Single Atlas Research Articles

Human pancreatic cancer single cell atlas reveals association of CXCL10+ fibroblasts and basal subtype tumor cells.

Pancreatic ductal adenocarcinoma (PDAC) patients with tumors enriched for the basal-like molecular subtype exhibit enhanced resistance to standard of care treatments and have significantly worse overall survival (OS) compared to patients with classical subtype enriched tumors. It is important to develop genomic resources, enabling identification of novel putative targets in a statistically rigorous manner. We compiled a single cell RNA sequencing (scRNAseq) atlas of the human pancreas with 229 patient samples, aggregated from publicly available raw data. We mapped cell-type specific scRNAseq gene signatures in bulk RNAseq (n=744) and spatial transcriptomics (ST) (n=22) and performed validation using multiplex immunostaining. Analysis of tumor cells from our scRNAseq atlas revealed nine distinct populations, two of which aligned with the basal subtype, correlating with worse OS in bulk RNAseq. Deconvolution identified one of the basal populations to be the predominant tumor subtype in non-dissociated ST tissues and in vitro tumor cell and patient-derived organoid lines. We discovered a novel enrichment and spatial association of CXCL10+ cancer associated fibroblasts (CAFs) with basal tumor cells. We identified that besides immune cells, ductal cells also express CXCR3, the receptor for CXCL10, suggesting a relationship between these cell types in PDAC tumor microenvironment. We show that our scRNAseq atlas (700,000 cells), integrated with ST data, has increased statistical power and is a powerful resource, allowing for expansion of current subtyping paradigms in PDAC. We uncovered a novel signaling niche marked by CXCL10+ CAFs and basal tumor cells that could be explored for future targeted therapies.

ScPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases

Multimodal single-cell assays profile multiple sets of features in the same cells and are widely used for identifying and mapping cell states between chromatin and mRNA and linking regulatory elements to target genes. However, the high dimensionality of input features and shallow sequencing depth compared to unimodal assays pose challenges in data analysis. Here we present scPair, a multimodal single-cell data framework that overcomes these challenges by employing an implicit feature selection approach. scPair uses dual encoder-decoder structures trained on paired data to align cell states across modalities and predict features from one modality to another. We demonstrate that scPair outperforms existing methods in accuracy and execution time, and facilitates downstream tasks such as trajectory inference. We further show scPair can augment smaller multimodal datasets with larger unimodal atlases to increase statistical power to identify groups of transcription factors active during different stages of neural differentiation.

TMIC-13. INTEGRATING SNRNASEQ AND SPATIAL TRANSCRIPTOME (ST) SHOWS DIVERSE TRANSCRIPTOMIC LANDSCAPE FOR TUMOR MICROENVIRONMENT (TME) IN INTRACRANIAL GERMINOMAS

Abstract The incidence of intracranial germ cell tumors is 5-8 times higher in East Asia and histologically, germinoma is well known to show intense lymphocytic infiltrates. In spite of germinoma’s well known immune infiltration, there have only been a few studies on its tumor micro-environment (TME). There has been no study using the recent and comprehensive techniques of single nucleus sequencing or spatial transcriptomics to study its TME. In this study, we studied snRNAseq (Chromium 10X) of FFPE tissues of 24 intracranial germinomas, and spatial transcriptomes (STs) (Visium Spatial) from fresh tissues from ten matched cases. For the TME, in contrast to common belief, macrophages and fibroblasts were equally represented as T- and NK-cells as immune cells. Clinical stage correlated with a lesser proportion of fibroblasts and T- and NK-cells. There was no correlation with proportion of tumor cells with either focal vs bifocal disease or clinical stage. Common gene modules (non-negative matrix factorization) were epithelial-mesenchymal transition (complete and partial), cell cycle and immune cells. Cell interaction analysis showed strong interaction between germ cells and macrophages involving CXCL, MIF and MK pathways. By ligand-receptor analysis, MIF combined with CD74 might promote tumor development. For ten cases, we studied STs with fresh tissues and integrated the results with snRNSeq, deconvoluting the spots for ST using snRNAseq cell types as reference (SPOTight) and achieved integrated single cell expression atlases of the STs (Seurat package). STs revealed a similar CNV plot pattern as those derived from snRNAseqs. Multimodal Intersection Analysis integrated significant genes based on snRNAseq and different regions’ significant genes based on STs. There was correlation between the expression of the four gene modules for tumor and immune cells for cell neighborhood. In conclusion, we showed in details the heterogenous expression landscape of TME of germinomas.

