Blood-based proteomics is approaching a translational inflection point. Driven by advances in measurement technologies, rapid expansion of analytical capabilities, and growing adoption across research and medical communities, there is increasing demand for clinically actionable biomarkers. As the field transitions away from purely large-scale discovery-oriented studies toward more informed, targeted, application-driven analyses, the generation of proteomic data is no longer the bottleneck. Instead, the central challenge is to translate these measurements into robust, reproducible, and clinically meaningful insights. In this Review, we assess recent technological and methodological developments, evaluate persistent preanalytical and interpretative limitations, and outline the key steps required for clinical translation. We focus on three deeply interconnected dimensions: the capabilities and constraints of current measurement platforms, the role of computational and machine learning approaches in extracting biological and clinical signals, and the emergence of large-scale population studies that create new opportunities for validation and generalization. Finally, we discuss a forward-looking vision in which proteomics plays a central role in dynamic, multilayered omics frameworks, where integration with genomics, temporal profiling, and imaging can deepen our understanding of health, disease, and therapeutic response.

Primary central nervous system lymphoma (PCNSL) is a rare and aggressive form of diffuse large B ‐ cell lymphoma (DLBCL) characterized by infiltration of malignant B cells into the central nervous system (CNS). 1 Fit patients are treated with high ‐ dose methotrexate (HD ‐ MTX) – based polychemotherapy followed by thiotepa ‐ containing high ‐ dose chemotherapy and autologous stem cell transplantation (ASCT). 2 For patients unsuitable for ASCT, induction treatment is consolidated with high ‐ dose cytarabine and low ‐ dose whole ‐ brain radiotherapy (WBRT). 1,2 Patients unsuitable for HD ‐ MTX can receive first ‐ line treatment with a single alkylator, such as temozolomide (TMZ) and WBRT. Despite therapeutic advances and high response rates, over half of the patients with PCNSL relapse within 2 years after diagnosis. 3 TMZ maintenance treatment has been suggested to reduce relapse rates and improve overall survival (OS) for patients with post ‐ induction complete remission (CR) or partial remission (PR). Therefore, its administration is supported by international guidelines, including the European Hematology Association and European Society for Medical Oncology (EHA ‐ ESMO) joint recommendations 2 and the National Comprehensive Cancer Network (NCCN) Guidelines for B ‐ Cell Lymphomas (Version 3.2026). However, implementation of TMZ maintenance varies across clinical settings, allowing real ‐ world evaluation of the association between TMZ

Background Studies linking self-reported symptoms to circulating protein biomarkers are increasing, partly driven by the rising number of protein biomarkers being identified. Despite research advances, current reviews that synthesise these studies are typically disease-specific. A broader, diagnosis-agnostic and multidisciplinary scoping review can uncover shared biological patterns associated with symptoms across different conditions and can generate insights that advance precision health and symptom science. This scoping review protocol builds on this need for a broad approach as it aims to consolidate current knowledge on self-reported symptom and circulating protein biomarker associations, identify potential patterns, and provide relevant insights for clinical practice and future research. Methods The protocol is aligned with the PRISMA and JBI guidelines for scoping reviews. The search strategy includes original peer-reviewed journal research articles examining associations between self-reported symptoms and protein biomarkers in blood plasma or serum. Searches will be conducted from PubMed, CINAHL, Embase, and Web of Science databases. A preliminary search retrieved more than 30,000 articles, prompting a pilot test of the Artificial Intelligence (AI)-assisted review screening tool ASReview. The pilot demonstrated time savings, refined methodological decisions, and confirmed the feasibility of proceeding with the review. Therefore, screening will be conducted using ASReview, with a total of five reviewers involved in the process. Data extraction will focus on single self-reported symptoms and circulating protein biomarker pairs that have a reported association. Analysis will involve counting and mapping these associations to identify potential patterns. The protocol was registered with the Open Science Framework (OSF) https://osf.io/bku3f. Discussion This protocol provides a structured and transparent approach for conducting a large-scale scoping review. By adhering to established guidelines, the protocol provides a more comprehensive, standardised, exact, and reproducible accumulation of knowledge, while acknowledging potential limitations. The expected results can contribute to summarise the current understanding of associations between self-reported symptoms and circulating protein biomarkers. This fosters the integration of symptom science and the omics field, specifically proteomics, to advance precision health.

