IDH1 Mutations Research Articles

Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis

ObjectiveThis systematic review aims to evaluate the quality and accuracy of ML algorithms in predicting ATRX and IDH mutation status in patients with glioma through the analysis of radiomic features extracted from medical imaging. The potential clinical impacts and areas for further improvement in non-invasive glioma diagnosis, classification and prognosis are also identified and discussed.MethodsThe review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic and Test Accuracy (PRISMA-DTA) statement. Databases including PubMed, Science Direct, CINAHL, Academic Search Complete, Medline, and Google Scholar were searched from inception to April 2024. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess the risk of bias and applicability concerns. Additionally, meta-regression identified covariates contributing to heterogeneity before a subgroup meta-analysis was conducted. Pooled sensitivities, specificities and area under the curve (AUC) values were calculated for the prediction of ATRX and IDH mutations.ResultsEleven studies involving 1,685 patients with grade I–IV glioma were included. Primary contributors to heterogeneity included the MRI modalities utilised (conventional only vs. combined) and the types of ML models employed. The meta-analysis revealed pooled sensitivities of 0.682 for prediction of ATRX loss and 0.831 for IDH mutations, specificities of 0.874 and 0.828, and AUC values of 0.842 and 0.948, respectively. Interestingly, incorporating semantics and clinical data, including patient demographics, improved the diagnostic performance of ML models.ConclusionsThe high AUC in the prediction of both mutations demonstrates an overall robust diagnostic performance of ML, indicating the potential for accurate, non-invasive diagnosis and precise prognosis. Future research should focus on integrating diverse data types, including advanced imaging, semantics and clinical data while also aiming to standardise the collection and integration of multimodal data. This approach will enhance clinical applicability and consistency.

Single cell analysis of Idh mutant growth plates identifies cell populations responsible for longitudinal bone growth and enchondroma formation

Enchondromas are a common tumor in bone that can occur as multiple lesions in enchondromatosis, which is associated with deformity of the affected bone. These lesions harbor somatic mutations in IDH and driving expression of a mutant Idh1 in Col2 expressing cells in mice causes an enchondromatosis phenotype. Here we compared growth plates from E18.5 mice expressing a mutant Idh1 with control littermates using single cell RNA sequencing. Data from Col2 expressing cells were analysed using UMAP and RNA pseudo-time analyses. A unique cluster of cells was identified in the mutant growth plates that expressed genes known to be upregulated in enchondromas. There was also a cluster of cells that was underrepresented in the mutant growth plates that expressed genes known to be important in longitudinal bone growth. Immunofluorescence showed that the genes from the unique cluster identified in the mutant growth plates were expressed in multiple growth plate anatomic zones, and pseudo-time analysis also suggested these cells could arise from multiple growth plate chondrocyte subpopulations. This data supports the notion that a subpopulation of chondrocytes become enchondromas at the expense of contributing to longitudinal growth.

The prognostic significance of genetics in acute myeloid leukemia under venetoclax-based treatment.

Acute myeloid leukemia (AML) is the most prevalent hematologic malignancy in adults. In 2022, the European LeukemiaNet (ELN) has updated its prognostic system that incorporates cytogenetics and molecular genetics based on data from patients undergoing intensive chemotherapy (IC). Recently, a risk stratification framework has been established for hypomethylating agents (HMA)-based low-intensity treatment (LIT) to fill the gaps in stratification for this treatment modality, but this needs further refinement. Venetoclax (VEN), a BH3 mimetic, targets BCL-2 to modulate apoptosis and metabolism in AML cells. Its combination with HMA or low-dose cytarabine (LDAC) has been shown to enhance the response rates and prolong the survival outcomes of older or unfit patients with AML. In this review, we delved into the prognostic significance of FLT3-ITD and IDH mutations when used in combination with VEN and HMA, as well as in conjunction with their specific inhibitors. We also explored the role of VEN in NPM1-mutated AML and its efficacy in splicing factor mutations AML. Additionally, we examined the response rates and survival outcomes of CBF-AML when treated with a VEN-based regimen. Moving forward, it is imperative that risk stratification for LIT becomes more nuanced to better align with the requirements of personalized diagnosis and treatment strategies.

