Urine biomarkers enable pancreatic cancer detection up to 2 years before diagnosis.

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly attributed to late diagnosis. We assessed the predictive performance of our previously reported urine biomarker panel for earlier detection of PDAC (LYVE1, REG1B and TFF1) in prediagnostic samples, alone and in combination with plasma CA19-9. This nested case-control study included 99 PDAC cases with urine samples prospectively collected up to 5 years prior to PDAC diagnosis and 198 matched controls. The samples were obtained from the Shanghai Women's Health Study (SWHS), the Shanghai Men's Health Studies (SMHS) and the Southern Community Cohort Study (SCCS). The urine biomarkers were measured by ELISA. Plasma CA19-9 was quantified by Luminex. Multiple logistic regression and Wilcoxon rank-sum and Mann-Whitney test were used for analysis. The internal validation approach was applied and the validated AUC estimators are reported on. The algorithm of urinary protein panel, urine creatinine and age named PancRISK, displayed similar AUC as CA19-9 up to 1 year before PDAC diagnosis (AUC = 0.79); however, the combination enhanced the AUCs to 0.89, and showed good discriminative ability (AUC = 0.77) up to 2 years. The combination showed sensitivity (SN) of 72% at 90% specificity (SP), and SP of 59% at 90% SN up to 1 year and 60% SN with 80% SP and 53% SP with 80% SN up to 2 years before PDAC diagnosis. Adding the clinical information on BMI value resulted in the overall improvement in performance of the PancRISK score. When combined with CA19-9, the urinary panel reached a workable model for detecting PDAC cases up to 2 years prior to diagnosis.

Deciphering the complex interplay between pancreatic cancer, diabetes mellitus subtypes and obesity/BMI through causal inference and mediation analyses.

OBJECTIVES: To characterise the association between type 2 diabetes mellitus (T2DM) subtypes (new-onset T2DM (NODM) or long-standing T2DM (LSDM)) and pancreatic cancer (PC) risk, to explore the direction of causation through Mendelian randomisation (MR) analysis and to assess the mediation role of body mass index (BMI). DESIGN: Information about T2DM and related factors was collected from 2018 PC cases and 1540 controls from the PanGenEU (European Study into Digestive Illnesses and Genetics) study. A subset of PC cases and controls had glycated haemoglobin, C-peptide and genotype data. Multivariate logistic regression models were applied to derive ORs and 95% CIs. T2DM and PC-related single nucleotide polymorphism (SNP) were used as instrumental variables (IVs) in bidirectional MR analysis to test for two-way causal associations between PC, NODM and LSDM. Indirect and direct effects of the BMI-T2DM-PC association were further explored using mediation analysis. RESULTS: T2DM was associated with an increased PC risk when compared with non-T2DM (OR=2.50; 95% CI: 2.05 to 3.05), the risk being greater for NODM (OR=6.39; 95% CI: 4.18 to 9.78) and insulin users (OR=3.69; 95% CI: 2.80 to 4.86). The causal association between T2DM (57-SNP IV) and PC was not statistically significant (ORLSDM=1.08, 95% CI: 0.86 to 1.29, ORNODM=1.06, 95% CI: 0.95 to 1.17). In contrast, there was a causal association between PC (40-SNP IV) and NODM (OR=2.85; 95% CI: 2.04 to 3.98), although genetic pleiotropy was present (MR-Egger: p value=0.03). Potential mediating effects of BMI (125-SNPs as IV), particularly in terms of weight loss, were evidenced on the NODM-PC association (indirect effect for BMI in previous years=0.55). CONCLUSION: Findings of this study do not support a causal effect of LSDM on PC, but suggest that PC causes NODM. The interplay between obesity, PC and T2DM is complex.