Background Glioblastoma (GBM) invasion is clinically decisive but difficult to model systematically. Existing patient-derived xenograft (PDX) resources rarely couple reproducible in vivo invasion phenotypes with matched multi-omic profiles at scale, limiting mechanistic insight and phenotype-informed therapeutic hypotheses. Methods We established the HGCC Phenobank, comprising 65 patient-derived GBM stem-like cultures with matched multi-omic profiling and orthotopic engraftment in 449 mice. Blinded histopathology quantified ten invasion traits per case. These phenotypes were integrated with RNA sequencing, DNA methylation, and mass-spectrometry-based proteomics. Multi-Omic Factor Analysis (MOFA) identified latent molecular programs. Phenotype-specific RNA signatures were matched to LINCS drug-perturbation profiles and validated in 3D gliomasphere and ex vivo brain-slice assays. Results Two dominant, reproducible invasion modes emerged across models: diffuse parenchymal infiltration and perivascular/condensed growth. Proneural cultures formed more aggressive tumors in immunodeficient mice, and mouse survival showed a modest correlation with patient survival in matched cases (Pearson 0.1832, 0.045). MOFA identified 15 latent factors; Factor 1, enriched for ASCL1/OLIG1/OLIG2 programs and associated with TP53/DCHS2/WNK2 alterations, was linked to increased tumor formation, diffuse invasion, and shorter mouse survival, and stratified GBM patients in TCGA and in our matched patient cohort. Drug-signature matching separated mechanisms targeting diffuse versus perivascular invasion. Experimental validation confirmed phenotype-selective sensitivities, and inhibitors PIK-75 and buparlisib suppressed invasion dynamics across representative models in 3D and brain-slice assays. Conclusions The HGCC Phenobank provides the first openly available PDX resource that systematically links GBM invasion phenotypes to multi-omic programs and therapeutic predictions. This framework enables reproducible model selection, mechanistic dissection of invasion modes, and phenotype-guided therapeutic discovery. Key Points Diffuse and perivascular invasion define orthogonal GBM axes ASCL1/OLIG factor links initiation, diffuse growth, and survival Phenotype-matched drugs validated; PIK-75 and buparlisib curb invasion dynamics Importance of the Study Glioblastoma invasion varies substantially between patients, yet existing patient-derived xenograft resources rarely combine reproducible in vivo phenotyping with matched multi-omic profiling at scale. The HGCC Phenobank addresses this gap with standardized, blinded scoring of ten invasion traits across 449 orthotopic xenografts from 65 molecularly characterized GBM stem-like cultures, integrated with transcriptomic, methylomic, and proteomic data. We identify two dominant, reproducible invasion modes and a cross-modal neurodevelopmental program, the ASCL1/OLIG1/2-associated Factor 1, that links tumor initiation, diffuse growth, and survival in mice, and stratifies GBM patients in TCGA and in our matched patient cohort. In a spatially resolved xenograft section, Factor 1 signal localizes to the invasive tumor periphery. By matching phenotype-specific RNA signatures to drug-induced transcriptional responses, we show that invasion phenotypes nominate selective vulnerabilities, exemplified by PIK-75. This openly shared resource enables reproducible model selection, mechanistic dissection of invasion programs, and phenotype-guided therapeutic discovery.

The translation of blood-based proteomics into healthcare is no longer constrained primarily by technological limitations, but by unresolved challenges in standardization, validation, and implementation. In this Commentary, we identify four structural bottlenecks at the current translational inflection point that limit the opportunities for the adoption of multi-protein blood biomarkers in routine clinical care: lack of harmonized reference frameworks, uncertainty around fit-for-purpose biological resolution, complexity in validating multi-analyte and algorithm-based tests, and misalignment between the design of discovery workflows and clinical requirements. We argue that progress will depend on shifting from exploratory profiling toward decision-oriented proteome analytics, with early alignment across academic, regulatory, clinical, and technical domains. Establishing coherent validation pathways for clinically actionable use cases will be essential to enable reliable integration of proteomics into healthcare. This Comment identifies key bottlenecks limiting the clinical adoption of multi-protein blood biomarkers and argues that progress requires a shift from exploratory profiling to decision-oriented proteome analytics with early cross-sector alignment.

