Drug dependence in cancer is exploitable by optimally constructed treatment holidays

Area of Science:

• Oncology
• Cancer Biology
• Evolutionary Medicine

Background:

• Acquired resistance to targeted therapies is a major challenge in cancer treatment.
• Some resistant cancers become dependent on the drug for survival, suggesting intermittent therapy could be effective.
• Optimal scheduling and identification of drug-dependent tumors remain poorly understood.

Purpose of the Study:

• To characterize drug dependence in BRAF-mutant melanoma models resistant to MAPK inhibitors.
• To elucidate the mechanisms driving drug dependence.
• To identify optimal intermittent therapy schedules and validate them in silico.

Main Methods:

• Characterization of over 75 MAPK-inhibitor-resistant BRAF V600E mutant melanoma models.
• Population and single-clone level analysis of drug dependence.
• Mathematical modeling to determine optimal 'drug holiday' schedules.
• Spatial agent-based modeling to simulate tumor dynamics.

Main Results:

• Melanocytic differentiated melanoma models showed a higher propensity for drug dependence compared to dedifferentiated models.
• Loss of microphthalmia-associated transcription factor in differentiated models drives ERK-JunB-p21 signaling, enforcing drug dependence.
• Simple adaptive therapy protocols using population size measurements yielded near-optimal outcomes.
• Optimized schedules remained effective in complex tumor microenvironment simulations.

Conclusions:

• Tumor differentiation status is a key predictor of drug dependence.
• Specific molecular pathways mediate drug dependence, offering therapeutic targets.
• Evolution-inspired adaptive therapy, even with simplified protocols, can effectively manage drug-dependent cancers.