Serum protein profiling reveals an inflammation signature as a predictor of early breast cancer survival.

BACKGROUND: Breast cancers exhibit considerable heterogeneity in their biology, immunology, and prognosis. Currently, no validated, serum protein-based tools are available to evaluate the prognosis of patients with early breast cancer. METHODS: The study population consisted of 521 early-stage breast cancer patients with a median follow-up of 8.9 years. Additionally, 61 patients with breast fibroadenoma or atypical ductal hyperplasia were included as controls. We used a proximity extension assay to measure the preoperative serum levels of 92 proteins associated with inflammatory and immune response processes. The invasive cancers were randomly split into discovery (n = 413) and validation (n = 108) cohorts for the statistical analyses. RESULTS: Using LASSO regression, we identified a nine-protein signature (CCL8, CCL23, CCL28, CSCL10, S100A12, IL10, IL10RB, STAMPB2, and TNFß) that predicted various survival endpoints more accurately than traditional prognostic factors. In the time-dependent analyses, the prognostic power of the model remained rather stable over time. We also developed and validated a 17-protein model with the potential to differentiate benign breast lesions from malignant lesions (Wilcoxon p < 2.2*10- 16; AUC 0.94). CONCLUSIONS: Inflammation and immunity-related serum proteins have the potential to rise above the classical prognostic factors of early-stage breast cancer. They may also help to distinguish benign from malignant breast lesions.

Identification of novel protein biomarkers from the blood and urine for the early diagnosis of bladder cancer via proximity extension analysis.

BACKGROUND: Bladder cancer (BC) is a very common urinary tract malignancy that has a high incidence and lethality. In this study, we identified BC biomarkers and described a new noninvasive detection method using serum and urine samples for the early detection of BC. METHODS: Serum and urine samples were retrospectively collected from patients with BC (n = 99) and healthy controls (HC) (n = 50), and the expression levels of 92 inflammation-related proteins were examined via the proximity extension analysis (PEA) technique. Differential protein expression was then evaluated by univariate analysis (p < 0.05). The expression of the selected potential marker was further verified in BC and adjacent tissues by immunohistochemistry (IHC) and single-cell sequencing. A model was constructed to differentiate BC from HC by LASSO regression and compared to the detection capability of FISH. RESULTS: The univariate analysis revealed significant differences in the expression levels of 40 proteins in the serum (p < 0.05) and 17 proteins in the urine (p < 0.05) between BC patients and HC. Six proteins (AREG, RET, WFDC2, FGFBP1, ESM-1, and PVRL4) were selected as potential BC biomarkers, and their expression was evaluated at the protein and transcriptome levels by IHC and single-cell sequencing, respectively. A diagnostic model (a signature) consisting of 14 protein markers (11 in serum and three in urine) was also established using LASSO regression to distinguish between BC patients and HC (area under the curve = 0.91, PPV = 0.91, sensitivity = 0.87, and specificity = 0.82). Our model showed better diagnostic efficacy than FISH, especially for early-stage, small, and low-grade BC. CONCLUSION: Using the PEA method, we identified a panel of potential protein markers in the serum and urine of BC patients. These proteins are associated with the development of BC. A total of 14 of these proteins can be used to detect early-stage, small, low-grade BC. Thus, these markers are promising for clinical translation to improve the prognosis of BC patients.

Proximity extension assay proteomics and renal single cell transcriptomics uncover novel urinary biomarkers for active lupus nephritis.

