A Plasma Biomarker Panel to Identify Surgically Resectable Early-Stage Pancreatic Cancer

The role of biomarkers in the early detection of pancreatic cancer

Pancreatic surveillance can detect early-stage pancreatic cancer and achieve long-term survival, but currently involves annual endoscopic ultrasound and MRI/MRCP, and is recommended only for individuals who meet familial/genetic risk criteria. To improve upon current approaches to pancreatic cancer early detection and to expand access, more accurate, inexpensive, and safe biomarkers are needed, but finding them has remained elusive. Newer approaches to early detection, such as using gene tests to personalize biomarker interpretation, and the increasing application of artificial intelligence approaches to integrate complex biomarker data, offer promise that clinically useful biomarkers for early pancreatic cancer detection are on the horizon.

Extracellular Vesicular Analysis of Glypican 1 mRNA and Protein for Pancreatic Cancer Diagnosis and Prognosis

Detecting pancreatic duct adenocarcinoma (PDAC) in its early stages and predicting late-stage patient prognosis undergoing chemotherapy is challenging. This work shows that the activation of specific oncogenes leads to elevated expression of mRNAs and their corresponding proteins in extracellular vesicles (EVs) circulating in blood. Utilizing an immune lipoplex nanoparticle (ILN) biochip assay, these findings demonstrate that glypican 1 (GPC1) mRNA expression in the exosomes-rich (Exo) EV subpopulation and GPC1 membrane protein (mProtein) expression in the microvesicles-rich (MV) EV subpopulation, particularly the tumor associated microvesicles (tMV), served as a viable biomarker for PDAC. A combined analysis effectively discriminated early-stage PDAC patients from benign pancreatic diseases and healthy donors in sizable clinical from multiple hospitals. Furthermore, among late-stage PDAC patients undergoing chemotherapy, lower GPC1 tMV-mProtein and Exo-mRNA expression before treatment correlated significantly with prolonged overall survival. These findings underscore the potential of vesicular GPC1 expression for early PDAC screenings and chemotherapy prognosis.

A Circulating Panel of circRNA Biomarkers for the Noninvasive and Early Detection of Pancreatic Ductal Adenocarcinoma

BACKGROUND & AIMS

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies. Delayed manifestation of symptoms and lack of specific diagnostic markers lead patients being diagnosed with PDAC at advanced stages. This study aimed to develop a circular RNA (circRNA)-based biomarker panel to facilitate noninvasive and early detection of PDAC.

METHODS

A systematic genome-wide discovery of circRNAs overexpressed in patients with PDAC was conducted. Subsequently, validation of the candidate markers in the primary tumors from patients with PDAC was performed, followed by their translation into a plasma-based liquid biopsy assay by analyzing 2 independent clinical cohorts of patients with PDAC and nondisease controls. The performance of the circRNA panel was assessed in conjunction with the plasma levels of cancer antigen 19-9 for the early detection of PDAC.

RESULTS

Initially, a panel of 10 circRNA candidates was identified during the discovery phase. Subsequently, the panel was reduced to 5 circRNAs in the liquid biopsy-based assay, which robustly identified patients with PDAC and distinguished between early-stage (stage I/II) and late-stage (stage III/IV) disease. The areas under the curve of this diagnostic panel for the detection of early-stage PDAC were 0.83 and 0.81 in the training and validation cohorts, respectively. Moreover, when this panel was combined with cancer antigen 19-9 levels, the diagnostic performance for identifying patients with PDAC improved remarkably (area under the curve, 0.94) for patients in the validation cohort. Furthermore, the circRNA panel could also efficiently identify patients with PDAC (area under the curve, 0.85) who were otherwise deemed clinically cancer antigen 19-9-negative (<37 U/mL).

CONCLUSIONS

A circRNA-based biomarker panel with a robust noninvasive diagnostic potential for identifying patients with early-stage PDAC was developed.

A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease that requires precise diagnosis for effective treatment. However, the diagnostic value of carbohydrate antigen 19 - 9 (CA19-9) is limited. Therefore, this study aims to identify novel tumor-associated autoantibodies (TAAbs) for PDAC diagnosis.

METHODS

A three-phase strategy comprising discovery, test, and validation was implemented. HuProt™ Human Proteome Microarray v3.1 was used to screen potential TAAbs in 49 samples. Subsequently, the levels of potential TAAbs were evaluated in 477 samples via enzyme-linked immunosorbent assay (ELISA) in PDAC, benign pancreatic diseases (BPD), and normal control (NC), followed by the construction of a diagnostic model.

RESULTS

In the discovery phase, protein microarrays identified 167 candidate TAAbs. Based on bioinformatics analysis, fifteen tumor-associated antigens (TAAs) were selected for further validation using ELISA. Ten TAAbs exhibited differentially expressed in PDAC patients in the test phase (P < 0.05), with an area under the curve (AUC) ranging from 0.61 to 0.76. An immunodiagnostic model including three TAAbs (anti-HEXB, anti-TXLNA, anti-SLAMF6) was then developed, demonstrating AUCs of 0.81 (58.0% sensitivity, 86.0% specificity) and 0.78 (55.71% sensitivity, 87.14% specificity) for distinguishing PDAC from NC. Additionally, the model yielded AUCs of 0.80 (58.0% sensitivity, 86.25% specificity) and 0.83 (55.71% sensitivity, 100% specificity) for distinguishing PDAC from BPD in the test and validation phases, respectively. Notably, the combination of the immunodiagnostic model with CA19-9 resulted in an increased positive rate of PDAC to 92.91%.

CONCLUSION

The immunodiagnostic model may offer a novel serological detection method for PDAC diagnosis, providing valuable insights into the development of effective diagnostic biomarkers.

Blood-based Migration Signature Biomarker Panel Discriminates Early Stage New Onset Diabetes related Pancreatic Ductal Adenocarcinoma from Type 2 Diabetes

BACKGROUND AND AIMS

While type 2 diabetes is a well-known risk factor for pancreatic ductal adenocarcinoma (PDAC), PDAC-induced new-onset diabetes (PDAC-NOD) is a manifestation of underlying PDAC. In this study, we sought to identify potential blood-based biomarkers for distinguishing PDAC-NOD from type 2 diabetes (T2DM) without PDAC.

MATERIALS AND METHODS

By ELISA analysis, a migration signature biomarker panel comprising tissue factor pathway inhibitor (TFPI), tenascin C (TNC-FNIII-C) and CA 19-9 was analyzed in plasma samples from 50 PDAC-NOD and 50 T2DM controls.

