Panel Of Serum Protein Biomarkers Research Articles

Novel serum protein biomarker panel for early diagnosis of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Late presentation of disease at the time of diagnosis is one of the major reasons for dismal prognostic outcomes for PDAC patients. Currently, there is a lack of clinical biomarkers, which can be used to diagnose PDAC patients at an early resectable stage. This study performed proteomic mass spectrometry to identify novel blood-based biomarkers for early diagnosis of PDAC. Serum specimens from 88 PDAC patients and 88 healthy controls (60 discovery cohort and 28 validation cohort) were analyzed using data independent acquisition high resolution mass spectrometry to identify candidate biomarker proteins. A total of 249 proteins were identified and quantified by the mass spectrometric analysis. Six proteins were markedly (>1.5 fold) and significantly (p < .05; q < 0.1) increased in PDAC patients compared to healthy controls in discovery cohort. Notably, four of these six proteins were significantly upregulated in an independent validation cohort. The top three upregulated proteins (i.e., Polymeric Immunoglobulin Receptor [PIGR], von Willebrand Factor [vWF], and Fibrinogen) were validated using enzyme linked immunosorbent assay, which led to selection of PIGR and vWF as a diagnostic biomarker panel for PDAC. The panel showed high ability to diagnose early stage (stage I and II) PDAC patients (area under the curve [AUC]: 0.8926), which was further improved after the addition of clinically used prognostic biomarker (Ca 19-9) to the panel (AUC: 0.9798). In conclusion, a novel serum protein biomarker panel for early diagnosis of PDAC was identified.

Correlation between Stool DNA-based SDC2 Methylation Tests Combined with Tumor Markers with Pathological Features in Colorectal Cancer.

Colorectal cancer (CRC) screening is the most efficient strategy to reduce disease-related mortality. In this study, we aimed to investigate the association of a methylation-based stool DNA test with serum protein biomarker panel (CEA, CA125, CA199, and AFP) in CSC patients and their relationship with pathological features to improve the diagnostic efficacy and applicability in CSC in the Chinese population. In this double-blinded case-control study, we enrolled 150 participants from our hospital, including 50 CRC patients, 50 adenomas, and 50 healthy controls. We compared the cycling threshold (Ct) values of stool DNA-based SDC2 measured by quantitative methylation-specific PCR (MSP) in the three groups. We also evaluated the differences and correlation between serum concentrations of tumor biomarker and pathological features in patients with CSC, including TNM stage (I, II, III), tumor size, and lymph node metastasis. The discrimination performance of indexes was assessed using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). CSC was more common in middle-aged people and men. The methylation-based stool DNA test was not significantly correlated with other tumor indicators except CEA, and the difference was statistically significant. Compared with the normal control group, the combined diagnostic value of the methylation-based stool DNA test and tumor indicators was significantly higher than individual biomarkers alone, especially the methylation-based stool DNA test combined with CEA and AFP, which improved the AUC to 0.96. This combination can increase the positive rate of pathological stage diagnosis. Combining a methylation-based stool DNA test with CEA and AFP can significantly improve the diagnostic value of CRC and can be used to confirm the diagnosis of colorectal cancer. This combination can also be used as a reliable indicator identifying early-stage CRC patients and pathology. A large-scale study is underway to further define the clinical application of this method for the diagnosis of CRC among Chinese populations.

Identification and Evaluation of Serum Protein BiomarkersThat Differentiate Psoriatic Arthritis From Rheumatoid Arthritis.