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types

Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited. We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties. Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells. This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism. National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Abstract B068: Human pancreatic cancer single cell atlas reveals association of CXCL10 positive fibroblasts and basal subtype tumor cells

Abstract Pancreatic ductal adenocarcinoma (PDAC) patients with tumors enriched for the basal-like molecular subtype exhibit severe resistance to standard of care treatments and have significantly worse overall survival, compared to patients with classical subtype enriched tumors. It is therefore important to develop resources that allow identification of novel targets in a statistically rigorous way to overcome therapeutic challenges. Here we compiled a single cell RNA sequencing (scRNAseq) atlas of the human pancreas with 229 individual patient samples, aggregated from raw data obtained from published studies. We mapped cell-type specific scRNAseq gene signatures in newly generated (n=12) and existing (n=13) spatial transcriptomics (ST) data from PDAC samples. Analysis of tumor cells from our scRNAseq atlas revealed nine transcriptionally distinct populations, two of which strongly align with a basal gene signature, correlating with worse overall survival in PDAC bulk RNAseq datasets. Deconvolution showed one of the basal populations identified to be the predominant tumor subtype in non- dissociated ST tissues and in vitro tumor cell and organoid PDAC lines. In bulk RNAseq datasets, we uncovered that myofibroblasts (myFb) expressing CXCL10 consistently correlated with both basal subtypes and were found near basal tumor cells using immunostaining. We also identified that besides immune cells, ductal cells also express CXCR3, the receptor for CXCL10, further suggesting a relationship between these cell types in PDAC tumor microenvironment (TME). Overall, we show that our newly built scRNAseq atlas, containing over 700,000 cells, integrated with ST data is a powerful resource, given that it corroborates numerous PDAC TME observations in the literature, with increased statistical power. Moreover, we uncovered a novel association between CXCL10+ myFb and basal tumor cells, underscoring the presence of unique spatial niches in PDAC that should be explored for the development of targeted therapies for this dismal disease. Citation Format: Ian M Loveless, Samantha B Kempt, Yuesong Wu, Daniel J Salas-Escabillas, Kailee Hartway, Madison George, Yetunde Makinwa, Thais Stockdale, Samuel D Zwernik, Kendyll Gartrelle, Rohit G Reddy, Daniel Long, Allison Wombell, Julie M Clark, Albert M Levin, David Kwon, Ling Huang, Ralph Francescone, Débora B Vendramini-Costa, Ben Stanger, Adam Alessio, Andrew M Waters, Yuehua Cui, Brian Theisen, Howard C Crawford, Nina G Steele. Human pancreatic cancer single cell atlas reveals association of CXCL10 positive fibroblasts and basal subtype tumor cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B068.

Molecular, Cellular, and Developmental Organization of the Mouse Vomeronasal organ at Single Cell Resolution.

We have generated single cell transcriptomic atlases of vomeronasal organs (VNO) from juvenile and adult mice. Combined with spatial molecular imaging, we uncover a distinct, previously unidentified class of cells that express the vomeronasal receptors and a population of canonical olfactory sensory neurons in the VNO. High resolution trajectory and cluster analyses reveal the lineage relationship, spatial distribution of cell types, and a putative cascade of molecular events that specify the V1r, V2r, and OR lineages from a common stem cell population. The expression of vomeronasal and olfactory receptors follow power law distributions, but there is high variability in average expression levels between individual receptor and cell types. Substantial co-expression is found between receptors across clades, from different classes, and between olfactory and vomeronasal receptors, with nearly half from pairs located on the same chromosome. Interestingly, the expression of V2r, but not V1r, genes is associated with various transcription factors, suggesting distinct mechanisms of receptor choice associated with the two cell types. We identify association between transcription factors, surface axon guidance molecules, and individual VRs, thereby uncovering a molecular code that guides the specification of the vomeronasal circuitry. Our study provides a wealth of data on the development and organization of the accessory olfactory system at both cellular and molecular levels to enable a deeper understanding of vomeronasal system function.