A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: A case-control study.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with around 9% of patients surviving >5 years. Asymptomatic in its initial stages, PDAC is mostly diagnosed late, when already a locally advanced or metastatic disease, as there are no useful biomarkers for detection in its early stages, when surgery can be curative. We have previously described a promising biomarker panel (LYVE1, REG1A, and TFF1) for earlier detection of PDAC in urine. Here, we aimed to establish the accuracy of an improved panel, including REG1B instead of REG1A, and an algorithm for data interpretation, the PancRISK score, in additional retrospectively collected urine specimens. We also assessed the complementarity of this panel with CA19-9 and explored the daily variation and stability of the biomarkers and their performance in common urinary tract cancers. METHODS AND FINDINGS: Clinical specimens were obtained from multiple centres: Barts Pancreas Tissue Bank, University College London, University of Liverpool, Spanish National Cancer Research Center, Cambridge University Hospital, and University of Belgrade. The biomarker panel was assayed on 590 urine specimens: 183 control samples, 208 benign hepatobiliary disease samples (of which 119 were chronic pancreatitis), and 199 PDAC samples (102 stage I-II and 97 stage III-IV); 50.7% were from female individuals. PDAC samples were collected from patients before treatment. The samples were assayed using commercially available ELISAs. Statistical analyses were performed using non-parametric Kruskal-Wallis tests adjusted for multiple comparisons, and multiple logistic regression. Training and validation datasets for controls and PDAC samples were obtained after random division of the whole available dataset in a 1:1 ratio. The substitution of REG1A with REG1B enhanced the performance of the panel to detect resectable PDAC. In a comparison of controls and PDAC stage I-II samples, the areas under the receiver operating characteristic curve (AUCs) increased from 0.900 (95% CI 0.843-0.957) and 0.926 (95% CI 0.843-1.000) in the training (50% of the dataset) and validation sets, respectively, to 0.936 in both the training (95% CI 0.903-0.969) and the validation (95% CI 0.888-0.984) datasets for the new panel including REG1B. This improved panel showed both sensitivity (SN) and specificity (SP) to be >85%. Plasma CA19-9 enhanced the performance of this panel in discriminating PDAC I-II patients from controls, with AUC = 0.992 (95% CI 0.983-1.000), SN = 0.963 (95% CI 0.913-1.000), and SP = 0.967 (95% CI 0.924-1.000). We demonstrate that the biomarkers do not show significant daily variation, and that they are stable for up to 5 days at room temperature. The main limitation of our study is the low number of stage I-IIA PDAC samples (n = 27) and lack of samples from individuals with hereditary predisposition to PDAC, for which specimens collected from control individuals were used as a proxy. CONCLUSIONS: We have successfully validated our urinary biomarker panel, which was improved by substituting REG1A with REG1B. At a pre-selected cutoff of >80% SN and SP for the affiliated PancRISK score, we demonstrate a clinically applicable risk stratification tool with a binary output for risk of developing PDAC ('elevated' or 'normal'). PancRISK provides a step towards precision surveillance for PDAC patients, which we will test in a prospective clinical study, UroPanc.

Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients.

BACKGROUND: An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK. METHODS: Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity. RESULTS: None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model. CONCLUSION: PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.

The Pancreatic Expression Database: 2018 update.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) continues to be a major resource for mining pancreatic -omics data a decade after its initial release. Here, we present recent updates to PED and describe its evolution into a comprehensive resource for extracting, analysing and integrating publicly available multi-omics datasets. A new analytical module has been implemented to run in parallel with the existing literature mining functions. This analytical module has been created using rich data content derived from pancreas-related specimens available through the major data repositories (GEO, ArrayExpress) and international initiatives (TCGA, GENIE, CCLE). Researchers have access to a host of functions to tailor analyses to meet their needs. Results are presented using interactive graphics that allow the molecular data to be visualized in a user-friendly manner. Furthermore, researchers are provided with the means to superimpose layers of molecular information to gain greater insight into alterations and the relationships between them. The literature-mining module has been improved with a redesigned web appearance, restructured query platforms and updated annotations. These updates to PED are in preparation for its integration with the Pancreatic Cancer Research Fund Tissue Bank (PCRFTB), a vital resource of pancreas cancer tissue for researchers to support and promote cutting-edge research.

Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case-control approaches.