To identify serum protein biomarkers that might distinguish patients with early inflammatory arthritis (IA) with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) and may be used to support appropriate early intervention. The serum proteome of patients with PsA and patients with RA was interrogated using nano-liquid chromatography mass spectrometry (nano-LC-MS/MS) (n = 64 patients), an aptamer-based assay (SomaScan) targeting 1,129 proteins (n = 36 patients), and a multiplexed antibody assay (Luminex) for 48 proteins (n = 64 patients). Multiple reaction monitoring (MRM) assays were developed to evaluate the performance of putative markers using the discovery cohort (n = 60 patients) and subsequently an independent cohort of PsA and RA patients (n = 167). Multivariate machine learning analysis of the protein discovery data from the 3 platforms revealed that it was possible to differentiate PsA patients from RA patients with an area under the curve (AUC) of 0.94 for nano-LC-MS/MS, 0.69 for bead-based immunoassay measurements, and 0.73 for aptamer-based analysis. Subsequently, in the separate verification and evaluation studies, random forest models revealed that a subset of proteins measured by MRM could differentiate PsA and RA patients with AUCs of 0.79 and 0.85, respectively. We present a serum protein biomarker panel that can separate patients with early-onset IA with PsA from those with RA. With continued evaluation and refinement using additional and larger patient cohorts, including those with other arthropathies, we suggest that the panel identified here could contribute to improved clinical decision making.

Auxiliary diagnosis of Lung Cancer on the basis of a Serum Protein Biomarker Panel.

Objectives: In this study, we established a serum protein biomarker panel (consisting of Pro-SFTPB, CA125, Cyfra21-1, and CEA) and evaluated the feasibility and performance for the auxiliary diagnosis of lung cancer in the Chinese population.Materials and Methods: The current study was a single-center study based on the Chinese population and performed in two cohorts (training cohort and validation cohort). Serum concentrations of Pro-SFTPB, CA125, Cyfra21-1, and CEA were measured by a bead-based flow fluorescence immunoassay. The discrimination performance of the model was assessed using sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC).Results: For the biomarker panel model, the AUC was 0.88 (95% CI, 0.85-0.91) in the training cohort and 0.90 (95% CI, 0.86-0.92) in the validation data cohort, which was significantly greater than the AUC of each biomarker alone. For the nodule risk model, the AUC was improved to 0.96 (95% CI, 0.94-0.98) in the training cohort and 0.95 (95% CI, 0.93-0.97) in the validation cohort. In addition, the biomarker panel model yielded an AUC of 0.78 (95% CI, 0.74-0.81) for stage I & II lung cancer, better than the performance of individual biomarker alone.Conclusions: It was demonstrated that 4-protein biomarker panel had a significant performance in identifying lung cancer patients from healthy controls, especially combining with the nodule size. Specifically, it yielded excellent discrimination for identifying early-stage lung cancer patients than individual biomarker alone. A future large-scale study is underway to further define the clinical application of this method for the early diagnosis of lung cancer among Chinese populations.

Discovery of Potential Serum Protein Biomarkers in Ankylosing Spondylitis Using Tandem Mass Tag-Based Quantitative Proteomics.

