Early Detection of Pancreatic Cancer: Role of Biomarkers in Pancreatic Fluid Samples

Emerging biomarkers for pancreatic cancer: from early detection to personalized therapy

Pancreatic cancer (PC) remains one of the most lethal malignancies, primarily due to its poor prognosis and late diagnosis. Biomarkers are essential in enhancing diagnostic accuracy, prognostic assessments, and therapeutic strategies, thereby addressing these challenges. Conventional biomarkers, such as CA 19-9, are widely used for monitoring disease progression but have limitations in early detection and specificity, necessitating complementary markers like CEA and MUC1. Emerging genetic biomarkers, including KRAS mutations and TP53 alterations, offer critical insights into tumorigenesis and serve as valuable diagnostic, prognostic, and therapeutic targets. Epigenetic biomarkers, such as DNA methylation and histone modifications, provide additional molecular layers, with aberrant methylation patterns and dysregulated histone modifications influencing tumor aggressiveness and therapy resistance. RNA-based biomarkers, particularly microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), play pivotal roles in regulating tumor biology and offer significant diagnostic and therapeutic potential. Protein-based biomarkers, including glycoproteins and cytokines, alongside liquid biopsy components like circulating tumor DNA (ctDNA), exosomes, and circulating tumor cells (CTCs), facilitate real-time disease monitoring and early detection. Personalized therapy is increasingly guided by these biomarkers, which predict responses to chemotherapy and immunotherapy. Despite challenges in biomarker validation and clinical implementation, advancements in multi-omics, artificial intelligence, and collaborative research hold promise for improving patient outcomes and survival rates in PC.

Development of PDAC diagnosis and prognosis evaluation models based on machine learning

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is difficult to detect early and highly aggressive, often leading to poor patient prognosis. Existing serum biomarkers like CA19-9 are limited in early diagnosis, failing to meet clinical needs. Machine learning (ML)/deep learning (DL) technologies have shown great potential in biomedicine. This study aims to establish PDAC differential diagnosis and prognosis assessment models using ML combined with serum biomarkers for early diagnosis, risk stratification, and personalized treatment recommendations, improving early diagnosis rates and patient survival.

METHODS

The study included serum biomarker data and prognosis information from 117 PDAC patients. ML models (Random Forest (RF), Neural Network (NNET), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM)) were used for differential diagnosis, evaluated by accuracy, Kappa test, ROC curve, sensitivity, and specificity. COX proportional hazards model and DeepSurv DL model predicted survival risk, compared by C-index and Log-rank test. Based on DeepSurv's risk predictions, personalized treatment recommendations were made and their effectiveness assessed.

RESULTS

Effective PDAC diagnosis and prognosis models were built using ML. The validation set data shows that the accuracy of the RF, NNET, SVM, and GBM models are 84.21%, 84.21%, 76.97%, and 83.55%; the sensitivity are 91.26%, 90.29%, 89.32%, and 88.35%; and the specificity are 69.39%, 71.43%, 51.02%, and 73.47%. The Kappa values are 0.6266, 0.6307, 0.4336, and 0.6215; and the AUC are 0.889, 0.8488, 0.8488, and 0.8704, respectively. BCAT1, AMY, and CA12-5 were selected as modeling parameters for the prognosis model using COX regression. DeepSurv outperformed the COX model on both training and validation sets, with C-indexes of 0.738 and 0.724, respectively. The Kaplan-Meier survival curves indicate that personalized treatment recommendations based on DeepSurv can help patients achieve survival benefits.

CONCLUSION

This study built efficient PDAC diagnosis and prognosis models using ML, improving early diagnosis rates and prognosis accuracy. The DeepSurv model excelled in prognosis prediction and successfully guided personalized treatment recommendations and supporting PDAC clinical management.

