Prospective prediction of resistance to neoadjuvant therapy in patients with locoregional esophageal adenocarcinoma
Authors Rosen D, Shan W, Lassen N, Johnson C, Oelschlager K, Bierman-Harrar Y, Kesler K, Maetzold D, Badve S, Cook R, Saxena R
Received 16 October 2014
Accepted for publication 12 December 2014
Published 19 February 2015 Volume 2015:5 Pages 53—59
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Dr Eileen O'Reilly
Daniel G Rosen,1 Weiwei Shan,2 Natalie Lassen,2 Clare Johnson,2 Kristen Oelschlager,2 Yaeli Bierman-Harrar,1 Kenneth A Kesler,3 Derek Maetzold,2 Sunil Badve,3 Robert W Cook,2 Romil Saxena3
1Baylor College of Medicine, Houston TX, USA; 2Castle Biosciences, Incorporated, Friendswood, TX, USA; 3Indiana University, Indianapolis, IN, USA
Background: To clinically validate a multianalyte algorithmic immunohistochemistry (IHC) assay that has been previously shown to accurately identify patients with locoregional esophageal adenocarcinoma (EC) who will exhibit extreme resistance to neoadjuvant chemoradiotherapy.
Methods: Archived biopsy specimens of EC were subject to IHC examination of compartmentalized immunoreactivity of nuclear factor kappa B (NF-κB), Sonic Hedgehog (SHH), and GLI family zinc finger 1 (Gli-1), and a labeling index score was assigned to each biomarker. Test prediction was generated by logistic regression predictive modeling, using the labeling index scores for all three analytes from each sample, referring to a validated training set of 167 EC patients. Accuracy of the test was determined by comparing the predicted outcomes with pathologically determined College of American Pathologists tumor response grade. Analytical validity of the test was measured by comparing validation set prediction results obtained in two independent Clinical Laboratory Improvement Amendment-certified laboratories, and by measuring concordance between two trained labeling index readers.
Results: Specimens from 64 patients that met specific criteria were collected. No technical failure was encountered during the IHC labeling procedures. The logistic regression algorithm generated an area under the curve of 0.96 and 0.85 for the 64 sample cohort in two independent clinical laboratories, respectively, comparing predictive results with the established training set. Positive predictive values of 88% and 82% were also achieved in each laboratory, respectively. A negative predictive value of 83% was reported by both laboratories. Interobserver concordance was 97%.
Discussion: We report the second validation of a multianalyte algorithmic IHC-based predictive test that accurately identifies EC patient response to fluorouracil-based neoadjuvant chemoradiotherapy regimens under College of American Pathologists-accredited Clinical Laboratory Improvement Amendment-certified laboratory protocols. The validated assay provides the opportunity to identify patients with EC who have extreme resistance to neoadjuvant chemoradiotherapy who are resistant to fluorouracil-based neoadjuvant chemoradiotherapy, allowing for more effective treatment planning by clinicians and less toxicity for patients.
Keywords: neoadjuvant chemoradiation, immunohistochemistry, predictive test, validation
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