Ankylosing spondylitis (AS) is a systemic, chronic, and inflammatory rheumatic disease that affects 0.2% of the population. Current diagnostic criteria for disease activity rely on subjective Bath Ankylosing Spondylitis Disease Activity Index scores. Here, we aimed to discover a panel of serum protein biomarkers. First, tandem mass tag (TMT)-based quantitative proteomics was applied to identify differential proteins between 15 pooled active AS and 60 pooled healthy subjects. Second, cohort 1 of 328 humans, including 138 active AS and 190 healthy subjects from two independent centers, was used for biomarker discovery and validation. Finally, biomarker panels were applied to differentiate among active AS, stable AS, and healthy subjects from cohort 2, which enrolled 28 patients with stable AS, 26 with active AS, and 28 healthy subjects. From the proteomics study, a total of 762 proteins were identified and 46 proteins were up-regulated and 59 proteins were down-regulated in active AS patients compared to those in healthy persons. Among them, C-reactive protein (CRP), complement factor H-related protein 3 (CFHR3), α-1-acid glycoprotein 2 (ORM2), serum amyloid A1 (SAA1), fibrinogen γ (FG-γ), and fibrinogen β (FG-β) were the most significantly up-regulated inflammation-related proteins and S100A8, fatty acid-binding protein 5 (FABP5), and thrombospondin 1 (THBS1) were the most significantly down-regulated inflammation-related proteins. From the cohort 1 study, the best panel for the diagnosis of active AS vs healthy subjects is the combination of CRP and SAA1. The area under the receiver operating characteristic (ROC) curve was nearly 0.900, the sensitivity was 0.970%, and the specificity was 0.805% at a 95% confidence interval from 0.811 to 0.977. Using 0.387 as the cutoff value, the predictive values reached 92.00% in the internal validation set (62 with active AS vs 114 healthy subjects) and 97.50% in the external validation phase (40 with active AS vs 40 healthy subjects). From the cohort 2 study, a panel of CRP and SAA1 can differentiate well among active AS, stable AS, and healthy subjects. For active AS vs stable AS, the area under the ROC curve was 0.951, the sensitivity was 96.43%, the specificity was 88.46% at a 95% confidence interval from 0.891 to 1, and the coincidence rate was 92.30%. For stable AS vs healthy humans, the area under the ROC curve was 0.908, the sensitivity was 89.29%, the specificity was 78.57% at a 95% confidence interval from 0.836 to 0.980, and the coincidence rate was 83.93%. For active AS vs healthy subjects, the predictive value was 94.44%. The results indicated that the CRP and SAA1 combination can potentially diagnose disease status, especially for active or stable AS, which will be conducive to treatment recommendation for patients with AS.

A Serum Protein Biomarker Panel Improves Outcome Prediction in Human Traumatic Brain Injury.

Brain-enriched protein biomarkers of tissue fate are being introduced clinically to aid in traumatic brain injury (TBI) management. The aim of this study was to determine how concentrations of six different protein biomarkers, measured in samples collected during the first weeks after TBI, relate to injury severity and outcome. We included neurocritical care TBI patients that were prospectively enrolled from 2007 to 2013, all having one to three blood samples drawn during the first 2 weeks. The biomarkers analyzed were S100 calcium-binding protein B (S100B), neuron-specific enolase (NSE), glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase-L1 (UCH-L1), tau, and neurofilament-light (NF-L). Glasgow Outcome Score (GOS) was assessed at 12 months. In total, 172 patients were included. All serum markers were associated with injury severity as classified on computed tomography scans at admission. Almost all biomarkers outperformed other known outcome predictors with higher levels the first 5 days, correlating with unfavorable outcomes, and UCH-L1 (0.260, pseduo-R2) displaying the best discrimination in univariate analyses. After adjusting for acknowledged TBI outcome predictors, GFAP and NF-L added most independent information to predict favorable/unfavorable GOS, improving the model from 0.38 to 0.51 pseudo-R2. A correlation matrix indicated substantial covariance, with the strongest correlation between UCH-L1, GFAP, and tau (r = 0.827–0.880). Additionally, the principal component analysis exhibited clustering of UCH-L1 and tau, as well as GFAP, S100B, and NSE, which was separate from NF-L. In summary, a panel of several different protein biomarkers, all associated with injury severity, with different cellular origin and temporal trajectories, improve outcome prediction models.

Abstract LB-219: Clinical utility of a serum protein biomarker panel (FLNA, KRT19) in stratification of prostate cancer from benign prostate hyperplasia patients