Integrating OMICS-based platforms and analytical tools for diagnosis and management of pancreatic cancer: a review

Cancer remains the second leading cause of death worldwide, surpassed only by cardiovascular disease. From the different types of cancer, pancreatic cancer (PaC) has one of the lowest survival rates, with a survival rate of about 20% after the first year of diagnosis and about 8% after 5 years. The lack of highly sensitive and specific biomarkers, together with the absence of symptoms in the early stages, determines a late diagnosis, which is associated with a decrease in the effectiveness of medical intervention, regardless of its nature - surgery and/or chemotherapy. This review provides an updated overview of recent studies combining multi-OMICs approaches (e.g., proteomics, metabolomics) with analytical tools, highlighting the synergy between high-throughput molecular data generation and precise analytical tools such as LC-MS, GC-MS and MALDI-TOF MS. This combination significantly improves the detection, quantification and identification of biomolecules in complex biological systems and represents the latest advances in understanding PaC management and the search for effective diagnostic tools. Large-scale data analysis coupled with bioinformatics tools enables the identification of specific genetic mutations, gene expression patterns, pathways, networks, protein modifications and metabolic signatures associated with PaC pathogenesis, progression and treatment response through the integration of multi-OMICs data.

Diagnostic Utility of MicroRNAs in Pancreatic Cancers

Pancreatic cancer (PC) is a serious malignant tumor with a high mortality rate, mainly due to late diagnosis and a lack of effective therapeutic interventions. The possibility of recognizing this cancer with reliable biomarkers using minimally invasive methods is of great importance for improving early detection, prognostic assessment, and targeted treatment methods. In recent years, small non-coding RNAs, especially microRNAs, have emerged as promising candidates for biomarkers of pancreatic cancer. Despite the methodological and analytical limitations of microRNA determination and a lack of available automated and standardized tests, a prospective systematic assessment of this new parameter's use in the detection of pancreatic cancer is warranted. This review provides general information on the diagnostic and prognostic utility of microRNAs, which appear to be well-established in many studies. In summary, microRNAs are promising non-invasive biomarkers of pancreatic cancer, offering potential opportunities for early detection, prognosis, and treatment monitoring. As research in this field progresses, microRNAs should become valuable tools in the fight against pancreatic cancer.

Genetic Assessment of Intraductal Papillary Mucinous Neoplasm for Predicting Concomitant Pancreatic Ductal Adenocarcinoma

OBJECTIVE

The role of Krüppel-like transcription factor 4 ( KLF4 ) mutations in IPMNs with concomitant pancreatic ductal adenocarcinoma (PDAC) remains unclear. This study clarified the rate and effect of KLF4 mutations in IPMN with concomitant PDAC.

MATERIALS AND METHODS

DNA was extracted from 65 formalin-fixed and paraffin-embedded samples from 52 patients including 13 IPMNs with concomitant PDAC and 39 IPMNs alone. A comprehensive screening using next-generation sequencing and then targeted sequencing for KLF4 , GNAS , and KRAS mutations were performed.

RESULTS

In next-generation sequencing screening, KRAS mutations were observed in all samples except for one, GNAS mutation in 2 IPMNs with concomitant PDAC, and a KLF4 mutation in 1 IPMN with concomitant PDAC. Targeted sequence detected KLF4 mutations in 11 of the 52 IPMNs. Concomitant PDAC developed only in the nonintestinal, noninvasive, and branch-duct IPMNs, and KLF4 mutations were more frequent in this IPMN type than in the other type. For this IPMN type with KLF4 mutation, PDAC-prediction sensitivity, specificity, and accuracy were 63%, 82%, and 79%, respectively.

CONCLUSION

For selected IPMNs with nonintestinal, noninvasive, and branch-duct, genetic assessment might be helpful for predicting the possible development of concomitant PDAC, although a prospective validation study using a larger study population is needed.

Duodenal Fluid Analysis as a Rewarding Approach to Detect Low-Abundance Mutations in Biliopancreatic Cancers

