A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma

Abstract

Human papillomavirus-related (p16-positive) oropharyngeal squamous cell carcinoma patients develop recurrent disease, mostly distant metastasis, in approximately 10% of cases, and the remaining patients, despite cure, can have major morbidity from treatment. Identifying patients with aggressive versus indolent tumors is critical. Hematoxylin and eosin-stained slides of a microarray cohort of p16-positive oropharyngeal squamous cell carcinoma cases were digitally scanned. A novel cluster cell graph was constructed using the nuclei as vertices to characterize and measure spatial distribution and cell clustering. A series of topological features defined on each node of the subgraph were analyzed, and a random forest decision tree classifier was developed. The classifier (QuHbIC) was validated over 25 runs of 3-fold cross-validation using case subsets for independent training and testing. Nineteen (11.9%) of the 160 patients on the array developed recurrence. QuHbIC correctly predicted outcomes in 140 patients (87.5% accuracy). There were 23 positive patients, of whom 11 developed recurrence (47.8% positive predictive value), and 137 negative patients, of whom only 8 developed recurrence (94.2% negative predictive value). The best other predictive features were stage T4 (18 patients; 83.1% accuracy) and N3 nodal disease (10 patients; 88.6% accuracy). QuHbIC-positive patients had poorer overall, disease-free, and disease-specific survival (P<0.001 for each). In multivariate analysis, QuHbIC-positive patients still showed significantly poorer disease-free and disease-specific survival, independent of all other variables. In summary, using just tiny hematoxylin and eosin punches, a computer-aided histomorphometric classifier (QuHbIC) can strongly predict recurrence risk. With prospective validation, this testing may be useful to stratify patients into different treatment groups.

FIGURE 1

A, Tissue microarray slide showing an array of 2 mm punches with H&E staining. B, A case on the array with tumor showing typical nonkeratinizing SCC morphology. C, p16 immunohistochemistry showing a tumor with strong and diffuse nuclear and cytoplasmic expression.

FIGURE 2

Image analysis of the tissue microarrays. H&E images of tumors from representative patients who either did not develop recurrent disease (A) or who went on to develop recurrent disease (D). Nuclei were identified by computerized image analysis (B and E) and CCGs (C and F), shown by blue nodes for the nuclei with interconnecting red lines, generated. Insets of each panel show magnified areas of analysis.

FIGURE 3

Unsupervised clustering of histomorphometric data in reduced dimensionality space showing patients with recurrent disease (+) versus those with no recurrence (−). There is a clear separation between the two groups even without any modeling of the data.

FIGURE 4

K survival curves for QuHbIC results for the entire patient cohort.