A Tissue Systems Pathology Assay for High-Risk Barrett's Esophagus

Abstract

Background Better methods are needed to predict risk of progression for Barrett's esophagus (BE). We aimed to determine whether a tissue systems pathology approach could predict progression in patients with non-dysplastic BE, indefinite for dysplasia, or low-grade dysplasia. Methods We performed a nested case–control study to develop and validate a test that predicts progression of BE to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC), based upon quantification of epithelial and stromal variables in baseline biopsies. Data were collected from BE patients at 4 institutions. Patients who progressed to HGD or EAC in ≥1 year (n=79) were matched with patients who did not progress (n=287). Biopsies were assigned randomly to training or validation sets. Immunofluorescence analyses were performed for 14 biomarkers and quantitative biomarker and morphometric features were analyzed. Prognostic features were selected in the training set and combined into classifiers. The top-performing classifier was assessed in the validation set. Results A 3-tier, 15-feature classifier was selected in the training set and tested in the validation set. The classifier stratified patients into low-, intermediate- and high-risk classes (hazard ratio, 9.42; 95% confidence interval, 4.6–19.24 (high-risk vs low-risk); P<0.0001). It also provided independent prognostic information that outperformed predictions based on pathology analysis, segment length, age, sex, or p53 overexpression. Conclusion We developed a tissue systems pathology test that better predicts risk of progression in BE than clinicopathologic variables. Impact The test has the potential to improve upon histologic analysis as an objective method to risk stratify BE patients.

A Tissue Systems Pathology Test Detects Abnormalities Associated with Prevalent High-Grade Dysplasia and Esophageal Cancer in Barrett's Esophagus

“…The image analysis algorithms have been described in detail previously (19) and are summarized in Supplementary Figure S1. The 15 features employed by the risk classifier (Supplementary Table 1 (20)) were extracted from the fluorescence whole slide tissue images.…”

“…A tissue systems pathology approach based upon an imaging platform that quantifies both epithelial and stromal abnormalities has been shown to aid in distinguishing HGD from non-dysplastic BE with reactive atypia (18, 19). This imaging approach has also been demonstrated to predict incident progression in BE, by objectively quantifying molecular and cellular features that precede definitive morphologic changes (20) (Figure 1). The assay employs multiplexed immunofluorescence labeling of 9 epithelial and stromal biomarkers in sections from formalin-fixed paraffin-embedded (FFPE) biopsies.…”

“…The fluorescently-labeled slides are imaged by whole slide fluorescence scanning, and automated image analysis software extracts quantitative expression and localization data on the biomarkers and morphology. The final step utilizes a multivariable classifier to integrate the quantitative image analysis data into individualized scores that are correlated with risk of HGD/EAC (Figure 1 (20)). This may have applications in detecting molecular and cellular changes in the expanded preneoplastic field associated with HGD/EAC.…”

Opportunities and Challenges in Implementation of Multiparameter Single Cell Analysis Platforms for Clinical Translation

“…The platform is valuable for identifying the optimal/minimal combination of biomarkers that could be incorporated into a smaller number of biomarkers for multiplexed tissue‐based diagnostic tests and biomarkers for drug development 30. Hyperplexed fluorescence with the MxIF using panels of biomarkers tailored to the cell types will be an important platform to define the functional interplay between the cells in the tumor microenvironment and to define the response to therapeutics when combined with automated machine learning software tools to identify and to quantify spatial patterns of biomarkers that reflect the heterogeneity of disease.…”

“…Multiplex protein expression (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37) …”