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

The cross-talk between the macro and micro-environment in precursor lesions of pancreatic cancer leads to new and promising circulating biomarkers

Pancreatic cancer (PC) is a clinically challenging tumor to combat due to its advanced stage at diagnosis as well as its resistance to currently available therapies. The absence of early symptoms and known detectable biomarkers renders this disease incredibly difficult to detect/manage. Recent advances in the understanding of PC biology have highlighted the importance of cancer-immune cell interactions, not only in the tumor micro-environment but also in distant systemic sites, like the bone marrow, spleen and circulating immune cells, the so-called macro-environment. The response of the macro-environment is emerging as a determining factor in tumor development by contributing to the formation of an increasingly immunogenic micro-environment promoting tumor homeostasis and progression. We will summarize the key events associated with the feedback loop between the tumor immune micro-environment (TIME) and the tumor immune macroenvironment (TIMaE) in pancreatic precancerous lesions along with how it regulates disease development and progression. In addition, liquid biopsy biomarkers capable of diagnosing PC at an early stage of onset will also be discussed. A clearer understanding of the early crosstalk between micro-environment and macro-environment could contribute to identifying new molecular therapeutic targets and biomarkers, consequently improving early PC diagnosis and treatment.

A Rigorous Multi-Laboratory Study of Known PDAC Biomarkers Identifies Increased Sensitivity and Specificity Over CA19-9 Alone

A blood test that enables surveillance for early-stage pancreatic ductal adenocarcinoma (PDAC) is an urgent need. Independent laboratories have reported PDAC biomarkers that could improve biomarker performance over CA19-9 alone, but the performance of the previously reported biomarkers in combination is not known. Therefore, we conducted a coordinated case/control study across multiple laboratories using common sets of blinded training and validation samples (132 and 295 plasma samples, respectively) from PDAC patients and non-PDAC control subjects representing conditions under which surveillance occurs. We analyzed the training set to identify candidate biomarker combination panels using biomarkers across laboratories, and we applied the fixed panels to the validation set. The panels identified in the training set, CA19-9 with CA199.STRA, LRG1, TIMP-1, TGM2, THSP2, ANG, and MUC16.STRA, achieved consistent performance in the validation set. The panel of CA19-9 with the glycan biomarker CA199.STRA improved sensitivity from 0.44 with 0.98 specificity for CA19-9 alone to 0.71 with 0.98 specificity (p < 0.001, 1000-fold bootstrap). Similarly, CA19-9 combined with the protein biomarker LRG1 and CA199.STRA improved specificity from 0.16 with 0.94 sensitivity for CA19-9 to 0.65 with 0.89 sensitivity (p < 0.001, 1000-fold bootstrap). We further validated significantly improved performance using biomarker panels that did not include CA19-9. This study establishes the effectiveness of a coordinated study of previously discovered biomarkers and identified panels of those biomarkers that significantly increased the sensitivity and specificity of early-stage PDAC detection in a rigorous validation trial.

Postbiotic butyrate: role and its effects for being a potential drug and biomarker to pancreatic cancer

Postbiotics are produced by microbes and have recently gained importance in the field of oncology due to their beneficial effects to the host, effectiveness against cancer cells, and their ability to suppress inflammation. In particular, butyrate dominates over all other postbiotics both in quantity and anticancer properties. Pancreatic cancer (PC), being one of the most malignant and lethal cancers, reported a decreased 5-year survival rate in less than 10% of the patients. PC causes an increased mortality rate due to its inability to be detected at an early stage but still a promising strategy for its diagnosis has not been achieved yet. It is necessary to diagnose Pancreatic cancer before the metastatic progression stage. The available blood biomarkers lack accurate and proficient diagnostic results. Postbiotic butyrate is produced by gut microbiota such as Rhuminococcus and Faecalibacterium it is involved in cell signalling pathways, autophagy, and cell cycle regulation, and reduction in butyrate concentration is associated with the occurrence of pancreatic cancer. The postbiotic butyrate is a potential biomarker that could detect PC at an early stage, before the metastatic progression stage. Thus, this review focused on the gut microbiota butyrate's role in pancreatic cancer and the immuno-suppressive environment, its effects on histone deacetylase and other immune cells, microbes in major butyrate synthesis pathways, current biomarkers in use for Pancreatic Cancer.

Improving the prognosis of pancreatic cancer: insights from epidemiology, genomic alterations, and therapeutic challenges

Pancreatic cancer, notorious for its late diagnosis and aggressive progression, poses a substantial challenge owing to scarce treatment alternatives. This review endeavors to furnish a holistic insight into pancreatic cancer, encompassing its epidemiology, genomic characterization, risk factors, diagnosis, therapeutic strategies, and treatment resistance mechanisms. We delve into identifying risk factors, including genetic predisposition and environmental exposures, and explore recent research advancements in precursor lesions and molecular subtypes of pancreatic cancer. Additionally, we highlight the development and application of multi-omics approaches in pancreatic cancer research and discuss the latest combinations of pancreatic cancer biomarkers and their efficacy. We also dissect the primary mechanisms underlying treatment resistance in this malignancy, illustrating the latest therapeutic options and advancements in the field. Conclusively, we accentuate the urgent demand for more extensive research to enhance the prognosis for pancreatic cancer patients.

