This Olink-sponsored webinar was originally broadcast by Labroots on February 21, 2023 and is now available as an on-demand recording
Background
Cancer is a highly heterogeneous disease in need of accurate and non-invasive diagnostic tools. Here, we 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. The implication for cancer precision medicine of next generation plasma profiling is discussed.
Learning Objectives
- Identify the proteome signature from patients representing 12 common cancer types using minute amounts of blood samples
- Predict disease using AI-based models to identify a set of proteins associated with each cancers type
- Show the result in the open access Human Disease Blood Atlas
- Discuss the implication for cancer precision medicine of next generation plasma profiling
