Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1132922 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR29733500_1.fastq.gz | Fastqsanger.gz | |||
| SRR29733500_2.fastq.gz | Fastqsanger.gz | |||
| SRR29733600_1.fastq.gz | Fastqsanger.gz | |||
| SRR29733600_2.fastq.gz | Fastqsanger.gz | |||
| SRR29733700_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 1717 |
Similar Datasets
Project description:Non–small cell lung cancer (NSCLC) has an increasing number of targeted and systemic therapies allowing subsets of patients to achieve long–term durable benefit. Fundamental to understanding responses to a given therapy is comprehensive molecular characterization of the underlying tumor immune microenvironment (TIME). The TIME may inform models to predict immunotherapy outcomes and features to delineate therapeutic responses and clinical endpoints. We hypothesize that an integrated multi–omics approach will uncover interactions within the NSCLC TIME and identify novel biomarkers that are predictive of immunotherapy responses, thus aiding precision oncology. To develop a spatially resolved TIME model for NSCLC immunotherapy, we utilized a multi–omics approach, combining spatial mapping of protein expression at the single–cell resolution by Phenocycler Fusion (PCF) and multi–cellular readout whole transcriptome profiling at cellular compartment resolution by Digital Spatial Profiling (DSP–GeoMx–WTA). This approach facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first–line immunotherapy. We studied two independent cohorts of advanced NSCLC tissue samples, treated with PD–1–based immunotherapies. We derived gene signatures from cell type signatures to predict treatment outcomes using a multistage Least Absolute Shrinkage and Selection Operator (LASSO) approach. Our spatial proteomic analysis identified three distinct cell types, proliferating tumor cells, granulocytes, and vessels, associated with resistance to immunotherapy. A high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the Yale training cohort and 1.8 (p = 0.05) in the UQ validation cohort. In the response cell–type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells had an HR of 0.4 (p = 0.019) in the training cohort and 0.49 (p = 0.036) in the validation. In the transcriptomic analysis, gene signatures derived from these cell types predicted outcomes with high accuracy. The resistance gene model, which included 8 genes associated with epithelial–mesenchymal transition (EMT) and cell migration, showed an HR of 5.3 (p < 0.001) in the Yale training cohort, 2.2 (p = 0.036) in the UQ validation cohort, and 1.7 (p = 0.042) in the Greece validation cohort. Conversely, the response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the Yale training cohort, 0.38 (p = 0.034) in the UQ validation cohort, and 0.56 (p = 0.041) in the Greece validation cohort. Multivariable analysis, adjusting for age, sex, disease stage, prior chemotherapy, type of immunotherapy, line of immunotherapy, histology, and smoking status, confirmed that the resistance signature remained significantly associated with worse outcomes, while the response signature remained associated with improved outcomes in both the UQ and Greece cohorts. This research highlights the potential of a multi–omics cell typing and gene expression profiling approach in advancing NSCLC treatment toward precision oncology. By offering insights into the TIME and unveiling novel biomarkers, our model seeks to define resistance and to improve prediction of response to treatment.
2024-08-22 | GSE271689 | GEO
Project description:Full title: Predictive Gene Signatures as Strong Prognostic Indicators of the Effectiveness of Bacillus Calmetteâ??Guérin (BCG) Immunotherapy in Primary pT1 Bladder Cancers Intravesical BCG immunotherapy is effective in prevention of recurrence and progression in many cases of non-muscle invasive bladder cancer, but many patients fail to respond. This study identified predictive gene signatures in primary pT1 bladder cancer with BCG immunotherapy. Fourty-Eight patients with primary pT1 bladder cancer treated with BCG immunotherapy were used. Microarray gene expression analysis of the 48 primary bladder cancers was carried out. Predictive gene signatures were individually selected based on the recurrence and progression status. Among 43,148 unique genes, 424 and 287 candidate predictive genes that could predict recurrence and progression, respectively, were selected. Time to recurrence or progression was shorter for patients with poor-predictive gene signatures than good-predictive gene signatures (log-rank test, p <0.001, respectively). Validation of predictive gene signatures with RT-PCR was nearly identical to those of microarray (log-rank test, p <0.05, respectively). In multivariate regression analysis, predictive gene signatures were the only independent predictors of recurrence (HR 3.38, p = 0.048) or progression (HR 10.49, p = 0.048) in validation cohorts. Predictive gene signatures have strong diagnostic value for determining the response to intravesical BCG immunotherapy in primary pT1 bladder cancer. Keywords: Gene expression, Bladder cancer, BCG Total RNA, extracted from the fresh frozen tissues of 48 pT1 bladder cancers, was isolated by TRIzol reagent (Life Technologies, NY, USA), according to the manufacturer's protocol. The quality and integrity of the RNA were confirmed by agarose gel electrophoresis and ethidium bromide staining, followed by visual examination under ultraviolet light. Five-hundred nanograms of total RNA were used for labeling hybridization according to the manufacturerâ??s protocol (Illumina HumanWG-6 BeadChip, version 2, Illumina, Inc., San Diego, CA, USA). Arrays were scanned with an Illumina Bead Array Reader confocal scanner (BeadStation 500GXDW; Illumina, Inc.) according to the manufacturer's instructions. Initial microarray gene expression data were obtained using Bead Studio software with gene expression analysis module (version 3.1.3, Illumina, Inc.).