Abstract Background: Prostate-specific antigen (PSA) based screening tests have been considered as a benchmark for PCa diagnosis (PSA &amp;gt;4 ng/mL), however, in recent years their use has been widely debated due to various limitations. In particular, increased PSA levels are also observed in patients with benign prostatic hyperplasia (BPH) thus generating a false positive. These patients are often subjected to unnecessary prostate biopsies and have to endure both physical and mental anguish. Thus, there is a clear unmet need to develop molecular diagnostics that stratifies men with BPH from those that have PCa and prevent unwarranted prostate biopsies in symptomatic, DRE negative men. Here in, we demonstrate clinical utility of a validated biomarker panel (FLNA, KRT 19) in combination with age and prostate volume in stratifying BPH from PCa patients. Methods: This study was conducted using retrospectively collected and clinically annotated serum samples from 203 BPH patients and 333 PCa patients with PSA values ranging from 4-10ng/mL. These samples included BPH patients with negative Digital Rectum Exam, single biopsy and repeat biopsies, and prostate cancer patients undergoing radical prostatectomy. Validated bioanalytical assays (FLNA ELISA, FLNA IPMRM and KRT19 ELISA) were used to quantitate the serum biomarkers. Regression models were built and compared for their ability to distinguish patients with BPH from those with PCa. Results: Table 1: Summary of predictive power analysis of PCa panel (49 characters) BERG TestPSAAUC0.80.54Sensitivity0.8n/aSpecificity0.62n/aPPV0.78n/aNPV0.65n/aOR (CI)6.67 (4.49,9.90)n/a Conclusion: The data demonstrates that these PCa biomarkers in combination with age, and prostatic volume were significantly better than (0.8) PSA (0.54 AUC in this population) in identifying men with BPH rather than PCa thus preventing unnecessary biopsies for this population. Moreover, this panel may be used in clinical decision support to physicians to fill a clinical unmet need. Citation Format: Michael A. Kiebish, Poornima Tekmulla, Shobha Ravipaty, Wenfang Wu, Tracey Friss, Chenchen Liao, Allison Klotz, Joe Andreazi, Elisabeth Hutchins, Albert Dobi, Shiv Srivastava, Jennifer Cullen, Amina Ali, Stephen Freedland, Kagan Griffin, Sandra Laszlo, Michele Petrovic, Neil Fleshner, Jeonifer Garren, Leonardo Rodrigues, Mark D. Kellog, Viatcheslav R. Akmaev, Rangaprasad Sarangarajan, Niven R. Narain. Clinical utility of a serum protein biomarker panel (FLNA, KRT19) in stratification of prostate cancer from benign prostate hyperplasia patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-219.

Panel of serum protein biomarkers to grade the severity of traumatic brain injury.

The Glasgow Coma Scale (GCS), which classifies patients into mild, moderate or severe traumatic brain injury (TBI), is a system used to prioritize treatment and prognosticate the severity of head injury. In this study, sera of patients with various stages of TBI, as well as control subjects, were analyzed to screen for proteins that may be used to complement the GCS system. By subjecting pooled serum samples to iTRAQ analysis for quantitative comparison of protein abundance, and attesting their altered levels using ELISA, we have detected increased levels of serum amyloid A, C-reactive protein, leucine-rich alpha-2-glycoprotein, lipopolysaccharide-binding protein, fibronectin, vitronectin and alpha-1-antichymotrypsin in patients across all strata of TBI relative to the controls. However, kininogen was decreased only in moderate and severe TBI, whereas apolipoprotein E and zinc-alpha-2-glycoprotein were only increased in severe TBI. Hence, we propose a panel of serum biomarkers, which if analyzed within 24 h of the injury, can be used to diagnose patients with TBI into mild, moderate or severe stratification objectively, thus complementing the traditional GCS.

Abstract P2-02-03: Assessment of an immune response panel of serum protein biomarkers for the non-invasive detection of breast cancer

