Biomarker For Pancreatic Ductal Adenocarcinoma Research Articles

Plasma COL10A1 Level, a Potential Diagnostic and Prognostic Biomarker for Pancreatic Ductal Adenocarcinoma.

COL10A1 expression was up-regulated and could promote tumor development in pancreatic cancer. As a secreted protein, plasma COL10A1 level was proven to have certain diagnostic efficacy in gastric cancer, breast cancer, and colorectal cancer. It is still unknown whether it has a biomarker role for pancreatic cancer. To explore and analyze the diagnostic and prognostic value of plasma COL10A1 level in pancreatic ductal adenocarcinoma (PDAC). The RNA-seq dataset of PDAC from The Cancer Genome Atlas (TCGA) and six expression profiling microarray datasets from Gene Expression Omnibus (GEO) were downloaded to analyze the expression of COL10A1 in tissues. Thirty-six patients with PDAC and eighteen healthy volunteers were enrolled to measure COL10A1 levels in tissues and plasmas, and the relationship between clinical characteristics and the COL10A1 levels was analyzed. The diagnostic and prognostic efficacy of plasma COL10A1 levels were calculated. Aspects of COL10A1 expression level in tissues, COL10A1 expression was significantly higher in PDAC tissue than adjacent normal tissue. The expression of COL10A1 was correlated with T, M, and AJCC stages. Patients with high COL10A1 expression had worse recurrence-free survival (RFS) and overall survival (OS) than those with low expression. Aspects of COL10A1 expression levels in plasma, its diagnostic area under the curve (AUC) for PDAC was 0.926 (95% CI 0.853-0.999), diagnostic sensitivity was 81% (95% CI 64-92%), and specificity was 100% (95% CI 81-100%). The time-dependent AUCs at 1-year and 3-year were 0.71 (95% CI 0.51-0.90) and 0.74 (95% CI 0.48-1.00), respectively. In PDAC, plasma COL10A1 levels showed certain diagnostic and prognostic efficacy. COL10A1 may be a diagnostic and prognostic biomarker for PDAC and play a role in liquid biopsy of this disease.

Circulating Neoplastic-Immune Hybrid Cells Are Biomarkers of Occult Metastasis and Treatment Response in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) presents significant diagnostic and prognostic challenges, as current biomarkers frequently fail to accurately stage disease, predict rapid metastatic recurrence (rPDAC), or assess response to neoadjuvant therapy (NAT). We investigated the potential for circulating neoplastic-immune hybrid cells (CHCs) as a non-invasive, multifunctional biomarker for PDAC. Peripheral blood specimens were obtained from patients diagnosed with PDAC. CHCs were detected by co-expression of pan-cytokeratin and CD45, normalized to 50,000 peripheral blood mononuclear cells. rPDAC was defined as metastatic recurrence within six months of margin-negative pancreatectomy. Cyclic immunofluorescence (CyCIF) analyses compared hybrid phenotypes in blood and tumors. Blood samples were collected from 42 patients with PDAC prior to resection. Those with radiographically occult metastatic disease and rPDAC had higher preoperative CHC numbers compared to patients who did not (65.0 and 74.4, vs. 11.52 CHCs; p < 0.001). Patients with complete or near-complete pathologic responses to NAT had lower preoperative CHC numbers than partial and/or non-responders (1.7 vs. 13.1 CHCs; p = 0.008). When assessed longitudinally, those with partial pathologic response saw CHC levels become undetectable while on treatment but increase in the interval between NAT completion and resection. In contrast, patients with poor responses or development of metastatic disease experienced persistent CHC detection during therapy or rising levels prior to radiographic evidence of metastases. Further, in metastatic PDAC patients, treatment-induced phenotypic changes in hybrid cells mirrored those in paired metastatic tumor samples. CHC enumeration and phenotyping display promise as a real-time indicator of disease burden, recurrence risk, and treatment response in PDAC. CHCs have great potential as tumor-derived biomarkers to optimize therapeutic strategies and improve survival in patients with PDAC.

