CD37 high expression as a potential biomarker and association with poor outcome in acute myeloid leukemia

CD37 in acute myeloid leukemia: a novel surface target for drug delivery

ABSTRACT

Acute myeloid leukemia (AML) is the most common and lethal leukemia in adults. AML consists of many genetic subtypes, which limits broad applicability of targeted therapy. We discovered that the hematopoiesis-restricted tetraspanin CD37 is expressed on the majority of primary AML blasts and thus may represent a common therapeutic target for AML regardless of subtype. We demonstrate that the internalization properties of CD37 are distinct in AML blasts when compared with normal blood cells, and that CD37 rapidly accumulates inside AML blasts via dynamin-dependent endocytosis. Our work revealed that the clinically relevant anti-CD37 antibody-drug conjugate (ADC) Debio 1562 (αCD37-DM1) is highly cytotoxic to AML blasts, but not normal hematopoietic stem cells. We found that αCD37-DM1 improved clinical outcomes and overall survival in multiple in vivo models of AML. Together, these data demonstrate that targeting CD37 with an ADC such as αCD37-DM1 is a feasible and promising therapeutic option for the treatment of AML.

A combined immune and exosome-related risk signature as prognostic biomakers in acute myeloid leukemia

OBJECTIVES

Acute myeloid leukemia (AML) is one of the common hematological diseases with low survival rates. Studies have highlighted the dysregulated expression of immune-related and exosome-related genes (ERGs) in cancers. Nevertheless, it remains to be determined whether combining these genes have a prognostic significance in AML.

METHODS

Immune-ERG profiles for 151 AML patients from TCGA were analyzed. A risk model was constructed and optimized through the combination of univariate Cox regression and LASSO regression analysis. GEO datasets were utilized as the external validation for the robustness of the risk model. In addition, we performed KEGG and GO enrichment analyses to investigate the role played by these genes in AML. The variations in immune cell infiltrations among risk groups were assessed through four algorithms. Expression of hub gene in specific cell was analyzed by single-cell RNA seq.

RESULTS

A total of 85 immune-ERGs associated with prognosis were identified, enabling the construction of a risk model for AML. The risk model based on five immune-ERGs (CD37, NUCB2, LSP1, MGST1, and PLXNB1) demonstrated a correlation with the clinical outcomes. Additionally, age, FAB classification, cytogenetics risk, and risk score were identified as independent prognostic factors. The five immune-ERGs exhibited correlations with cytokine-cytokine receptor interaction, and antigen processing and presentation. Notably, the risk model demonstrated significant associations with immune responses and the expression of immune checkpoints.

CONCLUSIONS

An immune-ERG-based risk model was developed to effectively predict prognostic outcomes for AML patients. There is potential for immune therapy in AML targeting the five hub genes.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

Phase 1 study of CAR-37 T cells in patients with relapsed or refractory CD37+ lymphoid malignancies

ABSTRACT

We report a first-in-human clinical trial using chimeric antigen receptor (CAR) T cells targeting CD37, an antigen highly expressed in B- and T-cell malignancies. Five patients with relapsed or refractory CD37+ lymphoid malignancies were enrolled and infused with autologous CAR-37 T cells. CAR-37 T cells expanded in the peripheral blood of all patients and, at peak, comprised >94% of the total lymphocytes in 4 of 5 patients. Tumor responses were observed in 4 of 5 patients with 3 complete responses, 1 mixed response, and 1 patient whose disease progressed rapidly and with relative loss of CD37 expression. Three patients experienced prolonged and severe pancytopenia, and in 2 of these patients, efforts to ablate CAR-37 T cells, which were engineered to coexpress truncated epidermal growth factor receptor, with cetuximab were unsuccessful. Hematopoiesis was restored in these 2 patients after allogeneic hematopoietic stem cell transplantation. No other severe, nonhematopoietic toxicities occurred. We investigated the mechanisms of profound pancytopenia and did not observe activation of CAR-37 T cells in response to hematopoietic stem cells in vitro or hematotoxicity in humanized models. Patients with pancytopenia had sustained high levels of interleukin-18 (IL-18) with low levels of IL-18 binding protein in their peripheral blood. IL-18 levels were significantly higher in CAR-37-treated patients than in both cytopenic and noncytopenic cohorts of CAR-19-treated patients. In conclusion, CAR-37 T cells exhibited antitumor activity, with significant CAR expansion and cytokine production. CAR-37 T cells may be an effective therapy in hematologic malignancies as a bridge to hematopoietic stem cell transplant. This trial was registered at www.ClinicalTrials.gov as #NCT04136275.

