Samuel Leung | PubMed Health

PubMed2026

Drug repurposing using transcriptomics: principles and unmet needs in cardiovascular disease.

Although cardiovascular disease is the leading cause of death globally, therapeutic development in this field is slow. Given the high cost of developing new drugs and running clinical trials for cardiovascular disease, repurposing of drugs with approved safety profiles is an attractive strategy for therapeutic development that can significantly reduce the time and cost investment before phase II clinical trials. In the era of "Omics," various new methods and several large databases have been developed to enable the use of transcriptomics data for drug repurposing. This review summarizes the principles and workflow of signature mapping, which forms the foundation of statistical models used for transcriptome-based drug repurposing. We highlight the features of different analysis pipelines and databases that have been developed for signature mapping. These analysis pipelines prioritize genes that are statistically important, an approach that fundamentally differs from the pharmacological approach of identifying disease-driving and therapeutically targetable pathways. Outcomes of signature mapping pipelines are sensitive to the quality of input data, and results are not always reproducible. Moreover, all widely used RNA-seq databases are derived from cancer research and lack high-quality molecular data for cardiovascular disease. These unmet needs call for interdisciplinary collaboration and large networks of cardiovascular research-oriented biobanks to create the databases needed for transcriptomic-based signature mapping for drug repurposing efforts.

Drug Repositioning↗

PubMed2025

Cross-Platform Methylation-Based Site of Origin Classification for Squamous Cell Carcinomas.

Squamous cell carcinomas (SCCs) are one of the most common cancer types and can arise at nearly any anatomic site. Because SCCs are one of the most common metastases, do not have reliable site-specific morphologic or genomic features, and have considerable morphologic and immunohistochemical overlap with urothelial carcinomas, distinguishing between primary and metastatic squamous-appearing tumors can be challenging. This distinction can be critical to clinical management. We present Squamous cell carcinoma Methylation for Origin Site (SquaMOS), a methylation-based classifier to predict site of origin of squamous-appearing carcinomas. Trained on publicly available array-based methylation data from 1062 primary SCCs (from lung, head and neck, cervix, and esophagus) and urothelial carcinomas, SquaMOS predicted site of origin in primary tumors with 96.1% accuracy in an internal test set (n = 458) and 97.4% accuracy in an external test set from 3 institutions (n = 78). On metastatic tumors (n = 51), SquaMOS predictions were 96.1% accurate. SquaMOS was directly applicable to shallow Nanopore sequencing data (CpG probe site coverage, 0.25-2.88×) with an accuracy of 91.7% (n = 36; 100% accurate for high-confidence predictions). When tested on SCCs outside the training set types (n = 15, including 3 metastases to lung), no cases were misclassified as of lung origin, supporting accuracy of lung vs nonlung origin classification for diverse SCC types. Overall, we demonstrate highly accurate performance of the SquaMOS classifier on primary and metastatic tumors from multiple data sources, robust to suboptimal tumor purity. We illustrate transferability of our array-based classifier to low-depth Nanopore sequencing data, a potentially rapid means of site of origin determination in a clinical setting.

Humans↗