Dataset Information

The Proteomic Landscape of Pancreatic Ductal Adenocarcinoma Liver Metastases Identifies Molecular Subtypes and Associations with Clinical Response.

ABSTRACT:

Purpose

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease that can be separated into distinct subtypes based on molecular signatures. Identifying PDAC subtype-specific therapeutic vulnerabilities is necessary to develop precision medicine approaches to treat PDAC.

Experimental design

A total of 56 PDAC liver metastases were obtained from the UNMC Rapid Autopsy Program and analyzed with quantitative proteomics. PDAC subtypes were identified by principal component analysis based on protein expression profiling. Proteomic subtypes were further characterized by the associated clinical information, including but not limited to survival analysis, drug treatment response, and smoking and drinking status.

Results

Over 3,960 proteins were identified and used to delineate four distinct PDAC microenvironment subtypes: (i) metabolic; (ii) progenitor-like; (iii) proliferative; and (iv) inflammatory. PDAC risk factors of alcohol and tobacco consumption correlate with subtype classifications. Enhanced survival is observed in FOLFIRINOX treated metabolic and progenitor-like subtypes compared with the proliferative and inflammatory subtypes. In addition, TYMP, PDCD6IP, ERAP1, and STMN showed significant association with patient survival in a subtype-specific manner. Gemcitabine-induced alterations in the proteome identify proteins, such as serine hydroxymethyltransferase 1, associated with drug resistance.

Conclusions

These data demonstrate that proteomic analysis of clinical PDAC liver metastases can identify molecular signatures unique to disease subtypes and point to opportunities for therapeutic development to improve the treatment of PDAC.

SUBMITTER: Law HC

PROVIDER: S-EPMC7056493 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

The Proteomic Landscape of Pancreatic Ductal Adenocarcinoma Liver Metastases Identifies Molecular Subtypes and Associations with Clinical Response.

Law Henry C-H HC Lagundžin Dragana D Clement Emalie J EJ Qiao Fangfang F Wagner Zachary S ZS Krieger Kimiko L KL Costanzo-Garvey Diane D Caffrey Thomas C TC Grem Jean L JL DiMaio Dominick J DJ Grandgenett Paul M PM Cook Leah M LM Fisher Kurt W KW Yu Fang F Hollingsworth Michael A MA Woods Nicholas T NT

Clinical cancer research : an official journal of the American Association for Cancer Research 20191217 5

<h4>Purpose</h4>Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease that can be separated into distinct subtypes based on molecular signatures. Identifying PDAC subtype-specific therapeutic vulnerabilities is necessary to develop precision medicine approaches to treat PDAC.<h4>Experimental design</h4>A total of 56 PDAC liver metastases were obtained from the UNMC Rapid Autopsy Program and analyzed with quantitative proteomics. PDAC subtypes were identified by principal compone ...[more]

PMID: 31848187

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Project description:PurposeHypodensity of pancreatic ductal adenocarcinoma (PDAC) during contrast-enhanced computed tomography (CECT) examination is common, but a minority of PDAC patients exhibit hyperdense images. The present study examined the clinical characteristics and protein landscape of PDAC with hyperdensity.Materials and methodsA total of 844 pathologically confirmed PDAC patients who underwent CECT before surgery were included. During the parenchymal phase of CECT, patients were assigned to the hyperdense or hypodense group based on CT values. Clinical and CT characteristics for predicting relapse-free survival (RFS) and overall survival (OS) were analyzed using the Kaplan-Meier method and Cox proportional hazards model. The expression of the tumor angiogenesis marker CD31 and stroma-related protein CTHRC1 were analyzed using immunohistochemistry (IHC) assay to evaluate differences between the two groups. Proteomics was performed to compare the possible mechanisms underlying the differential enhancement on CT scans.ResultsBased on CECT, 43 and 801 PDAC patients had hyperdense and hypodense lesions, respectively. All 43 patients presented a hyperdense lesion in the parenchymal phase. The mean CECT values of the hyperdense group were higher than the hypodense group (102.5 ± 17.4 and 53.7 ± 18.7, respectively, P < 0.001). The hyperdense group had a better prognosis than the hypodense group (median RFS, 19.97 vs. 12.34 months, P = 0.0176; median OS, 33.6 vs. 20.3 months, P = 0.047). Multivariate analysis showed that age, higher CA19-9 levels (> 300 U/ml), tumor stage, tumor differentiation, tumor CT density, and adjuvant chemotherapy were significant independent prognostic factors for OS. CD31 immunohistochemical staining showed that the hyperdense PDACs had a higher microvessel density than the hypodense group (P < 0.001). CTHRC1 expression was higher in the hypodense group (P = 0.019). Sixty-eight differentially expressed proteins were found using the tandem mass tag labeling-based quantification of the proteomes of PDAC tissue samples, and 7 proteins (POFUT1, PKP2, P0DOX4, ITPR1, HBG2, IGLC3, SAA2) were related to angiogenesis.ConclusionPatients who presented with a hyperdense mass on CECT had a higher microvessel density and better prognosis. Anti-angiogenic therapy may be suitable for these patients.

