Manifold Approximation Research Articles

Single‐cell analyses reveal impaired type B spermatogonia differentiation and meiotic entry in C‐Nap1‐null testes

AbstractSperm development is critical for male reproductive capability; any disruption during the process of spermatogenesis will result in male infertility. In this research, we used the C‐Nap1 encoded by the gene of Cep250 knockout mouse line as the model to evaluate the impact of absent C‐Nap1 on spermatogenesis. To investigate the interaction between C‐Nap1 and spermatogenesis, we utilized single‐cell RNA sequencing to analyze 10,332 C‐Nap1+/+ and 13,308 C‐Nap1−/− testicular cells. We identified five main cell types within seminiferous tubules, including spermatogonia, Sertoli cells, spermatogonia stem cells, Leydig cells, and spermatocytes. We found a critical reduction in testicular spermatogonia and spermatocytes in C‐Nap1‐null testes, compared to its C‐Nap1+/+ controls. By combining uniform manifold approximation and projection clustering and psedotime ordering, we distinguished five spermatogonial stages/subtypes, demonstrating that type B spermatogonia differentiation and meiotic initiation are impaired during C‐Nap1‐null spermatogenesis. Following gene ontology enrichment analysis, meiosis‐specific genes downregulated in the C‐Nap1−/− testicular cells were further verified by reverse transcription polymerase chain reaction (RT‐PCR). Based on the differential gene expression, certain downregulated genes such as Ctnnb1 and Aurka encoding C‐Nap1‐binding potential β‐Catenin and Aurka are encountered, which may account for defective type B spermatogonia differentiation and meiotic entry in C‐Nap1‐null testes.

Uniform manifold approximation and projection

Uniform manifold approximation and projection

Uniform manifold approximation and projection

Uniform manifold approximation and projection

Dimensionality Reduction for Data Analysis With Quantum Feature Learning

ABSTRACTTo improve data analysis and feature learning, this study compares the effectiveness of quantum dimensionality reduction (qDR) techniques to classical ones. In this study, we investigate several qDR techniques on a variety of datasets such as quantum Gaussian distribution adaptation (qGDA), quantum principal component analysis (qPCA), quantum linear discriminant analysis (qLDA), and quantum t‐SNE (qt‐SNE). The Olivetti Faces, Wine, Breast Cancer, Digits, and Iris are among the datasets used in this investigation. Through comparison evaluations against well‐established classical approaches, such as classical PCA (cPCA), classical LDA (cLDA), and classical GDA (cGDA), and using well‐established metrics like loss, fidelity, and processing time, the effectiveness of these techniques is assessed. The findings show that cPCA produced positive results with the lowest loss and highest fidelity when used on the Iris dataset. On the other hand, quantum uniform manifold approximation and projection (qUMAP) performs well and shows strong fidelity when tested against the Wine dataset, but ct‐SNE shows mediocre performance against the Digits dataset. Isomap and locally linear embedding (LLE) function differently depending on the dataset. Notably, LLE showed the largest loss and lowest fidelity on the Olivetti Faces dataset. The hypothesis testing findings showed that the qDR strategies did not significantly outperform the classical techniques in terms of maintaining pertinent information from quantum datasets. More specifically, the outcomes of paired t‐tests show that when it comes to the ability to capture complex patterns, there are no statistically significant differences between the cPCA and qPCA, the cLDA and qLDA, and the cGDA and qGDA. According to the findings of the assessments of mutual information (MI) and clustering accuracy, qPCA may be able to recognize patterns more clearly than standardized cPCA. Nevertheless, there is no discernible improvement between the qLDA and qGDA approaches and their classical counterparts.

Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources.

