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Integrated analysis of metabolome in a EUS-FNA sample with transcriptome in the TCGA cohort of pancreatic head and body/tail adenocarcinoma.

ABSTRACT: Metabolome profiles are largely unknown for pancreatic head cancers, in which the predominant anatomical feature is the exosure of bile, pancreatic juice, and duodenal juice. In this research, 30 head and 30 body/tail cytological samples acquired by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) of pancreatic adenocarcinoma were delivered for liquid chromatography coupled with mass spectrometry (LC-MS). Transcriptome analysis was performed using the sequencing data from The Cancer Genome Atlas (TCGA) cohort. LC-MS obtained 4,857 features in EUS-FNA cytological samples, and 586 metabolites were certified. Among them, 30 differential metabolites were identified. In the TCGA cohort, 247 differential metabolism genes were selected from 1,583 differential genes. The integrated analysis identified the top three enriched metabolic pathways as follows: branched chain amino acid (BCAA) biosynthesis; glycerophospholipid metabolism; and phenylalanine metabolism. In cell line, BCAA promoted pancreatic cancer proliferation and inhibited Oxaliplatin-induced apoptosis. In conclusion, metabolomic analysis with the EUS-FNA sample is feasible for pancreatic cancer. The integrated analysis can identify key metabolites and enzyme-coded genes between pancreatic head and body/tail adenocarcinoma. Anti-BCAA metabolism therapy may exert promising effect, especially for the body/tail cancer.

SUBMITTER: Ke C

PROVIDER: S-EPMC8034907 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Targeted transcriptomic analysis of pancreatic adenocarcinoma in EUS-FNA samples by NanoString technology

Project description:Background: Integration of transcriptomic testing into EUS-FNA samples is a growing need for precision oncology in pancreatic ductal adenocarcinoma (PDAC). The NanoString platform is suitable for transcriptome profiling in low yield RNA samples. Methods: Inclusion of patients that underwent EUS-FNA cytological diagnosis of pancreatic ductal adenocarcinoma using 19G and/or 22G needles and subsequent surgical resection. Formalin fixed paraffin embedded (FFPE) cytological and surgical samples underwent RNA extraction and transcriptomic analysis using a custom 52-gene NanoString panel of stromal PDAC features. Cell type abundance was quantified in FFPE specimens and correlated. Results: 18 PDAC patients were included. Mean EUS-FNA passes was 2 + 0.7. All FFPE passed the RNA quality control for genomic analysis. Hierarchical clustering on the global gene expression data showed that genes were differentially expressed between EUS and surgical samples. A more enriched cancerassociated fibroblasts and epithelial-mesenchymal transition transcriptomic profile was observed across surgical specimens whereas immunological biomarkers were more represented in EUS-FNA samples. Cytological examination confirmed a scanty representation of CAF and more immunological cell abundance in cytological samples in comparison to surgical specimens. Conclusion: Targeted transcriptomic NanoString profiling of PDAC samples obtained by EUS-FNA is a feasible approach for pre-surgical molecular analysis although stromal CAF/EMT mRNA biomarkers are underrepresented.

2023-05-04 | GSE225953 | GEO

Pancreatic hydatid cyst diagnosed on EUS-guided FNA.

Project description: Not available

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A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

Project description:Background and aimsEndoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens.MethodsHSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model.ResultsA total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei.ConclusionsAn HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

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Diagnostic performance and factors influencing the accuracy of EUS-FNA of pancreatic neuroendocrine neoplasms.

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| S-EPMC4990623 | biostudies-literature

EUS-FNA of a lesion in the pancreatic head using a forward-viewing echoendoscope in a patient with Billroth II gastrectomy (with video).

Project description: Not available

| S-EPMC9258016 | biostudies-literature

Comparative molecular profiling of pancreatic ductal adenocarcinoma of the head versus body and tail.

Project description:Pancreatic ductal adenocarcinoma (PDAC) of the head (H) and body/tail (B/T) differ in embryonic origin, cell composition, blood supply, lymphatic and venous drainage, and innervation. We aimed to compare the molecular and tumor immune microenvironment (TIME) profiles of PDAC of the H vs. B/T. A total of 3499 PDAC samples were analyzed via next-generation sequencing (NGS) of RNA (whole transcriptome, NovaSeq), DNA (NextSeq, 592 genes or NovaSeq, whole exome sequencing), and immunohistochemistry (Caris Life Sciences, Phoenix, AZ). Significance was determined as p values adjusted for multiple corrections (q) of <0.05. Anatomic subsites of PDAC tumors were grouped by primary tumor sites into H (N = 2058) or B/T (N = 1384). There were significantly more metastatic tumors profiled from B/T vs. H (57% vs. 44%, p < 0.001). KRAS mutations (93.8% vs. 90.2%), genomic loss of heterozygosity (12.7% vs. 9.1%), and several copy number alterations (FGF3, FGF4, FGF19, CCND1, ZNF703, FLT4, MUTYH, TNFRS14) trended higher in B/T when compared to H (p < 0.05 but q > 0.05). Expression analysis of immuno-oncology (IO)-related genes showed significantly higher expression of CTLA4 and PDCD1 in H (q < 0.05, fold change 1.2 and 1.3) and IDO1 and PDCD1LG2 expression trended higher in B/T (p < 0.05, fold change 0.95). To our knowledge, this is one of the largest cohorts of PDAC tumors subjected to broad molecular profiling. Differences in IO-related gene expression and TIME cell distribution suggest that response to IO therapies may differ in PDAC arising from H vs. B/T. Subtle differences in the genomic profiles of H vs. B/T tumors were observed.

| S-EPMC10998911 | biostudies-literature

Molecular Characterization and Xenotransplantation of Pancreatic Cancer Using Endoscopic Ultrasound-Guided Fine Needle Aspiration (EUS-FNA).

