We developed machine-learning models to predict isocitrate dehydrogenase (IDH) mutation status in acute myeloid leukemia (AML) from gene expression profiles and to reconstruct missing IDH annotations across public datasets. Transcriptomic data from 19 cohorts (5844 samples) were harmonized using batch correction, and 1546 samples with known IDH status were used to train a feed-forward neural network and a logistic regression (LR) classifier within a nested cross-validation framework, followed by independent validation in the TCGA-LAML dataset. The LR model showed superior performance, achieving receiver operating characteristic area under the curve [Formula: see text] 0.994 [Formula: see text] 0.007, accuracy [Formula: see text] 0.983 [Formula: see text] 0.006, balanced accuracy [Formula: see text] 0.979 [Formula: see text] 0.005, sensitivity for the IDH-mutant (IDH-MUT) class [Formula: see text] 0.972 [Formula: see text] 0.010, and specificity [Formula: see text] 0.986 [Formula: see text] 0.008, and correctly classified all IDH-MUT cases in the independent cohort. Applying the final model to samples lacking annotations enabled reconstruction of IDH status for 4148 AML cases, expanding the number of molecularly characterized transcriptomes available for downstream analyses. Predicted groups recapitulated known IDH-associated transcriptional signatures, supporting biological validity. This work demonstrates that IDH mutation status can be accurately inferred from transcriptomic data alone and provides a scalable framework to recover missing genomic annotations, thereby enhancing the utility of public AML resources for large-scale biological and translational research.

A newborn girl had typical "blueberry muffin" skin lesions, which shows histopathologic features of monocytic leukemia cutis. The systemic leukemia was demonstrated after one month of life. She was treated by chemotherapy, including induction and three consolidation cures, according to the ELAM02 protocol, which led to complete remission. This case report with congenital form of AML5 cutaneous localization, preceding systemic involvement, with a 5-year follow-up and positive outcome is remarkable.

Several studies have linked bad prognoses of acute myeloid leukemia (AML) to the ability of leukemic cells to reprogram their metabolism and, in particular, their lipid metabolism. In this context, we performed “in-depth” characterization of fatty acids (FAs) and lipid species in leukemic cell lines and in plasma from AML patients. We firstly showed that leukemic cell lines harbored significant differences in their lipid profiles at steady state, and that under nutrient stress, they developed common mechanisms of protection that led to variation in the same lipid species; this highlights that the remodeling of lipid species is a major and shared mechanism of adaptation to stress in leukemic cells. We also showed that sensitivity to etomoxir, which blocks fatty acid oxidation (FAO), was dependent on the initial lipid profile of cell lines, suggesting that only a particular “lipidic phenotype” is sensitive to the drug targeting of FAO. We then showed that the lipid profiles of plasma samples from AML patients were significantly correlated with the prognosis of patients. In particular, we highlighted the impact of phosphocholine and phosphatidyl-choline metabolism on patients’ survival. In conclusion, our data show that balance between lipid species is a phenotypic marker of the diversity of leukemic cells that significantly influences their proliferation and resistance to stress, and thereby, the prognosis of AML patients.

Table S1. Deregulated proteins between JAK2(+) PV and JAK2(-) ET/PMF. Table S2. Deregulated proteins between JAK2(+) ET and JAK2(-) ET/PMF. Table S3. Deregulated proteins between JAK2(+) PMF and JAK2(-) ET/PMF. Table S4. Deregulated proteins between JAK2(+) PV and JAK2(+) PMF. Table S5. Deregulated proteins between JAK2(+) ET and JAK2(+) PMF. Table S6. Deregulated proteins between JAK2(+) PV and JAK2(+) ET. Table S7. Deregulated pathways between JAK2(+) PV and JAK2(-) ET/PMF. Table S8. Deregulated pathways between JAK2(+) ET and JAK2(-) ET/PMF. Table S9. Deregulated pathways between JAK2(+) PMF and JAK2(-) ET/PMF. Table S10. Deregulated pathways between JAK2(+) PV and JAK2(+) PMF. Table S11. Deregulated pathways between JAK2(+) ET and JAK2(+) PMF. Table S12. Deregulated pathways between JAK2(+) PV and JAK2(+) ET.