Diagnosis of biliopancreatic cancers by the available serum tumor markers, imaging, and histopathological tissue specimen examination remains a challenge. Circulating cell-free DNA derived from matched pairs of secretin-stimulated duodenal fluid (DF) and plasma from 10 patients with biliopancreatic diseases and 8 control subjects was analyzed using AmpliSeq™ HD technology for Ion Torrent Next-Generation Sequencing to evaluate the potential of liquid biopsy with DF in biliopancreatic cancers. The median cfDNA concentration was greater in DF-derived than in plasma-derived samples. A total of 13 variants were detected: 11 vs. 1 were exclusive for DF relative to the plasma source, and 1 was shared between the two body fluids. According to the four-tier systems, 10 clinical tier-I-II (76.9%), 1 tier-III (7.7%), and 2 tier-IV (15.4%) variants were identified. Notably, the 11 tier-I-III variants were exclusively found in DF-derived cfDNA from five patients with biliopancreatic cancers, and were detected in seven genes (KRAS, TP53, BRAF, CDKN2A, RNF43, GNAS, and PIK3CA); 82% of the tier-I-III variants had a low abundance, with a VAF < 6%. The mutational profiling of DF seems to be a reliable and promising tool for identifying cancer-associated alterations in malignant cancers of the biliopancreatic tract.

New Function Annotation of PROSER2 in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis due to the absence of diagnostic markers and molecular targets. Here, we took an unconventional approach to identify new molecular targets for pancreatic cancer. We chose uncharacterized protein evidence level 1 without function annotation from extensive proteomic research on pancreatic cancer and focused on proline and serine-rich 2 (PROSER2), which ranked high in the cell membrane and cytoplasm. In our study using cell lines and patient-derived orthotopic xenograft cells, PROSER2 exhibited a higher expression in cells derived from primary tumors than in those from metastatic tissues. PROSER2 was localized in the cell membrane and cytosol by immunocytochemistry. PROSER2 overexpression significantly reduced the metastatic ability of cancer cells, whereas its suppression had the opposite effect. Proteomic analysis revealed that PROSER2 interacts with STK25 and PDCD10, and their binding was confirmed by immunoprecipitation and immunocytochemistry. STK25 knockdown enhanced metastasis by decreasing p-AMPK levels, whereas PROSER2-overexpressing cells increased the level of p-AMPK, indicating that PROSER2 suppresses invasion via the AMPK pathway by interacting with STK25. This is the first demonstration of the novel role of PROSER2 in antagonizing tumor progression via the STK25-AMPK pathway in PDAC. LC-MS/MS data are available at MassIVE (MSV000092953) and ProteomeXchange (PXD045646).

QN-302 demonstrates opposing effects between i-motif and G-quadruplex DNA structures in the promoter of the S100P gene

GC-rich sequences can fold into G-quadruplexes and i-motifs and are known to control gene expression in many organisms. The potent G-quadruplex experimental anticancer drug QN-302 down-regulates a number of cancer-related genes, in particular S100P. Here we show this ligand has strong opposing effects with i-motif DNA structures and is one of the most potent i-motif destabilising agents reported to date. QN-302 down-regulates the expression of numerous cancer-related genes by pan-quadruplex targeting. QN-302 exhibits exceptional combined synergistic effects compared to many other G-quadruplex and i-motif interacting compounds. This work further emphasises the importance of considering G-quadruplex and i-motif DNA structures as one dynamic system.

A comprehensive survey into the role of exosomes in pancreatic cancer; from the origin of cancer to the progress and possibility of diagnosis and treatment

Pancreatic cancer is the fourth most common malignant tumor in the world, which has a high mortality rate due to high invasiveness, early metastases, lack of specific symptoms, and high invasiveness. Recent studies have shown that exosomes can be essential sources of biomarkers in pancreatic cancer. Over the past ten years, exosomes have been implicated in multiple trials to prevent the growth and metastasis of many cancers, including pancreatic cancer. Exosomes also play essential roles in immune evasion, invasion, metastasis, proliferation, apoptosis, drug resistance, and cancer stemness. Exosomes help cells communicate by carrying proteins and genetic material, such as non-coding RNAs, including mRNAs and microRNAs. This review examines the biological significance of exosomes in pancreatic cancer and their functions in tumor invasion, metastasis, treatment resistance, proliferation, stemness, and immune evasion. We also emphasize recent advances in our understanding of the main functions of exosomes in diagnosing and treating pancreatic cancer.

