Prognosis Of Pancreatic Ductal Adenocarcinoma Research Articles

Taurine Synthesis by 2-Aminoethanethiol Dioxygenase as a Vulnerable Metabolic Alteration in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) exhibits an altered metabolic profile compared to normal pancreatic tissue. However, studies on actual pancreatic tissues are limited. Untargeted metabolomics analysis was conducted on 54 pairs of tumor and matched normal tissues. Taurine levels were validated via immunohistochemistry (IHC) on separate PDAC and normal tissues. Bioinformatics analysis of transcriptomics and proteomics data evaluated genes associated with taurine metabolism. Identified taurine-associated gene was validated through gene modulation. Clinical implications were evaluated using patient data. Metabolomics analysis showed a 2.51-fold increase in taurine in PDAC compared to normal tissues (n=54). IHC confirmed this in independent samples (n=99 PDAC, 19 normal). Bioinformatics identified 2-aminoethanethiol dioxygenase (ADO) as a key gene modulating taurine metabolism. IHC on a tissue microarray (39 PDAC, 10 normal) confirmed elevated ADO in PDAC. The ADOTaurine axis correlated with PDAC recurrence and disease-free survival. ADO knockdown reduced cancer cell proliferation and tumor growth in a mouse xenograft model. The MEK-related signaling pathway is suggested to be modulated by ADO-Taurine metabolism. Our multi-omics investigation revealed elevated taurine synthesis mediated by ADO upregulation in PDAC. The ADOTaurine axis may serve as a biomarker for PDAC prognosis and a therapeutic target.

Deep Multiple Instance Learning Model to Predict Outcome of Pancreatic Cancer Following Surgery

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is a cancer with very poor prognosis despite early surgical management. To date, only clinical variables are used to predict outcome for decision-making about adjuvant therapy. We sought to generate a deep learning approach based on hematoxylin and eosin (H&E) or hematoxylin, eosin and saffron (HES) whole slides to predict patients’ outcome, compare these new entities with known molecular subtypes and question their biological significance; Methods: We used as a training set a retrospective private cohort of 206 patients treated by surgery for PDAC cancer and a validation cohort of 166 non-metastatic patients from The Cancer Genome Atlas (TCGA) PDAC project. We estimated a multi-instance learning survival model to predict relapse in the training set and evaluated its performance in the validation set. RNAseq and exome data from the TCGA PDAC database were used to describe the transcriptomic and genomic features associated with deep learning classification; Results: Based on the estimation of an attention-based multi-instance learning survival model, we identified two groups of patients with a distinct prognosis. There was a significant difference in progression-free survival (PFS) between these two groups in the training set (hazard ratio HR = 0.72 [0.54;0.96]; p = 0.03) and in the validation set (HR = 0.63 [0.42;0.94]; p = 0.01). Transcriptomic and genomic features revealed that the poor prognosis group was associated with a squamous phenotype. Conclusions: Our study demonstrates that deep learning could be used to predict PDAC prognosis and offer assistance in better choosing adjuvant treatment.

Novel Amanitin-based Antibody Drug Conjugates (ATAC®) targeting TROP2 for the treatment of Pancreatic Cancer.

Trophoblast cell surface antigen 2 (TROP2) exhibits aberrant expression in pancreatic cancer, correlating with metastasis, advanced tumor stage and poor prognosis of pancreatic ductal adenocarcinoma (PDAC) patients. TROP2 has been recognized as a promising therapeutic target for antibody drug conjugates (ADCs), as evidenced by the approval of the anti-TROP2 ADC Trodelvy® for the treatment of triple negative breast cancer. In this study we report the generation of novel second-generation amanitin based ADCs (ATAC®s) targeting TROP2, comprising the humanized RS7 antibody of Trodelvy® (hRS7) and the highly potent payload amanitin. The specific in vitro binding, efficient antigen internalization, and high cytotoxicity of hRS7 ATAC®s with half maximal effective concentration (EC50) values in the picomolar range in TROP2-expressing cells constituted the foundation for preclinical in vivo evaluation. The hRS7 ATAC®s demonstrated a significant reduction in tumor growth in vivo in subcutaneous xenograft mouse models of pancreatic cancer and triple negative breast cancer at well-tolerated doses. The antitumor efficacy correlated with the level of TROP2 expression on the tumors and the in vivo tumor uptake of the ATAC®s. The long half-life of 9.7-10.7 days of hRS7 ATAC®s without premature payload release in serum supported a high therapeutic index. Notably, the efficacy of the hRS7 ATAC®s was superior to that of Trodelvy® with complete tumor eradication in both, refractory pancreatic and triple negative breast cancer xenograft models. In summary, hRS7 ATAC®s represent a highly effective and well-tolerated targeted therapy, and our data support their development for pancreatic cancer and other TROP2-expressing tumors.