Deciphering the underlying genetic basis behind pancreatic cancer (PC) and its associated multimorbidities will enhance our knowledge toward PC control. The study investigated the common genetic background of PC and different morbidities through a computational approach and further evaluated the less explored association between PC and autoimmune diseases (AIDs) through an epidemiological analysis. Gene-disease associations (GDAs) of 26 morbidities of interest and PC were obtained using the DisGeNET public discovery platform. The association between AIDs and PC pointed by the computational analysis was confirmed through multivariable logistic regression models in the PanGen European case-control study population of 1,705 PC cases and 1,084 controls. Fifteen morbidities shared at least one gene with PC in the DisGeNET database. Based on common genes, several AIDs were genetically associated with PC pointing to a potential link between them. An epidemiologic analysis confirmed that having any of the nine AIDs studied was significantly associated with a reduced risk of PC (Odds Ratio (OR) = 0.74, 95% confidence interval (CI) 0.58-0.93) which decreased in subjects having ≥2 AIDs (OR = 0.39, 95%CI 0.21-0.73). In independent analyses, polymyalgia rheumatica, and rheumatoid arthritis were significantly associated with low PC risk (OR = 0.40, 95%CI 0.19-0.89, and OR = 0.73, 95%CI 0.53-1.00, respectively). Several inflammatory-related morbidities shared a common genetic component with PC based on public databases. These molecular links could shed light into the molecular mechanisms underlying PC development and simultaneously generate novel hypotheses. In our study, we report sound findings pointing to an association between AIDs and a reduced risk of PC.

Demographic, clinical, and pathological features of early onset pancreatic cancer patients.

BACKGROUND: Early onset pancreatic cancer (EOPC), i.e. pancreatic ductal adenocarcinoma (PDAC) occurring in patients below 50 years of age, is rare and there is limited information regarding risk factors, molecular basis and outcome. This study aimed to determine the demographic and clinicopathological features and survival figures for EOPC. METHODS: A retrospective analysis of patients treated at the Royal London Hospital for PDAC between September 2004 and September 2015 was performed. Data on demographics, risk factors, presentation, pathological features, treatment and survival outcome were compared in EOPC and older PDAC patients. RESULTS: Of 369 PDAC cases identified, 35 (9.5%) were EOPC. Compared to older patients, EOPC patients were more frequently male (71% vs 54%, p = 0.043) and less commonly of British origin (37% vs 70%, p = 0.002). There was no significant difference regarding the prevalence of any of the risk factors known to be associated with older PDAC patients. Fewer EOPC patients presented with resectable disease (23% vs 44%, p = 0.015) and more received adjuvant chemo/radiotherapy (60% vs 46%, p = 0.008). The overall median survival and stage specific survival did not differ significantly between the two groups, although a longer survival for localized disease was seen in EOPC patients (25 months (12.9-37, 95%CI) vs 13 months (10.5-15.5 95%CI) for older PDAC patients). CONCLUSIONS: The EOPC patients had different demographics and were more likely than their older PDAC counterparts to be male. Typically they presented with more advanced disease, received more aggressive treatment, and had on overall similar survival outcome.

Reduced risk of pancreatic cancer associated with asthma and nasal allergies.

OBJECTIVE: Studies indicate an inverse association between ductal adenocarcinoma of the pancreas (PDAC) and nasal allergies. However, controversial findings are reported for the association with asthma. Understanding PDAC risk factors will help us to implement appropriate strategies to prevent, treat and diagnose this cancer. This study assessed and characterised the association between PDAC and asthma and corroborated existing reports regarding the association between allergies and PDAC risk. DESIGN: Information about asthma and allergies was collated from 1297 PDAC cases and 1024 controls included in the PanGenEU case-control study. Associations between PDAC and atopic diseases were studied using multilevel logistic regression analysis. Meta-analyses of association studies on these diseases and PDAC risk were performed applying random-effects model. RESULTS: Asthma was associated with lower risk of PDAC (OR 0.64, 95% CI 0.47 to 0.88), particularly long-standing asthma (>=17 years, OR 0.39, 95% CI 0.24 to 0.65). Meta-analysis of 10 case-control studies sustained our results (metaOR 0.73, 95% CI 0.59 to 0.89). Nasal allergies and related symptoms were associated with lower risk of PDAC (OR 0.66, 95% CI 0.52 to 0.83 and OR 0.59, 95% CI 0.46 to 0.77, respectively). These results were supported by a meta-analysis of nasal allergy studies (metaOR 0.6, 95% CI 0.5 to 0.72). Skin allergies were not associated with PDAC risk. CONCLUSIONS: This study shows a consistent inverse association between PDAC and asthma and nasal allergies, supporting the notion that atopic diseases are associated with reduced cancer risk. These results point to the involvement of immune and/or inflammatory factors that may either foster or restrain pancreas carcinogenesis warranting further research to understand the molecular mechanisms driving this association.