Diagnostic value of sphingolipid metabolism-related genes CD37 and CXCL9 in nonalcoholic fatty liver disease

The development of nonalcoholic fatty liver disease (NAFLD) has been reported to be caused by sphingolipid family inducing insulin resistance, mitochondrial dysfunction, and inflammation, which can be regulated by multiple sphingolipid metabolic pathways. This study aimed to explore the molecular mechanism of crucial sphingolipid metabolism related genes (SMRGs) in NAFLD. Firstly, the datasets (GSE48452, GSE126848, and GSE63067) from the Gene Expression Omnibus database and sphingolipid metabolism genes (SMGs) from previous research were collected for this study. The differentially expressed genes (DEGs) between different NAFLD and controls were acquired through "limma," and the SMRGs were authenticated via weighted gene co-expression network analysis (WGCNA). After overlapping the DEGs and SMRGs, the causality between the intersection genes (DE-SMRGs) and NAFLD was explored to sort out the candidate biomarkers by Mendelian randomization (MR) study. The receiver operating characteristic (ROC) curves of candidate biomarkers in GSE48452 and GSE126848 were yielded to determine the biomarkers, followed by the nomogram construction and enrichment analysis. Finally, the immune infiltration analysis, the prediction of transcription factors (TFs) and drugs targeting biomarkers were put into effect. A total of 23 DE-SMRGs were acquired based on the differential analysis and weighted gene co-expression network analysis (WGCNA), of which 3 DE-SMRGs (CD37, CXCL9 and IL7R) were picked out for follow-up analysis through univariate and multivariate MR analysis. The values of area under ROC curve of CD37 and CXCL9 were >0.7 in GSE48452 and GSE126848, thereby being regarded as biomarkers, which were mainly enriched in amino acid metabolism. With respect to the Spearman analysis between immune cells and biomarkers, CD37 and CXCL9 were significantly positively associated with M1 macrophages (P < .001), whose proportion was observably higher in NAFLD patients compared with controls. At last, TFs (ZNF460 and ZNF384) of CD37 and CXCL9 and a total of 79 chemical drugs targeting CD37 and CXCL9 were predicted. This study mined the pivotal SMRGs, CD37 and CXCL9, and systematically explored the mechanism of action of both biomarkers based on the public databases, which could tender a fresh reference for the clinical diagnosis and therapy of NAFLD.

Tetraspanins set the stage for bone marrow microenvironment-induced chemoprotection in hematologic malignancies

Despite recent advances in the treatment of hematologic malignancies, relapse still remains a consistent issue. One of the primary contributors to relapse is the bone marrow microenvironment providing a sanctuary to malignant cells. These cells interact with bone marrow components such as osteoblasts and stromal cells, extracellular matrix proteins, and soluble factors. These interactions, mediated by the cell surface proteins like cellular adhesion molecules (CAMs), induce intracellular signaling that leads to the development of bone marrow microenvironment-induced chemoprotection (BMC). Although extensive study has gone into these CAMs, including the development of targeted therapies, very little focus in hematologic malignancies has been put on a family of cell surface proteins that are just as important for mediating bone marrow interactions: the transmembrane 4 superfamily (tetraspanins; TSPANs). TSPANs are known to be important mediators of microenvironmental interactions and metastasis based on numerous studies in solid tumors. Recently, evidence of their possible role in hematologic malignancies, specifically in the regulation of cellular adhesion, bone marrow homing, intracellular signaling, and stem cell dynamics in malignant hematologic cells has come to light. Many of these effects are facilitated by associations with CAMs and other receptors on the cell surface in TSPAN-enriched microdomains. This could suggest that TSPANs play an important role in mediating BMC in hematologic malignancies and could be used as therapeutic targets. In this review, we discuss TSPAN structure and function in hematologic cells, their interactions with different cell surface and signaling proteins, and possible ways to target/inhibit their effects.

Prognostic value of an eighteen-genes panel in acute myeloid leukemia by analyzing TARGET and TCGA databases

BACKGROUND

Acute myeloid leukemia (AML) has a poor prognosis, and the current 5-year survival rate is less than 30%.

