Morphology-guided organoid classification reveals prognosis of oral cancer [dataset 2]

Oral cancer is an aggressive malignancy with a survival rate below 50% in advanced stages due to low mutation rates, lack of molecular subtypes, and limited treatment targets. This study presents a pioneering approach to classifying oral cancer subtypes based on the morphology of patient-derived organoids (PDOs) and proposes a novel therapeutic strategy. We successfully established 76 cancer and 81 normal PDOs. For cancer PDOs, both manual classification and AI-based scoring were utilized to categorize them into three distinct subtypes: normal-like, dense, and grape-like. These subtypes correlated with unique transcriptomic profiles, genetic mutations, and clinical outcomes, with patients harboring dense and grape-like organoids exhibiting poorer prognoses. Furthermore, drug response assessments of 14 single agents and Cisplatin combination therapies identified a synergistic treatment approach for resistant subtypes. This study highlights the potential of integrating morphology-based classification with genomic and transcriptomic analyses to refine oral cancer subtyping and develop effective treatment strategies. RNA-seq profiling was performed on 40 patient-derived oral squamous cell carcinoma (OSCC) organoid. Total RNA was extracted, and libraries were prepared using the TruSeq Stranded mRNA protocol. Sequencing was conducted on the Illumina NovaSeq 6000 platform (GSE293370). In addition, Exome-seq was performed as part of this study. Although the original raw Exome-seq files are no longer available, mutation annotation files (MAF format) are provided as supplementary files. The MAF files contain high-confidence somatic mutations annotated from tumor-normal paired Exome-seq. *************************************************************** Submitter states that missing raw files are due to file loss. ***************************************************************