Plasma Protein Biomarkers Research Articles

Serum and plasma protein biomarkers associated with frailty in patients with cirrhosis.

Frailty, a clinical phenotype of decreased physiological reserve, is a strong determinant of adverse health outcomes in patients with cirrhosis. The only cirrhosis-specific frailty metric is the Liver Frailty Index (LFI), which must be administered in person and may not be feasible for every clinical scenario. We sought to discover candidate serum/plasma protein biomarkers that could differentiate frail from robust patients with cirrhosis. A total of 140 adults with cirrhosis awaiting liver transplantation in the ambulatory setting with LFI assessments and available serum/plasma samples were included. We selected 70 pairs of patients on opposite ends of the frailty spectrum (LFI>4.4 for frail and LFI<3.2 for robust) who were matched by age, sex, etiology, HCC, and Model for End-Stage Liver Disease-Sodium. Twenty-five biomarkers with biologically plausible associations with frailty were analyzed using ELISA by a single laboratory. Conditional logistic regression was used to examine their association with frailty. Of the 25 biomarkers analyzed, we identified 7 proteins that were differentially expressed between frail and robust patients. We observed differences in 6 of the 7 proteins in the expected direction: (a) higher median values in frail versus robust with growth differentiation factor-15 (3682 vs. 2249pg/mL), IL-6 (17.4 vs. 6.4pg/mL), TNF-alpha receptor 1 (2062 vs. 1627pg/mL), leucine-rich alpha-2 glycoprotein (44.0 vs. 38.6μg/mL), and myostatin (4066 vs. 6006ng/mL) and (b) lower median values in frail versus robust with alpha-2-Heremans-Schmid glycoprotein (0.11 vs. 0.13mg/mL) and free total testosterone (1.2 vs. 2.4ng/mL). These biomarkers represent inflammatory, musculoskeletal, and endocrine/metabolic systems, reflecting the multiple physiological derangements observed in frailty. These data lay the foundation for confirmatory work and development of a laboratory frailty index for patients with cirrhosis to improve diagnosis and prognostication.

Plasma metabolites with mechanistic and clinical links to the neurovascular disease cavernous angioma

Background:Cavernous angiomas (CAs) affect 0.5% of the population, predisposing to serious neurologic sequelae from brain bleeding. A leaky gut epithelium associated with a permissive gut microbiome, was identified in patients who develop CAs, favoring lipid polysaccharide producing bacterial species. Micro-ribonucleic acids along with plasma levels of proteins reflecting angiogenesis and inflammation were also previously correlated with CA and CA with symptomatic hemorrhage.Methods:The plasma metabolome of CA patients and CA patients with symptomatic hemorrhage was assessed using liquid-chromatography mass spectrometry. Differential metabolites were identified using partial least squares-discriminant analysis (p < 0.05, FDR corrected). Interactions between these metabolites and the previously established CA transcriptome, microbiome, and differential proteins were queried for mechanistic relevance. Differential metabolites in CA patients with symptomatic hemorrhage were then validated in an independent, propensity matched cohort. A machine learning-implemented, Bayesian approach was used to integrate proteins, micro-RNAs and metabolites to develop a diagnostic model for CA patients with symptomatic hemorrhage.Results:Here we identify plasma metabolites, including cholic acid and hypoxanthine distinguishing CA patients, while arachidonic and linoleic acids distinguish those with symptomatic hemorrhage. Plasma metabolites are linked to the permissive microbiome genes, and to previously implicated disease mechanisms. The metabolites distinguishing CA with symptomatic hemorrhage are validated in an independent propensity-matched cohort, and their integration, along with levels of circulating miRNAs, enhance the performance of plasma protein biomarkers (up to 85% sensitivity and 80% specificity).Conclusions:Plasma metabolites reflect CAs and their hemorrhagic activity. A model of their multiomic integration is applicable to other pathologies.