Exosomal miR-199a-3p Secreted From Cancer-Associated Adipocytes Promotes Pancreatic Cancer Progression.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer. Recent studies indicated that cancer-associated adipocytes (CAAs) play crucial roles in tumor progression; however, the precise mechanism remains unknown. Here, we analyzed specific exosomal microRNAs (miRNA) signatures derived from pancreatic CAAs to investigate their role in cancer progression. CAAs were generated by co-culturing human adipocytes with human pancreatic cancer cells, and exosomes were isolated from the CAA-conditioned medium (CAA-CM). Small RNA-seq analysis was used to identify differentially expressed miRNAs in these exosomes. The effects of miRNAs on cell proliferation, migration/invasion, and drug sensitivity were examined. Luciferase reporter assays, real-time polymerase chain reaction, and western blotting were performed to investigate the molecular mechanisms of the miRNAs. The clinical relevance of the miRNAs was investigated using publicly available data and our cohort of patients with PDAC. miR-199a-3p expression was significantly increased in CAA-CM-derived exosomes. CAA-derived exosomes transferred miR-199a-3p to pancreatic cancer cells. Transfection with miR-199a-3p increased the proliferation, invasion, migration, and drug resistance of pancreatic cancer cells by downregulating SOCS7, increasing STAT3 phosphorylation, and upregulating SAA1 expression. High tissue miR-199a-3p expression is correlated with poor prognosis in patients with PDAC. Liquid biopsies revealed that exosomal miR-199a-3p could accurately differentiate patients with PDAC from healthy controls. Multivariate survival analysis indicated that miR-199a is an independent prognostic factor for PDAC. miR-199a-3p in CAA-derived exosomes contributes to the malignant transformation of pancreatic cancer via the SOCS7/STAT3/SAA1 pathway, suggesting its potential as a biomarker and therapeutic target for PDAC.

Evolution of Liquid Biopsies for Detecting Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy characterised by late diagnosis and poor prognosis. Despite advancements, current diagnostic and prognostic strategies remain limited. Liquid biopsy techniques, including circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), circulating tumour exosomes, and proteomics, offer potential solutions to improve PDAC diagnosis, prognostication, and management. A systematic search of Ovid MEDLINE identified studies published between 2019 and 2024, focusing on liquid biopsy biomarkers for PDAC. A total of 49 articles were included. ctDNA research shows some promise in diagnosing and prognosticating PDAC, especially through detecting mutant KRAS in minimal residual disease assays. CTC analyses had low sensitivity for early-stage PDAC and inconsistent prognostic results across subpopulations. Exosomal studies revealed diverse biomarkers with some diagnostic and prognostic potential. Proteomics, although relatively novel, has demonstrated superior accuracy in PDAC diagnosis, including early detection, and notable prognostic capacity. Proteomics combined with CA19-9 analysis has shown the most promising results to date. An update on multi-cancer early detection testing, given its significance for population screening, is also briefly discussed. Liquid biopsy techniques offer promising avenues for improving PDAC diagnosis, prognostication, and management. In particular, proteomics shows considerable potential, yet further research is needed to validate existing findings and comprehensively explore the proteome using an unbiased approach.

Prognosis prediction of PDAC via detection of O-glycan altered extracellular vesicles in perioperative sera.

Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignancy due to the difficulty in diagnosis and poor prognosis because of the high recurrence rate, necessitating reliable biomarkers to improve the diagnosis and prognosis. However, the existing markers have limitations. We previously identified extracellular vesicles (EVs) recognized by O-glycan-binding lectins (Amaranthus caudatus agglutinin [ACA]) as a novel diagnostic biomarker for PDAC using an EV-counting system (ExoCounter). This retrospective study analyzed changes in ACA-positive EVs in perioperative PDAC serum and its association with prognosis using ExoCounter. Absolute EV levels in the pre- and postoperative sera of 44 patients who underwent curative pancreatectomy for PDAC were quantified using ExoCounter. The carbohydrate antigen 19-9 levels declined in most samples postoperatively, and presented no correlation with poor prognosis. In contrast, ACA-positive EVs increased in serum at 7 days postoperatively in 27 of 44 patients (61.4%). We therefore divided participants with ACA-positive EVs before and after surgery into elevation and decline groups. The overall survival (OS) and recurrence-free survival (RFS) of patients with higher ACA-positive EVs were significantly shorter than those with lower ACA-positive EVs (26.1 months vs. not reached, P = 0.018; 11.9 vs. 38.6 months, P = 0.013). Multivariable analysis revealed that ACA-positive EV elevation in postoperative serum was an independent prognostic factor for poor OS (hazard ratio [HR] = 3.891, P = 0.023) and RFS (HR = 2.650, P = 0.024). The detection of ACA-positive EVs in perioperative serum may be used to predict the prognosis of PDAC in the early postoperative period.