OBJECTIVE: To identify urinary biomarkers that can distinguish active renal involvement in Lupus Nephritis (LN), a severe manifestation of systemic lupus erythematosus (SLE). METHODS: Urine from 117 subjects, comprised of inactive SLE, active non-renal lupus, active LN, and healthy controls, were subjected to Proximity Extension Assay (PEA) based comprehensive proteomics followed by ELISA validation in an independent, ethnically diverse cohort. Proteomic data is also cross-referenced to renal transcriptomic data to elucidate cellular origins of biomarkers. RESULTS: Systems biology analyses revealed progressive activation of cytokine signaling, chemokine activity and coagulation pathways, with worsening renal disease. In addition to validating 30 previously reported biomarkers, this study uncovers several novel candidates. Following ELISA validation in an independent cohort of different ethnicity, the six most discriminatory biomarkers for active LN were urinary ICAM-2, FABP4, FASLG, IGFBP-2, SELE and TNFSF13B/BAFF, with ROC AUC ≥80%, with most correlating strongly with clinical disease activity. Transcriptomic analyses of LN kidneys mapped the likely origin of these proteins to intra-renal myeloid cells (CXCL16, IL-1RT2, TNFSF13B/BAFF), T/NK cells (FASLG), leukocytes (ICAM2) and endothelial cells (SELE). CONCLUSION: In addition to confirming the diagnostic potential of urine ALCAM, CD163, MCP1, SELL, ICAM1, VCAM1, NGAL and TWEAK for active LN, this study adds urine ICAM-2, FABP4, FASLG, IGFBP-2, SELE, and TNFSF13B/BAFF as additional markers that warrant systematic validation in larger cross-sectional and longitudinal cohorts.

Identification of diagnostic and prognostic biomarkers of PD using a multiplex proteomics approach.

Given the complexity of Parkinson's disease (PD), achieving acceptable diagnostic and prognostic accuracy will require the support of a panel of diverse biomarkers. We used Proximity extension assays to measure a panel of 92 proteins in CSF of 120 newly diagnosed PD patients and 45 control subjects without neurological disease. From 75 proteins detectable in the CSF of >90% of the subjects, regularized regression analysis identified four proteins (ß-NGF, CD38, tau and NCAN) as downregulated in newly diagnosed PD patients (age at diagnosis 67.2 ± 9.4 years) compared to controls (age 65.4 ± 10.9 years). Higher tau (ß -0.82 transformed MMSE points/year, 95% CI -1.37 to -0.27, P = 0.005) was also linked to faster cognitive decline over the first ten years after PD diagnosis. These findings provide insights into multiple aspects of PD pathophysiology and may serve as the foundation for identifying new biomarkers and therapeutic targets.

Discovery of novel glioma serum biomarkers by proximity extension assay.

BACKGROUND: Gliomas are among the most malignant tumors, with a very poor prognosis. Early diagnosis is highly desirable since it can help implement more effective treatments for smaller tumors, which have not yet extensively metastasized. Improving early diagnosis may facilitate access of patients to clinical trials and prepare them for the future availability of new disease-modifying treatments. METHODS: We analyzed retrospective samples collected at diagnosis (before therapy initiation), with PEA (Olink Proteomics), quantifying about 3000 proteins. We utilized 30 plasmas from gliomas (20 glioblastomas, 5 anaplastic astrocytomas, 5 anaplastic oligodendrogliomas) and 20 meningiomas (as controls). We then analyzed the data to identify proteins which either alone, or in combination, could discriminate gliomas from meningiomas, or correlate with clinical and molecular alterations. RESULTS: We identified 8 plasma proteins which were increased in gliomas vs. meningiomas (GFAP, NEFL, EDDM3B, PROK1, MMP3, CTRL, GP2, SPINT3) and 4 proteins which were decreased in gliomas vs. meningiomas (FABP4, ALDH3A1, IL-12B and OXT). Partition algorithms and logistic regression algorithms with two biomarkers (GFAP and FABP4) achieved sensitivity of 83% and 93% at 100% and 90% specificity, respectively. The strongest single marker was GFAP with an area under the ROC curve (AUC) of 0.86. The AUC for the GFAP-FABP4 combination was 0.98. CONCLUSION: PEA is a powerful new proteomic technology for biomarker discovery. GFAP and a handful of other plasma biomarkers may be useful for early glioma detection and probably, prognosis. STATEMENT: Detecting gliomas as early as possible is highly desirable since it can significantly improve the chances of effective treatments. Reliable glioma biomarkers can timely inform glioma patients about the efficacy of their prescribed treatment. Our results reveal some novel putative glioma markers that may prove valuable, when used alone or in combination, towards improved clinical care of gliomas. In order to better appreciate the potential usefulness of these markers, their performance needs to be further validated in a larger cohort of samples.

Are the results from a multiplex proteomic assay and a conventional immunoassay for NT-proBNP and GDF-15 comparable?