RESULTS

Both TFPI (area under the curve (AUC) 0.71) and TNC-FNIII-C (AUC 0.69) outperformed CA 19-9 (AUC 0.60) in distinguishing all stages of PDAC-NOD from T2DM controls. The combined panel showed an AUC of 0.82 (95% CI = 0.73-0.90) (p = 0.002). In the PDAC-NOD early stage II samples, the three biomarkers had an AUC of 0.84 (95% CI = 0.73-0.93) vs CA 19-9, AUC = 0.60, (95% CI = 0.45-0.73), which also improved significance (p = 0.0123).

CONCLUSION

The migration signature panel adds significantly to CA 19-9 to discriminate PDAC-NOD from T2DM controls and warrants further validation for high-risk group stratification.

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.

Multi-biomarker panel prediction model for diagnosis of pancreatic cancer

BACKGROUND/PURPOSE

The current study aimed to develop a prediction model using a multi-marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma.

METHODS

Multi-center cohort of 1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19-9. Using a logistic regression model on a training data set, the predicted values for pancreatic ductal adenocarcinoma were obtained, and the result was classification into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and three biomarkers.

RESULTS

Participants were categorized into four groups as normal (n = 609), other cancer (n = 145), pancreatic benign disease (n = 314), and pancreatic ductal adenocarcinoma (n = 923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non-pancreatic ductal adenocarcinoma group (n = 1068). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively.

CONCLUSIONS

This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing pancreatic ductal adenocarcinoma from normal and benign pancreatic disease states, as well as patients with other cancers.

TFPI inhibits breast cancer progression by suppressing ERK/p38 MAPK signaling pathway

BACKGROUND

Tissue factor pathway inhibitor-1 (TFPI) is a serine protease inhibitor, which is responsible for inactivating TF-induced coagulation. Recently, increasing studies revealed that TFPI was lowly expressed in tumor cells and exhibited the antitumor activity.

OBJECTIVE

The aim of this study was to explore the role and underlying molecular mechanisms of TFPI in breast cancer.

METHODS

The expression and prognostic value of TFPI were analyzed using UALCAN and Kaplan-Meier plotter website. The expression level of TFPI in breast cancer tissues and cells was examined by immunohistochemistry (IHC) and western blot analysis, respectively. Cellular proliferation was evaluated by CCK-8 and colony formation assays. Cell migration and invasion were determined by transwell assay. The methylation level of TFPI promoter was determined by methylation-specific PCR.

RESULTS

TFPI expression was significantly lower in breast cancer tissues and cells compared to normal breast tissues and normal breast cells. Patients with low TFPI levels showed worse overall survival (OS). Furthermore, overexpression of TFPI significantly inhibited the proliferation, migration and invasion of breast cancer cells. Conversely, knockdown of TFPI promoted the proliferation, migration and invasion of breast cancer cells. Mechanistically, TFPI inhibited the ERK/p38 MAPK signaling pathway in breast cancer. Moreover, DNA hypermethylation of TFPI promoter was responsible for the downregulation of TFPI in breast cancer cells.

CONCLUSION

TFPI inhibited breast cancer cell proliferation, migration and invasion through inhibition of the ERK/p38 MAPK signaling pathway, suggesting that TFPI may serve as a novel prognostic biomarker and therapeutic target for breast cancer.

Oncogenic KRAS-Induced Protein Signature in the Tumor Secretome Identifies Laminin-C2 and Pentraxin-3 as Useful Biomarkers for the Early Diagnosis of Pancreatic Cancer

KRAS mutations characterize pancreatic cell transformation from the earliest stages of carcinogenesis, and are present in >95% of pancreatic ductal adenocarcinoma (PDAC) cases. In search of novel biomarkers for the early diagnosis of PDAC, we identified the proteins secreted by the normal human pancreatic cell line (HPDE) recently transformed by inducing the overexpression of the KRASG12V oncogene. We report a proteomic signature of KRAS-induced secreted proteins, which was confirmed in surgical tumor samples from resected PDAC patients. The putative diagnostic performance of three candidates, Laminin-C2 (LAMC2), Tenascin-C (TNC) and Pentraxin-3 (PTX3), was investigated by ELISA quantification in two cohorts of PDAC patients (n = 200) eligible for surgery. Circulating levels of LAMC2, TNC and PTX3 were significantly higher in PDAC patients compared to the healthy individuals (p < 0.0001). The Receiver Operating Characteristics (ROC) curve showed good sensitivity (1) and specificity (0.63 and 0.85) for LAMC2 and PTX3, respectively, but not for TNC, and patients with high levels of LAMC2 had significantly shorter overall survival (p = 0.0007). High levels of LAMC2 and PTX3 were detected at early stages (I−IIB) and in CA19-9-low PDAC patients. In conclusion, pancreatic tumors release LAMC2 and PTX3, which can be quantified in the systemic circulation, and may be useful in selecting patients for further diagnostic imaging.

The Impact of Biomarkers in Pancreatic Ductal Adenocarcinoma on Diagnosis, Surveillance and Therapy

Simple Summary

Pancreatic ductal adenocarcinoma is a leading cause of cancer death worldwide. Due to the frequently late diagnosis, early metastasis and high therapy resistance curation is rare and prognosis remains poor overall. To provide early diagnostic and therapeutic predictors, various molecules from blood, tissue and other origin e.g., saliva, urine and stool, have been identified as biomarkers. This review summarizes current trends in biomarkers for diagnosis and therapy of pancreatic ductal adenocarcinoma.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is still difficult to treat due to insufficient methods for early diagnosis and prediction of therapy response. Furthermore, surveillance after curatively intended surgery lacks adequate methods for timely detection of recurrence. Therefore, several molecules have been analyzed as predictors of recurrence or early detection of PDAC. Enhanced understanding of molecular tumorigenesis and treatment response triggered the identification of novel biomarkers as predictors for response to conventional chemotherapy or targeted therapy. In conclusion, progress has been made especially in the prediction of therapy response with biomarkers. The use of molecules for early detection and recurrence of PDAC is still at an early stage, but there are promising approaches in noninvasive biomarkers, composite panels and scores that can already ameliorate the current clinical practice. The present review summarizes the current state of research on biomarkers for diagnosis and therapy of pancreatic cancer.