A rigorous multi-laboratory study of known PDAC biomarkers identifies increased sensitivity and specificity over CA19-9 alone

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

Induction of oxidative- and endoplasmic-reticulum-stress dependent apoptosis in pancreatic cancer cell lines by DDOST knockdown

The dolichyl-diphosphooligosaccharide-protein glycosyltransferase non-catalytic subunit (DDOST) is a key component of the oligosaccharyltransferase complex catalyzing N-linked glycosylation in the endoplasmic reticulum lumen. DDOST is associated with several cancers and congenital disorders of glycosylation. However, its role in pancreatic cancer remains elusive, despite its enriched pancreatic expression. Using quantitative mass spectrometry, we identify 30 differentially expressed proteins and phosphopeptides (DEPs) after DDOST knockdown in the pancreatic ductal adenocarcinoma (PDAC) cell line PA-TU-8988T. We evaluated DDOST / DEP protein–protein interaction networks using STRING database, correlation of mRNA levels in pancreatic cancer TCGA data, and biological processes annotated to DEPs in Gene Ontology database. The inferred DDOST regulated phenotypes were experimentally verified in two PDAC cell lines, PA-TU-8988T and BXPC-3. We found decreased proliferation and cell viability after DDOST knockdown, whereas ER-stress, ROS-formation and apoptosis were increased. In conclusion, our results support an oncogenic role of DDOST in PDAC by intercepting cell stress events and thereby reducing apoptosis. As such, DDOST might be a potential biomarker and therapeutic target for PDAC.

Multiplexed Glycan Immunofluorescence Identification of Pancreatic Cancer Cell Subpopulations in Both Tumor and Blood Samples.

Pancreatic ductal adenocarcinoma (PDAC) tumor heterogeneity impedes the development of biomarker assays suitable for early disease detection that would improve patient outcomes. The CA19-9 glycan is currently used as a standalone biomarker for PDAC. Furthermore, previous studies have shown that cancer cells may display aberrant membrane-associated glycans. We therefore hypothesized that PDAC cancer cell subpopulations could be distinguished by aberrant glycan signatures. We used multiplexed glycan immunofluorescence combined with pathologist annotation and automated image processing to distinguish between PDAC cancer cell subpopulations within tumor tissue. Using a training-set/test-set approach, we found that PDAC cancer cells may be identified by signatures comprising 4 aberrant glycans (VVL, CA19-9, sTRA, and GM2) and that there are three glycan-defined PDAC tumor types: sTRA type, CA19-9 type, and intermixed. To determine whether the aberrant glycan signatures could be detected in blood samples, we developed hybrid glycan sandwich assays for membrane-associated glycans. In both patient-matched tumor and blood samples, the proportion of aberrant glycans detected was consistent. Furthermore, our multiplexed glycan immunofluorescent approach proved to be more sensitive and more specific than CA19-9 alone. Our results provide proof of concept for a novel methodology to improve early PDAC detection and patient outcomes.