By processing and abstracting diverse omics datasets into associations between genes and their attributes, the Harmonizome databaseenables researchers to explore and integrate knowledge about human genes from many central omics resources. Here, we introduce Harmonizome 3.0, a significant upgrade to the original Harmonizome database. The upgrade adds 26 datasets that contribute nearly 12 million associations between genes and various attribute types such as cells and tissues, diseases, and pathways.The upgrade has adatasetcrossing feature to identify gene modules that are shared across datasets. To further explain significantly high gene set overlap between dataset pairs, a large language model (LLM) composes a paragraph that speculates about the reasons behind the high overlap. The upgradealso adds more data formats and visualization options. Datasets aredownloadable as knowledge graph (KG) assertions and visualized withUniform Manifold Approximation and Projection (UMAP) plots. The KG assertions can be explored via a user interface that visualizes gene-attribute associations as ball-and-stick diagrams. Overall, Harmonizome 3.0 is a rich resource of processed omics datasets that are provided in several AI-ready formats.Harmonizome 3.0is available at https://maayanlab.cloud/Harmonizome/.

Integrative analysis of single-nucleus RNA sequencing and Mendelian randomization to explore novel risk genes for Alzheimer's disease.

In this study, we aimed to delineate cellular heterogeneity in Alzheimer's disease (AD) and identify genetic markers contributing to its pathogenesis using integrative analysis of single-nucleus RNA sequencing (sn-RNA-Seq) and Mendelian randomization (MR). The dorsolateral prefrontal cortex sn-RNA-Seq dataset (GSE243292) was sourced from the Gene Expression Omnibus (GEO) database. Data preprocessing was conducted using the Seurat R software package, employing principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) for cell clustering and annotation. MR analysis was used to identify instrumental variables from expression quantitative trait loci (eQTL) and GWAS data by applying inverse variance weighting (IVW), weighted median (WM) and MR-Egger methods. This was complemented by leave-one-out sensitivity analysis to validate the causal relationship on AD risk genes. We identified 23 distinct cell clusters, which were annotated into eight subgroups, including oligodendrocytes, oligodendrocyte precursors, astrocytes, macrophage cells, endothelial cells, glutamatergic neurons, neural stem cells, and neurons. Notably, the number of macrophages significantly increased in the AD group. Using genome-wide association study (GWAS) summaries and eQTL data, MR analysis identified causal relationships for 7 genes with significant impacts on AD risk. Among these genes, CACNA2D3, INPP5D, RBM47, and TBXAS1 were associated with a decreased risk of AD, whereas EPB41L2, MYO1F, and SSH2 were associated with an increased risk. A leave-one-out sensitivity analysis confirmed the robustness of these findings. Expression analysis revealed that these genes were variably expressed across different cell subgroups. Except for the CACNA2D3 gene, the other 6 genes showed increased expression levels in the macrophages, particularly EPB41L2 and SSH2. Our findings highlight the potential of specific genetic markers identified through integrative analysis of sn-RNA-Seq and MR in guiding the diagnosis and therapeutic strategies for Alzheimer's disease.

A comprehensive human embryo reference tool using single-cell RNA-sequencing data.

Stem cell-based embryo models offer unprecedented experimental tools for studying early human development. The usefulness of embryo models hinges on their molecular, cellular and structural fidelities to their in vivo counterparts. To authenticate human embryo models, single-cell RNA sequencing has been utilized for unbiased transcriptional profiling. However, an organized and integrated human single-cell RNA-sequencing dataset, serving as a universal reference for benchmarking human embryo models, remains unavailable. Here we developed such a reference through the integration of six published human datasets covering development from the zygote to the gastrula. Lineage annotations are contrasted and validated with available human and nonhuman primate datasets. Using stabilized Uniform Manifold Approximation and Projection, we constructed an early embryogenesis prediction tool, where query datasets can be projected on the reference and annotated with predicted cell identities. Using this reference tool, we examined published human embryo models, highlighting the risk of misannotation when relevant references are not utilized for benchmarking and authentication.

A machine learning approach to stratify patients with hypermobile Ehlers-Danlos syndrome/hypermobility spectrum disorders according to disorders of gut brain interaction, comorbidities and quality of life.