Bioorthogonal Chemical Labeling Probes Targeting Sialic Acid Isomers for N-Glycan MALDI Imaging Mass Spectrometry of Tissues, Cells, and Biofluids

Sialic acid isomers attached in either α2,3 or α2,6 linkage to glycan termini confer distinct chemical, biological, and pathological properties, but they cannot be distinguished by mass differences in traditional mass spectrometry experiments. Multiple derivatization strategies have been developed to stabilize and facilitate the analysis of sialic acid isomers and their glycoconjugate carriers by high-performance liquid chromatography, capillary electrophoresis, and mass spectrometry workflows. Herein, a set of novel derivatization schemes are described that result in the introduction of bioorthogonal click chemistry alkyne or azide groups into α2,3- and α2,8-linked sialic acids. These chemical modifications were validated and structurally characterized using model isomeric sialic acid conjugates and model protein carriers. Use of an alkyne-amine, propargylamine, as the second amidation reagent effectively introduces an alkyne functional group into α2,3-linked sialic acid glycoproteins. In tissues, serum, and cultured cells, this allows for the detection and visualization of N-linked glycan sialic acid isomers by imaging mass spectrometry approaches. Formalin-fixed paraffin-embedded prostate cancer tissues and pancreatic cancer cell lines were used to characterize the numbers and distribution of alkyne-modified α2,3-linked sialic acid N-glycans. An azide-amine compound with a poly(ethylene glycol) linker was evaluated for use in histochemical staining. Formalin-fixed pancreatic cancer tissues were amidated with the azide amine, reacted with biotin-alkyne and copper catalyst, and sialic acid isomers detected by streptavidin-peroxidase staining. The direct chemical introduction of bioorthogonal click chemistry reagents into sialic acid-containing glycans and glycoproteins provides a new glycomic tool set to expand approaches for their detection, labeling, visualization, and enrichment.

Amplification-Free, High-Throughput Nanoplasmonic Quantification of Circulating MicroRNAs in Unprocessed Plasma Microsamples for Earlier Pancreatic Cancer Detection

Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy that is often detected at an advanced stage. Earlier diagnosis of PDAC is key to reducing mortality. Circulating biomarkers such as microRNAs are gaining interest, but existing technologies require large sample volumes, amplification steps, extensive biofluid processing, lack sensitivity, and are low-throughput. Here, we present an advanced nanoplasmonic sensor for the highly sensitive, amplification-free detection and quantification of microRNAs (microRNA-10b, microRNA-let7a) from unprocessed plasma microsamples. The sensor construct utilizes uniquely designed -ssDNA receptors attached to gold triangular nanoprisms, which display unique localized surface plasmon resonance (LSPR) properties, in a multiwell plate format. The formation of -ssDNA/microRNA duplex controls the nanostructure-biomolecule interfacial electronic interactions to promote the charge transfer/exciton delocalization processes and enhance the LSPR responses to achieve attomolar (10^-18 M) limit of detection (LOD) in human plasma. This improve LOD allows the fabrication of a high-throughput assay in a 384-well plate format. The performance of nanoplasmonic sensors for microRNA detection was further assessed by comparing with the qRT-PCR assay of 15 PDAC patient plasma samples that shows a positive correlation between these two assays with the Pearson correlation coefficient value >0.86. Evaluation of >170 clinical samples reveals that oncogenic microRNA-10b and tumor suppressor microRNA-let7a levels can individually differentiate PDAC from chronic pancreatitis and normal controls with >94% sensitivity and >94% specificity at a 95% confidence interval (CI). Furthermore, combining both oncogenic and tumor suppressor microRNA levels significantly improves differentiation of PDAC stages I and II versus III and IV with >91% and 87% sensitivity and specificity, respectively, in comparison to the sensitivity and specificity values for individual microRNAs. Moreover, we show that the level of microRNAs varies substantially in pre- and post-surgery PDAC patients (n = 75). Taken together, this ultrasensitive nanoplasmonic sensor with excellent sensitivity and specificity is capable of assaying multiple biomarkers simultaneously and may facilitate early detection of PDAC to improve patient care.