Clinical Value of Circulating miRNA in Diagnosis, Prognosis, Screening and Monitoring Therapy of Pancreatic Ductal Adenocarcinoma-A Review of the Literature

Pancreatic cancer (PC) is considered to be the seventh most common cause of cancer-related deaths. The number of deaths caused by PC is estimated to increase in the future. An early diagnosis of PC is crucial for improving treatment outcomes. The most common histopathological subtype of PC is pancreatic ductal adenocarcinoma (PDAC). MicroRNAs (miRNAs)-which are endogenous non-coding RNAs involved in the posttranscriptional regulation of multiple gene expression-constitute useful diagnostic and prognostic biomarkers in various neoplasms, including PDAC. Circulating miRNAs detected in a patient's serum or plasma are drawing more and more attention. Hence, this review aims at evaluating the clinical value of circulating miRNA in the screening, diagnosis, prognosis and monitoring of pancreatic ductal adenocarcinoma therapy.

The Potent G-Quadruplex-Binding Compound QN-302 Downregulates S100P Gene Expression in Cells and in an In Vivo Model of Pancreatic Cancer

The naphthalene diimide compound QN-302, designed to bind to G-quadruplex DNA sequences within the promoter regions of cancer-related genes, has high anti-proliferative activity in pancreatic cancer cell lines and anti-tumor activity in several experimental models for the disease. We show here that QN-302 also causes downregulation of the expression of the S100P gene and the S100P protein in cells and in vivo. This protein is well established as being involved in key proliferation and motility pathways in several human cancers and has been identified as a potential biomarker in pancreatic cancer. The S100P gene contains 60 putative quadruplex-forming sequences, one of which is in the promoter region, 48 nucleotides upstream from the transcription start site. We report biophysical and molecular modeling studies showing that this sequence forms a highly stable G-quadruplex in vitro, which is further stabilized by QN-302. We also report transcriptome analyses showing that S100P expression is highly upregulated in tissues from human pancreatic cancer tumors, compared to normal pancreas material. The extent of upregulation is dependent on the degree of differentiation of tumor cells, with the most poorly differentiated, from more advanced disease, having the highest level of S100P expression. The experimental drug QN-302 is currently in pre-IND development (as of Q1 2023), and its ability to downregulate S100P protein expression supports a role for this protein as a marker of therapeutic response in pancreatic cancer. These results are also consistent with the hypothesis that the S100P promoter G-quadruplex is a potential therapeutic target in pancreatic cancer at the transcriptional level for QN-302.

Circulating tumor-associated autoantibodies as novel diagnostic biomarkers in pancreatic adenocarcinoma

To develop a superior diagnostic approach for pancreatic adenocarcinoma (PAAC), the present study prospectively included 338 PAAC patients, 294 normal healthy volunteers (NHV), 122 chronic pancreatitis (CP) patients and 100 patients with non-PAAC malignancies. In the identification phase, HuProt Human Proteome Microarray, comprising 21 065 proteins, was used to identify serum tumor-associated autoantibodies (TAAbs) candidates differentiating PAAC (n = 30) from NHV (n = 30). A PAAC-focused array containing 165 differentially expressed TAAbs identified was subsequently adopted in the validation phase (n = 712) for specificity and sensitivities. The multivariate TAAbs signature for differentiation PAAC from controls (NHV + CP) identified five candidates, namely the IgG-type TAAbs against CLDN17, KCNN3, SLAMF7, SLC22A11 and OR51F2. Multivariate logistic performance model of y = (22.893 × CA19-9 + 0.68 × CLDN17 - 4.012) showed a significant better diagnostic accuracy than that of CA19-9 and CLDN17 in differentiating PAAC from controls (NHV + CP) (AUC = 0.97, 0.92 and 0.82, respectively, P-value < .0001). We further tested the autoantigen level of CLDN17 by ELISA in 82 sera samples from PAAC (n = 42), CP (n = 24) and NHV (n = 16). Similarly, the model showed superior diagnostic performance than that of CA19-9 and CLDN17 (AUC = 0.93, 0.83 and 0.81, respectively, P-value < .0001) in differentiating PAAC from controls. In conclusion, our study is the first to characterize the circulating TAAbs signatures in PAAC. The results showed that CLDN17 combined with CA19-9 provided potentially clinical value and may serve as noninvasive novel biomarkers for PAAC diagnosis.

Exosomes as prognostic biomarkers in pancreatic ductal adenocarcinoma-a systematic review and meta-analysis

Extensive research is focused on the role of liquid biopsy in pancreatic cancer since reliable diagnostic and follow-up biomarkers represent an unmet need for this highly lethal malignancy. We performed a systematic review and meta-analysis on the prognostic value of exosomal biomarkers in pancreatic ductal adenocarcinoma (PDAC). MEDLINE, Embase, Scopus, Web of Science, and CENTRAL were systematically searched on the 18th of January, 2021 for studies reporting on the differences in overall (OS) and progression-free survival (PFS) in PDAC patients with positive vs negative exosomal biomarkers isolated from blood. The random-effects model estimated pooled multivariate-adjusted (AHR) and univariate hazard ratios (UHRs) with 95% confidence intervals (CIs). Eleven studies comprising 634 patients were eligible for meta-analysis. Detection of positive exosomal biomarkers indicated increased risk of mortality (UHR = 2.81, CI:1.31-6,00, I2 = 88.7%, P < 0.001), and progression (UHR = 3.33, CI: 2.33-4.77, I2 = 0, P = 0.879) across various disease stages. Positive exosomal biomarkers identified preoperatively revealed a higher risk of mortality in resectable stages (UHR = 5.55, CI: 3.24-9.49, I2 = 0, P = 0.898). The risk of mortality in unresectable stages was not significantly increased with positive exosomal biomarkers (UHR = 2.51, CI: 0.55-11.43, I2 = 90.3%, P < 0.001). Detectable exosomal micro ribonucleic acids were associated with a decreased OS (UHR = 4.08, CI: 2.16-7.69, I2 = 46.9%, P = 0.152) across various stages. Our results reflect the potential of exosomal biomarkers for prognosis evaluation in PDAC. The associated heterogeneity reflects the variability of study methods and need for their uniformization before transition to clinical use.

Role of PLEXIND1/TGFβ Signaling Axis in Pancreatic Ductal Adenocarcinoma Progression Correlates with the Mutational Status of KRAS

Simple Summary

Pancreatic cancer is among the most lethal cancers. The expression of PLEXIND1, a receptor, is upregulated in many cancers (including pancreatic cancer). Traditionally, PLEXIND1 is known to be involved in neuron development and mediate semaphorin signaling. However, its role and signaling in cancer is not fully understood. In our study, we present a new mechanism through which PLEXIND1 mediates its roles in cancer. For the first time, we demonstrate that it can function as a transforming growth factor beta coreceptor and modulate SMAD3 signaling. Around 90% of pancreatic cancer patients have mutant KRAS. Our work suggests that PLEXIND1 functions differently in pancreatic cancer cell lines, and the difference correlates with KRAS mutational status. Additionally, we demonstrate a novel peptide based therapeutic approach to target PLEXIND1 in cancer cells. Our work is valuable to both neuroscience and cancer fields, as it demonstrates an association between two previously unrelated signaling pathways.

Abstract

PLEXIND1 is upregulated in several cancers, including pancreatic ductal adenocarcinoma (PDAC). It is an established mediator of semaphorin signaling, and neuropilins are its known coreceptors. Herein, we report data to support the proposal that PLEXIND1 acts as a transforming growth factor beta (TGFβ) coreceptor, modulating cell growth through SMAD3 signaling. Our findings demonstrate that PLEXIND1 plays a pro-tumorigenic role in PDAC cells with oncogenic KRAS (KRAS^mut). We show in KRAS^mut PDAC cell lines (PANC-1, AsPC-1,4535) PLEXIND1 downregulation results in decreased cell viability (in vitro) and reduced tumor growth (in vivo). Conversely, PLEXIND1 acts as a tumor suppressor in the PDAC cell line (BxPC-3) with wild-type KRAS (KRAS^wt), as its reduced expression results in higher cell viability (in-vitro) and tumor growth (in vivo). Additionally, we demonstrate that PLEXIND1-mediated interactions can be selectively disrupted using a peptide based on its C-terminal sequence (a PDZ domain-binding motif), an outcome that may possess significant therapeutic implications. To our knowledge, this is the first report showing that (1) PLEXIND1 acts as a TGFβ coreceptor and mediates SMAD3 signaling, and (2) differential roles of PLEXIND1 in PDAC cell lines correlate with KRAS^mut and KRAS^wt status.

The significance of exosomal RNAs in the development, diagnosis, and treatment of pancreatic cancer

Exosomes are single-membrane, secreted organelles with a diameter of 30–200 nm, containing diverse bioactive constituents, including DNAs, RNAs, proteins, and lipids, with prominent molecular heterogeneity. Extensive studies indicate that exosomal RNAs (e.g., microRNAs, long non-coding RNAs, and circular RNAs) can interact with many types of cancers, associated with several hallmark features like tumor growth, metastasis, and resistance to therapy. Pancreatic cancer (PaCa) is among the most lethal cancers worldwide, emerging as the seventh foremost cause of cancer-related death in both sexes. Hence, revealing the specific pathogenesis and improving the clinical diagnosis and treatment process are urgently required. As the study of exosomes has become an active area of research, the functional connections between exosomes and PaCa have been deeply investigated. Among these, exosomal RNAs seem to play a significant role in the development, diagnosis, and treatment of PaCa. Exosomal RNAs delivery ultimately modulates the various features of PaCa, and many scholars have interpreted how exosomal RNAs contribute to the proliferation, angiogenesis, migration, invasion, metastasis, immune escape, and drug resistance in PaCa. Besides, recent studies emphasize that exosomal RNAs may serve as diagnostic and prognostic biomarkers or therapeutic targets for PaCa. In this review, we will introduce these recent insights focusing on the discoveries of the relationship between exosomal RNAs and PaCa, and the potentially diagnostic and therapeutic applications of exosomes in PaCa.

Serum Biomarker Panel for Diagnosis and Prognosis of Pancreatic Ductal Adenocarcinomas

Background

Patients with pancreatic ductal adenocarcinoma (PDAC) have late diagnosis which results in poor prognosis. Currently, surgical resection is the only option for curative intent. Identifying high-risk features for patients with aggressive PDAC is essential for accurate diagnosis, prognostication, and personalised care due to the disease burden and risk of recurrence despite surgical resection. A panel of three biomarkers identified in tumour tissue (S100A4, Ca125 and Mesothelin) have shown an association with poor prognosis and overall survival. The diagnostic and prognostic value of the serum concentration of this particular biomarker panel for patients with PDAC has not been previously studied.

Methods

Retrospectively collected blood samples of PDAC patients (n =120) and healthy controls (n =80) were evaluated for the serum concentration of select biomarkers - S100A4, S100A2, Ca-125, Ca 19-9 and mesothelin. Statistical analyses were performed for diagnostic and prognostic correlation.

Results

A panel of four biomarkers (S100A2, S100A4, Ca-125 and Ca 19-9) achieved high diagnostic potential (AUROC 0.913). Three biomarkers (S100A4, Ca-125 and Ca 19-9) correlated with poor overall survival in a univariable model (p < 0.05). PDAC patients with abnormal levels of 2 or more biomarkers in their serum demonstrated significantly lower survival compared to patients with abnormal levels of one or less biomarker (p < 0.05).

Conclusion and Impact

The identified biomarker panels have shown the potential to diagnose PDAC patients and stratify patients based on their prognostic outcomes. If independently validated, this may lead to the development of a diagnostic and prognosticating blood test for PDAC.

A phase I study of the safety and activity of K-001 in patients with advanced pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that lack of effective therapeutic drugs. K-001 is an oral antitumor drug made from active ingredients of marine microorganisms. The current study aimed to evaluate safety and antitumor activity of K-001 in patients with advanced PDAC.

METHODS

In this phase I, open-label trial, patients with advanced PDAC were recruited to a dose-escalation study in a standard 3 + 3 design. K-001 was administered twice daily in four-week cycles, and dose escalation from 1350 mg to 2160 mg was evaluated twice daily. Physical examination and laboratory tests were done at screening and then weekly. The safety, dose-limiting toxicity (DLT), and maximum tolerated dose (MTD) of K-001 were assessed while tumor response was estimated by Response Evaluation Criteria in Solid Tumor (RECIST).

RESULTS

Eighteen patients with advanced PDAC were screened, and twelve eligible patients were analyzed in the study. No DLT was observed. Totally, 47 adverse events (AEs) presented, and 14 drug-related AEs were reported in 7 patients, including 8 grade 1 events (57.1%) and 6 grade 2 events (42.9%). There was no grade 3 or 4 drug-related AE. In these 14 drug-related AEs, the most frequent ones were dyspepsia (21.4%), followed by flatulence, constipation, and hemorrhoid bleeding (above 10% of each). Among all 12 patients, 10 patients (83.3%) maintained stable disease (SD), and 2 patients (16.7%) had progressive disease (PD). The objective response rate (ORR) was 0% and the disease control rate (DCR) was 83.3%.

CONCLUSIONS

K-001 manifests satisfactory safety and tolerability, as well as meaningful antitumor activity in advanced PDAC patients. Further evaluation of K-001 in phase II/III appears warranted.

TRIAL REGISTRATION

NCT02720666 . Registered 28 Match 2016 - Retrospectively registered.

Artificial intelligence for pancreatic cancer detection: Recent development and future direction

Artificial intelligence (AI) has been increasingly utilized in medical applications, especially in the field of gastroenterology. AI can assist gastroenterologists in imaging-based testing and prediction of clinical diagnosis, for examples, detecting polyps during colonoscopy, identifying small bowel lesions using capsule endoscopy images, and predicting liver diseases based on clinical parameters. With its high mortality rate, pancreatic cancer can highly benefit from AI since the early detection of small lesion is difficult with conventional imaging techniques and current biomarkers. Endoscopic ultrasound (EUS) is a main diagnostic tool with high sensitivity for pancreatic adenocarcinoma and pancreatic cystic lesion. The standard tumor markers have not been effective for diagnosis. There have been recent research studies in AI application in EUS and novel biomarkers to early detect and differentiate malignant pancreatic lesions. The findings are impressive compared to the available traditional methods. Herein, we aim to explore the utility of AI in EUS and novel serum and cyst fluid biomarkers for pancreatic cancer detection.

Transcriptomic Profiling Identifies DCBLD2 as a Diagnostic and Prognostic Biomarker in Pancreatic Ductal Adenocarcinoma

Background: Accumulating evidence shows that the elevated expression of DCBLD2 (discoidin, CUB and LCCL domain-containing protein 2) is associated with unfavorable prognosis of various cancers. However, the correlation of DCBLD2 expression value with the diagnosis and prognosis of pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated. Methods: Univariate Cox regression analysis was used to screen robust survival-related genes. Expression pattern of selected genes was investigated in PDAC tissues and normal tissues from multiple cohorts. Kaplan–Meier (K–M) survival curves, ROC curves and calibration curves were employed to assess prognostic performance. The relationship between DCBLD2 expression and immune cell infiltrates was conducted by CIBERSORT software. Biological processes and KEGG pathway enrichment analyses were adopted to clarify the potential function of DCBLD2 in PDAC. Results: Univariate analysis, K–M survival curves and calibration curves indicated that DCBLD2 was a robust prognostic factor for PDAC with cross-cohort compatibility. Upregulation of DCBLD2 was observed in dissected PDAC tissues as well as extracellular vesicles from both plasma and serum samples of PDAC patients. Both DCBLD2 expression in tissue and extracellular vesicles had significant diagnostic value. Besides, DCBLD2 expression was correlated with infiltrating level of CD8⁺ T cells and macrophage M2 cells. Functional enrichment revealed that DCBLD2 might be involved in cell motility, angiogenesis, and cancer-associated pathways. Conclusion: Our study systematically analyzed the potential diagnostic, prognostic and therapeutic value of DCBLD2 in PDAC. All the findings indicated that DCBLD2 might play a considerably oncogenic role in PDAC with diagnostic, prognostic and therapeutic potential. These preliminary results of bioinformatics analyses need to be further validated in more prospective studies.