Pesticide-induced transgenerational alterations of genome-wide DNA methylation patterns in the pancreas of Xenopus tropicalis correlate with metabolic phenotypes

The unsustainable use of manmade chemicals poses significant threats to biodiversity and human health. Emerging evidence highlights the potential of certain chemicals to cause transgenerational impacts on metabolic health. Here, we investigate male transmitted epigenetic transgenerational effects of the anti-androgenic herbicide linuron in the pancreas of Xenopus tropicalis frogs, and their association with metabolic phenotypes. Reduced representation bisulfite sequencing (RRBS) was used to assess genome-wide DNA methylation patterns in the pancreas of adult male F2 generation ancestrally exposed to environmentally relevant linuron levels (44 ± 4.7 μg/L). We identified 1117 differentially methylated regions (DMRs) distributed across the X. tropicalis genome, revealing potential regulatory mechanisms underlying metabolic disturbances. DMRs were identified in genes crucial for pancreatic function, including calcium signalling (clstn2, cacna1d and cadps2), genes associated with type 2 diabetes (tcf7l2 and adcy5) and a biomarker for pancreatic ductal adenocarcinoma (plec). Correlation analysis revealed associations between DNA methylation levels in these genes and metabolic phenotypes, indicating epigenetic regulation of glucose metabolism. Moreover, differential methylation in genes related to histone modifications suggests alterations in the epigenetic machinery. These findings underscore the long-term consequences of environmental contamination on pancreatic function and raise concerns about the health risks associated with transgenerational effects of pesticides.

Development of a Fit-For-Purpose Multi-Marker Panel for Early Diagnosis of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

Unlocking the diagnostic power of plasma extracellular vesicle miR-200 family in pancreatic ductal adenocarcinoma

BackgroundDistinguishing benign from malignant pancreaticobiliary disease is challenging because of the absence of reliable biomarkers. Circulating extracellular vesicles (EVs) have emerged as functional mediators between cells. Their cargos, including microRNAs (miRNAs), are increasingly acknowledged as an important source of potential biomarkers. This multicentric, prospective study aimed to establish a diagnostic plasma EV-derived miRNA signature to discriminate pancreatic ductal adenocarcinoma (PDAC) from benign pancreaticobiliary disease.MethodsPlasma EVs were isolated using size exclusion chromatography (SEC) and characterised using nanoparticle tracking analysis, electron microscopy and Western blotting. EV-RNAs underwent small RNA sequencing to discover differentially expressed markers for PDAC (n = 10 benign vs. 10 PDAC). Candidate EV-miRNAs were then validated in a cohort of 61 patients (n = 31 benign vs. 30 PDAC) by RT-qPCR. Logistic regression and optimal thresholds (Youden Index) were used to develop an EV-miR-200 family model to detect cancer. This model was tested in an independent cohort of 95 patients (n = 30 benign, 33 PDAC, and 32 cholangiocarcinoma).ResultsSmall RNA sequencing and RT-qPCR showed that EV-miR-200 family members were significantly overexpressed in PDAC vs. benign disease. Combined expression of the EV-miR-200 family showed an AUC of 0.823. In an independent validation cohort, application of this model showed a sensitivity, specificity and AUC of 100%, 88%, and 0.97, respectively, for diagnosing PDAC.ConclusionsThis is the first study to validate plasma EV-miR-200 members as a clinically-useful diagnostic biomarker for PDAC. Further validation in larger cohorts and clinical trials is essential. These findings also suggest the potential utility in monitoring response and/or recurrence.Graphical