Targeting Tumor-Stromal Interactions in Pancreatic Cancer: Impact of Collagens and Mechanical Traits

Pancreatic ductal adenocarcinoma (PDAC) has one of the worst outcomes among cancers with a 5-years survival rate of below 10%. This is a result of late diagnosis and the lack of effective treatments. The tumor is characterized by a highly fibrotic stroma containing distinct cellular components, embedded within an extracellular matrix (ECM). This ECM-abundant tumor microenvironment (TME) in PDAC plays a pivotal role in tumor progression and resistance to treatment. Cancer-associated fibroblasts (CAFs), being a dominant cell type of the stroma, are in fact functionally heterogeneous populations of cells within the TME. Certain subtypes of CAFs are the main producer of the ECM components of the stroma, with the most abundant one being the collagen family of proteins. Collagens are large macromolecules that upon deposition into the ECM form supramolecular fibrillar structures which provide a mechanical framework to the TME. They not only bring structure to the tissue by being the main structural proteins but also contain binding domains that interact with surface receptors on the cancer cells. These interactions can induce various responses in the cancer cells and activate signaling pathways leading to epithelial-to-mesenchymal transition (EMT) and ultimately metastasis. In addition, collagens are one of the main contributors to building up mechanical forces in the tumor. These forces influence the signaling pathways that are involved in cell motility and tumor progression and affect tumor microstructure and tissue stiffness by exerting solid stress and interstitial fluid pressure on the cells. Taken together, the TME is subjected to various types of mechanical forces and interactions that affect tumor progression, metastasis, and drug response. In this review article, we aim to summarize and contextualize the recent knowledge of components of the PDAC stroma, especially the role of different collagens and mechanical traits on tumor progression. We furthermore discuss different experimental models available for studying tumor-stromal interactions and finally discuss potential therapeutic targets within the stroma.

Hemostatic Biomarkers and Cancer Prognosis: Where Do We Stand?

Cancer patients are characterized by hypercoagulable state and an increased rate of thrombotic events, the most common being venous thromboembolism. Several hemostatic pathways that are significantly implicated in mechanisms of thromboembolic disease are also involved in growth, invasion, and metastatic spread of malignant cells as well in tumor-induced neo-angiogenesis. This close connection between cancer and the hemostatic system has prompted numerous studies on the role of alterations in the level plasma biomarkers of the different compartments of hemostasis in predicting cancer prognosis. In this review, we collect the results of several exemplificative studies that have evaluated clotting activation biomarkers in relation to different cancer outcomes with a final emphasis on current research and forthcoming directions in this field.

Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning

BACKGROUND

Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients.

METHODS

A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation.

RESULTS

Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism.

CONCLUSIONS

We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.

Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review

Pancreatic cancer is a highly malignant digestive system tumor with a poor prognosis. Most pancreatic cancer patients are diagnosed at an advanced stage or even metastasis due to its highly aggressive characteristics and lack of typical early symptoms. Thus, an early diagnosis of pancreatic cancer is crucial for improving its prognosis. Currently, screening is often applied in high‐risk individuals to achieve the early diagnosis of pancreatic cancer. Fully understanding the risk factors of pancreatic cancer and pathogenesis could help us identify the high‐risk population and achieve early diagnosis and timely treatment of pancreatic cancer. Notably, accumulating studies have been undertaken to improve the detection rate of different imaging methods and the diagnostic accuracy of endoscopic ultrasound‐guided fine‐needle aspiration (EUS‐FNA) which is the golden standard for pancreatic cancer diagnosis. In addition, there are currently no biomarkers with sufficient sensitivity and specificity for the diagnosis of pancreatic cancer to be applied in the clinic. As the only serum biomarker approved by the United States Food and Drug Administration, carbohydrate antigen 19‐9 (CA19‐9) is not recommended to be used in the early screening of pancreatic cancer because of its limited specificity. Recently, increasing numbers of studies focused on the discovering of novel serum biomarkers and exploring their combination with CA19‐9 in the detection of pancreatic cancer. Besides, the application of liquid biopsy involving circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs (miRNAs), and exosomes in blood and biomarkers in urine, and saliva in pancreatic cancer diagnosis are drawing more and more attention. Furthermore, many innovative technologies such as artificial intelligence, computer‐aided diagnosis system, metabolomics technology, ion mobility spectrometry (IMS) associated technologies, and novel nanomaterials have been tested for the early diagnosis of pancreatic cancer and have shown promising prospects. Hence, this review aims to summarize the recent progress in the development of early screening and diagnostic methods, including imaging, pathological examination, serological examination, liquid biopsy, as well as other potential diagnostic strategies for pancreatic cancer.

Biomarkers in the diagnosis of pancreatic cancer: Are we closer to finding the golden ticket?

Pancreatic cancer (PC) is a leading cause of cancer related mortality on a global scale. The disease itself is associated with a dismal prognosis, partly due to its silent nature resulting in patients presenting with advanced disease at the time of diagnosis. To combat this, there has been an explosion in the last decade of potential candidate biomarkers in the research setting in the hope that a diagnostic biomarker may provide a glimmer of hope in what is otherwise quite a substantial clinical dilemma. Currently, serum carbohydrate antigen 19-9 is utilized in the diagnostic work-up of patients diagnosed with PC however this biomarker lacks the sensitivity and specificity associated with a gold-standard marker. In the search for a biomarker that is both sensitive and specific for the diagnosis of PC, there has been a paradigm shift towards a focus on liquid biopsy and the use of diagnostic panels which has subsequently proved to have efficacy in the diagnosis of PC. Currently, promising developments in the field of early detection on PC using diagnostic biomarkers include the detection of microRNA (miRNA) in serum and circulating tumour cells. Both these modalities, although in their infancy and yet to be widely accepted into routine clinical practice, possess merit in the early detection of PC. We reviewed over 300 biomarkers with the aim to provide an in-depth summary of the current state-of-play regarding diagnostic biomarkers in PC (serum, urinary, salivary, faecal, pancreatic juice and biliary fluid).

Extracellular Matrices and Cancer-Associated Fibroblasts: Targets for Cancer Diagnosis and Therapy?

Solid cancer progression is dictated by neoplastic cell features and pro-tumoral crosstalks with their microenvironment. Stroma modifications, such as fibroblast activation into cancer-associated fibroblasts (CAFs) and extracellular matrix (ECM) remodeling, are now recognized as critical events for cancer progression and as potential therapeutic or diagnostic targets. The recent appreciation of the key, complex and multiple roles of the ECM in cancer and of the CAF diversity, has revolutionized the field and raised innovative but challenging questions. Here, we rapidly present CAF heterogeneity in link with their specific ECM remodeling features observed in cancer, before developing each of the impacts of such ECM modifications on tumor progression (survival, angiogenesis, pre-metastatic niche, chemoresistance, etc.), and on patient prognosis. Finally, based on preclinical studies and recent results obtained from clinical trials, we highlight key mechanisms or proteins that are, or may be, used as potential therapeutic or diagnostic targets, and we report and discuss benefits, disappointments, or even failures, of recently reported stroma-targeting strategies.

Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer-A Comprehensive Review

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC), which represents approximately 90% of all pancreatic cancers, is an extremely aggressive and lethal disease. It is considered a silent killer due to a largely asymptomatic course and late clinical presentation. Earlier detection of the disease would likely have a great impact on changing the currently poor survival figures for this malignancy. In this comprehensive review, we assessed over 4000 reports on non-invasive PDAC biomarkers in the last decade. Applying the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, we selected and reviewed in more detail 49 relevant studies reporting on the most promising candidate biomarkers. In addition, we also highlight the present challenges and complexities of translating novel biomarkers into clinical use.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) carries a deadly diagnosis, due in large part to delayed presentation when the disease is already at an advanced stage. CA19-9 is currently the most commonly utilized biomarker for PDAC; however, it lacks the necessary accuracy to detect precursor lesions or stage I PDAC. Novel biomarkers that could detect this malignancy with improved sensitivity (SN) and specificity (SP) would likely result in more curative resections and more effective therapeutic interventions, changing thus the present dismal survival figures. The aim of this study was to systematically and comprehensively review the scientific literature on non-invasive biomarkers in biofluids such as blood, urine and saliva that were attempting earlier PDAC detection. The search performed covered a period of 10 years (January 2010—August 2020). Data were extracted using keywords search in the three databases: MEDLINE, Web of Science and Embase. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied for study selection based on establishing the risk of bias and applicability concerns in Patient Selection, Index test (biomarker assay) and Reference Standard (standard-of-care diagnostic test). Out of initially over 4000 published reports, 49 relevant studies were selected and reviewed in more detail. In addition, we discuss the present challenges and complexities in the path of translating the discovered biomarkers into the clinical setting. Our systematic review highlighted several promising biomarkers that could, either alone or in combination with CA19-9, potentially improve earlier detection of PDAC. Overall, reviewed biomarker studies should aim to improve methodological and reporting quality, and novel candidate biomarkers should be investigated further in order to demonstrate their clinical usefulness. However, challenges and complexities in the path of translating the discovered biomarkers from the research laboratory to the clinical setting remain and would have to be addressed before a more realistic breakthrough in earlier detection of PDAC is achieved.

Excessive risk of second-cancer incidence and cancer mortality in patients with esophageal cancer

BACKGROUND

Second primary cancers have impact on survival in patients who achieved cure for the first esophageal cancer. We, therefore, assessed the risk of incidence and mortality for second primary cancer by calculating standardized incidence ratio (SIR) and standardized mortality ratio (SMR) in patients with superficial or localized esophageal cancer without lymph node metastases as the first cancer (index cancer).

METHODS

Data on cancer development and subsequent causes of deaths were collected from integrated database of the Osaka Cancer Registry and the Vital Statistics of Japan. Records with information on patients with index esophageal cancer diagnosed between 2004 and 2013 were extracted from the database. Then, SIR and SMR for second primary cancers that developed in other organ were calculated with the reference to the general population during the same period. All probability values are two-tailed.

RESULTS

Of 473,784 case records, 3022 cases of patients with index esophageal cancer were identified. Significantly higher SMRs/SIRs for cancers in mouth/pharynx, larynx, pancreas, and leukemia were confirmed with the values of 10.78/16.16, 8.56/6.44, 2.33/2.31, and 3.96/4.42, respectively. Significantly, higher SIRs for stomach, lung, and skin cancers were confirmed with the values of 2.84, 2.36, and 3.38, respectively, while SMRs were not significantly higher in these cancers.

CONCLUSIONS

Significantly higher risks for mouth/pharynx, larynx, pancreas, and leukemia as second cancers were clarified. Careful surveillance for these cancers is required for esophageal cancer patients.

Plasma miRNA Biomarkers in Limited Volume Samples for Detection of Early-stage Pancreatic Cancer

Early detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes; however, PDAC is usually diagnosed late. Therefore, blood-based minimally invasive biomarker assays for limited volume clinical samples are urgently needed. A novel miRNA profiling platform (Abcam Fireplex-Oncology Panel) was used to investigate the feasibility of developing early detection miRNA biomarkers with 20 μL plasma from a training set (58 stage II PDAC cases and 30 controls) and two validation sets (34 stage II PDAC cases and 25 controls; 44 stage II PDAC cases and 18 controls). miR-34a-5p [AUC = 0.77; 95% confidence interval (CI), 0.66-0.87], miR-130a-3p (AUC = 0.74; 95% CI, 0.63-0.84), and miR-222-3p (AUC = 0.70; 95% CI, 0.58-0.81) were identified as significantly differentially abundant in plasma from stage II PDAC versus controls. Although none of the miRNAs individually outperformed the currently used serologic biomarker for PDAC, carbohydrate antigen 19-9 (CA19-9), combining the miRNAs with CA 19-9 improved AUCs from 0.89 (95% CI, 0.81-0.95) for CA 19-9 alone to 0.92 (95% CI, 0.86-0.97), 0.94 (95% CI, 0.89-0.98), and 0.92 (95% CI, 0.87-0.97), respectively. Gene set enrichment analyses of transcripts correlated with high and low expression of the three miRNAs in The Cancer Genome Atlas PDAC sample set. These miRNA biomarkers, assayed in limited volume plasma together with CA19-9, discriminate stage II PDAC from controls with good sensitivity and specificity. Unbiased profiling of larger cohorts should help develop an informative early detection biomarker assay for diagnostic settings. PREVENTION RELEVANCE: Development of minimally invasive biomarker assays for detection of premalignant disease and early-stage pancreatic cancer is key to improving patient survival. This study describes a limited volume plasma miRNA biomarker assay that can detect early-stage resectable pancreatic cancer in clinical samples necessary for effective prevention and clinical intervention.

Current State of "Omics" Biomarkers in Pancreatic Cancer

Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different "omics" levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.

Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Upper Gastrointestinal Cancers: A Systematic Review

Introduction

Detecting upper gastrointestinal (GI) cancers in primary care is challenging, as cancer symptoms are common, often non-specific, and most patients presenting with these symptoms will not have cancer. Substantial investment has been made to develop biomarkers for cancer detection, but few have reached routine clinical practice. We aimed to identify novel biomarkers for upper GI cancers which have been sufficiently validated to be ready for evaluation in low-prevalence populations.

Methods

We systematically searched MEDLINE, Embase, Emcare, and Web of Science for studies published in English from January 2000 to October 2019 (PROSPERO registration CRD42020165005). Reference lists of included studies were assessed. Studies had to report on second measures of diagnostic performance (beyond discovery phase) for biomarkers (single or in panels) used to detect pancreatic, oesophageal, gastric, and biliary tract cancers. We included all designs and excluded studies with less than 50 cases/controls. Data were extracted on types of biomarkers, populations and outcomes. Heterogeneity prevented pooling of outcomes.

