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.

Background COVID-19 remains a major public health challenge, requiring the development of tools to improve diagnosis and inform therapeutic decisions. As dysregulated inflammation and coagulation responses have been implicated in the pathophysiology of COVID-19 and sepsis, we studied their plasma proteome profiles to delineate similarities from specific features. Methods We measured 276 plasma proteins involved in Inflammation, organ damage, immune response and coagulation in healthy controls, COVID-19 patients during acute and convalescence phase, and sepsis patients; the latter included (i) community-acquired pneumonia (CAP) caused by Influenza, (ii) bacterial CAP, (iii) non-pneumonia sepsis, and (iv) septic shock patients. Results We identified a core response to infection consisting of 42 proteins altered in both COVID-19 and sepsis, although higher levels of cytokine storm-associated proteins were evident in sepsis. Furthermore, microbiologic etiology and clinical endotypes were linked to unique signatures. Finally, through machine learning, we identified biomarkers, such as TRIM21, PTN and CASP8, that accurately differentiated COVID-19 from CAP-sepsis with higher accuracy than standard clinical markers. Conclusions This study extends the understanding of host responses underlying sepsis and COVID-19, indicating varying disease mechanisms with unique signatures. These diagnostic and severity signatures are candidates for the development of personalized management of COVID-19 and sepsis.

Glioblastoma’s (GBM) origin, recurrence and resistance to treatment are driven by GBM cancer stem cells (GSCs). Existing transcriptomic characterisations of GBM classify the tumours to three subtypes: classical, proneural, and mesenchymal. The comprehension of how expression patterns of the GBM subtypes are reflected at global proteome level in GSCs is limited. To characterise protein expression in GSCs, we performed in-depth proteogenomic analysis of patient-derived GSCs by RNA-sequencing and mass-spectrometry proteomics. We identified and quantified over 10,000 proteins in two independent GSCs panels, and propose a GSC-associated proteomic signature (GSAPS) that defines two distinct morphological conditions; one defined by a set of proteins expressed in non-mesenchymal - proneural and classical - GSCs (GPC-like), and another expressed in mesenchymal GSCs (GM-like). The expression of GM-like protein set in GBM tissue was associated with hypoxia, necrosis, recurrence, and worse overall survival in GBM patients. In a proof-of-concept proteogenomic approach, we discovered 252 non-canonical peptides expressed in GSCs, i.e., protein sequences that are variant or derive from genome regions previously considered protein-non-coding. We report new variants of the heterogeneous ribonucleoproteins (HNRNPs), which are implicated in mRNA splicing. Furthermore, we show that per-gene mRNA-protein correlations in GSCs are moderate and vary compared to GBM tissue.