Diagnostic challenges in complicated case of glioblastoma

Glioblastoma is the commonest primary malignant brain tumor, with a very poor prognosis and short overall survival. It is characterized by its high intra- and intertumoral heterogeneity, in terms of both the level of single-nucleotide variants, copy number alterations, and aneuploidy. Therefore, routine diagnosis can be challenging in some cases. We present a complicated case of glioblastoma, which was characterized with five cytogenomic methods: interphase fluorescence in situ hybridization, multiplex ligation-dependent probe amplification, comparative genomic hybridization array and single-nucleotide polymorphism, targeted gene panel, and whole-genome sequencing. These cytogenomic methods revealed classical findings associated with glioblastoma, such as a lack of IDH and TERT mutations, gain of chromosome 7, and loss of chromosome 10. At least three pathological clones were identified, including one with whole-genome duplication, and one with loss of 1p and suspected loss of 19q. Deletion and mutation of the TP53 gene were detected with numerous breakends on 17p and 20q. Based on these findings, we recommend a combined approach to the diagnosis of glioblastoma involving the detection of copy number alterations, mutations, and aneuploidy. The choice of the best combination of methods is based on cost, time required, staff expertise, and laboratory equipment. This integrated strategy could contribute directly to tangible improvements in the diagnosis, prognosis, and prediction of the therapeutic responses of patients with brain tumors.

Loss of IDH1 and IDH2 in macrophages induces pathological phenotypes in cardiomyocytes

Abstract Introduction The clonal expansion of hematopoietic stem cells can be induced by a variety of age-related somatic mutations, a phenomenon known as "clonal hematopoiesis of indeterminate potential" (CHIP). CHIP is associated with an increased incidence and poor outcomes in patients with cardiovascular disease. The mechanisms underlying the crosstalk between CHIP cells and cardiac tissue are yet to be elucidated. Rare mutations in the metabolism-regulating isocitrate dehydrogenases encoding genes IDH1 and IDH2 have been identified as AML-like and cardiovascular high-risk CHIP-driving mutations in patient cohorts. However, the potential paracrine effects of these mutations in immune cells on cardiac tissue are unknown. Therefore, we assessed the impact of IDH1 and IDH2 mutations on the paracrine activities of macrophages. Methods and Results To gain first insights into the functional consequences of the IDH mutations, we used the AlphaMissense algorithm, which predicts the impact of mutations on protein function. IDH1 and IDH2 mutations identified in our patient cohorts were predicted to induce pathological alterations of protein function with a score >0.99. Therefore, we assessed the effects of silencing of the two genes in primary human CD14+ M0 macrophages on the paracrine activity on neonatal rat cardiomyocytes. Transfection with siRNA pools targeting IDH1 and IDH2 to mimic CHIP-driving conditions reduced mRNA abundance by 78% and 73%, respectively (both p<0.05). To assess the impact of IDH-silencing on cardiovascular cells, cardiomyocytes were treated with the supernatant of the macrophages. Interestingly, supernatants of IDH1 silenced macrophages significantly increased apoptosis of cardiomyocytes (8.6-fold increased expression of cleaved caspase-3; p<0.05) and reduced beating frequency. In addition, supernatants of IDH1 silenced macrophages induced a 1.5-fold increase in cardiomyocyte hypertrophy (p<0.01), whereas silencing of IDH2 showed no effect on cardiomyocyte hypertrophy and apoptosis. However, supernatants of both IDH1 and IDH2 silenced macrophages resulted in significantly increased numbers of bi- and multinucleated cardiomyocytes. Conclusion: Loss of isocitrate dehydrogenases in human macrophages affects cardiomyocyte functions in a paracrine fashion. Particularly silencing of IDH1 in macrophages introduced cardiomyocyte hypertrophy, apoptosis, and bi- and multinucleation, and reduced contractile function. Ongoing studies will address how IDH1 and IDH2 silencing-induced alterations in macrophages control their paracrine activity on cardiomyocytes.