Abstract Background: Breast cancer screening guidelines (ACS) recommend annual mammography for asymptomatic women ages 45 to 54 and once every two years for women age 55 and older. Women with suspicious screening mammograms are recommended for a diagnostic mammogram and may also undergo MRI or ultrasound. Ultimately, unresolved suspicious findings frequently result in the recommendation of a breast biopsy. Approximately 10% of suspicious diagnostic mammograms are recommended for breast biopsies and a large majority of these biopsies yield benign results. We conducted a screen for serum protein biomarkers and identified a novel panel for the non-invasive detection of breast cancer with the goal of developing a diagnostic test that can reduce the number of patients with benign pathology undergoing invasive biopsies. Methods: Serum samples were collected at 4 US sites from women with suspicious diagnostic mammogram findings (primarily BI-RADS category 4 and breast composition b/c) undergoing biopsy for evaluation of a potential malignancy. Serum samples from 136 patients (87 benign pathology and 49 malignant pathology) were evaluated on the olink® Proteomics Immune Response Panel (92 analytes). Statistical screening methodologies, such as individual t-tests with control for false discovery, were used to identify markers with the potential to distinguish benign from malignant pathology. The candidate markers were further studied and combined using generalized linear modeling to develop potential diagnostic models. Results: A 19-marker model resulted in an AUC of 0.94 with a sensitivity of 90% and a specificity of 80%. A 12-marker model resulted in an AUC of 0.93, yielding a sensitivity of 90% with a specificity of 77%. Conclusions: This study reveals a novel panel of serum protein biomarkers that may allow for the non-invasive and sensitive detection of breast cancer in patients presenting with suspicious findings on mammography, thus reducing the need for invasive biopsies. Citation Format: McQuary PR, Copeland KAF, Sheibani NM, Friedman LA, Korn RL, Fiorica JV, Lourenco AP, Struthers SE, Hesterberg LK. Assessment of an immune response panel of serum protein biomarkers for the non-invasive detection of breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-02-03.

Abstract P5-03-05: Development of a panel of serum-based protein biomarkers for the non-invasive detection of breast cancer in BI-RADS category 4 patients

Abstract Background: Current breast cancer screening guidelines call for annual mammography for asymptomatic women age 45 to 54 and once every two years for women age 55 and older. Women with suspicious screening mammograms are recommended for a diagnostic mammogram and may also undergo MRI or ultrasound. Ultimately, suspicious findings unresolved by imaging typically result in the recommendation of a breast biopsy. Approximately 10% of suspicious diagnostic mammograms are recommended for breast biopsies and 67% to 95% of these biopsies yield negative results. With the goal of reducing the number of patients with benign pathology undergoing invasive biopsies, we conducted a screen for serum protein biomarkers and identified a novel panel for the non-invasive detection of breast cancer. Methods: Serum samples were collected at two sites from women with suspicious diagnostic mammogram findings (primarily BI-RADS category 4) undergoing biopsy for the evaluation of a potential malignancy. Serum samples from 100-patients (50 benign pathology and 50 malignant pathology) were evaluated on the SOMAscan Assay 1.3k, which measures levels of 1,310 different protein analytes. Statistical screening methodologies, such as individual t-tests with control for false discovery, were used to identify markers with the potential to distinguish benign from malignant pathology. The candidate markers were further studied and combined using generalized linear modeling to develop three potential diagnostic models. K-fold cross validation was used to guard against over fitting of the models. Results: A 15-marker model resulted in an AUC of 0.92 with a sensitivity of 90% and specificity of 76%. Two 6-marker models (with 4 markers in common) each resulted in AUC of 0.85, yielding a sensitivity of 90% with a specificity of 56% or 64%. Conclusions: This study reveals a novel panel of serum protein biomarkers that may allow for the non-invasive and sensitive detection of breast cancer in BI-RADS category 4 patients. A multicenter study is underway to further refine and validate this panel in a larger set of prospectively collected patient samples. Citation Format: Chapman KB, Copeland K, Kidd J, Qiu L, Sheibani N, Tam O, Friedman L, Korn R, Fiorica J, Lourenco A, Suthers S, Hesterberg L. Development of a panel of serum-based protein biomarkers for the non-invasive detection of breast cancer in BI-RADS category 4 patients [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-03-05.

