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Background and purpose 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. Methods Four other language versions (German, French, Italian, and Spanish) of the R‐PAct were created and distributed among Pompe patients (≥16 years old) in Germany, France, Spain, Italy, and Switzerland and pooled with data of newly diagnosed patients from Australia, Belgium, Canada, the Netherlands, New Zealand, the USA, and the UK and the original validation cohort (n = 186). The psychometric properties of the scale were assessed by exploratory factor analysis and Rasch analysis. Results Data for 520 patients were eligible for analysis. Exploratory factor analysis suggested that the items separated into two domains: Activities of Daily Living and Mobility. Both domains independently displayed adequate Rasch model measurement properties, following the removal of one item (\"Are you able to practice a sport?\") from the Mobility domain, and can be added together to form a \"higher order\" factor as well. Differential item functioning (DIF)‐by‐language assessment indicated DIF for several items; however, the impact of accounting for DIF was negligible. We recalibrated the nomogram (raw score interval‐level transformation) for the updated 17‐item R‐PAct scale. The minimal detectable change value was 13.85 for the overall R‐PAct. Conclusions After removing one item, the modified‐R‐PAct scale is a valid disease‐specific patient‐reported outcome measure for patients with Pompe disease across multiple 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. Babačić et al . performed systematic analyses of blood proteins in COVID-19 patients through mass-spectrometry proteomics, showing that a large part of the soluble blood proteome is altered. The authors then developed an open-access resource, CoViMAPP, for meta-analysis of MS proteomics studies of COVID-19 patients.

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.

BackgroundImmune checkpoint inhibitors (ICIs) have significantly improved the outcome in metastatic cutaneous melanoma (CM). However, therapy response is limited to subgroups of patients and clinically useful predictive biomarkers are lacking.MethodsTo discover treatment-related systemic changes in plasma and potential biomarkers associated with treatment outcome, we analyzed serial plasma samples from 24 patients with metastatic CM, collected before and during ICI treatment, with mass-spectrometry-based global proteomics (high-resolution isoelectric focusing liquid chromatography–mass spectrometry (HiRIEF LC-MS/MS)) and targeted proteomics with proximity extension assays (PEAs). In addition, we analyzed plasma proteomes of 24 patients with metastatic CM treated with mitogen-activated protein kinase inhibitors (MAPKis), to pinpoint changes in protein plasma levels specific to the ICI treatment. To detect plasma proteins associated with treatment response, we performed stratified analyses in anti-programmed cell death protein 1 (anti-PD-1) responders and non-responders. In addition, we analyzed the association between protein plasma levels and progression-free survival (PFS) by Cox proportional hazards models.ResultsUnbiased HiRIEF LC-MS/MS-based proteomics showed plasma levels’ alterations related to anti-PD-1 treatment in 80 out of 1160 quantified proteins. Circulating PD-1 had the highest increase during anti-PD-1 treatment (log2-FC=2.03, p=0.0008) and in anti-PD-1 responders (log2-FC=2.09, p=0.005), but did not change in the MAPKis cohort. Targeted, antibody-based proteomics by PEA confirmed this observation. Anti-PD-1 responders had an increase in plasma proteins involved in T-cell response, neutrophil degranulation, inflammation, cell adhesion, and immune suppression. Furthermore, we discovered new associations between plasma proteins (eg, interleukin 6, interleukin 10, proline-rich acidic protein 1, desmocollin 3, C-C motif chemokine ligands 2, 3 and 4, vascular endothelial growth factor A) and PFS, which may serve as predictive biomarkers.ConclusionsWe detected an increase in circulating PD-1 during anti-PD-1 treatment, as well as diverse immune plasma proteomic signatures in anti-PD-1 responders. This study demonstrates the potential of plasma proteomics as a liquid biopsy method and in discovery of putative predictive biomarkers for anti-PD-1 treatment in metastatic CM.

Glioblastoma (GBM) cancer stem cells (GSCs) contribute to GBM's origin, recurrence, and resistance to treatment. However, the understanding of how mRNA expression patterns of GBM subtypes are reflected at global proteome level in GSCs is limited. To characterize protein expression in GSCs, we performed in‐depth proteogenomic analysis of patient‐derived GSCs by RNA‐sequencing and mass‐spectrometry. We quantified > 10 000 proteins in two independent GSC panels and propose a GSC‐associated proteomic signature characterizing two distinct phenotypic conditions; one defined by proteins upregulated in proneural and classical GSCs (GPC‐like), and another by proteins upregulated in mesenchymal GSCs (GM‐like). The GM‐like protein set in GBM tissue was associated with necrosis, recurrence, and worse overall survival. Through proteogenomics, we discovered 252 non‐canonical peptides in the GSCs, i.e., protein sequences that are variant or derive from genome regions previously considered non‐protein‐coding, including variants of the heterogeneous ribonucleoproteins implicated in RNA splicing. In summary, GSCs express two protein sets that have an inverse association with clinical outcomes in GBM. The discovery of non‐canonical protein sequences questions existing gene models and pinpoints new protein targets for research in GBM. Cancer stem cells (GSCs) drive malignancy in glioblastoma. However, their molecular phenotype is not well understood. Here, we report proteomic profiling of GSCs and protein sets that separate two GSC types, which are differentially associated with overall survival in glioblastoma. Through proteogenomics, we detect matching mRNA and protein sequences mapping to gene regions previously considered non‐coding for proteins, including variants of HNRNPs.

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.

Glioblastoma (GBM) is a highly invasive and heterogeneous brain tumor, where distinct patterns of growth and invasion critically influence disease progression and therapy response. However, the molecular drivers of these phenotypes remain poorly understood. Here, we present the HGCC Phenobank, a next-generation atlas of 76 patient-derived GBM cases engrafted in mice, integrating histopathology, transcriptomics, epigenomics, and proteomics. We identify two dominant invasion modes-diffuse parenchymal spread and perivascular/condensed growth-each governed by distinct gene regulatory programs. Using Multi-Omic Factor Analysis (MOFA), we link these invasion modes to patient survival, tumor-initiating capacity, and specific genetic alterations, revealing shared latent factors that structure GBM heterogeneity. The lead factor signature is defined by temporal lobe tumor localization, a high rate of successful xenografts with diffusely invasive growth, and recurrent mutations in TP53, DCHS2, and WNK2, and is associated with significantly worse patient survival. Through computational drug repurposing, we identify candidate inhibitors of invasive subtypes, including PIK-75, a multi-target PI3K/CDK/TAL1 inhibitor, and validate its efficacy across multiple models. Our findings offer a comprehensive framework for decoding GBM invasion and provide a resource for developing phenotype-guided therapies, accessible at hgcc.se/phenobank.