A high prevalence of disorders of gut-brain interaction (DGBI) exist in patients with hypermobile Ehlers-Danlos Syndrome (hEDS) and hypermobility spectrum disorders (HSD). However, it is unknown if clusters of hEDS/HSD patients exist which overlap with different DGBIs and whether this overlap influences presence of comorbidities and quality of life. We aimed to study these knowledge gaps. A prospectively collected hEDS/HSD cohort of 1044 individuals were studied. We undertook Uniform Manifold Approximation and Projection-enabled (UMAP) dimension reduction to create a representation of nonlinear interactions between hEDS/HSD and DGBIs, from which individuals were stratified into clusters. Somatization, Postural Tachycardia Syndrome (PoTS), autonomic symptoms, psychological factors and quality of life were statistically compared between clusters. The mean age of patients was 40 ± 13.2 years; 87.8% were female. Patients segregated into three clusters: Cluster 0 (n = 466): hEDS/HSD+ functional foregut disorders (FFD) + irritable bowel syndrome (IBS); Cluster 1 (n = 180): hEDS/HSD+ IBS and Cluster 2 (n = 337): hEDS/HSD alone. In cluster 0, we demonstrated increased somatization (p <0.0001), anxiety (p <0.0001), depression (p <0.0001), PoTS prevalence (p = 0.003), autonomic symptoms (p <0.0001) and reduced quality of life (p <0.0001) compared to cluster 2. Cluster 0 had greater comorbidity burden than cluster 1. Within hEDS/HSD, subgroups exist with a high prevalence of FFD and IBS. These subgroups have a higher prevalence of psychological disorders, dysautonomia and poorer quality of life compared with hEDS/HSD alone. Further research should focus on healthcare utilization, management and prognosis in hEDS/HSD and DGBI overlap.

Abstract 4135951: Single Nuclei RNA Sequencing Reveals Distinct Cellular Composition of Pericoronary Adipose Tissue

Introduction: Adipose tissue is a metabolically active organ with local paracrine interactions on adjacent tissues. Pericoronary adipose tissue (PcAT) is an emerging cardiovascular disease (CVD) risk factor and likely has paracrine effects on coronary vasculature that may promote coronary artery disease (CAD) development. PcAT may be functionally and clinically distinct from epicardial adipose tissue (EAT) given its proximity to the coronary artery adventitia. The cellular composition of PcAT and EAT and whether they have unique inflammatory cell signatures and functional expression at a single cell level has not been explored. Hypothesis: PcAT will have diverse cellular composition with heightened inflammatory cell content compared to EAT. Methods: Visual dissection of paired EAT not adjacent to an epicardial coronary artery and PcAT in direct apposition to an epicardial artery (left anterior descending artery) was performed from a single human heart immediately after explant (prior to heart transplantation). Nuclei were isolated using the Chromium Nuclei Isolation Kit (10x Genomics) with RNAse inhibitor. Cells were counted with a Cellometer K2 and Chromium Next GEM Single Cell libraries were prepared, followed by reverse transcription, PCR and NovaSeq 6000 platform sequencing. Raw sequence reads from single nuclei RNA-seq (snRNA-seq) were processed and after QC filtering, cells were clustered using Seurat. Clusters were annotated using singleR. Results: In PcAT and EAT, &gt;7000 cells were successfully extracted with 73755 and 48750 mean snRNA-seq reads per cell, respectively. snRNA-seq cellular composition analysis delineated a heightened inflammatory milieu and distinct cellular composition of human PcAT vs. EAT. A UMAP (Uniform Manifold Approximation and Projection) plot displays higher inflammatory cell infiltrate with a neutrophil predominance and lesser adipocyte composition in PcAT compared with EAT ( Figure 1) . Conclusions: We have generated snRNA-seq data from human PcAT and shown distinct cellular composition compared to EAT. Given the proximity of PcAT to coronary vasculature, these results are supportive of paracrine inflammatory effects and highlight adipose tissue biology in cardiovascular disease. Understanding this biology may identify novel therapeutic targets for CVD.

