Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Current progress in genomics and targeted therapies for neurofibromatosis type 2

Neurofibromatosis type 2 (NF2), a multiple neoplasia syndrome, is a manifestation of an impaired expression of the merlin protein, exerting inhibitory effects on cell proliferation signals due to abnormalities of the NF2 gene located on chromosome 22. About half of patients inherit a germline mutation from a parent, and nearly 60% of de novo NF2 patients are estimated to have somatic mosaicism. The development of technical methods to detect NF2 gene mutation, including targeted deep sequencing from multiple tissues, improved the diagnostic rate of mosaic NF2. With improved understanding of genetics and pathogenesis, the diagnostic criteria for NF2 were updated to assist in identifying and diagnosing NF2 at an earlier stage. The understanding of cell signaling pathways interacting with merlin has led to the development of molecular-targeted therapies. Currently, several translational studies are searching for possible therapeutic agents targeting VEGF or VEGF receptors. Bevacizumab, an anti-VEGF monoclonal antibody, is widely used in many clinical trials aiming for hearing improvement or tumor volume control. Currently, a randomized, double-masked trial to assess bevacizumab is underway. In this randomized control trial, 12 other Japanese institutions joined the principal investigators in the clinical trial originating at Fukushima Medical University. In this review, we will be discussing the latest research developments regarding NF2 pathophysiology, including molecular biology, diagnosis, and novel therapeutics.

Increasing Transfection Efficiency of Lipoplexes by Modulating Complexation Solution for Transient Gene Expression

Transient gene expression is a suitable tool for the production of biopharmaceutical candidates in the early stage of development and provides a simple and rapid alternative to the generation of stable cell line. In this study, an efficient transient gene expression methodology using DC-Chol/DOPE cationic liposomes and pDNA in Chinese hamster ovary suspension cells was established through screening of diverse lipoplex formation conditions. We modulated properties of both the liposome formation and pDNA solution, together called complexation solutions. Protein expression and cellular cytotoxicity were evaluated following transfection over the cell cultivation period to select the optimal complexation solution. Changes in hydrodynamic size, polydispersity index, and ζ potential of the liposomes and lipoplexes were analyzed depending on the various pH ranges of the complexation solutions using dynamic light scattering. The transfer of lipoplexes to the cytosol and their conformation were traced using fluorescence analysis until the early period of transfection. As a result, up to 1785 mg/L and 191 mg/L of human Fc protein and immunoglobulin G (bevacizumab), respectively, were successfully produced using acidic liposome formation and alkaline pDNA solutions. We expect that this lipoplex formation in acidic and alkaline complexation solutions could be an effective methodology for a promising gene delivery strategy.

Designed Amino Acid Feed in Improvement of Production and Quality Targets of a Therapeutic Monoclonal Antibody

Cell culture feeds optimization is a critical step in process development of pharmaceutical recombinant protein production. Amino acids are the basic supplements of mammalian cell culture feeds with known effect on their growth promotion and productivity. In this study, we reported the implementation of the Plackett-Burman (PB) multifactorial design to screen the effects of amino acids on the growth promotion and productivity of a Chinese hamster ovary DG-44 (CHO-DG44) cell line producing bevacizumab. After this screening, the amino acid combinations were optimized by the response surface methodology (RSM) to determine the most effective concentration in feeds. Through this strategy, the final monoclonal antibody (mAb) titre was enhanced by 70%, compared to the control group. For this particular cell line, aspartic acid, glutamic acid, arginine and glycine had the highest positive effects on the final mAb titre. Simultaneously, the impact of the designed amino acid feed on some critical quality attributes of bevacizumab was examined in the group with highest productivity. The product was analysed for N-glycan profiles, charge variant distribution, and low molecular weight forms. The results showed that the target product quality has been improved using this feeding strategy. It was shown how this strategy could significantly diminish the time and number of experiments in identifying the most effective amino acids and related concentrations in target product enhancement. This model could be successfully applied to other components of culture media and feeds.