Machine learning-based new classification for immune infiltration of gliomas.

Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of the immunosuppressive tumor microenvironment (TME) are one of the important factors hindering the effectiveness of immunotherapy. Therefore, this study aims to reveal the immune microenvironment (IME) characteristics of glioma and predict different immune subtypes using machine learning methods, providing guidance for immune therapy in glioma. We first performed unsupervised cluster analysis on the genes and arrays of 693 gliomas in CGGA database and 702 gliomas in TCGA database. Then establish and verify the classification model through Machine Learning (ML). Then, use DAVID to perform functional enrichment analysis for different immune subtypes. Next step, analyze the immune cell distribution, stemness maintenance, mesenchymal phenotype, neuronal phenotype, tumorigenic cytokines, molecular and clinical characteristics of different immune subtypes of gliomas. Firstly, we divide the IME of gliomas in the CGGA database into four different subtypes, namely IM1, IM2, IM3, and IM4; similarly, the IME of gliomas in the TCGA database can also be divided into four different subtypes (IMA, IMB, IMC, and IMD). Next, based on ML, we developed a highly reliable model for predicting different immune subtypes of glioma. Then, we found that Monocytic lineage, Myeloid dendritic cells, NK cells and CD8 T cells had the highest enrichment in the IM1/IMD subtypes. Cytotoxic lymphocytes were highest expressed in the IM4/IMA subtypes. Next step, Enrichment analysis revealed that the IM1-IMD subtypes were mainly closely related to the production and secretion of IL-8 and TNF signaling pathway. The IM2-IMB subtypes were strongly associated with leukocyte activation and NK cell mediated cytotoxicity. The IM3-IMC subtypes were closely related to mitotic nuclear division and mitotic cell cycle process. The IM4-IMA subtypes were strongly associated with Central Nervous System (CNS) development and striated muscle tissue development. Afterwards, Single sample gene set enrichment analysis (ssGSEA) showed that stemness maintenance phenotypes were mainly enriched in the IM4/IMA subtypes; Neuronal phenotypes were closely associated with the IM2/IMB subtypes; and mesenchymal phenotypes and tumorigenic cytokines were highly correlated with the IM2 /IMB subtypes. Finally, we found that compared with patients in the IM2/IMB and IM4/IMA subtypes, the IM1/IMD and IM3/IMC subtypes have the highest proportion of GBM patients, the shortest average overall survival of patients and the lowest proportion of patients with IDH mutation and 1p36/19q13 co-deletion. We developed a highly reliable model for predicting different immune subtypes of glioma by ML. Then, we comprehensively analyzed the immune infiltration, molecular and clinical features of different immune subtypes of gliomas and defined gliomas into four subtypes: immunogenic subtype, adaptive immune resistance subtype, mesenchymal subtype, and immune tolerance subtype, which represent different TMEs and different stages of tumor development.

De novo design of mIDH1 inhibitors by integrating deep learning and molecular modeling

BackgroundMutations in the IDH1 gene have been shown to be an important driver in the development of acute myeloid leukemia, gliomas and certain solid tumors, which is a promising target for cancer therapy.MethodsBidirectional recurrent neural network (BRNN) and scaffold hopping methods were used to generate new compounds, which were evaluated by principal components analysis, quantitative estimate of drug-likeness, synthetic accessibility analysis and molecular docking. ADME prediction, molecular docking and molecular dynamics simulations were used to screen candidate compounds and assess their binding affinity and binding stability with mutant IDH1 (mIDH1).ResultsBRNN and scaffold hopping methods generated 3890 and 3680 new compounds, respectively. The molecules generated by the BRNN performed better in terms of molecular diversity, druggability, synthetic accessibility and docking score. From the 3890 compounds generated by the BRNN model, 10 structurally diverse drug candidates with great docking score were preserved. Molecular dynamics simulations showed that the RMSD of the four systems, M1, M2, M3 and M6, remained stable, with local flexibility and compactness similar to the positive drug. The binding free energy results indicated that compound M1 exhibited the best binding properties in all energy aspects and was the best candidate molecule among the 10 compounds.ConclusionIn present study, compounds M1, M2, M3 and M6 generated by BRNN exhibited optimal binding properties. This study is the first attempt to use deep learning to design mIDH1 inhibitors, which provides theoretical guidance for the design of mIDH1 inhibitors.