Identification of a serum protein biomarker panel for the diagnosis of knee osteoarthritis

s / Osteoarthritis and Cartilage 24 (2016) S8eS62 S23 knee criteria and included features related to osteophytes, cartilage damage, bone marrow lesions, subchondral cysts and labral lesions. Logistic regression analysis was used to evaluate the association of FAI with moderate cartilage damage ( 2), moderate BML ( 2) and MRI-OA. Analyses were adjusted for age, sex, body mass index and family history of OA and, in secondary analyses, also for physical activity. Results: Subjects in the MRI study (n 1⁄4 182) had mean age 43.5 years, mean BMI 26.8, mean WOMAC pain 14.4 and 65.4% were female. FAI was present in 48.9% and hip pain in 51.7%. Cartilage score 2, BML 2 and MRI-OA were present in 11.0%, 9.9% and 8.9%, respectively. FAI was not significantly associated with cartilage damage 2 (OR 2.0, 95% CI 0.7e5.5) and this relationship remained non-significant after adjustment for physical activity (OR 1.8, 95% CI 0.6e5.2). There was a statistically significant association of FAI with BML 2 (OR 4.7, 95% CI 1.4e16.0) and with MRI-OA (OR 3.4, 95% CI 1.1e12.3). Additional adjusting for physical activity did not change the findings for BML (OR 4.2, 95% CI 1.4e14.7) and MRI-OA (OR 2.9, 95% CI 0.9e10.6), although the latter was only of borderline statistical significance. Conclusions: In this population-based cross-sectional study of young adults, the risk of moderate BML and MRI-defined OA was significantly increased in subjects with radiographic FAI compared to those without radiographic FAI. No association was found for FAI with moderate cartilage damage. These findings suggest that FAI is linked to early osteoarthritic changes onMRI in young adults. Whether BML is a precursor to future cartilage damage and radiographic OA requires longitudinal evaluation of this cohort. 27 IDENTIFICATION OF A SERUM PROTEIN BIOMARKER PANEL FOR THE DIAGNOSIS OF KNEE OSTEOARTHRITIS L. Lourido y, B. Ayoglu z, J. Fern andez-Tajes x, F. Henjes z, J.M. Schwenk z, C. Ruiz-Romero y, P. Nilsson z, F.J. Blanco y. yRheumatology Div., ProteoRed/ISCIII Proteomics Group, INIBIC e Hospital Universitario de A Coru~ na, Spain; zAffinity Proteomics, SciLifeLab, Sch. of Biotechnology, KTH-Royal Institute of Technology, Stockholm, Sweden; xWellcome Trust Ctr. For Human Genetics, McCarthy's Group, University of Oxford, Oxford, United Kingdom Purpose: Osteoarthritis (OA) is the most common rheumatic disease. Despite the active research in the OA biomarker field, no single protein is sufficiently reliable for its use in OA diagnosis. This is mainly due to the lack of validation studies in large populations, making the findings questionable to be considered as robust biomarkers for the OA joint turnover. In this study we addressed a large-scale study with the aim of search for proteins that could serve as potential indicators of knee OA. Methods: Antibody suspension bead arrays were applied to profile serum samples from patients with knee OA (n 1⁄4 288) with different Kellgren-Lawrence (K/L) scores and compare to patients with rheumatoid arthritis (RA, n 1⁄4 288), psoriatic arthritis (PsA, n 1⁄4 288) and healthy control (n 1⁄4 96) subjects with non-radiographic knee osteoarthritis. The serum protein content was labelled and protein profiles obtained using 174 antibodies from the Human Protein Atlas targeting 78 different proteins. A focused bead array was then built to further profile 46 selected protein targets as a replication of the first findings and also to validate these results in an independent cohort of serum samples composed by patients with OA (n 1⁄4 196), RA (n 1⁄4 192), PsA (n1⁄4 192) and healthy control individuals (n1⁄4 92). The panel of proteins proposed and analysed for this study was selected based on previous internal protein profiling studies in the context of rheumatic diseases. For the statistical analysis, a lineal regression analysis adjusting by sex, age and body mass index (BMI) was applied to observe differences in protein profiles between compared groups and these were denoted statistically significant if they concordantly revealed p.values 2-fold increase in the risk of incident ROA, while sarcopenia alone had no increased risk, compared with those who were non-obese non-sarcopenic (Table 1). This association of obesity persisted after adjustment for body weight. Similar results were

