Multisite Analytical Validation of a 92-Gene Molecular Classifier for Cancers of Uncertain Primary
Sarah E Kerr, Catherine A Schnabel, Peggy S Sullivan, Yi Zhang, Veena Singh, Brittany Carey, Mark G Erlander, W Edward Highsmith, Sarah M Dry, Elena F Brachtel. Mayo Clinic, Rochester, MN; bioTheranostics, Inc., San Diego, CA; University of California Los Angeles, Los Angeles, CA; Massachusetts General Hospital, Boston, MA
Background: With increasing use of site- and subtype-specific cancer therapies, accurate tumor classification is of paramount importance to optimize treatment and improve patient outcomes. Diagnosis remains uncertain in 2-5% of solid malignancies after standard histopathologic evaluation. Gene expression-based molecular classifiers have been proposed as a diagnostic aid. This study evaluates the performance of a 92-gene molecular classifier (CancerTYPE ID, bioTheranostics Inc.) for identification of tumor type and subtype in a large multi-institution cohort.
Design: Formalin-fixed paraffin-embedded tumors representing 28 tumor types and 50 subtypes predicted by the molecular classifier were selected and passed pathologist-adjudicated review at 3 institutions. Blinded tumor sections were submitted and tested using a prespecified classification model that reports computational algorithm results as rank probabilities. The top-ranking tumor type predicted by the classifier was compared to the adjudicated reference diagnosis.
Results: The study cohort was comprised of 790 tumors with at least 25 and 10 cases for each classifier type and subtype, respectively. Forty-seven (5.9%) cases were considered unclassified by the assay. The overall concordance rate of the assay prediction with the reference diagnosis was 87% at the main type level and 82% at the subtype level. The reference diagnosis was within the top two rank predicted types in 91% of cases. No statistically significant differences in performance were observed in subset analyses of primary versus metastatic tumors, by grade, or by biopsy type.
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