Atlas of Clinically Distinct Cell States and Cellular Ecosystems Across Human Solid Tumors
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA753520 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a new machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially-resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally-defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell state co-occurrence patterns, we discovered 10 clinically-distinct multicellular communities with unexpectedly strong conservation, including four with unique myeloid and stromal elements, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue (https://ecotyper.stanford.edu).
2021-08-12 | GSE181817 | GEO
Project description:Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.
| S-EPMC8526411 | biostudies-literature
Project description:Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell-state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at unprecedented resolution and identify opportunities for therapeutic targeting (https://ecotyper-stanford-edu.stanford.idm.oclc.org/lymphoma).
2021-09-30 | GSE182435 | GEO
Project description:Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell-state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at unprecedented resolution and identify opportunities for therapeutic targeting (https://ecotyper-stanford-edu.stanford.idm.oclc.org/lymphoma).
2021-09-30 | GSE182434 | GEO
Project description:The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1,070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8,887 TCGA, and 1,261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n=1,261) including our lung cancer cohort (n=497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.
2024-02-14 | GSE218989 | GEO
Project description:Innate lymphocytes in solid human tumors
| PRJNA863562 | ENA
Project description:We previously developed human CAR macrophages (CAR-M) and demonstrated redirection of macrophage anti-tumor function leading to tumor control in immunodeficient xenograft models. Here, we developed clinically relevant fully immunocompetent syngeneic models to evaluate the potential for CAR-M to remodel the tumor microenvironment (TME), induce T cell anti-tumor immunity, and sensitize solid tumors to PD1/PDL1 checkpoint inhibition. In vivo, anti-HER2 CAR-M significantly reduced tumor burden, prolonged survival, remodeled the TME, increased intratumoral T cell and natural killer (NK) cell infiltration, and induced epitope spreading. CAR-M therapy protected against antigen-negative relapse in a T cell dependent fashion, confirming long-term anti-tumor immunity. In HER2+ solid tumors resistant to anti-PD1(aPD1) monotherapy, the combination of CAR-M and aPD1 significantly improved tumor growth control, survival, and remodeling of the TME. These results demonstrate synergy between CAR-M and T cell checkpoint blockade and provide a strategy to enhance response to aPD1 therapy for patients with non-responsive tumors.
2024-11-20 | GSE275417 | GEO
Project description:We previously developed human CAR macrophages (CAR-M) and demonstrated redirection of macrophage anti-tumor function leading to tumor control in immunodeficient xenograft models. Here, we developed clinically relevant fully immunocompetent syngeneic models to evaluate the potential for CAR-M to remodel the tumor microenvironment (TME), induce T cell anti-tumor immunity, and sensitize solid tumors to PD1/PDL1 checkpoint inhibition. In vivo, anti-HER2 CAR-M significantly reduced tumor burden, prolonged survival, remodeled the TME, increased intratumoral T cell and natural killer (NK) cell infiltration, and induced epitope spreading. CAR-M therapy protected against antigen-negative relapse in a T cell dependent fashion, confirming long-term anti-tumor immunity. In HER2+ solid tumors resistant to anti-PD1(aPD1) monotherapy, the combination of CAR-M and aPD1 significantly improved tumor growth control, survival, and remodeling of the TME. These results demonstrate synergy between CAR-M and T cell checkpoint blockade and provide a strategy to enhance response to aPD1 therapy for patients with non-responsive tumors.
2024-12-22 | GSE285188 | GEO
Project description:Advanced Genetic and Molecular Analysis of Solid Tumors
| PRJNA615693 | ENA