Ovarian cancer is usually detected at a late stage and the overall 5-year survival is only 30–40%. Additional means for early detection and improved diagnosis are acutely needed. To search for novel biomarkers, we compared circulating plasma levels of 593 proteins in three cohorts of patients with ovarian cancer and benign tumors, using the proximity extension assay (PEA). A combinatorial strategy was developed for identification of different multivariate biomarker signatures. A final model consisting of 11 biomarkers plus age was developed into a multiplex PEA test reporting in absolute concentrations. The final model was evaluated in a fourth independent cohort and has an AUC = 0.94, PPV = 0.92, sensitivity = 0.85 and specificity = 0.93 for detection of ovarian cancer stages I–IV. The novel plasma protein signature could be used to improve the diagnosis of women with adnexal ovarian mass or in screening to identify women that should be referred to specialized examination.

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.

Background: The aim of this study is to evaluate the impact of lymphovascular space invasion (LVSI) on the risk of lymph node metastases and survival in endometrioid endometrial adenocarcinoma. Material and methods: As regard the study design, this is a cohort study based on prospectively recorded data. Patients with endometrioid endometrial adenocarcinoma registered in the Swedish Quality Registry for Gynecologic Cancer 2010–2017 with FIGO stages I–III and verified nodal status were identified (n = 1587). LVSI together with established risk factors, namely DNA ploidy, FIGO grade, myometrial invasion and age, were included in multivariable regression analyses with lymph node metastases as the dependent variable. Associations between the risk factors and overall and relative survival were included in multivariable models. Estimates of risk ratios (RR), hazard ratios (HR), excess mortality rate ratios (EMR), and 95% confidence intervals (95% CI) were calculated. Results: The presence of LVSI presented the strongest association with lymph node metastases (RR = 5.46, CI 3.69–8.07, p < .001) followed by deep myometrial invasion (RR = 1.64, CI 1.13–2.37). In the multivariable survival analyses, LVSI (EMR = 7.69, CI 2.03–29.10,) and non-diploidy (EMR = 3.23, CI 1.25–8.41) were associated with decreased relative survival. In sub-analyses including only patients with complete para-aortic and pelvic lymphadenectomy and negative lymph nodes (n = 404), only LVSI (HR = 2.50, CI 1.05–5.98) was associated with a worsened overall survival. Conclusion: This large nationwide study identified LVSI as the strongest independent risk factor for lymph node metastases and decreased survival in patients with endometrioid adenocarcinomas. Moreover, decreased overall survival was also seen in patients with LVSI-positive tumors and negative lymph nodes, indicating that hematogenous dissemination might also be important.

Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine ( N _urine = 220 cancer vs. 360 healthy) and plasma ( N _plasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83–0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.

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.