Organoid-based precision medicine in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) ranks among the leading causes of cancer-related deaths worldwide. Despite advances in precision oncology in other malignancies, treatment of PDAC still largely relies on conventional chemotherapy. Given the dismal prognosis and heterogeneity in PDAC, there is an urgent need for personalized therapeutic strategies to improve treatment response. Organoids, generated from patients' tumor tissue, have emerged as a powerful tool in cancer research. These three-dimensional models faithfully recapitulate the morphological and genetic features of the parental tumor and retain patient-specific heterogeneity. This review summarizes existing precision oncology approaches in PDAC, explores current applications and limitations of organoid cultures in personalized medicine, details preclinical studies correlating in vitro organoid prediction and patient treatment response, and provides an overview of ongoing organoid-based clinical trials.

Comparison of multiple machine learning models for predicting prognosis of pancreatic ductal adenocarcinoma based on contrast-enhanced CT radiomics and clinical features.

The aim of this study was to evaluate the prognostic potential of combining clinical features and radiomics with multiple machine learning (ML) algorithms in pancreatic ductal adenocarcinoma (PDAC). A total of 116 patients with PDAC who met the eligibility criteria were randomly assigned to a training or validation cohort. Seven ML algorithms, including Supervised Principal Components, stepwise Cox, Random Survival Forest, CoxBoost, Least absolute shrinkage and selection operation (Lasso), Ridge, and Elastic network, were integrated into 43 algorithm combinations. Forty-three radiomics models were constructed separately using radiomics features extracted from arterial phase (AP), venous phase (VP), and combined arterial and venous phase (AP+VP) images. The concordance index (C-index) of each model was calculated. The model with the highest mean C-index was identified as the best model for calculating the radiomics score (Radscore). Univariate and multivariate Cox analyses were used to identify independent prognostic indicators and create a clinical model for prognosis prediction. The multivariable Cox regression was used to combine Radscore with clinical features to create a combined model. The efficacy of the model was evaluated using the C-index, calibration curves, and decision curve analysis (DCA). The model based on the Lasso+StepCox[both] algorithm constructed using AP+VP radiomics features showed the best predictive ability among the 114 radiomics models. The C-indices of the model in the training and validation cohorts were 0.742 and 0.722, respectively. Based on the results of the univariate and multivariate Cox regression analyses, sex, Tumor-Node-Metastasis (TNM) stage, and systemic inflammation response index were included to build the clinical model. The combined model, incorporating three clinical factors and AP+VP-Radscore, achieved the highest C-indices of 0.764 and 0.746 in the training and validation cohorts, respectively. In terms of preoperative prognosis prediction for PDAC, the calibration curve and DCA showed that the combined model had a good consistency and greatest net benefit. A combined model of clinical features and AP+VP-Radscore screened using multiple ML algorithms has an excellent ability to predict the prognosis of PDAC and may provide a noninvasive and effective method for clinical decision-making.

Integrated analysis of microbiome and metabolome reveals signatures in PDAC tumorigenesis and prognosis.