Acute myeloid leukaemia (AML) is a highly heterogeneous disease, however the therapeutic approaches have hardly changed in the last decades. Metabolism rewiring and the enhanced production of reactive oxygen species (ROS) are hallmarks of cancer. A deeper understanding of these features could be instrumental for the development of specific AML-subtypes treatments. NADPH oxidases (NOX), the only cellular system specialised in ROS production, are also involved in leukemic metabolism control. NOX2 shows a variable expression in AML patients, so patients can be classified based on such difference. Here we have analysed whether NOX2 levels are important for AML metabolism control. The lack of NOX2 in AML cells slowdowns basal glycolysis and oxidative phosphorylation (OXPHOS), along with the accumulation of metabolites that feed such routes, and a sharp decrease of glutathione. In addition, we found changes in the expression of 725 genes. Among them, we have discovered a panel of 30 differentially expressed metabolic genes, whose relevance was validated in patients. This panel can segregate AML patients according to CYBB expression, and it can predict patient prognosis and survival. In summary, our data strongly support the relevance of NOX2 for AML metabolism, and highlights the potential of our discoveries in AML prognosis.

Acute myeloid leukemia (AML) remains a disease of gloomy prognosis despite intense efforts to understand its molecular foundations and to find efficient treatments. In search of new characteristic features of AML blasts, we first examined experimental conditions supporting the amplification of hematological CD34+ progenitors ex vivo. Both AML blasts and healthy progenitors heavily depended on iron availability. However, even if known features, such as easier engagement in the cell cycle and amplification factor by healthy progenitors, were observed, multiplying progenitors in a fully defined medium is not readily obtained without modifying their cellular characteristics. As such, we measured selected molecular data including mRNA, proteins, and activities right after isolation. Leukemic blasts showed clear signs of metabolic and signaling shifts as already known, and we provide unprecedented data emphasizing disturbed cellular iron homeostasis in these blasts. The combined quantitative data relative to the latter pathway allowed us to stratify the studied patients in two sets with different iron status. This categorization is likely to impact the efficiency of several therapeutic strategies targeting cellular iron handling that may be applied to eradicate AML blasts.

Within the PREDIMED Clinical Data Warehouse (CDW) of Grenoble Alpes University Hospital (CHUGA), we have developed a hypergraph based operational data model, aiming at empowering physicians to explore, visualize and qualitatively analyze interactively the complex and massive information of the patients treated in CHUGA. This model constitutes a central target structure, expressed in a dual form, both graphical and formal, which gathers the concepts and their semantic relations into a hypergraph whose implementation can easily be manipulated by medical experts. The implementation is based on a property graph database linked to an interactive graphical interface allowing to navigate through the data and to interact in real time with a search engine, visualization and analysis tools. This model and its agile implementation allow for easy structural changes inherent to the evolution of techniques and practices in the health field. This flexibility provides adaptability to the evolution of interoperability standards.

Leukemic cells display some alterations in metabolic pathways, which play a role in leukemogenesis and in patients’ prognosis. To evaluate the characteristics and the impact of this metabolic reprogramming, we explore the bone marrow samples from 54 de novo acute myeloid leukemia (AML) patients, using an untargeted metabolomics approach based on proton high-resolution magic angle spinning-nuclear magnetic resonance. The spectra obtained were subjected to multivariate statistical analysis to find specific metabolome alterations and biomarkers correlated to clinical features. We found that patients display a large diversity of metabolic profiles, according to the different AML cytologic subtypes and molecular statuses. The link between metabolism and molecular status was particularly strong for the oncometabolite 2-hydroxyglutarate (2-HG), whose intracellular production is directly linked to the presence of isocitrate dehydrogenase mutations. Moreover, patients’ prognosis was strongly impacted by several metabolites, such as 2-HG that appeared as a good prognostic biomarker in our cohort. Conversely, deregulations in phospholipid metabolism had a negative impact on prognosis through 2 main metabolites (phosphocholine and phosphoethanolamine), which could be potential aggressiveness biomarkers. Finally, we highlighted an overexpression of glutathione and alanine in chemoresistant patients. Overall, our results demonstrate that different metabolic pathways could be activated in leukemic cells according to their phenotype and maturation levels. This confirms that metabolic reprogramming strongly influences prognosis of patients and underscores a particular role of certain metabolites and associated pathways in AML prognosis, suggesting common mechanisms developed by leukemic cells to maintain their aggressiveness even after well-conducted induction chemotherapy.