Abstract 4114276: Identification of the heterogeneity in the risk of heart failure in those with left bundle branch block pattern using artificial intelligence

Introduction: The association between left bundle branch block (LBBB) and heart failure is well-established. However, the lack of a practical risk assessment tool for patients with LBBB poses a clinical challenge to the management despite the heterogeneity of the LBBB population. Hypothesis: Artificial intelligence (AI) can extract features to stratify the risk of heart failure (HF) within the heterogeneity of LBBB. Methods: All 12-lead ECGs diagnosed with LBBB recorded from 1/1/2015 to 12/31/2019 were identified in our institution. ECG data were analyzed as 2D matrices with a shape of 12×2500. The Uniform Manifold Approximation and Projection (UMAP) was used to visualize the heterogeneity of LBBB ECG. Additionally, a 2-dimensional convolutional neural network model was trained to detect LBBB ECGs associated with past HF diagnosis. The model was then applied to ECGs from an external cohort of patients with LBBB but without a history of HF. Cumulative incidences of HF admission were compared by stratifying according to tertiles of the model output (low, intermediate and high AI score groups) Results: We identified 15,124 LBBB ECGs in our institution (training dataset) and 2,463 individuals with LBBB ECGs in the external cohort (external test dataset). The UMAP projection of the ECGs revealed distinct heterogeneity in the data ( Fig A ). This heterogeneity was not fully explained by patient demographics, ECG features, or institutions. When the external cohort was divided into 3 groups according to the tertile of AI prediction, patients with high AI scores had a higher risk of HF admission (high vs low AI score group: hazard ratio (HR), 2.10; 95% confidence interval (CI), 1.66-2.65; intermediate vs low AI score group: HR, 1.39; 95%CI, 1.08-1.78: log-rank p &lt;0.0001; Fig 1B ). This finding was unchanged after adjusting for multiple clinical characteristics and ECG parameters, including QRS duration. Conclusion: We observed significant heterogeneity in ECGs presenting LBBB, and the AI model excellently stratified the risk of heart failure admission in patients with LBBB from a single recording of 12-lead ECG, suggesting great potential for clinical utility in managing patients with LBBB.

Abstract 4138373: Machine learning identifies clinically distinct phenotypes in patients with aortic regurgitation.

Background: Aortic regurgitation (AR) is a prevalent valve disease with a long latent period to symptoms. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation. Method: We sought to evaluate the role of unsupervised cluster analyses in identifying different clinical clusters, including clinical status, and a large number of echocardiographic variables including left ventricular (LV) volumes, and their association with mortality. Patients with ≥moderate-severe chronic AR identified using echocardiography at Mayo Clinic, Rochester were retrospectively analyzed. Primary outcome was all-cause mortality censored at aortic valve surgery/last follow-up. Uniform Manifold Approximation and Projection (UMAP) with K -means algorithm was used to cluster patients using clinical and, echocardiographic variables at the time of presentation. Missing data were imputed with the Multiple Imputation by Chained Equations (MICE) method. A supervised approach trained on the training set was used to find cluster membership in a hold-out validation set. Log-rank tests were used to assess differences in mortality rates between the clusters, both in the training and validation sets. Results: Three distinct clusters were identified among 1100 patients (log-rank P for survival &lt;0.001). Cluster 1 (n=337), which included younger males with severe AR but fewer symptoms, showed the best survival, 75.6% (69.5, 82.3). Cluster 2 (n=235), older and more females with elevated filling pressures, showed intermediate survival of 64.2 % (56.8, 72.5). Cluster 3 (n=253), characterized by severe symptomatic AR, demonstrated the lowest survival of 45.3 % (34.4, 59.8) at 5 years. Similar clusters were formed in the internal validation cohort. Conclusion: Distinct clusters with variable echocardiographic features and mortality differences exist within patients with chronic ≥moderate-severe AR. Recognizing these clusters can refine individual risk stratification and clinical decision-making after verification in future prospective studies.

Abstract 4129223: Large Language Modeling-Enabled Analysis of Atrial Fibrillation Topics and Sentiments on Social Media

