Abstract
Abstract
Background
Oral and esophageal squamous cell carcinomas (OSCC/ESCC) are commonly diagnosed late since histopathology is subjective and robust molecular tools are lacking. Leveraging interpretable machine learning, we aimed to develop an objective and platform-agnostic molecular measure of malignancy that overcomes the technical and geographic barriers of conventional genomic testing.
Methods
Using a discovery-to-generalization framework with 2,541 samples, we developed and validated a single-sample transcriptomic scoring system, EsoralPrint. We feature engineered 34 intra-sample ordinal gene pairs (isoPairs) that capture consistent expression-order reversals between malignant and healthy states. It was trained on oral tissues and externally validated across diverse global cohorts spanning the upper aerodigestive tract, technical platforms, and geographic regions. Independent single-cell RNA sequencing was used to provide post-hoc mechanistic explainability.
Results
In the discovery cohort, EsoralPrint distinguished patients histologically graded as low-risk hyperplasia who nonetheless progressed to cancer (log-rank p
Conclusion
EsoralPrint provides a platform-independent and quantitative complement to histological grading in oral and esophageal squamous malignancy, with the potential to give clinicians lead time for intervention. It can be accessed via https://github.com/aiphaqua/EsoralPrint.