BRIDGING PROTEOMIC DISCOVERY AND CLINICAL PREDICTION: A REVIEW OF TRANSLATIONAL PATHWAYS FROM CANCER BIOMARKER RESEARCH TO PREDICTIVE SURVIVAL MODELS IN THE UNITED STATES

Cancer proteomics has generated numerous candidate biomarkers and molecular signatures, but the translation of these discoveries into validated predictive survival models remains incomplete. This systematized review examined the translational pathway from cancer proteomic discovery to predictive survival modeling and clinical implementation in the U.S. oncology context. A structured evidence synthesis of 22 studies was conducted, focusing on biomarker discovery and signature development, survival prediction, analytical and clinical validation, multi-omics and artificial-intelligence-enabled modeling, and clinical translation. The reviewed evidence shows substantial progression from biomarker discovery toward proteomic signatures associated with survival and treatment response and increasingly integrated predictive models. However, the evidence remains uneven across the translational pathway. External validation, analytical reproducibility, integration of molecular and clinical variables, population representation, and prospective evaluation are limited, while direct evidence of clinical utility remains insufficient. The findings indicate that associations between proteomic signatures and survival should not be interpreted as evidence of clinically ready predictive models. Progress toward routine implementation will require rigorous analytical and clinical validation, external evaluation across relevant populations and settings, reproducibility across analytical platforms, and evidence that model outputs improve clinical decision-making.