We conducted a large sample-size pancreatic adenocarcinoma microbiome study using a novel microbiome sequencing method and two metabolomic assays. Two significant outcomes of our analysis are: (i) commensal opportunistic pathogens Staphylococcus aureus, Cutibacterium acnes, and Cutibacterium granulosum were enriched in pancreatic ductal adenocarcinoma (PDAC) tumors compared with normal adjacent tissues, and (ii) worse overall survival was found related to the presence of Ralstonia pickettii_B. Microbial species affect the tumorigenesis, metastasis, and prognosis of PDAC via unique microbe-enzyme-metabolite interaction. Thus, our study highlights the need for further investigation of the potential associations between pancreatic microbiota-derived omics signatures, which may drive the clinical transformation of microbiome-derived strategies toward therapy-targeted bacteria.

Endosomal Trafficking Bypassed by the RAB5B-CD109 Interplay Promotes Axonogenesis in KRAS-Mutant Pancreatic Cancer.

Perineural invasion (PNI) represents a unique biological feature associated with poor prognosis in pancreatic ductal adenocarcinoma (PDAC), especially in the presence of KRAS mutations. Extracellular vesicle (EV)-packaged circular RNAs (circRNAs) function as essential mediators of tumor microenvironment communication, triggering PDAC cell invasion and distant metastasis. However, the regulatory mechanisms of EV-packaged circRNAs in the PNI of KRAS-mutant PDAC have not yet been elucidated. Herein, a KRASG12D mutation-responsive EV-packaged circRNA, circPNIT, which positively correlated with PNI in PDAC patients is identified. Functionally, KRASG12D PDAC-derived EV-packaged circPNIT promoted axonogenesis and PNI both in vitro and in vivo. Mechanistically, the circPNIT-mediated Rab5B-CD109 interplay bypassed traditional endosomal trafficking to anchor Rab5B to the lipid rafts of multivesicular bodies and packaged circPNIT into CD109+ EVs. Subsequently, CD109+ EVs delivered circPNIT to neurons by binding to TRPV1 and facilitating DSCAML1 transcription-induced axonogenesis, which in turn enhanced the PNI by activating the GFRα1/RET pathway. Importantly, circPNIT-loaded CD109+ EVs are established to dramatically promote PNI in a KRASG12D/+ Trp53R172H/+ Pdx-1-Cre mouse model. Collectively, the findings highlight the mechanism underlying how EV-packaged circRNAs mediate the PNI of KRAS-mutant PDAC cells through the Rab5B endosomal bypass, identifying circPNIT as an effective target for the treatment of neuro-metastatic PDAC.

Lymphocyte Antigen 6 Family Member D (LY6D) Affects Stem Cell Phenotype and Progression of Pancreatic Adenocarcinoma.

The membrane-bound protein lymphocyte antigen 6 family member D (LY6D), a marker of early B cell lineage is reportedly expressed in several human malignancies and has been implicated in cancer stemness. However, its expression and role in cancer stemness remain largely unexplored in pancreatic ductal adenocarcinoma (PDAC). The aim of this study was to clarify the role of LY6D in PDAC. We conducted functional analysis of LY6D to evaluate its impact on the malignant features of PDAC cells in vitro. Using our in-house developed stem cell separation technique, which isolates cells with low proteasome activity and CD44 v9 cell surface marker for cancer stem cells, we performed sphere formation and chemosensitivity tests and tumor formation assay in mice, through knockdown of LY6D expression. Immuno-histopathological analysis was also conducted to reveal the clinical significance of LY6D in PDAC. In vitro functional assays demonstrated that LY6D was critically involved in promoting the cancer malignant phenotype, including increased invasive ability, drug resistance, migration capacity, and cancer stemness. Immunohistopathological analysis revealed that high LY6D expression levels were associated with high recurrence rates and poorer prognosis in PDAC. Our study showed that LY6D is a novel prognostic indicator and plays a key role in regulation of cancer stemness in PDAC.

