Biomedical subjects

Ken S Lau

Publications and source records attributed to Ken S Lau.

3 recordsLinked to original sources

PubMed2026

An integrated single-cell and spatial transcriptomic atlas of thyroid cancer progression identifies prognostic fibroblast subpopulations.

Although well-differentiated thyroid carcinoma (WDTC) is characterized by a robust treatment response, aggressive subtypes, such as anaplastic thyroid carcinoma (ATC), remain highly lethal. To understand thyroid cancer evolution in both children and adults, we analyzed single-cell transcriptomes of 423,733 cells from 81 samples and spatially resolved key tumor and microenvironment populations across 28 tumors with spatial transcriptomics, including rare and unique composite WDTC/ATC tumors and pediatric diffuse sclerosing thyroid carcinomas. Additionally, we identified gene signatures of stromal cell populations in 5 large thyroid cancer bulk RNA-sequencing cohorts. Through this multi-institutional effort, we defined a population of POSTN+ myofibroblast cancer-associated fibroblasts (myCAFs) that are intimately associated with invasive tumor cells and correlate with poor prognosis, lymph node metastasis, and disease progression in thyroid carcinoma. We also revealed a population of inflammatory CAFs that are distant to tumor cells and are found in the inflammatory stromal microenvironment of autoimmune thyroiditis. Together, our study provides spatial profiling of thyroid cancer evolution in samples with mixed WDTC/ATC histopathology and identifies a prognostic myCAF subtype with potential clinical utility in predicting aggressive disease in both children and adults.

Humans↗

PubMed2025

Multi-ancestry genome-wide and transcriptome-wide association analyses identified new risk loci and genes for inflammatory bowel disease.

To advance genetic understanding of inflammatory bowel disease (IBD), we conducted genome-wide association meta-analyses of 63,415 IBD cases of European and East Asian descendants and identified 90 previously unknown risk loci. Integrating multi-ancestry transcriptome-wide association studies (TWAS), cell type-specific TWAS, alternative splicing (AS-WAS), and alternative polyadenylation (APA-WAS) analyses using RNA-seq data from normal colon tissues of 707 European and 364 East Asian individuals, we uncovered 506 high-confidence IBD risk genes, including 384 not previously reported. These genes converge on immune regulation, microbial interaction, and other pathways central to IBD pathogenesis, with over half showing transcriptional dysregulation supported by single-cell and spatial omics analyses. Notably, 46 risk genes are targeted by 225 drugs that have been approved or in Phase II/III trials, including sulfasalazine already used in IBD therapy. Our study findings deepen the understanding of IBD genetics and support the development of precision medicine for its prevention and treatment.

GWAS↗

PubMed2025

Histology-Based Virtual RNA Inference Identifies Pathways Associated With Metastasis Risk in Colorectal Cancer.

Colorectal cancer (CRC) remains a major health concern, with >150,000 new diagnoses and >50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (eg, Visium and Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented virtual RNA inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST data set to date, comprising 45 patients and >300,000 Visium spots from primary tumors. Using state-of-the-art deep learning models (UNI, ResNet-50, Vision Transformer, and Vision Mamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, and inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Humans↗