Novel diagnostic biomarkers for pancreatic cancer: assessing methylation status with epigenetic-specific peptide nucleic acid and KRAS mutation in cell-free DNA.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor with a poor prognosis that poses challenges for diagnosis using traditional tissue-based techniques. DNA methylation alterations have emerged as potential and promising biomarkers for PDAC. In this study, we aimed to assess the diagnostic potential of a novel DNA methylation assay based on epigenetic-specific peptide nucleic acid (Epi-sPNA) in both tissue and plasma samples for detecting PDAC. The study involved 46 patients with PDAC who underwent surgical resection. Epi-TOP pancreatic assay was used to detect PDAC-specific epigenetic biomarkers. The Epi-sPNA allowed accurate and rapid methylation analysis without bisulfite sample processing. Genomic DNA extracted from paired normal pancreatic and PDAC tissues was used to assess the diagnostic efficacy of epigenetic biomarkers for PDAC. Subsequent validation was conducted on cell-free DNA (cfDNA) extracted from plasma samples, with 10 individuals represented in each group: PDAC, benign pancreatic cystic neoplasm, and healthy control. The combination of seven epigenetic biomarkers (HOXA9, TWIST, WT1, RPRM, BMP3, NPTX2, and BNC1) achieved 93.5% sensitivity and 96.7% specificity in discerning normal pancreatic from PDAC tissues. Plasma cfDNA, analyzed using these markers and KRAS mutations, exhibited a substantial 90.0% sensitivity, 95.0% specificity, and an overall 93.3% accuracy for discriminating PDAC. Notably, cancer antigen 19-9 and carcinoembryonic antigen both had an accuracy of 90.0%. Our study suggests that analyzing seven differentially methylated genes with KRAS mutations in cfDNA using the novel Epi-TOP pancreatic assay is a potential blood-based biomarker for the diagnosis of PDAC.

Circulating tumor DNA for recurrence monitoring following resection for pancreas adenocarcinoma.

e16365 Background: Detecting early recurrence among patients with resected pancreatic cancer may facilitate earlier treatment and better outcomes for patients with resected pancreatic ductal adenocarcinoma (PDAC). Circulating tumor DNA (ctDNA) has shown promise as a tumor-specific biomarker for PDAC but has not yet entered routine clinical use. This study aims to conduct analytical and clinical validation of Signatera ctDNA profiling to predict recurrence in patients with PDAC. Methods: A retrospective IRB-approved protocol evaluating the use of Signatera ctDNA testing among patients who underwent resection at Northwell Health during 2019-2023. The utility of ctDNA status to predict PDAC recurrence during follow-up was assessed using cox regression. Results: There were 91 ctDNA tests among 24 patients. With a median follow-up of 15 months, recurrence was observed in 38% (9/24) of patients and 8 patients died. Among patients with recurrence, most of them had positive ctDNA before showing signs of recurrence on the imaging (6/9, 66.7%) with mean lead time of 116 days, while 2 of them had positive ctDNA after recurrence and one patient never had positive ctDNA. At the time of recurrence, eight patients had elevated CA19-9, while two patients with normal CA19-9 had positive ctDNA. The median recurrence-free survival (RFS) was 23 months (95% CI, 11-not reached). Patients with detectable preoperative ctDNA had significantly decreased median RFS (14 months; 7 of 10 (70%) recurred) when compared with patients negative for ctDNA [not reached; 2/14 (14.3%) recurred; P= 0.005). In multivariable Cox model after adjusting for potential risk factors, detection of ctDNA was associated with RFS. (table). Conclusions: Signatera ctDNA profiling identified relapse earlier than imaging for most patients. These results suggest the potential for a prospective trial evaluating early treatment interventions in this deadly disease to improve survival rates. [Table: see text]

Development of a multi-analyte blood test for the early detection of pancreatic ductal adenocarcinoma (PDAC) and performance compared to CA19-9.

e16360 Background: Early detection of PDAC can improve survival but is hampered by the lack of recognizable symptoms in early disease. Imaging has been the mainstay for surveillance of high-risk individuals but has proven to be cumbersome and may lack sensitivity to detect small tumors. CA19-9 is the only available blood biomarker for PDAC, but it is neither sensitive nor specific enough for it to be recommended for high-risk surveillance. A sensitive and specific blood biomarker for PDAC could positively impact this situation. Methods: Serum samples from patients across multiple centers with stage I and II PDAC (n = 75) and matched controls at high risk for PDAC (genetic and familial) (n = 83) were assessed for more than 2,900 protein analytes using O-link multiplex technology and conventional immunoassays for selected protein candidates. Machine learning was used to combine the biomarker candidates to create 4-plex signatures discriminating PDAC and controls. Results: ROC (receiver operating characteristic) curve analysis of these revealed signatures with performance superior to CA19-9 and a previously commercially available assay, IMMRay PanCan-d (AUC of 0.97 and 0.94 vs. 0.87 for CA19-9 and 0.88 for IMMRay). A subset of these candidate biomarkers has now been transitioned to standardized ELISA assays which are being used to construct a final signature for clinical validation in an independent set of PDAC and high-risk control samples. Conclusions: A novel multi-analyte blood test utilizing newly-discovered PDAC biomarkers has the potential to improve early detection of PDAC in high-risk individuals.

