Biology subjects

Chung, Y. R.

Publications and source records attributed to Chung, Y. R..

5 recordsLinked to original sources

bioRxiv2026

Tumor-tropic E. coli engineered as living T and NK cell engagers

Despite advances in immunotherapy, most solid tumors remain resistant to treatment. Immune cell engagers redirect cytotoxic lymphocytes against cancer, but limited tumor access, immunosuppressive microenvironments and systemic immune activation limit efficacy. Here we develop live immune modulating engagers (LIME), a modular platform where non-pathogenic, tumor-tropic Escherichia coli display tandem single-chain variable fragments targeting a tumor-associated antigen and an activating receptor on T or natural killer cells. LIME bridged effector and tumor cells, induced transcriptional programs of T cell activation, metabolism and proliferation, and enhanced cytotoxicity across cancer cell lines and patient-derived organoids. In mouse models, LIME safely accumulated in tumors, outperformed tarlatamab in small cell lung cancer, and induced durable immunity in lymphoma. RAS inhibition and PD-L1 blockade enhanced LIME activity in pancreatic cancer and induced humoral responses. Multi-lineage immune modulation remained tumor-confined, without organ toxicity. These findings establish LIME as a versatile living therapeutic platform for programmable, tumor-restricted immune orchestration.

bioengineering↗

bioRxiv2023

Replication-attenuated r3LCMV vectors potentiate tumor control via IFN-I

Viral vectors are being used for the treatment of cancer. Yet their efficacy varies among tumors and their use poses challenges in immunosuppressed patients, underscoring the need for alternatives. We report striking antitumoral effects by a nonlytic viral vector based on attenuated lymphocytic choriomeningitis virus (r3LCMV). We show in multiple tumor models that injection of tumor-bearing mice with this novel vector results in improved tumor control and survival. Importantly, r3LCMV also improved tumor control in immunodeficient Rag1-/- mice. Single cell RNA-Seq analyses, antibody blockade experiments, and KO models revealed a critical role for host IFN-I in the antitumoral efficacy of r3LCMV vectors. Collectively, these data demonstrate potent antitumoral effects by a replication-attenuated LCMV vector and unveil mechanisms underlying its antitumoral efficacy.

immunology↗

bioRxiv2022

Unusual Morphological Changes of Rugositalea oryzae, A Novel Wrinkled Bacterium Isolated from The Rice Rhizosphere, Under Nutrient Stress

Bacterial cell morphology might result from natural selection to gain a competitive advantage under environmentally stressful conditions such as nutrient limitation. A bacterial strain YC6860T isolated from the rhizosphere of rice (Oryza sativa L.) showed pleomorphic behavior with smooth cell morphology and wrinkled surface rods depending upon nutritional conditions. Based on scanning and transmission electron microscopy studies, we hypothesized that the surface-to-volume ratio of cells increases with decreasing nutrient concentrations. The transition from smooth to wrinkled cell surface morphology could be one of the adaptation strategies by which YC6860T maximizes its ability to access available nutrients. To characterize the properties of the wrinkled strain, we performed taxonomic and phylogenetic analyses. 16S rRNA gene sequencing results showed that the strain represented a novel, deep-rooting lineage within the order Rhizobiales with the highest similarity of 94.2% to Pseudorhodoplanes sinuspersici RIPI 110T. Whole genome sequencing was also performed to characterize its genetic features. The strain YC6860T might belong to a new genus, named Rugositalea, and a new species, named Rugositalea oryzae, In addition, taxonomic analysis showed that YC6860T is Gram-negative, aerobic, and rod-shaped with large regular wrinkles resembling a delicate twist of fusilli, measuring 0.5-0.6 {micro}m in width and 1.5-1.6 {micro}m in length under nutrient-limiting conditions. This unique cell structure with regular rugosity could be the first finding that has not been reported in the existing bacterial morphology.

microbiology↗

bioRxiv2022

Comparative analysis of high- and low-level deep learning approaches in microsatellite instability prediction

Deep learning-based approaches in histopathology can be largely divided into two categories: a high-level approach using an end-to-end model and a low-level approach using feature extractors. Although the advantages and disadvantages of both approaches are empirically well known, there exists no scientific basis for choosing a specific approach in research, and direct comparative analysis of the two approaches has rarely been performed. Using the Cancer Genomic Atlas (TCGA)-based dataset, we compared these two different approaches in microsatellite instability (MSI) prediction and analyzed morphological image features associated with MSI. Our high-level approach was based solely on EfficientNet, while our low-level approach relied on LightGBM and multiple deep learning models trained on publicly available multiclass tissue, nuclei, and gland datasets. We compared their performance and important image features. Our high-level approach showed superior performance compared to our low-level approach. In both approaches, debris, lymphocytes, and necrotic cells were revealed as important features of MSI, which is consistent with clinical knowledge. Then, during qualitative analysis, we discovered the weaknesses of our low-level approach and demonstrated that its performance can be improved by using different image features in a complementary way. We performed our study using open-access data, and we believe this study can serve as a useful basis for discovering imaging biomarkers for clinical application.

pathology↗

bioRxiv2020

Aggregation of Cohorts for Histopathological Diagnosis with Deep Morphological Analysis

There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks (DNNs) either specialized in specific cohorts or an aggregate of all cohorts when there are only a few images available for the target cohort. A trade-off between decreasing the number of models and their cancer detection performance was evident in our experiments with The Cancer Genomic Atlas (TCGA) dataset, with the former approach achieving higher performance at the cost of having to acquire large datasets from the cohort of interest. Constructing annotated datasets for individual cohorts is extremely time-consuming, with the acquisition cost of such datasets growing linearly with the number of cohorts. Another issue associated with developing cohort-specific models is the difficulty of maintenance: all cohort-specific models may need to be adjusted when a new DL algorithm is to be used, where training even a single model may require a non-negligible amount of computation, or when more data is added to some cohorts. In resolving the sub-optimal behavior of a universal cancer detection model trained on an aggregate of cohorts, we investigated how cohorts can be grouped to augment a dataset without increasing the number of models linearly with the number of cohorts. This study introduces several metrics which measure the morphological similarities between cohort pairs and demonstrates how the metrics can be used to control the trade-off between performance and the number of models.

pathology↗