Reappraisal of prognostic factors in CNS WHO grade 3 oligodendrogliomas IDH-mutant and 1p/19q co-deleted: lessons from the French POLA cohort.

In POLA cohort, three pathological groups of CNS WHO grade 3 oligodendroglioma IDH-mutant and 1p/19q co-deleted have been described: group 1 (high mitotic count only), group 2 (microvascular proliferation MVP and no necrosis), and group 3 (MVP and necrosis). 494 patients from the POLA cohort, with a median follow up of 96 months were included. To identify the impact of the pathological groups and contrast enhancement in group 1 on overall survival (OS) or progression free survival (PFS), survival curves were obtained (Kaplan-Meier method) and compared (log-rank test). Prognostic value of clinical factors and CDKN2A homozygous deletion HD were also tested. Multivariate analysis was performed. Survival analysis demonstrated that the pathological groups were associated with both progression-free survival (PFS P=0.01) and overall survival (OS P=0.001). In group 1, patients with contrast enhancement (1CE+) had a poorer prognosis compared to those without (OS P=0.028, PFS P=0.006). Further stratification into group 1CE-, group 1CE+, group 2, and group 3 provided clearer prognostic distinctions (OS P=0.002, PFS P<0.0001). Other prognostic factors included age (OS P<0.0001, PFS P=0.002), extent of surgical resection (OS P=0.001, PFS P=0.003), KPS (OS P<0.0001, PFS P=0.002), postoperative treatment (OS P=0.007, PFS P<0.0001), and CDKN2A HD (OS and PFS P<0.0001). The pathological groups remained of prognostic significance for PFS in multivariate analysis. Necrosis and CDKN2A HD are adverse prognostic factors of WHO grade 3 oligodendrogliomas, IDH mutant and 1p/19q co-deleted. Besides, in group 1 patients, lack of contrast enhancement is a factor of better prognosis.