Applications and continued evolution of glycan imaging mass spectrometry

Glycosylation is an important posttranslational modifier of proteins and lipid conjugates critical for the stability and function of these macromolecules. Particularly important are N-linked glycans attached to asparagine residues in proteins. N-glycans have well-defined roles in protein folding, cellular trafficking and signal transduction, and alterations to them are implicated in a variety of diseases. However, the non-template driven biosynthesis of these N-glycans leads to significant structural diversity, making it challenging to identify the most biologically and clinically relevant species using conventional analyses. Advances in mass spectrometry instrumentation and data acquisition, as well as in enzymatic and chemical sample preparation strategies, have positioned mass spectrometry approaches as powerful analytical tools for the characterization of glycosylation in health and disease. Imaging mass spectrometry expands upon these strategies by capturing the spatial component of a glycan's distribution in-situ, lending additional insight into the organization and function of these molecules. Herein we review the ongoing evolution of glycan imaging mass spectrometry beginning with widely adopted tissue imaging approaches and expanding to other matrices and sample types with potential research and clinical implications. Adaptations of these techniques, along with their applications to various states of disease, are discussed. Collectively, glycan imaging mass spectrometry analyses broaden our understanding of the biological and clinical relevance of N-glycosylation to human disease.

Establishment and validation of a plasma oncofetal chondroitin sulfated proteoglycan for pan-cancer detection

Various biomarkers targeting cell-free DNA (cfDNA) and circulating proteins have been tested for pan-cancer detection. Oncofetal chondroitin sulfate (ofCS), which distinctively modifies proteoglycans (PGs) of most cancer cells and binds specifically to the recombinant Plasmodium falciparum VAR2CSA proteins (rVAR2), is explored for its potential as a plasma biomarker in pan-cancer detection. To quantitate the plasma ofCS/ofCSPGs, we optimized an ELISA using different capture/detection pairs (rVAR2/anti-CD44, -SDC1, and -CSPG4) in a case-control study with six cancer types. We show that the plasma levels of ofCS/ofCSPGs are significantly higher in cancer patients (P values, 1.2 × 10^-2 to 4.4 × 10^-10). Validation studies are performed with two independent cohorts covering 11 malignant tumors. The individuals in the top decile of ofCS-CD44 have more than 27-fold cancer risk (OR = 27.8, 95%CI = 18.8-41.4, P = 2.72 × 10^-62) compared with the lowest 20%. Moreover, the elevated plasma ofCS-CD44 could be detected at the early stage of pan-cancer with strong dose-dependent odds risk prediction.

Pancreatic Cancer in Chronic Pancreatitis: Pathogenesis and Diagnostic Approach

Chronic pancreatitis is one of the main risk factors for pancreatic cancer, but it is a rare event. Inflammation and oncogenes work hand in hand as key promoters of this disease. Tobacco is another co-factor. During alcoholic chronic pancreatitis, the cumulative risk of cancer is estimated at 4% after 15 to 20 years. This cumulative risk is higher in hereditary pancreatitis: 19 and 12% in the case of PRSS1 and SPINK1 mutations, respectively, at an age of 60 years. The diagnosis is difficult due to: (i) clinical symptoms of cancer shared with those of chronic pancreatitis; (ii) the parenchymal and ductal remodeling of chronic pancreatitis rendering imaging analysis difficult; and (iii) differential diagnoses, such as pseudo-tumorous chronic pancreatitis and paraduodenal pancreatitis. Nevertheless, the occurrence of cancer during chronic pancreatitis must be suspected in the case of back pain, weight loss, unbalanced diabetes, and jaundice, despite alcohol withdrawal. Imaging must be systematically reviewed. Endoscopic ultrasound-guided fine-needle biopsy can contribute by targeting suspicious tissue areas with the help of molecular biology (search for KRAS, TP53, CDKN2A, DPC4 mutations). Short-term follow-up of patients is necessary at the clinical and paraclinical levels to try to diagnose cancer at a surgically curable stage. Pancreatic surgery is sometimes necessary if there is any doubt.

A combination of circulating tumor cells and CA199 improves the diagnosis of pancreatic cancer

BACKGROUND

Early diagnosis of pancreatic ductal adenocarcinoma (PDAC) is difficult due to the lack of effective screening tests. CA199, the standard biomarker for PDAC management, is not sufficiently reliable for early diagnosis. This prospective study aimed to evaluate whether circulating tumor cells (CTCs) could complement or perform better than CA199 in determining PDAC.

METHODS

A total of 168 blood samples were collected from 80 patients with PDAC, 32 patients with acute pancreatitis, 22 patients with benign pancreatic masses, and 34 healthy donors. CTCs were detected by a novel system combining negative enrichment with immunostaining and fluorescence in situ hybridization (NE-imFISH). Next, ROC curves and AUC analyses were conducted to assess diagnostic abilities of CA199, CTCs, and the combination of the two biomarkers in PDAC.