Comprehensive analysis of KLF family reveals KLF6 as a promising prognostic and immune biomarker in pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors worldwide, with extremely aggressive and complicated biology. Krüppel-like factors (KLFs) encode a series of transcriptional regulatory proteins and play crucial roles in a variety of processes, including tumor cell differentiation and proliferation. However, the potential biological functions and possible pathways of KLFs in the progression of PDAC remain elusive.MethodsWe systematically evaluated the transcriptional variations and expression patterns of KLFs in pancreatic cancer from the UCSC Xena. Based on difference analysis, the non-negative matrix factorization (NMF) algorithm was utilized to identify the immune characteristics and clinical significance of two different subtypes. The multivariate Cox regression was used to construct the risk model and then explore the differences in tumor immune microenvironment (TIME) and drug sensitivity between high and low groups. Through single-cell RNA sequencing (scRNA-seq) analysis, we screened KLF6 and further investigated its biological functions in pancreatic cancer and pan-cancer.ResultsThe KLFs exhibited differential expression and mutations in the transcriptomic profile of PDAC. According to the expression of KLFs, patients were classified into two distinct subtypes, each exhibiting significant differences in prognosis and TIME. Moreover, the KLF signature was developed using univariate Cox and Lasso regression, which proved to be a reliable and effective prognostic model. Furthermore, the KLF_Score was closely associated with immune infiltration, response to immunotherapy, and drug sensitivity and we screened small molecule compounds targeting prognostic genes separately. Through scRNA-seq analysis, KLF6 was selected to further demonstrate its role in the malignance of PC in vitro. Finally, pan-cancer analysis emphasized the biological significance of KLF6 in multiple types of tumors and its clinical utility in assessing cancer prognosis.ConclusionThis study elucidated the pivotal role of KLF family genes in the malignant development of PC through comprehensive analysis and revealed that KLF6 would be a novel diagnostic biomolecule marker and potential therapeutic target for PDAC.

Identification of potential biomarkers for pancreatic ductal adenocarcinoma: a bioinformatics analysis.

PDA is an aggressive cancer with a 5-year survival rate, which is very low. There is no effective prognosis or therapy for PDA because of the lack of target biomarkers. The objective of this article is to identify the target biomarkers for PDA using a bioinformatics approach. In this work, we have analysed the three microarray datasets from the NCBI GEO database. We used the Geo2R tool to analyse the microarray data with the Benjamini and Hochberg false discovery rate method, and the significance level cut-off was set to 0.05. We have identified 659 DEGs from the datasets. There are a total of 15 hub genes that were selected from the PPI network constructed using the STRING application. Furthermore, these 15 genes were evaluated on PDA patients using TCGA and GTEx databases in (GEPIA). The online tool DAVID was used to analyse the functional annotation information for the DEGs. The functional pathway enrichment was performed on the GO and KEGG. The hub genes were mainly enriched for cell division, chromosome segregation, protein binding and microtubule binding. Further, the gene alteration study was performed using the cBioportal tool and screened out six hub genes (ASPM, CENPF, BIRC5, TTK, DLGAP5, and TOP2A) with a high alteration rate in PDA samples. Furthermore, Kaplan-Meier survival analysis was performed on the six hub genes and identified poor-survival outcomes that may be involved in tumorigenesis and PDA development. So, this study concludes that, these six hub genes may be potential prognostic biomarkers for PDA.

Keratin 17 is a prognostic and predictive biomarker in pancreatic ductal adenocarcinoma.