BACKGROUND: We aimed to compare absolute plasma concentrations of N-terminal pro-brain natriuretic peptide (NT-proBNP) and growth differentiation factor 15 (GDF-15) obtained by a conventional immunoassay with the corresponding relative concentrations from a proximity extension assay (PEA) and compare the prognostic impact of the protein levels obtained from these assays. METHODS: We evaluated 437 patients with peripheral arterial disease (PAD) and a population-based cohort of 643 individuals without PAD. Correlations were calculated using Spearman's rank correlation coefficients (rho). The discriminatory accuracy of the protein levels to predict future cardiovascular events was analyzed with Cox regression and presented as time-dependent areas under the receiver-operator-characteristic curves (tdAUCs). RESULTS: For NT-proBNP, the two assays correlated with rho 0.93 and 0.93 in the respective cohort. The PEA values leveled off at higher values in both cohorts. The corresponding correlations for GDF-15 were 0.91 and 0.89. At 5 years follow-up, the tdAUCs in the patient cohort were similar for NT-proBNP and GDF-15 regardless of assay used (0.65-0.66). The corresponding tdAUCs in the population-based cohort were between 0.72 and 0.77. CONCLUSION: Except for the highest levels of NT-proBNP, we suggest that PEA data for NT-proBNP and GDF-15 reliably reflects absolute plasma levels and contains similar prognostic information.

Elevated levels of MMP12 sourced from macrophages are associated with poor prognosis in urothelial bladder cancer.

BACKGROUND: Urothelial bladder cancer is most frequently diagnosed at the non-muscle-invasive stage (NMIBC). However, recurrences and interventions for intermediate and high-risk NMIBC patients impact the quality of life. Biomarkers for patient stratification could help to avoid unnecessary interventions whilst indicating aggressive measures when required. METHODS: In this study, immuno-oncology focused, multiplexed proximity extension assays were utilised to analyse plasma (n = 90) and urine (n = 40) samples from 90 newly-diagnosed and treatment-naïve bladder cancer patients. Public single-cell RNA-sequencing and microarray data from patient tumour tissues and murine OH-BBN-induced urothelial carcinomas were also explored to further corroborate the proteomic findings. RESULTS: Plasma from muscle-invasive, urothelial bladder cancer patients displayed higher levels of MMP7 (p = 0.028) and CCL23 (p = 0.03) compared to NMIBC patients, whereas urine displayed higher levels of CD27 (p = 0.044) and CD40 (p = 0.04) in the NMIBC group by two-sided Wilcoxon rank-sum tests. Random forest survival and multivariable regression analyses identified increased MMP12 plasma levels as an independent marker (p < 0.001) associated with shorter overall survival (HR = 1.8, p < 0.001, 95% CI:1.3-2.5); this finding was validated in an independent patient OLINK cohort, but could not be established using a transcriptomic microarray dataset. Single-cell transcriptomics analyses indicated tumour-infiltrating macrophages as a putative source of MMP12. CONCLUSIONS: The measurable levels of tumour-localised, immune-cell-derived MMP12 in blood suggest MMP12 as an important biomarker that could complement histopathology-based risk stratification. As MMP12 stems from infiltrating immune cells rather than the tumor cells themselves, analyses performed on tissue biopsy material risk a biased selection of biomarkers produced by the tumour, while ignoring the surrounding microenvironment.

Machine learning models of plasma proteomic data predict mood in chronic stroke and tie it to aberrant peripheral immune responses.

Post-stroke depression is common, long-lasting and associated with severe morbidity and death, but mechanisms are not well-understood. We used a broad proteomics panel and developed a machine learning algorithm to determine whether plasma protein data can predict mood in people with chronic stroke, and to identify proteins and pathways associated with mood. We used Olink to measure 1,196 plasma proteins in 85 participants aged 25 and older who were between 5 months and 9 years after ischemic stroke. Mood was assessed with the Stroke Impact Scale mood questionnaire (SIS3). Machine learning multivariable regression models were constructed to estimate SIS3 using proteomics data, age, and time since stroke. We also dichotomized participants into better mood (SIS3 > 63) or worse mood (SIS3 ≤ 63) and analyzed candidate proteins. Machine learning models verified that there is indeed a relationship between plasma proteomic data and mood in chronic stroke, with the most accurate prediction of mood occurring when we add age and time since stroke. At the individual protein level, no single protein or set of proteins predicts mood. But by using univariate analyses of the proteins most highly associated with mood we produced a model of chronic post-stroke depression. We utilized the fact that this list contained many proteins that are also implicated in major depression. Also, over 80% of immune proteins that correlate with mood were higher with worse mood, implicating a broadly overactive immune system in chronic post-stroke depression. Finally, we used a comprehensive literature review of major depression and acute post-stroke depression. We propose that in chronic post-stroke depression there is over-activation of the immune response that then triggers changes in serotonin activity and neuronal plasticity leading to depressed mood.