Results

We identified 149 eligible studies, involving 22,264 cancer cases and 49,474 controls. A total of 431 biomarkers were identified (183 microRNAs and other RNAs, 79 autoantibodies and other immunological markers, 119 other proteins, 36 metabolic markers, 6 circulating tumour DNA and 8 other). Over half (n = 231) were reported in pancreatic cancer studies. Only 35 biomarkers had been investigated in at least two studies, with reported outcomes for that individual marker for the same tumour type. Apolipoproteins (apoAII-AT and apoAII-ATQ), and pepsinogens (PGI and PGII) were the most promising biomarkers for pancreatic and gastric cancer, respectively.

Conclusion

Most novel biomarkers for the early detection of upper GI cancers are still at an early stage of matureness. Further evidence is needed on biomarker performance in low-prevalence populations, in addition to implementation and health economic studies, before extensive adoption into clinical practice can be recommended.

Electronic Supplementary Material

The online version of this article (10.1007/s12325-020-01571-z) contains supplementary material, which is available to authorized users.

Plasma protein biomarkers for early detection of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, mainly due to late diagnosis at advanced tumor stages. In this study, we aimed to identify plasma protein biomarkers for early detection of PDAC. Totally, 135 PDAC patients (early PDAC, Stage I/II, n = 71; advanced PDAC, Stage III/IV, n = 64), 13 benign lesions/chronic pancreatitis patients and 72 healthy individuals, with corresponding plasma samples from a case-control study in Sweden were included. A proximity extension assay was used to detect 92 cancer-related proteins, and an enzyme-linked immunosorbent assay/electrochemiluminescence immunoassay was used to detect CA19-9. Predictive features were selected from these 93 candidate proteins and three covariates in the Swedish participants, and then validated in Spanish participants, including 37 early PDAC patients, 38 advanced PDAC patients, 19 chronic pancreatitis patients and 36 healthy controls. A panel of eight proteins discriminating early PDAC from healthy individuals was identified, and the cross-validated area under the curves (AUCs) were 0.85 (95% confidence interval, 95% CI, 0.78-0.91) and 0.81 (95% CI, 0.70-0.92) in the Swedish and Spanish participants, respectively. Another eight-protein panel was predictive for classifying advanced PDAC from healthy controls in two populations, with cross-validated AUCs of 0.89 (95% CI, 0.83-0.95) and 0.90 (95% CI, 0.83-0.98), respectively. In conclusion, eight protein biomarkers were identified and externally validated, potentially allowing early detection of PDAC patients if validated in additional prospective studies.

Biomarkers and Strategy to Detect Preinvasive and Early Pancreatic Cancer: State of the Field and the Impact of the EDRN

Patients afflicted with pancreatic ductal adenocarcinoma (PDAC) face a dismal prognosis, but headway could be made if physicians could identify the disease earlier. A compelling strategy to broaden the use of surveillance for PDAC is to incorporate molecular biomarkers in combination with clinical analysis and imaging tools. This article summarizes the components involved in accomplishing biomarker validation and an analysis of the requirements of molecular biomarkers for disease surveillance. We highlight the significance of consortia for this research and highlight resources and infrastructure of the Early Detection Research Network (EDRN). The EDRN brings together the multifaceted expertise and resources needed for biomarker validation, such as study design, clinical care, biospecimen collection and handling, molecular technologies, and biostatistical analysis, and studies coming out of the EDRN have yielded biomarkers that are moving forward in validation. We close the article with an overview of the current investigational biomarkers, an analysis of their performance relative to the established benchmarks, and an outlook on the current needs in the field. The outlook for improving the early detection of PDAC looks promising, and the pace of further research should be quickened through the resources and expertise of the EDRN and other consortia.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Advances on diagnostic biomarkers of pancreatic ductal adenocarcinoma: A systems biology perspective

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is usually diagnosed at an advanced stage when curative surgery is no longer an option. Robust diagnostic biomarkers with high sensitivity and specificity for early detection are urgently needed. Systems biology provides a powerful tool for understanding diseases and solving challenging biological problems, allowing biomarkers to be identified and quantified with increasing accuracy, sensitivity, and comprehensiveness. Here, we present a comprehensive overview of efforts to identify biomarkers of PDAC using genomics, transcriptomics, proteomics, metabonomics, and bioinformatics. Systems biology perspective provides a crucial “network” to integrate multi-omics approaches to biomarker identification, shedding additional light on early PDAC detection.

A Transcriptomics-Based Meta-Analysis Combined With Machine Learning Identifies a Secretory Biomarker Panel for Diagnosis of Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is generally incurable due to the late diagnosis and absence of markers that are concordant with expression in several sample sources (i.e., tissue, blood, plasma) and platforms (i.e., Microarray, sequencing). We optimized meta-analysis of 19 PDAC (tissue and blood) transcriptome studies from multiple platforms. The key biomarkers for PDAC diagnosis with secretory potential were identified and validated in different cohorts. Machine learning approach i.e., support vector machine supported by leave-one-out cross-validation was used to build and test the classifier. We identified a 9-gene panel (IFI27, ITGB5, CTSD, EFNA4, GGH, PLBD1, HTATIP2, IL1R2, CTSA) that achieved ∼0.92 average sensitivity and ∼0.90 average specificity in distinguishing PDAC from healthy samples in five training sets using cross-validation. These markers were also validated in proteomics and single-cell transcriptomics studies suggesting their prognostic role in the diagnosis of PDAC. Our 9-gene classifier can not only clearly discriminate between better and poor survivors but can also precisely discriminate PDAC from chronic pancreatitis (AUC = 0.95), early stages of progression [Stage I and II (AUC = 0.82), IPMA and IPMN (AUC = 1), and IPMC (AUC = 0.81)]. The 9-gene marker outperformed the previously known markers in blood studies particularly (AUC = 0.84). The discrimination of PDAC from early precursor lesions in non-malignant tissue (AUC > 0.81) and peripheral blood (AUC > 0.80) may assist in an early diagnosis of PDAC in blood samples and thus will also facilitate risk stratification upon validation in clinical trials.

Potential biomarkers for early detection of pancreatic ductal adenocarcinoma

Pancreatic cancer has the highest mortality amongst all major organ cancers. Early detection is key to reduce deaths related to pancreatic cancer. However, early detection has been challenged by the lack of non-invasive biomarkers with enough sensitivity and specificity to allow for screening. The gold standard is still carbohydrate antigen (CA 19-9), against which all new biomarkers must be evaluated. In this paper, we describe recent progress in the development of new pancreatic cancer biomarkers, focusing on proteins, metabolites, and genetic and epigenetic biomarkers. Although several promising biomarkers have been identified, they are all derived from retrospective studies and additional prospective studies are needed to confirm their clinical validity.