RESULTS

CTCs were stained as CD45-/DAPI+/CEP8 ≥3. With 2 CTCs/3.2 ml as the cut-off value, the sensitivity/specificity of the CTC number was 0.76/0.94, which was comparable to that of CA199 (0.78/0.83; Delong test p = 0.3360). Improved performance was achieved through a logistic regression model integrating CA199 and CTC number (AUCCTC+CA199  = 0.95, AUCCA199  = 0.80, AUCCTC number  = 0.85; Delong test p vs . CA199  < 0.0001 and p vs . CTC number  = 0.0002). CTC subtype was inferior to CTC number as a diagnostic marker (AUCCTC subtype  = 0.73; Delong test p vs . CTC number  < 0.0001).

CONCLUSION

The dual-marker panel consisting of CA199 and CTC number can significantly improve upon the diagnostic performance of CA199 alone, highlighting the promising clinical utilization as an effective strategy for PDAC surveillance.

The Diagnostic Value of the CA19-9 and Bilirubin Ratio in Patients with Pancreatic Cancer, Distal Bile Duct Cancer and Benign Periampullary Diseases, a Novel Approach

Distinction of pancreatic ductal adenocarcinoma (PDAC) in the head of the pancreas, distal cholangiocarcinoma (dCCA), and benign periampullary conditions, is complex as they often share similar clinical symptoms. However, these diseases require specific management strategies, urging improvement of non-invasive tools for accurate diagnosis. Recent evidence has shown that the ratio between CA19-9 and bilirubin levels supports diagnostic distinction of benign or malignant hepatopancreaticobiliary diseases. Here, we investigate the diagnostic value of this ratio in PDAC, dCCA and benign diseases of the periampullary region in a novel fashion. To address this aim, we enrolled 265 patients with hepatopancreaticobiliary diseases and constructed four logistic regression models on a subset of patients (n = 232) based on CA19-9, bilirubin and the ratio of both values: CA19-9/(bilirubin^-1). Non-linearity was investigated using restricted cubic splines and a final model, the 'Model Ratio', based on these three variables was fitted using multivariable fractional polynomials. The performance of this model was consistently superior in terms of discrimination and calibration compared to models based on CA19-9 combined with bilirubin and CA19-9 or bilirubin alone. The 'Model Ratio' accurately distinguished between malignant and benign disease (AUC [95% CI], 0.91 [0.86-0.95]), PDAC and benign disease (AUC 0.91 [0.87-0.96]) and PDAC and dCCA (AUC 0.83 [0.74-0.92]) which was confirmed by internal validation using 1000 bootstrap replicates. These findings provide a foundation to improve minimally-invasive diagnostic procedures, ultimately ameliorating effective therapy for PDAC and dCCA.

Pancreatic Cancer Surveillance and Novel Strategies for Screening

Individuals with a genetic susceptibility to pancreatic ductal adenocarcinoma (PDAC) may benefit from surveillance to increase the likelihood of early detection. Currently, candidates for surveillance are identified based on genetic test results and family history of PDAC, and surveillance is accomplished through imaging of the pancreas (endoscopic ultrasound or MRI). Novel methods that incorporate personalized risk, biomarkers, and radiomics are being investigated in an attempt to improve identification of at-risk individuals and to increase detection of precursor and early-stage lesions.

Diagnostic biomarkers for pancreatic cancer: An update

Pancreatic ductal adenocarcinoma accounts for the primary type of pancreatic cancer (PC) with a 5-year survival rate of only about 10% in the United States. Early diagnosis will improve chances for curative treatment. To date, a broadly used serum marker for PC diagnosis is carbohydrate antigen 19-9, which is the only approved biomarker currently by the United States Food and Drug Administration. However, it has low specificity; therefore, development of novel biomarkers is urgently needed. Clinical trials are ongoing to evaluate candidate biomarkers for PC diagnosis, and the use of a multi-biomarker panel with current PC diagnostic biomarkers appears promising.

Neoadjuvant Treatment for Pancreatic Adenocarcinoma: A False Promise or an Opportunity to Improve Outcome?

Simple Summary

Pancreatic cancer usually presents late when it has spread to distant sites. In a small proportion of patients, the cancer can be removed by surgery. Surgery is usually followed by chemotherapy, as studies have shown that this improves survival. However, due to complications after surgery and a decline in fitness, some patients do not start their chemotherapy and many do not complete the planned course. The cancer returns in the majority of patients. Chemotherapy or a combination of chemotherapy and radiotherapy before surgery are being investigated to improve survival. The best treatment regime and patient selection for different treatment strategies remains to be defined and is discussed here.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) has an aggressive tumor biology and is associated with poor survival outcomes. Most patients present with metastatic or locally advanced disease. In the 10–20% of patients with upfront resectable disease, surgery offers the only chance of cure, with the addition of adjuvant chemotherapy representing an established standard of care for improving outcomes. Despite resection followed by adjuvant chemotherapy, at best, 3-year survival reaches 63.4%. Post-operative complications and poor performance mean that around 50% of the patients do not commence adjuvant chemotherapy, and a significant proportion do not complete the intended treatment course. These factors, along with the advantages of early treatment of micrometastatic disease, the ability to downstage tumors, and the increase in R0 resection rates, have increased interest in neo-adjuvant treatment strategies. Here we review biomarkers for early diagnosis of PDAC and patient selection for a neo-adjuvant approach. We also review the current evidence for different chemotherapy regimens in this setting, as well as the role of chemoradiotherapy and immunotherapy, and we discuss ongoing trials.

