Beyond the Surface: Deciphering the Role of Genetic Susceptibility in BIA-ALCL Pathogenesis.

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is the sentinel implant-associated malignancy, illustrating how long-lived biomaterials can reshape local tissue-immune ecology. Although textured (high-surface-area) implants show the strongest epidemiologic association, the rarity of disease despite widespread exposure suggests additional host modifiers. We synthesize evidence supporting a gene-environment (G × E) framework and critically appraise emerging host-susceptibility signals (including and HLA associations). We conducted a narrative, evidence-based synthesis of peer-reviewed epidemiologic and registry studies, peri-implant niche biology (biofilm/foreign-body response and cytokine milieu), tumor genomic profiling, and current guidelines/regulatory communications, prioritizing primary studies for key claims. Textured exposure dominates risk attribution, whereas absolute-risk estimates vary with denominators, exposure ascertainment, and follow-up duration. Mechanistic studies support a chronically inflamed capsule niche. Genomic analyses repeatedly converge on JAK/STAT pathway activation with frequent co-alterations in epigenetic regulators and recurrent copy-number changes, consistent with stepwise evolution under sustained selection. Immune-evasion features-including frequent PD-L1 expression and (9p24.1) copy-number alterations-provide a plausible checkpoint route, while host-susceptibility signals remain preliminary and require multi-center, multi-ancestry replication. BIA-ALCL is a multistep, context-dependent lymphoma in which implant-mediated inflammation intersects with host susceptibility to enable somatic evolution and immune escape. Clinically, prevention currently relies on exposure mitigation, standardized risk communication, and symptom-driven evaluation; precision prevention will require integrative cohorts linking verified device exposure, immunogenetics, microenvironment profiling, and tumor multi-omics.