Development and validation of a novel nomogram for pretreatment prediction of liver metastasis in pancreatic cancer

PURPOSE

The diagnostic value of nomogram in pancreatic cancer (PC) with liver metastasis (PCLM) is still largely unknown. We sought to develop and validate a novel nomogram for the prediction of liver metastasis in patients with PC.

METHOD

About 604 pathologically confirmed PC patients from the Sun Yat-sen University Cancer Center (SYSUCC) between July, 2001 and December, 2013 were retrospectively studied. The SYSUCC cohort was randomly assigned to as the training set and internal validation set. Using these two sets, we derived and validated a prognostic model by using concordance index and calibration curves. Another two independent cohorts between August, 2002 and December, 2013 from the Sun Yat-sen Memorial Hospital (SYSMH, n = 335) and Guangdong General Hospital (GDGH, n = 503) was used for external validation.

RESULT

Computed tomography (CT) reported liver metastasis status, carcinoembryonic antigen (CEA) level and differentiation type were identified as risk factors for PCLM in the training set. The final diagnostic model demonstrated good calibration and discrimination with a concordance index of 0.97 and had a robust internal validation. The score ability to diagnose PCLM was further externally validated in SYSMH and GDGH with a concordance index of 0.93. The model showed better calibration and discrimination than CT, CEA and differentiation in each cohort.

CONCLUSION

Based on a large multi-institution database and on the routinely observed CT-reported status, CEA level and tumor differentiation in clinical practice, we developed and validated a novel nomogram to predict PLCM.

PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression

BACKGROUND

Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment.

METHODS

We performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples.

RESULTS

This microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1.

CONCLUSION

The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.

Soluble stroma-related biomarkers of pancreatic cancer

The clinical management of pancreatic ductal adenocarcinoma (PDAC) is hampered by the lack of reliable biomarkers. This study investigated the value of soluble stroma‐related molecules as PDAC biomarkers. In the first exploratory phase, 12 out of 38 molecules were associated with PDAC in a cohort of 25 PDAC patients and 16 healthy subjects. A second confirmatory phase on an independent cohort of 131 PDAC patients, 30 chronic pancreatitis patients, and 131 healthy subjects confirmed the PDAC association for MMP7, CCN2, IGFBP2, TSP2, sICAM1, TIMP1, and PLG. Multivariable logistic regression model identified biomarker panels discriminating respectively PDAC versus healthy subjects (MMP7 + CA19.9, AUC = 0.99, 99% CI = 0.98–1.00) (CCN2 + CA19.9, AUC = 0.96, 99% CI = 0.92–0.99) and PDAC versus chronic pancreatitis (CCN2 + PLG+FN+Col4 + CA19.9, AUC = 0.94, 99% CI = 0.88–0.99). Five molecules were associated with PanIN development in two GEM models of PDAC (PdxCre/LSL‐Kras^G12D and PdxCre/LSL‐Kras^G12D/+/LSL‐Trp53^R172H/+), suggesting their potential for detecting early disease. These markers were also elevated in patient‐derived orthotopic PDAC xenografts and associated with response to chemotherapy. The identified stroma‐related soluble biomarkers represent potential tools for PDAC diagnosis and for monitoring treatment response of PDAC patients.

CA19-9 and apolipoprotein-A2 isoforms as detection markers for pancreatic cancer: a prospective evaluation

Recently, we identified unique processing patterns of apolipoprotein A2 (ApoA2) in patients with pancreatic cancer. Our study provides a first prospective evaluation of an ApoA2 isoform ("ApoA2-ATQ/AT"), alone and in combination with carbohydrate antigen 19-9 (CA19-9), as an early detection biomarker for pancreatic cancer. We performed ELISA measurements of CA19-9 and ApoA2-ATQ/AT in 156 patients with pancreatic cancer and 217 matched controls within the European EPIC cohort, using plasma samples collected up to 60 months prior to diagnosis. The detection discrimination statistics were calculated for risk scores by strata of lag-time. For CA19-9, in univariate marker analyses, C-statistics to distinguish future pancreatic cancer patients from cancer-free individuals were 0.80 for plasma taken ≤6 months before diagnosis, and 0.71 for >6-18 months; for ApoA2-ATQ/AT, C-statistics were 0.62, and 0.65, respectively. Joint models based on ApoA2-ATQ/AT plus CA19-9 significantly improved discrimination within >6-18 months (C = 0.74 vs. 0.71 for CA19-9 alone, p = 0.022) and ≤ 18 months (C = 0.75 vs. 0.74, p = 0.022). At 98% specificity, and for lag times of ≤6, >6-18 or ≤ 18 months, sensitivities were 57%, 36% and 43% for CA19-9 combined with ApoA2-ATQ/AT, respectively, vs. 50%, 29% and 36% for CA19-9 alone. Compared to CA19-9 alone, the combination of CA19-9 and ApoA2-ATQ/AT may improve detection of pancreatic cancer up to 18 months prior to diagnosis under usual care, and may provide a useful first measure for pancreatic cancer detection prior to imaging.

CD44 Predicts Early Recurrence in Pancreatic Cancer Patients Undergoing Radical Surgery

BACKGROUND/AIM

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive types of digestive cancer. Recurrence within one year after surgery is inevitable in most PDAC patients. Recently, cluster of differentiation 44 (CD44) has been shown to be associated with tumor initiation, metastasis and prognosis. This study aimed to explore the correlation of CD44 expression with clinicopathological factors and the role of CD44 in predicting early recurrence (ER) in PDAC patients after radical surgery.

MATERIALS AND METHODS

PDAC patients who underwent radical resection between January 1999 and March 2015 were enrolled in this study. Tumor recurrence within 6 months after surgery was defined as ER. Immunohistochemical staining was performed with anti-CD44 antibodies. The association between clinicopathological parameters and CD44 expression was analyzed. Predictors for ER were also assessed with univariate and multivariate analyses.

RESULTS

Overall, 155 patients were included in this study. Univariate analysis revealed CA19-9 levels (p=0.014), CD44 histoscores (H-scores; p=0.002), differentiation (p=0.010), nodal status (p=0.005), stage (p=0.003), vascular invasion (p=0.007), lymphatic invasion (p<0.001) and perineural invasion (p=0.042) as risk factors for ER. In multivariate analysis, high CA19-9 levels and CD44 H-scores and poor differentiation independently predicted ER.

CONCLUSION

High CA19-9 levels, CD44 H-scores and poor differentiation are independent predictors for ER in PDAC patients undergoing radical resection. Therefore, the determination of CD44 expression might help in identifying patients at a high risk of ER for more aggressive treatment after radical surgery.

