Cancer-specific risk prediction with a serum microRNA signature.

We recently derived and validated a serum-based microRNA risk score (miR-score) that predicted colorectal cancer (CRC) occurrence with very high accuracy within 14 years of follow-up in a population-based cohort study from Germany (ESTHER cohort). Here, we aimed to evaluate associations of the CRC-specific miR-score with the risk of developing other common cancers, including female breast cancer (BC), lung cancer (LC), and prostate cancer (PC), in the ESTHER cohort. MicroRNAs (miRNAs) were profiled by quantitative real-time PCR in serum samples collected at baseline from randomly selected incident cases of BC (n = 90), LC (n = 88), and PC (n = 93) and participants without diagnosis of CRC, LC, BC, or PC (controls, n = 181) until the end of the 17-year follow-up. Multivariate logistic regression models were used to evaluate the associations of the miR-score with BC, LC, and PC incidence. The miR-score showed strong inverse associations with BC and LC incidence [odds ratio per 1 standard deviation increase: 0.60 (95% confidence interval [CI] 0.43-0.82), p = 0.0017, and 0.64 (95% CI 0.48-0.84),p = 0.0015, respectively]. Associations with PC were not statistically significant but pointed in the positive direction. Our study highlights the potential of serum-based miRNA biomarkers for cancer-specific risk prediction. Further large cohort studies aiming to investigate, validate, and optimize the use of circulating miRNA signatures for cancer risk assessment are warranted.

Potential of Fecal Carcinoembryonic Antigen for Noninvasive Detection of Colorectal Cancer: A Systematic Review.

Carcinoembryonic antigen (CEA) is more abundant in feces than in serum; however, evidence for the role of fecal CEA (FCEA) in the detection of colorectal cancer (CRC) is limited. We conducted a systematic review of studies that evaluated FCEA for the noninvasive detection and diagnosis of CRC. PubMed and Web of Science were searched for relevant studies published until 18 January 2023. Information on publication year, study design, country, study population characteristics, FCEA and serum CEA (SCEA) concentrations, and diagnostic performance was summarized. Two authors independently extracted data and assessed the risk of bias and applicability of each included study. Seven studies published between 1979 and 2021, all conducted in clinical settings and together involving 399 CRC patients and 889 controls, were identified. Significant differences in FCEA concentrations were observed between CRC and control groups in all studies. Methods for detecting FCEA varied, with the electronic chemiluminescence immunoassay (ECLIA) being used in the most recent studies. Reported sensitivities, specificities, and area under the curves of FCEA ranged from 50.0% to 85.7%, 73.0% to 100.0%, and 0.704 to 0.831, respectively. In direct comparisons, the diagnostic performance of FCEA was better than that of SCEA. The potential role of FCEA as a novel, noninvasive, easily measurable biomarker for the diagnosis of CRC requires further evaluation in screening settings.

Comparison of discrimination performance of 11 lung cancer risk models for predicting lung cancer in a prospective cohort of screening-age adults from Germany followed over 17 years.

Randomized trials have demonstrated considerable reduction in lung cancer (LC) mortality by screening pre-selected heavy smokers with low-dose computed tomography (LDCT). Newer screening guidelines recommend refined LC risk models for selecting the target population for screening. We aimed to evaluate and compare the discrimination performance of LC risk models and previously used trial criteria in predicting LC incidence and mortality in a large German cohort of screening-age adults. Within ESTHER, a population-based prospective cohort study conducted in Saarland, Germany, 4812 ever smokers aged 50-75 years were followed up with respect to LC incidence and mortality for up to 17 years. We quantified the performance of 11 different LC risk models by the area under the curve (AUC) and compared the proportion of correctly predicted LC cases between the best performing models and the LDCT trial criteria. Risk prediction of LC incidence in the ESTHER ever smokers was best for the Bach model, LCRAT and LCDRAT with AUCs ranging from 0.782 to 0.787, from 0.770 to 0.774, and from 0.765 to 0.771 for the follow-up time periods of cases identified at 6, 11, and 17 years, respectively. At cutoffs yielding comparable positivity rates as the LDCT trial criteria, these models would have identified between 11.8 (95% CI 3.0-20.5) and 17.6 (95% CI 10.1-25.2) percent units higher proportions of LC cases occurring during the initial 6 years of follow-up. Use of LC risk models is expected to result in substantially greater potential to identify people at highest risk of LC, suggesting enhanced potential for reducing LC mortality by LC screening.