The role of metabolic alterations in cancer cells has been discussed and clarified over the years.Researchers focused on key metabolites and the mechanisms they initiate in cancer.Acute myeloid leukemia (AML) is one such cancer that is dependent on certain metabolites and the pathways they trigger.www.videleaf.comEven though several advances have been made in understanding the mechanisms underlying the initiation and progression of AML, therapies targeted toward these mechanisms, although potent, do not always achieve the desired effect.Hence, studying the metabolic alterations in AML is one of the many approaches researchers currently employ in order to glean a deeper understanding of the intricacies that govern this cancer.Amino acids are crucial players in AML.They trigger several cell survival and replication processes, as well as modulate key epigenetic processesall of which are critical in carcinogenesis.Moreover, several amino acids have been found to play a role in the maintenance of leukemic stem cells, which are correlated to poor prognosis in AML.The role of a few amino acids in AML are highlighted in this review.

La protéine S100A8 est dérégulée dans de nombreux types de cancer. Dans les leucémies aiguës myéloïdes (LAM), son expression intracellulaire est associée à un pronostic défavorable. Bien qu'elle puisse être sécrétée et qu'elle exerce une activité d'alarmine, le rôle extracellulaire de la protéine S100A8 dans la niche hématopoïétique reste méconnu. Nous avons mesuré la protéine S100A8 par technique ELISA chez 78 plasmas médullaires, dont 50 LAM. La concentration en S100A8 ([S100A8]) est significativement plus élevée chez les patients atteints de LAM que chez les patients sains ou présentant des états pré-leucémiques (syndrome myéloprolifératif, syndrome myélodysplasique). Dans les LAM, nous montrons que la [S100A8] est significativement associée à la leucocytose. Fait intéressant, la [S100A8] est fortement corrélée avec le pourcentage de monocytes mais négativement corrélée avec le pourcentage de blastes médullaires. Ce lien entre la [S100A8] et monocytes a été confirmé par la corrélation avec le pourcentage de cellules CD36+ CD64+ CD14+ déterminé par immunophénotypage. De plus, la [S100A8] est plus élevée dans la leucémie aiguë monocytaire et myélomonocytaire (LAM4/5) que dans les autres sous-types de FAB. En analyse multivariée, la [S100A8] est principalement exprimée lorsque les blastes exprimaient des marqueurs monocytaires CD11c+, CD4+, CD3- ou CD117-. Pour confirmer l'origine monocytaire de la protéine S100A8, nous avons mesuré par cytométrie en flux la protéine S100A8 intracellulaire dans les blastes, les lymphocytes, les PNN, les promonocytes et les monocytes de patients atteints de LAM. Les blastes CD34+ expriment peu de S100A8 intracellulaire. La protéine S100A8 est significativement plus exprimée par les monocytes et les promonocytes, confirmant par une seconde approche l'origine monocytaire de la S100A8 dans les LAM. Pour finir, nous avons étudié l'impact pronostique de la [S100A8] qui semble associée à une diminution de la survie globale dans les LAM4/M5. En conclusion, nos travaux caractérisent la secrétion de S100A8 dans les plasmas médullaires et son rôle dans la leucémogenèse.

Deregulations of the expression of the S100A8 and S100A9 genes and/or proteins, as well as changes in their plasma levels or their levels of secretion in the bone marrow microenvironment, are frequently observed in acute myeloblastic leukemias (AML) and acute lymphoblastic leukemias (ALL). These deregulations impact the prognosis of patients through various mechanisms of cellular or extracellular regulation of the viability of leukemic cells. In particular, S100A8 and S100A9 in monomeric, homodimeric, or heterodimeric forms are able to modulate the survival and the sensitivity to chemotherapy of leukemic clones through their action on the regulation of intracellular calcium, on oxidative stress, on the activation of apoptosis, and thanks to their implications, on cell death regulation by autophagy and pyroptosis. Moreover, biologic effects of S100A8/9 via both TLR4 and RAGE on hematopoietic stem cells contribute to the selection and expansion of leukemic clones by excretion of proinflammatory cytokines and/or immune regulation. Hence, the therapeutic targeting of S100A8 and S100A9 appears to be a promising way to improve treatment efficiency in acute leukemias.

