Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Single-Cell Tumor Immune Atlas for Precision Oncology

ABSTRACT: The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell resolved tumor immune cell atlas, jointly analyzing >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting immune cells from external tumors onto the atlas facilitated an automated cell annotation system for a harmonized interpretation. To enable in situ mapping of immune populations for digital pathology, we developed SPOTlight, a computational tool that identified striking spatial immune cell patterns in tumor sections. We expect the atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification strategies for prognosis and immuno-therapy response.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE158803 | GEO | 2020/10/27

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

A Single-Cell Tumor Immune Atlas for Precision Oncology

Project description:A Single-Cell Tumor Immune Atlas for Precision Oncology

| PRJNA666613 | ENA

Refined molecular taxonomy and treatment remodeling of pancreatic cancer using single-cell resolution

Project description:Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here we construct a high-resolution molecular landscape of the multicellular subtypes and spatial communities that compose PAC using single-nucleus RNA-seq and whole-transcriptome digital spatial profiling (SP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment-naïve. We uncovered expression programs across malignant cells and fibroblasts, including a newly-identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities: classical, squamoid-basaloid, and treatment-enriched. Our refined molecular and cellular taxonomy can advance precision oncology in PAC through stratification in clinical trials and as roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.

2022-05-18 | GSE202051 | GEO

Spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment

2022-05-12 | GSE199102 | GEO

Spatially informed gene signatures for response to immunotherapy in Melanoma

Project description:Gene signatures have been shown to predict the response/resistance to immunotherapies but with only modest accuracy. Reduction in this precision might be due to the lack of spatial information which prevents the ability from distinguish tumor from tumor-microenvironment (TME) genes. Here we collected gene expression data spatially from three compartments (CD68+macrophages, CD45+leukocytes and S100+tumor cells) of 59-immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. We achieved AUC≥0.90 for CD45, AUC≥0.94 for CD68, and AUC≥0.86 for S100B signatures, whereas AUC≥0.70 for pseudo-bulk () signature. Cross-testing in different compartments (e.g., CD45 signature in CD68 and S100B compartments) showed poor performance indicating compartment-specificity. Our novel spatial S100B signature showed the best performance with AUC≥0.80 in the validation cohort (N=46). Testing our signatures in computationally deconvolved pseudo-compartments revealed lower AUCs. We conclude that the spatially defined compartment signatures utilize tumor and TME-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical

2023-05-24 | GSE233305 | GEO

Assessment of Spatial Genomics for Oncology Discovery

Project description:Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for Oncology Discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis that provide a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

2022-11-01 | GSE211895 | GEO

Spatially resolved transcriptomic analysis of acute kidney injury in a female murine model

Project description:We optimized and validated a female bilateral ischemia reperfusion injury model. Using the 10X Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics. An ischemia time of 34 minutes in a female murine model resulted in comparable injury to males across the time course of injury and repair. We report increased resolution of cell and gene expression with Giotto, a computational toolbox for spatial data analysis. Using a seeded non-negative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we find that injured proximal tubule cells are characterized by increasing macrophage and lymphocyte interactions even at 6 weeks after injury, consistent with a pro-inflammatory role for this cell state. In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created a data visualization web application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).

2021-11-22 | GSE182939 | GEO

Synthetic lethality-mediated precision oncology via the tumor transcriptome

Project description:Synthetic lethality-mediated precision oncology via the tumor transcriptome

| PRJNA700986 | ENA

Characterization of a large panel of patient-derived hepatocellular carcinoma xenograft tumor models for preclinical development

Project description:We have generated a collection of patient-derived xenograft (PDX) tumor models and characterized them at the molecular level to facilitate precision oncology.

2014-03-13 | GSE55828 | GEO

Single-cell and spatial transcriptome analysis reveals the cellular heterogeneity of liver metastatic colorectal cancer

Project description:In this study, we comprehensively charted the cellular landscape of colorectal cancer (CRC) and well-matched liver metastatic CRC using single-cell and spatial transcriptome RNA sequencing. We generated 41892 CD45- non-immune cells and 196473 CD45+ immune cells from 27 samples of 6 CRC patients, and found that CD8_CXCL13 and CD4_CXCL13 subsets increased significantly in liver metastatic samples that exhibited high proliferation ability and tumor-activating characterization, contributing to better prognosis of patients. Distinct fibroblast profiles were observed in primary and liver metastatic tumors. The F3+ fibroblasts enriched in primary tumors contributed to worse overall survival by expressing pro-tumor factors. However, the MCAM+ fibroblasts enriched in liver metastatic tumors might promote generation of CD8_CXCL13 cells through Notch signaling. In summary, we extensively analyzed the transcriptional differences of cell atlas between primary and liver metastatic tumors of CRC by single-cell and spatial transcriptome RNA sequencing, providing different dimensions of the development of liver metastasis in CRC

2023-02-22 | GSE225857 | GEO

Development of an immune-related prognostic biomarker for triple-negative breast cancer

Project description:Triple-negative breast cancer represents approximately 15–20% of all reported breast cancer cases, and is characterized by a shorter survival time and higher mortality rates compared to other breast cancer sub-types. Tumor microenvironment (TME) refers to the internal and external environment of tumor tissue. Increasing evidence indicates that a tumor’s microenvironment is tightly associated with the immunological surveillance and defense during the development of breast cancer. Although oncology studies employing digital dissection methodologies have provided some insight on the biological features of TME, the development of methods to investigate the cellular composition of the tumor microenvironment remain an important research priority. In this study, we extracted whole transcriptome from 30 Triple-negative breast cancer (TNBC) patients and then used bioinformatics approaches to characterize cell type content in tumor tissue compared with para-cancerous tissue. We identified 4 types of enriched immune cells and 6 types of downregulated immune cells in the tumor tissue samples. After comprehensive bioinformatics analyses, we developed an ‘immune infiltration score’ (IIS) to quantitatively model immune cell infiltration in TNBC. To demonstrate the utility of the IIS, we used 2 independent datasets for validation. We found that patients with a higher IIS showing a longer progression-free survival time and significantly better prognosis than those with a lower IIS value. In sum, we explored the immune infiltration landscape in 30 TNBC patients and provided a novel and reliable biomarker IIS to evaluate the progression-free survival and prognosis in the TNBC patients.

2021-10-18 | E-MTAB-10886 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data