T-distributed Stochastic Neighbor Embedding Research Articles

Single-nucleus RNA transcriptome profiling reveals murine adipose tissue endothelial cell proliferation gene networks involved in obesity development

Endothelial cells play an important role in the metabolism of adipose tissue (AT). This study aimed to analyze the changes that adipose tissue in AT endothelial cells undergo during the development of obesity, using single-nucleus RNA sequence (snRNA-seq). Mouse paraepididymal AT cells were subjected to snRNA-seq with the 10X Genomics platform. The cell types were then clustered using t-distributed stochastic neighbor embedding and unbiased computational informatics analyses. Protein–protein interactions network was established using the STRING database and visualized using Cytoscape. The dataset was subjected to differential gene enrichment analysis. In total, 21,333 cells acquired from 24 mouse paraepididymal AT samples were analyzed using snRNA-seq. This study identified 18 distinct clusters and annotated macrophages, fibroblasts, epithelial cells, T cells, endothelial cells, stem cells, neutrophil cells, and neutrophil cell types based on representative markers. Cluster 12 was defined as endothelial cells. The proportion of endothelial cells decreased with the development of obesity. Inflammatory factors, such as Vegfa and Prdm16 were upregulated in the medium obesity group but downregulated in the obesity group. Genes, such as Prox1, Erg, Flt4, Kdr, Flt1, and Pecam1 promoted the proliferation of AT endothelial cells and maintained the internal environment of AT. This study established a reference model and general framework for studying the mechanisms, biomarkers, and therapeutic targets of endothelial cell dysfunction-related diseases at the single-cell level.

A PD-1highCD4+ T Cell Population With a Cytotoxic Phenotype is Associated With Interstitial Lung Disease in Systemic Sclerosis.

T cells contribute to tissue injury in systemic sclerosis (SSc), yet the specific T cell subsets expanded in patients with SSc remain incompletely defined. Here we evaluated specific phenotypes and functions of peripheral helper T (Tph) and follicular helper T (Tfh) cells, which have been implicated in autoantibody production, and assessed their associations with clinical features in a well-characterized cohort of patients with SSc. Mass cytometry of T cells from peripheral blood mononuclear cells of patients with SSc and controls were evaluated using t-distributed stochastic neighbor embedding visualization, biaxial gating, and marker expression levels. Findings were validated with flow cytometry and in vitro assays. The frequencies of PD-1highCXCR5+ Tfh cells and PD-1highCXCR5- Tph cells were similar in patients with SSc and controls. t-distributed stochastic neighbor embedding visualization (tSNE) revealed distinct populations within the PD-1highCXCR5- cells distinguished by expression of HLA-DR and inducible costimulator (ICOS). Among PD-1highCXCR5- cells, only the HLA-DR+ICOS- cell population was expanded in patients with SSc. Cytometric and RNA sequencing analyses indicated that these cells expressed cytotoxic rather than B cell helper features. HLA-DR+ICOS- PD-1highCXCR5- cells were less potent in inducing B cell plasmablast differentiation and antibody production than comparator T helper cell populations. HLA-DR+ICOS-PD-1highCXCR5- cells were significantly associated with the presence and severity of interstitial lung disease among patients with SSc. Among PD-1highCXCR5- T cells, a subset of HLA-DR+ICOS- cells with cytotoxic features is specifically expanded in patients with SSc and is significantly associated with interstitial lung disease severity. This potential cytotoxicity appearing in the CD4 T cell population can be evaluated as a prognostic disease biomarker in patients with SSc.

Assessment and accumulation of microplastics in the Indian riverine systems: Risk assessment and implications of translocation across the water-to-fish continuum.

