Hybrid Model Integrating Immunohistochemistry and Expression Profiling for the Classification of Carcinomas of Unknown Primary Site

Abstract

Identification of the site of origin for 'malignancy with unknown primary' remains a challenge for modern pathology. Correct diagnosis is critical to defining the most beneficial treatment for the patient. Standard pathological approaches combine morphology and immunohistochemical (IHC) studies to first subclassify cytokeratin-positive carcinomas into adenocarcinoma, squamous cell carcinoma, neuroendocrine carcinoma, and urothelial carcinoma. Subsequently, organ-specific IHC-markers, if available, are used to assign the tumor's primary site of origin. Previous gene expression classifiers have shown promise in tumor classification but cannot readily be integrated into standard practice because they ignore the algorithmic hierarchy used by pathologists. Here we present a novel hybrid approach integrating a hierarchy of gene expression classifiers into the algorithmic method used with IHC. In this method, a tumor is initially assigned to one of the carcinoma subclasses by the top tier classifier. Dependent on initial classification, one of three second-tier classifiers assign primary site resulting in both carcinoma subtype and primary site classification. First tier classifier accuracies were 89%, 88%, and 75% for cross-validation, independent, and institutional independent test sets, respectively. Second tier accuracies were 87%, 90%, and 87% for adenocarcinoma, squamous, and neuroendocrine carcinoma respectively. Therefore, we can successfully separate the four main subtypes of carcinoma and subsequently assign primary site by incorporation of gene expression-based classifiers into the standard algorithmic pathology approach.