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Background Hypomagnesemia has been correlated with inferior outcomes in patients with large B cell lymphoma (LBCL) undergoing stem cell transplants. As T-cell and myeloid cell dysfunction have been associated with low magnesium conditions, we investigated whether serum magnesium (Mg) levels could predict clinical outcomes in LBCL patients who received chimeric antigen receptor T-cell therapy. Methods Patients with LBCL who received axi-cel under the ZUMA-1 trial or as FDA approved therapy at Mayo Clinic were examined. Serum samples were obtained at specified time points and cytokine analysis was performed. Single cell RNA sequencing was performed on peripheral blood mononuclear cells. The Student T-test, Kruskal Wallis, or Fisher's Exact Tests were used to compare differences in demographics across Mg levels. Survival curves were plotted using the Kaplan–Meier methodology and compared using the Wilcoxon test. Results We found that hypomagnesemia before lymphodepletion chemotherapy predicted inferior progression-free and overall survival in the pivotal study ZUMA-1 (NCT02348216). These results were validated in an independent cohort of LBCL patients receiving axicabtagene ciloleucel (axi-cel) at Mayo Clinic. Hypomagnesemia correlated with increased inflammatory serum markers and cytokine levels including ferritin, IL-6, IL1Ra, IL-8, and MIP1a. scRNAseq analysis unveiled altered immune interactions between monocytes and T cells with a concordant immune suppressive transcriptome. Conclusions Hypomagnesemia at the time of CAR-T infusion is associated with an unfavorable inflammatory profile and decreased response and survival in LBCL patients receiving axi-cel. These findings suggest a potentially actionable prognostic factor for patients with large cell lymphoma undergoing CAR-T.

BackgroundCAR T-cell therapy has achieved remarkable responses in patients with relapsed/refractory aggressive B-cell non-Hodgkin’s lymphoma (NHL). However, 20-30% of patients present with primary refractory disease, and 30-40% relapse, often within 6 months. We examined the metabolic profiles of T-cell subsets in patients who received CD19 CAR T-cell therapy to identify correlations to clinical response and uncover potential targets for therapy.MethodsSingle-cell RNA sequencing (scRNA-seq) and T-cell receptor sequencing (scTCR-seq) were performed on peripheral blood mononuclear cells at three timepoints: before lymphodepletion (BL), peak CAR-T expansion (PK), and one-month post-infusion (M1). scRNA-seq/scTCR-seq data were processed with CellRanger v7.0.1, followed by downstream analysis using the immunopipe pipeline.1 Metabolic landscape analysis was conducted on T cell clusters, and pathway enrichment was compared across clinical response groups: CR (complete remission for at least 6 months), PD1 (primary refractory), and PD2 (relapsed after partial or complete response).ResultsWe analyzed 107,035 T cells from 77 serial samples among 32 subjects (16 CR, 12 PD2, 4 PD1). Patients in CR showed an expansion of Th2 cell cluster with enrichment of steroid hormone biosynthesis pathway at BL and PK compared to PD1 patients. Additionally, Tregs were enriched for inositol phosphate metabolism at PK in CR, but not in PD1 and PD2.By contrast, patients in PD1, displayed Treg and Th2 cell clusters enriched for the citric acid cycle (TCA) pathway at BL and PK compared to CR and PD2. Furthermore, these patients presented CD8 effector memory (Tem) and CD8 naïve cell clusters enriched for oxidative phosphorylation (OxPhos), and Th2 clusters with increased glycolysis/gluconeogenesis (GLY) activity at CART peak. GLY pathway was also enriched in tumor recirculating CD8 effector memory (Tem) at PK in PD1 compared to PD2.Finally, patients in PD2 had enrichment for inositol phosphate metabolism in CD8 naïve population at BL and in Tregs and Th1 cells at month 1 compared to CR.ConclusionsWe identified dynamic metabolic profile changes in different T cell populations early in the course of CAR-T therapy that are associated with clinical response. The enrichment of TCA and GLY pathways may reflect altered metabolic function in exhausted T cells. OxPhos has been implicated in mitochondrial functions, whereas inositol phosphate metabolism may play a role in modulating therapeutic response. Further studies of T cell metabolic functions could inform therapeutic strategies to improve clinical response in CAR-T therapy.ReferenceWang P, Yu Y, Dong H, et al. Immunopipe: a comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline. NAR Genomics and Bioinformatics. 2025;7(2).Ethics ApprovalIRB ID: 16-008261 Mayo clinic RochesterConsentWritten informed consent was obtained from the patient for publication of this abstract. A copy of the written consent is available for review by the Editor of this journal

