Dataset Information

A Visually Apparent and Quantifiable CT Imaging Feature Identifies Biophysical Subtypes of Pancreatic Ductal Adenocarcinoma.

ABSTRACT: PURPOSE:Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with variable presentations and natural histories of disease. We hypothesized that different morphologic characteristics of PDAC tumors on diagnostic computed tomography (CT) scans would reflect their underlying biology. EXPERIMENTAL DESIGN:We developed a quantitative method to categorize the PDAC morphology on pretherapy CT scans from multiple datasets of patients with resectable and metastatic disease and correlated these patterns with clinical/pathologic measurements. We modeled macroscopic lesion growth computationally to test the effects of stroma on morphologic patterns, hypothesizing that the balance of proliferation and local migration rates of the cancer cells would determine tumor morphology. RESULTS:In localized and metastatic PDAC, quantifying the change in enhancement on CT scans at the interface between tumor and parenchyma (delta) demonstrated that patients with conspicuous (high-delta) tumors had significantly less stroma, higher likelihood of multiple common pathway mutations, more mesenchymal features, higher likelihood of early distant metastasis, and shorter survival times compared with those with inconspicuous (low-delta) tumors. Pathologic measurements of stromal and mesenchymal features of the tumors supported the mathematical model's underlying theory for PDAC growth. CONCLUSIONS:At baseline diagnosis, a visually striking and quantifiable CT imaging feature reflects the molecular and pathological heterogeneity of PDAC, and may be used to stratify patients into distinct subtypes. Moreover, growth patterns of PDAC may be described using physical principles, enabling new insights into diagnosis and treatment of this deadly disease.

SUBMITTER: Koay EJ

PROVIDER: S-EPMC6279613 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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A Visually Apparent and Quantifiable CT Imaging Feature Identifies Biophysical Subtypes of Pancreatic Ductal Adenocarcinoma.

Koay Eugene J EJ Lee Yeonju Y Cristini Vittorio V Lowengrub John S JS Kang Ya'an Y Lucas F Anthony San FAS Hobbs Brian P BP Ye Rong R Elganainy Dalia D Almahariq Muayad M Amer Ahmed M AM Chatterjee Deyali D Yan Huaming H Park Peter C PC Rios Perez Mayrim V MV Li Dali D Garg Naveen N Reiss Kim A KA Yu Shun S Chauhan Anil A Zaid Mohamed M Nikzad Newsha N Wolff Robert A RA Javle Milind M Varadhachary Gauri R GR Shroff Rachna T RT Das Prajnan P Lee Jeffrey E JE Ferrari Mauro M Maitra Anirban A Taniguchi Cullen M CM Kim Michael P MP Crane Christopher H CH Katz Matthew H MH Wang Huamin H Bhosale Priya P Tamm Eric P EP Fleming Jason B JB

Clinical cancer research : an official journal of the American Association for Cancer Research 20180806 23

<h4>Purpose</h4>Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with variable presentations and natural histories of disease. We hypothesized that different morphologic characteristics of PDAC tumors on diagnostic computed tomography (CT) scans would reflect their underlying biology.<h4>Experimental design</h4>We developed a quantitative method to categorize the PDAC morphology on pretherapy CT scans from multiple datasets of patients with resectable and metastatic disease and ...[more]

PMID: 30082477

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Project description:Comprehensive characterization of signaling in pancreatic ductal adenocarcinoma (PDAC) promises to enhance our understanding of the molecular aberrations driving this devastating disease, and may identify novel therapeutic targets as well as biomarkers that enable stratification of patients for optimal therapy. Here, we use immunoaffinity-coupled high-resolution mass spectrometry to characterize global tyrosine phosphorylation patterns across two large panels of human PDAC cell lines: the ATCC series (19 cell lines) and TKCC series (17 cell lines). This resulted in the identification and quantification of over 1800 class 1 tyrosine phosphorylation sites and the consistent segregation of both PDAC cell line series into three subtypes with distinct tyrosine phosphorylation profiles. Subtype-selective signaling networks were characterized by identification of subtype-enriched phosphosites together with pathway and network analyses. This revealed that the three subtypes characteristic of the ATCC series were associated with perturbations in signaling networks associated with cell-cell adhesion and epithelial-mesenchyme transition, mRNA metabolism, and receptor tyrosine kinase (RTK) signaling, respectively. Specifically, the third subtype exhibited enhanced tyrosine phosphorylation of multiple RTKs including the EGFR, ERBB3 and MET. Interestingly, a similar RTK-enriched subtype was identified in the TKCC series, and 'classifier' sites for each series identified using Random Forest models were able to predict the subtypes of the alternate series with high accuracy, highlighting the conservation of the three subtypes across the two series. Finally, RTK-enriched cell lines from both series exhibited enhanced sensitivity to the small molecule EGFR inhibitor erlotinib, indicating that their phosphosignature may provide a predictive biomarker for response to this targeted therapy. These studies highlight how resolution of subtype-selective signaling networks can provide a novel taxonomy for particular cancers, and provide insights into PDAC biology that can be exploited for improved patient management.

Dataset Information

A Visually Apparent and Quantifiable CT Imaging Feature Identifies Biophysical Subtypes of Pancreatic Ductal Adenocarcinoma.

Publications

A Visually Apparent and Quantifiable CT Imaging Feature Identifies Biophysical Subtypes of Pancreatic Ductal Adenocarcinoma.

Similar Datasets

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