We aimed to study the prognostic impact of the mutational landscape in primary and secondary myelofibrosis. The study included 479 patients with myelofibrosis recruited from 24 French Intergroup of Myeloproliferative Neoplasms (FIM) centers. The molecular landscape was studied by high-throughput sequencing of 77 genes. A Bayesian network allowed the identification of genomic groups whose prognostic impact was studied in a multistate model considering transitions from the 3 conditions: myelofibrosis, acute leukemia, and death. Results were validated using an independent, previously published cohort (n = 276). Four genomic groups were identified: patients with TP53 mutation; patients with ≥1 mutation in EZH2, CBL, U2AF1, SRSF2, IDH1, IDH2, NRAS, or KRAS (high-risk group); patients with ASXL1-only mutation (ie, no associated mutation in TP53 or high-risk genes); and other patients. A multistate model found that both TP53 and high-risk groups were associated with leukemic transformation (hazard ratios [HRs] [95% confidence interval], 8.68 [3.32-22.73] and 3.24 [1.58-6.64], respectively) and death from myelofibrosis (HRs, 3.03 [1.66-5.56] and 1.77 [1.18-2.67], respectively). ASXL1-only mutations had no prognostic value that was confirmed in the validation cohort. However, ASXL1 mutations conferred a worse prognosis when associated with a mutation in TP53 or high-risk genes. This study provides a new definition of adverse mutations in myelofibrosis with the addition of TP53, CBL, NRAS, KRAS, and U2AF1 to previously described genes. Furthermore, our results argue that ASXL1 mutations alone cannot be considered detrimental.

Mitochondria are not only essential for cell metabolism and energy supply but they are also engaged in calcium homeostasis, reactive oxygen species generation and play a key role in apoptosis. As a consequence, functional mitochondria disorders are involved in many human cancers including acute myeloid leukemia (AML). However, very little data are available about the deregulation of their number and/or shape in leukemic cells, despite the evident link between ultrastructure and function. In this context, we analyzed the ultrastructural mitochondrial parameters (number per cell, mitochondria area, number of cristae/mitochondria, cristae thickness) in five leukemia cell lines (HEL, HL60, K562, KG1 and OCI-AML3) together with the functional assay of their respiratory profile. First of all, we show significant differences within basal respiration, maximal respiration, ATP production and spare respiratory capacity between our cell lines, confirming the various respiratory profiles between leukemia subtypes. Second, we highlight that these variations were obviously associated with significant inter-leukemia heterogeneity of the number and/or shape of mitochondria. For instance, KG1 characterized by the lowest number of mitochondria together with reduced cristae diameter displayed a very particularly deficient respiratory profile. In comparison, HEL and K562, both cell lines with high respiratory profiles, harbored the highest number of mitochondria/cells with high cristae diameters. We show the leukemia lines present ultrastructural alterations of their mitochondria likely to impact the regulatory pathways of cell mortality, such as the process of mitophagy or calcium homeostasis. Indeed, a significant disparity in the presence of Mitochondrial-derived vesicles (MDVs) precursors among AML cell lines, suggesting that leukemic cells displayed alteration of mitophagy, is also shown. For instance, few MDV precursors were observed in K562, carrying ASXL1 mutation. Moreover, HL60 carried high levels of matrix granules and Mitochondria-associated Endoplasmic Reticulum membranes (MAMs) both implicated in calcium-dependent apoptosis. In conclusion, this study offers new and original data on mitochondria heterogeneity linked to the deregulation of respiration profiles in AMLs, suggesting that modifications of mitochondria shape and/or number in leukemic cells could be a targeted mechanism to regulate their proliferative potential.

