A comprehensive characterization of blood proteome profiles in cancer patients could provide a better understanding of disease biology, enabling earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes.
Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing 12 major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients of the Uppsala-Umeå Comprehensive Cancer Consortium (U-CAN) biobank was measured at the time of diagnosis and before treatment. Using machine learning methods, the differentially expressed proteins identified were used to derive models to discriminate among different cancer types. A panel of 83 proteins was found to identify the correct cancer types with AUCs ranging between 0.93 and 1. Preliminary analysis indicated that the protein panel was able to discriminate all cancers from healthy controls and showed promising performance in both staging some of the cancer types, and in detecting very early-stage cancer. The data from this study was made available via the Disease Blood Atlas, an open-access resource.
The results were used as a foundation to establish the Olink Insight platform, an open-access digital data resource to accelerate adoption of proteomics in the research community. In Olink Insight, we are creating a collection of proteomic profiles for important diseases, beginning with cancer. Olink Insight and the Human Disease Blood Atlas represent a significant step towards uncovering human disease proteome and will be a valuable resource for researchers in many areas of medicine and biology.