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Calcium is a ubiquitous messenger that regulates a wide range of cellular functions, but its involvement in the pathophysiology of acute myeloid leukemia (AML) is not widely investigated. Here, we identified, from an analysis of The Cancer Genome Atlas and genotype‐tissue expression databases, stromal interaction molecule 2 (STIM2) as being highly expressed in AML with monocytic differentiation and negatively correlated with overall survival. This was confirmed on a validation cohort of 407 AML patients. We then investigated the role of STIM2 in cell proliferation, differentiation, and survival in two leukemic cell lines with monocytic potential and in normal hematopoietic stem cells. STIM2 expression increased at the RNA and protein levels upon monocyte differentiation. Phenotypically, STIM2 knockdown drastically inhibited cell proliferation and induced genomic stress with DNA double‐strand breaks, as shown by increased levels of phosphorylate histone H2AXγ (p‐H2AXγ), followed by activation of the cellular tumor antigen p53 pathway, decreased expression of cell cycle regulators such as cyclin‐dependent kinase 1 (CDK1)–cyclin B1 and M‐phase inducer phosphatase 3 (CDC25c), and a decreased apoptosis threshold with a low antiapoptotic/proapoptotic protein ratio. Our study reports STIM2 as a new actor regulating genomic stability and p53 response in terms of cell cycle and apoptosis of human normal and malignant monocytic cells.

Bladder cancer remains a prevalent and challenging malignancy. Myeloid-derived suppressor cells (MDSCs) have emerged as key contributors to the immunosuppressive tumor microenvironment, facilitating tumor progression, immune evasion, and resistance to therapies. This review explores the role of MDSC in bladder cancer, highlighting their involvement in immune regulation; tumor progression; and resistance to therapies such as bacillus Calmette–Guérin (BCG) therapy, chemotherapy, and immune checkpoint inhibitors (ICIs). We also discuss their potential as biomarkers and therapeutic targets, with current evidence suggesting that targeting MDSCs, either alone or in combination with existing treatments such as BCG and ICIs, may enhance anti-tumor immunity and improve clinical outcomes. However,, challenges remain, particularly regarding the identification and therapeutic modulation of MDSC subpopulations. Further research is warranted to fully elucidate their role in bladder cancer and to optimize MDSC-targeted therapies.

Summary: Hepatocellular carcinoma (HCC) is an inflammation-associated cancer arising from viral or non-viral etiologies including steatotic liver diseases (SLDs). Expansion of immunosuppressive myeloid cells is a hallmark of inflammation and cancer, but their heterogeneity in HCC is not fully resolved and might underlie immunotherapy resistance. Here, we present a high-resolution atlas of innate immune cells from patients with HCC that unravels an SLD-associated contexture characterized by influx of inflammatory and immunosuppressive myeloid cells, including a discrete population of THBS1+ regulatory myeloid (Mreg) cells expressing monocyte- and neutrophil-affiliated genes. THBS1+ Mreg cells expand in SLD-associated HCC, populate fibrotic lesions, and are associated with poor prognosis. THBS1+ Mreg cells are CD163+ but distinguished from macrophages by high expression of triggering receptor expressed on myeloid cells 1 (TREM1), which contributes to their immunosuppressive activity and promotes HCC tumor growth in vivo. Our data support myeloid subset-targeted immunotherapies to treat HCC.

The diagnosis of myelodysplastic syndromes (MDS) might be challenging and relies on the convergence of cytological, cytogenetic, and molecular factors. Multiparametric flow cytometry (MFC) helps diagnose MDS, especially when other features do not contribute to the decision-making process, but its usefulness remains underestimated, mostly due to a lack of standardization of cytometers. We present here an innovative model integrating artificial intelligence (AI) with MFC to improve the diagnosis and the classification of MDS. We develop a machine learning model through an elasticnet algorithm directed on a cohort of 191 patients, only based on flow cytometry parameters selected by the Boruta algorithm, to build a simple but reliable prediction score with five parameters. Our AI-assisted MDS prediction score greatly improves the sensitivity of the Ogata score while keeping an excellent specificity validated on an external cohort of 89 patients with an Area Under the Curve of 0.935. This model allows the diagnosis of both high- and low-risk MDS with 91.8% sensitivity and 92.5% specificity. Interestingly, it highlights a progressive evolution of the score from clonal hematopoiesis of indeterminate potential (CHIP) to highrisk MDS, suggesting a linear evolution between these different stages. By significantly decreasing the overall misclassification of 52% for patients with MDS and of 31.3% for those without MDS (P=0.02), our AI-assisted prediction score outperforms the Ogata score and positions itself as a reliable tool to help diagnose MDS.