Fibrinogen to pre-albumin ratio is an independent prognostic index for patients with pancreatic ductal adenocarcinoma after radical resection

BackgroundThis study aims to elucidate the significance of the preoperative fibrinogen to pre-albumin ratio (FPR) in predicting the prognosis of pancreatic ductal adenocarcinoma (PDAC), a correlation not extensively explored previously.MethodsA cohort of 563 patients diagnosed with PDAC and subjected to radical surgical resection was examined. We meticulously documented a range of inflammatory markers, clinical-pathological features, and oncological outcomes. The prognostic value of preoperative FPR was assessed using Kaplan–Meier survival analysis and Cox proportional hazards regression modeling. Furthermore, the predictive accuracy of FPR was evaluated through time-dependent receiver operating characteristic (ROC) curves and decision curve analyses (DCA).ResultsThe determined optimal threshold for FPR was 14.77, which facilitated the stratification of patients into groups with low and high FPR levels. Notably, patients in the high FPR cohort exhibited significantly reduced recurrence-free survival (RFS) and overall survival (OS) rates compared to their low FPR counterparts. Multivariate Cox regression analysis underscored FPR as an independent prognostic indicator for both RFS and OS. In comparison to the neutrophil-to-lymphocyte ratio (NLR), FPR demonstrated superior prognostic accuracy and clinical utility.ConclusionThe preoperative fibrinogen to pre-albumin ratio serves as an independent prognostic marker for RFS and OS among PDAC patients undergoing radical resection. Our findings suggest that FPR could be a valuable addition to the current prognostic models, potentially guiding therapeutic decision-making and patient management strategies in PDAC.

HOXA9 and CD163 potentiate pancreatic ductal adenocarcinoma progression

BackgroundThe role of HOXA9 requires investigations in pancreatic ductal adenocarcinoma (PDAC) as HOXA9 inhibitors are being developed. HOXA9 might attract CD163 expressed tumor associated macrophages (TAM) and could affect PDAC prognosis. This work aims to study the expression and relevance of HOXA9 and CD163 in PDAC progression.Materials and methodsSelected 98 PDAC and 98 adjacent non tumor tissues as a control group were immunostained with HOXA9 and CD163 antibodies.ResultsPDAC displayed highly significant higher HOXA9 staining intensity, percent and H score values than control group. HOXA9 staining of PDAC cases showed significant associations with poor prognostic indicators including larger tumor size, higher grade and advanced stage. PDAC showed highly significant differences regarding CD163 macrophage-specific staining intensity, percent and H score values than control group. CD163 showed significant higher expressions with larger tumor size, higher histological grade and advanced stage group. HOXA9 staining in PDAC showed highly significant direct correlations with CD163 positive macrophages. Follow up of PDAC cases revealed that high median H score of HOXA9 and CD163 were significantly associated with worse overall survival. CD163 was an independent prognostic marker of worse survival.ConclusionsIn conclusion, HOXA9 could potentiate PDAC progression by stimulating CD163 expressed TAM attraction in tumors. HOXA9 and CD163 could participate in PDAC therapy. HOXA9 and CD163 could be predictors of worse prognosis and shorter survival in PDAC.

Stereotactic Radiotherapy Plus Nivolumab in Patients with Locally Advanced Pancreatic Cancer: Results from Phase 1/2 Clinical CA209-9KH Trial.

The dismal prognosis of pancreatic ductal adenocarcinoma (PDAC) highlights the urgent need for novel therapeutic strategies. Immune checkpoint inhibitors (ICIs) seem to be ineffective in most PDAC studies. Therefore, we conducted an open-label, multicenter phase 1/2 study (CA209-9KH) to evaluate the safety of stereotactic radiotherapy (SRT) and sequential ICI therapy in PDAC, as well as to validate the efficacy of this regimen as a potential activator of antitumor immunity. Patients aged ≥ 18years with unresectable non-metastatic PDAC following four FOLFIRINOX induction cycles were included. Treatment comprised SRT (4 × 8Gy) and sequential nivolumab administration until disease progression or unacceptable toxicity. The primary endpoints were safety and toxicity assessment. Secondary endpoints included progression-free survival (PFS), overall survival (OS), biomarker evaluation, and quality of life (QoL) analysis. Twenty-two patients were screened, with 15 enrolled. Eleven (median) nivolumab cycles were administered. SRT demonstrated low and clinically nonsignificant toxicity, whereas nivolumab toxicity aligned with prior safety profiles, without grade 4-5 events observed. Three patients discontinued therapy owing to toxicity. Median PFS and OS were 8.1 and 13.0months, respectively, with 12-month PFS and OS rates of 0% and 66.7%, respectively, and a 24-month OS rate of 8.9%. Biomarker levels correlated with clinical or radiological disease control. Patient-reported QoL remained acceptable, deteriorating with disease progression. SRT and nivolumab therapy exhibited manageable toxicity profiles consistent with previous findings; however, long-term treatment responses were not achieved with this regimen in locally advanced PDAC. Another strategy to trigger antitumor immunity in PDAC needs to be sought. EudraCT: 2017-003404-52; ClinicalTrials.gov: NCT04098432.