To determine the role of keratin 17 (K17) as a predictive biomarker for response to chemotherapy by defining thresholds of K17 expression based on immunohistochemical tests that could be used to optimize therapeutic intervention for patients with pancreatic ductal adenocarcinoma (PDAC). We profiled K17 expression, a hallmark of the basal molecular subtype of PDAC, by immunohistochemistry in 2 cohorts of formalin-fixed, paraffin-embedded PDACs (n = 305). We determined a K17 threshold of expression to optimize prognostic stratification according to the lowest Akaike information criterion and explored the potential relationship between K17 and chemoresistance by multivariate predictive analyses. Patients with advanced-stage, low K17 PDACs treated using 5-fluorouracil (5-FU)-based chemotherapeutic regimens had 3-fold longer survival than corresponding cases treated with gemcitabine-based chemotherapy. By contrast, PDACs with high K17 did not respond to either regimen. The predictive value of K17 was independent of tumor mutation status and other clinicopathologic variables. The detection of K17 in 10% or greater of PDAC cells identified patients with shortest survival. Among patients with low K17 PDACs, 5-FU-based treatment was more likely than gemcitabine-based therapies to extend survival.

Integration of single-nucleus and exosome RNA sequencing dissected inter-cellular communication and biomarkers in pancreatic ductal adenocarcinoma

BackgroundMounting evidence underscores the importance of cell communication within the tumor microenvironment, which is pivotal in tumor proliferation, invasion, and metastasis. Exosomes play a crucial role in cell-to-cell communication. Although single-cell RNA sequencing (scRNA-seq) provides insights into individual cell transcriptional characteristics, it falls short of comprehensively capturing exosome-mediated intercellular communication. MethodWe analyzed Pancreatic Ductal Adenocarcinoma (PDAC) tissues, separating supernatant and precipitate for exosome purification and single-cell nucleus suspension. We then constructed Single-nucleus RNA sequencing (snRNA-seq) and small RNA-seq libraries from these components. Our bioinformatic analysis integrated these sequences with ligand-receptor analysis and public miRNA data to map the cell communication network. ResultsWe established intercellular communication networks using bioinformatic analysis to track exosome miRNA effects and ligand-receptor pairs. Significantly, hsa-miR-1293 emerged as a prognostic biomarker for pancreatic cancer, linked to immune evasion, increased myeloid-derived suppressor cells, and poorer prognosis. Targeting this miRNA may enhance anti-tumor immunity and improve outcomes. ConclusionOur study offers a novel approach to constructing intercellular communication networks using snRNA-seq and exosome-small RNA sequencing. By integrating miRNA tracing with ligand-receptor analysis, we illuminate the complex interactions in the pancreatic cancer microenvironment, highlighting the pivotal role of miRNAs and identifying potential biomarkers and therapeutic targets.

Abstract 3514: A deep learning model for detection and characterization of tertiary lymphoid structures in H&amp;E-stained images from pancreatic ductal adenocarcinoma

Abstract Tertiary lymphoid structures (TLSs) are ectopic aggregates of a number of immune cells in nonlymphoid tissues under chronically inflamed environments and cancer. Emergence evidence suggested that TLSs were found in diverse cancers and TLSs have been referred as an independently predictive biomarker for immunotherapy response, especially immune checkpoint inhibitors. Moreover, the density and mature status of TLSs often positively associated with the favorable outcomes in cancers. Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related mortality with low survival rate in the whole world. Here, we developed a multi-resolution machine learning model for detection of TLSs from hema-toxylin and eosin (H&amp;E) stained digital pathology slides as a potential clinical biomarker in PDAC. We selected 120 H&amp;E slides (patients) with a total of 1,896 TLSs manually annotated as immature or mature TLSs by two pathologists. We also used immunohistochemistry (IHC) for CD20 and CD23 markers as a reference standard for H&amp;E-based manual annotations. Our model mainly included detection and segmentation of individual TLSs. First, we used a transformer convolutional neural network to build a classification task to detect the presence of immature or mature TLSs. Then the instance segmentation model was applied to quantify the number of lymphocytes per unit square to validate the presence and maturation of TLSs. F1-score, calculated by the harmonic mean of precision (positive predictive value) and recall (sensitivity), was finally used to quantitatively evaluate the performance of model for each H&amp;E slide. In validation dataset with 250 H&amp;E slides (patients), we selected 50 slides to calculate F1-score, suggesting that our machine learning model achieved high F1-scores for detection and classification of immature TLSs (mean = 0.87, range from 0.79 to 0.95), and mature TLSs (mean = 0.92, range from 0.86 to 0.97). We further stratified 370 PDAC patients into three groups with mature TLSs (23%), immature TLSs (16%), and no TLSs (61%), respectively. Combined the clinical information, survival analysis revealed that patients with mature TLS had better survival outcomes than those without TLS (HR = 0.63; 95% CI, 0.44 - 0.90; P = 0.010). We developed a machine learning model that can accurately detect and classify TLSs in PDAC, and also demonstrated the prognosis value of predictive TLSs in H&amp;E slides by automatedly machine learning model. These data highlight the promise of machine learning model for automated identification and quantification of the TLS on H&amp;E slides in PDAC pathology. Citation Format: Chaoxian Zhao, Jidong Jia, Mingxi Lv, Jiayi Feng, Yijun Wang, Yingbin Liu. A deep learning model for detection and characterization of tertiary lymphoid structures in H&amp;E-stained images from pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3514.