2010-04-07 | E-GEOD-19423 | biostudies-arrayexpress
Project description:Integrative multi-omics analysis uncovers tumor-immune-gut axis influencing immunotherapy outcomes in ovarian cancer
| PRJNA850634 | ENA
Project description:Full title: Predictive Gene Signatures as Strong Prognostic Indicators of the Effectiveness of Bacillus Calmette–Guérin (BCG) Immunotherapy in Primary pT1 Bladder Cancers Intravesical BCG immunotherapy is effective in prevention of recurrence and progression in many cases of non-muscle invasive bladder cancer, but many patients fail to respond. This study identified predictive gene signatures in primary pT1 bladder cancer with BCG immunotherapy. Fourty-Eight patients with primary pT1 bladder cancer treated with BCG immunotherapy were used. Microarray gene expression analysis of the 48 primary bladder cancers was carried out. Predictive gene signatures were individually selected based on the recurrence and progression status. Among 43,148 unique genes, 424 and 287 candidate predictive genes that could predict recurrence and progression, respectively, were selected. Time to recurrence or progression was shorter for patients with poor-predictive gene signatures than good-predictive gene signatures (log-rank test, p <0.001, respectively). Validation of predictive gene signatures with RT-PCR was nearly identical to those of microarray (log-rank test, p <0.05, respectively). In multivariate regression analysis, predictive gene signatures were the only independent predictors of recurrence (HR 3.38, p = 0.048) or progression (HR 10.49, p = 0.048) in validation cohorts. Predictive gene signatures have strong diagnostic value for determining the response to intravesical BCG immunotherapy in primary pT1 bladder cancer. Keywords: Gene expression, Bladder cancer, BCG
2010-03-26 | GSE19423 | GEO
Project description:Gestational diabetes mellitus (GDM) and preeclampsia (PE) are major contributors to maternal and perinatal morbidity and mortality, characterized by dysregulated lipid metabolism, chronic inflammation, and immune dysfunction, disrupting maternal-fetal homeostasis. The coexistence of GDM and PE (PG) presents a uniquely complex clinical scenario with molecular mechanisms that remain poorly understood. Peripheral blood samples from 42 pregnant women were categorized into CTL (n=10), GDM (n=12), PE (n=10), and PG (n=10) groups. Lipidomic profiling via LC-MS and transcriptomic analysis through RNA sequencing were performed. Multi-omics integration correlated molecular signatures with clinical and neonatal outcomes, and a predictive model for PE was constructed using differentially expressed lipids. Additionally, UK Biobank data was analyzed to explore associations between pregnancy complications and maternal postpartum depression (PD).PE exhibited the most pronounced lipidomic alterations, correlating with adverse neonatal outcomes, including low birth weight, while GDM showed the most significant transcriptomic changes. PG demonstrated unique transcriptomic signatures associated with neuronal and immune dysfunction, suggesting a potential link to maternal PD. Seven lipid biomarkers were identified, significantly correlating with neonatal outcomes, and a lipid-based predictive model for PE showed robust performance across PE and PG cohorts. GDM and PE were significantly associated with PD, though PG’s association remains inconclusive due to limited data.This study highlights the utility of multi-omics in unraveling shared and disease-specific mechanisms underlying pregnancy complications. The identified biomarkers and PE predictive model offer promising avenues for advancing diagnosis, prognosis, and personalized maternal-fetal care.
2025-07-30 | GSE284329 | GEO
Project description:Multi-Omics Analysis of Brain Metastasis Outcomes Following Craniotomy
| PRJNA689295 | ENA
Project description:Eomesodermin+ CD4 T cells are critical for curative immunotherapy outcomes
| PRJNA1307456 | ENA
Project description:Eomesodermin+ CD4 T cells are critical for curative immunotherapy outcomes
| PRJNA1307310 | ENA
Project description:Enhancing sensitivity and versatility of Tn5-based single cell omics
| PRJNA986156 | ENA
Project description:IL-10-producing CD4+ type-1 regulatory T cells (Tr1) promote immune tolerance during chronic infection, autoimmunity, and transplantation. However, specific Eomes-dependent stages of Tr1 differentiation and function remain unclear. Using preclinical models of bone marrow transplantation (BMT) and chimeric antigen receptor (CAR) T cell immunotherapy, we demonstrate a Tr1 differentiation trajectory in vivo from Eomes+IL-10– to Eomes+IL-10+ subsets with the acquisition of cytokine, cytolytic and exhaustion features. The Eomes+CD4+ fraction represents the dominant cytotoxic subset after BMT, mediating graft-versus-leukemia effects while limiting inflammation. In CD19-targeted CAR T cell immunotherapy, Eomes drives the same CD4+ Tr1 phenotype that controls cytolysis, whilst mitigating immune toxicity and promoting persistence. In patients receiving commercial CD19-targeted CAR T cells with long term disease control, Eomes+ Tr1 cells represent a stable population comprising 40-80% of the CD4+ CAR T cell population. Hence, Eomes controls both regulatory and cytotoxic programs in CD4+ T cells, essential for curative immunotherapy outcomes.
2025-10-01 | GSE305683 | GEO