The genomic sequencing classifier demonstrates high sensitivity and accuracy for identifying benign nodules. Its 36% increase in specificity compared with the gene expression classifier potentially increases the number of patients with benign nodules who can safely avoid unnecessary diagnostic surgery.

NCI-V01-1646).

MammaPrint, a prognostic 70-gene profile for early-stage breast cancer, has been available for fresh tissue. Improvements in RNA processing have enabled microarray diagnostics for formalin-fixed, paraffin-embedded (FFPE) tissue. Here, we describe method optimization, validation, and performance of MammaPrint using analyte from FFPE tissue. Laboratory procedures for enabling the assay to be run on FFPE tissue were determined using 157 samples, and the assay was established using 125 matched FFPE and fresh tissues. Validation of MammaPrint-FFPE, compared with MammaPrint-fresh, was performed on an independent series of matched tissue from five hospitals (n = 211). Reproducibility, repeatability, and precision of the FFPE assay (n = 87) was established for duplicate analysis of the same tumor, interlaboratory performance, 20-day repeat experiments, and repeated analyses over 12 months. FFPE sample processing had a success rate of 97%. The MammaPrint assay using FFPE analyte demonstrated an overall equivalence of 91.5% (95% confidence interval, 86.9% to 94.5%) between the 211 independent matched FFPE and fresh tumor samples. Precision was 97.3%, and repeatability was 97.8%, with highly reproducible results between replicate samples of the same tumor and between two laboratories (concordance, 96%). Thus, with 580 tumor samples, MammaPrint was successfully translated to FFPE tissue. The assay has high precision and reproducibility, and FFPE results are substantially equivalent to results derived from fresh tissue.

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We demonstrate proof of principle that genomic analysis and machine learning improves the utility of TBB for the diagnosis of UIP, with greater sensitivity and specificity than pathology in TBB alone. Combining multiple individual subject samples results in increased test accuracy over single sample testing. This approach requires validation in an independent cohort of subjects before application in the clinic.