Microplastic (MP) pollution has engulfed global aquatic systems, and the concerns about microplastic translocation and bioaccumulation in fish and other aquatic organisms are now an unpleasant truth. In the past few years, MP pollution in freshwater systems, particularly rivers and subsequently in freshwater organisms, especially in fish, has caught the attention of researchers. Rivers provide livelihood to approximately 40 % of the global population through food and potable water. Hence, assessment of emerging contaminants like microplastics in rivers and the associated fauna is crucial. This study assessed microplastics (MPs) in fish, sediment and freshwater samples across the third largest riverine system of peninsular India, the Mahanadi River. The number concentrations of MPs measured in water, sediment and fish ranged from 337.5±54.4-1333.3±557.2 MPs/m3, 14.7±3.7-69.3±10.1 MPs/kg. Dry weight and 0.4-3.2 MPs/Fish, respectively. Surprisingly, MPs were found in every second fish sample, with a higher MP number in the gut than in the gills. Black and blue coloured filaments with <0.5 mm size were the dominant MPs with polypropylene and polyethylene polymers in abundance. The Polymer Hazard Index (PHI) and the Potential Ecological Risk Index (PERI) studies revealed that the majority of the sampling sites fell in Risk category V (dangerous category). An irregular trend in the MP concentration was observed downstream of the river, though relatively elevated MP concentrations in water and fish samples were observed downstream of the river. t-Distributed Stochastic Neighbour Embedding (t-SNE) unveiled distinct patterns in MP distribution with a higher similarity exhibited in the MPs found in fish gill and gut samples, unlike water and sediment, which shared certain characteristics. The findings in the current study contribute to filling the knowledge gap of MP assessment and accumulation in global freshwater systems and highlight the microplastic contamination and accumulation in fish with its potential implications on human health.

K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell–cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis. Traditional PCA, a main workhorse in dimensionality reduction, lacks the ability to capture geometrical structure information embedded in the data, and previous graph Laplacian regularizations are limited by the analysis of only a single scale. We propose a topological Principal Components Analysis (tPCA) method by the combination of persistent Laplacian (PL) technique and L2,1 norm regularization to address multiscale and multiclass heterogeneity issues in data. We further introduce a k-Nearest-Neighbor (kNN) persistent Laplacian technique to improve the robustness of our persistent Laplacian method. The proposed kNN-PL is a new algebraic topology technique which addresses the many limitations of the traditional persistent homology. Rather than inducing filtration via the varying of a distance threshold, we introduced kNN-tPCA, where filtrations are achieved by varying the number of neighbors in a kNN network at each step, and find that this framework has significant implications for hyper-parameter tuning. We validate the efficacy of our proposed tPCA and kNN-tPCA methods on 11 diverse benchmark scRNA-seq datasets, and showcase that our methods outperform other unsupervised PCA enhancements from the literature, as well as popular Uniform Manifold Approximation (UMAP), t-Distributed Stochastic Neighbor Embedding (tSNE), and Projection Non-Negative Matrix Factorization (NMF) by significant margins. For example, tPCA provides up to 628%, 78%, and 149% improvements to UMAP, tSNE, and NMF, respectively on classification in the F1 metric, and kNN-tPCA offers 53%, 63%, and 32% improvements to UMAP, tSNE, and NMF, respectively on clustering in the ARI metric.

Optimized data driven fault detection and diagnosis in chemical processes

Fault detection and diagnosis (FDD) is crucial for ensuring process safety and product quality in the chemical industry. Despite the large amounts of process data recorded and stored in chemical plants, most of them are not well-labeled, and their conditions are not adequately specified. In this study, an optimized data-driven FDD model was developed for a chemical process based on automatic clustering algorithms. Due to data preprocessing importance, feature selection was performed by a non-dominated sorting genetic algorithm (NSGAII) based on k-means clustering. The optimal subset of features is selected by comparing clustering results for each subset. The performance of the proposed feature selection method was compared with the Fisher discriminant ratio (FDR), and XGBoost methods. The t-distributed stochastic neighbor embedding (t-SNE), Isomap, and KPCA dimension reduction methods were also employed for feature extraction. Finally, automatic clustering was performed based on metaheuristic algorithms for fault detection and diagnosis. Results were compared with non-automatic clustering methods. The performance of the proposed method was evaluated by examining the Tennessee Eastman and four water tank processes as case studies. The results showed that the proposed method is reliable and capable of online and offline chemical process fault detection and diagnosis. As a result, the findings of this study can be used to stabilize the operation of chemical processes.

Integration of multiomics analyses reveals unique insights into CD24-mediated immunosuppressive tumor microenvironment of breast cancer.