Clinical and biological markers predictive of treatment response associated with metastatic pancreatic adenocarcinoma

BackgroundChemotherapy for metastatic pancreatic adenocarcinoma (PDAC) offers limited benefits, but survival outcomes vary. Reliable predictive response biomarkers to guide patient management are lacking.MethodsPatient performance status, tumour burden (determined by the presence or absence of liver metastases), plasma protein biomarkers (CA19-9, albumin, C-reactive protein and neutrophils) and circulating tumour DNA (ctDNA) were assessed in 146 patients with metastatic PDAC prior to starting either concomitant or sequential nab-paclitaxel + gemcitabine chemotherapy in the SIEGE randomised prospective clinical trial, as well as during the first 8 weeks of treatment. Correlations were made with objective response, death within 1 year and overall survival (OS).ResultsInitial poor patient performance status, presence of liver metastases and detectable mutKRAS ctDNA all correlated with worse OS after adjusting for the different biomarkers of interest. Objective response at 8 weeks also correlated with OS (P = 0.026). Plasma biomarkers measured during treatment and prior to the first response assessment identified ≥10% decrease in albumin at 4 weeks predicted for worse OS (HR 4.75, 95% CI 1.43–16.94, P = 0.012), while any association of longitudinal evaluation of mutKRAS ctDNA with OS was unclear (β = 0.024, P = 0.057).ConclusionsReadily measurable patient variables can aid the prediction of outcomes from combination chemotherapy used to treat metastatic PDAC. The role of mutKRAS ctDNA as a tool to guide treatment warrants further exploration.Clinical trial registrationISRCTN71070888; ClinialTrials.gov (NCT03529175).

Abstract 13: Aptamer-Based Plasma Proteomics Nominates Biomarkers Of Neurological Severity, Stroke Diagnosis And Age

Background: Plasma protein biomarkers measured in the hyperacute setting that are associated with stroke diagnosis or with clinical neurological severity, can be diagnostically and prognostically meaningful in patient care. We hypothesized that high-throughput proteomics can identify panels of plasma proteins that can differentiate stroke mimics from ischemic and hemorrhagic stroke, and are associated with neurological severity. Methods: From a clinically-characterized biorepository of 320 plasma samples collected at time of patient presentation to the Emergency Room at Grady Memorial Hospital (Atlanta), 40 samples (10 stroke mimics, 10 intracerebral hemorrhage [ICH], 20 acute ischemic stroke [AIS]) were included (Fig A). 7,000 proteins were measured per sample using aptamer-based proteomics (SomaLogic). A combination of differential-expression and variance partitioning analyses (VPA) identified proteins associated with diagnosis (mimic vs ICH vs AIS), neurological severity (NIHSS), age and sex. Results: Cohort characteristics are shown (Fig B). VPA identified top proteins that accounted for majority of variance in several traits (Fig C). One-way ANOVA identified &gt;400 proteins showing group-wise differences, of which a panel of 56 proteins collectively distinguished mimics from both AIS and ICH, ICH from both mimics and AIS, as well as distinguished between AIS based on NIHSS (Fig D). The pattern of expression of these 56 plasma proteins across diagnostic groups is shown in Fig E. We also identified plasma biomarkers associated with increasing age, indicative of age-related vascular dysfunction. Conclusions: This pilot study of AIS, ICH and stroke mimics demonstrates the applicability of aptamer-based proteomics as a promising approach for plasma protein biomarker discovery in stroke. We identified acute plasma protein biomarkers of neurological severity, diagnostic group and age-related vascular dysfunction that need further validation.

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.

Perinatal mood and anxiety disorders: biomarker discovery using plasma proteomics