The pancreatic expression database: recent extensions and updates.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) is the only device currently available for mining of pancreatic cancer literature data. It brings together the largest collection of multidimensional pancreatic data from the literature including genomic, proteomic, microRNA, methylomic and transcriptomic profiles. PED allows the user to ask specific questions on the observed levels of deregulation among a broad range of specimen/experimental types including healthy/patient tissue and body fluid specimens, cell lines and murine models as well as related treatments/drugs data. Here we provide an update to PED, which has been previously featured in the Database issue of this journal. Briefly, PED data content has been substantially increased and expanded to cover methylomics studies. We introduced an extensive controlled vocabulary that records specific details on the samples and added data from large-scale meta-analysis studies. The web interface has been improved/redesigned with a quick search option to rapidly extract information about a gene/protein of interest and an upload option allowing users to add their own data to PED. We added a user guide and implemented integrated graphical tools to overlay and visualize retrieved information. Interoperability with biomart-compatible data sets was significantly improved to allow integrative queries with pancreatic cancer data.

Analysis of urinary potassium isotopes and association with pancreatic health: healthy, diabetic and cancerous states.

Background: More than 700 million people worldwide suffer from diseases of the pancreas, such as diabetes, pancreatitis and pancreatic cancer. Often dysregulation of potassium (K+) channels, co-transporters and pumps can promote development and progression of many types of these diseases. The role of K+ transport system in pancreatic cell homeostasis and disease development remains largely unexplored. Potassium isotope analysis (δ41K), however, might have the potential to detect minute changes in metabolic processes relevant for pancreatic diseases. Methods: We assessed urinary K isotope composition in a case-control study by measuring K concentrations and δ41K in spot urines collected from patients diagnosed with pancreatic cancer (n=18), other pancreas-related diseases (n=14) and compared those data to healthy controls (n=16). Results: Our results show that urinary K+ levels for patients with diseased pancreas (benign and pancreatic cancer) are significantly lower than the healthy controls. For δ41K, the values tend to be higher for individuals with pancreatic cancer (mean δ41K = -0.58 ± 0.33‰) than for healthy individuals (mean δ41K = -0.78 ± 0.19‰) but the difference is not significant (p=0.08). For diabetics, urinary K+ levels are significantly lower (p=0.03) and δ41K is significantly higher (p=0.009) than for the healthy controls. These results suggest that urinary K+ levels and K isotopes can help identify K disturbances related to diabetes, an associated factors of all-cause mortality for diabetics. Conclusion: Although the K isotope results should be considered exploratory and hypothesis-generating and future studies should focus on larger sample size and δ41K analysis of other K-disrupting diseases (e.g., chronic kidney disease), our data hold great promise for K isotopes as disease marker.

Molecular characteristics of early-onset pancreatic ductal adenocarcinoma.

The median age of patients with pancreatic ductal adenocarcinoma (PDAC) at diagnosis is 71 years; however, around 10% present with early-onset pancreatic cancer (EOPC), i.e., before age 50. The molecular mechanisms underlying such an early onset are unknown. We assessed the role of common PDAC drivers (KRAS, TP53, CDKN2A and SMAD4) and determined their mutational status and protein expression in 90 formalin-fixed, paraffin-embedded tissues, including multiple primary and matched metastases, from 37 EOPC patients. KRAS was mutated in 88% of patients; p53 was altered in 94%, and p16 and SMAD4 were lost in 86% and 71% of patients, respectively. Meta-synthesis showed a higher rate of p53 alterations in EOPC than in late-onset PDAC (94% vs. 69%, P = 0.0009) and significantly higher loss of SMAD4 (71% vs. 44%, P = 0.0025). The majority of EOPC patients accumulated aberrations in all four drivers; in addition, high tumour heterogeneity was observed across all tissues. The cumulative effect of an exceptionally high rate of alterations in all common PDAC driver genes combined with high tumour heterogeneity suggests an important mechanism underlying the early onset of PDAC.