BackgroundIn May 2024, the FDA approved tarlatamab, a Delta-like ligand (DLL3)/CD3-targeted bispecific T-cell engager, for patients with extensive-stage small cell lung cancer (SCLC) with disease progression following platinum-based chemotherapy and at least one other prior line of therapy. We aim to examine the cellular immune cell profile changes seen in patients receiving this therapy in standard-of-care (SOC) practice.MethodsPatients who received tarlatamab at Mayo Clinic Rochester consented to research blood. Immune phenotyping was performed on whole blood by flow cytometry and analyzed by Kaluza. Data analysis was performed with Excel and PRISM.ResultsThirteen patients with a median age 64 years (range 41-80) who were treated and evaluable for clinical response were included, 53% were women. All had a history of tobacco use.Nine (69%) patients had progressive disease (PD) after a median of 2 cycles, whereas 4 patients had partial response (PR) or stable disease (SD) after at least 2 cycles and remain on therapy (no-PD). Compared to the no-PD group, those whose disease PD early had higher levels of exhausted CD8 T cells at baseline (PD1+TIGIT+CD57+, PD vs no-PD, cells/µL: 17.4±5.6, 7.6±2.6, p=0.006). Interestingly, a CD8 TIGIT+PD1negCD57neg population was also identified which was higher at baseline (PD vs no-PD, cells/µL: 21.5±16.9, 12.2±5.33, p=0.039) and decreased significantly for the PD group from baseline to day 7 (21.5±16.9 to 9.30±10, p=0.039; figure 1A). This population was found to have a different functional profile than the exhausted phenotype in other solid tumors and its role in small cell lung cancer has not been defined. At day 7, compared to the no-PD group, the PD group also had a high level of B-cells (PD vs no-PD, cells/µL: 10.3±15, 4.07±1.83, p=0.027), classical monocytes (PD vs no-PD, cells/µL: 365±279, 226±121, p=0.035), and immunosuppressive monocytes (CD14+HLA-DRneg monocytes, cells/µL: 119±93, 33±42, p=0.05). In addition, the no-PD group had a statistically significant decrease from baseline to day 7 in intermediate monocytes (32.5±7.9 to 17±2.6, p=0.049; figure 1B).ConclusionsIn this study investigating the SOC outcomes of tarlatamab, early PD was associated with higher presence of exhausted CD8 T cells, B cells, and immunosuppressive monocytes. Analysis of additional patients will be shared at SITC meeting.Abstract 844 Figure 1ACD8 T cell phenotype associated with clinical response[Image Omitted. See PDF.]Abstract 844 Figure 1BMonocyte phenotype associated with clinical response[Image Omitted. See PDF.]

was a filamentous fungus that produced sexual spores under hypotonic stress and asexual spores under hypertonic stress. It could be useful for understanding filamentous fungi's sporulation mechanism. Previously, we conducted functional studies on , which regulated the hyperosmotic glycerol signaling (HOG) pathway and found that SI65_02513 was significantly downregulated in the transcriptomics data of Δ knockout strain. This gene was located at multiple locations in the HOG pathway, indicating that it might play an important role in the HOG pathway of . Furthermore, the function of this gene had not been identified in Aspergillus fungi, necessitating further investigation. This gene's conserved domain study revealed that it has the same protein tyrosine phosphatases (PTPs) functional domain as , hence SI65_02513 was named . The function of this gene was mostly validated using gene knockout and gene complementation approaches. Knockout strains exhibited sexual and asexual development, as well as pigments synthesis. Morphological observations of the knockout strain were carried out under several stress conditions (osmotic stress, oxidative stress, Congo Red, and sodium dodecyl sulfate (SDS). Real-time fluorescence polymerase chain reaction (PCR) identified the expression of genes involved in sporulation, stress response, and pigments synthesis. The deletion of reduced sexual and asexual spore production by 4.4 and 4.6 times, demonstrating that positively regulated the sporulation of . The sensitivity tests to osmotic stress revealed that Δ strains did not respond to sorbitol-induced osmotic stress. However, Δ strains grew considerably slower than the wild type in high concentration sucrose medium. The Δ strains grew slower than the wild type on media containing hydrogen peroxide, Congo red, and SDS. These findings showed that favorably controlled osmotic stress, oxidative stress, and cell wall-damaging chemical stress in . Deleting resulted in a deeper colony color, demonstrating that regulated pigment synthesis in . The expression levels of numerous stress-and pigments-related genes matched the phenotypic data. According to our findings, played an important role in the regulation of sporulation, stress response, and pigments synthesis in . This was the first study on the function of PTPs in Aspergillus fungi.

Background Chronic cough affects approximately 10% of adults. The lack of ICD codes for chronic cough makes it challenging to apply supervised learning methods to predict the characteristics of chronic cough patients, thereby requiring the identification of chronic cough patients by other mechanisms. We developed a deep clustering algorithm with auto-encoder embedding (DCAE) to identify clusters of chronic cough patients based on data from a large cohort of 264,146 patients from the Electronic Medical Records (EMR) system. We constructed features using the diagnosis within the EMR, then built a clustering-oriented loss function directly on embedded features of the deep autoencoder to jointly perform feature refinement and cluster assignment. Lastly, we performed statistical analysis on the identified clusters to characterize the chronic cough patients compared to the non-chronic cough patients. Results The experimental results show that the DCAE model generated three chronic cough clusters and one non-chronic cough patient cluster. We found various diagnoses, medications, and lab tests highly associated with chronic cough patients by comparing the chronic cough cluster with the non-chronic cough cluster. Comparison of chronic cough clusters demonstrated that certain combinations of medications and diagnoses characterize some chronic cough clusters. Conclusions To the best of our knowledge, this study is the first to test the potential of unsupervised deep learning methods for chronic cough investigation, which also shows a great advantage over existing algorithms for patient data clustering.

Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure-property relationship (QSPR) analysis to effectively predict flash points. In recent years graph-based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, message-passing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average R2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3% lower and 2.0 K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data R2 of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.against 12 previous QSPR studies using more traditional