Perinatal mood and anxiety disorders encompass a range of mental health disorders that occur during pregnancy and up to 1 year postpartum, affecting approximately 20% of women. Traditional risk factors, such as a history of depression and pregnancy complications including preeclampsia, are known. Their predictive utility, however, is not specific or sensitive enough to inform clinical decision-making or prevention strategies for perinatal mood and anxiety disorders. Better diagnostic and prognostic models are needed for early identification and referral to treatment. This study aimed to determine if a panel of novel third-trimester plasma protein biomarkers in pregnant women can be used to identify those who have a high predisposed risk for perinatal mood and anxiety disorders within 3 months postpartum. We studied 52 women (n=34 with a risk for perinatal mood and anxiety disorders and n=18 controls) among whom mental health screening was conducted at 2 time points, namely in the third trimester and again at 3 months postdelivery. An elevated perinatal mood and anxiety disorder risk was identified by screening individuals with above-validated cutoffs for depression (Edinburgh Postnatal Depression Scale ≥12), anxiety (Overall Anxiety Severity and Impairment Scale ≥7), and/or posttraumatic stress disorder (Impact of Events Scale >26) at both time points. Plasma samples collected in the third trimester were screened using the aptamer-based SomaLogic SomaScan proteomic assay technology to evaluate perinatal mood and anxiety disorder-associated changes in the expression of 1305 protein analytes. Ingenuity Pathway Analysis was conducted to highlight pathophysiological relationships between perinatal mood and anxiety disorder-specific proteins found to be significantly up- or down-regulated in all subjects with perinatal mood and anxiety disorder and in those with perinatal mood and anxiety disorders and no preeclampsia. From a panel of 53 significant perinatal mood and anxiety disorder-associated proteins, a unique 20-protein signature differentiated perinatal mood and anxiety disorder cases from controls in a principal component analysis (P<.05). This protein signature included NCAM1, NRCAM, and NTRK3 that converge around neuronal signaling pathways regulating axonal guidance, astrocyte differentiation, and maintenance of GABAergic neurons. Interestingly, when we restricted the analysis to subjects without preeclampsia, a 30-protein signature differentiated perinatal mood and anxiety disorder cases from all controls without overlap on the principal component analysis (P<.001). In the nonpreeclamptic perinatal mood and anxiety disorder group, we observed increased expression of proteins, such as CXCL11, CXCL6, MIC-B, and B2MG, which regulate leucocyte migration, inflammation, and immune function. Participants with perinatal mood and anxiety disorders had a unique and distinct plasma protein signature that regulated a variety of neuronal signaling and proinflammatory pathways. Additional validation studies with larger sample sizes are needed to determine whether some of these molecules can be used in conjunction with traditional risk factors for the early detection of perinatal mood and anxiety disorders.

Differential Proteins Expression Distinguished Between Patients With Infectious and Noninfectious Uveitis

ABSTRACT Purpose We investigated the aqueous humor proteome and associated plasma proteome in patients with infectious or noninfectious uveitis. Methods AH and plasma were obtained from 28 patients with infectious uveitis (IU), 29 patients with noninfectious uveitis (NIU) and 35 healthy controls undergoing cataract surgery. The proteins profile was analyzed by SomaScan technology. Results We found 1844 and 2484 proteins up-regulated and 124 and 161 proteins down-regulated in the AH from IU and NIU groups, respectively. In the plasma, three proteins were up-regulated in NIU patients, and one and five proteins were down-regulated in the IU and NIU patients, respectively. The results of pathway enrichment analysis for both IU and NIU groups were related mostly to inflammatory and regulatory processes. Conclusion SomaScan was able to detect novel AH and plasma protein biomarkers in IU and NIU patients. Also, the unique proteins found in both AH and plasma suggest a protein signature that could distinguish between infectious and noninfectious uveitis.

A Multiplex Biomarker Assay Improves the Prediction of Survival in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) is usually diagnosed in advanced stages and has a high mortality rate. In this study, we used the proximity extension assay from Olink Proteomics to search for new plasma protein biomarkers to predict overall survival (OS) in patients with EOC. Peripheral blood samples were obtained preoperatively from 116 EOC patients undergoing primary debulking surgery: 28 early EOC cases (FIGO stage I-II) and 88 advanced EOC cases (FIGO stage III-IV). Proteins were measured using the Olink Oncology II and Inflammation panels. In total, 177 unique protein biomarkers were analysed. Cross-validation and LASSO regression were combined to select prediction models for OS. The model including age and the three-biomarker combination of neurotrophin-3 (NT-3)+transmembrane glycoprotein NMB (GPNMB)+mesothelin (MSLN) predicted worse OS with AUC=0.79 (p=0.004). Adding cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) to the model further improved performance (AUC=0.83; p=0.003). In a postoperative model including age and stage (III+IV vs. I+II), the three-biomarker panel of chemokine (C-C motif) ligand 28 (CCL28)+T-cell leukaemia/lymphoma protein 1A (TCL1A)+GPNMB improved the prediction of OS (from AUC=0.83 to AUC=0.90; p=0.05). In the postoperative model including age and dichotomized stage (III vs. I+II), the biomarkers CCL28 and GPNMB1 improved the prediction of OS (AUC=0.86; p<0.001). The combination of high levels of both CA125 and HE4 predicted worse survival (p=0.05). In this explorative study evaluating the performance of plasma protein biomarkers in predicting OS, we found that adding biomarkers, especially NT-3, to the panel improved the prediction of OS.

