A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in 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 many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.

The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.

Mass spectrometry based on data-independent acquisition (DIA) has developed into a powerful quantitative tool with a variety of implications, including precision medicine. Combined with stable isotope recombinant protein standards, this strategy provides confident protein identification and precise quantification on an absolute scale. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1800 blood plasma samples representing 15 different cancer types. We successfully performed an absolute quantification of 253 proteins in multiplex. The assay had low intra-assay variability with a coefficient of variation below 20% (CV = 17.2%) for a total of 1013 peptides quantified across almost two thousand injections. This study identified a potential biomarker panel of seven protein targets for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of markers, including the complement C1 complex, JCHAIN, and CD5L, resulted in a prediction model with an AUC of 0.96 for the identification of multiple myeloma patients across various cancer patients. All these proteins are known to interact with immunoglobulins.

Cancer detection is challenging, especially in patients with diffuse symptoms that overlap with non-malignant conditions. Here we show that plasma protein profiling can identify cancer among patients with non-specific symptoms. Using proximity extension assay-based proteomics of 1463 plasma proteins from 456 patients presenting with non-specific symptoms sampled prior to cancer diagnostic work-up and diagnosis, we identify 29 proteins associated with new cancer diagnoses. We develop a model able to stratify 160 cancer cases and 296 non-cancer cases with an area under the curve of 0.80, maintaining performance (0.82) in an independent replication cohort of 238 patients. The model also distinguishes cancer from autoimmune, inflammatory and infectious diseases. Designed as a triage tool, our model based on a blood test could help prioritize patients at higher cancer risk for rapid and highly sensitive diagnostic modalities such as positron emission tomography–computed tomography. These findings emphasize the potential of blood proteome profiling to support timely diagnosis and transform clinical medicine.

Proteomic research enhances our understanding of health- and disease-related biological processes. Protein profiling during healthy childhood provides important insights into normal physiological development. We longitudinally measured 5416 plasma proteins at four follow-ups during childhood (4-, 8-, 16 years) and early adulthood (24 years) in 100 randomly selected subjects participating in a population-based Swedish cohort, using Olink Explore HT. In total, 3509 proteins were included in the analysis. 54% of the proteins were found to be associated with age, and we observed several protein trajectories from childhood to adulthood based on clustering. In addition to proteins involved in bone, teeth and cartilage formation, we identified differences in proteins involved in neural function, drug metabolism, and hormonal control. There were pronounced sex-related differences in protein levels, particularly at follow-ups 16 and 24, characterized by, for example, growth, response to stimuli and regulation of catabolic processes. We demonstrate dynamic age- and sex-related changes in protein levels during the first two decades of life. Our study results may serve as an important resource in understanding human physiological development, disease etiology, and for future protein biomarker research.

Systemic autoimmune rheumatic diseases (SARDs) are a heterogeneous group of autoimmune conditions characterized by immune system dysregulation leading to chronic inflammation and tissue damage. The overlapping clinical manifestations make differential diagnosis challenging, highlighting the need for novel biomarkers to facilitate early diagnosis, stratification, and personalized treatment. Five SARDs including idiopathic inflammatory myopathies (n = 210), rheumatoid arthritis (n = 84), systemic sclerosis (n = 100), Sjögren disease (n = 99), and systemic lupus erythematosus (n = 99), as well as healthy controls (n = 400) and controls with acute infectious diseases (n = 218) were selected for plasma protein profiling using Olink Explore 1536. Differential abundance analysis and machine learning were used to identify proteins with both known and novel association to SARDs. The five SARDs share hundreds of proteins with consistently altered abundance compared to both healthy and infectious controls, reflecting common underlying molecular dysregulation. Despite the overlap, we identify multiple proteins with higher abundance specific to individual SARDs. Machine learning further enables accurate classification of the five SARDs, identifying a panel of 48 proteins with high discriminatory performance, several of which are also supported by differential abundance analysis. Altogether, this explorative cross-sectional study demonstrates the importance of a pan-disease approach, including also infectious and healthy controls, to identify robust and disease-informative protein panels for improved classification of SARDs. Protein levels from this study are available open access through the Human Protein Atlas, facilitating further plasma proteome research on autoimmune disease.