Association of the inflammation-related proteome with dementia development at older age: results from a large, prospective, population-based cohort study.

BACKGROUND: Chronic inflammation is a central feature of several forms of dementia. However, few details on the associations of blood-based inflammation-related proteins with dementia incidence have been explored yet. METHODS: The Olink Target 96 Inflammation panel was measured in baseline serum samples (collected 07/2000-06/2002) of 1782 older adults from a German, population-based cohort study in a case-cohort design. Logistic regression models were used to assess the associations of biomarkers with all-cause dementia, Alzheimer's disease, and vascular dementia incidence. RESULTS: During 17 years of follow-up, 504 participants were diagnosed with dementia, including 163 Alzheimer's disease and 195 vascular dementia cases. After correction for multiple testing, 58 out of 72 tested (80.6%) biomarkers were statistically significantly associated with all-cause dementia, 22 with Alzheimer's disease, and 33 with vascular dementia incidence. We identified four biomarker clusters, among which the strongest representatives, CX3CL1, EN-RAGE, LAP TGF-beta-1, and VEGF-A, were significantly associated with dementia endpoints independently from other inflammation-related proteins. CX3CL1 (odds ratio [95% confidence interval] per 1 standard deviation increase: 1.41 [1.24-1.60]) and EN-RAGE (1.41 [1.25-1.60]) were associated with all-cause dementia incidence, EN-RAGE (1.51 [1.25-1.83]) and LAP TGF-beta-1 (1.46 [1.21-1.76]) with Alzheimer's disease incidence, and VEGF-A (1.43 [1.20-1.70]) with vascular dementia incidence. All named associations were stronger among APOE ε4-negative subjects. CONCLUSION: With this large, population-based cohort study, we show for the first time that the majority of inflammation-related proteins measured in blood samples are associated with total dementia incidence. Future studies should concentrate not only on single biomarkers but also on the complex relationships in biomarker clusters.

Assessment of a Serum Microrna Risk Score for Colorectal Cancer among Participants of Screening Colonoscopy at Various Stages of Colorectal Carcinogenesis.

We recently derived and validated a serum-based microRNA risk score (miR-score) which predicted colorectal cancer (CRC) occurrence with very high accuracy within 14 years of follow-up in a large population-based cohort. Here, we aimed to assess and compare the distribution of the miR-score among participants of screening colonoscopy at various stages of colorectal carcinogenesis. MicroRNAs (miRNAs) were profiled by quantitative-real-time-polymerase-chain-reaction in the serum samples of screening colonoscopy participants with CRC (n = 52), advanced colorectal adenoma (AA, n = 100), non-advanced colorectal adenoma (NAA, n = 88), and participants free of colorectal neoplasms (n = 173). The mean values of the miR-score were compared between groups by the Mann-Whitney U test. The associations of the miR-score with risk for colorectal neoplasms were evaluated using logistic regression analyses. MicroRNA risk scores were significantly higher among participants with AA than among those with NAA (p = 0.027) and those with CRC (p = 0.014), whereas no statistically significant difference was seen between those with NAA and those with no colorectal neoplasms (p = 0.127). When comparing adjacent groups, miR-scores were inversely associated with CRC versus AA and positively associated with AA versus NAA [odds ratio (OR), 0.37 (95% confidence interval (CI), 0.16-0.86) and OR, 2.22 (95% CI, 1.06-4.64) for the top versus bottom tertiles, respectively]. Our results are consistent with the hypothesis that a high miR-score may be indicative of an increased CRC risk by an increased tendency of progression from non-advanced to advanced colorectal neoplasms, along with a change of the miR-patterns after CRC manifestation.

Potential of Inflammatory Protein Signatures for Enhanced Selection of People for Lung Cancer Screening.