Hepatic stellate cell single cell atlas reveals a highly similar activation process across liver disease aetiologies

Background & AimsThe progression of chronic liver disease (CLD) is characterized by excessive extracellular matrix deposition, disrupting hepatic architecture and function. Upon liver injury, hepatic stellate cells (HSCs) differentiate towards myofibroblasts and become inflammatory, proliferative and fibrogenic. To date it is still unclear whether HSC activation is driven by similar mechanisms in different aetiologies. MethodsHSCs from multiple publicly available single-cell RNA-sequencing datasets were annotated and merged into a single-cell HSC activation atlas. Spheroid co-cultures of primary mouse hepatocytes/HSCs (n=5) and ELISAs on patient plasma samples (n=80) were performed to validate the mechanistic insight obtained from the HSC atlas. ResultsWe established an HSC activation atlas in which HSCs are clearly divided into 3 distinct transcriptomic profiles: quiescent HSCs, initiatory HSCs and myofibroblasts. These transcriptomic profiles are present in each of the investigated mouse liver injury models as well as in human chronic liver diseases, indicating that HSC activation is a conserved process. This activation process is driven by a core set of transcription factors independent of liver injury or species. Furthermore, we reveal novel ligands associated with activation of HSCs in multiple liver injury models and validate the profibrotic effect of parathyroid hormone. Finally, we identify COLEC10 as a conserved marker for quiescent HSCs and a biomarker of liver fibrosis in patients with different CLDs (p<0.0001). ConclusionsWe reveal unexpected similarities in the regulatory mechanisms of HSCs across diverse liver injury settings and species. The HSC activation atlas has the potential to provide novel insights into liver fibrosis and steer novel treatment options. Impact and implicationsThis study establishes a single-cell atlas of hepatic stellate cells across various liver injuries, highlighting a conserved activation process between different injuries and across species. The discovery of novel activating ligands and the biomarker COLEC10 in human plasma enhances diagnostic and therapeutic strategies. Additionally, the conserved activation process supports the use of any mouse model for mechanistic studies and testing of new anti-fibrotic compounds, streamlining preclinical research efforts.

MAFT-SO: A novel multi-atlas fusion template based on spatial overlap for ASD diagnosis

Autism spectrum disorder (ASD) is a common neurological condition. Early diagnosis and treatment are essential for enhancing the life quality of individuals with ASD. However, most existing studies either focus solely on the brain networks of subjects within a single atlas or merely employ simple matrix concatenation to represent the fusion of multi-atlas. These approaches neglected the natural spatial overlap that exists between brain regions across multi-atlas and did not fully capture the comprehensive information of brain regions under different atlases. To tackle this weakness, in this paper, we propose a novel multi-atlas fusion template based on spatial overlap degree of brain regions, which aims to obtain a comprehensive representation of brain networks. Specifically, we formally define a measurement of the spatial overlap among brain regions across different atlases, named spatial overlap degree. Then, we fuse the multi-atlas to obtain brain networks of each subject based on spatial overlap. Finally, the GCN is used to perform the final classification. The experimental results on Autism Brain Imaging Data Exchange (ABIDE) demonstrate that our proposed method achieved an accuracy of 0.757. Overall, our method outperforms SOTA methods in ASD/TC classification.

Unveiling novel double-negative prostate cancer subtypes through single-cell RNA sequencing analysis

Recent advancements in single-cell RNA sequencing (scRNAseq) have facilitated the discovery of previously unrecognized subtypes within prostate cancer (PCa), offering new insights into cancer heterogeneity and progression. In this study, we integrated scRNAseq data from multiple studies, comprising publicly available cohorts and data generated by our research team, and established the Human Prostate Single cell Atlas (HuPSA) and Mouse Prostate Single cell Atlas (MoPSA) datasets. Through comprehensive analysis, we identified two novel double-negative PCa populations: KRT7 cells characterized by elevated KRT7 expression and progenitor-like cells marked by SOX2 and FOXA2 expression, distinct from NEPCa, and displaying stem/progenitor features. Furthermore, HuPSA-based deconvolution re-classified human PCa specimens, validating the presence of these novel subtypes. We then developed a user-friendly web application, “HuPSA–MoPSA” (https://pcatools.shinyapps.io/HuPSA-MoPSA/), for visualizing gene expression across all newly established datasets. Our study provides comprehensive tools for PCa research and uncovers novel cancer subtypes that can inform clinical diagnosis and treatment strategies.