Purpose: We utilized large language modeling (LLM) and automated topic modeling techniques to describe public perceptions regarding AF on Reddit, a social media platform. Methods: In this qualitative study, we curated all AF-related discussions on Reddit from 2006 to 2023 by searching for all posts and comments containing AF-related terms (“atrial fibrillation”, “afib”, and “atrial flutter”). Discussions were embedded using a state-of-the-art embedding model (BAAI/bge-base-en-v1.5). Embeddings are reduced in dimensionality using Uniform Manifold Approximation and Project and clustered using K-Means Clustering to find topics and groups. Topic and group labels are enhanced using large language models (Llama2 and GPT4, respectively). Results: We identified a total of 86,323 discussions related to AF from 38,183 unique users between April 14, 2006 and November 20, 2023. Discussions around wearables and ablations increased over time, while discussions around anticoagulation had periodic spikes. The topic modeling pipeline automatically identified 64 topics and 9 groups (Figure 1). Groups emphasized the following themes: lifestyle factors and triggers associated with AF (groups 1, 5, 7); patient experiences with management strategies including anticoagulation, rate control, pharmacological rhythm control, and ablation (groups 2, 4, 5); experiences with wearable devices (group 8); psychological burden of living with AF (group 6); and online advertisements for AF-related medications (groups 3, 9). Conclusions: Our AI-enabled topic modeling analysis revealed public perceptions about AF including triggers, experiences with different management strategies, impact of wearables, and psychological burden of the diagnosis, which may help guide potential strategies for improving shared decision making around the AF patient journey.

IMMU-03. EXPLORING CANCER-SPECIFIC T CELLS AND ANTIGENS IN GLIOBLASTOMA BASED ON SINGLE-CELL SEQUENCING OF CD8+ TILS

Abstract BACKGROUND The effectiveness of cancer immunotherapy against glioblastoma (GBM) remains limited. This study aims to identify cancer-specific antigens to develop antigen-based cancer immunotherapies for GBM. We investigated candidate tumor antigen-specific T cells in GBM by single-cell RNA sequencing (scRNA-seq) and single-cell TCR sequencing (scTCR-seq), and we explored candidate antigens through genetic analysis of tumor tissues. METHODS Flow cytometry analysis was conducted on fresh tumor digest samples to evaluate tumor-infiltrating lymphocytes (TILs) of GBM. Single-cell RNA and TCR sequencing were performed on CD8+ T cells in TILs. Both data generated by Cell Ranger software were loaded into Seurat at R studio. The dimensional reduction for clustering was performed with uniform manifold approximation and projection (UMAP). Additionally, we performed bulk RNA sequencing (RNA-seq) and whole exome sequencing (WES) on tumor tissues. RESULTS Two out of 20 patients (10%) exhibited abundant TILs in GBM. From these two patients, approximately 20,000 CD8+ T cells in total were analyzed in single-cell analysis. Clustering based on UMAP revealed a predominance of clusters expressing exhaustion markers, with high TCR clonality centered on exhausted T cell (TEX) clusters, like our previous date of lung cancer TILs recognizing tumor antigens. Moreover, within the TEX clusters, the expression of CXCL13, often reported as an antigen-specific marker in recent years, was significantly higher in our data compared to two publicly available datasets. Subsequently, based on RNA-seq and WES, 61 candidate neoantigens were estimated using machine-learning prediction algorithms from NEC Corporation. Furthermore, 5 overexpressed cancer/testis antigens, and 3 bacterial species-derived microbial peptides using Kraken2 were selected as antigen candidates. CONCLUSIONS We identified prospective tumor antigen-specific T cell subsets with high CXCL13 expression from two GBM patients. We plan to identify cancer-specific T cells and antigens from these candidates by T cell activation assay.

PATH-22. SINGLE-CELL RNA (SCRNA) SEQUENCING OF PAIRED PRIMARY AND RECURRENT ATYPICAL TERATOID/RHABDOID TUMORS (AT/RT) REVEALED MOLECULAR DIFFERENCES AND THERAPY-RESISTANT PRIMARY TUMOR CELLS

