Biology subjects

Soong, T. R.

Publications and source records attributed to Soong, T. R..

3 recordsLinked to original sources

bioRxiv2026

SpaReg: sparsity-based 3D reconstruction of tissue microenvironments at native resolution across morphological and spatial molecular modalities

Tissue microenvironments comprise cellular and acellular components whose three-dimensional (3D) architecture guides disease fate. Direct imaging of intact specimens by light-sheet and multiphoton microscopy, and computational reconstruction from serial sections, have established that 3D spatial context reveals cell and tissue organization inaccessible at single planes. Computational reconstruction in particular can leverage archived human tissue, benefiting from the cost-effectiveness, robustness, scalable storage, workflow compatibility, and century-long pathobiology knowledge of histology, and can integrate multiple spatial modalities. However, sectioning can introduce tears and folds, and computational alignment can further distort tissue integrity. Here we introduce SpaReg, a sparsity-based 3D reconstruction method spanning histology, spatial proteomics and spatial transcriptomics. Across multiple organs, SpaReg robustly reconstructs large tissue volumes with preserved subcellular morphology despite sectioning artifacts. On a standardized histology benchmark, SpaReg achieves the best balance between 3D reconstruction accuracy and tissue integrity, and on spatial transcriptomics benchmarks it ranks among the leading methods while scaling to hundreds of sections and millions of cells in a dataset that several existing methods fail to process. Preservation of subcellular morphology by SpaReg also enables training of a Hematoxylin and Eosin (H&E)-based epithelial, T and B cell classifier, generating single-cell-resolved 3D maps directly from H&E. Applied to pancreatic tissue containing pancreatic ductal adenocarcinoma arising from an intraductal papillary mucinous neoplasm, these maps reveal that 2D sections overestimate immune exclusion, and resolve lymphoid aggregates in 3D. SpaReg, therefore, provides a scalable foundation for morphologically faithful, multimodal 3D atlases and spatially informed disease modeling

systems biology↗

bioRxiv2025

HRDPath: An Explainable Multi-Model Deep Learning Architecture for Predicting Homologous Recombination Deficiency from Histopathology Images

Homologous recombination deficiency (HRD) is a critical biomarker for guiding treatment decisions in high-grade serous tubo-ovarian carcinoma (HGSOC), a cancer with few reliable biomarkers. However, existing genomic-based tests for HRD are variable, expensive, and time-consuming. To this end, we developed HRDPath, a novel patient-level deep learning architecture that combines the strengths of two complementary models with a multi-task design, to predict genomically derived HRD status from whole slide images in HGSOC. HRDPath was comprehensively validated across three datasets and benchmarked against leading deep learning models. It achieved an AUC of 0.846, surpassing previously reported H&E-based HRD prediction results for HGSOC images by 0.09, and for the first time, reporting a specificity of 0.938, where accuracy significantly increased when multiple slides per patient were used. Our proposed patient-level approach and interpretability pipeline enhance model trustworthiness and reveal important clinical and biological insights into HRD-positive cancers, highlighting the associated morphological and pathological changes at the cellular and tissue levels. HRDPath is a potentially accessible and scalable digital biomarker that could improve ovarian cancer diagnosis and therapy selection.

bioinformatics↗

bioRxiv2024

Stromal mediated DNA damage promotes high grade serous ovarian cancer initiation

The fundamental steps in high-grade serous ovarian cancer (HGSOC) initiation are unclear, thus providing critical barriers to the development of prevention or early detection strategies for this deadly disease. Increasing evidence demonstrates most HGSOC starts in the fallopian tube epithelium (FTE). Current models propose HGSOC initiates when FTE cells acquire increasing numbers of mutations allowing cells to evolve into serous tubal intraepithelial carcinoma (STIC) precursors and then to full blown cancer. Here we report that epigenetically altered mesenchymal stem cells (termed high risk MSC-hrMSCs) can be detected prior to the formation of ovarian cancer precursor lesions. These hrMSCs drive DNA damage in the form of DNA double strand breaks in FTE cells while also promoting the survival of FTE cells in the face of DNA damage. Indicating the hrMSC may actually drive cancer initiation, we find hrMSCs induce full malignant transformation of otherwise healthy, primary FTE resulting in metastatic cancer in vivo. Further supporting a role for hrMSCs in cancer initiation in humans, we demonstrate that hrMSCs are highly enriched in BRCA1/2 mutation carriers and increase with age. Combined these findings indicate that hrMSCs may incite ovarian cancer initiation. These findings have important implications for ovarian cancer detection and prevention.

cancer biology↗