Enasidenib in relapsed aggressive systemic mastocytosis with IDH2 mutation

Enasidenib in relapsed aggressive systemic mastocytosis with IDH2 mutation

P18.42.B TAU AND AMYLOID BETA PATHOLOGY IN IDH-MUTANT GLIOMA

Abstract BACKGROUND With an aging population the incidence of brain tumors and neurodegenerative diseases is increasing. Although biological associations between glioma and Alzheimer’s disease (AD) have been suggested, the frequency of comorbidity is insufficiently defined. We here screen tumor-adjacent cortex of a representative cohort of patients with low grade glioma for AD neuropathological changes (ADNC) specifically for phosphorylated tau (pTau) and amyloid beta (Abeta) deposition, microglial activation, amyloid precursor protein (APP) expression, diffuse axonal injury, and cortical tumor cell infiltration. MATERIAL AND METHODS A total of 85 patients with astrocytoma, IDH mutant, CNS-WHO grade 2-3, (N=37, median age: 39) and oligodendroglioma, IDH mutant and 1p-19q co-deleted, CNS-WHO grades 2-3 (N=48 median age: 50) were included. 2µm sections were immunohistochemically stained for the markers b-A4, t-AT8, NeuN, APP and Iba1. Whole slide quantification was performed using Matlab and QuPath codes. For a subset of 15 patients, longitudinal samples were available for analysis of post-treatment effects. RESULTS The median cell density in the tumor-adjacent cortex was 1355/mm2 for astrocytoma and 1392/mm2 for oligodendroglioma, which was significantly higher than in non-tumor infiltrated cortex (median: 1030 cells/mm², p-value&amp;lt;0.0001). A total of 32 patients (38%) showed Abeta (14%, n=12/85) and/or pTau pathology (36%, n=31/85) in the tumor-adjacent cortex. No difference between astrocytoma and oligodendroglioma was reported. ADNC was mostly frequently observed in older patients but was also present in young individuals (median: 34 years, range: 17 to 77 years). pTau was significantly positively correlated with tumor cell infiltration (Kendall´s tau=0.13, p=0.002). Both Abeta and pTau deposits were frequently observed in the frontal cortex (30%, N=18/60). The tumor infiltration into the cortical regions was associated with neuronal loss (Kendall´s tau=-0.29, p&amp;lt;0.0001). Microglial activation depended on the degree of tumor infiltration (R=0.29), followed by increasing patient age (R=0.12). Diffuse axonal injury was associated with ADNC (Chi²=19.6, p&amp;lt;0.05). APP expression of tumor cells was highest in patients of old age (R=0.35). Longitudinal samples showed stable ANDC pathology in 15/15 subjects. CONCLUSION Our preliminary findings suggest presence of ADNC in roughly a third of patients with low-grade glioma, which increases with tumor cell infiltration into the cortex, microglial activation and patient age. Notably, isolated pTau pathology was more pronounced than Abeta pathology suggesting the acquisition of a secondary tauopathy.

P10.09.A BLOOD-BASED CLASSIFICATION OF IDH-STATUS OF NON-ENHANCING GLIOMAS: A MACHINE LEARNING APPROACH

Abstract BACKGROUND Non-invasive determination of IDH mutational status in patients with glioma could offer significant therapeutic opportunities. While IDH wildtype tumors (e.g., GBM) typically show enhancement on MRI, IDH mutant tumors often lack this enhancement. However, relying solely on anatomic radiology may lead to misclassification, and currently, tissue acquisition is the primary method for assessing IDH status in gliomas. This limitation hampers the development of neoadjuvant or intraoperative therapeutic strategies based on IDH status; thus, more minimally invasive methods to determine IDH status are needed. In this study, we assessed immune cell proportions, which vary by glioma subtype, from pre-surgery peripheral blood samples as an alternative method of classifying the IDH status in gliomas without enhancement. MATERIAL AND METHODS First, we employed a highly accurate large language model (GPT-4-Turbo-128k) with over 99% accuracy to read radiology notes and exclude patients with enhancing gliomas. Then, we identified twelve immune cell subtypes from whole blood using deconvolution algorithms based on DNA methylation data. These immune cell subtypes, along with patient age, were integrated into a machine learning model (random forest) to predict IDH status (wildtype vs. mutant), leveraging conditional Generative Adversarial Networks to generate synthetic data and mitigate bias in the datasets. Two independent datasets were included for training and validating the model. RESULTS The random forest model had an area under the ROC curve (AUC) of 0.90 and accurately identified IDH status in 81% of a training set of 287 gliomas (65 wildtype and 222 mutant tumors at varied time points after diagnosis). In an independent validation data set of 99 gliomas (6 wildtype and 93 mutant) from pre-surgery blood samples, the AUC was 0.93, and accuracy predicted IDH status in 92% of cases with a sensitivity of 93% and specificity of 83%. CONCLUSION We could accurately predict IDH status in pre-surgical peripheral blood samples from patients with non-enhancing gliomas. This minimally invasive approach is a promising step toward reducing risk and exploring opportunities for earlier therapies based on IDH status.

Research trends of glioma-related epilepsy: A bibliometric analysis from 2004 to 2023.

