Combining causal and correlative approaches to discover biomarkers of response to paclitaxel

Area of Science:

• Oncology
• Genomics
• Pharmacogenomics

Background:

• Glioblastoma treatment efficacy is highly variable, necessitating predictive biomarkers.
• Paclitaxel is a key chemotherapeutic agent, but response prediction remains challenging.
• Identifying biomarkers for taxane therapy improves patient stratification and treatment outcomes.

Purpose of the Study:

• To discover and validate a predictive biomarker for paclitaxel response in glioblastoma and breast cancer.
• To assess the clinical utility of a novel biomarker in taxane-treated patients.
• To enable personalized medicine approaches in neuro-oncology and breast cancer treatment.

Main Methods:

• Whole genome CRISPR knockout screens were employed for biomarker discovery.
• Biomarker candidate was validated in two independent breast cancer patient cohorts.
• Prospective validation in glioblastoma patient cohorts is planned within a Phase II clinical trial (NCT04528680).

Main Results:

• A putative biomarker for paclitaxel response was identified.
• The biomarker candidate demonstrated successful validation in breast cancer patient cohorts.
• Further validation is required to confirm its predictive value in glioblastoma.

Conclusions:

• The identified biomarker holds potential for predicting paclitaxel response in glioblastoma and breast cancer.
• Clinical validation in glioblastoma patients is crucial for therapeutic decision-making.
• This biomarker could enhance the efficacy of taxane-based therapies and improve patient outcomes.