Immune Gene Signatures and Immunotypes in Immune Microenvironment Are Associated With Glioma Prognose

Integrated MRI radiomics, tumor microenvironment, and clinical risk factors for improving survival prediction in patients with glioblastomas

PURPOSE

To construct a comprehensive model for predicting the prognosis of patients with glioblastoma (GB) using a radiomics method and integrating clinical risk factors, tumor microenvironment (TME), and imaging characteristics.

MATERIALS AND METHODS

In this retrospective study, we included 148 patients (85 males and 63 females; median age 53 years) with isocitrate dehydrogenase-wildtype GB between January 2016 and April 2022. Patients were randomly divided into the training (n = 104) and test (n = 44) sets. The best feature combination related to GB overall survival (OS) was selected using LASSO Cox regression analyses. Clinical, radiomics, clinical-radiomics, clinical-TME, and clinical-radiomics-TME models were established. The models' concordance index (C-index) was evaluated. The survival curve was drawn using the Kaplan-Meier method, and the prognostic stratification ability of the model was tested.

RESULTS

LASSO Cox analyses were used to screen the factors related to OS in patients with GB, including MGMT (hazard ratio [HR] = 0.642; 95% CI 0.414-0.997; P = 0.046), TERT (HR = 1.755; 95% CI 1.095-2.813; P = 0.019), peritumoral edema (HR = 1.013; 95% CI 0.999-1.027; P = 0.049), tumor purity (TP; HR = 0.982; 95% CI 0.964-1.000; P = 0.054), CD163 + tumor-associated macrophages (TAMs; HR = 1.049; 95% CI 1.021-1.078; P < 0.001), CD68 + TAMs (HR = 1.055; 95% CI 1.018-1.093; P = 0.004), and the six radiomics features. The clinical-radiomics-TME model had the best survival prediction ability, the C‑index was 0.768 (0.717-0.819). The AUC of 1‑, 2‑, and 3‑year OS prediction in the test set was 0.842, 0.844, and 0.795, respectively.

CONCLUSION

The clinical-radiomics-TME model is the most effective for predicting the survival of patients with GB. Radiomics features, TP, and TAMs play important roles in the prognostic model.

Pan-cancer analysis of IRF1 focusing on prognostic and immunological roles in non-small cell lung cancer

Interferon regulatory factor 1 (IRF1) significantly affects tumour occurrence and development. This study aimed to analyse its function as a pan-cancer prognostic indicator. We compared IRF1 expression and prognostic significance in normal and tumour samples from different databases. Accordingly, we performed in vitro experiments and immunohistochemistry (IHC) to investigate the role of IRF1 in non-small cell lung cancer (NSCLC). Our findings indicate that IRF1 expression is significantly correlated with prognosis, the tumour microenvironment, and immune cell infiltration. Furthermore, receiver operating characteristic (ROC) analysis revealed that IRF1 had high accuracy in distinguishing cancerous tissues from normal ones. Notably, IRF1 expression was linked to immune-related and immune checkpoint genes. Cell proliferation, invasion, and migration were significantly related to IRF1 expression. IHC indicated that IRF1 was downregulated in NSCLC tissues. Our study provides comprehensive bioinformatic analysis and experimental verification of IRF1, suggesting its potential as a prognostic biomarker in cancer.

Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data

A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma.

The Immunomodulatory Effects of Fluorescein-Mediated Sonodynamic Treatment Lead to Systemic and Intratumoral Depletion of Myeloid-Derived Suppressor Cells in a Preclinical Malignant Glioma Model

Fluorescein-mediated sonodynamic therapy (FL-SDT) is an extremely promising approach for glioma treatment, resulting from the combination of low-intensity focused ultrasound (FUS) with a sonosensitizer. In the present study, we evaluated the efficacy and immunomodulation of SDT with fluorescein as the sonosensitizer in immunocompetent GL261 glioma mice for the first time. In vitro studies demonstrated that the exposure of GL261 cells to FL-SDT induced immunogenic cell death and relevant upregulation of MHC class I, CD80 and CD86 expression. In vivo studies were then performed to treat GL261 glioma-bearing mice with FL-SDT, fluorescein alone, or FUS alone. Perturbation of the glioma-associated macrophage subset within the immune microenvironment was induced by all the treatments. Notably, a relevant depletion of myeloid-derived suppressor cells (MDSCs) and concomitant robust infiltration of CD8+ T cells were observed in the SDT-FL-treated mice, resulting in a significant radiological delay in glioma progression and a consequent improvement in survival. Tumor control and improved survival were also observed in mice treated with FL alone (median survival 41.5 days, p > 0.0001 compared to untreated mice), reflecting considerable modulation of the immune microenvironment. Interestingly, a high circulating lymphocyte-to-monocyte ratio and a very low proportion of MDSCs were predictive of better survival in FL- and FL-SDT-treated mice than in untreated and FUS-treated mice, in which elevated monocyte and MDSC frequencies correlated with worse survival. The immunostimulatory potential of FL-SDT treatment and the profound modulation of most immunosuppressive components within the microenvironment encouraged the exploration of the combination of FL-SDT with immunotherapeutic strategies.

Drivers of heterogeneity in the glioblastoma immune microenvironment

Glioblastoma is the most common and aggressive primary brain tumor, characterized by a highly complex and heterogeneous tumor immune microenvironment (TIME). In this review, we discuss the impact of tumor-intrinsic and tumor-extrinsic drivers that contribute to heterogeneity in the adult glioblastoma TIME, focusing on four main factors: genetic drivers, sex, age, and standard of care therapy. We describe recent insights into how each of these factors affects key aspects ranging from TIME composition to therapy response, with an emphasis on the cross-talk between tumor and immune cells. Deciphering these local interactions is fundamental to understanding therapy resistance and identifying novel immunomodulatory strategies.