Glioma-related epilepsy (GRE) is a hotspot in recent years and there remains many urgent unsolved issues. This study aimed to conduct bibliometric analysis on GRE research over the past 2 decades. We collected scientific outputs relating to GRE on Web of Science Core Collection (WoSCC) from 2004 to 2023 and conducted visual analysis using VOSviewer and Microsoft Excel. A total of 2697 publications were retrieved with an increasing trend over the past 20years. The USA ranked first in publication number, total citation and H-index. Institut National de la Sante et de la Recherche Medicale (Inserm) was the institution with the most publications. In the field of GRE, core journals were Journal of Neurosurgery, Epilepsia and Neurology. Duffau, Hugues was the author with the most papers and total citations, and the highest H-index. Co-occurrence analysis revealed that the latest research focus of GRE were awake craniotomy, immunotherapy, cognitive impairment, and basic research on pathogenesis, with particular emphasis on the IDH1 mutation. This study intended to gain a deeper understanding of the current global GRE research and identify hotspots, as well as to provide theoretical reference for further studies.

MATHEMATICAL MODELLING OF SURVIVAL IN LOW GRADE GLIOMAS AT MALIGNANT TRANSFORMATION WITH XGBOOST

Abstract AIMS To develop non-linear machine learning models using the XGBoost algorithm to predict a continuous (overall survival (OS) and a binary survival outcome (OS &amp;gt; 5 years) using clinical, molecular and genetic, and radiomic data. METHOD Patients with LGGs treated at a single institution (2005-2020) with histology and MRIs at the time of malignant transformation (MT) were retrospectively included in this study. MRIs underwent in-house tumour segmentation pipeline with radiomic feature extraction of whole-tumour, enhancing, non-enhancing and oedema components, and masked disconnectome map components. Patients were split into training and testing sets for the development of the survival models, which were assessed with mean absolute error (MAE) and root mean square error (RMSE) for the prediction of OS; and receiver operating characteristics analysis for the prediction of OS &amp;gt; 5 years. RESULTS Of 553 patients, 415 patients were included in the training set and 138 patients in the testing set. The XGB Regressor model was able to predict overall survival (OS) from the time of malignant transformation (tMRI) with an MAE of 953 days (RMSE: 1163 days). The XGB Classifier model was able to predict the probability of OS &amp;gt; 5 years from tMRI with an accuracy of 64% (sensitivity: 58%, specificity: 70%). Age, IDH1 mutation, 1p/19q co- deletion, regularity of tumour shape, and disconnectome-related perilesional components were most predictive of survival outcome. CONCLUSION This study has investigated the predictive capabilities of clinical, molecular and genetic, and radiomic data to develop survival analysis models, using XGBoost, to predict OS and OS &amp;gt; 5 years in patients with LGG at tMRI. We corroborate previous findings that age, 1p/19q co-deletion and IDH1 mutation are positive prognosticators for survival. However, further investigation into the radiomics of the disconnectome, especially of the perilesional oedema compartment, presents an intriguing and novel avenue for survival analysis of patients with LGG.

MAPPING GLIOMAS: A RADIOGRAPHIC AND SPATIAL ANALYSIS ACCORDING TO THE 2021 WHO CLASSIfiCATION

Abstract AIMS The spatial positioning of gliomas within the brain significantly influences prognosis, potentially mirroring the genetic landscape of their precursor cells. Furthermore, the association between glioma subtypes and their spatial distribution underscores a complex interplay that may illuminate underlying pathogenetic mechanisms. This study explores the intricate relationship between the location of gliomas and their classification according to the 2021 WHO guidelines, aiming to unravel the spatial and genetic nuances that define glioma heterogeneity. METHOD In this study, preoperative anatomical MRI data from 242 glioma patients were meticulously analyzed to elucidate spatial distribution patterns in relation to tumor genetics. The imaging data underwent registration to the MNI-152 standard brain space, facilitating uniform analysis. Subsequent tumor segmentation and division of the brain into 84 distinct subregions allowed for a detailed examination of tumor locations. A statistical analysis was conducted to explore the stereospecific frequency of glioma occurrence across these subregions, correlating findings with tumor grade and pivotal genetic markers, including MGMT promoter methylation, IDH1 mutation, 1p/19q co-deletion, and CDKN2A/B homozygous deletion. RESULTS We developed 21 distinct tumor frequency atlases based on subtypes (Glioblastoma, Oligodendroglioma, Astrocytoma), biomarker status (IDH, 1p19q, MGMT, CDKN2A/B), and grade (2, 3, 4). Employing probability vectors for statistical analysis revealed a significant correlation among different subtypes, locations and overall survival. Furthermore, we used a Visualization tool to build a 3D heatmap for each tumor subtype, more intuitively illustrating spatial correlations. CONCLUSION Our study underscores the prognostic value of tumor location and its correlation with glioma subtypes and patient outcomes, suggesting a potential pathway to personalized treatments. Future efforts will focus on incorporating multi-omics data into spatial radiographic analyses.