OBJECTIVE

The present study was designed to identify the significant genes closely related to AML prognosis and predict the prognostic value by constructing a risk model based on their expression.

METHODS

Using bioinformatics (Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, univariate and multivariate Cox regression analysis, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) analysis) to identify a prognostic gene signature for AML. Finally, The Cancer Genome Atlas (TCGA) database was used to validate this prognostic signature.

RESULTS

Based on univariate and multivariate Cox regression analysis, eighteen prognostic genes were identified, and the gene signature and risk score model were constructed. Multivariate Cox analysis showed that the risk score was an independent prognostic factor [hazard ratio (HR) = 1.122, 95% confidence interval (CI) = 1.067-1.180, P< 0.001]. ROC analysis showed a high predictive value of the risk model with an area under the curve (AUC) of 0.705.

CONCLUSIONS

This study evaluated a potential prognostic signature with eighteen genes and constructed a risk model significantly related to the prognosis of AML patients.

A Six-Gene Risk Model Based on the Immune Score Reveals Prognosis in Intermediate-Risk Acute Myeloid Leukemia

Tumor microenvironment (TME) has been revealed as an important determinant of diagnosis and treatment response in AML patients. The scores of immune and stromal cell scores of AML in the intermediate-risk group from The Cancer Genome Atlas (TCGA) database were calculated using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Differentially expressed genes were identified between high and low scores. Gene set enrichment and pathway analyses were performed. A risk score model based on TME for six immune-related genes was established and validated. Patients with a lower immune score had a longer overall survival than those with a higher score (P = 0.044). A total of 805 intersected genes as differentially expressed genes were identified and selected according to the comparison of both immune and stromal scores. The functional enrichment analysis shows that these genes are mainly associated with the immune/inflammatory response. The risk score model based on TME for six immune-related genes (including MEF2C, ENPP2, FAM107A, CD37, TNFAIP8L2, and CASS4) was established and validated in the TCGA database and well validated in the TARGET database (P = 0.005). A key microenvironment-related gene signature was identified that affects the outcomes of AML patients in the intermediate-risk group and might serve as therapeutic targets.

Tetraspanins as Potential Modulators of Glutamatergic Synaptic Function

Tetraspanins (Tspans) comprise a membrane protein family structurally defined by four transmembrane domains and intracellular N and C termini that is found in almost all cell types and tissues of eukaryotes. Moreover, they are involved in a bewildering multitude of diverse biological processes such as cell adhesion, motility, protein trafficking, signaling, proliferation, and regulation of the immune system. Beside their physiological roles, they are linked to many pathophysiological phenomena, including tumor progression regulation, HIV-1 replication, diabetes, and hepatitis. Tetraspanins are involved in the formation of extensive protein networks, through interactions not only with themselves but also with numerous other specific proteins, including regulatory proteins in the central nervous system (CNS). Interestingly, recent studies showed that Tspan7 impacts dendritic spine formation, glutamatergic synaptic transmission and plasticity, and that Tspan6 is correlated with epilepsy and intellectual disability (formerly known as mental retardation), highlighting the importance of particular tetraspanins and their involvement in critical processes in the CNS. In this review, we summarize the current knowledge of tetraspanin functions in the brain, with a particular focus on their impact on glutamatergic neurotransmission. In addition, we compare available resolved structures of tetraspanin family members to those of auxiliary proteins of glutamate receptors that are known for their modulatory effects.

CD37 in B Cell Derived Tumors-More than Just a Docking Point for Monoclonal Antibodies

CD37 is a tetraspanin expressed prominently on the surface of B cells. It is an attractive molecular target exploited in the immunotherapy of B cell-derived lymphomas and leukemia. Currently, several monoclonal antibodies targeting CD37 as well as chimeric antigen receptor-based immunotherapies are being developed and investigated in clinical trials. Given the unique role of CD37 in the biology of B cells, it seems that CD37 constitutes more than a docking point for monoclonal antibodies, and targeting this molecule may provide additional benefit to relapsed or refractory patients. In this review, we aimed to provide an extensive overview of the function of CD37 in B cell malignancies, providing a comprehensive view of recent therapeutic advances targeting CD37 and delineating future perspectives.