Tumor immunotherapy brings new light and vitality to breast cancer patients, but low response rate and limitations of therapeutic targets become major obstacles to its clinical application. Recent studies have shown that CD24 is involved in an important process of tumor immune regulation in breast cancer and is a promising target for immunotherapy. In this study, singleR was used to annotate each cell subpopulation after t-distributed stochastic neighbor embedding (t-SNE) methods. Pseudo-time trace analysis and cell communication were analyzed by Monocle2 package and CellChat, respectively. A prognostic model based on CD24-related genes was constructed using several machine learning methods. Multiple quantitative immunofluorescence (MQIF) was used to evaluate the spatial relationship between CD24+PANCK+cells and exhausted CD8+T cells. Based on the scRNA-seq analysis, 1488 CD24-related differential genes were identified, and a risk model consisting of 15 prognostic characteristic genes was constructed by combining the bulk RNA-seq data. Patients weredivided into high- and low-risk groups based on the median risk score. Immune landscape analysis showed that the low-risk group showed higher infiltration of immune-promoting cells and stronger immune reactivity. The results of cell communication demonstrated a strong interaction between CD24+epithelial cells and CD8+T cells. Subsequent MQIF demonstrated a strong interaction between CD24+PANCK+ and exhausted CD8+T cells with FOXP3+ in breast cancer. Additionally, CD24+PANCK+ and CD8+FOXP3+T cells were positively associated with lower survival rates. This study highlights the importance of CD24+breast cancer cells in clinical prognosis and immunosuppressive microenvironment, which may provide a new direction for improving patient outcomes.

Modeling the decay of free residual chlorine in water distribution networks in Brazilian rural communities using artificial neural network

Disinfecting water with chlorine is crucial for maintaining sanitation and preventing waterborne diseases. However, managing Free Residual Chlorine (FRC) concentrations becomes challenging in water supply systems, as its levels can decrease throughout the distribution network, impacting water quality and public health. By leveraging water quality data from the Water Quality Surveillance Information System for Human Consumption (Sisagua) and employing the variable selection method t-Distributed Stochastic Neighbor Embedding, we crafted a Multilayer Perceptron (MLP) neural network. This MLP, fine-tuned with weighted mean square error (WMSE), was designed to evaluate the decline of free residual chlorine in water distribution networks within rural Brazilian communities. Training the model with the weighted mean squared error (WMSE) increased the average hit rate from 3.6 % to 5.0 %. In contrast to the traditional colorimetric method for predicting free residual chlorine, the proposed methodology showed superior performance, with accuracy rates of 85.45 %, 95.58 % and 99.84 % for equipment accuracies of 0.5 mg. /L, 1.0 mg/L and 2.0 mg/L, respectively. The MLP approach not only anticipates nonconformities related to FRC but also aids in adjusting chlorine dosage proactively.

On the use of contrastive learning for standard-plane classification in fetal ultrasound imaging

Background:To investigate the effectiveness of contrastive learning, in particular SimClr, in reducing the need for large annotated ultrasound (US) image datasets for fetal standard plane identification. Methods:We explore SimClr advantage in the cases of both low and high inter-class variability, considering at the same time how classification performance varies according to different amounts of labels used. This evaluation is performed by exploiting contrastive learning through different training strategies. We apply both quantitative and qualitative analyses, using standard metrics (F1-score, sensitivity, and precision), Class Activation Mapping (CAM), and t-Distributed Stochastic Neighbor Embedding (t-SNE). Results:When dealing with high inter-class variability classification tasks, contrastive learning does not bring a significant advantage; whereas it results to be relevant for low inter-class variability classification, specifically when initialized with ImageNet weights. Conclusions:Contrastive learning approaches are typically used when a large number of unlabeled data is available, which is not representative of US datasets. We proved that SimClr either as pre-training with backbone initialized via ImageNet weights or used in an end-to-end dual-task may impact positively the performance over standard transfer learning approaches, under a scenario in which the dataset is small and characterized by low inter-class variability.

