Title: Rational Manual and Automated Scoring Thresholds for the Immunohistochemical Detection of TP53 Missense Mutations in Human Breast Carcinomas.

Authors: Taylor, Nicholas J; Nikolaishvili-Feinberg, Nana; Midkiff, Bentley R; Conway, Kathleen; Millikan, Robert C; Geradts, Joseph

Published In Appl Immunohistochem Mol Morphol, (2016 Jul)

Abstract: Missense mutations in TP53 are common in human breast cancer, have been associated with worse prognosis, and may predict therapy effect. TP53 missense mutations are associated with aberrant accumulation of p53 protein in tumor cell nuclei. Previous studies have used relatively arbitrary cutoffs to characterize breast tumors as positive for p53 staining by immunohistochemical assays. This study aimed to objectively determine optimal thresholds for p53 positivity by manual and automated scoring methods using whole tissue sections from the Carolina Breast Cancer Study. p53-immunostained slides were available for 564 breast tumors previously assayed for TP53 mutations. Average nuclear p53 staining intensity was manually scored as negative, borderline, weak, moderate, or strong and percentage of positive tumor cells was estimated. Automated p53 signal intensity was measured using the Aperio nuclear v9 algorithm combined with the Genie histology pattern recognition tool and tuned to achieve optimal nuclear segmentation. Receiver operating characteristic curve analysis was performed to determine optimal cutoffs for average staining intensity and percent cells positive to distinguish between tumors with and without a missense mutation. Receiver operating characteristic curve analysis demonstrated a threshold of moderate average nuclear staining intensity as a good surrogate for TP53 missense mutations in both manual (area under the curve=0.87) and automated (area under the curve=0.84) scoring systems. Both manual and automated immunohistochemical scoring methods predicted missense mutations in breast carcinomas with high accuracy. Validation of the automated intensity scoring threshold suggests a role for such algorithms in detecting TP53 missense mutations in high throughput studies.

PubMed ID: 26200835

MeSH Terms: Automation; Breast Neoplasms/genetics*; Female; Humans; Immunohistochemistry; Mutation, Missense*; Tumor Suppressor Protein p53/genetics*