Multiplex proteomics using proximity extension assay for the identification of protein biomarkers predictive of acute graft-vs.-host disease in allogeneic hematopoietic cell transplantation.

OBJECTIVES: Allogeneic hematopoietic cell transplantation (HCT) is associated with acute graft-vs.-host disease (aGVHD). The presented study applied a novel multiplex antibody-based proximity extension assay (PEA) proteomic platform that can detect thousands of serum proteins simultaneously for the identification of potential biomarkers of aGVHD. METHODS: Serum samples from 28 patients who underwent allogeneic HCT for acute myeloid leukemia (AML) were analyzed; 17 were diagnosed with grade II-IV aGVHD while 11 patients were not. Samples collected on day -6, day 0, +14, +30, +60 and +90 post-HCT were analyzed for the relative concentrations of 552 proteins. The concentration of each protein from baseline to the closest time point before onset of aGVHD, or to the latest time point in control patients, was documented. RESULTS: Individualized analysis identified 26 proteins demonstrating ≥3-fold increase at aGVHD onset compared to baseline, eliminating proteins with a similar increase in controls. Another approach used paired t-testing and logistic regression that identified a four-marker panel, including SLAMF7, IL-1ra, BTN3A2 and DAB2, where individual log-likelihood ratios ranged from 3.99 to 8.15 (logistic regression, p=0.004-0.046). When combined, the four-marker panel demonstrated an area under the curve (AUC) of 0.90 (95% CI: 0.78-1.00; p=0.0006) with high negative predictive value of 81.8% and positive predictive value of 86.7%. All four markers play a physiological role in immune regulation. Among these, three were also present in the individualized analysis (SLAMF7, IL-1ra and BTN3A2). CONCLUSIONS: We conclude that serum proteins identified using multiplex proteomics, particularly SLAMF7, IL-1ra, BTN3A2 and DAB2, may potentially predict aGVHD.

Assessing the Preanalytical Variability of Plasma and Cerebrospinal Fluid Processing and Its Effects on Inflammation-Related Protein Biomarkers.

Proteomics studies are important for the discovery of new biomarkers as clinical tools for diagnosis and disease monitoring. However, preanalytical variations caused by differences in sample handling protocol pose challenges for assessing biomarker reliability and comparability between studies. The purpose of this study was to examine the effects of delayed centrifuging on measured protein levels in plasma and cerebrospinal fluid (CSF). Blood from healthy individuals and patients with multiple sclerosis along with CSF from patients with suspected neurological disorders were left at room temperature for different periods (blood: 1, 24, 48, 72 h; CSF: 1 and 6 h) prior to centrifuging. Ninety-one inflammation-related proteins were analyzed using a proximity extension assay, a high-sensitivity multiplex immunoassay. Additional metabolic and neurology-related markers were also investigated in CSF. In summary, many proteins, particularly in plasma, had increased levels with longer delays in processing likely due in part to intracellular leakage. Levels of caspase 8, interleukin 8, interleukin 18, sirtuin 2, and sulfotransferase 1A1 increased 2-fold to 10-fold in plasma after 24 h at room temperature. Similarly, levels of cathepsin H, ectonucleoside triphosphate diphosphohydrolase 5, and WW domain containing E3 ubiquitin protein ligase 2 differentiated in CSF with <6 h delay in processing. However, the rate of change for many proteins was relatively consistent; therefore, we were able to characterize biomarkers for detecting sample handling variability. Our findings highlight the importance of timely and consistent sample collection and the need for increased awareness of protein susceptibility to sample handling bias. In addition, suggested biomarkers may be used in certain situations to detect and correct for preanalytical variation in future studies.

Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis.