Randomized trials have demonstrated a substantial reduction in lung cancer (LC) mortality by screening heavy smokers with low-dose computed tomography (LDCT). The aim of this study was to assess if and to what extent blood-based inflammatory protein biomarkers might enhance selection of those at highest risk for LC screening. Ever smoking participants were chosen from 9940 participants, aged 50-75 years, who were followed up with respect to LC incidence for 17 years in a prospective population-based cohort study conducted in Saarland, Germany. Using proximity extension assay, 92 inflammation protein biomarkers were measured in baseline plasma samples of ever smoking participants, including 172 incident LC cases and 285 randomly selected participants free of LC. Smoothly clipped absolute deviation (SCAD) penalized regression with 0.632+ bootstrap for correction of overoptimism was applied to derive an inflammation protein biomarker score (INS) and a combined INS-pack-years score in a training set, and algorithms were further evaluated in an independent validation set. Furthermore, the performances of nine LC risk prediction models individually and in combination with inflammatory plasma protein biomarkers for predicting LC incidence were comparatively evaluated. The combined INS-pack-years score predicted LC incidence with area under the curves (AUCs) of 0.811 and 0.782 in the training and the validation sets, respectively. The addition of inflammatory plasma protein biomarkers to established nine LC risk models increased the AUCs up to 0.121 and 0.070 among ever smoking participants from training and validation sets, respectively. Our results suggest that inflammatory protein biomarkers may have potential to improve the selection of people for LC screening and thereby enhance screening efficiency.

Correlation of Repeat Measurements of 27 Candidate Protein Markers for Colorectal Cancer Screening Taken Three Years and Multiple Freeze-Thaw Cycles Apart.

In recent years the blood proteome has been increasingly researched for biomarkers for early detection of colorectal cancer (CRC). Blood samples from screening studies are often subject to preanalytical variability and repeated freeze−thaw cycles. We aimed to assess the correlation of repeat measurements of 27 candidate protein markers for CRC screening taken three years and multiple freeze−thaw cycles apart. The concentrations of 27 protein markers were measured in plasma samples of 39 newly detected CRC cases from a cohort of 9245 participants of screening colonoscopies. The proteins were measured using proximity extension assays (PEA) carried out on the same set of samples twice, three years apart, with an average of three freeze−thaw cycles in between the two measurements. Pearson's product moment correlation coefficients were calculated. Correlation coefficients ranged from +0.43 to +0.97, with a median of 0.67 and an interquartile range of +0.58 to +0.84, with all p-values of correlation being <0.01 (<0.0005 for 22 proteins, <0.001 for 4 proteins). Repeat measurements of the 27 protein biomarkers for CRC screening performed three years later, and on average three freeze−thaw cycles apart, showed moderate to high levels of correlation. Apart from the effects of freeze−thaw cycles, slightly different preprocessing performed on the data may have contributed to recorded differences between measurements.

Novel associations between inflammation-related proteins and adiposity: A targeted proteomics approach across four population-based studies.

Chronic low-grade inflammation has been proposed as a linking mechanism between obesity and the development of inflammation-related conditions such as insulin resistance and cardiovascular disease. Despite major advances in the last 2 decades, the complex relationship between inflammation and obesity remains poorly understood. Therefore, we aimed to identify novel inflammation-related proteins associated with adiposity. We investigated the association between BMI and waist circumference and 72 circulating inflammation-related proteins, measured using the Proximity Extension Assay (Olink Proteomics), in 3,308 participants of four independent European population-based studies (KORA-Fit, BVSII, ESTHER, and Bialystok PLUS). In addition, we used body fat mass measurements obtained by Dual-energy X-ray absorptiometry (DXA) in the Bialystok PLUS study to further validate our results and to explore the relationship between inflammation-related proteins and body fat distribution. We found 14 proteins associated with at least one measure of adiposity across all four studies, including four proteins for which the association is novel: DNER, SLAMF1, RANKL, and CSF-1. We confirmed previously reported associations with CCL19, CCL28, FGF-21, HGF, IL-10RB, IL-18, IL-18R1, IL-6, SCF, and VEGF-A. The majority of the identified inflammation-related proteins were associated with visceral fat as well as with the accumulation of adipose tissue in the abdomen and the trunk. In conclusion, our study provides new insights into the immune dysregulation observed in obesity that might help uncover pathophysiological mechanisms of disease development.