Biomolecular Recognition of the Glycan Neoantigen CA19-9 by Distinct Antibodies

Glycans decorate the cell surface, secreted glycoproteins and glycolipids, and altered glycans are often found in cancers. Despite their high diagnostic and therapeutic potential, however, glycans are polar and flexible molecules that are quite challenging for the development and design of high-affinity binding antibodies. To understand the mechanisms by which glycan neoantigens are specifically recognized by antibodies, we analyze the biomolecular recognition of the tumor-associated carbohydrate antigen CA19-9 by two distinct antibodies using X-ray crystallography. Despite the potential plasticity of glycans and the very different antigen-binding surfaces presented by the antibodies, both structures reveal an essentially identical extended CA19-9 conformer, suggesting that this conformer's stability selects the antibodies. Starting from the bound structure of one of the antibodies, we use the AbLIFT computational algorithm to design a variant with seven core mutations in the variable domain's light-heavy chain interface that exhibits tenfold improved affinity for CA19-9. The results reveal strategies used by antibodies to specifically recognize glycan antigens and show how automated antibody-optimization methods may be used to enhance the clinical potential of existing antibodies.

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).

Roles of CA19-9 in pancreatic cancer: Biomarker, predictor and promoter

Carbohydrate antigen 19-9 (CA19-9) is the best validated biomarker and an indicator of aberrant glycosylation in pancreatic cancer. CA19-9 functions as a biomarker, predictor, and promoter in pancreatic cancer. As a biomarker, the sensitivity is approximately 80%, and the major challenges involve false positives in conditions of inflammation and nonpancreatic cancers and false negatives in Lewis-negative Individuals. Lewis antigen status should be determined when using CA19-9 as a biomarker. CA19-9 has screening potential when combined with symptoms and/or risk factors. As a predictor, CA19-9 could be used to assess stage, prognosis, resectability, recurrence, and therapeutic efficacy. Normal baseline levels of CA19-9 are associated with long-term survival. As a promoter, CA19-9 could be used to evaluate the biology of pancreatic cancer. CA19-9 can accelerate pancreatic cancer progression by glycosylating proteins, binding to E-selectin, strengthening angiogenesis, and mediating the immunological response. CA19-9 is an attractive therapeutic target for cancer, and strategies include therapeutic antibodies and vaccines, CA19-9-guided nanoparticles, and inhibition of CA19-9 biosynthesis.

Blood biomarkers for differential diagnosis and early detection of pancreatic cancer

Pancreatic cancer is currently the most lethal tumor entity and case numbers are rising. It will soon be the second most frequent cause of cancer-related death in the Western world. Mortality is close to incidence and patient survival after diagnosis stands at about five months. Blood-based diagnostics could be one crucial factor for improving this dismal situation and is at a stage that could make this possible. Here, we are reviewing the current state of affairs with its problems and promises, looking at various molecule types. Reported results are evaluated in the overall context. Also, we are proposing steps toward clinical utility that should advance the development toward clinical application by improving biomarker quality but also by defining distinct clinical objectives and the respective diagnostic accuracies required to achieve them. Many of the discussed points and conclusions are highly relevant to other solid tumors, too.

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

ABSTRACT

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

Identification of Serum microRNA-25 as a novel biomarker for pancreatic cancer

To identify serum microRNA-25 (miR-25) as a diagnostic biomarker for pancreatic cancer (PCa) and to evaluate its supplementary role with serum carbohydrate antigen 19-9 (CA19-9) in early identification of cancers.Eighty patients with pancreatic cancer and 91 non-cancer controls were enrolled in this study. Quantitative reverse transcription-polymerase chain reaction (RT-PCR) was used to detect the expression level of miR-25. Levels of CA19-9, carcinoembryonic antigen (CEA) and carbohydrate antigen 125 (CA125) were measured by chemiluminescent immunoassay. The logistic model was established to evaluate the correlation of miR-25 with clinical characteristics. A risk model for PCa was conducted by R statistical software. Diagnostic utility for PCa and correlation with clinical characteristics were analyzed.The expression level of miR-25, in the PCa group was significantly higher (P < .05). Risk Model illustrated the relation between miR-25 and pancreatic cancer. With the combination of CA19-9, the performance of miR-25 in early stages (I+II) in the diagnosis of PCa was profoundly better than CA19-9 and miR-25 alone. This combination was more effective for discriminating PCa from non-cancer controls (AUC-ROC, 0.985; sensitivity, 97.50%; specificity, 90.11%) compared with CA19-9 alone or the combination of CA19-9 and CA125.The expression level of miR-25 among pancreatic cancer patients was significantly higher than that in the control group. miR-25 existed as one of the most relevant factors of PCa. miR-25 can serve as a novel noninvasive approach for PCa diagnosis, and with the supplementary of CA19-9, the combination was more effective, especially in early tumor screening.