Abstract 4153: PET/CT imaging of pancreatic ductal adenocarcinoma with a claudin-4 binding peptide in preclinical mouse model

Abstract There is a critical need to develop new imaging and therapeutic strategies for pancreatic cancer. Current positron emission tomography (PET) imaging assessments of pancreatic ductal adenocarcinoma (PDAC) rely on 18F-FDG as a radiopharmaceutical. To increase the sensitivity of PDAC detection, we identified Claudin-4 (CLDN4), a tight junction transmembrane protein, as a PDAC biomarker through spatial transcriptomic and proteomic analyses. CLDN4 expression has previously been reported to be enhanced in 99% of primary and 100% of metastatic pancreatic cancer tissue samples and in 10 of 11 precancerous pancreatic intraepithelial neoplasia (PanIN) specimens. A small fragment of naturally occurring Clostridium perfringens enterotoxin (CPE) was previously reported to bind to the extracellular loop domain of CLDN4 protein (PDB:7kp4) with nanomolar (nM) affinity (Kd). A CLDN4 binding peptide (C4BP) was developed as a radioligand by modifying the n-terminus of C4BP which does not impact receptor binding. Here, we designed and synthesized a DOTA-PEG1-C4BP ligand on a rink amide resin using a microwave-assisted peptide synthesizer. Radiolabeling of DOTA-PEG1-C4BP (2 nmol) with 64CuCl2 (70 MBq) conferred 64Cu-C4BP (28 MBq/nmol) with 80% non-decay corrected yield and &amp;gt;99% radiochemical purity. PET/CT imaging of 64Cu-C4BP (~0.1 μg/mouse) performed in an inducible PDAC (KrasLSL-G12D/+;Rosa26LSL-tdTomato/LSL-tdTomato(KT);Ptf1aCreER;Trp53fl/fl) model at 84 day after tamoxifen injection resulted in ~25% injected activity per cubic centimeter (IA/cc) accumulation in metastases and at ~18% IA/cc retention in pancreatic tumors within 30 minutes of tail vein injection with minimal background accumulation. Ex vivo PET imaging confirmed the localized accumulation of 64Cu-C4BP in transgenic mice within the colon, pancreas, and liver. Blocking of 64Cu-C4BP accumulation with the cold compound in a xenograft model (NOD/SCID) bearing Capan-1 PDAC tumors demonstrated the specificity of 64Cu-C4BP binding. Here we report that CLDN4 is a promising target and 64Cu-C4BP is a promising ligand for the detection of pancreatic cancer. Citation Format: Jai Woong Seo, James Wang, Aris J. Kare, Spencer K. Tumbale, Bo Wu, Marina N. Raie, Mallesh Pandrala, Katherine W. Ferrara. PET/CT imaging of pancreatic ductal adenocarcinoma with a claudin-4 binding peptide in preclinical mouse model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4153.