Summary: Background: Schistocyte counts are a cornerstone of the diagnosis of thrombotic microangiopathy syndrome (TMA). Their manual quantification is complex and alternative automated methods suffer from pitfalls that limit their use. We report a method combining imaging flow cytometry (IFC) and artificial intelligence for the direct label-free and operator-independent quantification of schistocytes in whole blood. Methods: We used 135,045 IFC images from blood acquisition among 14 patients to extract 188 features with IDEAS® software and 128 features from a convolutional neural network (CNN) with Keras framework in order to train a support vector machine (SVM) blood elements’ classifier used for schistocytes quantification. Finding: Keras features showed better accuracy (94.03%, CI: 93.75-94.31%) than ideas features (91.54%, CI: 91.21-91.87%) in recognising whole-blood elements, and together they showed the best accuracy (95.64%, CI: 95.39-95.88%). We obtained an excellent correlation (0.93, CI: 0.90-0.96) between three haematologists and our method on a cohort of 102 patient samples. All patients with schistocytosis (>1% schistocytes) were detected with excellent specificity (91.3%, CI: 82.0-96.7%) and sensitivity (100%, CI: 89.4-100.0%). We confirmed these results with a similar specificity (91.1%, CI: 78.8-97.5%) and sensitivity (100%, CI: 88.1-100.0%) on a validation cohort (n=74) analysed in an independent healthcare centre. Simultaneous analysis of 16 samples in both study centres showed a very good correlation between the 2 imaging flow cytometers (Y=1.001x). Interpretation: We demonstrate that IFC can represent a reliable tool for operator-independent schistocyte quantification with no pre-analytical processing which is of most importance in emergency situations such as TMA. Funding: None.

Breast cancers are commonly associated with an immunosuppressive microenvironment responsible for tumor escape from anti-cancer immunity. Cells of the myeloid lineage account for a major part of this tumor-promoting landscape. These myeloid cells are composed of heterogeneous subsets at different stages of differentiation and have traditionally been described by their cardinal ability to suppress innate and adaptive anticancer immunity. However, evidence has accumulated that, beyond their immunosuppressive properties, breast cancer-induced myeloid cells are also equipped with a broad array of “non-immunological” tumor-promoting functions. They therefore represent major impediments for anticancer therapies, particularly for immune-based interventions. We herein analyze and discuss current literature related to the versatile properties of the different myeloid cell subsets engaged in breast cancer development. We critically assess persisting difficulties and challenges in unequivocally discriminate dedicated subsets, which has so far prevented both the selective targeting of these immunosuppressive cells and their use as potential biomarkers. In this context, we propose the concept of IMCGL, “pro-tumoral immunosuppressive myeloid cells of the granulocytic lineage”, to more accurately reflect the contentious nature and origin of granulocytic cells in the breast tumor microenvironment. Future research prospects related to the role of this myeloid landscape in breast cancer are further considered.

Myelodysplastic syndromes (MDSs) are associated with a significant risk of transformation to acute myeloid leukemia (AML), supported by alterations affecting malignant stem cells. This review focuses on the metabolic, phenotypic and genetic characteristics underlying this dynamic evolution, from myelodysplastic stem cells (MDS-SCs) to leukemic stem cells (LSCs). MDS-SCs are more likely to be derived from healthy hematopoietic stem cells (HSCs), whereas LSCs may originate from healthy progenitors, mostly LMPP (lymphoid-primed multipotential progenitors). Moreover, overexpression of CD123 and CLL1 markers by LSCs and MDS-SCs in high risk-MDS [HR-MDS] has led to exciting therapeutic applications. Single-cell sequencing has suggested that clonal evolution in the stem cell compartment was non-linear during MDS initiation and progression to AML, with pre-MDS-SC acquiring distinct additional mutations in parallel, that drive either MDS blast production or AML transformation. In AML and HR-MDS, common metabolic alterations have been identified in malignant stem cells, including activation of the protein machinery and dependence on oxidative phosphorylation. Targeting these metabolic abnormalities could prevent HR-MDS from progressing to AML. Strikingly, in low risk-MDS-SC, the expression of ribosomal proteins is decreased, which may be accompanied by a reduction in protein synthesis.

Synchronous diagnosis of acute myeloid leukemia (AML) and symptomatic multiple myeloma (MM) is a rare situation that poses serious therapeutic difficulties. We report the case of a 68-year-old male which evolved simultaneously to symptomatic MM and AML. Both diseases first responded to treatment for 40 months after 7+3 induction and maintenance therapy of azacytidine + lenalidomide. MM relapsed first and was treated with azacytidine + daratumumab, which led an additional 15 months of progression-free survival. Little myeloid clonal size reduction over time was seen. This case shows that AML and MM can be effectively treated simultaneously using appropriate combinations.