Project description:Background & aimsPancreatic ductal adenocarcinomas (PDACs) frequently metastasize to the lymph nodes; strategies are needed to identify patients at highest risk for lymph node metastases. We performed genome-wide expression profile analyses of PDAC specimens, collected during surgery or endoscopic ultrasound-guided fine-need aspiration (EUS-FNA), to identify a microRNA (miRNA) signature associated with metastasis to lymph nodes.MethodsFor biomarker discovery, we analyzed miRNA expression profiles of primary pancreatic tumors from 3 public data sets (The Cancer Genome Atlas, GSE24279, and GSE32688). We then analyzed 157 PDAC specimens (83 from patients with lymph node metastases and 74 without) from Japan, collected from 2001 through 2017, for the training cohort and 107 PDAC specimens (63 from patients with lymph node metastases and 44 without) from a different medical center in Japan, from 2002 through 2016, for the validation cohort. We also analyzed samples collected by EUS-FNA before surgery from 47 patients (22 patients with lymph node metastases and 25 without; 17 for the training cohort and 30 from the validation cohort) and 62 specimens before any treatment from patients who received neoadjuvant chemotherapy (9 patients with lymph node metastasis and 53 without) for additional validation. Multivariate logistic regression analyses were used to evaluate the statistical differences in miRNA expression between patients with vs without metastases.ResultsWe identified an miRNA expression pattern associated with diagnosis of PDAC metastasis to lymph nodes. Using logistic regression analysis, we optimized and trained a 6-miRNA risk prediction model for the training cohort; this model discriminated patients with vs without lymph node metastases with an area under the curve (AUC) of 0.84 (95% confidence interval [CI], 0.77-0.89). In the validation cohort, the model identified patients with vs without lymph node metastases with an AUC of 0.73 (95% CI, 0.64-0.81). In EUS-FNA biopsy samples, the model identified patients with vs without lymph node metastases with an AUC of 0.78 (95% CI, 0.63-0.89). The miRNA expression pattern was an independent predictor of PDAC metastasis to lymph nodes in the validation cohort (odds ratio, 17.05; 95% CI, 2.43-119.57) and in the EUS-FNA cohort (95% CI, 0.65-0.87).ConclusionsUsing data and tumor samples from 3 independent cohorts, we identified an miRNA signature that identifies patients at risk for PDAC metastasis to lymph nodes. The signature has similar levels of accuracy in the analysis of resected tumor specimens and EUS-FNA biopsy specimens. This model might be used to select treatment and management strategies for patients with PDAC.

Dataset Information

The Proteomic Landscape of Pancreatic Ductal Adenocarcinoma Liver Metastases Identifies Molecular Subtypes and Associations with Clinical Response.

Purpose

Experimental design

Results

Conclusions

Publications

The Proteomic Landscape of Pancreatic Ductal Adenocarcinoma Liver Metastases Identifies Molecular Subtypes and Associations with Clinical Response.

Similar Datasets

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