Meta-analysis of the Microbial Diversity Cultured in Bioreactors Simulating the Gut Microbiome

Understanding the intricate ecological interactions within the gut microbiome and unravelling its impact on human health is a challenging task. Bioreactors are valuable tools that have contributed to our understanding of gut microbial ecology. However, there is a lack of studies describing and comparing the microbial diversity cultivated in these models. This knowledge is crucial for refining current models to reflect the gastrointestinal microbiome accurately. In this study, we analysed the microbial diversity of 1512 samples from 18 studies available in public repositories that employed cultures performed in batches and various bioreactor models to cultivate faecal microbiota. Community structure comparison between samples using t-distributed stochastic neighbour embedding and the Hellinger distance revealed a high variation between projects. The main driver of these differences was the inter-individual variation between the donor faecal inocula. Moreover, there was no overlap in the structure of the microbial communities between studies using the same bioreactor platform. In addition, α-diversity analysis using Hill numbers showed that highly complex bioreactors did not exhibit higher diversities than simpler designs. However, analyses of five projects in which the samples from the faecal inoculum were also provided revealed an amplicon sequence variants enrichment in bioreactors compared to the inoculum. Finally, a comparative analysis of the taxonomy of the families detected in the projects and the GMRepo database revealed bacterial families exclusively found in the bioreactor models. These findings highlight the potential of bioreactors to enrich low-abundance microorganisms from faecal samples, contributing to uncovering the gut microbial “dark matter”.

Expansion of HLA-DR Positive Peripheral Helper T and Naive B cells in Anticitrullinated Protein Antibody-Positive Individuals At Risk for Rheumatoid Arthritis.

To investigate immune dysregulation in the peripheral blood that contributes to the pre-rheumatoid arthritis (RA) stage of RA development in anticitrullinated protein antibody (ACPA)+ individuals. Using 37 markers by mass cytometry, we investigated peripheral blood mononuclear cells (PBMCs) from ACPA+ at-risk individuals, ACPA+ early untreated patients with RA, and ACPA- controls in the Tokyo Women's Medical University cohort (n = 17 in each group). Computational algorithms, FlowSOM and Optimized t-Distributed Stochastic Neighbor Embedding, were employed to explore specific immunologic differences between study groups. These findings were further evaluated, and longitudinal changes were explored, using flow cytometry and PBMCs from the US-based Targeting Immune Responses for Prevention of RA cohort that included 11 ACPA+ individuals who later developed RA (pre-RA), of which 9 had post-RA diagnosis PBMCs (post-RA), and 11 ACPA- controls. HLA-DR+ peripheral helper T (Tph) cells, activated regulatory T cells, PD-1hi CD8+ T cells, and CXCR5-CD11c-CD38+ naive B cells were significantly expanded in PBMCs from at-risk individuals and patients with early RA from the Tokyo Women's Medical University cohort. Expansion of HLA-DR+ Tph cells and CXCR5-CD11c-CD38+ naive B cells was likewise found in both pre-RA and post-RA time points in the Targeting Immune Responses for Prevention of RA cohort. The expansion of HLA-DR+ Tph cells and CXCR5-CD11c-CD38+ naive B cells in ACPA+ individuals, including those who developed inflammatory arthritis and classified RA, supports a key role of these cells in transition from pre-RA to classified RA. These findings may identify a new mechanistic target for treatment and prevention in RA.

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard.

Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive Python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two-dimensional t-distributed stochastic neighbor embedding embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of state-of-the-art ms2deepscore pairwise spectral similarities as a quantitative backbone while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part of mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.

A comprehensive histomolecular characterization of meningioangiomatosis: Further evidence for a precursor neoplastic lesion.

Meningioangiomatosis (MAM) remains a poorly understood lesion responsible for epileptic disease. In the past, MAM was primarily described in the context of neurofibromatosis type 2 before being mainly reported sporadically. Moreover, the malformative or tumoral nature is still debated. Because a subset of MAM are associated with meningiomas, some authors argue that MAM corresponds to an infiltration pattern of these tumors. For these reasons, MAM has not been added to the World Health Organization (WHO) Classification of Central Nervous System Tumors as a specific entity. In the present study, we characterized a series of pure MAM (n = 7) and MAM associated with meningiomas (n = 4) using histopathology, immunohistochemistry, genetic (fluorescent in situ and DNA sequencing analyses), and epigenetic (DNA-methylation profiling) data. We evidenced two distinct morphological patterns: MAM with a fibroblastic-like pattern having few lesional cells, and MAM with a more cellular pattern. A subset was associated with the genetic alterations previously reported in meningiomas (such as a KMT2C mutation and a hemizygous deletion of chromosome 22q including the NF2 gene). The DNA-methylation profile, using a t-distributed stochastic neighbor embedding analysis, evidenced that MAM (pure or associated with meningiomas) clustered in a separate group from pediatric meningiomas. The present results seem to suggest that MAM represents a neoplastic lesion and encourage the further study of similar additional series so that it may be included in a future WHO classification.