Understanding the dynamics of the human proteome is crucial for developing biomarkers to be used as measurable indicators for disease severity and progression, patient stratification, and drug development. The Proximity Extension Assay (PEA) is a technology that translates protein information into actionable knowledge by linking protein-specific antibodies to DNA-encoded tags. In this report we demonstrate how we have combined the unique PEA technology with an innovative and automated sample preparation and high-throughput sequencing readout enabling parallel measurement of nearly 1500 proteins in 96 samples generating close to 150,000 data points per run. This advancement will have a major impact on the discovery of new biomarkers for disease prediction and prognosis and contribute to the development of the rapidly evolving fields of wellness monitoring and precision medicine.

Uncovering the Depths of the Human Proteome: Antibody-based Technologies for Ultrasensitive Multiplexed Protein Detection and Quantification.

Probing the human proteome in tissues and biofluids such as plasma is attractive for biomarker and drug target discovery. Recent breakthroughs in multiplex, antibody-based, proteomics technologies now enable the simultaneous quantification of thousands of proteins at as low as sub fg/ml concentrations with remarkable dynamic ranges of up to 10-log. We herein provide a comprehensive guide to the methodologies, performance, technical comparisons, advantages, and disadvantages of established and emerging technologies for the multiplexed ultrasensitive measurement of proteins. Gaining holistic knowledge on these innovations is crucial for choosing the right multiplexed proteomics tool for applications at hand to critically complement traditional proteomics methods. This can bring researchers closer than ever before to elucidating the intricate inner workings and cross talk that spans multitude of proteins in disease mechanisms.

Inflammation-related plasma and CSF biomarkers for multiple sclerosis.

Effective biomarkers for multiple sclerosis diagnosis, assessment of prognosis, and treatment responses, in particular those measurable in blood, are largely lacking. We have investigated a broad set of protein biomarkers in cerebrospinal fluid (CSF) and plasma using a highly sensitive proteomic immunoassay. Cases from two independent cohorts were compared with healthy controls and patients with other neurological diseases. We identified and replicated 10 cerebrospinal fluid proteins including IL-12B, CD5, MIP-1a, and CXCL9 which had a combined diagnostic efficacy similar to immunoglobulin G (IgG) index and neurofilament light chain (area under the curve [AUC] = 0.95). Two plasma proteins, OSM and HGF, were also associated with multiple sclerosis in comparison to healthy controls. Sensitivity and specificity of combined CSF and plasma markers for multiple sclerosis were 85.7% and 73.5%, respectively. In the discovery cohort, eotaxin-1 (CCL11) was associated with disease duration particularly in patients who had secondary progressive disease (PCSF < 4 × 10-5, Pplasma < 4 × 10-5), and plasma CCL20 was associated with disease severity (P = 4 × 10-5), although both require further validation. Treatment with natalizumab and fingolimod showed different compartmental changes in protein levels of CSF and peripheral blood, respectively, including many disease-associated markers (e.g., IL12B, CD5) showing potential application for both diagnosing disease and monitoring treatment efficacy. We report a number of multiple sclerosis biomarkers in CSF and plasma for early disease detection and potential indicators for disease activity. Of particular importance is the set of markers discovered in blood, where validated biomarkers are lacking.

Multiplex protein analysis and ensemble machine learning methods of fine needle aspirates from prostate cancer patients reveal potential diagnostic signatures associated with tumour grade.

BACKGROUND: Improved molecular diagnosis is needed in prostate cancer (PC). Fine needle aspiration (FNA) is a minimally invasive biopsy technique, less traumatic compared to core needle biopsy, and could be useful for diagnosis of PC. Molecular biomarkers (BMs) in FNA-samples can be assessed for prediction, eg of immunotherapy efficacy before treatment as well as at treatment decision time points during disease progression. METHODS: In the present pilot study, the expression levels of 151 BM proteins were analysed by proximity extension assay in FNA-samples from 16 patients, including benign prostate lesions (n = 3) and cancers (n = 13). An ensemble data analysis strategy was applied using several machine learning models. RESULTS: Twelve potentially predictive BM proteins correlating with International Society of Urological Pathology grade groups were identified, among them vimentin, tissue factor pathway inhibitor 2, and integrin beta-5. The validity of the results was supported by network analysis that showed functional associations between most of the identified putative BMs. We also showed that multiple immune checkpoint targets can be assessed (eg PD-L1, CD137, and Galectin-9), which may support the selection of immunotherapy in advanced PC. Results are promising but need further validation in a larger cohort. CONCLUSIONS: Our pilot study represents a "proof of concept" and shows that multiplex profiling of potential diagnostic and predictive BM proteins is feasible on tumour material obtained by FNA sampling of prostate cancer. Moreover, our results demonstrate that an ensemble data analysis strategy may facilitate the identification of BM signatures in pilot studies when the patient cohort is limited.