Abstract Atypical teratoid/rhabdoid tumors (AT/RT) are the most common malignant brain tumors during infancy. They split into four molecular types. The major three (AT/RT-SHH, AT/RT-TYR, and AT/RT-MYC) all carry mutations in SMARCB1, the fourth smaller type is characterized by SMARCA4 mutations (AT/RT-SMARCA4). AT/RT are associated with a dismal outcome, and 50% of the patients suffer a progress or recurrence of the tumor. Molecular mechanisms of disease recurrence are currently unclear. Here, we identified molecular characteristics of AT/RT primaries and recurrences, which could help to improve the therapy for relapsed patients. Previously, we investigated tumor tissue from 26 patients revealing signatures of recurrences in comparison to matched primary tumor samples using histology, DNA methylation analysis, and bulk RNA sequencing. To further identify mechanisms regarding tumor progression and recurrence, we performed single-cell RNA (scRNA) sequencing for five of these primary-recurrence pairs so far using formalin-fixed paraffin-embedded (FFPE) tissue. Using Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction, we observed a distinct separation in the gene expression profiles of primary and recurrent tumor cells. In addition, we aimed to identify primary tumor cells which potentially persisted after therapy and developed to recurrences. Therefore, we identified primary tumor cells showing highest similarity to recurrent tumor cells using CIBERSORT and trajectory analysis. Differentially expressed genes of these specific primary tumor cells were investigated. They are involved in cell migration, -adhesion, -proliferation, and -stability, among others. Overall, molecular characteristics that occur in the course of the disease were identified, which may help to define new therapeutic targets for AT/RT recurrences.

PATH-21. SINGEL CELL TRANSCRIPTOMICS OF SPINAL EPENDYMOMA SUBTYPES IDENTIFIES INTRATUMORAL HETEROGENEITY

Abstract Spinal ependymomas comprise glial neoplasms that are classified by histological and molecular features. Two of the respective types, myxopapillary ependymomas (MPE) and spinal ependymomas (SP-EPN) are largely understudied. For each of these, our group identified clinically relevant subtypes with poorer (MPE-A; SP-EPN-A) and more favorable progression-free survival (MPE-B; SP-EPN-B) employing global methylation and transcriptomic profiling. As cellular origin and pathogenesis of these tumors are largely unknown, therapeutic options for patients with unfavorable subtypes are unavailable. We performed single cell transcriptomic sequencing of 12 formalin-fixed and paraffin-embedded spinal ependymomas including all MPE and SP-EPN subtypes and obtained a median of 10,091 cells per tumor after quality control. Uniform Manifold Approximation and Projection (UMAP) analysis revealed intertumoral heterogeneity as most samples formed separate clusters with tumors of the same subtype located in proximity. The expression of literature-defined neoplastic consensus cell states was examined for each cell of the data set and revealed intratumoral heterogeneity. Tumors expressed predominantly two programs, being a ciliated (ependymal-like) cell state (61% of all neoplastic cells) and the astrocytic state (31 % of all neoplastic cells). Interestingly, third and fourth most abundant states (mesenchymal and hypoxia) were almost exclusively expressed in MPE-A and SP-EPN-A. Furthermore, distinct clones of chromosomal copy number variations were identified across all tumors. These clones can be distinguished in UMAP representation of MPE single cell transcriptomes and to a lesser extend in SP-EPN. Next, we sought to characterize the resemblance of integrated tumor subtypes to normal spinal cord cell populations and observed a high similarity to human spinal cord ependymal cells. All spinal ependymoma subtypes display intratumoral heterogeneity with respect to the transcriptomic cell state resemblance of the neoplastic cells. Together with the transcriptomic similarity to ependymal and astrocytic cells, our data suggest the tumor origin within the astro-ependymal lineage.

Unsupervised learning analysis on the proteomes of Zika virus

Background The Zika virus (ZIKV), which is transmitted by mosquito vectors to nonhuman primates and humans, causes devastating outbreaks in the poorest tropical regions of the world. Molecular epidemiology, supported by clustering phylogenetic gold standard studies using sequence data, has provided valuable information for tracking and controlling the spread of ZIKV. Unsupervised learning (UL), a form of machine learning algorithm, can be applied on the datasets without the need of known information for training. Methods In this work, unsupervised Random Forest (URF), followed by the application of dimensional reduction algorithms such as principal component analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders were used to uncover hidden patterns from polymorphic amino acid sites extracted on the proteome ZIKV multi-alignments, without the need of an underlying evolutionary model. Results The four UL algorithms revealed specific host and geographical clustering patterns for ZIKV. Among the four dimensionality reduction (DR) algorithms, the performance was better for UMAP. The four algorithms allowed the identification of imported viruses for specific geographical clusters. The UL dimension coordinates showed a significant correlation with phylogenetic tree branch lengths and significant phylogenetic dependence in Abouheif’s Cmean and Pagel’s Lambda tests (p value &lt; 0.01) that showed comparable performance with the phylogenetic method. This analytical strategy was generalizable to an external large dengue type 2 dataset. Conclusion These UL algorithms could be practical evolutionary analytical techniques to track the dispersal of viral pathogens.