AML typical mutations (CEBPA, FLT3, NPM1) identify a high-risk chronic myelomonocytic leukemia independent of CPSS molecular

AbstractMutations commonly associated with acute myeloid leukemia (AML), such as CEBPA, FLT3, IDH1/2, and NPM1, are rarely found in chronic myelomonocytic leukemia (CMML), and their prognostic significance in CMML has not been clearly identified. In 127 patients with CMML, we have retrospectively analyzed next-generation sequencing and polymerase chain reaction data from bone marrow samples collected at the time of CMML diagnosis. Seven patients harbored CEBPA mutations, 8 FLT3 mutations, 12 IDH1 mutations, 26 IDH2 mutations, and 11 NPM1 mutations. Patients with CMML harboring CEBPA, FLT3, and/or NPM1 mutations (mutCFN) more frequently had the myeloproliferative subtype, a high prevalence of severe cytopenia, and elevated blast counts. Regardless of their CMML Prognostic Scoring System molecular classification, mutCFN patients with CMML had a poor prognosis, and the multivariate analysis identified mutCFN as an independent marker of overall survival. The genetic profile of these mutCFN patients with CMML closely resembled that of patients with AML, with higher-risk clinical characteristics. Our findings lead us to suggest including the assessment of these mutations in CMML prognostic models and treating these patients with AML-type therapies, including intensive chemotherapy and allogeneic stem cell transplantation, whenever feasible. Furthermore, certain targeted therapies approved for use in AML should be considered.

HMA/VEN treatment modifications and associated outcomes in IDH-mutant AML

Hypomethylating agents (HMA) and venetoclax (VEN) are commonly used in patients with IDH-mutated (IDHm) acute myeloid leukemia (AML) ineligible for induction chemotherapy. While prior studies demonstrated high response and survival rates with HMA/VEN in IDHm AML, the impact of treatment modifications in real-world settings is unclear. We retrospectively reviewed 89 IDHm AML patients treated with HMA/VEN from January 2018 to June 2023. CR/CRi rates were 76% in newly diagnosed (ND) and 55% in relapsed/refractory (R/R) patients, and median overall survival was 29.2 months (ND) and 17.1 months (R/R), respectively. Treatment modifications were common. Early VEN reductions were associated with lower response rates but not worse survival. Prolonged cycles were not associated with worse response rates or survival. Significant neutropenia and ED visits or unplanned hospitalizations were considerable before and after CR/CRi, though febrile neutropenia decreased afterward. HMA/VEN is efficacious, with treatment modifications not affecting survival, though long-term toxicities are notable.

Vorasidenib: First Approval.

Vorasidenib (VORANIGO®; Servier) is an orally administered, first-in-class, highly brain-penetrant dual inhibitor of mutant isocitrate dehydrogenase 1 and 2 (IDH1/2) enzymes being developed for use in IDH-mutant diffuse glioma. Vorasidenib received its first approval on 6 August 2024, in the USA, for the treatment of adult and pediatric patients aged 12 years and older with grade 2 astrocytoma or oligodendroglioma with a susceptible IDH1 or IDH2 mutation following surgery, including biopsy, sub-total resection, or gross total resection. Approval was based on results from the multinational phase III INDIGO trial, in which vorasidenib significantly improved progression-free survival and time to the next anticancer intervention relative to placebo. In the EU and other countries worldwide, regulatory review of vorasidenib in IDH-mutant glioma is currently underway. This article summarizes the milestones in the development of vorasidenib leading to this first approval for glioma.