Imaging Mass Spectrometry and Lectin Analysis of N-Linked Glycans in Carbohydrate Antigen-Defined Pancreatic Cancer Tissues

The early detection of pancreatic ductal adenocarcinoma (PDAC) is a complex clinical obstacle yet is key to improving the overall likelihood of patient survival. Current and prospective carbohydrate biomarkers carbohydrate antigen 19-9 (CA19-9) and sialylated tumor-related antigen (sTRA) are sufficient for surveilling disease progression yet are not approved for delineating PDAC from other abdominal cancers and noncancerous pancreatic pathologies. To further understand these glycan epitopes, an imaging mass spectrometry (IMS) approach was used to assess the N-glycome of the human pancreas and pancreatic cancer in a cohort of patients with PDAC represented by tissue microarrays and whole-tissue sections. Orthogonally, these same tissues were characterized by multiround immunofluorescence that defined expression of CA19-9 and sTRA as well as other lectins toward carbohydrate epitopes with the potential to improve PDAC diagnosis. These analyses revealed distinct differences not only in N-glycan spatial localization across both healthy and diseased tissues but importantly between different biomarker-categorized tissue samples. Unique sulfated biantennary N-glycans were detected specifically in normal pancreatic islets. N-glycans from CA19-9-expressing tissues tended to be biantennary, triantennary, and tetra-antennary structures with both core and terminal fucose residues and bisecting GlcNAc. These N-glycans were detected in less abundance in sTRA-expressing tumor tissues, which favored triantennary and tetra-antennary structures with polylactosamine extensions. Increased sialylation of N-glycans was detected in all tumor tissues. A candidate new biomarker derived from IMS was further explored by fluorescence staining with selected lectins on the same tissues. The lectins confirmed the expression of the epitopes in cancer cells and revealed different tumor-associated staining patterns between glycans with bisecting GlcNAc and those with terminal GlcNAc. Thus, the combination of lectin-immunohistochemistry and lectin-IMS techniques produces more complete information for tumor classification than the individual analyses alone. These findings potentiate the development of early assessment technologies to rapidly and specifically identify PDAC in the clinic that may directly impact patient outcomes.

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."

Detection of Chemotherapy-resistant Pancreatic Cancer Using a Glycan Biomarker, sTRA

PURPOSE

A subset of pancreatic ductal adenocarcinomas (PDACs) is highly resistant to systemic chemotherapy, but no markers are available in clinical settings to identify this subset. We hypothesized that a glycan biomarker for PDACs called sialylated tumor-related antigen (sTRA) could be used for this purpose.

EXPERIMENTAL DESIGN

We tested for differences between PDACs classified by glycan expression in multiple systems: sets of cell lines, organoids, and isogenic cell lines; primary tumors; and blood plasma from human subjects.

RESULTS

The sTRA-expressing models tended to have stem-like gene expression and the capacity for mesenchymal differentiation, in contrast to the nonexpressing models. The sTRA cell lines also had significantly increased resistance to seven different chemotherapeutics commonly used against pancreatic cancer. Patients with primary tumors that were positive for a gene expression classifier for sTRA received no statistically significant benefit from adjuvant chemotherapy, in contrast to those negative for the signature. In another cohort, based on direct measurements of sTRA in tissue microarrays, the patients who were high in sTRA again had no statistically significant benefit from adjuvant chemotherapy. Furthermore, a blood plasma test for the sTRA glycan identified the PDACs that showed rapid relapse following neoadjuvant chemotherapy.

CONCLUSIONS

This research demonstrates that a glycan biomarker could have value to detect chemotherapy-resistant PDAC in clinical settings. This capability could aid in the development of stratified treatment plans and facilitate biomarker-guided trials targeting resistant PDAC.

