Multiomics empowers predictive drug resistance in advanced prostate cancer

Prostate cancer (PCa) remains a leading cause of cancer-related morbidity and mortality among men worldwide, with advanced stages often exhibiting resistance to standard therapies. Drug resistance in advanced prostate cancer is a multifactorial process influenced by genetic mutations, hereditary factors, epigenetic alterations, lifestyle choices, and dietary habits. Understanding this complex interplay is crucial for developing effective predictive and therapeutic strategies. In this context, a multiomics approach integrating genomics, transcriptomics, proteomics, metabolomics, and epigenomics offers a comprehensive framework to dissect the molecular mechanisms driving drug resistance.Current challenges include tumor heterogeneity, limited access to longitudinal patient data, and insufficient representation of diverse populations in omics datasets. This study highlights the novel application of multiomics integration to stratify patient subgroups based on molecular signatures, enabling early prediction of therapy resistance. We also emphasize key biomarkers and signaling pathways associated with treatment failure, including alterations in androgen receptor signaling, PI3K-AKT-motor pathways, and metabolic rewiring. The integration of these datasets not only enhances diagnostic precision but also aids in identifying actionable therapeutic targets. It also opens up new avenues for developing targeted therapies by integrative profiling and quantifying a broad spectrum of biomolecular features across distinct subtypes of malignant cells.