In an Olink-sponsored Labroots webinar Professor Mathias Uhlen will describe a novel strategy based on plasma profiling and machine learning that explores the proteome signatures of 12 major cancer types and then uses the information to identify all the individual cancer types as a first step towards new diagnostic tools for cancer.

The pan-cancer study is based on proximity extension assay plasma profiles generated from minute samples of blood and AI-based prediction models to identify proteins associated with the different cancer types. Almost 1,500 proteins from more than 1,500 cancer patients representing 12 different cancer types have been analysed, and the results are presented in the Human Disease Blood Atlas section of the Human Protein Atlas. During the webinar Mathias Uhlen, Professor at KTH and Science for Life Laboratory, will discuss the strategy and the results of this study as well as implications of next generation plasma profiling for cancer precision medicine in general.

By joining this webinar you will get to learn more about:

- How to identify proteome signatures in small amounts of blood from cancer patients

- How to predict proteins associated with the disease using AI-based prediction models

- Implications of next generation plasma profiling for cancer precision medicine

- The open access Human Disease Blood Atlas

Click here for more information and to sign up