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Abstract Background The five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in blood, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery. Results We conducted a comprehensive proteomic profiling of EVs isolated from blood plasma, ascites, and cell lines of patients, employing both data-dependent (DDA) and data-independent acquisition (DIA) methods to construct a spectral library tailored for targeted proteomics. Our investigation aimed at uncovering novel biomarkers for the early detection of HGSC by comparing the proteomic signatures of EVs from women with HGSC to those with benign gynecological conditions. The initial cohort, comprising 19 donors, utilized DDA proteomics for spectral library development. The subsequent cohort, involving 30 HGSC patients and 30 control subjects, employed DIA proteomics for a similar purpose. Support vector machine (SVM) classification was applied in both cohorts to identify combinatorial biomarkers with high specificity and sensitivity (ROC-AUC > 0.90). Notably, MUC1 emerged as a significant biomarker in both cohorts when used in combination with additional biomarkers. Validation through an ELISA assay on a subset of benign (n = 18), Stage I (n = 9), and stage II (n = 9) plasma samples corroborated the diagnostic utility of MUC1 in the early-stage detection of HGSC. Conclusions This study highlights the value of EV-based proteomic analysis in the discovery of combinatorial biomarkers for early ovarian cancer detection.

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor in adults. The standard treatment achieves a median overall survival for GBM patients of only 15 months. Hence, novel therapies based on an increased understanding of the mechanistic underpinnings of GBM are desperately needed. In this study, we show that elevated expression of 28S rRNA (cytosine‐C(5))‐methyltransferase NSUN5, which methylates cytosine 3782 of 28S rRNA in GBM cells, is strongly associated with the poor survival of GBM patients. Moreover, we demonstrate that overexpression of NSUN5 increases protein synthesis in GBM cells. NSUN5 knockdown decreased protein synthesis, cell proliferation, sphere formation, migration, and resistance to temozolomide in GBM cell lines. NSUN5 knockdown also decreased the number and size of GBM neurospheres in vitro. As a corollary, mice harboring U251 tumors wherein NSUN5 was knocked down survived longer than mice harboring control tumors. Taken together, our results suggest that NSUN5 plays a protumorigenic role in GBM by enabling the enhanced protein synthesis requisite for tumor progression. Accordingly, NSUN5 may be a hitherto unappreciated target for the treatment of GBM.

Abstract Ovarian cancer is commonly diagnosed in its late stages, and new treatment modalities are needed to improve patient outcomes and survival. We have recently established the synergistic effects of combination tumor necrosis factor‐related apoptosis‐inducing ligand (TRAIL) and procaspase activating compound (PAC‐1) therapies in granulosa cell tumors (GCT) of the ovary, a rare form of ovarian cancer, using a mathematical model of the effects of both drugs in a GCT cell line. Here, to understand the mechanisms of combined TRAIL and PAC‐1 therapy, study the viability of this treatment strategy, and accelerate preclinical translation, we leveraged our mathematical model in combination with population pharmacokinetics (PKs) models of both TRAIL and PAC‐1 to expand a realistic heterogeneous cohort of virtual patients and optimize treatment schedules. Using this approach, we investigated treatment responses in this virtual cohort and determined optimal therapeutic schedules based on patient‐specific PK characteristics. Our results showed that schedules with high initial doses of PAC‐1 were required for therapeutic efficacy. Further analysis of individualized regimens revealed two distinct groups of virtual patients within our cohort: one with high PAC‐1 elimination and one with normal PAC‐1 elimination. In the high elimination group, high weekly doses of both PAC‐1 and TRAIL were necessary for therapeutic efficacy; however, virtual patients in this group were predicted to have a worse prognosis when compared to those in the normal elimination group. Thus, PAC‐1 PK characteristics, particularly clearance, can be used to identify patients most likely to respond to combined PAC‐1 and TRAIL therapy. This work underlines the importance of quantitative approaches in preclinical oncology.