Cervical cancer, which is strongly linked to persistent HPV infection, presents a challenge for immunotherapy development due to the complexity of the tumor microenvironment and diverse host immune responses. Traditional approaches have struggled to accurately characterize interactions between T-cell receptors and viral components, largely due to difficulty in reliably extracting and analyzing TCR repertoires from heterogeneous patient datasets. Furthermore, conventional methods often fail to capture biochemical properties such as electrostatic and hydrophobic interactions, which dictate TCR-epitope binding affinity, limiting precision in identifying immune targets that may inform better prognostic markers and therapeutic strategies.
Our inventors have developed a novel bioinformatic method that evaluates the chemical complementarity between T-cell receptor CDR3 regions and specific HPV16 epitopes—extracted from large cervical cancer datasets—using RNA sequencing data, a sliding-window alignment algorithm, and scoring based on electrostatic and hydrophobic interactions. This unique approach correlates high complementarity scores with improved overall and disease-free survival, independent of other clinical covariates such as age, smoking history, and tumor staging, highlighting its potential as a biomarker and opening a novel window for therapeutic HPV vaccine and immunotherapy development. Specifically, three approaches identified the same few HPV16 epitopes as being important TCR targets, and thus, using these epitopes as immunotherapy development targets may provide for a greater likelihood of success.
Kaplan-Meier (KM) analyses of overall survival (OS) of TCGA-CESC cases based on chemical complementarity of tumor resident TRA and TRB CDR3s, recovered from either tumor WXS files or RNAseq files; and based on HPV Epitopes-110943, -174148, -1625373.