Chatter Detection Approach Based on Wavelet Synchrosqueezing and t-Distributed Stochastic Neighbor Embedding for a Turning Process

Chatter Detection Approach Based on Wavelet Synchrosqueezing and t-Distributed Stochastic Neighbor Embedding for a Turning Process

Development of a physical reservoir that operates by the diffusion of Cu cations

We developed a physical reservoir using Cu2S and Cu-doped Ta2O5 as a material of a reservoir layer, in both of which Cu cations contribute to the reservoir operation. The reservoirs showed nonlinearity and short-term memory required as reservoirs. The memory capacity becomes maximum with the input frequency at around 104 Hz. The t-distributed stochastic neighbor embedding analysis revealed that a Cu2S reservoir can classify input of five bit pulse trains, and a Cu-doped Ta2O5 reservoir can classify input of six bit pulse trains. These are longer than four bit pulse trains that a Ag2S island network reservoir achieved in our previous study. Using the superior performance, NARMA task was also carried out.

Characterization of CD34+ Cells from Patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS) Using a t-Distributed Stochastic Neighbor Embedding (t-SNE) Protocol.

Using multi-color flow cytometry analysis, we studied the immunophenotypical differences between leukemic cells from patients with AML/MDS and hematopoietic stem and progenitor cells (HSPCs) from patients in complete remission (CR) following their successful treatment. The panel of markers included CD34, CD38, CD45RA, CD123 as representatives for a hierarchical hematopoietic stem and progenitor cell (HSPC) classification as well as programmed death ligand 1 (PD-L1). Rather than restricting the evaluation on a 2- or 3-dimensional analysis, we applied a t-distributed stochastic neighbor embedding (t-SNE) approach to obtain deeper insight and segregation between leukemic cells and normal HPSCs. For that purpose, we created a t-SNE map, which resulted in the visualization of 27 cell clusters based on their similarity concerning the composition and intensity of antigen expression. Two of these clusters were "leukemia-related" containing a great proportion of CD34+/CD38- hematopoietic stem cells (HSCs) or CD34+ cells with a strong co-expression of CD45RA/CD123, respectively. CD34+ cells within the latter cluster were also highly positive for PD-L1 reflecting their immunosuppressive capacity. Beyond this proof of principle study, the inclusion of additional markers will be helpful to refine the differentiation between normal HSPCs and leukemic cells, particularly in the context of minimal disease detection and antigen-targeted therapeutic interventions. Furthermore, we suggest a protocol for the assignment of new cell ensembles in quantitative terms, via a numerical value, the Pearson coefficient, based on a similarity comparison of the t-SNE pattern with a reference.

Classification model of electricity consumption behavior based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering

The development of electrical measurement technology has brought high latitude residential electricity consumption data to power companies, which contains the characteristics of users' electricity consumption behavior and provides data support for the behavior classification. In order to improve the efficiency of data feature extraction and the accuracy of electricity consumption behavior identification, a classification model based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering is proposed in this paper. Firstly, the sparse denoising autoencoder (SDAE) and the manually defining electricity consumption characteristic indicators are deployed to extract features from the residential daily electricity consumption data, and then the spectral clustering is employed to classify the extracted electricity consumption characteristics. Secondly, the t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize and analyze the classification results, and on this basis, the secondary classification is implemented to fix the issue of the confused electricity consumption behaviors. Finally, the typical consumption behavior curves are calculated by Gaussian distance weighting method, and the characteristics of power consumption behavior are analyzed and summarized. The proposed approach is evaluated and verified by using the electricity dataset in Fujian, China.

