Functional precision cancer medicine: drug sensitivity screening enabled by cell culture models

Area of Science:

• Oncology
• Translational Medicine
• Computational Biology

Background:

• Current precision cancer medicine primarily relies on genomic biomarkers, which benefit only a subset of patients.
• Molecular tumor boards utilize genomic data for treatment recommendations, highlighting a need for complementary approaches.
• Functional precision medicine offers a novel strategy by analyzing direct responses of tumor cells to therapeutic agents.

Purpose of the Study:

• To provide an overview of the current landscape of functional precision medicine.
• To highlight advancements in drug-sensitivity screening using cell culture models.
• To explore the integration of artificial intelligence (AI) with functional precision medicine for enhanced patient stratification.

Main Methods:

• Review of current literature on functional precision medicine and its applications.
• Discussion of cell culture models enabling drug-sensitivity screening.
• Exploration of AI methodologies for analyzing functional data and guiding treatment decisions.

Main Results:

• Functional precision medicine captures real-time cellular responses to drug perturbations, offering insights beyond static genomic data.
• Cell culture models have advanced to facilitate high-throughput drug sensitivity screening.
• AI demonstrates potential in integrating diverse functional data for sophisticated patient stratification.

Conclusions:

• Functional precision medicine represents a promising frontier in personalized cancer care.
• Integrating AI with functional screening can overcome limitations of purely genomic approaches.
• This combined strategy holds the potential to significantly improve treatment selection and patient outcomes in oncology.