A microRNA panel compared to environmental and polygenic scores for colorectal cancer risk prediction.

Circulating microRNAs (miRNAs) could improve colorectal cancer (CRC) risk prediction. Here, we derive a blood-based miRNA panel and evaluate its ability to predict CRC occurrence in a population-based cohort of adults aged 50-75 years. Forty-one miRNAs are preselected from independent studies and measured by quantitative-real-time-polymerase-chain-reaction in serum collected at baseline of 198 participants who develop CRC during 14 years of follow-up and 178 randomly selected controls. A 7-miRNA score is derived by logistic regression. Its predictive ability, quantified by the optimism-corrected area-under-the-receiver-operating-characteristic-curve (AUC) using .632+ bootstrap is 0.794. Predictive ability is compared to that of an environmental risk score (ERS) based on known risk factors and a polygenic risk score (PRS) based on 140 previously identified single-nucleotide-polymorphisms. In participants with all scores available, optimism-corrected-AUC is 0.802 for the 7-miRNA score, while AUC (95% CI) is 0.557 (0.498-0.616) for the ERS and 0.622 (0.564-0.681) for the PRS.

Comparison of Proteomic Technologies for Blood-Based Detection of Colorectal Cancer.

Blood-based protein biomarkers are increasingly being explored as supplementary or efficient alternatives for population-based screening of colorectal cancer (CRC). The objective of the current study was to compare the diagnostic potential of proteins measured with different proteomic technologies. The concentrations of protein biomarkers were measured using proximity extension assays (PEAs), liquid chromatography/multiple reaction monitoring-mass spectrometry (LC/MRM-MS) and quantibody microarrays (QMAs) in plasma samples of 56 CRC patients and 99 participants free of neoplasms. In another approach, proteins were measured in serum samples of 30 CRC cases and 30 participants free of neoplasm using immunome full-length functional protein arrays (IpAs). From all the measurements, 9, 6, 35 and 14 protein biomarkers overlapped for comparative evaluation of (a) PEA and LC/MRM-MS, (b) PEA and QMA, (c) PEA and IpA, and (d) LC/MRM-MS and IpA measurements, respectively. Correlation analysis was performed, along with calculation of the area under the curve (AUC) for assessing the diagnostic potential of each biomarker. DeLong's test was performed to assess the differences in AUC. Evaluation of the nine biomarkers measured with PEA and LC/MRM-MS displayed correlation coefficients >+0.6, similar AUCs and DeLong's p-values indicating no differences in AUCs for biomarkers like insulin-like growth factor binding protein 2 (IGFBP2), matrix metalloproteinase 9 (MMP9) and serum paraoxonase lactonase 3 (PON3). Comparing six proteins measured with PEA and QMA showed good correlation and similar diagnostic performance for only one protein, growth differentiation factor 15 (GDF15). The comparison of 35 proteins measured with IpA and PEA and 14 proteins analyzed with IpA and LC/MRM-MS revealed poor concordance and comparatively better AUCs when measured with PEA and LC/MRM-MS. The comparison of different proteomic technologies suggests the superior performance of novel technologies like PEA and LC/MRM-MS over the assessed array-based technologies in blood-protein-based early detection of CRC.

Fecal microRNAs, Fecal microRNA Panels, or Combinations of Fecal microRNAs with Fecal Hemoglobin for Early Detection of Colorectal Cancer and Its Precursors: A Systematic Review.

