Dataset Information

SIO: A Spatioimageomics Pipeline to Identify Prognostic Biomarkers Associated with the Ovarian Tumor Microenvironment.

ABSTRACT: Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients' survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.

SUBMITTER: Zhu Y

PROVIDER: S-EPMC8068305 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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SIO: A Spatioimageomics Pipeline to Identify Prognostic Biomarkers Associated with the Ovarian Tumor Microenvironment.

Zhu Ying Y Ferri-Borgogno Sammy S Sheng Jianting J Yeung Tsz-Lun TL Burks Jared K JK Cappello Paola P Jazaeri Amir A AA Kim Jae-Hoon JH Han Gwan Hee GH Birrer Michael J MJ Mok Samuel C SC Wong Stephen T C STC

Cancers 20210408 8

Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients' survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of vari ...[more]

PMID: 33917869

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Project description:BackgroundOvarian cancer (OC) is a globally prevalent malignancy with significant morbidity and mortality, yet its heterogeneity poses challenges in treatment and prognosis. Recognizing the crucial role of the tumor microenvironment (TME) in OC progression, this study leverages integrative multi-omics and machine learning to uncover TME-associated prognostic biomarkers, paving the way for more personalized therapeutic interventions.MethodsEmploying a rigorous multi-omics approach, this study analyzed single-cell RNA sequencing (scRNA-seq) data from OC and normal tissue samples, including high-grade serous OC (HGSOC) from the Gene Expression Omnibus (GEO: GSE184880) and The Cancer Genome Atlas (TCGA) OC cohort, utilizing the Seurat package to annotate 700 TME-related genes. A prognostic model was developed using the least absolute shrinkage and selection operator (LASSO) regression and independently validated against similarly composed HGSOC datasets. Comprehensive gene expression and immune cell infiltration analyses were conducted, employing advanced algorithms like xCell to delineate the immune landscape of HGSOC.ResultsOur investigation unveiled distinctive immune cell infiltration patterns and gene expression profiles within the TME of HGSOC. Notably, the prevalence of exhausted CD8+ T cells in high-risk patient samples emerged as a critical finding, underscoring the dualistic nature of the immune response in OC. The developed prognostic model, incorporating immune cell markers, exhibited robust predictive accuracy for patient outcomes, showing significant correlations with immunotherapy responses and drug sensitivities.ConclusionsThis study presents a groundbreaking exploration of the OC TME, offering vital insights into its molecular intricacies. By systematically deciphering the TME-associated gene signatures, the research illuminates the potential of these biomarkers in refining patient prognosis and guiding treatment strategies. Our findings underscore the necessity for personalized medicine in OC treatment, potentially enhancing patient survival rates and quality of life. This study marks a significant stride in understanding and combatting the complexities of OC.

Project description:Background: The tumor microenvironment (TME) has been reported to have significant value in the diagnosis and prognosis of cancers. This study aimed to identify key biomarkers in the TME of luminal breast cancer (BC). Methods: We obtained immune scores (ISs) and stromal scores (SSs) for The Cancer Genome Atlas (TCGA) luminal BC cohort from the online ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) portal. The relationships between ISs and SSs and the overall survival of luminal BC patients were assessed by the Kaplan-Meier method. The differentially expressed messenger RNAs (DEmRNAs) related to the ISs and SSs were subjected to functional enrichment analysis. Additionally, a competing endogenous RNA (ceRNA) network was constructed with differentially expressed microRNAs (DEmiRNAs) and long noncoding RNAs (DElncRNAs). Furthermore, a protein-protein interaction (PPI) network was established to analyze the DEmRNAs in the ceRNA network. Then, survival analysis of biomarkers involved in the ceRNA network was carried out to explore their prognostic value. Finally, these biomarkers were validated using the luminal BC dataset from the Gene Expression Omnibus (GEO) database. Results: The results showed that ISs were significantly associated with longer survival times of luminal BC patients. Functional enrichment analysis showed that the DEmRNAs were mainly associated with immune response, antigen binding, and the extracellular region. In the PPI network, the top 10 DEmRNAs were identified as hub genes that affected the TME of luminal BC. Finally, two DEmiRNAs, two DElncRNAs, and 17 DEmRNAs of the ceRNA network associated with the TME were shown to have prognostic value. Subsequently, the expression of 15 prognostic biomarkers was validated in one additional dataset (GSE81002). In particular, one lncRNA (GVINP1) and five mRNAs (CCDC69, DOCK2, IKZF1, JCHAIN, and NCKAP1L) were novel biomarkers. Conclusions: Our studies demonstrated that ISs were associated with the survival of luminal BC patients, and a set of novel biomarkers that might play a prognostic role in the TME of luminal BC was identified.

Project description:BackgroundThe tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression.MethodsThe TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts.ResultsThe TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples.ConclusionOur study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy.

Dataset Information

SIO: A Spatioimageomics Pipeline to Identify Prognostic Biomarkers Associated with the Ovarian Tumor Microenvironment.

Publications

SIO: A Spatioimageomics Pipeline to Identify Prognostic Biomarkers Associated with the Ovarian Tumor Microenvironment.

Similar Datasets

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