BIOM-04. SINGLE-CELL RNA SEQUENCING PROTOCOL OF THE HUMAN METASTATIC PROSTATE SPINE TUMOR MICROENVIRONMENT

Abstract INTRODUCTION 90% of spine tumor patients can develop metastases with debilitating complications, such as sensorimotor dysfunction, paralysis, and spinal instability. As spine tumor patients have limited eligibility to participate in clinical trials and have heterogenous tumor pathology, there is a distinct lack of therapeutic targets and prognostication markers of disease. Thus, there is a distinct need for molecular profiling of spine tumors. OBJECTIVE We describe an OR-to-bench-top processing of spine tumors for single-cell omic studies, with samples from a metastatic prostate cancer patient to demonstrate the ability to compare molecular markers and microenvironments of difficult-to-process spine tumors and adjacent normal tissue. METHODS Under IRB protocol #Pro00101198, patients undergoing spine surgery with the Department of Neurosurgery at Duke University were consented for tissue collection. Vertebral osseous metastases and adjacent normal tissue were confirmed histologically (H&amp;E, immunohistochemistry) and radiologically (MRI, PET, CT, and x-ray). Tissues were manually and enzymatically homogenized in serum-free media with collagenase A and DNAse I to obtain a single cell suspension. Cells were then filter sterilized, erythrolyzed, counted to determine live-death ratio, resuspended to 10-20 million cells per mL, then cryopreserved for downstream standard library preparation for single-cell sequencing using the 10X genomics platform. Downstream analysis was performed using Seurat pipeline, InferCNV analysis, and SingleR. RESULTS P404S and PSA expression confirmed tumor was prostate in origin. Radiological imaging and H&amp;E staining confirmed the presence of T7 osseous metastasis. Uniform manifold approximation and project (UMAP) plots identified 12 unique clusters of immune cell subpopulations between normal and tumorous vertebrae, with their own subsets of differentially expressed genes. CONCLUSION We have developed a protocol to generate granular single cell molecular profiling for spine tumors and metastases. Applications of this protocol can aid in identifying therapeutic targets, molecular predictors of disease, and potentiate increased eligibility of patients for clinical trials.

BIOM-67. DIFFERENTIAL DNA METHYLATION PATTERNS IN THE CSF OF PATIENTS WITH DIFFUSE GLIOMAS

Abstract Studies have shown that different central nervous system (CNS) tumor types exhibit unique DNA methylation profiles. DNA methylation-based machine learning classification of CNS tumor tissue has been demonstrated to be highly-accurate in differentiating between tumor types, including between diffuse glioma subtypes. While similar analyses have been performed using cell-free DNA (cfDNA) from blood, few studies have looked at the potential of DNA methylation as a biomarker for diffuse gliomas using CSF-derived cfDNA. In this study, we analyzed CSF cfDNA methylation patterns in diffuse glioma patients. DNA methylation profiling of CSF of patients with glioblastoma, IDH-wildtype (GBM) (n=37), IDH-mutant diffuse gliomas (n=12), and non-neoplastic conditions (n=5) was performed using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). Methylation quantification was estimated for each genomic region. Significance testing was performed between diagnostic groups to discover differentially-methylated regions (DMRs). The 300 most differentially-methylated regions (DMRs) for each comparison (IDH-wildtype vs. IDH-mutant, IDH-mutant vs. control, IDH-wildtype vs. control), for a total of 900 DMRs, were utilized as features in dimensionality reduction analysis and machine learning. Out of ~1×107 defined genomic regions, there were ~7×105 DMRs between GBM and control samples and ~1.5×106 DMRs between GBM and IDH-mutant samples, accounting for ~7% and ~15% of the genome, respectively. Uniform manifold approximation and projection (UMAP) and hierarchical clustering analysis revealed that samples were mostly clustered by diagnosis. Random forest analysis of the 900 DMRs with an 80/20 train/test split resulted in an overall accuracy of 0.815. In conclusion, CSF cfDNA methylation profiles of diffuse glioma patients exhibit differential methylation patterns between IDH-mutant and IDH-wildtype tumors, as well as between tumor and control, and could be informative biomarkers for the diagnosis of patients with diffuse gliomas.