Plasma Protein Biomarkers of Healthy Dietary Patterns: Results from the Atherosclerosis Risk in Communities Study and the Framingham Heart Study

BackgroundMolecular mechanisms underlying the benefits of healthy dietary patterns are poorly understood. Identifying protein biomarkers of dietary patterns can contribute to characterizing biological pathways influenced by food intake. ObjectivesThis study aimed to identify protein biomarkers associated with four indexes of healthy dietary patterns: Healthy Eating Index-2015 (HEI-2015); Alternative Healthy Eating Index-2010 (AHEI-2010); DASH diet; and alternate Mediterranean Diet (aMED). MethodsAnalyses were conducted on 10,490 Black and White men and women aged 49–73 y from the ARIC study at visit 3 (1993–1995). Dietary intake data were collected using a food frequency questionnaire, and plasma proteins were quantified using an aptamer-based proteomics assay. Multivariable linear regression models were used to examine the association between 4955 proteins and dietary patterns. We performed pathway overrepresentation analysis for diet-related proteins. An independent study population from the Framingham Heart Study was used for replication analyses. ResultsIn the multivariable-adjusted models, 282 out of 4955 proteins (5.7%) were significantly associated with at least one dietary pattern (HEI-2015: 137; AHEI-2010: 72; DASH: 254; aMED: 35; P value < 0.05/4955 = 1.01 × 10−5). There were 148 proteins that were associated with only one dietary pattern (HEI-2015: 22; AHEI-2010: 5; DASH: 121; aMED: 0), and 20 proteins were associated with all four dietary patterns. Five unique biological pathways were significantly enriched by diet-related proteins. Seven out of 20 proteins associated with all dietary patterns in the ARIC study were available for replication analyses, and 6 out of these 7 proteins were consistent in direction and significantly associated with at least 1 dietary pattern in the Framingham Heart Study (HEI-2015: 2; AHEI-2010: 4; DASH: 6; aMED: 4; P value < 0.05/7 = 7.14 × 10−3). ConclusionsA large-scale proteomic analysis identified plasma protein biomarkers that are representative of healthy dietary patterns among middle-aged and older US adult population. These protein biomarkers may be useful objective indicators of healthy dietary patterns.

Novel plasma protein biomarkers from critically ill sepsis patients

BackgroundDespite the high morbidity and mortality associated with sepsis, the relationship between the plasma proteome and clinical outcome is poorly understood. In this study, we used targeted plasma proteomics to identify novel biomarkers of sepsis in critically ill patients.MethodsBlood was obtained from 15 critically ill patients with suspected/confirmed sepsis (Sepsis-3.0 criteria) on intensive care unit (ICU) Day-1 and Day-3, as well as age- and sex-matched 15 healthy control subjects. A total of 1161 plasma proteins were measured with proximal extension assays. Promising sepsis biomarkers were narrowed with machine learning and then correlated with relevant clinical and laboratory variables.ResultsThe median age for critically ill sepsis patients was 56 (IQR 51–61) years. The median MODS and SOFA values were 7 (IQR 5.0–8.0) and 7 (IQR 5.0–9.0) on ICU Day-1, and 4 (IQR 3.5–7.0) and 6 (IQR 3.5–7.0) on ICU Day-3, respectively. Targeted proteomics, together with feature selection, identified the leading proteins that distinguished sepsis patients from healthy control subjects with ≥ 90% classification accuracy; 25 proteins on ICU Day-1 and 26 proteins on ICU Day-3 (6 proteins overlapped both ICU days; PRTN3, UPAR, GDF8, NTRK3, WFDC2 and CXCL13). Only 7 of the leading proteins changed significantly between ICU Day-1 and Day-3 (IL10, CCL23, TGFα1, ST2, VSIG4, CNTN5, and ITGAV; P < 0.01). Significant correlations were observed between a variety of patient clinical/laboratory variables and the expression of 15 proteins on ICU Day-1 and 14 proteins on ICU Day-3 (P < 0.05).ConclusionsTargeted proteomics with feature selection identified proteins altered in critically ill sepsis patients relative to healthy control subjects. Correlations between protein expression and clinical/laboratory variables were identified, each providing pathophysiological insight. Our exploratory data provide a rationale for further hypothesis-driven sepsis research.