Besides leukemia-intrinsic molecular abnormalities in acute myeloid leukemia (AML) that are decisive on clonal initiation and leukemic cell proliferation, bone marrow (BM) microenvironment plays a critical role in leukemogenesis and disease progression. The microenvironment is composed not only of non-tumoral cells such as mesenchymal cells, lymphocytes, macrophages, and myeloid-derived cells but also of extracellular products like chemokines, alarmins, or exosomes that could activate different signaling pathways. AML cells are hence bathed in extracellular signals that influence their clonal evolution and thereby potentially influence disease prognosis. S100A8, otherwise known as calgranulin A due to its physiologically abundant expression in neutrophils and its calcium chelating property, is upregulated in many types of cancer. In hematologic disorders, intracellular S100A8 coupled to S100A9 protein regulates leukemic proliferation through myeloid differentiation and has been shown to induce a defect in erythroid differentiation in a mouse model of myelodysplastic syndrome (MDS). High expression of S100A8 mRNA by blasts cells has been observed in some AML patients, and increased intracellular S100A8 has been associated with poor prognosis in AML. In addition to its intracellular activity, S100A8 can be secreted and exerts extracellular activities of damage-associated molecular pattern molecules (DAMPs). High levels have been found in plasma or extracellular fluids of adults with chronic inflammatory diseases, including rheumatoid arthritis and Crohn's disease. In this context, we hypothesized that extracellular S100A8 can be secreted into BM niche and potentially influence leukemic cell behavior. We thus investigated the concentration of S100A8 (referred to as [S100A8] in the text) in bone marrow plasma from 50 de novo AML. The percentage of monocytes was highly correlated with [S100A8] in BM. The percentage of CD36 + CD64 + CD14 + cells determined by immunophenotyping confirmed this relevant link between monocyte and S100A8 detected in BM. Secondly, intracellular measurement of S100A8 by flow cytometry highlighted that it mainly originated from monocytes and leukemic cells expressing monocytic markers such as promonocytes. To the contrary, CD34+ leukemic cells expressed fewer intracellular S100A8. Finally, we report that high levels of S100A8 appear associated with a worst overall survival in M4/M5 subgroups but not in all AML subgroups. Altogether, our study underlines the specific link between monocytic lineage and S100A8 in AML. Firstly, we measured S100A8 from 78 bone marrow (BM) plasmas, including 50 acute myeloid leukemia (AML) patients, myeloproliferative neoplasms (MPN), myelodysplastic syndrome (MDS), and healthy donors (HD). Briefly, samples were collected from 50 patients with de novo AML at diagnosis, between June 2013 and March 2016 by the Grenoble Alpes University Hospital Biobank (BRIF: BB-0033-00069) with informed consent and project approval by the research ethics board of Comité de Protection des Personnes Sud Est V. Clinical data are provided in Table S1. Other samples (MDS, MPN) were collected between May 2010 and January 2017 by the Grenoble Alpes University Hospital Biobank (hematological malignancies collection BRIF: BB0033-00069). Control bone marrow samples were collected between October 2010 and March 2016 with informed consent. BM and peripheral blood (PB) plasma were rapidly collected after centrifugation for 10 minutes at 800g and protease inhibitor (Complete EDTA free protease inhibitor cocktail, Roche) added to avoid protein degradation before freezing. ELISA was performed to quantify levels of S100A8 in BM and PB plasma, according to the manufacturer's instructions (Life Technologies). We observed that BM plasma [S100A8] was significantly higher in AML patients than in patients with pre-leukemic states (MPN and MDS) or in HD (Figure 1A). BM [S100A8] was significantly associated with leukocytosis (P < .001, R = 0.5) (Figure 1B). As expected, a positive correlation between BM and PB was observed in AML (P < .001, R = 0.85). However [S100A8] was 7to 10-fold higher in BM plasma than in PB (mean BM = 1246.7 μg/L [30.7; 10 055.3], mean PB = 131.5 μg/L [10.24; 1794.2], respectively, P < .01) (Figure 1C). Consequently, high BM [S100A8] reinforces the hypothesis of its role in AML physiopathology within the microenvironment. To determine the provenance of S100A8 in BM niche, we correlated BM [S100A8] to the percentage of cells determined by cytological analysis. As S100A8 and S100A9 represent 40% of the cytoplasmic protein in neutrophils, one could have supposed that S100A8 is released from neutrophils or their precursors. However, no linear correlation was observed between BM [S100A8] and myeloid precursors or granulocytes in BM, (P = .21, P = .29, respectively; Figure S1). Interestingly, the percentage of blasts in BM was negatively correlated with BM [S100A8] (P < .05, R = −0.31, Figure 1D), suggesting that blasts were not the main source of S100A8. However, the percentage of monocytes was highly correlated with [S100A8] in both BM and PB (Figure 1E, P < .001, R = 0.68 and P < .001, R = 0.68, respectively). Percentage of CD36+ CD64+ CD14+ cells determined by immunophenotyping confirmed the relevant link between monocyte and S100A8 detected in BM (P < .0001, R = 0.58) (Figure 1F). To our knowledge, this is the first report of an increased S100A8 secretion by maturing cells in AML subtypes. Further, we observed that BM [S100A8] was higher in acute monocytic and acute myelomonocytic leukemia (M4 and M5) than in Received: 8 August 2019 Revised: 6 December 2019 Accepted: 16 December 2019

