โก Quick Summary
This study introduces a machine learning-based classification for immune infiltration in gliomas, identifying four distinct immune subtypes. The findings provide crucial insights into the tumor microenvironment and potential pathways for enhancing immunotherapy effectiveness.
๐ Key Details
- ๐ Dataset: 693 gliomas from CGGA and 702 gliomas from TCGA
- โ๏ธ Technology: Machine Learning for classification model development
- ๐ฌ Analysis tools: Unsupervised cluster analysis and functional enrichment analysis
- ๐ Subtypes identified: IM1, IM2, IM3, IM4 (CGGA); IMA, IMB, IMC, IMD (TCGA)
๐ Key Takeaways
- ๐งฌ Immune microenvironment characteristics are critical for glioma treatment.
- ๐ค Machine learning effectively predicts immune subtypes of gliomas.
- ๐ Subtype IM1/IMD shows the highest enrichment of immune cells like NK and CD8 T cells.
- ๐ IM1/IMD and IM3/IMC subtypes correlate with shorter overall survival rates.
- ๐งช Enrichment analysis links IM2-IMB subtypes to leukocyte activation and cytotoxicity.
- ๐ง IM4/IMA subtypes are associated with CNS and muscle tissue development.
- ๐ Study highlights the heterogeneity of gliomas and their immune responses.
- ๐ Potential for improved immunotherapy strategies based on subtype classification.
๐ Background
Gliomas are known for their heterogeneity and poor immunogenicity, which significantly limits the effectiveness of current immunotherapy approaches. Understanding the immune microenvironment (IME) is essential for developing targeted therapies that can enhance patient outcomes. This study aims to classify gliomas based on their immune infiltration characteristics using advanced machine learning techniques.
๐๏ธ Study
The research utilized data from the CGGA and TCGA databases, encompassing a total of 1,395 glioma samples. Through unsupervised cluster analysis, the team identified distinct immune subtypes and developed a reliable machine learning model to predict these classifications. The study also included functional enrichment analysis to explore the biological significance of the identified subtypes.
๐ Results
The study successfully categorized gliomas into four immune subtypes in both databases. Notably, the IM1/IMD subtypes exhibited the highest levels of immune cell infiltration, while the IM4/IMA subtypes were linked to CNS development. Furthermore, the analysis revealed that patients in the IM1/IMD and IM3/IMC subtypes had the shortest overall survival and the lowest rates of favorable genetic mutations, highlighting the clinical relevance of these findings.
๐ Impact and Implications
This research has significant implications for the future of glioma treatment. By classifying gliomas into distinct immune subtypes, clinicians can better tailor immunotherapy strategies to individual patients. The insights gained from this study could lead to more effective treatments and improved survival rates for glioma patients, ultimately enhancing the quality of care in oncology.
๐ฎ Conclusion
The development of a machine learning-based classification for glioma immune subtypes marks a promising advancement in understanding the tumor microenvironment. This study not only highlights the complexity of gliomas but also opens new avenues for targeted immunotherapy approaches. Continued research in this area is essential for translating these findings into clinical practice, paving the way for improved patient outcomes.
๐ฌ Your comments
What are your thoughts on the role of machine learning in cancer research? Weโd love to hear your insights! ๐ฌ Share your comments below or connect with us on social media:
Machine learning-based new classification for immune infiltration of gliomas.
Abstract
BACKGROUND: Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of the immunosuppressive tumor microenvironment (TME) are one of the important factors hindering the effectiveness of immunotherapy. Therefore, this study aims to reveal the immune microenvironment (IME) characteristics of glioma and predict different immune subtypes using machine learning methods, providing guidance for immune therapy in glioma.
METHODS: We first performed unsupervised cluster analysis on the genes and arrays of 693 gliomas in CGGA database and 702 gliomas in TCGA database. Then establish and verify the classification model through Machine Learning (ML). Then, use DAVID to perform functional enrichment analysis for different immune subtypes. Next step, analyze the immune cell distribution, stemness maintenance, mesenchymal phenotype, neuronal phenotype, tumorigenic cytokines, molecular and clinical characteristics of different immune subtypes of gliomas.
RESULTS: Firstly, we divide the IME of gliomas in the CGGA database into four different subtypes, namely IM1, IM2, IM3, and IM4; similarly, the IME of gliomas in the TCGA database can also be divided into four different subtypes (IMA, IMB, IMC, and IMD). Next, based on ML, we developed a highly reliable model for predicting different immune subtypes of glioma. Then, we found that Monocytic lineage, Myeloid dendritic cells, NK cells and CD8 T cells had the highest enrichment in the IM1/IMD subtypes. Cytotoxic lymphocytes were highest expressed in the IM4/IMA subtypes. Next step, Enrichment analysis revealed that the IM1-IMD subtypes were mainly closely related to the production and secretion of IL-8 and TNF signaling pathway. The IM2-IMB subtypes were strongly associated with leukocyte activation and NK cell mediated cytotoxicity. The IM3-IMC subtypes were closely related to mitotic nuclear division and mitotic cell cycle process. The IM4-IMA subtypes were strongly associated with Central Nervous System (CNS) development and striated muscle tissue development. Afterwards, Single sample gene set enrichment analysis (ssGSEA) showed that stemness maintenance phenotypes were mainly enriched in the IM4/IMA subtypes; Neuronal phenotypes were closely associated with the IM2/IMB subtypes; and mesenchymal phenotypes and tumorigenic cytokines were highly correlated with the IM2 /IMB subtypes. Finally, we found that compared with patients in the IM2/IMB and IM4/IMA subtypes, the IM1/IMD and IM3/IMC subtypes have the highest proportion of GBM patients, the shortest average overall survival of patients and the lowest proportion of patients with IDH mutation and 1p36/19q13 co-deletion.
CONCLUSIONS: We developed a highly reliable model for predicting different immune subtypes of glioma by ML. Then, we comprehensively analyzed the immune infiltration, molecular and clinical features of different immune subtypes of gliomas and defined gliomas into four subtypes: immunogenic subtype, adaptive immune resistance subtype, mesenchymal subtype, and immune tolerance subtype, which represent different TMEs and different stages of tumor development.
Author: [‘Yuan F’, ‘Wang Y’, ‘Yuan L’, ‘Ye L’, ‘Hu Y’, ‘Cheng H’, ‘Li Y’]
Journal: PLoS One
Citation: Yuan F, et al. Machine learning-based new classification for immune infiltration of gliomas. Machine learning-based new classification for immune infiltration of gliomas. 2024; 19:e0312071. doi: 10.1371/journal.pone.0312071