Plasma proteomic profiling in postural orthostatic tachycardia syndrome (POTS) reveals new disease pathways

Postural orthostatic tachycardia syndrome (POTS) is a cardiovascular autonomic disorder characterized by excessive heart rate increase on standing, leading to debilitating symptoms with limited therapeutic possibilities. Proteomics is a large-scale study of proteins that enables a systematic unbiased view on disease and health, allowing stratification of patients based on their protein background. The aim of the present study was to determine plasma protein biomarkers of POTS and to reveal proteomic pathways differentially regulated in POTS. We performed an age- and sex-matched, case–control study in 130 individuals (case–control ratio 1:1) including POTS and healthy controls. Mean age in POTS was 30 ± 9.8 years (84.6% women) versus controls 31 ± 9.8 years (80.0% women). We analyzed plasma proteins using data-independent acquisition (DIA) mass spectrometry. Pathway analysis of significantly differently expressed proteins was executed using a cutoff log2 fold change set to 1.2 and false discovery rate (p-value) of < 0.05. A total of 393 differential plasma proteins were identified. Label-free quantification of DIA-data identified 30 differentially expressed proteins in POTS compared with healthy controls. Pathway analysis identified the strongest network interactions particularly for proteins involved in thrombogenicity and enhanced platelet activity, but also inflammation, cardiac contractility and hypertrophy, and increased adrenergic activity. Our observations generated by the first use a label-free unbiased quantification reveal the proteomic footprint of POTS in terms of a hypercoagulable state, proinflammatory state, enhanced cardiac contractility and hypertrophy, skeletal muscle expression, and adrenergic activity. These findings support the hypothesis that POTS may be an autoimmune, inflammatory and hyperadrenergic disorder.

Abstract 11559: Prognostic Significance of Senescence-Associated Secretory Phenotype (SASP) Biomarkers in Heart Failure (HF)

Background: Cell senescence is involved in various age-related pathologies, including HF. The SASP is a characteristic secretory phenotype from senescent cells. We aimed to assess the prognostic significance of the SASP assessed by a combination of plasma protein biomarkers in human HF. Methods: We measured 25 known SASP associated proteins among 2248 Penn HF Study (PHFS) participants using the SomaScan V4 assay. Factor analysis was used to extract the common variance among these proteins. We assessed the relationship between factor scores and: (1) All-cause death; (2) The composite of death or heart failure hospital admission (DHFA). Hazard ratios (HR) were standardized (per unit increase in z score) to facilitate comparisons. Results: Two SASP factors were identified. The levels of both factors did not differ between HF with reduced EF (n=1761) and HF with preserved EF (n=253). Both factors were associated with the risk of death (Factor 1: HR 1.43 [95%CI, 1.32-1.54]; P&lt;0.0001; Factor 2: HR 1.63 [95%CI, 1.54-1.72]; P&lt;0.0001; Figure 1A ) and DHFA (Factor 1: HR 1.36 [95%CI, 1.29-1.43]; P&lt;0.0001; Factor 2: HR 1.32 [95%CI, 1.26-1.38]; P&lt;0.0001; Figure 1B ). After adjustment for the MAGGIC score and NTproBNP levels, both factors remained associated with death (Factor 1: HR 1.14 [95%CI, 1.03-1.26]; P=0.008; Factor 2: HR 1.47 [95%CI, 1.34-1.61]; P&lt;0.0001; Figure 2A ) and DHFA (Factor 1: HR 1.10; [95%CI, 1.03-1,18]; P=0.004; Factor 2: HR 1.09 [95%CI, 1.02-1.17]; P=0.012; Figure 2B ). Conclusion: Increased SASP components are independently associated with adverse outcomes in HF. Further study is needed to assess whether SASP components are suitable therapeutic targets in heart failure.

Abstract 12661: Prognostic Significance of Tubular Injury Biomarkers in Heart Failure With Preserved Ejection Fraction (HFpEF)