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer mortality globally. Fecal miRNAs have been suggested to be promising biomarkers for CRC early detection. We aimed to conduct a systematic literature review on the diagnostic performance of fecal miRNA markers for CRC and its precursors. PubMed and Web of Science were searched to retrieve relevant articles published up to 7 December 2021. Information on study design, characteristics of study population, pre-analytics (sample collection, processing, and storage), fecal miRNA extraction and quantification technologies, and diagnostic performance (including sensitivity, specificity, and area under the curve (AUC)) were summarized. Twenty studies reporting on 31 individual miRNAs and 16 miRNA panels (with 2-9 markers) for CRC diagnosis were identified. Substantial heterogeneity existed regarding stool sample collection, processing, storage, and miRNA extraction and normalization. For two individual miRNAs and one miRNA panel, values ≥ 80% were reported for both sensitivity and specificity; however, none of these results were either internally or externally validated. In a study among fecal immunochemical test-positive cases recruited from a true screening setting, better diagnostic performance was identified and internally validated for a combination panel including two miRNAs, fecal hemoglobin level, and patient age and sex, compared with fecal hemoglobin concentration alone. Fecal miRNAs or miRNA panels, possibly in combination with fecal hemoglobin test, may be promising candidates for noninvasive CRC early detection. However, large prospective and well-designed studies in CRC screening cohorts are required to validate promising miRNAs or miRNA panels.

Multiplex quantitation of 270 plasma protein markers to identify a signature for early detection of colorectal cancer.

Blood-based protein biomarker signatures might be an alternative or supplement to existing methods for early detection of colorectal cancer (CRC) for population-based screening. The objective of this study was to derive a protein biomarker signature for early detection of CRC and its precursor advanced adenoma (AA). In a two-stage design, 270 protein markers were measured by liquid chromatography/multiple reaction monitoring/mass spectrometry in plasma samples of discovery and validation sets. In the discovery set consisting of 100 newly diagnosed CRC cases and 100 age- and sex-matched controls free of neoplasm at screening colonoscopy, the algorithms predicting the presence of early- or late-stage CRC were derived by Lasso regression and .632 + bootstrap. The prediction algorithms were then externally validated in an independent validation set consisting of participants of screening colonoscopy including 56 participants with CRC, 99 with AA and 99 controls without any colorectal neoplasms. Three different signatures for all-, early- and late-stage CRC consisting of five-, three- and eight-protein markers were obtained in the discovery set with areas under the curves (AUCs) after .632 + bootstrap adjustment of 0.85, 0.83 and 0.96, respectively. External validation in the representative screening population yielded AUCs of 0.79 (95% CI, 0.70-0.86), 0.79 (95% CI, 0.66-0.89) and 0.80 (95% CI, 0.70-0.89) for all-, early- and late-stage CRCs, respectively. The three-marker early-stage algorithm yielded an AUC of 0.65 (95% CI, 0.56-0.73) for detection of AA in the validation set. Although not yet competitive with available stool-based tests for CRC early detection, the identified proteins may contribute to the development of powerful blood-based tests for early detection of CRC and its precursors AAs.

Multiplex screening of 275 plasma protein biomarkers to identify a signature for early detection of colorectal cancer.

Blood-based protein biomarkers may be an attractive option for early detection of colorectal cancer (CRC). Here, we used a two-stage design to measure 275 protein markers by proximity extension assay (PEA), first in plasma samples of a discovery set consisting of 98 newly diagnosed CRC cases and 100 age- and gender-matched controls free of neoplasm at screening colonoscopy. An algorithm predicting the presence of early- or late-stage CRC was derived by least absolute shrinkage and selection operator regression with .632+ bootstrap method, and the algorithms were then validated using PEA again in an independent validation set consisting of participants of screening colonoscopy with and without CRC (n = 56 and 102, respectively). Three different signatures for all-, early-, and late-stage CRC consisting of 9, 12, and 11 protein markers were obtained in the discovery set with areas under the curves (AUCs) after .632 + bootstrap adjustment of 0.92, 0.91, and 0.96, respectively. External validation among participants of screening colonoscopy yielded AUCs of 0.76 [95% confidence interval (95% CI), 0.67-0.84], 0.75 (95% CI, 0.62-0.87), and 0.80 (95% CI, 0.68-0.89) for all-, early-, and late-stage CRC, respectively. Although the identified protein markers are not competitive with the best available stool tests, these proteins may contribute to the development of powerful blood-based tests for CRC early detection in the future.

