Tabanelli, Valentina

Lymphomatoid papulosis (LyP) has several histopathologic presentations. LyP featuring gamma-delta (γδ) T-cell receptor expression may masquerade as and may be misdiagnosed as aggressive cutaneous T-cell lymphoma, particularly primary cutaneous γδ T-cell lymphoma (PCGDTL) or γδ mycosis fungoides. We performed a clinicopathologic analysis of the largest series of LyP featuring γδ T-cell expression. We identified 26 patients with a diagnosis of LyP with γδ T cells from our institutions, as well as through a comprehensive review of the literature, and characterized these cases. Most cases were treated with topical steroids or not treated at all. The majority of cases showed a CD4-CD8+ phenotype and featured at least one cytotoxic marker. Histopathologic features included an intraepidermal or dermal infiltrate with large cells and frequent angiotropism. One case was initially misdiagnosed as PCGDTL, requiring further therapy. Our case series, the largest international cohort of γδ T cell predominant LyP cases, confirms marked clinicopathologic heterogeneity that may contribute to misdiagnosis, reasserting the need to identify classic clinical features, CD30+ T-cell components, and markers of cytotoxicity when dealing with this differential diagnosis. A limitation of this study includes somewhat limited follow-up, histologic, and immunophenotypic information for some cases.

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.