S100P-binding protein, S100PBP, mediates adhesion through regulation of cathepsin Z in pancreatic cancer cells.

Several S100 proteins are up-regulated in pancreatic ductal adenocarcinoma (PDAC), the most significant being S100P. We previously reported on S100PBP, a binding partner of S100P, that shows no homology to any described protein and whose functions are completely unknown. To determine S100PBP expression across human tissues and organs, immunohistochemistry was performed using both multiorgan- and in-house-constructed pancreatic tissue microarrays. To establish S100PBP functions, cell lines with either stably overexpressed or silenced S100PBP were generated and investigated using Affymetrix gene expression arrays and complementary functional assays. We show that S100PBP is differentially expressed in various healthy and tumor specimens, which is both cancer- and tissue-type dependent. In healthy pancreas, S100PBP is expressed in the nuclear/perinuclear region of both exocrine and endocrine compartments. In early precancerous lesions, S100PBP is translocated to the cytoplasm, whereas in PDAC and metastatic lesions, its expression is significantly diminished. The most pronounced phenotypic change after manipulation of S100PBP expression was seen in adhesion; this was significantly reduced after S100PBP up-regulation and increased after S100PBP silencing. Up-regulation or silencing of S100PBP also led to a concomitant change in the levels of the protease cathepsin Z, the silencing of which significantly reduced PDAC cell adhesion. We further demonstrate that the interaction of cathepsin Z with arginine-glycine-aspartic acid-binding integrins, specifically αvß5, mediates the changes seen in adhesion of PDAC cells.

Proteome of formalin-fixed paraffin-embedded pancreatic ductal adenocarcinoma and lymph node metastases.

Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer-related death, largely due to metastatic disease. To better understand PDAC metastatic spread and identify novel therapeutic targets, we analysed the proteome of primary tumours and matched lymph node (LN) metastases. As frozen specimens of metastatic lesions are scarce, we examined formalin-fixed paraffin-embedded (FFPE) tissues. This poses technical challenges because of the cross-linkages induced by fixation. Using laser capture microdissection (PALM system), we isolated malignant epithelia from seven FFPE primary PDAC tumours and matched LN metastases. Following dissection, samples were analysed in duplicate using Multidimensional Protein Identification Technology (MudPIT); this resulted in the identification of 1504 proteins, 854 of which were common to all samples analysed. Comparison of the obtained proteins with data from previous proteomics studies on pancreatic tissue, pancreatic juice, serum, and urine resulted in a less than 30% overlap, indicating that our study has substantially expanded the current database of proteins expressed in this malignancy. Statistical analysis further showed that 115/854 proteins (13.5%) were significantly differentially expressed (g-value ≥ 3.8). Two proteins, S100P and 14-3-3 sigma, with highly significant g-values were confirmed to be significantly differentially expressed (S100P: p = 0.05 and 14-3-3 sigma: p < 0.001) in a larger series of 55 cases of matched primary PDAC and LN metastases using immunohistochemistry. Thus, laser capture microdissection of FFPE tissue coupled with downstream proteomic analysis is a valid approach for the investigation of metastatic PDAC. This is the first study to establish and compare the protein composition of primary PDAC and matched LN metastases, and has resulted in the identification of several potential epithelial-specific therapeutic targets, including 14-3-3 sigma and S100P.

The Pancreatic Expression database: 2011 update.