Evaluation and Validation of Plasma Proteins Using Two Different Protein Detection Methods for Early Detection of Colorectal Cancer.

OBJECTIVE: Plasma protein biomarkers could be an efficient alternative for population-based screening for early detection of colorectal cancer (CRC). The objective of this study was to evaluate and validate plasma proteins individually and as a signature for early detection of CRC. METHODS: In a three-stage design, proteins were measured firstly by liquid chromatography/multiple reaction monitoring-mass spectrometry (LC/MRM-MS) and later by proximity extension assay (PEA) in a discovery set consisting of 96 newly diagnosed CRC cases and 94 controls free of neoplasms at screening colonoscopy. Two algorithms (one for each measurement method) were derived by Lasso regression and .632+ bootstrap based on 11 proteins that were included in both the LC/MRM-MS and PEA measurements. Additionally, another algorithm was constructed from the same eleven biomarkers plus amphireglin, the most promising protein marker in the PEA measurements that had not been available from the LC/MRM-MS measurements. Lastly the three prediction signatures were validated with PEA in independent samples of participants of screening colonoscopy (CRC (n = 56), advanced adenoma (n = 101), and participants free of neoplasm (n = 102)). RESULTS: The same four proteins were included in all three prediction signatures; mannan binding lectin serine protease 1, osteopontin, serum paraoxonase lactonase 3 and transferrin receptor protein 1, and the third prediction signature additionally included amphiregulin. In the independent validation set from a true screening setting, the five-marker blood-based signature including AREG presented areas under the curves of 0.82 (95% CI, 0.74-0.89), 0.86 (95% CI, 0.77-0.92) and 0.76 (95% CI, 0.64-0.86) for all, early and late stages CRC, respectively. CONCLUSION: Two different measurement methods consistently identified four protein markers and an algorithm additionally including amphiregulin, a marker measured by PEA only, showed promising performance for detecting early stage CRC in an independent validation in a true screening setting. These proteins may be potential candidates for blood-based tests for early detection of CRC.

Metabolomics Biomarkers for Detection of Colorectal Neoplasms: A Systematic Review.

BACKGROUND: Several approaches have been suggested to be useful in the early detection of colorectal neoplasms. Since metabolites are closely related to the phenotype and are available from different human bio-fluids, metabolomics are candidates for non-invasive early detection of colorectal neoplasms. OBJECTIVES: We aimed to summarize current knowledge on performance characteristics of metabolomics biomarkers that are potentially applicable in a screening setting for the early detection of colorectal neoplasms. DESIGN: We conducted a systematic literature search in PubMed and Web of Science and searched for biomarkers for the early detection of colorectal neoplasms in easy-to-collect human bio-fluids. Information on study design and performance characteristics for diagnostic accuracy was extracted. RESULTS: Finally, we included 41 studies in our analysis investigating biomarkers in different bio-fluids (blood, urine, and feces). Although single metabolites mostly had limited ability to distinguish people with and without colorectal neoplasms, promising results were reported for metabolite panels, especially amino acid panels in blood samples, as well as nucleosides in urine samples in several studies. However, validation of the results is limited. CONCLUSIONS: Panels of metabolites consisting of amino acids in blood and nucleosides in urinary samples might be useful biomarkers for early detection of advanced colorectal neoplasms. However, to make metabolomic biomarkers clinically applicable, future research in larger studies and external validation of the results is required.

Quantitative fecal immunochemical tests for colorectal cancer screening.