To the Editor, The high demand for COVID19 vaccines, combined with a significant lack of supply, leaves smaller and developing countries behind in mass immunization. This prompts the question whether administering a single vaccine dose in SARSCoV2 seropositive individuals could be a method for rationing available vaccine doses. We report results from a prospective study on Macedonian healthcare workers who received two doses of the Pfizer/BioNTech BNT162b2 mRNA vaccine, comparing antibody titres and frequency of side effects after vaccine administration between individuals who were SARSCoV2 seropositive (SeroPOS group) and seronegative (SeroNEG group) prior to immunization. The study included 226 participants recruited through convenience sampling, of whom 41 were SeroPOS (73.17% female; mean age 43 years, SD: 10.571), and 185 were SeroNEG (68.11% female, mean age 46 years, SD: 10.523). Baseline patients’ characteristics are provided in the Supplementary Appendix (Table S1). Blood samples were collected 18– 21 days after the first vaccine dose and 25– 28 days after the second dose. Baseline antibody levels were obtained from patient records. All participants gave blood samples after the first dose and filed a questionnaire for side effects, and 189 participants (83.63%) returned for assessment four weeks after the second dose. Serological testing was performed using the commercially available quantitative CLIA antiSARSCoV2 RBD kit (Snibe, Shenzhen, China),1 which targets the S1 subunit of the viral spike protein. More details on methods are available in the Supplementary Appendix. AntiSARSCoV2 RBD IgG antibody levels after the first dose of BNT162b2 were on average 11.7times higher in SeroPOS individuals (mean: 923.40 AU/ml, SD: 948.119, range 15.04– 5034.70) compared to SeroNEG individuals (mean: 79.06 AU/ml, SD: 253.243, range 0.912– 1867.30; Wilcoxon ranksum test, p < 2e16, Figure 1A). After the second dose, antiSARSCoV2 RBD IgG antibody levels were still higher in SeroPOS individuals (mean: 602.59 AU/ml, SD: 511.545, range 25.41– 1986.00) compared to SeroNEG individuals (mean: 375.567 AU/ml, SD: 437.088, range 9.617– 3704.40; Wilcoxon ranksum test, p = 0.006, Figure 1B). SeroNEG individuals had on average a 5.35fold increase in antiSARSCoV2 RBD IgG antibody levels after the second dose (Wilcoxon signedrank test, p < 2e16, Figure 1B), whereas SeroPOS individuals had no benefit of increased antibody levels after the second dose (Wilcoxon signedrank test, p = 0.529, Figure 1B). SeroPOS individuals had higher antibody levels after the first dose than SeroNEG individuals after the second dose (Wilcoxon ranksum test, p = 0.0039, Figure 1B). Exploratory analysis of the influence of sex and age on antibody response showed that older age had a reducing effect on antibody levels after the first and second vaccine dose (Supplementary Appendix, Table S2). The vast majority of the study participants reported at least one side effect after the first dose (91.15%, Figure 1C), mostly minor local pain (69.47%). A higher proportion of study participants reported at least one side effect after the second dose (97.35%, Figure 1D), again mostly minor local pain (53.97%). Our findings are in line with previous reports of higher antibody levels in SeroPOS individuals after a single dose of BNT162b2 compared to SeroNEG individuals25 and support the hypothesis that a single dose of BNT162b2 in SARSCoV2 seropositive individuals might provide sufficient humoral immunity towards SARSCoV2. These findings should be validated in a clinical trial setting as soon as possible, due to direct implications for public health policy in developing countries with limited access to vaccines. Future investigations should incorporate analyses of the cellular immunity and take into consideration the duration of the immune response, which have not been evaluated in this study. The more rational use of vaccines could accelerate the attainment of collective immunity at reduced costs.

Objective: To explain the global between-countries variance in number of deaths per million citizens (nDpm) and case fatality rate (CFR) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Design: Systematic analysis. Data sources: Worldometer, European Centre for Disease Prevention and Control, United Nations Main outcome measures: The explanators of nDpm and CFR were mathematically hypothesised and tested on publicly-available data from 88 countries with linear regression models on May 1st 2020. The derived explanators - age-adjusted infection fatality rate (IFRadj) and case detection rate (CDR) - were estimated for each country based on a SARS-CoV-2 model of China. The accuracy and agreement of the models with observed data was assessed with R2 and Bland-Altman plots, respectively. Sensitivity analyses involved removal of outliers and testing the models at five retrospective and two prospective time points. Results: Globally, IFRadj estimates varied between countries, ranging from below 0.2% in the youngest nations, to above 1.3% in Portugal, Greece, Italy, and Japan. The median estimated global CDR of SARS-CoV-2 infections on April 16th 2020 was 12.9%, suggesting that most of the countries have a much higher number of cases than reported. At least 93% and up to 99% of the variance in nDpm was explained by reported prevalence expressed as cases per million citizens (nCpm), IFRadj, and CDR. IFRadj and CDR accounted for up to 97% of the variance in CFR, but this model was less reliable than the nDpm model, being sensitive to outliers (R2 as low as 67.5%). Conclusions: The current differences in SARS-CoV-2 mortality between countries are driven mainly by reported prevalence of infections, age distribution, and CDR. The nDpm might be a more stable estimate than CFR in comparing mortality burden between countries.

Background Immune 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. Methods To 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. Results Unbiased 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. Conclusions We 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.