The Pancreatic Expression database (PED, http://www.pancreasexpression.org) has established itself as the main repository for pancreatic-derived -omics data. For the past 3 years, its data content and access have increased substantially. Here we describe several of its new and improved features, such as data content, which now includes over 60,000 measurements derived from transcriptomics, proteomics, genomics and miRNA profiles from various pancreas-centred reports on a broad range of specimen and experimental types. We also illustrate the capabilities of its interface, which allows integrative queries that can combine PED data with a growing number of biological resources such as NCBI, Ensembl, UniProt and Reactome. Thus, PED is capable of retrieving and integrating different types of -omics, annotations and clinical data. We also focus on the importance of data sharing and interoperability in the cancer field, and the integration of PED into the International Cancer Genome Consortium (ICGC) data portal.

A global insight into a cancer transcriptional space using pancreatic data: importance, findings and flaws.

Despite the increasing wealth of available data, the structure of cancer transcriptional space remains largely unknown. Analysis of this space would provide novel insights into the complexity of cancer, assess relative implications in complex biological processes and responses, evaluate the effectiveness of cancer models and help uncover vital facets of cancer biology not apparent from current small-scale studies. We conducted a comprehensive analysis of pancreatic cancer-expression space by integrating data from otherwise disparate studies. We found (i) a clear separation of profiles based on experimental type, with patient tissue samples, cell lines and xenograft models forming distinct groups; (ii) three subgroups within the normal samples adjacent to cancer showing disruptions to biofunctions previously linked to cancer; and (iii) that ectopic subcutaneous xenografts and cell line models do not effectively represent changes occurring in pancreatic cancer. All findings are available from our online resource for independent interrogation. Currently, the most comprehensive analysis of pancreatic cancer to date, our study primarily serves to highlight limitations inherent with a lack of raw data availability, insufficient clinical/histopathological information and ambiguous data processing. It stresses the importance of a global-systems approach to assess and maximise findings from expression profiling of malignant and non-malignant diseases.

Nutraceuticals as Supportive Therapeutic Agents in Diabetes and Pancreatic Ductal Adenocarcinoma: A Systematic Review.

The correlation between pancreatic ductal adenocarcinoma (PDAC) and diabetes-related mechanisms support the hypothesis that early therapeutic strategies targeting diabetes can contribute to PDAC risk reduction and treatment improvement. A systematic review was conducted, using PubMed, Embase and Cochrane Library databases, to evaluate the current evidence from clinical studies qualitatively examining the efficacy of four natural products: Curcumin-Curcuma longa L.; Thymoquinone-Nigella sativa L.; Genistein-Glycine max L.; Ginkgo biloba L.; and a low-carbohydrate ketogenic diet in type 2 diabetes (T2D) and PDAC treatment. A total of 28 clinical studies were included, showing strong evidence of inter-study heterogeneity. Used as a monotherapy or in combination with chemo-radiotherapy, the studied substances did not significantly improve the treatment response of PDAC patients. However, pronounced therapeutic efficacy was confirmed in T2D. The natural products and low-carbohydrate ketogenic diet, combined with the standard drugs, have the potential to improve T2D treatment and thus potentially reduce the risk of cancer development and improve multiple biological parameters in PDAC patients.

Dual-color proteomic profiling of complex samples with a microarray of 810 cancer-related antibodies.

Antibody microarrays have the potential to enable comprehensive proteomic analysis of small amounts of sample material. Here, protocols are presented for the production, quality assessment, and reproducible application of antibody microarrays in a two-color mode with an array of 1,800 features, representing 810 antibodies that were directed at 741 cancer-related proteins. In addition to measures of array quality, we implemented indicators for the accuracy and significance of dual-color detection. Dual-color measurements outperform a single-color approach concerning assay reproducibility and discriminative power. In the analysis of serum samples, depletion of high-abundance proteins did not improve technical assay quality. On the contrary, depletion introduced a strong bias in protein representation. In an initial study, we demonstrated the applicability of the protocols to proteins derived from urine samples. We identified differences between urine samples from pancreatic cancer patients and healthy subjects and between sexes. This study demonstrates that biomedically relevant data can be produced. As demonstrated by the thorough quality analysis, the dual-color antibody array approach proved to be competitive with other proteomic techniques and comparable in performance to transcriptional microarray analyses.

Non-Invasive Biomarkers for Early Lung Cancer Detection.

Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.