Dataset Information

Two distinct phenotypes of immunologically hot gastric cancer subtypes.

ABSTRACT: An in-depth understanding of the tumor microenvironment (TME) is required for the development of improved combination immunotherapies for gastric cancer. Recently, we classified these cancers into four main types defined by their immunological attributes, namely Hot 1, Hot 2, Intermediate and Cold. Of these, the T cell-inflamed "Hot" tumors were further divided into Hot 1 and Hot 2 with different clinical outcomes. Thus, overall survival and progression-free survival of patients with Hot 1 tumors were shorter than with Hot 2. In the present study, we re-evaluated RNA-Seq data of 6 Hot 1 and 6 Hot 2 gastric cancers to elucidate the underlying reason for the poor prognosis and T cell dysfunction in the former. In addition, 56 Hot 1 and 27 Hot 2 tumors in The Cancer Genome Atlas (TCGA) were analyzed. We report that single sample Gene Set Enrichment Analysis (ssGSEA) and differential gene expression analysis identified differences between Hot 1 and Hot 2 tumors involved in metabolism and cell adhesion pathways. Therefore, it is suggested that strategies to modulate active metabolism in Hot 1 tumors should be integrated into the treatment of these gastric cancers.

SUBMITTER: Saito N

PROVIDER: S-EPMC8579120 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Two distinct phenotypes of immunologically hot gastric cancer subtypes.

Saito Noriyuki N Kobayashi Yukari Y Nagaoka Koji K Kushihara Yoshihiro Y Sato Yasuyoshi Y Wada Ikuo I Kakimi Kazuhiro K Seto Yasuyuki Y

Biochemistry and biophysics reports 20211103

An in-depth understanding of the tumor microenvironment (TME) is required for the development of improved combination immunotherapies for gastric cancer. Recently, we classified these cancers into four main types defined by their immunological attributes, namely Hot 1, Hot 2, Intermediate and Cold. Of these, the T cell-inflamed "Hot" tumors were further divided into Hot 1 and Hot 2 with different clinical outcomes. Thus, overall survival and progression-free survival of patients with Hot 1 tumor ...[more]

PMID: 34786494

Similar Datasets

Project description:BackgroundOvarian cancer (OC) is a highly heterogeneous and malignant gynecological cancer, thereby leading to poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC and develop a diagnostic model that can precisely stratify OC patients, providing more diagnostic clues for OC patients to access focused therapeutic and preventative strategies.MethodsGene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, the ESTIMATE algorithm was applied. A univariate Cox analysis and the two-sided log-rank test were used to screen OC risk factors. We adopted the ConsensusClusterPlus algorithm to determine OC subtypes. Enrichment analysis based on KEGG and GO was performed to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select the feature gene and develop a diagnostic model. A ROC curve was depicted to evaluate the model performance.ResultsA total of 1,273 survival-related genes (SRGs) were firstly determined and used to clarify OC samples into different subtypes based on their different molecular pattern. SRGs were successfully stratified in OC patients into three robust subtypes, designated S-I (Immunoreactive and DNA Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III had the worst prognosis and was enriched with OC patients at advanced stages. Meanwhile, comprehensive functional analysis highlighted differences in biological pathways: genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, and RBX1 were enriched in S-I; S-II combined multiple gene signatures including genes associated with metabolism and transcription; and the gene signature of S-III was extensively involved in pathways reflecting malignancies, including many core kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC values of 0.922 and 0.901, respectively. Furthermore, a new dataset of the independent cohort could be automatically analyzed by this innovative pipeline and yield similar results.ConclusionThis study exploited an innovative approach to construct previously unexplored robust subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation also illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.

Project description:Background: Iron is an essential nutrient involved in the redox cycle and the formation of free radicals. The reprogramming of iron metabolism is the main link to tumor cell survival. Ferroptosis is an iron-dependent form of regulated cell death associated with cancer; the characteristics of ferroptosis in cancers are still uncertain. This study aimed to explore the application value and gender difference of ferroptosis in prognosis and immune prediction to provide clues for targeted therapy of gastric cancer. Methods: We comprehensively evaluated the ferroptosis levels of 1,404 gastric cancer samples from six independent GC cohorts based on ferroptosis-related specific genes and systematically correlated ferroptosis with immune cell infiltrating and gender characteristics. The ferroptosis score was constructed to quantify the ferroptosis levels of individual tumors using principal component analysis (PCA) algorithms. Results: We identified two distinct ferroptosis subtypes in gastric cancer, namely Subtype-A and Subtype-B. We found that male patients in Subtype-B had the worst prognosis in contrast with the other groups. Three sex hormone receptors (AR, ER, and PR) in Subtype-B tumor patients were higher than in Subtype-A tumor patients in GC, while the HER2 displayed an opposite trend. We developed a risk model termed ferroptosis score to evaluate ferroptosis levels within individual tumors. The low-ferroptosis score group was characterized by activation of immune cells and increased mutation burden, which is also linked to increased neoantigen load and enhanced response to anti-PD-1/L1 immunotherapy. The patients with a low-ferroptosis score showed a high microsatellite instability status (MSI-H) and had a higher response to immunotherapy. Furthermore, the patients with low-ferroptosis scores have a lower estimated IC50 in the several chemotherapy drugs, including paclitaxel, gemcitabine, and methotrexate. Conclusions: We revealed that sex hormone receptors and immune cell infiltration were markedly different between ferroptosis subtypes in GC patients. The results suggested that gender difference may be critical when the ferroptosis-related strategy is applied in GC treatment. Further, ferroptosis levels were identified with an extreme variety of prognosis and tumor immune characteristics, which might benefit GC individualized treatment.

Project description:Purpose: To identify novel breast cancer subtypes by extracting quantitative imaging phenotypes of the tumor and surrounding parenchyma and to elucidate the underlying biologic underpinnings and evaluate the prognostic capacity for predicting recurrence-free survival (RFS).Experimental Design: We retrospectively analyzed dynamic contrast-enhanced MRI data of patients from a single-center discovery cohort (n = 60) and an independent multicenter validation cohort (n = 96). Quantitative image features were extracted to characterize tumor morphology, intratumor heterogeneity of contrast agent wash-in/wash-out patterns, and tumor-surrounding parenchyma enhancement. On the basis of these image features, we used unsupervised consensus clustering to identify robust imaging subtypes and evaluated their clinical and biologic relevance. We built a gene expression-based classifier of imaging subtypes and tested their prognostic significance in five additional cohorts with publically available gene expression data but without imaging data (n = 1,160).Results: Three distinct imaging subtypes, that is, homogeneous intratumoral enhancing, minimal parenchymal enhancing, and prominent parenchymal enhancing, were identified and validated. In the discovery cohort, imaging subtypes stratified patients with significantly different 5-year RFS rates of 79.6%, 65.2%, 52.5% (log-rank P = 0.025) and remained as an independent predictor after adjusting for clinicopathologic factors (HR, 2.79; P = 0.016). The prognostic value of imaging subtypes was further validated in five independent gene expression cohorts, with average 5-year RFS rates of 88.1%, 74.0%, 59.5% (log-rank P from <0.0001 to 0.008). Each imaging subtype was associated with specific dysregulated molecular pathways that can be therapeutically targeted.Conclusions: Imaging subtypes provide complimentary value to established histopathologic or molecular subtypes and may help stratify patients with breast cancer. Clin Cancer Res; 23(13); 3334-42. ©2017 AACR.

Dataset Information

Two distinct phenotypes of immunologically hot gastric cancer subtypes.

Publications

Two distinct phenotypes of immunologically hot gastric cancer subtypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets