Early Diagnosis of Pancreatic Cancer: The Key for Survival

Personalized Medicine in Pancreatic Cancer: The Promise of Biomarkers and Molecular Targeting with Dr. Michael J. Pishvaian

Pancreatic cancer, with its alarming rising incidence, is predicted to become the second deadliest type of solid tumor by 2040, highlighting the urgent need for improved diagnostic and treatment strategies. Despite medical advancements, the five-year survival rate for pancreatic cancer remains about 14%, dropping further when metastasized. This review explores the promise of biomarkers for early detection, personalized treatment, and disease monitoring. Molecular classification of pancreatic cancer into subtypes based on genetic mutations, gene expression, and protein markers guides treatment decisions, potentially improving outcomes. A plethora of clinical trials investigating different strategies are currently ongoing. Targeted therapies, among which those against CLAUDIN 18.2 and inhibitors of Claudin 18.1, have shown promise. Next-generation sequencing (NGS) has emerged as a powerful tool for the comprehensive genomic analysis of pancreatic tumors, revealing unique genetic alterations that drive cancer progression. This allows oncologists to tailor therapies to target specific molecular abnormalities. However, challenges remain, including limited awareness and uptake of biomarker-guided therapies. Continued research into the molecular mechanisms of pancreatic cancer is essential for developing more effective treatments and improving patient survival rates.

Bidirectional relationship between pancreatic cancer and diabetes mellitus: a comprehensive literature review

Pancreatic cancer (PC) is a fatal malignant disease. It is well known that the relationship between PC and type 2 diabetes mellitus (T2DM) is a complicated bidirectional relationship. The most important factors causing increased risks of pancreatic cancer are hyperglycaemia, hyperinsulinemia, pancreatitis, and dyslipidemia. Genetics and the immune system also play an important role in the relationship between diabetes mellitus and pancreatic cancer. The primary contributors to this association involve insulin resistance and inflammatory processes within the tumour microenvironment. The combination of diabetes and obesity can contribute to PC by inducing hyperinsulinemia and influencing leptin and adiponectin levels. Given the heightened incidence of pancreatic cancer in diabetes patients compared to the general population, early screening for pancreatic cancer is recommended. Diabetes negatively impacts the survival of pancreatic cancer patients. Among patients receiving chemotherapy, it reduced their survival. The implementation of a healthy lifestyle, including weight management, serves as an initial preventive measure to mitigate the risk of disease development. The role of anti-diabetic drugs on survival is controversial; however, metformin may have a positive impact, especially in the early stages of cancer, while insulin therapy increases the risk of PC.

Targeted Nanoparticle-Based Diagnostic and Treatment Options for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest cancers, presents significant challenges in diagnosis and treatment due to its aggressive, metastatic nature and lack of early detection methods. A key obstacle in PDAC treatment is the highly complex tumor environment characterized by dense stroma surrounding the tumor, which hinders effective drug delivery. Nanotechnology can offer innovative solutions to these challenges, particularly in creating novel drug delivery systems for existing anticancer drugs for PDAC, such as gemcitabine and paclitaxel. By using customization methods such as incorporating conjugated targeting ligands, tumor-penetrating peptides, and therapeutic nucleic acids, these nanoparticle-based systems enhance drug solubility, extend circulation time, improve tumor targeting, and control drug release, thereby minimizing side effects and toxicity in healthy tissues. Moreover, nanoparticles have also shown potential in precise diagnostic methods for PDAC. This literature review will delve into targeted mechanisms, pathways, and approaches in treating pancreatic cancer. Additional emphasis is placed on the study of nanoparticle-based delivery systems, with a brief mention of those in clinical trials. Overall, the overview illustrates the significant advances in nanomedicine, underscoring its role in transcending the constraints of conventional PDAC therapies and diagnostics.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Genetic Signature of Human Pancreatic Cancer and Personalized Targeting

Pancreatic cancer is a highly lethal disease with a 5-year survival rate of around 11-12%. Surgery, being the treatment of choice, is only possible in 20% of symptomatic patients. The main reason is that when it becomes symptomatic, IT IS the tumor is usually locally advanced and/or has metastasized to distant organs; thus, early diagnosis is infrequent. The lack of specific early symptoms is an important cause of late diagnosis. Unfortunately, diagnostic tumor markers become positive at a late stage, and there is a lack of early-stage markers. Surgical and non-surgical cases are treated with neoadjuvant and/or adjuvant chemotherapy, and the results are usually poor. However, personalized targeted therapy directed against tumor drivers may improve this situation. Until recently, many pancreatic tumor driver genes/proteins were considered untargetable. Chemical and physical characteristics of mutated KRAS are a formidable challenge to overcome. This situation is slowly changing. For the first time, there are candidate drugs that can target the main driver gene of pancreatic cancer: KRAS. Indeed, KRAS inhibition has been clinically achieved in lung cancer and, at the pre-clinical level, in pancreatic cancer as well. This will probably change the very poor outlook for this disease. This paper reviews the genetic characteristics of sporadic and hereditary predisposition to pancreatic cancer and the possibilities of a personalized treatment according to the genetic signature.

Genomic insights and prognostic significance of novel biomarkers in pancreatic ductal adenocarcinoma: A comprehensive analysis

Pancreatic ductal adenocarcinoma (PDAC) is a highly prevalent digestive system malignancy, with a significant impact on public health, especially in the elderly population. The advent of the Human Genome Project has opened new avenues for precision medicine, allowing researchers to explore genetic markers and molecular targets for cancer diagnosis and treatment. Despite significant advances in genomic research, early diagnosis of pancreatic cancer remains elusive due to the lack of highly sensitive and specific markers. Therefore, there is a need for in-depth research to identify more precise and reliable diagnostic markers for pancreatic cancer. In this study, we utilized a combination of public databases from different sources to meticulously screen genes associated with prognosis in pancreatic cancer. We used gene differential analysis, univariate cox regression analysis, least absolute selection and shrinkage operator (LASSO) regression, and multivariate cox regression analysis to identify genes associated with prognosis. Subsequently, we constructed a scoring system, validated its validity using survival analysis and ROC analysis, and further confirmed its reliability by nomogram and decision curve analysis (DCA). We evaluated the diagnostic value of this scoring system for pancreatic cancer prognosis and validated the function of the genes using single cell data analysis. Our analysis identifies six genes, including GABRA3, IL20RB, CDK1, GPR87, TTYH3, and KCNA2, that were strongly associated with PDAC prognosis. Clinical prognostic models based on these genes showed strong predictive power not only in the training set but also in external datasets. Functional enrichment analysis revealed significant differences between high- and low-risk groups mainly in immune-related functions. Additionally, we explored the potential of the risk score as a marker for immunotherapy response and identified key factors within the tumor microenvironment. The single-cell RNA sequencing analysis further enriched our understanding of cell clusters and six hub genes expressions. This comprehensive investigation provides valuable insights into pancreatic PDAC and its intricate immune landscape. The identified genes and their functional significance underscore the importance of continued research into improving diagnosis and treatment strategies for PDAC.

Characteristics of Early Pancreatic Cancer: Comparison between Stage 1A and Stage 1B Pancreatic Cancer in Multicenter Clinical Data Warehouse Study

BACKGROUND

Little is known about the characteristics of early pancreatic cancer. We aimed to identify the characteristics, clues for early detection, and prognostic factors for early pancreatic cancer by analyzing a large number of patients with stage 1 pancreatic cancer.

METHODS

A clinical data warehouse that includes databases of all the medical records of eight academic institutions was used to select and analyze patients with pancreatic cancer that had been diagnosed from January 2010 to May 2023.

RESULTS

In total, 257 stage 1 pancreatic cancer patients were included. There were 134 men (52%), and the average age was 67.2 ± 9.9 years. Compared to patients with stage 1B pancreatic cancer (2-4 cm), patients with stage 1A pancreatic cancer (≤2 cm) had more tumors in the body and tail than in the head (p = 0.028), more new-onset diabetes and less old diabetes (p = 0.010), less jaundice (p = 0.020), more follow-up of IPMN (intraductal papillary mucinous neoplasm, p = 0.029), and more histories of acute pancreatitis (p = 0.013). The pathological findings showed that stage 1A pancreatic cancer involved more IPMNs (p < 0.001) and lower pancreatic intraepithelial neoplasia (p = 0.004). IPMN was present in all 13 pancreatic tumors that were smaller than 1 cm. In multivariate analysis, positive resection margin (odds ratio [OR] 1.536, p = 0.040), venous invasion (OR 1.710, p = 0.010), and perineural invasion (OR 1.968, p = 0.002) were found to be risk factors affecting disease-free survival, while old diabetes (odds ratio [OS] 1.981, p = 0.003) and perineural invasion (OR 2.270, p = 0.003) were found to be risk factors affecting overall survival.

CONCLUSIONS

IPMN is closely associated with early pancreatic cancer and may provide an opportunity for early detection. The presence of perineural invasion was a crucial prognostic factor for both overall and disease-free survival in patients with stage 1 pancreatic cancer.

Targeted Cyclo[8]pyrrole-Based NIR-II Photoacoustic Tomography Probe for Suppression of Orthotopic Pancreatic Tumor Growth and Intra-abdominal Metastases

Pancreatic cancer is highly lethal. New diagnostic and treatment modalities are desperately needed. We report here that an expanded porphyrin, cyclo[8]pyrrole (CP), with a high extinction coefficient (89.16 L/g·cm) within the second near-infrared window (NIR-II), may be formulated with an αvβ3-specific targeting peptide, cyclic-Arg-Gly-Asp (cRGD), to form cRGD-CP nanoparticles (cRGD-CPNPs) with promising NIR-II photothermal (PT) therapeutic and photoacoustic (PA) imaging properties. Studies with a ring-array PA tomography system, coupled with analysis of control nanoparticles lacking a targeting element (CPNPs), revealed that cRGD conjugation promoted the delivery of the NPs through abnormal vessels around the tumor to the solid tumor core. This proved true in both subcutaneous and orthotopic pancreatic tumor mice models, as confirmed by immunofluorescent studies. In combination with NIR-II laser photoirradiation, the cRGD-CPNPs provided near-baseline tumor growth inhibition through PTT both in vitro and in vivo. Notably, the combination of the present cRGD-CPNPs and photoirradiation was found to inhibit intra-abdominal metastases in an orthotopic pancreatic tumor mouse model. The cRGD-CPNPs also displayed good biosafety profiles, as inferred from PA tomography, blood analyses, and H&E staining. They thus appear promising for use in combined PA imaging and PT therapeutic treatment of pancreatic cancer.

Patient-Derived Models of Cancer in the NCI PDMC Consortium: Selection, Pitfalls, and Practical Recommendations

For over a century, early researchers sought to study biological organisms in a laboratory setting, leading to the generation of both in vitro and in vivo model systems. Patient-derived models of cancer (PDMCs) have more recently come to the forefront of preclinical cancer models and are even finding their way into clinical practice as part of functional precision medicine programs. The PDMC Consortium, supported by the Division of Cancer Biology in the National Cancer Institute of the National Institutes of Health, seeks to understand the biological principles that govern the various PDMC behaviors, particularly in response to perturbagens, such as cancer therapeutics. Based on collective experience from the consortium groups, we provide insight regarding PDMCs established both in vitro and in vivo, with a focus on practical matters related to developing and maintaining key cancer models through a series of vignettes. Although every model has the potential to offer valuable insights, the choice of the right model should be guided by the research question. However, recognizing the inherent constraints in each model is crucial. Our objective here is to delineate the strengths and limitations of each model as established by individual vignettes. Further advances in PDMCs and the development of novel model systems will enable us to better understand human biology and improve the study of human pathology in the lab.

Unveiling the potential of galectin-3 as a diagnostic biomarker for pancreatic cancer: a review

Early detection of pancreatic cancer is crucial for improving patient outcomes, and identifying reliable biomarkers is a critical research area in this field. Galectin-3 (Gal-3) is a promising candidate for utilisation as a diagnostic biomarker in early-stage pancreatic cancer. This review aims to explore the potential of Gal-3 in pancreatic cancer diagnosis and its implications for precision medicine. Rigorous validation studies are essential to establish the clinical utility of Gal-3, including large-scale investigations to assess its sensitivity, specificity, and predictive value. Combining Gal-3 with existing biomarkers and advanced imaging techniques may enhance the accuracy of early detection. Moreover, Gal-3 holds promise for risk stratification, enabling the identification of high-risk individuals who could benefit from intensified surveillance and early interventions. However, challenges in standardised testing protocols, establishing reference ranges, assay reliability, workflow integration, cost-effectiveness, and healthcare provider education must be addressed for successful implementation. Despite these challenges, Gal-3 presents significant implications for precision medicine in pancreatic cancer management. By unravelling its potential and overcoming the hurdles, Gal-3 could revolutionise early detection, risk stratification, and personalised approaches in pancreatic cancer care. Collaborative efforts and continued research will be crucial in harnessing the full potential of Gal-3 as a diagnostic biomarker for early-stage pancreatic cancer.

Expression of GRP78 and its copartners in HEK293 and pancreatic cancer cell lines (BxPC-3/PANC-1) exposed to MRI and CT contrast agents

Endoplasmic reticulum (ER) stress-associated chaperones trigger a defense mechanism called as unfolded protein response (UPR) which can manage apoptosis and be determinative in cell fate. Both anticancer drug effects and potential toxicity effects of magnetic resonance imaging (MRI) and computed tomography (CT) contrast agents were aimed to be evaluated. For this purpose, we investigated expression profiles of endoplasmic reticulum stress-associated chaperone molecules in human pancreatic tumor lines BxPC-3 and PANC-1 and control human embryonic kidney cells 293 (HEK293) induced with a variety of gadolinium and iohexol contrast agents. Protein expression levels of ER stress-associated chaperones (master regulator: GRP78/Bip and its copartners: Calnexin, Ero1, PDI, CHOP, IRE1α and PERK) were evaluated with Western blotting. Expression levels at mRNA level were also assessed for GRP78/Bip and CHOP with real-time PCR. Induction of cells was carried out with four different Gd-based contrast agents (GBCAs): (Dotarem, Optimark, Primovist and Gadovist) and two different iohexol agents (Omnipol, Omnipaque). CT contrast agents tested in the study did not result in significant ER stress in HEK293 cells. However, they do not seem to have theranostic potential in pancreas cancer through ER pathway. The potential efficiency of macrocyclic MRI contrast agents to provoke apoptosis via ER stress-associated chaperones in BxPC-3 cells lends credibility for their future theranostic use in pancreas cancer as long as undesired toxicity effects were carefully considered. ER stress markers and/or contrast agents seem to have promising potential to be translated into the clinical practice to manage pancreas cancer progression.

Graphene Sensor Arrays for Rapid and Accurate Detection of Pancreatic Cancer Exosomes in Patients' Blood Plasma Samples

Biosensors based on graphene field effect transistors (GFETs) have the potential to enable the development of point-of-care diagnostic tools for early stage disease detection. However, issues with reproducibility and manufacturing yields of graphene sensors, but also with Debye screening and unwanted detection of nonspecific species, have prevented the wider clinical use of graphene technology. Here, we demonstrate that our wafer-scalable GFETs array platform enables meaningful clinical results. As a case study of high clinical relevance, we demonstrate an accurate and robust portable GFET array biosensor platform for the detection of pancreatic ductal adenocarcinoma (PDAC) in patients' plasma through specific exosomes (GPC-1 expression) within 45 min. In order to facilitate reproducible detection in blood plasma, we optimized the analytical performance of GFET biosensors via the application of an internal control channel and the development of an optimized test protocol. Based on samples from 18 PDAC patients and 8 healthy controls, the GFET biosensor arrays could accurately discriminate between the two groups while being able to detect early cancer stages including stages 1 and 2. Furthermore, we confirmed the higher expression of GPC-1 and found that the concentration in PDAC plasma was on average more than 1 order of magnitude higher than in healthy samples. We found that these characteristics of GPC-1 cancerous exosomes are responsible for an increase in the number of target exosomes on the surface of graphene, leading to an improved signal response of the GFET biosensors. This GFET biosensor platform holds great promise for the development of an accurate tool for the rapid diagnosis of pancreatic cancer.

Targeted Bioluminescent Imaging of Pancreatic Ductal Adenocarcinoma Using Nanocarrier-Complexed EGFR-Binding Affibody-Gaussia Luciferase Fusion Protein

In vivo imaging has enabled impressive advances in biological research, both preclinical and clinical, and researchers have an arsenal of imaging methods available. Bioluminescence imaging is an advantageous method for in vivo studies that allows for the simple acquisition of images with low background signals. Researchers have increasingly been looking for ways to improve bioluminescent imaging for in vivo applications, which we sought to achieve by developing a bioluminescent probe that could specifically target cells of interest. We chose pancreatic ductal adenocarcinoma (PDAC) as the disease model because it is the most common type of pancreatic cancer and has an extremely low survival rate. We targeted the epidermal growth factor receptor (EGFR), which is frequently overexpressed in pancreatic cancer cells, using an EGFR-specific affibody to selectively identify PDAC cells and delivered a Gaussia luciferase (GLuc) bioluminescent protein for imaging by engineering a fusion protein with both the affibody and the bioluminescent protein. This fusion protein was then complexed with a G5-PAMAM dendrimer nanocarrier. The dendrimer was used to improve the protein stability in vivo and increase signal strength. Our targeted bioluminescent complex had an enhanced uptake into PDAC cells in vitro and localized to PDAC tumors in vivo in pancreatic cancer xenograft mice. The bioluminescent complexes could delineate the tumor shape, identify multiple masses, and locate metastases. Through this work, an EGFR-targeted bioluminescent-dendrimer complex enabled the straightforward identification and imaging of pancreatic cancer cells in vivo in preclinical models. This argues for the targeted nanocarrier-mediated delivery of bioluminescent proteins as a way to improve in vivo bioluminescent imaging.

Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.

The Link between Diabetes, Pancreatic Tumors, and miRNAs-New Players for Diagnosis and Therapy?

Despite significant progress in medicine, pancreatic cancer is one of the most tardily diagnosed cancer and is consequently associated with a poor prognosis and a low survival rate. The asymptomatic clinical picture and the lack of relevant diagnostic markers for the early stages of pancreatic cancer are believed to be the major constraints behind an accurate diagnosis of this disease. Furthermore, underlying mechanisms of pancreatic cancer development are still poorly recognized. It is well accepted that diabetes increases the risk of pancreatic cancer development, however the precise mechanisms are weakly investigated. Recent studies are focused on microRNAs as a causative factor of pancreatic cancer. This review aims to provide an overview of the current knowledge of pancreatic cancer and diabetes-associated microRNAs, and their potential in diagnosis and therapy. miR-96, miR-124, miR-21, and miR-10a were identified as promising biomarkers for early pancreatic cancer prediction. miR-26a, miR-101, and miR-200b carry therapeutic potential, as they not only regulate significant biological pathways, including the TGF-β and PI3K/AKT, but their re-expression contributes to the improvement of the prognosis by reducing invasiveness or chemoresistance. In diabetes, there are also changes in the expression of microRNAs, such as in miR-145, miR-29c, and miR-143. These microRNAs are involved, among others, in insulin signaling, including IRS-1 and AKT (miR-145), glucose homeostasis (hsa-miR-21), and glucose reuptake and gluconeogenesis (miR-29c). Although, changes in the expression of the same microRNAs are observed in both pancreatic cancer and diabetes, they exert different molecular effects. For example, miR-181a is upregulated in both pancreatic cancer and diabetes mellitus, but in diabetes it contributes to insulin resistance, whereas in pancreatic cancer it promotes tumor cell migration, respectively. To conclude, dysregulated microRNAs in diabetes affect crucial cellular processes that are involved in pancreatic cancer development and progression.

Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics

BACKGROUND

The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with histologically proven pancreatic cancer.

METHODS

In this study, a total of 143 participants (63 years ± 13, 48 females) who underwent third-generation dual-source DECT and DWI between November 2014 and October 2022 were included. Among these, 83 received a final diagnosis of pancreatic cancer, 20 had pancreatitis, and 40 had no evidence of pancreatic pathologies. Data comparisons were performed using chi-square statistic tests, one-way ANOVA, or two-tailed Student's t-test. For the assessment of the association of texture features with overall survival, receiver operating characteristics analysis and Cox regression tests were used.

RESULTS

Malignant pancreatic tissue differed significantly from normal or inflamed tissue regarding radiomics features (overall P < .001, respectively) and iodine uptake (overall P < .001, respectively). The performance for the distinction of malignant from normal or inflamed pancreatic tissue ranged between an AUC of ≥ 0.995 (95% CI, 0.955-1.0; P < .001) for radiomics features, ≥ 0.852 (95% CI, 0.767-0.914; P < .001) for DECT-IC, and ≥ 0.690 (95% CI, 0.587-0.780; P = .01) for DWI, respectively. During a follow-up of 14 ± 12 months (range, 10-44 months), the multiparametric approach showed a moderate prognostic power to predict all-cause mortality (c-index = 0.778 [95% CI, 0.697-0.864], P = .01).

CONCLUSIONS

Our reported multiparametric approach allowed for accurate discrimination of pancreatic cancer and revealed great potential to provide independent prognostic information on all-cause mortality.

Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis

Pancreatic cancer (PC) has a low incidence rate but a high mortality, with patients often in the advanced stage of the disease at the time of the first diagnosis. If detected, early neoplastic lesions are ideal for surgery, offering the best prognosis. Preneoplastic lesions of the pancreas include pancreatic intraepithelial neoplasia and mucinous cystic neoplasms, with intraductal papillary mucinous neoplasms being the most commonly diagnosed. Our study focused on predicting PC by identifying early signs using noninvasive techniques and artificial intelligence (AI). A systematic English literature search was conducted on the PubMed electronic database and other sources. We obtained a total of 97 studies on the subject of pancreatic neoplasms. The final number of articles included in our study was 44, 34 of which focused on the use of AI algorithms in the early diagnosis and prediction of pancreatic lesions. AI algorithms can facilitate diagnosis by analyzing massive amounts of data in a short period of time. Correlations can be made through AI algorithms by expanding image and electronic medical records databases, which can later be used as part of a screening program for the general population. AI-based screening models should involve a combination of biomarkers and medical and imaging data from different sources. This requires large numbers of resources, collaboration between medical practitioners, and investment in medical infrastructures.

RNA-Seq Analysis of Extradomain A and Extradomain B Fibronectin as Extracellular Matrix Markers for Cancer

Alternatively spliced forms of fibronectin, called oncofetal fibronectin, are aberrantly expressed in cancer, with little to no expression in normal tissue, making them attractive biomarkers to exploit for tumor-targeted therapeutics and diagnostics. While prior studies have explored oncofetal fibronectin expression in limited cancer types and limited sample sizes, no studies have performed a large-scale pan-cancer analysis in the context of clinical diagnostics and prognostics to posit the utility of these biomarkers across multiple cancer types. In this study, RNA-Seq data sourced from the UCSC Toil Recompute project were extracted and analyzed to determine the correlation between the expression of oncofetal fibronectin, including extradomain A and extradomain B fibronectin, and patient diagnosis and prognosis. We determined that oncofetal fibronectin is significantly overexpressed in most cancer types relative to corresponding normal tissues. In addition, strong correlations exist between increasing oncofetal fibronectin expression levels and tumor stage, lymph node activity, and histological grade at the time of diagnosis. Furthermore, oncofetal fibronectin expression is shown to be significantly associated with overall patient survival within a 10-year window. Thus, the results presented in this study suggest oncofetal fibronectin as a commonly upregulated biomarker in cancer with the potential to be used for tumor-selective diagnosis and treatment applications.

Utility of Comprehensive Genomic Profiling Tests for Patients with Incurable Pancreatic Cancer in Clinical Practice

Although comprehensive genomic profiling (CGP) tests have been covered under the Japanese national health insurance program since 2018, the utility and issues of CGP tests have not been clarified. We retrospectively reviewed 115 patients with incurable pancreatic cancer (IPC) who underwent CGP tests in a Japanese cancer referral center from November 2019 to August 2021. We evaluated the results of CGP tests, treatments based on CGP tests, and survival time. Eight cases (6.9%) were diagnosed as tumor mutation burden-high (TMB-H) and/or microsatellite instability-high (MSI-H). The gene mutation rates of KRAS/TP53/CDKN2A/SMAD4 were 93.0/83.0/53.0/25.2%, respectively. Twenty-five patients (21.7%) had homologous recombination deficiency (HRD)-related genetic mutations. Four patients (3.5%) having TMB-H and/or MSI-H were treated with pembrolizumab, and only two patients (1.7%) participated in the clinical trials. Patient characteristics were not significantly different between patients with and without HRD-related gene mutations. The median OS was significantly longer in the HRD (+) group than in the HRD (-) group (749 days vs. 519 days, p = 0.047). In multivariate analysis, HRD-related gene mutation was an independent prognostic factor associated with favorable OS. CGP tests for patients with IPC have the potential utility of detecting HRD-related gene mutations as prognostic factors as well as a therapeutic search.

Development and validation of a gene signature for pancreatic cancer: based on inflammatory response-related genes

Pancreatic cancer (PC) is one of the most common malignant tumors in the world with a poor prognosis. There were limited studies investigating the genetic signatures associated with inflammatory responses, tumor microenvironment (TME), and tumor drug sensitivity prediction. In the Cancer Genome Atlas (TCGA) dataset, we constructed an inflammatory response-related genes prognostic signature for PC, and predictive ability of the model was assessed via the International Cancer Genome Consortium (ICGC) database. Then, we explored the differences of TME, immune checkpoint genes and drug resistance genes, and the cancer cell sensitivity to chemotherapy drugs between different risk score group. Based on the TCGA and ICGC databases, we constructed and validated a prognostic model, which consisted of 5 genes (including AHR, F3, GNA15, IL18, and INHBA). Moreover, the prognostic model was independent prognostic factors affecting overall survival (OS). The low-risk score group had better OS, and lower stromal score, compared with patients in the high-risk score group. The difference of antigen-presenting cells, T cell regulation, and drug resistance genes between different risk score groups was found. In addition, the immune checkpoint genes were positively correlation to risk score. The expression levels of AHR, GNA15, IL18, and INHBA were related to the sensitivity of anti-tumor chemotherapy drugs. Gene set enrichment analysis (GSEA) showed significant pathway such as calcium signaling pathway and p53 signaling pathway. We successfully constructed a 5-inflammatory response-related gene signature to predict survival, TME, and cancer cell sensitivity to chemotherapy drugs in PC patients. Furthermore, substantiation was warranted to verify the role of these genes in tumorigenesis.

Clinical and Biological Data in Patients with Pancreatic Cancer vs. Chronic Pancreatitis-A Single Center Comparative Analysis

INTRODUCTION

In some patients with chronic pancreatitis, the diagnosis of pancreatic cancer can be missed. The objective of the study was to identify clinical and paraclinical data with statistical significance in the differential diagnosis between chronic pancreatitis and pancreatic cancer.

MATERIALS AND METHODS

We conducted a retrospective, observational study on a cohort of 120 patients hospitalized over 3 years. The patients were equally distributed in two groups: group A, with 60 patients with pancreatic cancer, and group B, with 60 patients with chronic pancreatitis. The statistical analysis was carried out by using the R program.

RESULTS

The comparative analysis of pancreatic cancer vs. chronic pancreatitis revealed a stronger link between pancreatic cancer, female gender (p = 0.001) and age over 60 years (p < 0.001). Patients with pancreatic cancer had higher serum values of aspartate aminotransferase (p 0.005), alanine aminotransferase (p 0.006), total bilirubin (p < 0.001), direct bilirubin (p < 0.001), alkaline phosphatase (p 0.030), C-reactive protein (p = 0.049) and uric acid (p 0.001), while patients with chronic pancreatitis presented slightly higher values of amylase (p 0.020) and lipase (p 0.029).

CONCLUSIONS

Female gender, advanced age, elevated aminotransferases, cholestasis markers and uric acid were associated with a higher probability of pancreatic cancer.

Cardio-Renal Metabolic Syndrome: Interdisciplinary Diagnostic Methods

Medical interdisciplinarity in making a correct diagnosis is of the utmost importance for an optimal treatment, which should include both effective therapeutic means (drugs and/or surgery) and the complex aspects (nutrition, lifestyle, rehabilitation, etc [...].

Involvement of angiogenesis in cancer-associated acinar-to-ductal metaplasia lesion of pancreatic cancer invasive front

PURPOSE

This study aimed to demonstrate the involvement of angiogenesis in cancer-associated acinar-to-ductal metaplasia (CA-ADM) lesion of invasive front pancreatic ductal adenocarcinoma (PDAC) and investigate the possible mechanism.

METHODS

Tissue samples from 128 patients with PDAC and 36 LSL-Kras^G12D/+; LSL-Trp53^R172H/+; Pdx-1-Cre mice were analyzed. Immunohistochemical assay was performed using HE, anti-CK19 and anti-amylase to confirm the presence of CA-ADM lesions, using anti-CD34 and anti-CD31 to measure microvessel density (MVD), and using anti-CD68, anti-CD163, anti-iNOS, or anti-MMP9 to evaluate the immune microenvironment. We performed multiplex immunohistochemical assay to detect the co-expression of MMP9 and CD68 on macrophage. We examined clinical outcomes and other clinicopathological factors to determine the significance of high-level MVD of CA-ADM on survival and liver metastasis. We performed tube formation assay to evaluate the effect of macrophage on angiogenic capacity in vitro.

RESULTS

Angiogenesis was significantly abundant in CA-ADM lesions compared with that in PDAC lesions in human and mouse tissues. High-level MVD in CA-ADM lesions was an independent predictor of poor prognosis (P = 0.0047) and the recurrence of liver metastasis (P = 0.0027). More CD68-positive and CD163-positive macrophages were detected in CA-ADM lesions than in PDAC. The percentage of CD68-positive macrophages was positively correlated with MVD in CA-ADM lesions. Multiplex-immunostaining revealed that MMP9 was expressed in CD68-positive macrophages of CA-ADM lesions. In CA-ADM lesions, the percentage of macrophages was positively correlated with MMP9 expression, which positively correlated with microvessel density.

CONCLUSION

CA-ADM related angiogenesis is a promising predictive marker for poor prognosis of PDAC and may provide an attractive therapeutic target for PDAC.

Periplocin exerts antitumor activity by regulating Nrf2-mediated signaling pathway in gemcitabine-resistant pancreatic cancer cells

Although gemcitabine-based chemotherapy is common and effective for pancreatic cancer (PC), acquired drug resistance is one of the major reasons for treatment failure. Therefore, a novel therapeutic approach for gemcitabine-resistant PC is required. Nuclear factor erythroid 2-related factor 2 (Nrf2) is an oxidative stress-responsive transcription factor regulating antioxidant responses and plays a crucial role in chemoresistance. In the present study, the antitumor activity of periplocin, a natural cardiac glycoside, was evaluated in an established gemcitabine-resistant PC cell line (PANC-GR). Nrf2 was overexpressed in gemcitabine-resistant cells, and Nrf2 knockdown recovered gemcitabine sensitivity in PANC-GR cells. The antiproliferative activity of periplocin was highly associated with Nrf2 downregulation and Nrf2-mediated signaling pathways in PANC-GR cells. Periplocin also increased reactive oxygen species production inducing G₀/G₁ cell cycle arrest and apoptosis in PANC-GR cells. Periplocin and gemcitabine combined significantly inhibited tumor growth in a PANC-GR cells-implanted xenograft mouse model via Nrf2 downregulation. Overall, these findings suggest that periplocin might be a novel therapeutic agent against gemcitabine resistance, as it could recover sensitivity to gemcitabine by regulating Nrf2-mediated signaling pathways in gemcitabine-resistant PC cells.

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.

OBJECTIVE

This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.

METHODS

A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.

RESULTS

Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of &lt;1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.

CONCLUSIONS

This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

Pancreatic Tuberculosis-A Condition That Mimics Pancreatic Cancer

Tuberculosis is a disease with serious consequences in terms of morbidity and mortality. Pancreatic localization is very rare and is mostly encountered in patients with immunosuppressive disorders. A 59-year-old woman with arterial hypertension, grade 2 obesity, and a history of cholecystectomy, was admitted for fever (38.5 °C), jaundice, and marked physical asthenia. The blood tests showed severe metabolic acidosis, with partial respiratory compensation, mild microcytic normochromic anemia, inflammatory syndrome, procalcitonin value ten times the upper limit of normal, nitrogen retention syndrome, hypoalbuminemia, hypertriglyceridemia, hypercholesterolemia, and moderate hyponatremia. The electrocardiogram, chest X-ray, and abdominal ultrasound did not show any significant pathological changes. Contrast-enhanced computed tomography raised the suspicion of acute-on-chronic pancreatitis and subsequent evaluation by magnetic resonance imaging raised the suspicion of a pancreatic tumor. Pancreatic fine needle biopsy under echoendoscopic guidance revealed purulent material, which was sent for cytological and bacteriological examination. The Ziehl-Neelsen stain showed acid-alcoholic resistant bacilli, while bacterial cultures were positive for gentamicin and tigecycline-sensitive Klebsiella. The diagnosis of pancreatic tuberculosis was established. Pancreatic tuberculosis is a very rare condition that often mimics pancreatic cancer. The peculiarity of the case is the appearance of pancreatic tuberculosis in an immunocompetent woman and the association with Klebsiella infection.

Recent nanotechnology advancements to treat multidrug-resistance pancreatic cancer: Pre-clinical and clinical overview

Pancreatic cancer (PC) remains one of the most lethal and incurable forms of cancer and has a poor prognosis. One of the significant therapeutic challenges in PC is multidrug resistance (MDR), a phenomenon in which cancer cells develop resistance toward administered therapy. Development of novel therapeutic platforms that could overcome MDR in PC is crucial for improving therapeutic outcomes. Nanotechnology is emerging as a promising tool to enhance drug efficacy and minimize off-target responses via passive and/or active targeting mechanisms. Over the past decade, tremendous efforts have been made to utilize nanocarriers capable of targeting PC cells while minimizing off-target effects. In this review article, we first give an overview of PC and the major molecular mechanisms of MDR, and then we discuss recent advancements in the development of nanocarriers used to overcome PC drug resistance. In doing so, we explore the developmental stages of this research in both pre-clinical and clinical settings. Lastly, we discuss current challenges and gaps in the literature as well as potential future directions in the field.

Integrated analysis of senescence-associated genes in pancreatic ductal adenocarcinoma

Background: Cellular senescence plays a critical role in the occurrence and development, and immune modulation of cancer. This research primarily investigated the role of senescence-associated genes (SAGs) in the survival and tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC).

Methods: From the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, the gene expression profiles and clinical data of PDAC samples were downloaded. SAGs in the TCGA cohort were used to build a novel prognostic model and validated in the ICGC cohort. The relationship of signature with the immune landscape, tumor mutational burden (TMB), as well as the sensitivity of different therapies, was explored. Moreover, a nomogram was developed to predict the overall survival of PDAC patients.

Results: A prognostic signature was constructed on basis of three SAGs, and patients in the low-risk score group had a longer survival time. The accuracy of the signature to distinguish different score groups was confirmed through principal component analysis (PCA) and the Receiver operator curves curve. The mRNA expression of the three signature genes was also verified in normal pancreatic and PDAC cell lines by RT-qPCR. The signature could independently predict the prognosis of PDAC patients and had broad applicability. Meanwhile, the nomogram predicted that 1- and 3-years survival rates were in good agreement with the observed overall survival rates. Low-risk patients had lower tumor mutational burden, and low-TMB patients had a better prognosis. Low- and high-risk patients exhibit distinct immune cell infiltration and immune checkpoint changes. By further analyzing the risk score, patients in the low-risk group were more responsive to immunotherapy and a variety of commonly used chemotherapeutic drugs.

Conclusion: The prognostic signature can well predict the prognosis and assess the possibility of immunotherapy in personalized PDAC treatment.

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Simple Summary

Early image-based diagnosis is crucial to improve outcomes in pancreatic ductal adenocarcinoma (PDAC) patients, but is challenging even for experienced radiologists. Artificial intelligence has the potential to assist in early diagnosis by leveraging high amounts of data to automatically detect small (<2 cm) lesions. In this study, the state-of-the-art, self-configuring framework for medical segmentation nnUnet was used to develop a fully automatic pipeline for the detection and localization of PDAC lesions on contrast-enhanced computed tomography scans, with a focus on small lesions. Furthermore, the impact of integrating the surrounding anatomy (which is known to be relevant to clinical diagnosis) into deep learning models was assessed. The developed automatic framework was tested in an external, publicly available test set, and the results showed that state-of-the-art deep learning can detect small PDAC lesions and benefits from anatomy information.

Abstract

Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC), but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis; however, current models still fail to identify small (<2 cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating the surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor (nnUnet_TP), and (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures (nnUnet_MS). An external, publicly available test set was used to compare the performance of the three networks. The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors <2 cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.

Risk Factors for Pancreatic Cancer: Emerging Role of Viral Hepatitis

Pancreatic cancer is one of the most aggressive malignant neoplastic diseases. The incidence and mortality rates of this disease vary depending on geographical area, which might be explained by the different exposure to risk factors. To improve the prognosis of patients with pancreatic cancer, different approaches are needed for an earlier diagnosis. Identification of risk factors and implementation of screening strategies are essential for a better prognosis. Currently, the risk factors for pancreatic cancer fall into two broad categories, namely extrinsic and intrinsic factors. Extrinsic factors include alcohol consumption, smoking, a diet rich in saturated fats, and viral infections such as chronic infection with hepatitis B and C viruses. The pathophysiological mechanisms explaining how these hepatotropic viruses contribute to the development of pancreatic cancer are not fully elucidated. The common origin of hepatocytes and pancreatic cells in the multipotent endodermal cells, the common origin of the blood vessels and biliary ducts of the pancreas and the liver, or chronic inflammatory changes may be involved in this interaction. A careful monitoring of patients with viral liver infections may contribute to the early diagnosis of pancreatic cancer and improve the prognosis of these patients.

Clinicopathological significance and underlying molecular mechanism of downregulation of basonuclin 1 expression in ovarian carcinoma

In this study, we aim to identify the clinical significance of basonuclin 1 (BNC1) expression in ovarian carcinoma (OV) and to explore its latent mechanisms. Via integrating in-house tissue microarrays, gene chips, and RNA-sequencing data, we explored the expression and clinical value of BNC1 in OV. Immunohistochemical staining was utilized to confirm the protein expression status of BNC1. A combined SMD of -2.339 (95% CI: -3.649 to -1.028, P < 0.001) identified that BNC1 was downregulated based on 1346 samples, and the sROC (AUC = 0.93) showed a favorable discriminatory ability of BNC1 in OV patients. We used univariate and multivariate Cox regulation to evaluate the prognostic role of BNC1 for OV patients, and a combined hazard ratio of 0.717 (95% CI: 0.445-0.989, P < 0.001) revealed that BNC1 was a protective factor for OV. Furthermore, the fraction of infiltrating naive B cells, memory B cells, and other immune cells showed statistical differences between the high- and low-BNC1 expression groups through cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. Enrichment analysis showed that BNC1 may have a relationship with immune-related items in OV. By predicting the potential regulatory transcription factors (TFs) of BNC1, friend leukemia virus integration 1 (FLI1) may be a potential upstream TF of BNC1. Corporately, a decreasing trend of BNC1 may serve as a tumor suppressor and prognostic biomarker in OV patients. Moreover, BNC1 may take part in immune-related pathways and influence the fraction of tumor-infiltrating immune cells.

Pancreatic Cancer and Cellular Senescence: Tumor Microenvironment under the Spotlight

Pancreatic ductal adenocarcinoma (PDAC) has one of the most dismal prognoses of all cancers due to its late manifestation and resistance to current therapies. Accumulating evidence has suggested that the malignant behavior of this cancer is mainly influenced by the associated strongly immunosuppressive, desmoplastic microenvironment and by the relatively low mutational burden. PDAC develops and progresses through a multi-step process. Early in tumorigenesis, cancer cells must evade the effects of cellular senescence, which slows proliferation and promotes the immune-mediated elimination of pre-malignant cells. The role of senescence as a tumor suppressor has been well-established; however, recent evidence has revealed novel pro-tumorigenic paracrine functions of senescent cells towards their microenvironment. Understanding the interactions between tumors and their microenvironment is a growing research field, with evidence having been provided that non-tumoral cells composing the tumor microenvironment (TME) influence tumor proliferation, metabolism, cell death, and therapeutic resistance. Simultaneously, cancer cells shape a tumor-supportive and immunosuppressive environment, influencing both non-tumoral neighboring and distant cells. The overall intention of this review is to provide an overview of the interplay that occurs between senescent and non-senescent cell types and to describe how such interplay may have an impact on PDAC progression. Specifically, the effects and the molecular changes occurring in non-cancerous cells during senescence, and how these may contribute to a tumor-permissive microenvironment, will be discussed. Finally, senescence targeting strategies will be briefly introduced, highlighting their potential in the treatment of PDAC.

DNA Methylation of PI3K/AKT Pathway-Related Genes Predicts Outcome in Patients with Pancreatic Cancer: A Comprehensive Bioinformatics-Based Study

Simple Summary

Pancreatic cancer is a highly lethal malignancy. Dysregulation of epigenetic mechanisms leads to abnormal patterns of gene expression contributing to the development and progression of cancer. We explored the ability of DNA methylation of PI3K-related genes to differentiate between malignant and healthy pancreatic tissue using distinct pancreatic cancer cohorts, and found that the methylation levels of the ITGA4, SFN, ITGA2, and PIK3R1 genes are altered in tumour samples since the early stages of malignant transformation and could serve as new diagnostic tools. We also demonstrate that these alterations correlate with overall survival and recurrence-free survival of the patients suggesting that its assessment can serve as independent prognostic indicators of patients’ survival with higher sensitivity and specificity than the currently implemented biomarkers. Therefore, the methylation profile of genes involved in this pathway may be an alternative method for predicting cell malignancy and help doctors’ decisions on patient care.

Abstract

Pancreatic cancer (PCA) is one of the most lethal malignancies worldwide with a 5-year survival rate of 9%. Despite the advances in the field, the need for an earlier detection and effective therapies is paramount. PCA high heterogeneity suggests that epigenetic alterations play a key role in tumour development. However, only few epigenetic biomarkers or therapeutic targets have been identified so far. Here we explored the potential of distinct DNA methylation signatures as biomarkers for early detection and prognosis of PCA. PI3K/AKT-related genes differentially expressed in PCA were identified using the Pancreatic Expression Database (n = 153). Methylation data from PCA patients was obtained from The Cancer Genome Atlas (n = 183), crossed with clinical data to evaluate the biomarker potential of the epigenetic signatures identified and validated in independent cohorts. The majority of selected genes presented higher expression and hypomethylation in tumour tissue. The methylation signatures of specific genes in the PI3K/AKT pathway could distinguish normal from malignant tissue at initial disease stages with AUC > 0.8, revealing their potential as PCA diagnostic tools. ITGA4, SFN, ITGA2, and PIK3R1 methylation levels could be independent prognostic indicators of patients’ survival. Methylation status of SFN and PIK3R1 were also associated with disease recurrence. Our study reveals that the methylation levels of PIK3/AKT genes involved in PCA could be used to diagnose and predict patients’ clinical outcome with high sensitivity and specificity. These results provide new evidence of the potential of epigenetic alterations as biomarkers for disease screening and management and highlight possible therapeutic targets.

GRP78 expression and prognostic significance in patients with pancreatic ductal adenocarcinoma treated with neoadjuvant therapy versus surgery first

BACKGROUND

Glucose-regulated protein 78 (GRP78) plays an essential role in protein folding, transportation, and degradation, thus regulates ER homeostasis and promotes cell survival, proliferation and invasion. GRP78 expression in PDAC patients who received neoadjuvant therapy has not been reported.

METHODS

This retrospective study of resected PDAC patients included 125 patients treated with neoadjuvant therapy (NAT) and 140 patients treated with surgery first (SF). The expression of GRP78 was evaluated by immunohistochemistry on tissue microarrays and the results were correlated with clinicopathologic parameters and survival.

RESULTS

GRP78 expression was higher in SF patients compared to NAT patients (P < 0.001). In SF cohort, the median disease-free survival (DFS) and overall survival (OS) for patients with GRP78-positive tumors were 11.2 months and 25.0 months, respectively, compared to DFS of 52.1 months (P = 0.008) and OS of 69.5 months (P = 0.02) for those with GRP78-negative tumors. GRP78 expression correlated with higher frequency of recurrent/metastasis (P = 0.045). In NAT cohort, GRP78 expression correlated with shorter OS (P = 0.03), but not DFS (P = 0.08). GRP78 expression was an independent prognosticator for both DFS (P = 0.02) and OS (P = 0.049) in SF cohort and was an independent prognosticator for OS (P = 0.03), but not for DFS (P = 0.06) in NAT cohort by multivariate analysis.

