptions (SF3B1 or biTET2). Ref 1.Kewan, Durmaz et al.Nat Commun 2023 2.Bernard, et al.Blood 2024