A targeted gene signature stratifying mediastinal gray zone lymphoma into classical Hodgkin lymphoma-like or primary mediastinal B-cell lymphoma-like subtypes

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A targeted gene signature stratifying mediastinal gray zone lymphoma into classical Hodgkin lymphoma-like or primary mediastinal B-cell lymphoma-like subtypesMediastinal gray zone lymphoma (MGZL), a B-cell lymphoma with overlapping features between primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (CHL), is a unique entity and a diagnostic challenge.1,2 MGZL typically exhibits discordant morphological and immunophenotypic traits and a molecular profile straddling between PMBL and CHL.3,4 However, the diagnosis of MGZL largely relies on morphological/immunophenotypic criteria 5 and unstandardized connotations such as CHL-or PMBL-like entities may affect therapeutic choices and patients' outcome.Retrospective studies revealed common reclassification of diagnosis and heterogeneous treatments associated with high relapse rates even following intensified chemotherapy, 6,7 emphasizing the need for new tools to improve the pathobiological stratification of MGZL.Here, we report the development of a signature -comprising both tumor-and tumor microenvironment (TME)-related genes -that enables MGZL categorization based on transcriptomic proximity to either CHL or PMBL.The study, conducted in line with the Declaration of Helsinki and with formal ethical approval (Comitato Etico Regionale per la Sperimentazione Clinica della Toscana with protocol number bioGZL-2020, Rif.CEAVC Em. 2022-263, Study number 18236_oss, 21/06/2022), was designed as reported in Figure 1.With the speculative idea of a molecular allocation of MG-ZL between CHL and PMBL, 3,4 we first sought to identify a unique set of transcripts capable of discerning these two entities, considering potential unbalanced contributions to gene expression offered by tumor and TME cells.A discovery cohort comprising 84 CHL and 51 PMBL, further subdivided into training (50 CHL and 31 PMBL) and testing (34 CHL and 20 PMBL) sets, was generated by pooling Affymetrix-HG-133plus2 raw data from three different gene expression profile (GEP) datasets of fresh-frozen biopsy tissues (GSE17920, GSE11318, GSE87371), 8-10 and processed as two distinct expression matrices (data not shown).A combination of CIBERSORTx deconvolution (http://cibersortx.stanford.edu)11 and a non-negative matrix factorization (NMF)-based approach 12 was used to identify gene sets that more accurately discriminate CHL from PMBL in an unsupervised fashion.CIBERSORTx was applied to create a customized signature matrix, including GEP of both tumor (N=2) and TME cytotypes (N=22) (Figure 2), used to derive two purified GEP matrices from the bulk transcriptome of the training cohort.Each purified matrix was independently decomposed by the NMF algorithm.12 The method was run 100 times varying the rank value in the interval [2,7].

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