Single-cell transcriptome analysis identifies a novel tumor-associated macrophage subtype predicting better prognosis in pancreatic ductal adenocarcinoma.

Characterized by an immune-suppressive tumor microenvironment (TME), pancreatic ductal adenocarcinoma (PDAC) is well-known for its poor prognosis. Tumor associated macrophages (TAMs) play a critical role in PDAC TME. An in-depth understanding of TAMs is helpful to develop new strategies for immunotherapy. A large number of single-cell RNA sequencing data and bulk RNA sequencing data of PDAC were collected for systematic bioinformatics analysis. Characterize subtypes of TAMs at single-cell resolution and its effect on prognosis. Differential gene analysis and cell-cell communication were used to describe the effect on prognosis and validated by the TCGA dataset. We used two prognosis-favorable genes, SLC12A5 and ENPP2, to identify a benign M2-like TAMs (bM2-like TAMs), which shared similarities with C1QC + TAMs, CXCL9+ TAMs and CD169+ TAMs, by analyzing scRNA-seq data and bulk RNA data of PDAC. The bM2-like TAMs were revealed to promote T cell activation and proliferation through ALCAM/CD6 interaction. Meanwhile, the bM2-like TAMs were responsible for stroma modeling by altering αSMA+/αSMA-cell ratio. On the contrast, the rest of the M2-like TAMs were defined as malignant M2-like TAMs (mM2-like TAMs), partly overlapping with SPP1+ TAMs. mM2-like TAMs were revealed to promote tumor progression by secretion of MIF and SPP1. Our study used two prognosis-favorable genes to divide M2-like TAMs of PDAC into anti-tumor bM2-like TAMs and pro-tumor mM2-like TAMs. The bM2-like TAMs activate T cells through ALCAM/CD6 and generate prognosis-favorable αSMA+ myofibroblasts through secreting TGFβ, which brings insight into heterogeneity of TAMs, prognosis prediction and immunotherapy of PDAC.

Oncogenic GALNT5 confers FOLFIRINOX resistance via activating the MYH9/ NOTCH/ DDR axis in pancreatic ductal adenocarcinoma.

Chemotherapy resistance has been a great challenge in pancreatic ductal adenocarcinoma(PDAC) treatments. Current first-line chemotherapy regimens for PDAC include gemcitabine-based regimens such as AG regimen (albumin paclitaxel and gemcitabine), fluorouracil-based regiments such as FOLFIRINOX regimen ((5-fluorouracil5-FU), oxaliplatin, Irinotecan) and platinum-based regimens for patients with BRCA mutations. large amounts of work have been done on exploring the mechanism underlying resistance of gemcitabine-based and platinum-based regimens, while little research has been achieved on the mechanism of FOLFIRINOX regimens resistance. Hence, we identified Polypeptide N-Acetylgalactosaminyltransferase 5, (GALNT5) as a vital regulator and a potential therapeutic target in FOLFIRINOX regimens resistance. Colony formation assays and flow cytometry assays were performed to explore the roles of GALNT5 in cell proliferation and apoptosis in PDAC treated with FOLFIRINOX. IC50 alterations were calculated in GALNT5 knockdown and overexpressed cell lines. RNA-seq followed by GSEA (gene set enrichment analysis) was displayed to explore the potential mechanism. WB (western blotting), real-time PCR, and IF (immunofluorescence) were performed to validate relative pathways. The mouse orthotopic xenograft PDAC model was established to examine GALNT5 functions in vivo. GALNT5 was highly expressed in PDAC tissues and predicted poor prognosis in PDAC. Upregulation of GALNT5 in PDAC cells conferred FOLFIRINOX resistance on PDAC by inhibiting DNA damage. Moreover, GALNT5 interacted with MYH9, thus participating in the activation of the NOTCH pathways, resulting in hampering FOI-induced DNA damage. Functions of GALNT5 promoting FOLFIRINOX resistance were validated in vivo. In this study, we found that aberrantly overexpressed GALNT5 in PDAC took part in the activation of the NOTCH pathway by interacting with MYH9, thus inhibiting the DDR to achieve FOLFIRINOX resistance and causing poor prognosis. We identified GALNT5 as a potential therapeutic target for PDAC patients resistant to FOLFIRINOX chemotherapy.