Evaluation of Sialyl-Lactotetra as a Marker for Epithelial Ovarian Tumors

Ovarian carcinoma is a heterogeneous disease with distinct molecular and histological profiles, ranging from low grade atypia to highly aggressive tumors associated with a poor prognosis. In the present study, glycosphingolipids were isolated from human high-grade serous ovarian carcinoma, whereby the novel stem cell marker Sialyl-lactotetra (S-Lc4) was characterized in two out of three cases. The presence and level of S-Lc4 was further evaluated immunohistochemically in a cohort of patients with ovarian tumors ranging from benign lesions to high grade serous carcinoma (n = 478). Its expression was assessed in association with tumor grade, stage, histology, and survival. The data showed that S-Lc4 is most common and highly expressed in borderline type tumors and carcinomas with low levels of aggressiveness, such as mucinous, endometrioid, and low grade serous. Accordingly, S-Lc4-positivity was associated with better disease-free survival. The expression of S-Lc4 was seemingly associated with lineage continuity and could be traced from premalignant lesions to carcinoma, suggesting inheritance by a stem cell lineage that gives rise to generally indolent tumors.

Advances in the discovery of novel biomarkers for cancer: spotlight on protein N-glycosylation

Progress on glycosylation and tumor markers has not been extensively reported. Glycosylation plays an important part in post-translational modification. Previous research on glycosylation-modified biomarkers has lagged behind due to insufficient understanding of glycosylation-related regulations. However, some new methods and ideas illustrated in recent research may provide new inspirations in the field. This article aims to review current advances in revealing relationship between tumors and abnormal N-glycosylation and discuss leading-edge applications of N-glycosylation in developing novel tumor biomarkers.

Identification of a competing endogenous RNA network associated with prognosis of pancreatic adenocarcinoma

Background

Emerging evidence suggests that competing endogenous RNAs plays a crucial role in the development and progress of pancreatic adenocarcinoma (PAAD). The objective was to identify a new lncRNA-miRNA-mRNA network as prognostic markers, and develop and validate a multi-mRNAs-based classifier for predicting overall survival (OS) in PAAD.

Methods

Data on pancreatic RNA expression and clinical information of 445 PAAD patients and 328 normal subjects were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype-Tissue Expression (GTEx). The weighted correlation network analysis (WGCNA) was used to analyze long non-coding RNA (lncRNA) and mRNA, clustering genes with similar expression patterns. MiRcode was used to predict the sponge microRNAs (miRNAs) corresponding to lncRNAs. The downstream targeted mRNAs of miRNAs were identified by starBase, miRDB, miRTarBase and Targetscan. A multi-mRNAs-based classifier was develop using least absolute shrinkage and selection operator method (LASSO) COX regression model, which was tested in an independent validation cohort.

Results

A lncRNA-miRNA-mRNA co-expression network which consisted of 60 lncRNAs, 3 miRNAs and 3 mRNAs associated with the prognosis of patients with PAAD was established. In addition, we constructed a 14-mRNAs-based classifier based on a training cohort composed of 178 PAAD patients, of which the area under receiver operating characteristic (AUC) in predicting 1-year, 3-year, and 5-year OS was 0.719, 0.806 and 0.794, respectively. The classifier also shown good prediction function in independent verification cohorts, with the AUC of 0.604, 0.639 and 0.607, respectively.

Conclusions

A novel competitive endogenous RNA (ceRNA) network associated with progression of PAAD could be used as a reference for future molecular biology research.

Phage Display Selection, Identification, and Characterization of Novel Pancreatic Cancer Targeting Peptides

Pancreatic cancer is characterized by a 5-year survival rate of 3%, in part due to inadequate detection methods. The small size of peptides offers advantages regarding molecular targeting. Thus, peptides may be used in detection of pancreatic cancer. Here, peptides that target pancreatic cancer cells were selected using phage display technology using a 15-mer fUSE5 library. Phage were pre-cleared against immortalized pancreatic cells (hTERT-HPNE), followed by selections against pancreatic cancer (Mia Paca-2) cells. Next-generation sequencing identified two peptides, MCA1 and MCA2, with a Log2 fold change (Mia Paca-2/ hTERT-HPNE) >1.5. Modified ELISA and fluorescent microscopy showed that both peptides bound significantly higher to Mia Paca-2 cells, and not to hTERT-HPNE, embryonic kidney (HEK 293), ovarian (SKOV-3) and prostate cancer (LNCaP) cell lines. Further characterization of MCA1 and MCA2 revealed EC₅₀ values of 16.11 µM (95% CI [9.69, 26.31 µM]) and 97.01 µM (95% CI [58.64, 166.30 µM]), respectively. Based on these results, MCA1 was selected for further studies. A competitive dose response assay demonstrated specific binding and an IC₅₀ value of 2.15 µM (95% CI [1.28, 3.62 µM]). Taken together, this study suggests that MCA1 may be used as a pancreatic cancer targeting ligand for detection of the disease.

Predictive value of CONUT score combined with serum CA199 levels in postoperative survival of patients with pancreatic ductal adenocarcinoma: a retrospective study

Background

The preoperative controlling nutritional status (CONUT) score and serum carbohydrate antigen 199 (CA199) levels are individually correlated with the prognosis of pancreatic ductal adenocarcinoma (PDAC). The objective of this study aimed to investigate the efficacy of CONUT score and CA199 (CONUT-CA199) combination in predicting the prognosis of PDAC patients undergoing radical surgery.

