Comprehensive Multiomics Characterization of Perineural Invasion in Cervical Cancer Reveals Diagnostic Markers, Molecular Drivers, and Therapeutic Strategies
Abstract
Perineural invasion (PNI) is an important pathologic feature of cervical cancer that is associated with poor prognosis and provides key information for clinical decisions. A better understanding of the molecular mechanisms underlying PNI could lead to improved patient treatment strategies. Here, we generated whole-exome, whole-genome, and RNA sequencing data from tumors and matched normal clinical samples of 45 patients with cervical cancer and performed a comparative analysis between 23 PNI and 22 non-PNI tumors. A robust machine learning approach identified a three-gene expression signature of MT1G, NPAS1, and SPRY1 that could predict the tumor PNI status with high accuracy, which was validated using an independent cohort (18 PNI and 19 non-PNI). Loss-of-function FBXW7 mutations were identified as driver events for PNI that lead to increased MYC activity and an immunosuppressive tumor microenvironment. Finally, a deep learning model for predicting drug efficacy over patients’ transcriptomic data revealed OTX015, a BET inhibitor, as a promising treatment that targets mutated FBXW7 PNI tumors. This study provides a rich resource for elucidating the molecular mechanisms of PNI tumors, laying a critical foundation for developing effective diagnostic and therapeutic strategies for PNI tumors in cervical cancer.
Significance:
Generation of a rich resource for characterizing the molecular basis of perineural invasion in tumors lays a critical foundation for developing effective diagnostic and therapeutic strategies in cervical cancer.
This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .
