Dataset Information

Deciphering the tumor microenvironment cell-infiltrating landscape reveals microenvironment subtypes and therapeutic potentials for nonsquamous NSCLC.

ABSTRACT: Recent studies highlighted the clinicopathologic importance of the tumor microenvironment (TME) in delineating molecular attributes and therapeutic potentials. However, the overall TME cell infiltration landscape in nonsquamous non-small cell lung cancer (NSCLC) has not been comprehensively characterized. In this study, we used consensus non-negative matrix factorization molecular subtyping to determine TME cell infiltration patterns and identified 3 TME clusters (TME-C1, -C2, -C3) characterized by distinct clinicopathologic features, infiltrating cells, and biological processes. Proteomics analyses revealed that cyclic GMP-AMP-stimulator of interferon genes immune signaling-mediated protein and phosphorylation levels were significantly upregulated in inflammation-related TME-C2 clusters. The score extracted from the TME-related signature (TMEsig-score) divided patients with NSCLC into high- and low-score subgroups, where a high score was associated with favorable prognosis and immune infiltration. The genomic landscape revealed that patients with low TMEsig-score harbored more somatic copy number alterations and higher mutation frequency of driver genes involving STK11, KEAP1, SMARCA4, and others. Drug sensitivity analyses suggested that tumors with high TMEsig-score were responsible for favorable clinical response to immune checkpoint inhibitor treatment. In summary, this study highlights that comprehensive recognizing of the TME cell infiltration landscape will contribute to enhancing our understanding of TME immune regulation and promote effectiveness of precision biotherapy strategies.

SUBMITTER: Chen H

PROVIDER: S-EPMC9309061 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundNeuroblastoma (NB) is a childhood cancer originating from immature nerve cells in the sympathetic nervous system. Current clinical and molecular subtyping methods for NB have limitations in providing accurate prognostic information and guiding treatment decisions.ResultsTo overcome these challenges, we explored the microenvironment of NB based on the knowledge-based functional gene expression signatures (Fges), which revealed heterogeneous subtypes. Consensus clustering of Fges activity scores identified three subtypes (Cluster 1, Cluster 2, and Cluster 3) that demonstrated significant differences in prognosis compared to mainstream subtypes. We assessed the immune infiltration, immunogenicity, CD8T cytotoxicity, and tumor purity of these subtypes, uncovering their distinct biological functions. Cluster 1 and Cluster 2 exhibited higher immunoreactivity, while Cluster 3 displayed higher tumor purity and poor prognosis. Gene ontology annotation and pathway analysis identified immune activation in Cluster 1, epithelial-mesenchymal transition (EMT) in Cluster 2, and cell cycle processes in Cluster 3. Notably, the impact of EMT activity on prognosis may vary across NB subtypes. A classification model using XGBoost accurately predicted subtypes in independent NB cohorts, with significant prognostic differences. GPR125, CDK4, and GREB1 emerged as potential therapeutic targets in Cluster 3. CD4K inhibitors showed subtype-specific responses, suggesting tailored treatment strategies. Single-cell analysis highlighted unfavorable clinical features in Cluster 3, including high-risk classification and reduced cytotoxicity. Suppressed interactions between monocytes, macrophages, and regulatory T cells were observed, affecting immune regulation and patient prognosis.ConclusionTo summarize, we have identified a new independent prognostic factor in NB that underscores the significant correlation between tumor phenotype and immune contexture. These findings deepen our understanding of NB subtypes and immune cell interactions, paving the way for more effective treatment approaches.

Project description:Motor Evoked Potentials (MEPs) are used to monitor disability progression in multiple sclerosis (MS). Their morphology plays an important role in this process. Currently, however, there is no clear definition of what constitutes a normal or abnormal morphology. To address this, five experts independently labeled the morphology (normal or abnormal) of the same set of 1,000 MEPs. The intra- and inter-rater agreement between the experts indicates they agree on the concept of morphology, but differ in their choice of threshold between normal and abnormal morphology. We subsequently performed an automated extraction of 5,943 time series features from the MEPs to identify a valid proxy for morphology, based on the provided labels. To do this, we compared the cross-validation performances of one-dimensional logistic regression models fitted to each of the features individually. We find that the approximate entropy (ApEn) feature can accurately reproduce the majority-vote labels. The performance of this feature is evaluated on an independent test set by comparing to the majority vote of the neurologists, obtaining an AUC score of 0.92. The model slightly outperforms the average neurologist at reproducing the neurologists consensus-vote labels. We can conclude that MEP morphology can be consistently defined by pooling the interpretations from multiple neurologists and that ApEn is a valid continuous score for this. Having an objective and reproducible MEP morphological abnormality score will allow researchers to include this feature in their models, without manual annotation becoming a bottleneck. This is crucial for large-scale, multi-center datasets. An exploratory analysis on a large single-center dataset shows that ApEn is potentially clinically useful. Introducing an automated, objective, and reproducible definition of morphology could help overcome some of the barriers that are currently obstructing broad adoption of evoked potentials in daily care and patient follow-up, such as standardization of measurements between different centers, and formulating guidelines for clinical use.

Dataset Information

Deciphering the tumor microenvironment cell-infiltrating landscape reveals microenvironment subtypes and therapeutic potentials for nonsquamous NSCLC.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets