Next generation pan-cancer blood proteome profiling with Olink Explore


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. Professor Mathias Uhlén’s team from the Royal Institute of Technology (KTH) in Stockholm used Olink® Explore 1536 to measure 1436 plasma proteins in over 1400 patients presenting with 12 different types of common cancers, using samples taken at the time of diagnosis, before the initiation of treatment. Using machine learning methods, the differentially expressed proteins identified were used to derive models to discriminate among different cancer types, with quite remarkable results.


Differentially expressed proteins were identified for each of the 12 cancer types (two forms of leukemia, lymphoma, myeloma, colorectal cancer, lung cancer, glioma, breast cancer, cervical cancer, endometrial cancer, ovarian cancer and prostate cancer). The most significant discriminatory proteins for each cancer type were identified as PRDX5, CEACAM5, PRTG, GLO1, DNER, PLAT, GFAP, CXCL9, CD244, PAEP, TCL1A, and CNTN5. Separate diagnostic models for each cancer type were then derived using the machine learning algorithm glmnet, with 70% of samples used as a training set and all other cancer types used as the control for each specific cancer. The number of proteins contributing to the classification of each cancer type varied considerably, from 473 for colorectal to just 9 for myeloma.

The classification models were then evaluated in the remaining 30% of the data excluded from the model training. As shown in the figure below in this post (taken from the original article), the performances of these models in distinguishing each cancer from all the other types were very high, with AUCs ranging from 0.82 to 1 (0.95 or above for six of the twelve cancer types). Reducing the number of proteins used to derive the models (especially when using fewer than the top 50 as model input) affected the AUCs significantly in most cases, demonstrating the value of including many proteins in the classification model to gain higher confidence.

Further data analysis identified a single panel of 83 proteins designed to measure all 12 cancer types (based on the highest contributing proteins from the individual models). This pan-cancer panel identified the correct cancer types with AUCs ranging between 0.93 and 1, with performances only marginally inferior to the initial models based on all ~1400 proteins. Preliminary analysis also 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. Further investigations in larger cohorts will be needed to confirm these potentially important findings.

Alvez et al (2023) Fig 4 B

ROC curves showing the ability of selected protein signatures to discriminate 12 types of cancer from all other 11 types. Area under the curve (AUC) values quantify the discriminatory performance, where a value of 1 equates to 100% discrimination between the classes being compared. Figure taken from Álvez et al. (2023) Nature Communications, DOI: 10.1038/s41467-023-39765-y, under Creative Commons 4.0 license

The data from this study was made available via the Disease Blood Atlas, an open-access resource that allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. In collaboration with Professor Uhlén, the data can also be viewed as an interactive data story on Olink® Insight. This presents a variety of analyses and visualizations, including differential expression analysis, pathway enrichment, pathway annotation with hexmaps, and predictive protein groups identified by machine learning. Access this free resource using the link below.



Álvez MB, Edfors F, von Feilitzen K, et al. Next generation pan-cancer blood proteome profiling using proximity extension assay. (2023) Nature Communications, DOI: 10.1038/s41467-023-39765-y

..cost-effective pan-cancer population screening can be foreseen in which a panel of proteins are used to identify multiple cancer types in a single assay

Álvez et al. (2023)

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Olink’s Proximity Extension Assay (PEA) technology has been used for protein biomarker discovery and analysis across a very broad range of disease areas and applications, providing actionable insights into disease biology and helping to drive future development of new and better therapeutics. There are now well over 1000 publications citing the use of our assays and the list is growing rapidly. Please visit our library of publications to see some of the extraordinary work produced by Olink customers.

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