CONCLUSIONS

Our study showed that GRP78 expression in NAT cohort is lower than that in SF cohort. GRP78 expression correlated with shorter survival in both SF and NAT patients. Our findings suggest that targeting GRP78 may help to improve the prognosis in PDAC patients.

Extracellular vesicles from pancreatic ductal adenocarcinoma endoscopic ultrasound-fine needle aspiration samples contain a protein barcode

BACKGROUND

The survival rate of pancreatic ductal adenocarcinoma (PDAC) is very poor because early detection is difficult. Extracellular vesicles (EVs) are released from cells associating with the cellular condition and circulated in the blood. We aimed to identify EV proteins from endoscopic ultrasound-fine needle aspiration (EUS-FNA) biopsy samples in order to develop novel biomarkers for PDAC.

METHODS

Extracellular vesicles were isolated from EUS-FNA samples of 40 PDAC patients and six autoimmune pancreatitis (AIP) patients to be used as a control. EV proteins were identified using nanoLC-MS/MS.

RESULTS

Intact EVs approximately 200 nm in diameter were detected from EUS-FNA samples. We identified 2059 or 1032 EV proteins in PDAC or AIP, respectively, and 1071 EV proteins were detected only in PDAC. One hundred and fifty-three EV proteins were significantly different between PDAC and AIP: 64 proteins were down-regulated in PDAC whereas 89 EV proteins were up-regulated in PDAC including mucins, keratins, Ras-related proteins, and olfactomedin-4, which proteins have been reported to be elevated in PDAC tissue/blood, or cultured pancreatic cancer cell lines. Notably, in the 89 up-regulated PDAC EV proteins we identified novel proteins including ADP-ribosylation factor 3, CD55, pyruvate kinase, and lipopolysaccharide-induced tumor necrosis factor. Out of 89 proteins, a total of 13 proteins including Ras-related proteins were significantly elevated in PDAC stages II-IV compared to PDAC stage I, including Ras-related proteins, moesin, and CD55.

CONCLUSIONS

The EV proteins obtained from EUS-FNA samples contain a PDAC-specific protein barcode. The EV proteins identified from EUS-FNA samples include promising biomarkers for the diagnosis and clinical staging of PDAC.

A spotlight on underlying the mechanism of AMPK in diabetes complications

OBJECTIVE

Type 2 diabetes (T2D) is one of the centenarian metabolic disorders and is considered as a stellar and leading health issue worldwide. According to the International Diabetes Federation (IDF) Diabetes Atlas and National Diabetes Statistics, the number of diabetic patients will increase at an exponential rate from 463 to 700 million by the year 2045. Thus, there is a great need for therapies targeting functions that can help in maintaining the homeostasis of glucose levels and improving insulin sensitivity. 5' adenosine monophosphate-activated protein kinase (AMPK) activation, by various direct and indirect factors, might help to overcome the hurdles (like insulin resistance) associated with the conventional approach.

MATERIALS AND RESULTS

A thorough review and analysis was conducted using various database including MEDLINE and EMBASE databases, with Google scholar using various keywords. This extensive review concluded that various drugs (plant-based, synthetic indirect/direct activators) are available, showing tremendous potential in maintaining the homeostasis of glucose and lipid metabolism, without causing insulin resistance, and improving insulin sensitivity. Moreover, these drugs have an effect against diabetes and are therapeutically beneficial in the treatment of diabetes-associated complications (neuropathy and nephropathy) via mechanism involving inhibition of nuclear translocation of SMAD4 (SMAD family member) expression and association with peripheral nociceptive neurons mediated by AMPK.

CONCLUSION

From the available information, it may be concluded that various indirect/direct activators show tremendous potential in maintaining the homeostasis of glucose and lipid metabolism, without resulting in insulin resistance, and may improve insulin sensitivity, as well. Therefore, in a nut shell, it may be concluded that the regulation of APMK functions by various direct/indirect activators may bring promising results. These activators may emerge as a novel therapy in diabetes and its associated complications.

Serum miRNA Profiling for Early PDAC Diagnosis and Prognosis: A Retrospective Study

Background: Tumor stage predicts pancreatic cancer (PDAC) prognosis, but prolonged and short survivals have been described in patients with early-stage tumors. Circulating microRNA (miRNA) are an emerging class of suitable biomarkers for PDAC prognosis. Our aim was to identify whether serum miRNA signatures predict survival of early-stage PDAC. Methods: Serum RNA from archival 15 stage I-III PDAC patients and 4 controls was used for miRNAs expression profile (Agilent microarrays). PDAC patients with comparable age, gender, diabetes, jaundice and surgery were classified according to survival: less than 14 months (7/15 pts, group A) and more than 22 months (8/15 pts, group B). Bioinformatic data analysis was performed by two-class Significance Analysis of Microarray (SAM) algorithm. Binary logistic regression analyses considering PDAC diagnosis and outcome as dependent variables, and ROC analyses were also performed. Results: 2549 human miRNAs were screened out. At SAM, 76 differentially expressed miRNAs were found among controls and PDAC (FDR = 0.4%), the large majority (50/76, 66%) of them being downregulated in PDAC with respect to controls. Six miRNAs were independently correlated with early PDAC, and among these, hsa-miR-6821-5p was associated with the best ROC curve area in distinguishing controls from early PDAC. Among the 71 miRNAs differentially expressed between groups A and B, the most significant were hsa-miR-3135b expressed in group A only, hsa-miR-6126 and hsa-miR-486-5p expressed in group B only. Eight miRNAs were correlated with the presence of lymph-node metastases; among these, hsa-miR-4669 is of potential interest. hsa-miR-4516, increased in PDAC and found as an independent predictor of survival, has among its putative targets a series of gens involved in key pathways of cancer progression and dissemination, such as Wnt and p53 signalling pathways. Conclusions: A series of serum miRNAs was identified as potentially useful for the early diagnosis of PDAC, and for establishing a prognosis.

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

Colorectal cancer is the third most common and second most lethal tumor globally, causing 900,000 deaths annually. In this research, a computer aided diagnosis system was designed that detects colorectal cancer, using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumor position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localization). The intelligent computer aided colorectal cancer diagnosis system was designed using different machine learning techniques, such as classification and shallow and deep neural networks. The maximum accuracy obtained from solving the binary classification problem with traditional machine learning algorithms was 77.8%. However, the regression problem solved with deep neural networks yielded with significantly better performance in terms of mean squared error minimization, reaching the value of 0.0000529.

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.