Matrix metalloproteinase 2 (MMP-2) and its tissue inhibitor 2 (TIMP-2) in pancreatic cancer (PC)

Objectives

The incidence rate of pancreatic cancer (PC) is similar to mortality rate, thus searching specific tumor biomarkers of PC is sorely needed. Matrix metalloproteinase-2 (MMP-2) and the imbalance between MMP-2 and its tissue inhibitor (TIMP-2) play a critical role in tumor progression. We aim to assess the diagnostic and prognostic usefulness of serum MMP-2 and TIMP-2 as potential biomarkers in comparison to well-established tumor markers of PC (CA 19-9, carbohydrate antigen 19-9 and CEA, carcinoembryonic antigen).

Results

We indicated the significant differences between serum TIMP-2 concentrations in PC patients, CP individuals and control group. The diagnostic sensitivity of TIMP-2 was the highest among all proteins tested and increased up to 96% in combined measurement with MMP-2. The area under ROC curve (AUC) for TIMP-2 was larger than for MMP-2, but lower than for classical tumor markers.

Methods

Presented study comprised on 226 subjects, including 92 PC patients, 43 chronic pancreatitis (CP) patients and 91 healthy volunteers. The serum concentrations of these proteins were measured using immunological methods.

Conclusions

Presented findings suggest higher usefulness of TIMP-2 than MMP-2 as potential biomarker in the diagnosis of PC patients, however more studies on large population are needed to support our results.

The sTRA Plasma Biomarker: Blinded Validation of Improved Accuracy Over CA19-9 in Pancreatic Cancer Diagnosis

PURPOSE

The CA19-9 biomarker is elevated in a substantial group of patients with pancreatic ductal adenocarcinoma (PDAC), but not enough to be reliable for the detection or diagnosis of the disease. We hypothesized that a glycan called sTRA (sialylated tumor-related antigen) is a biomarker for PDAC that improves upon CA19-9.

EXPERIMENTAL DESIGN

We examined sTRA and CA19-9 expression and secretion in panels of cell lines, patient-derived xenografts, and primary tumors. We developed candidate biomarkers from sTRA and CA19-9 in a training set of 147 plasma samples and used the panels to make case-control calls, based on predetermined thresholds, in a 50-sample validation set and a blinded, 147-sample test set.

RESULTS

The sTRA glycan was produced and secreted by pancreatic tumors and models that did not produce and secrete CA19-9. Two biomarker panels improved upon CA19-9 in the training set, one optimized for specificity, which included CA19-9 and 2 versions of the sTRA assay, and another optimized for sensitivity, which included 2 sTRA assays. Both panels achieved statistical improvement (P < 0.001) over CA19-9 in the validation set, and the specificity-optimized panel achieved statistical improvement (P < 0.001) in the blinded set: 95% specificity and 54% sensitivity (75% accuracy), compared with 97%/30% (65% accuracy). Unblinding produced further improvements and revealed independent, complementary contributions from each marker.

CONCLUSIONS

sTRA is a validated serological biomarker of PDAC that yields improved performance over CA19-9. The new panels may enable surveillance for PDAC among people with elevated risk, or improved differential diagnosis among patients with suspected pancreatic cancer.

Hemostatic biomarkers in cancer progression

Malignant disease is characterized by a hemostatic imbalance, usually shifted towards a procoagulant direction, and a high incidence of thrombotic complications. The mechanisms of hemostasis that are critically involved in thrombosis are also implicated in tumor progression, angiogenesis, and metastatic spread. As there is a close relationship between cancer and the clotting system, circulating biomarkers of activation of various hemostasis compartments (i.e. coagulation, fibrinolysis, platelets, endothelium, and other blood cells) have been extensively studied to predict cancer outcomes along with predicting the thrombotic risk. In this review, we will summarize the results of published studies and will focus on ongoing research and future directions of clotting activation bioproducts as biomarkers of cancer disease and progression.

Pancreatic Cancer and Diabetes Mellitus

PURPOSE OF REVIEW

The relationship between pancreatic ductal adenocarcinoma (PDAC) and diabetes mellitus (DM) is complex. We reviewed the recent medical literature regarding the effect of anti-diabetic medication on PDAC risk and survival, risk of PDAC in DM, and role of DM in early detection of PDAC.

RECENT FINDINGS

Studies report that while some anti-diabetic medications (e.g., metformin) may decrease the risk of PDAC, others (insulin, sulfonylureas and incretin-based therapies) may increase the risk. However, these observations may be subject to protopathic biases. Metformin's anti-tumor activity may have influence overall survival of PDAC, but epidemiological reports have largely been inconsistent to defend these findings due to heterogeneous methodologies. There is congruent data to support the association between DM and PDAC, with an inverse relationship to DM duration. Older subjects with new-onset DM are the only known high-risk group for PDAC, and strategy using this group for early detection has led to development of clinical risk prediction models that define a very high-risk PDAC group. Role of anti-diabetic medication in PDAC risk modification or survival is controversial. With successful efforts to distinguish type 2-DM from PDAC-DM using risk-stratifying models, there is an opportunity to initiate screening protocols for early detection of PDAC in a sub-set of DM subjects.

Glycoprotein biomarkers for the detection of pancreatic ductal adenocarcinoma

Pancreatic cancer (PaC) shows a clear tendency to increase in the next years and therefore represents an important health and social challenge. Currently, there is an important need to find biomarkers for PaC early detection because the existing ones are not useful for that purpose. Recent studies have indicated that there is a large window of time for PaC early detection, which opens the possibility to find early biomarkers that could greatly improve the dismal prognosis of this tumor. The present manuscript reviews the state of the art of the existing PaC biomarkers. It focuses on the anomalous glycosylation process and its role in PaC. Glycan structures of glycoconjugates such as glycoproteins are modified in tumors and these modifications can be detected in biological fluids of the cancer patients. Several studies have found serum glycoproteins with altered glycan chains in PaC patients, but they have not shown enough specificity for PaC. To find more specific cancer glycoproteins we propose to analyze the glycan moieties of a battery of glycoproteins that have been reported to increase in PaC tissues and that can also be found in serum. The combination of these new candidate glycoproteins with their aberrant glycosylation together with the existing biomarkers could result in a panel, which would expect to give better results as a new tool for early diagnosis of PaC and to monitor the disease.