This study aimed to describe and compare background factors and symptoms at diagnosis of patients with non-advanced or advanced stage lung cancer and patients without cancer, and to develop predictive models identifying key variables that contribute to the detection of early and late-stage lung cancer. Univariate logistic regression and three machine learning algorithms were used. Compared to patients without cancer, six background factors and two symptoms differed in non-advanced lung cancer, while 11 background factors and 19 symptoms differed in advanced cases. The machine learning models showed moderate performance in classifying patients with lung cancer from those without cancer. Notably, top predictors extended beyond classic respiratory symptoms. Demographic and lifestyle factors, particularly age, smoking status, and living situation, remained essential alongside symptoms such as pain, appetite loss, weight reduction, and respiratory problems. These findings support integrating clinical, demographic, and patient-reported symptoms to improve lung cancer risk models and refine referral decisions in screening pathways. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-026-46710-8.

Plasma proteomics technologies are advancing rapidly, offering new opportunities for biomarker discovery and precision medicine. Direct comparisons of available technologies are needed to understand how platform selection affects downstream findings. We compared the performance of a peptide fractionation-based mass spectrometry method (HiRIEF LC-MS/MS) and the Olink Explore 3072 proximity extension assays on 88 plasma samples, analyzing 1129 proteins with both methods. The platforms exhibited complementary proteome coverage, high precision, and concordance in estimating sex differences in protein levels. Quantitative agreement between platforms was moderate (median correlation 0.59, interquartile range 0.33-0.75), mainly influenced by technical factors. Finally, we present a publicly available tool for peptide-level analysis of platform agreement and demonstrate its utility in clarifying cross-platform discrepancies in protein and proteoform measurements. Our findings provide insights for platform selection and study design, and highlight the value of combining mass spectrometry and affinity-based approaches for more comprehensive and reliable plasma proteome profiling. Advancements in plasma proteomics have opened new avenues for biomarker discovery, necessitating a clear understanding of technological capabilities. Here, the authors compare HiRIEF LC-MS/MS and Olink Explore 3072, revealing complementary strengths and moderate quantitative agreement, and introduce PeptAffinity, a resource facilitating detailed peptide-level exploration of differences in protein quantification between platforms.

Highlights • MBM tumors show significant intertumor and intratumor heterogeneity in cellular composition, gene mutations, and pathway enrichment.• Therapy-treated tumors (P2, P4) exhibited immune activation, while untreated tumors (P1, P3) showed cold tumor signatures.• P1 and P4 tumors were enriched in CAFs, correlating with epithelial-mesenchymal transition and angiogenesis pathways.• Proteomic analysis revealed activation of oncogenic pathways like JAK-STAT, NF-κB, MAPK, and EMT, driving tumor progression.

Highlights • MBM tumors show significant intertumor and intratumor heterogeneity in cellular composition, gene mutations, and pathway enrichment.• Therapy-treated tumors (P2, P4) exhibited immune activation, while untreated tumors (P1, P3) showed cold tumor signatures.• P1 and P4 tumors were enriched in CAFs, correlating with epithelial-mesenchymal transition and angiogenesis pathways.• Proteomic analysis revealed activation of oncogenic pathways like JAK-STAT, NF-κB, MAPK, and EMT, driving tumor progression.

