Identification of hyperparameters with high effects on performance of deep neural networks: application to clinicopathological data of ovarian cancer

Recent advances in deep learning have emerged as an effective approach for precision medicine. The applications of deep learning to medicine have been applied mainly to medical image data but not clinicopathological data. One of challenges of deep learning model to clinicopathological data is to optimize hyperparameters to get high predictive power. In this study, we identified hyperparameters of deep learning model that have large effects on power. Specifically, we focused on predicting platinum-based chemotherapy response for ovarian cancer patients. As a performance metric, we used the area under the curve. We optimized six hyperparameters: the number of hidden layers, number of hidden units, optimization algorithm, weight initialization, activation function, and dropout rate. We also identified significant interaction effects between hyperparameters. We successfully found the combination of hyperparameters having large effects on prediction. These optimal combinations are expected to increase the prediction accuracy for the response to chemotherapy for a variety of cancer patients.N

Stearoyl-CoA desaturase 1 inhibition induces ER stress-mediated apoptosis in ovarian cancer cells

Abstract Ovarian cancer is a leading cause of death among gynecologic tumors, often detected at advanced stages. Metabolic reprogramming and increased lipid biosynthesis are key factors driving cancer cell growth. Stearoyl-CoA desaturase 1 (SCD1) is a crucial enzyme involved in de novo lipid synthesis, producing mono-unsaturated fatty acids (MUFAs). Here, we aimed to investigate the expression and significance of SCD1 in epithelial ovarian cancer (EOC). Comparative analysis of normal ovarian surface epithelial (NOSE) tissues and cell lines revealed elevated SCD1 expression in EOC tissues and cells. Inhibition of SCD1 significantly reduced the proliferation of EOC cells and patient-derived organoids and induced apoptotic cell death. Interestingly, SCD1 inhibition did not affect the viability of non-cancer cells, indicating selective cytotoxicity against EOC cells. SCD1 inhibition on EOC cells induced endoplasmic reticulum (ER) stress by activating the unfolded protein response (UPR) sensors and resulted in apoptosis. The addition of exogenous oleic acid, a product of SCD1, rescued EOC cells from ER stress-mediated apoptosis induced by SCD1 inhibition, underscoring the importance of lipid desaturation for cancer cell survival. Taken together, our findings suggest that the inhibition of SCD1 is a promising biomarker as well as a novel therapeutic target for ovarian cancer by regulating ER stress and inducing cancer cell apoptosis

Wnt/beta-Catenin Inhibition by CWP232291 as a Novel Therapeutic Strategy in Ovarian Cancer

The poor prognosis of ovarian cancer patients mainly results from a lack of early diagnosis approaches and a high rate of relapse. Only a very modest improvement has been made in ovarian cancer patient survival with traditional treatments. More targeted therapies precisely matching each patient are strongly needed. The aberrant activation of Wnt/beta-catenin signaling pathway plays a fundamental role in cancer development and progression in various types of cancer including ovarian cancer. Recent insight into this pathway has revealed the potential of targeting Wnt/beta-catenin in ovarian cancer treatment. This study aims to investigate the effect of CWP232291, a small molecular Wnt/beta-catenin inhibitor on ovarian cancer progression. Various in vitro, in vivo and ex vivo models are established for CWP232291 testing. Results show that CWP232291 could significantly attenuate ovarian cancer growth through inhibition of beta-catenin. Noticeably, CWP232291 could also s suppress the growth of cisplatin-resistant cell lines and ovarian cancer patient-derived organoids. Overall, this study has firstly demonstrated the anti-tumor effect of CWP232291 in ovarian cancer and proposed Wnt/beta-catenin pathway inhibition as a novel therapeutic strategy against ovarian cancer.N

Accurate Diagnosis of COVID-19 from Self-Collectable Biospecimens Using Synthetic Apolipoprotein H Peptide-Coated Nanoparticle Assay

A high-throughput, accurate screening is crucial for the prevention and control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Current methods, which involve sampling from the nasopharyngeal (NP) area by medical staffs, constitute a fundamental bottleneck in expanding the testing capacity. To meet the scales required for population-level surveillance, self-collectable specimens can be used; however, its low viral load has hindered its clinical adoption. Here, we describe a magnetic nanoparticle functionalized with synthetic apolipoprotein H (ApoH) peptides to capture, concentrate, and purify viruses. The ApoH assay demonstrates a viral enrichment efficiency of >90% for both SARS-CoV-2 and its variants, leading to an order of magnitude improvement in analytical sensitivity. For validation, we apply the assay to a total of 84 clinical specimens including nasal, oral, and mouth gargles obtained from COVID-19 patients. As a result, a 100% positivity rate is achieved from the patient-collected nasal and gargle samples, which exceeds that of the traditional NP swab method. The simple 12 min pre-enrichment assay enabling the use of self-collectable samples will be a practical solution to overcome the overwhelming diagnostic capacity. Furthermore, the methodology can easily be built on various clinical protocols, allowing its broad applicability to various disease diagnoses