Neutrophils infiltrating pancreatic ductal adenocarcinoma indicate higher malignancy and worse prognosis

CD177 is considered to represent neutrophils. We analyzed mRNA expression level of CD177 and clinical follow-up survey of PDAC to estimate overall survival (OS) from Gene Expression Omnibus (GEO) dataset (GSE21501, containing samples from 102 PDAC patients) by R2 platform (http://r2.amc.nl). We also analyzed correlated genes of CD177 by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to predict the potential relationship between neutrophils and prognosis of PDAC. We then performed hematoxylin and eosin (H&E) staining and immunohistochemical staining of surgical specimens to verify infiltration of neutrophils in PDAC tissues. After analyzing mRNA expression data and clinical follow-up survey provided in the GEO dataset (GSE21501, containing samples from 102 PDAC patients) and clinicopathological data of 23 PDAC patients, we demonstrated that CD177 was correlated with poor prognosis. The univariate Kaplan-Meier survival analysis revealed that OS was inversely associated with increased expression of CD177 (P = 0.012). Expression of phosphodiesterase (PDE)4D was positively related to CD177 in gene correlation analysis (R = 0.413, P < 0.001) by R2 platform. H&E staining and immunohistochemistry of CD177 in 23 PDAC surgical samples showed accumulation of neutrophils in the stroma and blood vessels around the cancer cells. In addition, immunohistochemical staining showed that CD177 was highly expressed in the stroma and blood vessels around tumor tissues of PDAC, which was similar to H&E staining. Expression of CD177 can be used to represent infiltration of neutrophils, which may have potential prognostic value in PDAC.

Proteomic biomarkers in body fluids associated with pancreatic cancer

Pancreatic cancer (PC) is a highly malignant disease that represents the fourth leading cancer-related death worldwide. There has been very little improvement in survival rates over recent years, and surgical resection remains the only reliable curative approach. Factors that contribute to this dismal prognosis for PC include its rapid progression and invasion, the absence of specific symptoms, and the little impact of available chemotherapy. Importantly, the management of this malignancy is also limited by the lack of highly specific and sensitive biomarkers for its diagnosis and follow-up, and their identification is therefore considered a promising strategy to improve outcomes in these patients. Numerous translational studies have explored the usefulness of body fluids as a non-invasive source of PC-specific biomarkers, and innovations in proteomic methods and technologies have provided a myriad of protein biomarkers for different cancers. The adoption of a proteomic approach has improved understanding of the biology of PC and contributed to the potential identification of protein biomarkers for this disease. This review considers the most recent research efforts to develop novel proteomic biomarkers in body fluids for PC.

Reimagining the diagnostic pathway for gastrointestinal cancer

A crisis is looming for the diagnosis of gastrointestinal cancers, one grounded only partly in the steady increase in their overall incidence. Public demand for diagnostic tests to be undertaken early and at lower levels of risk is reflected in early diagnosis being a widely held policy objective for reasons of both clinical outcome and patient experience. In the UK, urgent referrals for suspected lower gastrointestinal cancer have increased by 78% in the past 6 years, with parallel increases in endoscopy and imaging activity. Such growth in demand is unsustainable with current models of care. If gastrointestinal cancer diagnosis is to be affordable, the roles of professionals and their interactions with each other will need to be reframed while retaining public confidence in the process. In this Perspective, we consider how the relationship between medical specialists and generalists could be redefined to make better use of the skills of each while delivering optimal clinical outcomes and a good patient experience.

Validation of Biomarkers for Early Detection of Pancreatic Cancer: Summary of The Alliance of Pancreatic Cancer Consortia for Biomarkers for Early Detection Workshop

Pancreatic cancer is the third leading cause of cancer death in the United States, and the 5-year relative survival for patients diagnosed with pancreatic cancer is less than 10%. Early intervention is the key to a better survival outcome. Currently, there are no biomarkers that can reliably detect pancreatic cancer at an early stage or identify precursors that are destined to progress to malignancy. The National Cancer Institute in partnership with the Kenner Family Research Fund and the Pancreatic Cancer Action Network convened a Data Jamboree on Biomarkers workshop on December 5, 2016, to discuss and evaluate existing or newly developed biomarkers and imaging methods for early detection of pancreatic cancer. The primary goal of this workshop was to determine if there are any promising biomarkers for early detection of pancreatic cancer that are ready for clinical validation. The Alliance of Pancreatic Cancer Consortia for Biomarkers for Early Detection, formed under the auspices of this workshop, will provide the common platform and the resources necessary for validation. Although none of the biomarkers evaluated seemed ready for a large-scale biomarker validation trial, a number of them had sufficiently high sensitivity and specificity to warrant additional research, especially if combined with other biomarkers to form a panel.

Pancreatic Cancer: Molecular Characterization, Clonal Evolution and Cancer Stem Cells

Pancreatic Ductal Adenocarcinoma (PDAC) is the fourth most common cause of cancer-related death and is the most lethal of common malignancies with a five-year survival rate of <10%. PDAC arises from different types of non-invasive precursor lesions: intraductal papillary mucinous neoplasms, mucinous cystic neoplasms and pancreatic intraepithelial neoplasia. The genetic landscape of PDAC is characterized by the presence of four frequently-mutated genes: KRAS, CDKN2A, TP53 and SMAD4. The development of mouse models of PDAC has greatly contributed to the understanding of the molecular and cellular mechanisms through which driver genes contribute to pancreatic cancer development. Particularly, oncogenic KRAS-driven genetically-engineered mouse models that phenotypically and genetically recapitulate human pancreatic cancer have clarified the mechanisms through which various mutated genes act in neoplasia induction and progression and have led to identifying the possible cellular origin of these neoplasias. Patient-derived xenografts are increasingly used for preclinical studies and for the development of personalized medicine strategies. The studies of the purification and characterization of pancreatic cancer stem cells have suggested that a minority cell population is responsible for initiation and maintenance of pancreatic adenocarcinomas. The study of these cells could contribute to the identification and clinical development of more efficacious drug treatments.

The Roles of Matricellular Proteins in Oncogenic Virus-Induced Cancers and Their Potential Utilities as Therapeutic Targets

Matricellular proteins differ from other classical extracellular matrix proteins; for instance, they are transiently expressed as soluble proteins rather than being constitutively expressed in pathological conditions, such as acute viral infections. Accumulating studies have revealed that matricellular proteins, including osteopontin and tenascin-C, both of which interact with integrin heterodimers, are involved in inflammatory diseases, autoimmune disorders, and cancers. The concentrations of these matricellular proteins are elevated in the plasma of patients with certain types of cancers, indicating that they play important roles in oncogenesis. Chronic viral infections are associated with certain cancers, which are distinct from non-viral cancers. Viral oncogenes play critical roles in the development and progression of such cancers. It is vital to investigate the mechanisms of tumorigenesis and, particularly, the mechanism by which viral proteins induce tumor progression. Viral proteins have been shown to influence not only the viral-infected cancer cells, but also the stromal cells and matricellular proteins that constitute the extracellular matrix that surrounds tumor tissues. In this review, we summarize the recent progress on the involvement of matricellular proteins in oncogenic virus-induced cancers to elucidate the mechanism of oncogenesis and consider the possible role of matricellular proteins as therapeutic targets in virus-induced cancers.