Exosome-Associated Gene Signature for Predicting the Prognosis of Ovarian Cancer Patients

Diagnostic and Prognostic Significance of Exosomes and Their Components in Patients With Cancers

BACKGROUND

Cancer is the second leading cause of human mortality worldwide. Extracellular vesicles (EVs) from liquid biopsy samples are used in early cancer detection, characterization, and surveillance. Exosomes are a subset of EVs produced by all cells and present in all body fluids. They play an important role in the development of cancer because they are active transporters capable of carrying the contents of any type of cell. The objective of this review was to provide a brief overview of the clinical implication of exosomes or exosomal components in cancer diagnosis and prognosis.

METHODS

An extensive review of the current literature of exosomes and their components in cancer diagnosis and prognosis were carried out in the current study.

RESULTS

Tumor cells release exosomes that contribute to the formation of the pre-metastatic microenvironment, angiogenesis, invasion, and treatment resistance. On the contrary, tumor cells release more exosomes than normal cells, and these tumor-specific exosomes can carry the genomic and proteomic signature contents of the tumor cells, which can act as tools for the diagnosis and prognosis of patients with cancers.

CONCLUSION

This information may help clinicians to improve the management of cancer patients in clinical settings in the future.

Tumor-derived Exosomes and Antitumor Immunity

Cancer immunotherapy, including immune checkpoint blockade, has been approved for treatment of patients with many cancer types. However, some patients fail to respond to immunotherapy, and emerging evidence indicates that tumor-derived exosomes (TEX) play a major role in reprogramming the host immune cells by inducing their dysfunction. Focusing on effector T cells, this review illustrates mechanisms of suppression that TEX use, thus promoting tumor escape from the host immune system. TEX carry multiple suppressive signals that drive T cell dysfunction and convert the tumor microenvironment into "an immune desert" in which activated T cells either die or are reprogrammed to mediate protumor functions. The reprogrammed T cells produce a new crop of CD3+ immunoinhibitory exosomes that further amplify suppression mediated by TEX. The result is a profound depletion of antitumor immune effector cells that reflects the defective immune competence of the cancer patient and partly explains why TEX are a significant barrier for cancer immunotherapy.

An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer

Background

Ovarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.

Methods

420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.

Results

64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.

Conclusion

Our study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.

Simultaneously targeting extracellular vesicle trafficking and TGF-β receptor kinase activity blocks signaling hyperactivation and metastasis

Metastasis is the leading cause of cancer-related deaths. Transforming growth factor beta (TGF-β) signaling drives metastasis and is strongly enhanced during cancer progression. Yet, the use of on-target TGF-β signaling inhibitors in the treatment of cancer patients remains unsuccessful, highlighting a gap in the understanding of TGF-β biology that limits the establishment of efficient anti-metastatic therapies. Here, we show that TGF-β signaling hyperactivation in breast cancer cells is required for metastasis and relies on increased small extracellular vesicle (sEV) secretion. Demonstrating sEV's unique role, TGF-β signaling levels induced by sEVs exceed the activity of matching concentrations of soluble ligand TGF-β. Further, genetic disruption of sEV secretion in highly-metastatic breast cancer cells impairs cancer cell aggressiveness by reducing TGF-β signaling to nearly-normal levels. Otherwise, TGF-β signaling activity in non-invasive breast cancer cells is inherently low, but can be amplified by sEVs, enabling invasion and metastasis of poorly-metastatic breast cancer cells. Underscoring the translational potential of inhibiting sEV trafficking in advanced breast cancers, treatment with dimethyl amiloride (DMA) decreases sEV secretion, TGF-β signaling activity, and breast cancer progression in vivo. Targeting both the sEV trafficking and TGF-β signaling by combining DMA and SB431542 at suboptimal doses potentiated this effect, normalizing the TGF-β signaling in primary tumors to potently reduce circulating tumor cells, metastasis, and tumor self-seeding. Collectively, this study establishes sEVs as critical elements in TGF-β biology, demonstrating the feasibility of inhibiting sEV trafficking as a new therapeutic approach to impair metastasis by normalizing TGF-β signaling levels in breast cancer cells.

New Achievements from Molecular Biology and Treatment Options for Refractory/Relapsed Ovarian Cancer-A Systematic Review

Ovarian cancer (OC) has a high rate of mortality and is the fifth most common cause of death in females all over the world. The etiology is still unclear. Numerous factors such as smoking, obesity, and unhealthy diet may affect the risk of OC. Having a family history of breast and OC is one of the main risks for developing OC. Mutations of BRCA1/2 are associated with OC risk as well. The histopathological classification of OC reveals the four most common types: serous, clear cell, endometrioid, and mucinous; these are epithelial OC types, and other types are rare. Furthermore, OC can be subdivided into types I and II. Type I tumors are most probably caused by atypical proliferative tumors. Type II tumors include high-grade carcinoma of the serous type, carcinosarcoma, and carcinoma, which are not differentiated and generally originate from tubal intraepithelial carcinoma of the serous type. Typically, type I tumors are present in early stages, usually with good prognosis. Type II tumors are classified as high-grade tumors and are most often diagnosed at advanced FIGO stages with poor prognosis. High-grade serous OC accounts for 90% of serous OC. Tumor heterogeneity aggravates OC treatment. The standard care for primary epithelial ovarian cancer (EOC) is cytoreductive surgery followed by platinum-based chemotherapy. Neoadjuvant chemotherapy can be used in certain cases followed by cytoreductive surgery. The main prognostic factor is complete tumor resection. However, about 70% of patients relapse. Resistance to chemotherapeutic agents remains a major challenge in EOC treatment, in which many different factors are involved. In recent years, the examination of molecular parameters and their prognostic impact has become increasingly relevant in EOC, and furthermore, the use of immunotherapy has expanded the therapeutic range. As the clinical need is greatest for relapsed patients, this systematic review will focus on recent advances in molecular biology with prognostic and predictive markers and treatment options for recurrent/refractory OC. Inclusion criteria for the review: potential prospective or predictive biomarkers in preclinical or clinical use in relapsed and refractory OC, prognostic impact, clinical and preclinical trials, and immunotherapy. Exclusion criteria for the review: primary OC, no full text or abstract available, not the topic mentioned above, and text not available in English. Risk of bias: the included studies were evaluated descriptively for the topics mentioned above, and data were not compared with each other. The objective is to highlight the molecular mechanisms of the most promising targeted agents under clinical investigation to demonstrate their potential relevance in recurrent/refractory OC.