Investigator
Stellenbosch University
Resveratrol: Sensitising CD44+ Cervical Cancer Cells to Carboplatin and Mitigating Metastasis
ABSTRACT Background Cervical cancer remains a leading cause of cancer‐related mortality among women globally. Persistent infection with high‐risk human papillomavirus drives cervical carcinogenesis, and treatment outcomes are frequently challenged by metastasis and chemoresistance. The transmembrane glycoprotein cluster of differentiation 44 (CD44), a marker associated with cancer stem cells (CSCs), has emerged as a critical mediator of both processes in cervical cancer. Objective This review aims to critically evaluate current evidence on the role of CD44 in cervical cancer progression, metastasis, and treatment resistance. It also explores the potential of resveratrol, a naturally occurring polyphenol with known anticancer properties, as a chemo‐sensitizing agent to carboplatin therapy. Methods A comprehensive review of the literature was conducted using databases such as PubMed, Google Scholar, and Scopus to identify studies that investigate CD44‐mediated mechanisms in cervical cancer, as well as the modulatory and mechanistic effects of resveratrol on CD44 and chemoresistance across various cancer types. Results CD44 has been consistently implicated in promoting drug resistance, epithelial‐to‐mesenchymal transition (EMT), and stemness in cervical cancer. Resveratrol has demonstrated antimetastatic and chemo‐sensitizing effects in several malignancies, such as colorectal and breast cancers, often through modulation of CD44 and associated pathways. However, direct evidence in cervical cancer remains limited. Conclusion Current findings suggest a promising therapeutic avenue for combining resveratrol with carboplatin to overcome CD44‐mediated treatment resistance and metastasis in cervical cancer. Nonetheless, further cervical‐specific studies are needed to validate this approach. A clearer understanding of this relationship may facilitate lower chemotherapy dosing, reduced toxicity, and improved clinical outcomes.
Personalised Medicine in Cervical Cancer: Evaluating Therapy Resistance Through Multi‐Model Approaches
ABSTRACTIntroductionCervical 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.Methods and MaterialsThis 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.ResultsThe 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.ConclusionThis 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.