Methods

We retrospectively analyzed the preoperative CONUT scores and serum CA199 levels of 294 patients with PDAC who underwent radical resection at the Affiliated Hospital of Southwest Medical University between March 2012 and July 2019. Patients were divided into four groups on the basis of their preoperative CONUT scores and serum CA199 levels: CONUT^low/CA199^low (1), CONUT^low/CA199^high (2), CONUT^high/CA199^low (3) and CONUT^high/CA199^high (4). The prognostic effects were compared among the groups.

Results

CONUT^high was more frequent in patients with positive peripancreatic infiltration and Clavien–Dindo classification of ≥IIIa (P < 0.001). Kaplan–Meier analysis revealed obvious difference in overall survival (OS) and recurrence-free survival (RFS) among patients with PDAC having CONUT-CA199 scores of 1, 2, 3 and 4 (P < 0.001). Peripancreatic infiltration, lymph node metastasis, pTNM stage, CONUT score, serum CA199 levels and CONUT-CA199 classification were found to be the independent prognostic factors for OS and RFS in multivariate analyses. In time-dependent receiver operating characteristic (ROC) analyses, the area of the CONUT-CA199 score under the ROC curve (AUC) was higher than that of the preoperative CONUT score or serum CA199 levels for the prediction of OS and RFS.

Conclusion

CONUT-CA199 classification may be more effective in predicting the postoperative prognosis of PDAC patients.

Nanoparticle-enabled blood tests for early detection of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is often detected too late to allow adequate treatments with the result that patients are condemned to sufferings and early death. Most efforts have been therefore aimed at identifying sensitive PDAC biomarkers. Although biomarkers have numerous advantages, sample size, intra-individual variability, existence of several biases and confounding variables and cost of investigation make their clinical application challenging. In recent years, nanotechnology is providing new options for early cancer detection. Among recent discoveries, the concept is emerging that the protein corona, i.e. the layer of plasma proteins that surrounds nanomaterials in bodily fluids, is personalized. In particular, the protein corona of cancer patients is significantly different from that of healthy individuals. Herein, we review this concept with a particular focus on clinical relevance. We also discuss the recently developed nanoparticle-enabled blood (NEB) tests that demonstrated to be promising in discriminating PDAC patients from healthy volunteers by global change of the nanoparticle-protein corona. We conclude with a critical discussion of research perspectives aimed at further improving the prediction ability of the test.

An Integrated Workflow for Global, Glyco-, and Phospho-proteomic Analysis of Tumor Tissues

Recently, the rapid development and application of mass spectrometry (MS)-based technologies have markedly improved the comprehensive proteomic characterization of global proteome and protein post-translational modifications (PTMs). However, the current conventional approach for global proteomic analysis is often carried out separately from PTM analysis. In our study, we developed an integrated workflow for multiplex analysis of global, glyco-, and phospho-proteomics using breast cancer patient-derived xenograft (PDX) tumor samples. Our approach included the following steps: trypsin-digested tumor samples were enriched for phosphopeptides through immobilized metal ion affinity chromatography (IMAC), followed by enrichment of glycopeptides through mixed anion exchange (MAX) method, and then the flow-through peptides were analyzed for global proteomics. Our workflow demonstrated an increased identification of peptides and associated proteins in global proteome, as compared to those using the peptides without PTM depletion. In addition to global proteome, the workflow identified phosphopeptides and glycopeptides from the PTM enrichment. We also found a subset of glycans with unique distribution profiles in the IMAC flow-through, as compared to those enriched directly using the MAX method. Our integrated workflow provided an effective platform for simultaneous global proteomic and PTM analysis of biospecimens.

Automated Identification and Quantification of Signals in Multichannel Immunofluorescence Images: The SignalFinder-IF Platform

Multimarker fluorescence analysis of tissue specimens offers the opportunity to probe the expression levels and locations of multiple markers in a single sample. Software is needed to fully capitalize on the advantages of this technology for sensitive, quantitative, and multiplexed data collection. A major challenge has been the automated identification and quantification of signals. We report on the software SignalFinder-IF, which meets that need. SignalFinder-IF uses a newly developed algorithm called Segment-Fit Thresholding, which showed robust performance for automated signal identification in side-by-side comparisons with several current methods. Two utilities provided with SignalFinder-IF enable downstream analyses. The first allows the quantification and mapping of relationships between an unlimited number of markers through user-defined sequences of AND, OR, and NOT operators. The second produces composite pictures of the signals or colocalization analysis on brightfield hematoxylin and eosin images, which is useful for understanding the morphologies and locations of cells meeting specific marker criteria. SignalFinder-IF enables high-throughput, rigorous analyses of whole-slide, multimarker data, and it promises to open new possibilities in many research and clinical applications.