The Immune Signature of CSF in Multiple Sclerosis with and without Oligoclonal Bands: A Machine Learning Approach to Proximity Extension Assay Analysis.

Early diagnosis of multiple sclerosis (MS) relies on clinical evaluation, magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF) analysis. Reliable biomarkers are needed to differentiate MS from other neurological conditions and to define the underlying pathogenesis. This study aimed to comprehensively profile immune activation biomarkers in the CSF of individuals with MS and explore distinct signatures between MS with and without oligoclonal bands (OCB). A total of 118 subjects, including relapsing-remitting MS with OCB (MS OCB+) (n = 58), without OCB (MS OCB-) (n = 24), and controls with other neurological diseases (OND) (n = 36), were included. CSF samples were analyzed by means of proximity extension assay (PEA) for quantifying 92 immune-related proteins. Neurofilament light chain (NfL), a marker of axonal damage, was also measured. Machine learning techniques were employed to identify biomarker panels differentiating MS with and without OCB from controls. Analyses were performed by splitting the cohort into a training and a validation set. CSF CD5 and IL-12B exhibited the highest discriminatory power in differentiating MS from controls. CSF MIP-1-alpha, CD5, CXCL10, CCL23 and CXCL9 were positively correlated with NfL. Multivariate models were developed to distinguish MS OCB+ and MS OCB- from controls. The model for MS OCB+ included IL-12B, CD5, CX3CL1, FGF-19, CST5, MCP-1 (91% sensitivity and 94% specificity in the training set, 81% sensitivity, and 94% specificity in the validation set). The model for MS OCB- included CX3CL1, CD5, NfL, CCL4 and OPG (87% sensitivity and 80% specificity in the training set, 56% sensitivity and 48% specificity in the validation set). Comprehensive immune profiling of CSF biomarkers in MS revealed distinct pathophysiological signatures associated with OCB status. The identified biomarker panels, enriched in T cell activation markers and immune mediators, hold promise for improved diagnostic accuracy and insights into MS pathogenesis.

Long-Lasting Imprint in the Soluble Inflammatory Milieu Despite Early Treatment of Acute Symptomatic Hepatitis C.

BACKGROUND: Treatment with direct-acting antivirals (DAAs) in patients with chronic hepatitis C infection leads to partial restoration of soluble inflammatory mediators (SIMs). In contrast, we hypothesized that early DAA treatment of acute hepatitis C virus (HCV) with DAAs may normalize most SIMs. METHODS: In this study, we made use of a unique cohort of acute symptomatic hepatitis C patients who cleared HCV with a 6-week course of ledipasvir/sofosbuvir. Plasma samples were used for proximity extension assay measuring 92 proteins. RESULTS: Profound SIM alterations were observed in acute HCV patients, with marked upregulation of interleukin (IL)-6 and CXCL-10, whereas certain mediators were downregulated (eg, monocyte chemoattractant protein-4, IL-7). During treatment and follow-up, the majority of SIMs decreased but not all normalized (eg, CDCP1, IL-18). Of note, SIMs that were downregulated before DAA treatment remained suppressed, whereas others that were initially unchanged declined to lower values during treatment and follow-up (eg, CD244). CONCLUSIONS: Acute hepatitis C was associated with marked changes in the soluble inflammatory milieu compared with both chronic hepatitis patients and healthy controls. Whereas early DAA treatment partly normalized this altered signature, long-lasting imprints of HCV remained.

Proteomics technologies for cancer liquid biopsies.

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Systemic Inflammation in Preclinical Ulcerative Colitis.