Reconfiguration of the visual code and retinal cell type complement in closely related diurnal and nocturnal mice.

How does evolution act on neuronal populations to match computational characteristics to functional demands? We address this problem by comparing visual code and retinal cell composition in closely related murid species with different behaviours. Rhabdomys pumilio are diurnal and have substantially thicker inner retina and larger visual thalamus than nocturnal Mus musculus. High-density electrophysiological recordings of visual response features in the dorsal lateral geniculate nucleus (dLGN) reveals that Rhabdomys attains higher spatiotemporal acuity both by denser coverage of the visual scene and a selective expansion of elements of the code characterised by non-linear spatiotemporal summation. Comparative analysis of single cell transcriptomic cell atlases reveals that realignment of the visual code is associated with increased relative abundance of bipolar and ganglion cell types supporting OFF and ON-OFF responses. These findings demonstrate how changes in retinal cell complement can reconfigure the coding of visual information to match changes in visual needs.

Single Cell Atlas: a single-cell multi-omics human cell encyclopedia.

Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

Integrative Single Cell Atlas Revealed Intratumoral Heterogeneity Generation from an Adaptive Epigenetic Cell State in Human Bladder Urothelial Carcinoma.

Intratumor heterogeneity (ITH) of bladder cancer (BLCA) contributes to therapy resistance and immune evasion affecting clinical prognosis. The molecular and cellular mechanisms contributing to BLCA ITH generation remain elusive. It is found that a TM4SF1-positive cancer subpopulation (TPCS) can generate ITH in BLCA, evidenced by integrative single cell atlas analysis. Extensive profiling of the epigenome and transcriptome of all stages of BLCA revealed their evolutionary trajectories. Distinct ancestor cells gave rise to low-grade noninvasive and high-grade invasive BLCA. Epigenome reprograming led to transcriptional heterogeneity in BLCA. During early oncogenesis, epithelial-to-mesenchymal transition generated TPCS. TPCS has stem-cell-like properties and exhibited transcriptional plasticity, priming the development of transcriptionally heterogeneous descendent cell lineages. Moreover, TPCS prevalence in tumor is associated with advanced stage cancer and poor prognosis. The results of this study suggested that bladder cancer interacts with its environment by acquiring a stem cell-like epigenomic landscape, which might generate ITH without additional genetic diversification.

Abstract 903: AURIGIN: A comprehensive single-cell OMICs atlas of human development and an AI/ML framework to classify and identify the drivers of tumor plasticity and altered cellular state

Abstract Altered cellular state and plasticity have been increasingly identified as drivers of poor prognosis and treatment-resistance in cancer. Identifying therapeutics that correct altered cellular state and plasticity meets a broad need in the field of oncology, particularly in tumors resistant to standard-of-care therapy. Tumor plasticity and altered cellular states encompass many potential configurations, including dedifferentiated and transdifferentiated tumors, so classifying tumors based on their specific cell state profile is critical to identifying the gene drivers that are specific to that altered state. For that reason, we have developed AURIGIN - a platform that combines a comprehensive single-cell OMICs atlas of human development with an AI/ML framework that can classify cell state plasticity and identify the genes and pathways that drive those specific altered cellular states. AURIGIN is more comprehensive than previously disclosed single cell atlases in that it focuses and enriches the diverse stem, progenitor, and developmental states commonly involved in plastic cell states of cancer. The platform standardizes and integrates these states with multiple atlases of differentiated adult tissues to create a unified atlas of human development. In addition, the AI/ML paradigm of AURIGIN is embedded with developmental biology models that enable it to deconvolute state heterogeneity within tumor indications for precise identification of the most relevant gene targets for novel therapies. AURIGIN also unifies classified plasticity across tissue types by identifying altered cellular states that span multiple indications. Furthermore, we present the implementation of AURIGINDRIVE ML models that integrate pathway information, physical interaction datasets, and regulatory annotations to accurately predict the gene targets at the top of hierarchy of control of tumor plasticity. AURIGIN also enables and accelerates target validation by identifying models that map to the relevant classified plastic states. Similarly, AURIGIN defines clinically relevant patient selection and treatment efficacy biomarkers that are specifically defined from the classified plastic cellular state. We demonstrate the success of AURIGIN for therapeutic discovery. In sum, AURIGIN amplifies the value of multiOMIC tumor data in cellular state and plasticity target discovery by mapping the data into a developmental biology-informed ML framework. Citation Format: Joseph DeBartolo, Henry Wilson, Kimberly S. Straley, Maulasri Bhatta, Sambad Sharma, Sara Sinicropi-Yao, James Neef, Betty Chan, Andrew McRiner, Mark Bittinger, Laura Antipov, Katharine E. Yen, Thomas G. Graeber, David S. Millan. AURIGIN: A comprehensive single-cell OMICs atlas of human development and an AI/ML framework to classify and identify the drivers of tumor plasticity and altered cellular state [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 903.