Multiomic profiling identifies predictors of survival in African American patients with acute myeloid leukemia.

Genomic profiles and prognostic biomarkers in patients with acute myeloid leukemia (AML) from ancestry-diverse populations are underexplored. We analyzed the exomes and transcriptomes of 100 patients with AML with genomically confirmed African ancestry (Black; Alliance) and compared their somatic mutation frequencies with those of 323 self-reported white patients with AML, 55% of whom had genomically confirmed European ancestry (white; BeatAML). Here we find that 73% of 162 gene mutations recurrent in Black patients, including a hitherto unreported PHIP alteration detected in 7% of patients, were found in one white patient or not detected. Black patients with myelodysplasia-related AML were younger than white patients suggesting intrinsic and/or extrinsic dysplasia-causing stressors. On multivariable analyses of Black patients, NPM1 and NRAS mutations were associated with inferior disease-free and IDH1 and IDH2 mutations with reduced overall survival. Inflammatory profiles, cell type distributions and transcriptional profiles differed between Black and white patients with NPM1 mutations. Incorporation of ancestry-specific risk markers into the 2022 European LeukemiaNet genetic risk stratification changed risk group assignment for one-third of Black patients and improved their outcome prediction.

Can Ruxolitinib Crash TET2- and IDH2-Driven Clonal Hematopoiesis?

In this issue, Waarts and colleagues developed an advanced ex vivo CRISPR screening platform to identify vulnerabilities in clonal hematopoiesis (CH). This unique system allowed the authors to identify a link between IDH2 and TET2 CH mutations, histone demethylases, and altered cytokine signaling, which enabled targeting by ruxolitinib leading to the elimination of CH clones, offering a possible path for preventing the development of malignancy. See related article by Waarts et al., p. 1860.

Patterns of intersectional tumor volumes in T2-weighted MRI and [18F]FET PET in adult glioma: a prospective, observational study

Brain tumor volumes as assessed by magnetic resonance imaging (MRI) do not always spatially overlap with biological tumor volumes (BTV) measured by [18F]Fluoroethyltyrosine positron emission tomography ([18F]FET PET). We prospectively investigated volumetric patterns based on the extent of tumor volume overlap between the two modalities. Eighty-six patients with newly diagnosed glioma who had undergone MRI and [18F]FET PET between 2007 and 2009 were included in this prospective study and (re-)classified according to CNS WHO 2021 (Classification of Tumors of the Central Nervous System by the World Health Organization). Four different patterns of volume overlap were defined mathematically according to the extent of overlap between MRI-based T2 tumor volume (non-enhancing tumor volume, nCEV) and BTVs. Progression-free (PFS) and overall survival (OS) were determined. Seventy patients were diagnosed with isocitrate dehydrogenase wildtype (IDHwt) glioblastoma and 16 with IDH-mutant glioma, respectively. The most common pattern was characterized by a larger non-contrast-enhancing tumor volume (nCEV) that enclosed all or most of the BTV and was observed in 46 patients (54%) (pattern 1). This pattern was more frequent in IDH-mutant gliomas than in IDH-wildtype glioblastoma (81% versus 47%, p = 0.02). In multivariate analyses, pattern 1 was associated with prolonged PFS (HR 0.59; 95 CI 0.34-1.0; p = 0.05), but not OS (HR 0.66; 95 CI 0.4–1.08; p = 0.1). For OS, presence of an IDH mutation (p = 0.05) and lower age (p = 0.03) were associated with prolonged OS. The spatial relation between nCEV and BTV varies within and between glioma entities. Most frequently, a larger nCEV encases the BTV. Some patients show spatially dissociated nCEVs and BTVs. Not accounting for this phenomenon in surgery or radiotherapy planning might lead to undertreatment.