Histological variants of pancreatic ductal adenocarcinoma: a survival analysis.

Pancreatic ductal adenocarcinoma (PDAC) can be classified into distinct histological subtypes based on the WHO nomenclature. The aim of this study was to compare the prognosis of conventional PDAC (cPDAC) against the other histological variants at the population level. The Surveillance, Epidemiology and End Results (SEER) database was used to identify patients with microscopically confirmed PDAC. These patients were divided into 9 histological subgroups. Overall survival was assessed using the Kaplan-Meier method and Cox regression models stratified by tumor histology. A total of 159,548 patients with PDAC were identified, of whom 95.9% had cPDAC, followed by colloid carcinoma (CC) (2.6%), adenosquamous carcinoma (ASqC) (0.8%), signet ring cell carcinoma (SRCC) (0.5%), undifferentiated carcinoma (UC) (0.1%), undifferentiated carcinoma with osteoclast-like giant cells (UCOGC) (0.1%), hepatoid carcinoma (HC) (0.01%), medullary carcinoma of the pancreas (MCP) (0.006%) and pancreatic undifferentiated carcinoma with rhabdoid phenotype (PUCR) (0.003%). Kaplan-Meier curves showed that PUCR had the worst prognosis (median survival: 2 months; 5-year survival: 0%), while MCP had the best prognosis (median survival: 41 months; 5-year survival: 33.3%). In a multivariable Cox model, several histological subtypes (i.e. CC, ASqC, SRCC, UCOGC) were identified as independent predictors of overall survival when compared to cPDAC. PDAC is a heterogenous disease and accurate identification of variant histology is important for risk stratification, as these variants may have different biological behavior.

KLF15 suppresses stemness of pancreatic cancer by decreasing USP21-mediated Nanog stability

The existence of cancer stem cells (CSCs) in pancreatic ductal adenocarcinoma (PDAC) is considered to be the key factor for metastasis and chemoresistance. Thus, novel therapeutic strategies for eradicating CSCs are urgently needed. Here we aimed to explore the role of KLF15 in stemness and the feasibility of using KLF15 to inhibit CSCs and improve chemotherapy sensitivity in PDAC. In this study, we report that KLF15 is negatively associated with poor survival and advanced pathological staging of PDAC. Moreover, tumorous KLF15 suppresses the stemness of PDAC by promoting the degradation of Nanog, and KLF15 directly interacts with Nanog, inhibiting interaction between Nanog with USP21. We also demonstrate that the KLF15/Nanog complex inhibit the stemness in vivo and in PDX cells. Tazemetostat suppresses stemness and sensitizes PDAC cells to gemcitabine by promoting KLF15 expression in PDAC. In summary, the findings of our study confirm the value of KLF15 level in diagnosis and prognosis of PDAC, it is the first time to explore the inhibition role of KLF15 in stemness of PDAC and the regulation mechanism of Nanog, contributing to provide a new therapeutic strategy that using Tazemetostat sensitizes PDAC cells to gemcitabine by promoting KLF15 expression for PDAC.Graphical

Contribution of Immunoscore to Survival Prediction in Pancreatic Ductal Adenocarcinoma.

