The presentation will describe a novel strategy to explore the proteome signature by comprehensive analysis of protein levels using a pan-cancer approach of patients representing the major cancer types. Plasma profiles of 1,463 proteins from more than 1,500 cancer patients representing altogether 12 common cancer types were measured in minute amounts of blood plasma collected at the time of diagnosis and before treatment. AI-based disease prediction models allowed for the identification of a set of proteins associated with each of the analyzed cancers. By combining the results from all cancer types, a panel of proteins suitable for the identification of all individual cancer types was defined. The results are presented in a new open access Human Disease Blood Atlas.