Abstract 5887: Identifying new immunotherapy targets using machine learning and ex vivo validation

Abstract Overview: Immunotherapy has revolutionized cancer treatment. However, existing immune checkpoint inhibitors (CPI) yield low response rates in most cancers, highlighting the need for new therapeutic options. Traditional immune-oncology (IO) target discovery relies on preclinical models, which struggle to recapitulate human tumor complexity, limiting translation potential. Attention is thus directed at harnessing multimodal patient molecular data with modern machine learning (ML) techniques to identify new IO targets which may have higher clinical viability. Approach: We posed IO target discovery as a binary classification task, training ML systems on known candidate drug targets that have progressed to stage I or higher clinical trials. We constructed a rich knowledge graph database to support model development. Graph nodes (n=11,919) comprised genes with edges linking genes involved in n=99,275 protein-protein interactions (PPIs; PMID: 28936969). Genes were labelled with n=6,387 gene-disease associations alongside bulk exome and transcriptome (RNAseq) features from n=1,317 CPI-treated patients (PMID: 33508232). We considered the role of different immune subsets by incorporating cell type-specific PPIs from single cell RNAseq atlases of n=350 samples (PMID: 31786210). To capture how antigen processing affects immunotherapy response, we also examined the immunopeptidome of n=60 patients (PMID: 30556813). Causal data on immune responses to genetic perturbation stemmed from n=7 publicly available CRISPR tumor-T cell co-cultures and n=15,442 SNP-phenotype links. Lastly, we used interpretability analysis to understand drivers of model predictions and elucidate critical genes that influence multiple IO target pathways. Results: Firstly, we developed an ensemble ML approach which achieved test ROC-AUC&amp;gt;0.75. Next, we used a graph-based ML framework which yielded superior performance (test ROC-AUC&amp;gt;0.90). Orthogonal validation confirmed these models can discriminate known targets by trial phase (p&amp;lt;0.001), predict patient response in new CPI trials (p&amp;lt;0.05), and identify genes that rank highly in unseen genome-wide CRISPR screens. Targets from both methods have entered experimental validation in patient-derived explants and organoid-immune co-cultures. Already, we have identified 2 novel targets predicted to relate to macrophage activity. Early data suggest that perturbing them leads to macrophage repolarization. Further validation experiments are ongoing. Conclusions: We will reveal candidate targets, showing that our method is effective at uncovering new IO targets and can identify critical nodes in biological networks that might be attractive hits. In addition, deciphering data types that drive model predictions provides a broader immunobiological understanding of anti-tumor immune responses. Thus, our results endorse using ML and multimodal data for novel IO target discovery. Citation Format: Marcellus Augustine, Nuno Rocha Nene, Krupa Thakkar, Danwen Qian, Evelyn Fitzsimons, Benjamin S. Simpson, Chris Watkins, Charles Swanton, Kevin Litchfield. Identifying new immunotherapy targets using machine learning and ex vivo validation [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 5887.