9574 Background: The introduction of immune checkpoint inhibitors (ICIs) or therapies targeting the MAPK-pathway (MAPKis) has significantly improved clinical outcomes in metastatic melanoma patients. Still, a large proportion of the patients become resistant to therapy and there is a need for treatment predictive biomarkers. The aim of this study was to analyze the treatment predictive biomarkers based on the plasma proteome of patients with metastatic melanoma treated with ICIs or MAPKis. Methods: We analyzed serial plasma samples from 48 patients with metastatic melanoma collected; 24 patients were treated with ICIs and with MAPKis, respcetively. A non-biased, high-resolution isoelectric focusing of peptides-liquid chromatography-mass spectrometry (HiRIEFLC-MS/MS)-based method, and with proximity ligation assays (PEA) targeting 92 immuno-oncology-related proteins were used.We analyzed the change in protein levels during treatment with a paired t-test, and their association with progression free survival (PFS) with Cox proportional hazards models. Results: HiRIEFLC-MS/MS detected 1,835 proteins.We detected statistically-significant log2-fold-changes in 109 protein levels out of 1,160 proteins tested (not corrected for multiple testing). PDCD-1 had the highest log2-fold change (FC = 1.27) after treatment (p = 0.02). After stratifying for treatment type, PDCD-1 levels increased in patients treated with ICIs (FC = 2.13, p= 0.0008), but not in MAPKis-treated patients. PEA analyses confirmed this observation. The PEA panel showed association between 44 proteins and shorter PFS (pcoefficient <0.05, pLRT<0.05, qLRT<0.05), among them: LGALS1, CSF1, VEGFA, CASP8, CCL2, TNFSF14, ANGPT2, IL10, IL6, and ADGRG1. Of these, increase in plasma levels during treatment of LGALS1, CCL2 and ADGRG1 were associated with longer PFS. HiRIEF LC-MS/MS detected 69 proteins associated with PFS (pcoefficient< 0.05, pLRT< 0.05, qLRT < 0.05). Conclusions: By using HiRIEFLC-MS/MS, we could detect putative treatment predictive proteins in plasma from patients with metastatic melanoma treated with ICIs or MAPKis. Our findings require further validation.

Introduction The system of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (cas) is a new technology that allows easier manipulation of the genome. Its potential to edit genes opened a new door in treatment development for incurable neurological monogenic diseases (NMGDs). The aim of this systematic review was to summarise the findings on the current development of CRISPR-cas for therapeutic purposes in the most frequent NMGDs and provide critical assessment. Methods and data acquisition We searched the MEDLINE and EMBASE databases, looking for original studies on the use of CRISPR-cas to edit pathogenic variants in models of the most frequent NMGDs, until end of 2017. We included all the studies that met the following criteria: 1. Peer-reviewed study report with explicitly described experimental designs; 2. In vitro, ex vivo, or in vivo study using human or other animal biological systems (including cells, tissues, organs, organisms); 3. focusing on CRISPR as the gene-editing method of choice; and 5. featured at least one NMGD. Results We obtained 404 papers from MEDLINE and 513 from EMBASE. After removing the duplicates, we screened 490 papers by title and abstract and assessed them for eligibility. After reading 50 full-text papers, we finally selected 42 for the review. Discussion Here we give a systematic summary on the preclinical development of CRISPR-cas for therapeutic purposes in NMGDs. Furthermore, we address the clinical interpretability of the findings, giving a comprehensive overview of the current state of the art. Duchenne’s muscular dystrophy (DMD) paves the way forward, with 26 out of 42 studies reporting different strategies on DMD gene editing in different models of the disease. Most of the strategies aimed for permanent exon skipping by deletion with CRISPR-cas. Successful silencing of the mHTT gene with CRISPR-cas led to successful reversal of the neurotoxic effects in the striatum of mouse models of Huntington’s disease. Many other strategies have been explored, including epigenetic regulation of gene expression, in cellular and animal models of: myotonic dystrophy, Fraxile X syndrome, ataxias, and other less frequent dystrophies. Still, before even considering the clinical application of CRISPR-cas, three major bottlenecks need to be addressed: efficacy, safety, and delivery of the systems. This requires a collaborative approach in the research community, while having ethical considerations in mind.