BACKGROUND & AIMS: Preclinical ulcerative colitis is poorly defined. We aimed to characterize the preclinical systemic inflammation in ulcerative colitis, using a comprehensive set of proteins. METHODS: We obtained plasma samples biobanked from individuals who developed ulcerative colitis later in life (n = 72) and matched healthy controls (n = 140) within a population-based screening cohort. We measured 92 proteins related to inflammation using a proximity extension assay. The biologic relevance of these findings was validated in an inception cohort of patients with ulcerative colitis (n = 101) and healthy controls (n = 50). To examine the influence of genetic and environmental factors on these markers, a cohort of healthy twin siblings of patients with ulcerative colitis (n = 41) and matched healthy controls (n = 37) were explored. RESULTS: Six proteins (MMP10, CXCL9, CCL11, SLAMF1, CXCL11 and MCP-1) were up-regulated (P < .05) in preclinical ulcerative colitis compared with controls based on both univariate and multivariable models. Ingenuity Pathway Analyses identified several potential key regulators, including interleukin-1ß, tumor necrosis factor, interferon-gamma, oncostatin M, nuclear factor-κB, interleukin-6, and interleukin-4. For validation, we built a multivariable model to predict disease in the inception cohort. The model discriminated treatment-naïve patients with ulcerative colitis from controls with leave-one-out cross-validation (area under the curve = 0.92). Consistently, MMP10, CXCL9, CXCL11, and MCP-1, but not CCL11 and SLAMF1, were significantly up-regulated among the healthy twin siblings, even though their relative abundances seemed higher in incident ulcerative colitis. CONCLUSIONS: A set of inflammatory proteins are up-regulated several years before a diagnosis of ulcerative colitis. These proteins were highly predictive of an ulcerative colitis diagnosis, and some seemed to be up-regulated already at exposure to genetic and environmental risk factors.

A pilot study using proximity extension assay of cerebrospinal fluid and its extracellular vesicles identifies novel amyotrophic lateral sclerosis biomarker candidates.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder which is characterized by progressive degeneration of the motor system. Typically, the disease starts with focal weakness which spreads to involve most muscles and leads to death from respiratory failure within five years of diagnosis. Due to the heterogenic nature of the disease, diagnostics is complex, and it generally takes twelve months from symptom-onset to diagnosis. The discovery of novel biomarkers could lead to accelerated diagnosis, earlier start of treatment, improved patient-segmentation, and treatment follow-up as well as an increased insight into the pathology. Here, we analyzed cerebrospinal fluid (CSF) and CSF-derived extracellular vesicles (CSF-EVs) from ALS-patients and matched controls (n = 9 each) using the ultra-sensitive proximity extension assay (PEA), cardiovascular III-panel. On average, 84 and 61 proteins could be detected in CSF and CSF-EVs respectively. In CSF, three proteins were significantly upregulated in ALS-patients (Junctional Adhesion Molecule A Protein, Tumor necrosis factor receptor 2 and Chitinase 1) while myoglobin was down-regulated. In CSF-EVs, no significantly differentially expressed proteins were identified, but there was a trend for downregulation of Perlecan. To our knowledge, only CHIT1 has been previously described as a CSF-based biomarker candidate for ALS. By combining the four differentially expressed markers in CSF and support vector machine algorithm, all ALS patients and 8 of 9 controls were correctly classified. In conclusion, we here demonstrate the feasibility of using PEA of CSF and CSF-EVs for biomarker discovery and propose three de novo biomarker candidates for ALS, however, further studies are necessary to demonstrate clinical usability.

Soluble inflammatory mediators identify HCV patients who may be cured with four weeks of antiviral treatment.

Soluble inflammatory mediators (SIM) can be predictive of treatment outcome in antiviral treatment of chronic hepatitis C. Recently, it was shown that a subgroup of patients can be cured with four weeks of therapy. We here profiled patients for 70 SIM before and during treatment of hepatitis C with glecaprevir/pibrentasvir (GLE/PIB) +/- ribavirin. Proximity extension assay was performed in a total of 32 patients. Pre-treatment SIM profiles did not distinguish patients achieving an SVR (n = 21) from patients experiencing antiviral relapse (n = 11). However, after 4 weeks of therapy, eight markers were identified that could distinguish patients with SVR from the relapsed group, namely MMP-10, CCL20, CXCL11, FGF-23, TNF, MCP-2, IL-18R1 and CXCL10. Thus, this study shows that a distinct on-treatment immune profile is associated with cure of HCV infection after ultrashort treatment.