Proteomic Profiling of Nodal T-Follicular Helper Cell Lymphoma Reveals Distinct Biological Signatures and Improves Prognostic Stratification Beyond the IPI

David Beauvais, Yanis Zirem, Tristan Cardon, Soulaimane Aboulouard, Romain Dubois, Isabelle Fournier, Franck Morschhauser, Michel Salzet, Ibrahim Yakoub-Agha, Marie Duhamel

American Journal of Hematology

https://doi.org/10.1002/ajh.70502

Abstract

Nodal T-follicular helper cell lymphoma (nTFHL), most commonly of angioimmunoblastic type, exhibits heterogeneous clinical outcomes that are not fully explained by current clinical and biological variables. While genetic alterations have been extensively investigated, its proteomic landscape remains largely unexplored. Here, in a discovery cohort, the classical and alternative proteome of 63 nTFHL patients at diagnosis was profiled by mass spectrometry and analyzed using unsupervised k-means clustering. Two nTFHL signatures with distinct pathway enrichment and significantly different overall survival (p=0.02) were identified: the nTFHL-stromal profile, characterized by a complex microenvironment, and the nTFHL-translational profile, enriched in protein biosynthesis and cell proliferation pathways. Profile discrimination was confirmed across multiple supervised learning algorithms, enabling selection of a 10-protein biomarker panel validated by immunofluorescence. In an independent validation cohort including 31 nTFHL patients, the 10-protein panel accurately predicted the proteomic profiles, reproducing the observed difference in overall survival. In multivariable Cox regression, the nTFHL-translational profile remained associated with poorer OS (HR=1.92, p=0.01), independently of the International Prognostic Index (IPI). Integration of the proteomic profile with the IPI generated the IPI-P score, which refined risk stratification and identified three groups with significantly different overall survival (p<0.001). Overall, these findings reveal distinct proteomic programs underlying nTFHL heterogeneity and provide a clinically applicable framework for proteome-based prognostic stratification.