Abstract 2914: AI/ML-driven discovery of CTHRC1, collagen triple helix repeat-containing 1, a novel proteoglycan for stroma + tumor targeting and delivery of 4-1BB costimulation

Abstract Background: While checkpoint inhibitors have demonstrated efficacy in a number of solid tumor indications, those with high stromal presence have been difficult to treat with minimal objective responses observed. Phenomic has developed a proprietary machine learning/artificial intelligence platform to identify novel stromal targets with superior expression profiles that enable selective targeting of immune activating agents that will relieve these immunosuppressive barriers in difficult to treat indications. Methods: Using our single cell RNA Atlas, we assessed cancer-associated fibroblasts (CAFs) in several solid tumor indications for identification of novel targets, including proteoglycans. Our Atlas was also used to identify immune activating payloads whose cognate receptors were present in indications of interest. Antibodies were generated, and lead clones who demonstrated potent ligand binding and cell staining were used to generate fusion proteins to immune activating ligands. Efficacy and PD were assessed in multiple syngeneic tumor models. Results: Bioinformatic analysis identified a unique subset of pathogenic CAFs, which are TGF beta responsive, secrete several ECM proteins, and their presence tracks with poor outcome and resistance to immunotherapy in several solid tumor types. CTHRC1 was identified as a novel matrix protein highly expressed in this CAF subtype as well as tumor epithelium and is highly selective in a range of tumor types such as ovarian cancer, triple negative breast cancer, and pancreatic ductal adenocarcinoma. While secreted, CTHRC1 is complexed on the cell surface and affords the opportunity to drug this target in a variety of ways. We identified an absence of 4-1BBL expression across indications of interest, and fusion proteins were generated to deliver 4-1BBL via CTHRC1 targeting. In checkpoint-resistant tumor models, we observed significant increases in CD8 T cells and robust anti-tumor activity with anti-CTHRC1-targeted 4-1BBL. Biodistribution studies were conducted using our targeting mAb and demonstrate uptake only in sites of primary and metastatic tumors, even at doses 20-fold higher than those that achieve maximal therapeutic activity, suggesting the potential to minimize the toxicity that has been observed with other 4-1BB agonists. Conclusions: We have identified CTHRC1 as a novel proteoglycan expressed by both pathogenic CAFs and tumor cells that is highly selective for tumors, enabling the therapeutic targeting of immune activating payloads with the potential for safely delivering payloads while limiting toxicity. Given the specificity and selectivity afforded by CTHRC1 expression, ADC and CD3 engager approaches are also being pursued. These data represent novel approaches aimed at breaking down stromal barriers in tumors previously unresponsive to immunotherapies. Citation Format: Christopher Harvey, Elizabeth Koch, Amanda Hanson, Lindsey Rice, Amy Berkley, Kerry White, Reza Saberianfar, Nikolai Suslov, Sam Cooper, Michael Briskin. AI/ML-driven discovery of CTHRC1, collagen triple helix repeat-containing 1, a novel proteoglycan for stroma + tumor targeting and delivery of 4-1BB costimulation [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 2914.

Comprehensive analysis of pain genes in prognosis of kidney renal clear cell carcinoma and tumor immunotherapy: A comprehensive bioinformatic study.

The effect of pain genes (NAV1, EHMT2, SP1, SLC6A4, COMT, OPRM1, OPRD1, CYP2D6, and CYP3A4) have not been reported previously in kidney renal clear cell carcinoma (KIRC) patients and thus we made a comprehensive analysis of pain genes in the prognosis of KIRC and tumor immunotherapy. In this study, TCGA, Kaplan-Meier plotter, Metascape, STRING, Human Protein Atlas, Single Cell Expression Atlas database, LinkedOmics, cBioPortal, MethSurv, CancerSEA, COSMIC database and R package (ggplot2, version 3.3.3) were used for comprehensive analysis of pain genes in KIRC. Pearson and Spearman correlation coefficients were for co-expression analysis. Immunotherapy and TISIDB database were used for tumor Immunotherapy. Representative pain genes (SP1, SLC6A4, COMT, OPRD1, CYP2D6, and CYP3A4) were statistically significant (p < 0.0001) in the prognosis of KIRC. Immunotherapy (anti-PD-1 therapy, anti-PD-L1 therapy, and anti-CTLA4 therapy) and immunomodulator (immunoinhibitor, immunostimulator, and MHC molecule) in KIRC were significant associated with pain genes (SP1, SLC6A4, COMT, OPRD1, CYP2D6, and CYP3A4), which were the important addition to clinical decision making for patients. Our study uncovered a mechanism for the effect of pain genes on KIRC outcome via the modulation of associated co-expression gene networks, gene variation, and tumor Immunotherapy.

