Multi-omics-based molecular classification of adrenocortical carcinoma predicts response to immunotherapy and targeted treatments

Adrenal cortical carcinoma (ACC) is a rare and highly aggressive malignant tumor with dismal outcomes. Once metastasis occurs, the 5-year survival rate falls below 15%. Current treatment options offer LIMited benefit for advanced disease, Largely due to the absence of well-defined therapeutic targets, and there is an urgent need to develop new molecular classifications to achieve precise treatment strategies. In this study, we integrated multi-omics data including transcriptome, epigenetic, and genomic variation profiles and applied 10 clustering algorithms, identifying two robust molecular subtypes of ACC: Multi-Omics ACC Consensus Subtyping (MACCS)1 and MACCS2. Biologically, MACCS1 exhibits a proliferation-driven phenotype, whereas MACCS2 displays an immune activation state. Drug sensitivity analysis further revealed that MACCS2 tumors were more responsive to immune checkpoint inhibitors, while MACCS1 showed sensitivity to antiangiogenic tyrosine kinase inhibition. Using a random forest algorithm, we identified HOXC11 as a key prognostic factor within MACCS1, with high expression associated with tumor progression. Functional assays confirmed that silencing HOXC11 significantly reduced the proliferation of ACC cells. Survival analysis showed that the prognosis of patients with MACCS1 had markedly worse outcomes compared to those with MACCS2. Collectively, this study provides a theoretical basis for the molecular classification of ACC and personalized precision treatment, such as immunotherapy and targeted therapy, and highlight HOXC11 as a potential therapeutic target.