Ex vivo tissue modelling informs drug selection for rare cancers

Area of Science:

• Oncology
• Genomics
• Translational Medicine

Background:

• Targeting actionable gene mutations improves cancer treatment response in some patients.
• Precision medicine in rare cancers often relies on broad genetic data, lacking functional validation.
• Functional data can reveal oncogene dependence and refine treatment selection.

Purpose of the Study:

• To evaluate an ex vivo cancer explant modeling approach for guiding clinical treatment decisions.
• To assess the utility of functional data in precision medicine for rare cancers.
• To validate the model's accuracy in predicting treatment response in a specific patient case.

Main Methods:

• An ex vivo cancer explant modeling technique was developed for clinical integration.
• The model allows for pathological review within 10 days of tissue collection.
• Longitudinal response data was collected from the ex vivo model and the patient.

Main Results:

• The ex vivo model accurately predicted longitudinal response in a patient with BRAF V600E-mutant papillary thyroid cancer with squamous differentiation.
• The model guided the selection of targeted therapy for the patient.
• The model confirmed treatment resistance upon disease progression after 8 months.

Conclusions:

• Ex vivo cancer explant modeling is a viable tool for guiding precision medicine in clinical settings.
• Functional data from explant models enhances treatment selection beyond genetic profiling alone.
• This approach shows promise for improving outcomes in rare cancers lacking standard therapies.