Introduction Techniques for assessing the blood plasma proteome with high precision and at great depth are rapidly developing and have demonstrated utility in carrying diagnostic and prognostic information for patients with cancer, including hematological malignancies. However, it is not known whether the plasma proteome can be useful in distinguishing the more closely related cancer entities, such as different B-cell lymphomas (BCLs). Performing affinity-based plasma proteomics analyses in a population-based cohort of BCLs, we aimed at discovering plasma proteome differences between BCL subtypes and identifying potential biomarkers that can aid differential diagnosis. Material and Methods We analyzed 592 BCLs (221 diffuse large BCL (DLBCL), 94 follicular lymphoma (FL), 123 Hodgkin lymphoma (HL), 91 mantle cell lymphoma (MCL), and 63 primary CNS lymphoma (PCNSL)) from the U-CAN biobank (www.u-can.uu.se). Plasma samples collected at diagnosis were analyzed using the Olink Explore 1536 platform, which provided relative quantification of 1463 unique proteins. The plasma proteomes between a given group and all the remaining groups were compared with a two-sided t test and further adjusted for age and sex in multivariable linear limma models. To identify panels of plasma proteins that can differentiate between the different subtypes of BCLs, we trained two types of machine learning (ML) models based on the random forest (RF) algorithm and logistic regression with regularization (LRR). The entire dataset was proportionally partitioned into a training (70%) and testing (30%) dataset. Both model types were trained in one thousand iterations, with cross-validation, on a non-filtered dataset and implementing different filtering approaches based on varying cut-offs of mean log2-difference (log2-diff) of differentially altered proteins (DAPs) and 0.1% false discovery rate (FDR). Finally, the best-performing model from the iterations of the two ML methods on the training data was selected and tested on the testing dataset for performance. Both balanced accuracy and area under the curve (AUC) were considered as main outcomes of performance. Results Comparing the plasma proteomes between BCL subtypes showed many DAPs in each subtype compared to the rest of the cohort at 5% FDR. PCNSL patients had the largest number of DAPs, followed by HL, MCL, DLBCL, and FL. However, most of these alterations were of smaller log2-diff between the subgroups. Less than ten proteins per group had a log2-diff > 1 in a subgroup compared to other subtypes, apart from MCL patients, who had 64 DAPs with log2-diff > 1. The findings remained consistent in the multivariable analyses, where the log2-diff between subgroups was adjusted for age and sex. Yet, each subgroup had more DAPs that were uniquely altered in that subgroup and in no other group, regardless of the log2-FC, with most DAPs observed again in the MCL, followed by DLBCL, HL, FL, and PCNSL. This was reflected in the ML models, where combining smaller differences in protein levels into multivariate models showed reliable performance in differentiating the BCLs. Filtering improved the model's accuracy, and the derived best-performing LRR model showed moderate to high accuracy in differentiating the BCLs on testing data. The LRR model had the highest accuracy in classifying MCL, with AUC of 91%, followed by HL (90%), PCNSL (89%), DLBCL (85%), and FL (80%), the latter being repeatedly misclassified in the ML iterations. Although the model's sensitivity was variable, being highest for HL and lowest for FL, the specificity was very high (>93%) for excluding FL (94%), HL (96%), MCL (98%), and particularly PCNSL (99%), with the negative predictive value of the model for CNS involvement being 98%. Conclusions Plasma proteomics can differentiate between distinct types of BCLs with a moderate to high accuracy, between 80% and 91%. The models showed the highest accuracy in classifying MCL, likely due to the highest number of unique DAPs and proteins with large log2-diff observed in this subtype On average, the models showed better specificity, which is highly relevant for DLBCL, where a blood biomarker can serve as a quick diagnostic tool for initial exclusion of CNS involvement in a patient, with very high predictive value. This suggests that plasma proteomics could assist in the differential diagnosis of B-cell lymphomas and potentially for CNS-involvement.

The Rasch‐Built Pompe‐Specific Activity (R‐PAct) scale is a patient‐reported outcome measure specifically designed to quantify the effects of Pompe disease on daily life activities, developed for use in Dutch‐ and English‐speaking countries. This study aimed to validate the R‐PAct for use in other countries.

COVID-19 is characterised by systemic immunological perturbations in the human body, which can lead to multi-organ damage. Many of these processes are considered to be mediated by the blood. Therefore, to better understand the systemic host response to SARS-CoV-2 infection, we performed systematic analyses of the circulating, soluble proteins in the blood through global proteomics by mass-spectrometry (MS) proteomics. Here, we show that a large part of the soluble blood proteome is altered in COVID-19, among them elevated levels of interferon-induced and proteasomal proteins. Some proteins that have alternating levels in human cells after a SARS-CoV-2 infection in vitro and in different organs of COVID-19 patients are deregulated in the blood, suggesting shared infection-related changes.The availability of different public proteomic resources on soluble blood proteome alterations leaves uncertainty about the change of a given protein during COVID-19. Hence, we performed a systematic review and meta-analysis of MS global proteomics studies of soluble blood proteomes, including up to 1706 individuals (1039 COVID-19 patients), to provide concluding estimates for the alteration of 1517 soluble blood proteins in COVID-19. Finally, based on the meta-analysis we developed CoViMAPP, an open-access resource for effect sizes of alterations and diagnostic potential of soluble blood proteins in COVID-19, which is publicly available for the research, clinical, and academic community.