Acute myeloid leukemia (AML) is characterized by a set of malignant proliferations leading to an accumulation of blasts in the bone marrow and blood. The prognosis is pejorative due to the molecular complexity and pathways implicated in leukemogenesis. Our research was focused on comparing the metabolic profiles of leukemic cells in basal culture and deprivation conditions to investigate their behaviors under metabolic stress. We performed untargeted metabolomics using 1H HRMAS-NMR. Five human leukemic cell lines—KG1, K562, HEL, HL60 and OCIAML3—were studied in the basal and nutrient deprivation states. A multivariate analysis of the metabolic profile was performed to find over- or under- expressed metabolites in the different cell lines, depending on the experimental conditions. In the basal state, each leukemic cell line exhibited a specific metabolic signature related to the diversity of AML subtypes represented and their phenotypes. When cultured in a serum-free medium, they showed quick metabolic adaptation and continued to proliferate and survive despite the lack of nutrients. Low apoptosis was observed. Increased phosphocholine and glutathione was a common feature of all the observed cell lines, with the maximum increase in these metabolites at 24 h of culture, suggesting the involvement of lipid metabolism and oxidative stress regulators in the survival mechanism developed by the leukemic cells. Our study provides new insights into the metabolic mechanisms in leukemogenesis and suggests a hierarchy of metabolic pathways activated within leukemic cells, some dependent on their genotypes and others conserved among the subtypes but commonly induced under micro-environmental stress.

Intensive systemic chemotherapy is the gold standard of acute myeloid leukemia (AML) treatment and is associated with considerable off-target toxicities. Safer and targeted delivery systems are thus urgently needed. In this study, we evaluated a virus-like particle derived from the human type 3 adenovirus, called the adenoviral dodecahedron (Dd) to target AML cells. The vectorization of leukemic cells was proved very effective at nanomolar concentrations in a time- and dose-dependent manner, without vector toxicity. The internalization involved clathrin-mediated energy-dependent endocytosis and strongly correlated with the expression of αVβ3 integrin. The treatment of healthy donor peripheral blood mononuclear cells showed a preferential targeting of monocytes compared to lymphocytes and granulocytes. Similarly, monocytes but also AML blasts were the best-vectorized populations in patients while acute lymphoid leukemia blasts were less efficiently targeted. Importantly, AML leukemic stem cells (LSCs) could be addressed. Finally, Dd reached peripheral monocytes and bone marrow hematopoietic stem and progenitor cells following intravenous injection in mice, without excessive spreading in other organs. These findings reveal Dd as a promising myeloid vector especially for therapeutic purposes in AML blasts, LSCs, and progenitor cells.

Grenoble Alpes University Hospital (CHUGA) is currently deploying a health data warehouse called PREDIMED [1], a platform designed to integrate and analyze for research, education and institutional management the data of patients treated at CHUGA. PREDIMED contains healthcare data, administrative data and, potentially, data from external databases. PREDIMED is hosted by the CHUGA Information Systems Department and benefits from its strict security rules. CHUGA's institutional project PREDIMED aims to collaborate with similar projects in France and worldwide. In this paper, we present how the data model defined to implement PREDIMED at CHUGA is useful for medical experts to interactively build a cohort of patients and to visualize this cohort.

Adult patients with de novo acute myeloid leukemia show a functional deregulation of redox balance at diagnosis which is correlated with molecular subtypes and overall survival