CNSC-52. OLIGODENDROGLIOMA SPATIAL TRANSCRIPTOMIC SEQUENCING REVEALS THERAPY-DEPENDENT EVOLUTION OVER TIME

Abstract Oligodendroglioma evolution is incompletely understood. Here we analyze serial oligodendroglioma samples to define genomic and cellular mechanisms underlying treatment resistance. RNA sequencing was performed on 53,406 spatial transcriptomes from 31 oligodendrogliomas (IDH mutant, 1p/19q-codeleted) that were resected from 16 patients. There were 15 CNS WHO grade 2 and 16 grade 3 oligodendrogliomas, which were stratified for temporal progression across grades (n=5 grade 2 to 2, n=5 grade 2 to 3, n=6 grade 3 to 3) and treatments between serial resections (n=2 TMZ/RT, n=2 TMZ, n=1 mTOR inhibition, n=1 IDH inhibition, n=10 surgical monotherapy). Spatial transcriptomic clusters were defined using cell signature gene sets, cell cycle analysis, differentially expressed marker genes, immunohistochemistry, spatial copy number alterations (CNAs), and deconvolution of 18,628 oligodendroglioma single-cell transcriptomes from 16 independent patients. Specificity was tested using immunofluorescence on independent oligodendrogliomas that did not recur after surgical monotherapy (n=8 samples, median follow-up 8.4 years) and spatial transcriptomic sequencing on 8 matched-paired astrocytomas from 4 patients. Integrated uniform manifold approximation and projection revealed a proneural/NPC-like cluster that was enriched in grade 3 oligodendroglioma samples and represented the starting point in oligodendroglioma lineage differentiation using RNA velocity, Monocle, and subclonal spatiotemporal CNA evolution. Comparison across treatment regimens showed (1) enrichment of the proneural/NPC-like cluster in recurrent versus primary tumors that were treated with surgical monotherapy, (2) enrichment of a proneural/OPC-like cluster and depletion of an oligodendrocyte-like cluster after TMZ/RT, (3) enrichment of proneural/NPC-like and immediate-early response clusters after TMZ/RT, (4) enrichment of the oligodendrocyte-like cluster following mTOR inhibition, and (5) enrichment of cancer associated fibroblasts (CAFs) following IDH inhibition. These data show the spatiotemporal landscape of oligodendroglioma is associated with branched lineage differentiation that is modified by postoperative therapy, and suggest that therapy-dependent druggable dependencies may exist to treat oligodendrogliomas that are resistant to standard interventions.

Machine Learning-enhanced Signature of Metastasis-related T Cell Marker Genes for Predicting Overall Survival in Malignant Melanoma.

In this study, we aimed to investigate disparities in the tumor immune microenvironment (TME) between primary and metastatic malignant melanoma (MM) using single-cell RNA sequencing (scRNA-seq) and to identify metastasis-related T cell marker genes (MRTMGs) for predicting patient survival using machine learning techniques. We identified 6 distinct T cell clusters in 10×scRNA-seq data utilizing the Uniform Manifold Approximation and Projection (UMAP) algorithm. Four machine learning algorithms highlighted SRGN, PMEL, GPR143, EIF4A2, and DSP as pivotal MRTMGs, forming the foundation of the MRTMGs signature. A high MRTMGs signature was found to be correlated with poorer overall survival (OS) and suppression of antitumor immunity in MM patients. We developed a nomogram that combines the MRTMGs signature with the T stage and N stage, which accurately predicts 1-year, 3-year, and 5-year OS probabilities. Furthermore, in an immunotherapy cohort, a high MRTMG signature was associated with an unfavorable response to anti-programmed death 1 (PD-1) therapy. In conclusion, primary and metastatic MM display distinct TME landscapes with different T cell subsets playing crucial roles in metastasis. The MRTMGs signature, established through machine learning, holds potential as a valuable biomarker for predicting the survival of MM patients and their response to anti-PD-1 therapy.