Longitudinal single cell atlas identifies complex temporal relationship between type I interferon response and COVID-19 severity

Due to the paucity of longitudinal molecular studies of COVID-19, particularly those covering the early stages of infection (Days 1-8 symptom onset), our understanding of host response over the disease course is limited. We perform longitudinal single cell RNA-seq on 286 blood samples from 108 age- and sex-matched COVID-19 patients, including 73 with early samples. We examine discrete cell subtypes and continuous cell states longitudinally, and we identify upregulation of type I IFN-stimulated genes (ISGs) as the predominant early signature of subsequent worsening of symptoms, which we validate in an independent cohort and corroborate by plasma markers. However, ISG expression is dynamic in progressors, spiking early and then rapidly receding to the level of severity-matched non-progressors. In contrast, cross-sectional analysis shows that ISG expression is deficient and IFN suppressors such as SOCS3 are upregulated in severe and critical COVID-19. We validate the latter in four independent cohorts, and SOCS3 inhibition reduces SARS-CoV-2 replication in vitro. In summary, we identify complexity in type I IFN response to COVID-19, as well as a potential avenue for host-directed therapy.

Ultra High‐Plex Spatial Proteogenomic Investigation of Giant Cell Glioblastoma Multiforme Immune Infiltrates Reveals Distinct Protein and RNA Expression Profiles

AbstractBackgroundThe advancement of spatially resolved, multiplex proteomic and transcriptomic technologies has revolutionized and redefined the approaches to complex biological questions pertaining to tissue heterogeneity, tumor microenvironments, cellular interactions, cellular diversity, and therapeutic response. Most spatial technologies yield single analyte proteomic or transcriptomic datasets from separate formalin‐fixed paraffin‐embedded (FFPE) tissues sections. Multiple studies have demonstrated a poor correlation between RNA expression and protein abundance owing to transcriptional and translational regulation, target turnover, and post‐translational protein modifications. Therefore, a workflow that accurately measures RNA and protein simultaneously within a single tissue section with distinct spatial context is critical to a more complete biological understanding of cellular interactions and activities. Such multimodal omic datasets of protein and DNA or RNA have been termed “spatial proteogenomics”.MethodHere we present a novel spatial proteogenomic (SPG) assay on the GeoMx® Digital Spatial Profiler platform with NGS readout that enables ultra high‐plex digital quantitation of proteins (147‐plex) and RNA (whole transcriptome,&gt;18,000‐plex) from a single FFPE sample. We demonstrated high concordance, R&gt;0.85, and minor change in sensitivity (&lt;11%) between the SPG assay and the single analyte GeoMx Whole Transcriptome Atlas and GeoMx NGS Protein assays. We used the SPG assay to interrogate 23 different glioblastoma multiforme samples across 4 pathologies.ResultsWe observed clustering of both RNA and protein based on cancer pathology and anatomic location. The in‐depth investigation of giant cell glioblastoma multiforme (gcGBM) revealed distinct protein and RNA expression profiles compared to that of glioblastoma multiforme (GBM). Spatial proteogenomics allowed simultaneous interrogation of critical protein post‐translational modifications alongside whole transcriptomic profiles within the same distinct cellular neighborhoods.ConclusionsWithin our dataset, we observed &gt;2‐fold higher protein expression levels of phospho‐GSK3β (Ser9) in gcGBM compared to GBM. Inactivation of GSK3β through phosphorylation has been shown to enhance proliferation of GBM cells. We also observed differential protein expression phosphorylated Tau variants. Phospho‐Thr231 Tau was &gt;2‐fold higher in GBM compared to gcGBM. Associated with neurodegenerative Alzheimer’s disease, changes in Tau expression and phosphorylation have also been observed in glioblastoma. Our study exemplifies the utility of the SPG assay in expanding our understanding of glioblastoma multiforme molecular pathology.