Project description:Pancreatic cancer has one of the worst prognoses among all malignancies and few available treatment options. Patient-derived xenografts can be used to develop personalized therapy for pancreatic cancer. Endoscopic ultrasound fine-needle aspiration (EUS-FNA) may provide a powerful alternative to surgery for obtaining sufficient tissue for the establishment of patient-derived xenografts. In this study, EUS-FNA samples were obtained for 30 patients referred to the Ottawa Hospital, Ottawa, Ontario, Canada. These samples were used for xenotransplantation in NOD-SCID mice and for genetic analyses. The gene expression of pancreatic-cancer-relevant genes in xenograft tumors was examined by immunohistochemistry. Targeted sequencing of both the patient-derived tumors and xenograft tumors was performed. The xenografts' susceptibility to oncolytic virus infection was studied by infecting xenograft-derived cells with VSV∆51-GFP. The xenograft take rate was found to be 75.9% for passage 1 and 100% for passage 2. Eighty percent of patient tumor samples were successfully sequenced to a high depth for 42 cancer genes. Xenograft histological characteristics and marker expression were maintained between passages. All tested xenograft samples were susceptible to oncoviral infection. We found that EUS-FNA is an accessible, minimally invasive technique that can be used to acquire adequate pancreatic cancer tissue for the generation of patient-derived xenografts and for genetic sequencing.

| S-EPMC11311391 | biostudies-literature

Transoral EUS-guided FNA of prevertebral cervical metastasis.

Project description: Not available

| S-EPMC6732263 | biostudies-literature

Diagnostic yield of EUS-guided through-the-needle microforceps biopsy versus EUS-FNA of pancreatic cystic lesions: a systematic review and meta-analysis.

Project description:Background and study aims Accurate diagnosis and risk stratification of pancreatic cysts (PCs) is challenging. The aim of this study was to perform a systematic review and meta-analysis to assess the feasibility, safety, and diagnostic yield of endoscopic ultrasound-guided through-the-needle biopsy (TTNB) versus fine-needle aspiration (FNA) in PCs. Methods Comprehensive search of databases (PubMed, EMBASE, Cochrane, Web of Science) for relevant studies on TTNB of PCs (from inception to June 2019). The primary outcome was to compare the pooled diagnostic yield and concordance rate with surgical pathology of TTNB histology and FNA cytology of PCs. The secondary outcome was to estimate the safety profile of TTNB. Results: Eight studies (426 patients) were included. The diagnostic yield was significantly higher with TTNB over FNA for a specific cyst type (OR: 9.4; 95 % CI: [5.7-15.4]; I 2 = 48) or a mucinous cyst (MC) (OR: 3.9; 95 % CI: [2.0-7.4], I 2 = 72 %). The concordance rate with surgical pathology was significantly higher with TTNB over FNA for a specific cyst type (OR: 13.5; 95 % CI: [3.5-52.3]; I 2 = 48), for a MC (OR: 8.9; 95 % [CI: 1.9-40.8]; I 2 = 29), and for MC histologic severity (OR: 10.4; 95 % CI: [2.9-36.9]; I 2 = 0). The pooled sensitivity and specificity of TTNB for MCs were 90.1 % (95 % CI: [78.4-97.6]; I 2 = 36.5 %) and 94 % (95 % CI: [81.5-99.7]; I 2 = 0), respectively. The pooled adverse event rate was 7.0 % (95 % CI: [2.3-14.1]; I 2 = 82.9). Conclusions TTNB is safe, has a high sensitivity and specificity for MCs and may be superior to FNA cytology in risk-stratifying MCs and providing a specific cyst diagnosis.

| S-EPMC7164999 | biostudies-literature

Diagnostic accuracy of EUS-FNA in the evaluation of pancreatic neuroendocrine neoplasms grading: Possible clinical impact of misclassification.

Project description:Background and objectivesPrognosis of pancreatic neuroendocrine neoplasms (PanNENs) mostly depend on tumor stage and grade, determined by Ki-67 labeling index. EUS-FNA is considered the gold-standard technique to obtain it. The aims of our study were to establish diagnostic accuracy of preoperative EUS-FNA Ki-67 evaluation considering final pathological assessment on surgical specimen as gold standard and to investigate the possible impact on prognosis of misclassification.MethodsThis is a retrospective study from a prospectively collected database. EUS-FNA grading (eG) and surgical one (sG) measured according to Ki-67 proliferative index values, according to 2017 WHO classification, were compared. eG-sG correlation was evaluated by Spearman index. Logistic regression investigated factors associated with misclassification. Prognostic difference in terms of progression-free survival was evaluated by Kaplan Meier method.ResultsOne hundred and twelve PanNENs patients enrolled. In 13.4% of patients (15/112) EUS-FNA "undergraded" patients (eG1 vs. sG2), while in 12.5% (n = 14) it "overgraded" PanNENs (eG2 to sG1). No misclassifications in G3 patients. In patients with tumors <20 mm (n = 44), 2 (4.5%) eG1 and 10 (22.7%) eG2 were finally classified respectively as G2 and G1 at surgical histology. No factors, as i.e. the lesions' size or their morphological aspect, were associated with misclassification. In overgraded PanNENs, no progression occurred, while in patients correctly classified/undergraded the progression rate was 14.3%.ConclusionsThis is the largest cohort of surgical PanNENs with preoperative EUS-FNA grading evaluation. Despite an acceptable eG-sG correlation, about 25% of patients are misclassified. Clinical impact of misclassification should be carefully considered especially in small tumors undergoing observation.

| S-EPMC8544016 | biostudies-literature

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