Tumor metabolism plays a crucial role in sustaining tumorigenesis. There have been increasing reports regarding the role of tumor metabolism in the control of immune cell functions, generating a potent immunosuppressive contexture that can lead to immune escape. The metabolic reprogramming of tumor cells and the immune escape are two major hallmarks of cancer, with several instances of crosstalk between them. In this paper, we review the effects of tumor metabolism on immune cells, focusing on myeloid cells due to their important role in tumorigenesis and immunosuppression from the early stages of the disease. We also discuss ways to target this specific crosstalk in cancer patients.

Physical theories are nowadays very mathematized, and what scientists manipulate so as to describe, predict and control the phenomena, are (in part) equations, which consist in many mathematical symbols. These mathematical objects don't have any physical signification by themselves: they don't “speak” about the phenomena. An interpretation is required. In this article, we are interested in the interpretation which a physical theory needs so as to fulfill its role. We start by making explicit a traditional distinction: the “lean” interpretation (a simple instrument which allows the symbols of the theory to receive a physical meaning, strictly limited to the experimental results) differs from the “rich” interpretation (which makes up an image of the world compatible with the way the theory mathematically describes the experimental results). Our aim in this article is to show that this distinction should be amended. We rely on the example of Quantum Mechanics, but the distinction intends to be general, for any physical theory.

Background: Hepatocellular carcinoma (HCC) is a highly lethal cancer with few treatment options available to patients. Most HCC cases in Arizona, a state with a high proportion of Hispanic adults, have not been included in recent reports of HCC incidence. This study describes trends in HCC incidence and stage at diagnosis among Arizona residents between 2009-2017 and reports on racial and ethnic disparities for these outcomes., Methods: The Arizona Cancer Registry was used to identify Arizonans aged 19 or older diagnosed with liver cell carcinoma diagnosed between 2009-2017. A total of 5043 cases were examined. Adjusted annual and 3-year HCC incidence rates (per 100,000) were examined for non-Hispanic White (NHW) and Hispanic adults., Results: The total age-adjusted HCC incidence rate increased significantly between 2009-2012 and then declined significantly between 2012-2017. Across nearly all years, age-adjusted HCC incidence in Hispanic adults was twice that of NHW adults. Hispanic adults were more likely to be diagnosed at a later stage across all time periods. The disparity in 3-year age-adjusted HCC incidence rate between NHW and Hispanic adults decreased between 2009-2017., Conclusion: Whe total age-adjusted HCC incidence rate increased significantly between 2009-2012 and then declined significantly between 2012-2017. Across nearly all years, age-adjusted HCC incidence in Hispanic adults was twice that of NHW adults. Hispanic adults were more likely to be diagnosed at a later stage across all time periods. The disparity in 3-year age-adjusted HCC incidence rate between NHW and Hispanic adults decreased between 2009-2017., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background & Aims: Although patient knowledge is modifiable, there are no widely accepted tools to measure patient understanding during cirrhosis care. We aimed to develop and validate "My Cirrhosis Coach" (MCC), a personalized, self-administered questionnaire to evaluate cirrhosis-related medication use, obstacles, and understanding., Methods: Adults with cirrhosis were prospectively enrolled at 3 tertiary centers from July 2016 through July 2020. Psychometrics including confirmatory factor analysis was used to develop and validate a final questionnaire. Content validity was measured via the content validity index and expert performance. Discriminant validity was assessed by comparing scores between groups hypothesized to have varying performance., Results: The MCC was tested in a diverse cohort (n = 713) with cirrhosis and its complications including ascites (45%) and hepatic encephalopathy (33%) with median Model for End-Stage Liver Disease-Sodium 10 (interquartile range, 9-15). A 6-factor model of the MCC fit the data well (root mean square error of approximation, 0.22; comparative fit index, 0.96; standardized root mean squared residual, 0.104; final domains: Medication Use & Accessibility, Medication Obstacles, Lactulose Use, Diuretic Use, Beta Blocker Use, and Dietary Sodium Use). The MCC had excellent content validity (content validity index, 81%-94%) and accuracy (91%-100%) ratings by experts. Mean domain scores ranged from 1.1 to 2.6 (range, 0-3; 3 indicating better performance). Those with a cirrhosis complication scored higher in the relevant medication domain (ie, diuretic use score in ascites). Compared with outpatients, inpatients scored higher in all knowledge domains except salt use and reported more medication obstacles. Scores differed by income, education level, and having an adult at home., Conclusions: In a large, diverse cohort, we validated the MCC, which can serve to standardize medication use and knowledge measurement in clinical practice and education-based studies in cirrhosis., (Copyright © 2023 AGA Institute. Published by Elsevier Inc. All rights reserved.)