Background: The onset and progression of CKD in HFpEF may be driven by different etiologies, including tubular injury, glomerular disease or interstitial remodeling. We assessed the prognostic significance of tubular injury assessed with a combination of plasma protein biomarkers. Methods: We analyzed data and samples from the TOPCAT trial. We utilized the SomaScan assay to measure tubular injury biomarkers: Alpha-1 microglobulin (A1M), Beta 2 microglobulin (B2M), neutrophil gelatinase associated lipocalin (NGAL), kidney injury molecule 1 (KIM1), and interleukin 18 (IL-18). Factor analysis was used to extract the common variability underlying these biomarkers. We assessed the relationship between factor scores and: (1) All-cause mortality; (2) The composite of death or heart failure hospital admission (DHFA). All hazard ratios (HR) are standardized (per unit increase in z score) to facilitate comparisons. Linear regression was used to assess the relationship of each factor to 4,746 other proteins, followed by pathway analyses. Results: There were 2 underlying factors: factor 1 was associated predominantly with KIM1, whereas factor 2 was strongly associated with NGAL. Both factors were associated with estimated GFR. Factor 1 was predominantly associated with diabetes, whereas factor 2 was predominantly associated with a lower mean arterial pressure. In a model that included the 2 factors, both were significantly and independently associated with DHFA (Factor 1 HR=1.75; 95%CI=[1.47,2.07]; P&lt;0.0001; Factor 2 HR=1.37; 95%CI=[1.17,1.62]; P=0.0001), but only factor 1 was significantly associated with death (Factor 1 HR=1.22; 95%CI=[1.06,1.40]; P=0.0046; Factor 2 HR=1.23; 95%CI=[0.98,1.54]; P=0.066). In analyses that adjusted for estimated GFR and the MAGGIC risk score, Factor 1 was significantly associated with death and DHFA, whereas Factor 2 was only associated with DHFA. These factors were associated with pathways related to immunity, fibrosis, oxidative stress, cell death, and myocardial remodeling. Conclusion: Tubular injury is associated with adverse outcomes in HFpEF. This relationship is independent of baseline GFR, suggesting that ongoing injury contributes to poor outcomes and may represent a target for therapy.

P032: Circulating immune biomarkers in classical Hodgkin lymphoma in relation to tumor burden and response to treatment.

Background: In classical Hodgkin lymphoma (cHL) the malignant cells represent only a small fraction of the tumor mass. Yet, they orchestrate a lymphocyte-dominated tumor microenvironment (TME) that supports their survival and growth. The systemic effects of this local immunomodulation are not fully elucidated. In this study, we aimed at characterising circulating lymphocytes and plasma proteins in cHL patients in relation to clinical parameters and treatment effect. Methods: Peripheral blood (PB) samples were obtained from 48 consecutive patients at diagnosis (baseline, BL) and at 2 time points after primary treatment, right at the end of treatment (EoT) and at follow-up 6 months after EoT (FU). Twenty-eight patients had limited-stage (I-IIA) and 20 had advanced-stage (IIB-IV) disease. All the patients in the FU analysis were in complete remission. Twenty healthy individuals were included as controls. Cells from PB and LN were freshly analysed by flow cytometry, and plasma proteins by proximity extension assay (PEA). Concentrations of CCL17/TARC in plasma were also measured by Enzyme-Linked ImmunoSorbent Assay. Results: At BL, the numbers of B cells were lower in both limited- and advanced-stage cHL compared to controls, while T cells were normal. Advanced-stage patients had fewer NK cells with a functionally impaired phenotype. Compared to controls, cHL patients had higher frequencies of proliferating T cells as well as higher expression of programmed death (PD)-1 and cytotoxic T lymphocyte antigen (CTLA)-4 in circulating T cells, and lower naïve T-cell frequencies. The plasma concentration of CCL17/TARC was elevated in both limited- and advanced-stage cHL compared to controls. The frequencies of T and B cells positively correlated between the PB and LN compartments. Distinct immune cellular and plasma protein biomarker profiles were observed in patients with a high tumor burden (i.e. bulky tumor and/or >2 nodal sites involved and/or stage IV) and those with high inflammation (i.e. ESR ≥50). T-cell exhaustion and NK-cell depletion were reversed by standard first-line treatment and CCL17/TARC concentrations also dropped back to control levels. Patients who received radiotherapy involving the mediastinum had low T-cell counts for a prolonged period. Conclusion: The immunomodulation of lymphocytes in the TME of cHL might affect immune biomarkers in the PB. Most immunological changes are reverted after successful standard primary treatment.

Utility of Plasma Protein Biomarkers and Mid-infrared Spectroscopy for Diagnosing Fracture-related Infections: A Pilot Study.

