Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice

作者：Catalina Amador, Alyssa Bouska, George W. Wright, Dennis D. Weisenburger, Andrew L. Feldman, Timothy C. Greiner, Waseem Lone, Tayla B. Heavican, Lynette M. Smith, Stefano Pileri, Valentina Tabanelli, German Ott, Andreas Rosenwald, Kerry J. Savage, Graham W. Slack, Won Seog Kim, Young Hyeh, Yuping Li, Gehong Dong, Joo Y. Song, Sarah L. Ondrejka, James R. Cook, Carlos Barrionuevo, Soon Thye Lim, Choon Kiat Ong, Jennifer R. Chapman, Giorgio Inghirami, Philipp W. Raess, Sharathkumar Bhagavathi, Clare Gould, Piers Blombery, Elaine S. Jaffe, Stephan W. Morris, Lisa M. Rimsza, Julie M. Vose, Louis M. Staudt, Wing C. Chan, Javeed Iqbal · 发表于：Journal of Clinical Oncology · 年份：2022 · DOI：10.1200/jco.21.02707 · 被引用次数：44 · 研究领域：Lymphoma Diagnosis and Treatment、T-cell and Retrovirus Studies、Cutaneous lymphoproliferative disorders research

PURPOSE: Peripheral T-cell lymphoma (PTCL) includes heterogeneous clinicopathologic entities with numerous diagnostic and treatment challenges. We previously defined robust transcriptomic signatures that distinguish common PTCL entities and identified two novel biologic and prognostic PTCL-not otherwise specified subtypes (PTCL-TBX21 and PTCL-GATA3). We aimed to consolidate a gene expression-based subclassification using formalin-fixed, paraffin-embedded (FFPE) tissues to improve the accuracy and precision in PTCL diagnosis. 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 ma...