Validation of biomarkers that complement CA19.9 in detecting early pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a significant cause of cancer mortality. Carbohydrate antigen 19.9 (CA19.9), the only tumor marker available to detect and monitor PDAC, is not sufficiently sensitive and specific to consistently differentiate early cancer from benign disease. In this study, we aimed to validate recently discovered serum protein biomarkers for the early detection of PDAC and ultimately develop a biomarker panel that could discriminate PDAC from other benign disease better than the existing marker CA19.9. We performed a retrospective blinded evaluation of 400 serum samples collected from individuals recruited on a consecutive basis. The sample population consisted of 250 individuals with PDAC at various stages, 130 individuals with benign conditions and 20 healthy individuals. The serum levels of each biomarker were determined by ELISAs or automated immunoassay. By randomly splitting matched samples into a training (n = 186) and validation (n = 214) set, we were able to develop and validate a biomarker panel consisting of CA19.9, CA125, and LAMC2 that significantly improved the performance of CA19.9 alone. Improved discrimination was observed in the validation set between all PDAC and benign conditions (AUCCA19.9 = 0.80 vs. AUCCA19.9+CA125+LAMC2 = 0.87; P < 0.005) as well as between early-stage PDAC and benign conditions (AUCCA19.9 = 0.69 vs. AUCCA19.9+CA125+LAMC2 = 0.76; P < 0.05) and between early-stage PDAC and chronic pancreatitis (CP; AUCCA19.9 = 0.59 vs. AUCCA19.9+CA125+LAMC2 = 0.74; P < 0.05). The data demonstrate that a serum protein biomarker panel consisting of CA125, CA19.9, and LAMC2 is able to significantly improve upon the performance of CA19.9 alone in detecting PDAC.

Biomarkers for early detection of colorectal cancer and polyps: systematic review.

There is growing interest in early detection of colorectal cancer as current screening modalities lack compliance and specificity. This study systematically reviewed the literature to identify biomarkers for early detection of colorectal cancer and polyps. Literature searches were conducted for relevant papers since 2007. Human studies reporting on early detection of colorectal cancer and polyps using biomarkers were included. Methodologic quality was evaluated, and sensitivity, specificity, and the positive predictive value (PPV) were reported. The search strategy identified 3,348 abstracts. A total of 44 papers, examining 67 different tumor markers, were included. Overall sensitivities for colorectal cancer detection by fecal DNA markers ranged from 53% to 87%. Combining fecal DNA markers increased the sensitivity of colorectal cancer and adenoma detection. Canine scent detection had a sensitivity of detecting colorectal cancer of 99% and specificity of 97%. The PPV of immunochemical fecal occult blood test (iFOBT) is 1.26%, compared with 0.31% for the current screening method of guaiac fecal occult blood test (gFOBT). A panel of serum protein biomarkers provides a sensitivity and specificity above 85% for all stages of colorectal cancer, and a PPV of 0.72%. Combinations of fecal and serum biomarkers produce higher sensitivities, specificities, and PPVs for early detection of colorectal cancer and adenomas. Further research is required to validate these biomarkers in a well-structured population-based study.

Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer.

