Oral oncology · 2025
Tumor subtype classification tool for HPV-associated head and neck cancers.
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Li Shiting, Garb Bailey F, Qin Tingting, Soppe Sarah E, Lopez Elizabeth, Patil Snehal, D'Silva Nisha J, Rozek Laura S, Sartor Maureen A
Аннотация
BACKGROUND: Molecular subtypes of HPV-associated Head and Neck Squamous Cell Carcinoma (HNSCC), named IMU (immune strong) and KRT (highly keratinized), are well-recognized due to distinct molecular features, tumor microenvironments, clinical outcomes, and potentially differing optimal treatment strategies. Currently, no standardized method exists to subtype a new HPV + HNSCC tumor. Our paper introduces a machine learning-based classifier and webtool to reliably subtype HPV + HNSCC tumors using the IMU/KRT paradigm and highlights the importance of subtype in HPV + HNSCC.
METHODS: We conducted RNA-seq on 67 HNSCC tumors from University of Michigan Health. Combining this with three publicly available datasets, we utilized a total of 229 HPV + HNSCC RNA-seq samples. The classifier was trained and tested using 84 subtype-labeled HPV + RNA-seq samples and validated with the remaining samples. We also tested the association of 37 clinicodemographic and molecular variables with subtype.
RESULTS: The classifier achieved 100% accuracy in the test set. Validation on two additional cohorts demonstrated successful separation by known features of the subtypes. Investigation of the relationship between subtype and the molecular and clinicodemographic variables revealed 21 significant associations, both confirming previous findings and revealing novel subtype associations.
CONCLUSIONS: This study provides a reliable classifier for subtyping HPV + HNSCC tumors as either IMU or KRT based on bulk RNA-seq data and improves our understanding of the HPV + HNSCC subtypes.
METHODS: We conducted RNA-seq on 67 HNSCC tumors from University of Michigan Health. Combining this with three publicly available datasets, we utilized a total of 229 HPV + HNSCC RNA-seq samples. The classifier was trained and tested using 84 subtype-labeled HPV + RNA-seq samples and validated with the remaining samples. We also tested the association of 37 clinicodemographic and molecular variables with subtype.
RESULTS: The classifier achieved 100% accuracy in the test set. Validation on two additional cohorts demonstrated successful separation by known features of the subtypes. Investigation of the relationship between subtype and the molecular and clinicodemographic variables revealed 21 significant associations, both confirming previous findings and revealing novel subtype associations.
CONCLUSIONS: This study provides a reliable classifier for subtyping HPV + HNSCC tumors as either IMU or KRT based on bulk RNA-seq data and improves our understanding of the HPV + HNSCC subtypes.