Background: Patients with solid cancers and hematopoietic malignancy can experience systemic symptoms compatible with adult-onset Still’s disease (AOSD). The newly described VEXAS, associated with somatic UBA1 mutations, exhibits an overlap of clinical and/or biological pictures with auto inflammatory signs and myelodysplastic syndrome (MDS). Objectives: To describe a cohort of patients with signs of undifferentiated systemic autoinflammatory disorder (USAID) concordant with AOSD and MDS/chronic myelomonocytic leukemia (CMML) and the prevalence of VEXAS proposed management and outcome. Methods: A French multicenter retrospective study from the MINHEMON study group also used for other published works with the support of multidisciplinary and complementary networks of physicians and a control group of 104 MDS/CMML. Results: Twenty-six patients were included with a median age at first signs of USAID of 70.5 years with male predominance (4:1). Five patients met the criteria for confirmed AOSD. The most frequent subtypes were MDS with a blast excess (31%) and MDS with multilineage dysplasia (18%). Seven patients presented with acute myeloid leukemia and twelve died during a median follow-up of 2.5 years. Six out of 18 tested patients displayed a somatic UBA1 mutation concordant with VEXAS, including one woman. High-dose corticosteroids led to a response in 13/16 cases and targeted biological therapy alone or in association in 10/12 patients (anakinra, tocilizumab, and infliximab). Azacytidine resulted in complete or partial response in systemic symptoms for 10/12 (83%) patients including 3 VEXAS. Conclusions: Systemic form of VEXAS syndrome can mimic AOSD. The suspicion of USAID or AOSD in older males with atypia should prompt an evaluation of underlying MDS and assessment of somatic UBA1 mutation.

Systemic auto-inflammatory or autoimmune diseases (SIADs) develop in up to a quarter of patients with myelodysplastic syndromes (MDS) or chronic myelomonocytic leukemia (CMML). With or without the occurrence of SIADs, the distribution of MDS subtypes and the international or CMML-specific prognostic scoring systems have been similar between MDS/CMML patients. Moreover, various SIADs have been described in association with MDS, ranging from limited clinical manifestations to systemic diseases affecting multiple organs. Defined clinical entities including systemic vasculitis, connective tissue diseases, inflammatory arthritis and neutrophilic diseases are frequently reported; however, unclassified or isolated organ impairment can also be seen. Although the presence of SIADs does not impact the overall survival nor disease progression to acute myeloid leukemia, they can help with avoiding steroid dependence and make associated adverse events of immunosuppressive drugs challenging. While therapies using steroids and immunosuppressive treatment remain the backbone of first-line treatment, increasing evidence suggests that MDS specific therapy (hypomethylating agents) and sparing steroids may be effective in treating such complications based on their immunomodulatory effect. The aim of this review was to analyze the epidemiological, pathophysiological, clinical and therapeutic factors of systemic inflammatory and immune disorders associated with MDS.

Background/Objectives: Massively parallel sequencing technologies have advanced chronic lymphocytic leukemia (CLL) diagnostics and precision oncology. Illumina platforms, while offering robust performance, require substantial infrastructure investment and a large number of samples for cost-efficiency. Conversely, third-generation long-read nanopore sequencing from Oxford Nanopore Technologies (ONT) can significantly reduce sequencing costs, making it a valuable tool in resource-limited settings. However, nanopore sequencing faces challenges with lower accuracy and throughput than Illumina platforms, necessitating additional computational strategies. In this paper, we demonstrate that integrating publicly available short-read data with in-house generated ONT data, along with the application of machine learning approaches, enables the characterization of the CLL transcriptome landscape, the identification of clinically relevant molecular subtypes, and the assignment of these subtypes to nanopore-sequenced samples. Methods: Public Illumina RNA sequencing data for 608 CLL samples were obtained from the CLL-Map Portal. CLL transcriptome analysis, gene module identification, and transcriptomic subtype classification were performed using the oposSOM R package for high-dimensional data visualization with self-organizing maps. Eight CLL patients were recruited from the Hematology Center After Prof. R. Yeolyan (Yerevan, Armenia). Sequencing libraries were prepared from blood total RNA using the PCR-cDNA sequencing-barcoding kit (SQK-PCB109) following the manufacturer’s protocol and sequenced on an R9.4.1 flow cell for 24–48 h. Raw reads were converted to TPM values. These data were projected into the SOMs space using the supervised SOMs portrayal (supSOM) approach to predict the SOMs portrait of new samples using support vector machine regression. Results: The CLL transcriptomic landscape reveals disruptions in gene modules (spots) associated with T cell cytotoxicity, B and T cell activation, inflammation, cell cycle, DNA repair, proliferation, and splicing. A specific gene module contained genes associated with poor prognosis in CLL. Accordingly, CLL samples were classified into T-cell cytotoxic, immune, proliferative, splicing, and three mixed types: proliferative–immune, proliferative–splicing, and proliferative–immune–splicing. These transcriptomic subtypes were associated with survival orthogonal to gender and mutation status. Using supervised machine learning approaches, transcriptomic subtypes were assigned to patient samples sequenced with nanopore sequencing. Conclusions: This study demonstrates that the CLL transcriptome landscape can be parsed into functional modules, revealing distinct molecular subtypes based on proliferative and immune activity, with important implications for prognosis and treatment that are orthogonal to other molecular classifications. Additionally, the integration of nanopore sequencing with public datasets and machine learning offers a cost-effective approach to molecular subtyping and prognostic prediction, facilitating more accessible and personalized CLL care.