Abstract 7565: Tumor classification and deconvolution in liquid biopsy using enriched methylation sequencing

Abstract Background: Cell-free DNA (cfDNA) detected in proximal body fluids has demonstrated potential for cancer detection using minimally invasive methodology. Our past work showed that tumor cfDNA is present in the cerebrospinal fluid (CSF) and other body fluids of patients with inconclusive standard of care testing. However, past work measuring copy number aberrations or somatic mutations was limited in cancer classification. To facilitate reliable classification, even at low tumor fractions and with fragmented DNA, we developed XR-methylSeq, a methylation sequencing platform to enrich for cell type-specific markers. Methods: We benchmarked XR-methylSeq with the K562 cell line and correlated the methylation values with gold standard measurement − whole genome bisulfite sequencing (WGBS). Methylation classifiers were applied for at least 22 cytology-positive body fluids, incorporating methylation array data from public references. T-distributed stochastic neighbor embedding (t-SNE) analysis was used for visualization in R. Deconvolution of cell type fractions for at least 29 (seven cytology-negative) body fluids and plasma samples was conducted using wgbstools. Cell type-specific markers were identified from a human DNA methylation atlas. Results: Benchmarks: XR-methylSeq has a 5-fold enrichment of the cell type-specific markers compared with WGBS. XR-methylSeq at 20 ng input highly correlates with WGBS at 2 μg (Pearson’s r = 0.97). Body Fluids: Thirteen cytology-positive CSF samples had copy number aberrations, 77% of them had concordant tumor classification, while the remaining 23% clustered with low tumor fraction samples. All nine lung primaries, including a low tumor fraction case that did not originally classify, showed a consistent cell-of-origin through deconvolution, as indicated by increased contributions from lung alveolar epithelial cells. Among six other body fluids, three exhibited the highest fractions aligning with the clinically identified cancer cell-of-origin. Additionally, the plasma cfDNA of a patient with acute liver injury had a higher fraction of hepatocyte signatures (22%) than the healthy control (8%). Conclusions: This research highlights the potential of XR-methylSeq as an enriched methylation profiling method useful for liquid biopsy applications. Citation Format: Jingru Yu, Lauren S. Ahmann, Yvette Y. Yao, Angus Toland, Alicia Snowden, Chandler Ho, Benjamin Pinsky, Hannes Vogel, Ruben Y. Luo, Linlin Wang, Brooke Howitt, Brittany Holmes, Alarice C. Lowe, Wei Gu. Tumor classification and deconvolution in liquid biopsy using enriched methylation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7565.

Abstract 2780: Induced pluripotent stem cell (iPSC) based vaccines potentializes anti-CTLA4 immune response in a murine lung carcinoma model

Abstract Despite the development of new immunotherapy strategies, lung cancer remains the leading cause of cancer-related deaths. One of the reasons is attributed to the persistence of cancer stem cells (CSCs) within the tumor, contributing to drug resistance, dormancy and metastatic spread. To eradicate CSCs, iPSC-based vaccination represents a novel approach based on the paradigm that CSCs and iPSCs share common tumor associated antigens (TAAs) with the ability to elicit a strong anti-tumor immune response.We previously showed that induced allogeneic pluripotent stem cells (miPSCs), combined with a histone deacetylase inhibitor (HDACi), induced a major reduction of tumor growth and metastatic spread to lungs in an aggressive triple negative breast cancer model (4T1) in syngeneic mice. We report here the use of the same protocol in a lung carcinoma model using LLC1 cell line in combination with an anti-CTLA4 antibody. We compared the effectiveness of allogenic miPSCs treatment alone or with an anti-CTLA4 mouse antibody on mice-bearing LLC1-Luc tumors (mean+se: 17,5+0,6 mm3). Five mice received 5 subcutaneous (SC) injection of 2 × 106 miPSCs, 5 mice received 3 intraperitoneal injections of anti-CTLA4 antibody at 5mg/kg and 5 mice were treated with the combinatory regimen (miPSCs and anti- CTLA4) with the same doses. In addition, all treated mice received a HDACi in the drink water throughout the experience.After 24 days, all tumor volumes from the 3 groups were significantly reduced compared to the control mice receiving only PBS. We obtained a reduction of 72% (p=0.007) and 74% (p=0.009) respectively for the mice treated solely with miPSCs or anti-CTLA4. An important synergic effect was obtained for the mice treated with miPSCs and anti- CTLA4 with a reduction of the tumor size by 92% (p=0.005) compared to the control mice. We also found a significant inhibition of metastatic spread to lungs for the 3 groups that was more pronounced in mice receiving the combination treatment with a significant reduction of 91 % compared to the control group. All these therapeutic effects were correlated with a significant increase of CD4+ and CD8+ cells that was more pronounced in mice receiving the combinatory regimen.We also have performed single-cell RNA sequencing analysis (10x Genomics) of LLC1 tumors treated with miPSCs and anti- CTLA4 compared to mice treated with monotherapy and untreated mice. T-distributed stochastic neighbor embedding plot reveals distinct clusters of stromal and immune cells across tumors with a significant upregulation of CD8+ and CD4+ T cells in mice receiving the combinatory regimen.Overall, these results demonstrate for the first time the clinical relevance of using an off-the-shelf allogeneic iPSC-based vaccine combined with an anti-CTLA4 antibody as a new anti-cancer immunotherapy strategy against aggressive non-small cell lung cancer (NSCLC) with metastatic potential. Citation Format: Frank Griscelli, Diana Chaker, Noufissa Oudrhiri, Christophe Desterke, Ali G. Turhan, Annelise Bennaceur Griscelli. Induced pluripotent stem cell (iPSC) based vaccines potentializes anti-CTLA4 immune response in a murine lung carcinoma model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2780.

