Decoding Drug Resistance: Quantitative Proteomics Reveals Signal Rewiring in Melanoma

Overview

Comprehensive Cancer Center Melanoma is an aggressive skin cancer with high metastatic potential and a significant rate of treatment resistance, particularly to targeted therapies such as BRAF and MEK inhibitors. A major contributor to this resistance is signal rewiring, where tumor cells adapt by activating alternative pathways to maintain survival, proliferation, or resistance despite therapeutic pressure. This study employed quantitative proteomics to investigate the molecular mechanisms underlying drug resistance in melanoma cells treated with the combination therapy of trametinib and vemurafenib, specifically investigating which proteins undergo changes in expression and activity that lead to signal rewiring and resistance. Proteomic analysis revealed 2,112 differentially expresse d proteins, with 742 upregulated and 1,370 downregulated in resistant (RES) compared to wild-type (WT) melanoma cells. Key upregulated proteins, including RHBDF2 and PAI2, were implicated in tumor progression and therapy evasion, while downregulated proteins, such as Endoribonuclease YbeY and Nocturnin, suggested disruptions in mitochondrial function and apoptosis regulation. Gene Ontology analysis indicated that resistance mechanisms were associated with cytoskeletal reorganization, metabolic adaptations, and altered cell signaling. These findings highlight critical molecular adaptations that contribute to therapeutic failure and suggest potential targets for overcoming drug resistance. By elucidating the proteomic landscape of resistant melanoma, this study provides insights that may inform the development of novel therapeutic strategies aimed at preventing or reversing signal rewiring. Future research should explore novel combination strategies to preempt adaptive resistance and improve patient outcomes. BeeMind AI: Development of an Artificial Intelligence-Based System to Assess Honeybee Health, Behavior, and Nutrient Effects Matthew Lo The Haverford School, Haverford, PA Lately, honeybees have been facing increasing population loss due to a collection of environmental issues, including global warming, habitat loss, pesticides, and parasites. To address these issues, this research proposed and built an AI-based honeybee health assessment system called BeeMind AI. The BeeMind AI system integrated eight sensors including temperature and humidity, carbon dioxide, and atmospheric pressure sensors combined with microphone and camera modules. Due to its many functions including th e ability to analyze honeybee movement and behavioral patterns, the BeeMind AI system was used to evaluate the effects of four nutrients on honeybee learning and memory through video analysis in two experimental settings, one in a newly designed tri -chambered maze, and another in a free-flying homing paradigm. The free -flying experiment was conducted to study the effect of nutrients on return rates of honeybees at distances of 300 m, 500 m, and 800 m, and it was found that the base return rates of the contr ol group even at 800 m was close to 75%. Additionally, it was observed for the first time that C60 nanoparticles had significant positive effects on honeybee learning, memory, and flying capabilities, improving return rates by around 9% at 300 m, 16% at 500 m, and 20% at 800 m, while neonicotinoid pesticides had negative effects on return rates, reducing them significantly by up to 30%. Combined with the methods and findings achieved in this research, the developed BeeMind AI system demonstrates significant potential for application in the beekeeping industry as a powerful tool.

Competition history

• JSHS 2025 Category not listed

Resources

• National JSHS abstract book ↗

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