Is there a role for immunotherapy in malignant pleural mesothelioma?

Integrated analysis of DNA methylation and mRNA expression profiles to identify key genes in head and neck squamous cell carcinoma

DNA methylation has been demonstrated to play significant roles in the etiology and pathogenesis of head and neck squamous cell carcinoma (HNSCC). In the present study, methylation microarray dataset (GSE87053) and gene expression microarray dataset (GSE23558) were downloaded from GEO database and analyzed through R language. A total of 255 hypermethylated-downregulated genes and 114 hypomethylated-upregulated genes were finally identified. Functional enrichment analyses were performed and a comprehensive protein–protein interaction (PPI) network was constructed. Subsequently, the top ten hub genes selected by Cytoscape software were subjected to further analyses. It was illustrated that the expression level of CSF2, CTLA4, ETS1, PIK3CD, and CFTR was intimately associated with HNSCC. Survival analysis suggested that CTLA4 and FGFR2 could serve as effective independent prognostic biomarkers for HNSCC patients. Overall, our study lay a groundwork for further investigation into the underlying molecular mechanisms in HNSCC carcinogenesis, providing potential biomarkers and therapeutic targets for HNSCC.

Assessment of the expression of the immune checkpoint molecules PD-1, CTLA4, TIM-3 and LAG-3 across different cancers in relation to treatment response, tumor-infiltrating immune cells and survival

Immune checkpoint molecules have been identified as crucial regulators of the immune response, which motivated the emergence of immune checkpoint-targeting therapeutic strategies. However, the prognostic significance of the immune checkpoint molecules PD-1, CTLA4, TIM-3 and LAG-3 remains controversial. The aim of our study was to conduct a systematic assessment of the expression of these immune checkpoint molecules across different cancers in relation to treatment response, tumor-infiltrating immune cells and survival. Oncomine and PrognoScan database analyses were used to investigate the expression levels and prognostic values of these immune checkpoint molecule genes across various cancers. Then, we used Kaplan-Meier plotter to validate the associations between the checkpoint molecules and cancer survival identified in the PrognoScan analysis. TIMER analysis was used to evaluate immune cell infiltration data from The Cancer Genome Atlas. Finally, we used Gene Expression Profiling Interactive Analysis to investigate the prognostic value of these four checkpoint molecules and assess the correlations between these four checkpoint molecules and genetic markers. These immune checkpoint molecules may potentially serve as prognostic factors and therapeutic targets in breast cancer, ovarian cancer and lung cancer. The prognostic roles of these checkpoint molecules varied greatly across cancers, which implied a noteworthy amount of heterogeneity among tumors, even within the same molecular subtype. In addition, the expression patterns of these checkpoint molecules were closely associated with treatment response and provided some useful direction when choosing chemotherapeutic drugs. These findings enhance our understanding of these checkpoints in cancer treatment and identify strategies to promote synergistic activities in the context of other immunotherapies.

Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center

INTRODUCTION

Histologic subtypes of malignant pleural mesothelioma are a major prognostic indicator and decision denominator for all therapeutic strategies. In an ambiguous case, a rare transitional mesothelioma (TM) pattern may be diagnosed by pathologists either as epithelioid mesothelioma (EM), biphasic mesothelioma (BM), or sarcomatoid mesothelioma (SM). This study aimed to better characterize the TM subtype from a histological, immunohistochemical, and molecular standpoint. Deep learning of pathologic slides was applied to this cohort.

METHODS

A random selection of 49 representative digitalized sections from surgical biopsies of TM was reviewed by 16 panelists. We evaluated BAP1 expression and CDKN2A (p16) homozygous deletion. We conducted a comprehensive, integrated, transcriptomic analysis. An unsupervised deep learning algorithm was trained to classify tumors.

RESULTS

The 16 panelists recorded 784 diagnoses on the 49 cases. Even though a Kappa value of 0.42 is moderate, the presence of a TM component was diagnosed in 51%. In 49% of the histological evaluation, the reviewers classified the lesion as EM in 53%, SM in 33%, or BM in 14%. Median survival was 6.7 months. Loss of BAP1 observed in 44% was less frequent in TM than in EM and BM. p16 homozygous deletion was higher in TM (73%), followed by BM (63%) and SM (46%). RNA sequencing unsupervised clustering analysis revealed that TM grouped together and were closer to SM than to EM. Deep learning analysis achieved 94% accuracy for TM identification.

CONCLUSION

These results revealed that the TM pattern should be classified as non-EM or at minimum as a subgroup of the SM type.

Extracellular vesicles as biomarkers in malignant pleural mesothelioma: A review

Extracellular vesicles (EV) are secreted by all cells, including cancer cells, as a mode of intercellular transport and communication. The main types of EV known to date include exosomes, microvesicles and apoptotic bodies, as well as oncosomes and large oncosomes, which are specific to cancer cells. These different EV populations carry specific cargo from one cell to another to stimulate a specific response. They can be found in all body fluids and can be detected in liquid biopsies. EV released from mesothelioma cells can reveal important information about the molecules and signalling pathways involved in the development and progression of the tumour. The presence of tumour-derived EV in circulating body fluids makes them potential novel biomarkers for early diagnosis, prognostication and surveillance of cancer. In this review, we explore the characteristics and functional roles of EV reported in the literature, with a focus on their role in malignant pleural mesothelioma.