Personalised Medicine in Cervical Cancer: Evaluating Therapy Resistance Through Multi-Model Approaches

Cervical cancer remains a leading cause of malignancy among women globally, disproportionately affecting women from low-to-middle-income countries, including South Africa. The high prevalence in impoverished communities places significant pressure on the public healthcare system. In these regions, human papillomavirus (HPV); the primary risk factor for cervical cancer-along with co-occurring immunosuppressive conditions such as HIV, is common. Compounding this burden is the widespread development of treatment resistance to conventional therapies like cisplatin and carboplatin. Resistance is frequently associated with therapy-induced cellular senescence, underscoring the need for more personalised treatment strategies tailored to individual patient profiles.

This study aimed to assess ex vivo methods' utility in predicting patient-specific therapy responses. Biopsy samples from cervical cancer patients were cultured and subjected to various chemotherapies. Cell viability, senescence markers and treatment resistance pathways were analysed to determine optimal treatment outcomes.

The findings revealed significant variability in optimal treatment responses, with ex vivo methods demonstrating limitations in fully capturing the complexity of patient-specific reactions to therapy. No single experimental model provided comprehensive predictive insights into treatment outcomes.

This study underscores the need for integrative and multidisciplinary approaches when evaluating treatment strategies for cervical cancer. While ex vivo models offer valuable insights, combining multiple experimental methods is crucial for a more reliable and comprehensive understanding of treatment response and resistance mechanisms. Standardiszing approaches or employing method combinations may enhance personalised medicine efforts, particularly in resource-limited settings.