BRAF inhibitor resistance in melanoma: from resistance mechanisms to therapeutic innovations

TL;DR

This paper reviews how melanoma becomes resistant to BRAF inhibitors and explores new therapies to overcome this resistance.

Contribution

The paper provides a structured framework linking resistance mechanisms to targeted therapies in BRAF inhibitor-resistant melanoma.

Findings

Resistance mechanisms include genetic, epigenetic, metabolic, and tumor microenvironment changes.

Next-generation BRAFi, combination therapies, and RNA-based agents are proposed to combat resistance.

Technologies like liquid biopsies and AI are highlighted for precision monitoring and treatment.

Abstract

BRAF inhibitors (BRAFi) have transformed the treatment of BRAF mutant melanoma, but inherent and acquired resistance remains a major barrier to curative outcomes. Resistance arises from interconnected mechanisms: genetic alterations reactivating the MAPK pathway or bypass cascades (e.g., PI3K/AKT/RTK), epigenetic modulation, metabolic reprogramming, and the tumor microenvironment (TME) remodeling. Despite extensive research into these mechanisms, a cohesive framework linking each resistance module to targeted therapeutic strategies is lacking. This review systematically categorizes resistance into intrinsic and acquired subtypes: intrinsic resistance is driven by constitutive molecular traits of BRAF mutant melanoma (e.g., persistent MAPK activation, baseline PI3K/AKT hyperactivity), while acquired resistance emerges via therapeutic pressure-induced genetic mutations, epigenetic shifts, metabolic reprogramming, or TME modifications. For each identified resistance mechanism, we provide a detailed examination of corresponding therapeutic advancements. These encompass the development of next-generation BRAFi, strategically designed combination therapies, epigenetic modulators, immunotherapeutic approaches, and RNA-based therapeutic agents. Furthermore, we underscore the pivotal role of state-of-the-art technologies, such as liquid biopsies, single-cell multi-omics analyses, and artificial intelligence, in facilitating precise resistance monitoring and personalized therapy selection. By integrating these insights, we present a structured, translationally focused framework to guide basic research and clinical decision-making, ultimately advancing precision salvage therapy and trials aimed at preventing or overcoming BRAFi resistance.

Figures