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

Catalina Amador, Alyssa Bouska, George Wright, Dennis D. Weisenburger, Andrew L. Feldman, Timothy C. Greiner, Waseem Lone, Tayla Heavican, Lynette Smith, Stefano Pileri, Valentina Tabanelli, German Ott, Andreas Rosenwald, Kerry J. Savage, Graham Slack, Won Seog Kim, Young Hyeh, Yuping Li, Gehong Dong, Joo SongSarah Ondrejka, James R. Cook, Carlos Barrionuevo, Soon Thye Lim, Choon Kiat Ong, Jennifer Chapman, Giorgio Inghirami, Philipp W. Raess, Sharathkumar Bhagavathi, Clare Gould, Piers Blombery, Elaine Jaffe, Stephan W. Morris, Lisa M. Rimsza, Julie M. Vose, Louis Staudt, Wing C. Chan, Javeed Iqbal

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

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 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.

Original languageEnglish (US)
Pages (from-to)4261-4275
Number of pages15
JournalJournal of Clinical Oncology
Volume40
Issue number36
DOIs
StatePublished - Dec 20 2022

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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