To compare a large panel of plasma protein inflammatory biomarkers and mid-infrared (MIR) spectral patterns in patients with confirmed fracture-related infections (FRIs) with those in controls without infection. Prospective case-control study. Academic, Level 1 trauma center. Thirteen patients meeting confirmatory FRI criteria were matched to 13 controls based on age, time after surgery, and fracture region. Plasma levels of 49 proteins were measured using enzyme-linked immunosorbent assay techniques. Fourier transform infrared spectroscopy of dried films was used to obtain MIR spectra of plasma samples. The main outcome measurements included plasma protein levels and MIR spectra of samples. Multivariate analysis-based predictive model developed using enzyme-linked immunosorbent assay-based biomarkers had sensitivity, specificity, and accuracy of 69.2% ± 0.0%, 99.9% ± 1.0%, and 84.5% ± 0.6%, respectively, with platelet-derived growth factor-AB/BB, C-reactive protein, and MIG selected as the minimum number of variables explaining group differences ( P &lt; 0.05). Sensitivity, specificity, and accuracy of the predictive model based on MIR spectra were 69.9% ± 6.2%, 71.9% ± 5.9%, and 70.9% ± 4.8%, respectively, with 6 wavenumbers as explanatory variables ( P &lt; 0.05). This pilot study demonstrates the feasibility of using a select panel of plasma proteins and Fourier transform infrared spectroscopy to diagnose FRIs. Preliminary data suggest that the measurement of these select proteins and MIR spectra may be potential clinical tools to detect FRIs. Further investigation of these biomarkers in a larger cohort of patients is warranted. Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Men’s perception of information and psychological distress in the diagnostic phase of prostate cancer: a comparative mixed methods study

BackgroundPrevious studies indicate that men experience frustration and uncertainty when confronted with an elevated prostate specific antigen (PSA) test and during further diagnostics for prostate cancer. The novel Stockholm3 test is an algorithm-based test that combines plasma protein biomarkers, genetic markers and clinical variables in predicting the risk of PCa. The test was introduced in a western part of Norway as a new tool for detecting prostate cancer. This study aimed to explore and compare men’s perception of information and possible experience of distress between a PSA group and a Stockholm3 group during the diagnostic phase of prostate cancer.MethodsThis study is a part of the trailing research evaluating the impact of the change from PSA to Stockholm3. It is a multicenter study using a comparative mixed method design. Data were collected in a PSA group (n = 130) and a Stockholm3 group (n = 120) between 2017 and 2019. Quantitative data were collected using questionnaires and qualitative data were collected using semi-structured interviews (n = 20). The quantitative and qualitative data were analysed and compared separately and then merged in a side-by-side discussion. The study adheres to the GRAMMS guidelines for reporting mixed-methods research.ResultsCompared with the PSA group, men in the Stockholm3 group reported that the information from the general practitioners was better. Similarly, men in the Stockholm3 group were more likely to indicate that they had received sufficient information regarding how examinations would be conducted. No differences were found between the groups regarding waiting time and distress. Three themes emerged from the qualitative analysis of the two groups: “Information affects the experience of comprehension”, “Stepping into the world of the healthcare system”, and “Periodically feelings of distress”.ConclusionThe Stockholm3 test may facilitate the provision of information to patients. However, some patients in both groups experienced distress and would benefit from more information and additional support from healthcare professionals. Routines that ensure sufficient information from the interdisciplinary healthcare team should be of priority during the diagnostic phase of prostate cancer in order to provide patients with predictability and to avoid unnecessary distress.

Plasma protein biomarkers for primary graft dysfunction after lung transplantation: a single-center cohort analysis

The clinical use of circulating biomarkers for primary graft dysfunction (PGD) after lung transplantation has been limited. In a prospective single-center cohort, we examined the use of plasma protein biomarkers as indicators of PGD severity and duration after lung transplantation. The study comprised 40 consecutive lung transplant patients who consented to blood sample collection immediately pretransplant and at 6, 24, 48, and 72 h after lung transplant. An expert grader determined the severity and duration of PGD and scored PGD at T0 (6 h after reperfusion), T24, T48, and T72 h post-reperfusion using the 2016 ISHLT consensus guidelines. A bead-based multiplex assay was used to measure 27 plasma proteins including cytokines, growth factors, and chemokines. Enzyme-linked immunoassay was used to measure cell injury markers including M30, M65, soluble receptor of advanced glycation end-products (sRAGE), and plasminogen activator inhibitor-1 (PAI-1). A pairwise comparisons analysis was used to assess differences in protein levels between PGD severity scores (1, 2, and 3) at T0, T24, T48, and T72 h. Sensitivity and temporal analyses were used to explore the association of protein expression patterns and PGD3 at T48–72 h (the most severe, persistent form of PGD). We used the Benjamini–Hochberg method to adjust for multiple testing. Of the 40 patients, 22 (55%) had PGD3 at some point post-transplant from T0 to T72 h; 12 (30%) had PGD3 at T48–72 h. In the pairwise comparison, we identified a robust plasma protein expression signature for PGD severity. In the sensitivity analysis, using a linear model for microarray data, we found that differential perioperative expression of IP-10, MIP1B, RANTES, IL-8, IL-1Ra, G-CSF, and PDGF-BB correlated with PGD3 development at T48–72 h (FDR < 0.1 and p < 0.05). In the temporal analysis, using linear mixed modeling with overlap weighting, we identified unique protein patterns in patients who did or did not develop PGD3 at T48–72 h. Our findings suggest that unique inflammatory protein expression patterns may be informative of PGD severity and duration. PGD biomarker panels may improve early detection of PGD, predict its clinical course, and help monitor treatment efficacy in the current era of lung transplantation.