IntroductionSerum profiling using proteomic techniques has great potential to detect biomarkers that might improve diagnosis and predict outcome for breast cancer patients (BC). This study used surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) to identify differentially expressed proteins in sera from BC and healthy volunteers (HV), with the goal of developing a new prognostic biomarker panel.MethodsTraining set serum samples from 99 BC and 51 HV subjects were applied to four adsorptive chip surfaces (anion-exchange, cation-exchange, hydrophobic, and metal affinity) and analyzed by time-of-flight MS. For validation, 100 independent BC serum samples and 70 HV samples were analyzed similarly. Cluster analysis of protein spectra was performed to identify protein patterns related to BC and HV groups. Univariate and multivariate statistical analyses were used to develop a protein panel to distinguish breast cancer sera from healthy sera, and its prognostic potential was evaluated.ResultsFrom 51 protein peaks that were significantly up- or downregulated in BC patients by univariate analysis, binary logistic regression yielded five protein peaks that together classified BC and HV with a receiver operating characteristic (ROC) area-under-the-curve value of 0.961. Validation on an independent patient cohort confirmed the five-protein parameter (ROC value 0.939). The five-protein parameter showed positive association with large tumor size (P = 0.018) and lymph node involvement (P = 0.016). By matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, immunoprecipitation and western blotting the proteins were identified as a fragment of apolipoprotein H (ApoH), ApoCI, complement C3a, transthyretin, and ApoAI. Kaplan-Meier analysis on 181 subjects after median follow-up of >5 years demonstrated that the panel significantly predicted disease-free survival (P = 0.005), its efficacy apparently greater in women with estrogen receptor (ER)-negative tumors (n = 50, P = 0.003) compared to ER-positive (n = 131, P = 0.161), although the influence of ER status needs to be confirmed after longer follow-up.ConclusionsProtein mass profiling by MS has revealed five serum proteins which, in combination, can distinguish between serum from women with breast cancer and healthy control subjects with high sensitivity and specificity. The five-protein panel significantly predicts recurrence-free survival in women with ER-negative tumors and may have value in the management of these patients.

AB0290 Serum protein biomarkers for response of rheumatoid arthritis to infliximab

BackgroundBiological therapies have been a great advance in Rheumatoid Arthritis (RA) treatment. However, up to a third of the RA patients do not respond to a given biologic and a...

A combined blood based gene expression and plasma protein abundance signature for diagnosis of epithelial ovarian cancer--a study of the OVCAD consortium.

BackgroundThe immune system is a key player in fighting cancer. Thus, we sought to identify a molecular ‘immune response signature’ indicating the presence of epithelial ovarian cancer (EOC) and to combine this with a serum protein biomarker panel to increase the specificity and sensitivity for earlier detection of EOC.MethodsComparing the expression of 32,000 genes in a leukocytes fraction from 44 EOC patients and 19 controls, three uncorrelated shrunken centroid models were selected, comprised of 7, 14, and 6 genes. A second selection step using RT-qPCR data and significance analysis of microarrays yielded 13 genes (AP2A1, B4GALT1, C1orf63, CCR2, CFP, DIS3, NEAT1, NOXA1, OSM, PAPOLG, PRIC285, ZNF419, and BC037918) which were finally used in 343 samples (90 healthy, six cystadenoma, eight low malignant potential tumor, 19 FIGO I/II, and 220 FIGO III/IV EOC patients). Using new 65 controls and 224 EOC patients (thereof 14 FIGO I/II) the abundances of six plasma proteins (MIF, prolactin, CA125, leptin, osteopondin, and IGF2) was determined and used in combination with the expression values from the 13 genes for diagnosis of EOC.ResultsCombined diagnostic models using either each five gene expression and plasma protein abundance values or 13 gene expression and six plasma protein abundance values can discriminate controls from patients with EOC with Receiver Operator Characteristics Area Under the Curve values of 0.998 and bootstrap .632+ validated classification errors of 3.1% and 2.8%, respectively. The sensitivities were 97.8% and 95.6%, respectively, at a set specificity of 99.6%.ConclusionsThe combination of gene expression and plasma protein based blood derived biomarkers in one diagnostic model increases the sensitivity and the specificity significantly. Such a diagnostic test may allow earlier diagnosis of epithelial ovarian cancer.

Introduction

Introduction