Abstract 155: Clinico-radiological and histomolecular analyses identify of a new subtype of diffuse midline glioma, H3 K27 and BRAF/FGFR1 co-altered

Abstract Diffuse midline gliomas (DMG) H3 K27-altered are incurable grade 4 gliomas and represent a major challenge in neuro-oncology. This tumor type is now classified in four subtypes by the 2021 edition of the WHO Classification of the Central Nervous System (CNS) tumors. However, the H3.3-K27M subgroup still appears clinically and molecularly heterogenous. Recent publications reported that rare patients presenting a co-occurrence of H3.3K27M with BRAF or FGFR1 alterations tended to have a better prognosis. To better study the role of these co-driver alterations, we assembled a large pediatric and adult cohort of 29 tumors H3K27-altered with co-occurring activating mutation in BRAF or FGFR1 as well as 31 previous cases from the literature. We performed a comprehensive histological, radiological, genomic, transcriptomic and DNA methylation analysis. Interestingly, unsupervised t-distributed Stochastic Neighbor Embedding (tSNE) analysis of DNA methylation profiles regrouped BRAFV600E and all but one FGFR1MUT DMG in a unique methylation cluster, distinct from the other DMG subgroups and also from ganglioglioma (GG) or high-grade astrocytoma with piloid features (HGAP). This new DMG subtype harbors atypical radiological and histopathological profiles with calcification and/or a solid tumor component both for BRAFV600E and FGFR1MUT cases. Contrary to other DMG, these tumors occur more frequently in the thalamus (70% for BRAFV600E and 58% for FGFR1MUT) and patients have a longer overall survival with a median above three years. The analyses of a H3.3-K27M BRAFV600E tumor at diagnosis and corresponding in vitro cellular model showed that mutation in H3-3A was the first event in the oncogenesis, indicating a distinct oncogenesis from classical DMG despite an identical founder H3K27 alteration, and possible subclonal evolution.In conclusion, DMG, H3 K27 and BRAF/FGFR1 co-altered represent a new subtype of DMG with distinct genotype-phenotype characteristics, which deserve further attention with respect to trial interpretation and patient management. Our work also lays the foundations for therapeutic development highly-needed against this deadly disease. Citation Format: Lucie Auffret, Yassine Ajlil, Arnault Tauziede-Espariat, Thomas Kergrohen, Chloé Puiseux, Laurent Riffaud, Pascale Blouin, Anne-Isabelle Bertozzi, Pierre Leblond, Klas Blomgren, Sebastien Froelich, Alberto Picca, Mehdi Touat, Marc Sanson, Kevin Beccaria, Thomas Blauwblomme, Volodia Dangouloff-Ros, Nathalie Boddaert, Pascale Varlet, Marie-Anne Debily, Jacques Grill, David Castel. Clinico-radiological and histomolecular analyses identify of a new subtype of diffuse midline glioma, H3 K27 and BRAF/FGFR1 co-altered [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 155.

Establishment and Application of Steel Composition Prediction Model Based on t-Distributed Stochastic Neighbor Embedding (t-SNE) Dimensionality Reduction Algorithm

Establishment and Application of Steel Composition Prediction Model Based on t-Distributed Stochastic Neighbor Embedding (t-SNE) Dimensionality Reduction Algorithm