The presence of tumor-infiltrating lymphocytes (TILs) has been demonstrated as a prognostic factor in colorectal cancer after surgical resection of malignancy, but knowledge on their role in pancreatic cancer is lacking. The Immunoscore (IS) assesses TILs at the core of the tumor (CT) and invasive margin (IM) and is being evaluated as a new concept in tumor immunity. The aim of this study was to evaluate the relationship between IS and prognosis in PDAC. The IS was analyzed by immunohistochemistry using surgical tissue blocks from 131 patients with pancreatic ductal adenocarcinoma (PDAC) who underwent surgery to investigate the relationship between immune cell infiltration into the tumor and prognosis in PDAC. A high IS in both CT and IM of the tumor was significantly associated with prolonged overall survival (OS) (p<0.01) and relapse-free survival (RFS) (p<0.01). In multivariate logistic regression models adjusted for clinical pathology data, IS predicted survival and recurrence (p<0.01 and p<0.01, respectively). Moreover, in patients who received preoperative chemotherapy, a high IS was statistically significantly associated with longer OS and RFS (p<0.01 and p<0.01, respectively). The immunohistochemically assessed IS might be a useful prognostic marker for patients with PDAC who underwent primary tumor resection.

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.

Abstract B018: UroPanc: A large prospective observational study for validation of urinary biomarker test for the earlier detection of pancreatic adenocarcinoma

Abstract Introduction. A bleak prognosis of Pancreatic ductal adenocarcinoma (PDAC) is largely due to late diagnosis and highly accurate, non-invasive diagnostic tests are urgently needed to improve patients’ survival. Our group has previously reported on urinary biomarker panel, comprising of LYVE1, REG1B and TFF1 (1-2), which distinguished controls from PDAC stages I-II with AUC 0.936 and sensitivity/specificity (SN/SP)&amp;gt;85%. We constructed the logistic regression based algorithm for interpretation of the data, PancRISK (3), which enables the stratification of high- risk patients into those with ‘average’ or ‘elevated’ risk of developing PDAC. Recently, we showed that our panel combined with CA19-9 can detect PDAC up to 2 years before clinical diagnosis (4). Aim. Here, we report on the design and interim analysis of the UroPanc Trial (ClinicalTrials.gov NCT04449406), which aims to evaluate the accuracy of the urinary biomarker panel and the affiliated PancRISK. We plan to recruit two cohorts: the first one comprises asymptomatic individuals that have high risk of developing PDAC due to familial history and genetic syndroms - these will be recruited through the EUropean Registry Of familial PAncreatic Cancer and hereditary pancreatitis (EUROPAC) (PI: Prof W Greenhalf and team, Liverpool, UK). The second cohort comprises patients with symptoms suggestive of PDAC – such patients are recruited at gastrointestinal clinics at University College London Hospitals (UCLH, PI: Prof S Pereira and team), and The Royal London Hospital (PI: Dr P Wilson).Methods. The urinary proteins are assayed by commercially available ELISAs. Plasma CA19-9 is measured at The Doctors Laboratory in London, UK using Cobas601E, Roche platform. The statistical analysis and accuracy of the test is measured by c-statistics, SN/SP and positive/negative predictive value. The results are compared to imaging and histopathology records (where available). Human factor and budget impact analysis of the future inclusion of the urine test into the diagnostic and surveillance pathways for PDAC patients is ongoing (PI: Dr M Z Ni and the team, Imperial College London, UK). Results and conclusions. At present, 1067 patients have been recruited, and urine samples from 825 patients have been assayed. An interim analysis has shown promising results with SP of 91% and SN of 86%, and accuracy of cancer detection of 90%. Combined with the health economic analysis, we currently work towards the future implementation of this test into clinical practice, with the potential to significantly improve the current diagnostic pathway for individuals at risk of pancreatic cancer. 1. Radon, TP, et al. Clin Cancer Res, 2015; 21:3512-21. 2. Debernardi, S, et al. PLOSMed, 17(12); e1003489. 3. Blyuss, O, et al. Br J Cancer, 2020; 122:692-6. 4. Debernardi, S, et al. Int J Cancer, 2023; 152:769–80. Citation Format: Silvana Debernardi, Evelyn Kurotova, Nurshad Ali, Mariam Mahamood, Ismita Chhetri, Steve Pereira, Patrick Wilson, Bill Greenhalf, Melody Z Ni, Oleg Blyuss, Tatjana Crnogorac-Jurcevic. UroPanc: A large prospective observational study for validation of urinary biomarker test for the earlier detection of pancreatic adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B018.