73P Multi-cancer early detection through evaluation of aneuploidy, methylation, and protein biomarkers in plasma

Multi-cancer early detection (MCED) using a blood test represents a rapidly emerging, potentially transformative advance in preventive oncology. Given the complexity of carcinogenesis across organs, simultaneous analysis of several biomarkers will maximize assay performance, particularly for early stage tumors.

Quantitative Plasma Proteomics to Identify Candidate Biomarkers of Relapse in Pediatric/Adolescent Hodgkin Lymphoma.

Classical pediatric Hodgkin Lymphoma (HL) is a rare malignancy. Therapeutic regimens for its management may be optimized by establishing treatment response early on. The aim of this study was to identify plasma protein biomarkers enabling the prediction of relapse in pediatric/adolescent HL patients treated under the pediatric EuroNet-PHL-C2 trial. We used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics at the time of diagnosis—before any therapy—as semiquantitative method to profile plasma proteins specifically associated with relapse in 42 children with nodular sclerosing HL. In both the exploratory and the validation cohorts, six proteins (apolipoprotein E, C4b-binding protein α chain, clusterin, fibrinogen γ chain, prothrombin, and vitronectin) were more abundant in the plasma of patients whose HL relapsed (|fold change| ≥ 1.2, p < 0.05, Student’s t-test). Predicting protein function with the Gene Ontology classification model, the proteins were included in four biological processes (p < 0.01). Using immunoblotting and Luminex assays, we validated two of these candidate biomarkers—C4b-binding protein α chain and clusterin—linked to innate immune response function (GO:0045087). This study identified C4b-binding protein α chain and clusterin as candidate early plasma biomarkers of HL relapse, and important for the purpose of shedding light on the molecular scenario associated with immune response in patients treated under the EuroNet-PHL-C2 trial.

Integrated analysis of dermal blister fluid proteomics and genome-wide skin gene expression in systemic sclerosis: an observational study.

Skin fibrosis is a hallmark feature of systemic sclerosis. Skin biopsy transcriptomics and blister fluid proteomics give insight into the local environment of the skin. We have integrated these modalities with the aim of developing a surrogate for the modified Rodnan skin score (mRSS), using candidate genes and proteins from the skin and blister fluid as anchors to identify key analytes in the plasma. In this single-centre, prospective observational study at the Royal Free Campus, University College London, London, UK, transcriptional and proteomic analyses of blood and skin were performed in a cohort of patients with systemic sclerosis (n=52) and healthy controls (n=16). Weighted gene co-expression network analysis was used to explore the association of skin transcriptomics data, clinical traits, and blister fluid proteomic results. Candidate hub analytes were identified as those present in both blister and skin gene sets (modules), and which correlated with plasma (module membership >0·7 and gene significance >0·6). Hub analytes were confirmed using RNA transcript data obtained from skin biopsy samples from patients with early diffuse cutaneous systemic sclerosis at 12 months. We identified three modules in the skin, and two in blister fluid, which correlated with a diagnosis of early diffuse cutaneous systemic sclerosis. From these modules, 11 key hub analytes were identified, present in both skin and blister fluid modules, whose transcript and protein levels correlated with plasma protein concentrations, mRSS, and showed statistically significant correlation on repeat transcriptomic samples taken at 12 months. Multivariate analysis identified four plasma analytes as correlates of mRSS (COL4A1, COMP, SPON1, and TNC), which can be used to differentiate disease subtype. This unbiased approach has identified potential biological candidates that might be drivers of local skin pathogenesis in systemic sclerosis. By focusing on measurable analytes in the plasma, we generated a promising composite plasma protein biomarker that could be used for assessment of skin severity, case stratification, and as a potential outcome measure for clinical trials and practice. Once fully validated, the biomarker score could replace a clinical score such as the mRSS, which carries substantial variability. GlaxoSmithKline and UK Medical Research Council.