Fecal immunochemical tests (FITs) for hemoglobin (Hb) are increasingly used for colorectal cancer (CRC) screening. We aimed to review, summarize and compare reported diagnostic performance of various FITs. PubMed and Web of Science were searched from inception to July 24, 2017. Data on diagnostic performance of quantitative FITs, conducted in colonoscopy-controlled average-risk screening populations, were extracted. Summary receiver operating characteristic (ROC) curves were plotted and correlations between thresholds, positivity rates (PRs), sensitivities and specificities were assessed. Seven test brands were investigated across 22 studies. Although reported sensitivities for CRC, advanced adenoma (AA) and any advanced neoplasm (AN) varied widely (ranges: 25-100%, 6-44% and 9-60%, respectively), with specificities for AN ranging from 82% to 99%, the estimates were very close to the respective summary ROC curves whose areas under the curve (95% CI) were 0.905 (0.88-0.94), 0.683 (0.67-0.70) and 0.710 (0.70-0.72) for CRC, AA and AN, respectively. The seemingly large heterogeneity essentially reflected variations in test thresholds (range: 2-82 µg Hb/g feces) and showed moderate correlations with sensitivity (r = -0.49) and specificity (r = 0.60) for AN. By contrast, observed PRs (range: 1-21%) almost perfectly correlated with sensitivity (r = 0.84) and specificity (r = -0.94) for AN. The apparent large heterogeneity in diagnostic performance between various FITs can be almost completely overcome by appropriate threshold adjustments. Instead of simply applying the threshold recommended by the manufacturer, screening programs should adjust the threshold to yield a desired PR which is a very good proxy indicator for the specificity and the subsequent colonoscopy workload.

Blood-Based Protein Signatures for Early Detection of Colorectal Cancer: A Systematic Review.

OBJECTIVES: Blood-based proteins might be an attractive option for early detection of colorectal cancer (CRC), but individually they are unlikely to achieve the diagnostic performance required for population based screening. We aimed at summarizing current evidence of diagnostic performance of signatures based on multiple proteins for early detection of CRC. METHODS: A systematic literature review adhering to the PRISMA (preferred reporting items for systematic reviews and meta-analysis) guidelines was performed. PubMed and Web of Science databases were searched for potentially relevant studies published until 28th August, 2017. Relevant studies were identified by predefined eligibility criteria. Estimates of indicators of diagnostic performance such as sensitivity, specificity, and the area under the curve (AUC), along with information on validation and other key methodological procedures were extracted. Study quality was assessed by a QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) instrument tool. RESULTS: Thirty six eligible studies with numbers of CRC cases ranging from 23 to 512 and the number of proteins included in signatures ranged from 3 to 13 were identified. Reported Youden's Index and AUC ranged from 0.19 to 0.95 and from 0.62 to 0.996, respectively. However most studies, especially those reporting better diagnostic performance, were conducted in clinical rather than screening setting and many studies lacked any internal or external validation of identified algorithm. CONCLUSIONS: Blood-based tests using signatures of multiple proteins may be a promising approach for non-invasive CRC screening. However, promising signatures identified in clinical settings still require rigorous evaluation in large studies conducted in true screening setting.

Cell Line Secretome and Tumor Tissue Proteome Markers for Early Detection of Colorectal Cancer: A Systematic Review.

Objective: In order to find low abundant proteins secretome and tumor tissue proteome data have been explored in the last few years for the diagnosis of colorectal cancer (CRC). In this review we aim to summarize the results of studies evaluating markers derived from the secretome and tumor proteome for blood based detection of colorectal cancer. Methods: Observing the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines PubMed and Web of Science databases were searched systematically for relevant studies published up to 18 July 2017. After screening for predefined eligibility criteria a total of 47 studies were identified. Information on diagnostic performance indicators, methodological procedures and validation was extracted. Functions of proteins were identified from the UniProt database and the the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess study quality. Results: Forty seven studies meeting inclusion criteria were identified. Overall, 83 different proteins were identified, with carcinoembryonic Antigen (CEA) being by far the most commonly reported (reported in 24 studies). Evaluation of the markers or marker combinations in blood samples from CRC cases and controls yielded apparently very promising diagnostic performances, with area under the curve >0.9 in several cases, but lack of internal or external validation, overoptimism due to overfitting and spectrum bias due to evaluation in clinical setting rather than screening settings are major concerns. Conclusions: Secretome and tumor proteome-based biomarkers when validated in blood yield promising candidates. However, for discovered protein markers to be clinically applicable as screening tool they have to be specific for early stages and need to be validated externally in larger studies with participants recruited in true screening setting.