Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice.
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Amador Catalina, Bouska Alyssa, Wright George, Weisenburger Dennis D, Feldman Andrew L, Greiner Timothy C, Lone Waseem, Heavican Tayla, Smith Lynette, Pileri Stefano, Tabanelli Valentina, Ott German, Rosenwald Andreas, Savage Kerry J, Slack Graham, Kim Won Seog, Hyeh Young, Li Yuping, Dong Gehong, Song Joo, Ondrejka Sarah, Cook James R, Barrionuevo Carlos, Lim Soon Thye, Ong Choon Kiat, Chapman Jennifer, Inghirami Giorgio, Raess Philipp W, Bhagavathi Sharathkumar, Gould Clare, Blombery Piers, Jaffe Elaine, Morris Stephan W, Rimsza Lisa M, Vose Julie M, Staudt Louis, Chan Wing C, Iqbal Javeed
Аннотация
MATERIALS AND METHODS: We assembled a well-characterized PTCL training cohort (n = 105) with gene expression profiling data to derive a diagnostic signature using fresh-frozen tissue on the HG-U133plus2.0 platform (Affymetrix, Inc, Santa Clara, CA) subsequently validated using matched FFPE tissues in a digital gene expression profiling platform (nCounter, NanoString Technologies, Inc, Seattle, WA). Statistical filtering approaches were applied to refine the transcriptomic signatures and then validated in another PTCL cohort (n = 140) with rigorous pathology review and ancillary assays.
RESULTS: In the training cohort, the refined transcriptomic classifier in FFPE tissues showed high sensitivity (> 80%), specificity (> 95%), and accuracy (> 94%) for PTCL subclassification compared with the fresh-frozen-derived diagnostic model and showed high reproducibility between three independent laboratories. In the validation cohort, the transcriptional classifier matched the pathology diagnosis rendered by three expert hematopathologists in 85% (n = 119) of the cases, showed borderline association with the molecular signatures in 6% (n = 8), and disagreed in 8% (n = 11). The classifier improved the pathology diagnosis in two cases, validated by clinical findings. Of the 11 cases with disagreements, four had a molecular classification that may provide an improvement over pathology diagnosis on the basis of overall transcriptomic and morphological features. The molecular subclassification provided a comprehensive molecular characterization of PTCL subtypes, including viral etiologic factors and translocation partners.
CONCLUSION: We developed a novel transcriptomic approach for PTCL subclassification that facilitates translation into clinical practice with higher precision and uniformity than conventional pathology diagnosis.