203P Identification of high confidence candidate markers for macrophage infiltrating tumor microenvironment through single cell genomic atlases

203P Identification of high confidence candidate markers for macrophage infiltrating tumor microenvironment through single cell genomic atlases

A Single Cell Atlas of Diffuse Large B Cell Lymphomas Reveals Distinct Cellular States Predictive of Outcomes

Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive B-cell lymphoma with high clinical and biological heterogeneity. Despite current effective immunochemotherapy, up to 40% of patients do not respond or develop refractory disease. DLBCL is characterized by two major cell-of-origin (COO) subtypes, germinal center B cell-like (GCB) and activated B cell-like (ABC), with recent research uncovering additional molecular subgroups based on genomic alterations or tumor microenvironment (TME) features. However, current classifiers use bulk or deconvolution profiling which blurs intra-tumor heterogeneity, hindering the discovery of true functional cell states underlying clinical diversity. Here, we constructed a multimodal single-cell atlas of 62 DLBCL cases, integrating single-cell transcriptomes, immune repertoire and genetic features, to decrypt recurrent B cell states and TME ecosystems while identifying clinically relevant prognostic biomarkers (Fig.1). We performed simultaneous 5'-end single-cell RNA, B-cell receptor (BCR) and T-cell receptor sequencing on 63 DLBCL lymph node biopsies, recovering 109,294 cells after stringent quality controls. These biopsies were obtained from the real-world, clinically-annotated, multicentric CeVi collection and included 43 patient samples at diagnosis (ABC = 17, GCB = 11, Unclassified = 6, Not Evaluated = 9; 21 of which did not achieve event-free survival at 24 months (EFS24) after R-chemotherapy regimens), 11 relapsed/refractory DLBCL and 8 DLBCL transformed from indolent lymphoma. We distinguished malignant B-cells from TME using canonical markers and BCR sequence, exploring heterogeneity in both components. We developed an original bioinformatic approach to define malignant B cell transcriptional archetypes shared across patients. To assess whether subclonal genetics are associated with transcriptional states, we inferred copy number variations (CNV) and studied BCR intraclonal variations of malignant cells. We also integrated TME data (39,674 cells) to quantify subpopulations and clonally expanded T-cell subsets, and performed robust statistical analyses to identify clinically relevant features linked to patient outcomes. To circumvent interpatient heterogeneity in malignant B cells, we defined transcriptional cellular archetypes conserved across patients by combining 3 unsupervised methods: rank-biased overlap meta-clustering, variational autoencoder and canonical correlation analysis integration. We identified 5 cellular archetypes (Arch.1-5): Arch.1 was characterized by plasma cell identity, Arch.2 by MYC and NFKB pathways, Arch.3 by MYC pathway only, Arch.4 by quiescence markers, and Arch.5 by GC markers. Several archetypes co-existed in each tumor sample, and archetype identity was superimposed to the transcriptional profile conferred by COO molecular class as an additional layer of transcriptional heterogeneity. Cell state-specific CNV and BCR subclones were very rare, suggesting that subclonal genetic changes were not the main drivers of malignant B cell plasticity and archetypes. Kaplan-Meier analysis showed a significant negative association between proportion of Arch.4 cells and continuous EFS in our diagnosis cohort. Unsupervised analysis of archetype composition further distinguished ABC patients who achieved EFS24 (enriched in Arch.3 cells) from those with a particularly poor prognosis (enriched in Arch.4 cells). Integrated analysis of the TME revealed three major ecosystems, including one enriched for effector and cytotoxic T cells. In diagnosis samples, pseudo-temporal trajectory analysis revealed the enrichment of clonally-expanded hyperactivated CD8+ T cells expressing canonical exhaustion markers in patients achieving EFS24, suggesting a positive link between the extent of CD8+ T cell differentiation and response to R-CHOP based regimens. Shedding new light on DLBCL biological and clinical heterogeneity, our comprehensive single-cell atlas of DLBCL uncovered recurrent tumor cell states and TME features crucial for stratifying non-responsive ABC patients and guiding treatment decisions. Ongoing single-cell spatial transcriptomics analyses will be used to further validate our findings and elucidate archetype-TME interactions that may drive malignant B cell plasticity, with the potential to uncover novel therapeutic targets.