Intratumoral microbiota of pancreatic ductal adenocarcinoma impact patient prognosis by influencing tumor microenvironment

BackgroundPancreatic ductal adenocarcinoma (PDAC) is characterized by a highly metastatic potential and a heterogeneous tumor microenvironment. It exhibits limited sensitivity to conventional therapies, necessitating a deeper understanding of its pathogenesis. The role of the intratumoral microbiome in regulating cancer development in PDAC has been the subject of debate. Previous investigations into intra-tumor microbiomes have yielded uncertain results due to sample size limitations and insufficient decontamination procedures. Further research is imperative to elucidate the intricate relationship between intra-tumor microbiomes, the immune landscape of PDAC, and overall prognosis.ResultsOur findings revealed that the intratumor microbiota in PDAC tissue exhibited lower diversity and distinct communities compared to non-tumor tissues. The top microorganisms distinguishing between patients with long or short survival were used to construct the risk signature. We found that Stenotrophomonas is implicated in short survival of PDAC patients, while Neorickettia and Mediterraneibacter are correlated with long survival. This microbiome-based PDAC subtyping, grounded in prognosis-related signatures, exhibited significant correlations with distinct clinical prognoses and immune microenvironments. Microorganisms associated with negative prognoses were linked to pro-tumor immune activation, while those associated with positive prognoses were linked to anti-tumor immune response activation and a more favorable prognosis.ConclusionsOur PDAC subtyping approach, based on a microbiome-derived prognostic risk signature, unveiled compelling associations between the PDAC microbiota and disparities in both clinical prognosis and the tumor microenvironment. These findings suggest that microbiota may serve as a promising biomarker for predicting the prognosis of PDAC.

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma

Background and ObjectiveIn Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients’ follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project. Materials and MethodsAnalyzed datasets included tumor-annotated radiologic images, clinical, and mutational data. A feature selection was based on univariate (UV) and multivariate (MV) survival analyses according to Overall Survival (OS) and recurrence (REC). In this study, we considered seven multi-omic datasets and compared four SML classifiers: Cox, survival random forest, generalized boosted, and support vector machines (SVM). For each classifier, we assessed the concordance (C) index on the validation set. The best classifiers for the validation set on both OS and REC underwent explainability analyses using SurvSHAP(t), which extends SHapley Additive exPlanations (SHAP). ResultsAccording to OS, after UV and MV analyses we selected 18/37 and 10/37 multi-omic features, respectively. According to REC, based on UV and MV analyses we selected 10/35 and 5/35 determinants, respectively. Generally, SML classifiers including radiomics outperformed those modelled on clinical or mutational predictors. For OS, the Cox model encompassing radiomic, clinical, and mutational features reached 75 % of C index, outperforming other classifiers. On the other hand, for REC, the SVM model including only radiomics emerged as the best-performing, with 68 % of C index. For OS, SurvSHAP(t) identified the first order Median Gray Level (GL) intensities, the gender, the tumor grade, the Joint Energy GL Co-occurrence Matrix (GLCM), and the GLCM Informational Measures of Correlations of type 1 as the most important features. For REC, the first order Median GL intensities, the GL size zone matrix Small Area Low GL Emphasis, and first order variance of GL intensities emerged as the most discriminative. ConclusionsIn this work, radiomics showed the potential for improving patients’ risk stratification in PDA. Furthermore, a deeper understanding of how radiomics can contribute to prognosis in PDA was achieved with a time-dependent explainability of the top multi-omic predictors.