NF-κB fingerprinting reveals heterogeneous NF-κB composition in diffuse large B-cell lymphoma

IntroductionImproving treatments for Diffuse Large B-Cell Lymphoma (DLBCL) is challenged by the vast heterogeneity of the disease. Nuclear factor-kappa B (NF-kappa B) is frequently aberrantly activated in DLBCL. Transcriptionally active NF-kappa B is a dimer containing either RelA, RelB or cRel, but the variability in the composition of NF-kappa B between and within DLBCL cell populations is not known. ResultsHere we describe a new flow cytometry-based analysis technique termed NF-kappa B fingerprinting and demonstrate its applicability to DLBCL cell lines, DLBCL core-needle biopsy samples, and healthy donor blood samples. We find each of these cell populations has a unique NF-kappa B fingerprint and that widely used cell-of-origin classifications are inadequate to capture NF-kappa B heterogeneity in DLBCL. Computational modeling predicts that RelA is a key determinant of response to microenvironmental stimuli, and we experimentally identify substantial variability in RelA between and within ABC-DLBCL cell lines. We find that when we incorporate NF-kappa B fingerprints and mutational information into computational models we can predict how heterogeneous DLBCL cell populations respond to microenvironmental stimuli, and we validate these predictions experimentally. DiscussionOur results show that the composition of NF-kappa B is highly heterogeneous in DLBCL and predictive of how DLBCL cells will respond to microenvironmental stimuli. We find that commonly occurring mutations in the NF-kappa B signaling pathway reduce DLBCL's response to microenvironmental stimuli. NF-kappa B fingerprinting is a widely applicable analysis technique to quantify NF-kappa B heterogeneity in B cell malignancies that reveals functionally significant differences in NF-kappa B composition within and between cell populations.

Automated summary

A new flow cytometry-based analysis technique, NF-kappa B fingerprinting, reveals the heterogeneity of NF-kappa B composition in DLBCL, which is not captured by commonly used cell-of-origin classifications. Computational modeling predicts that RelA is a key determinant of DLBCL's response to microenvironmental stimuli, and substantial variability in RelA is identified within ABC-DLBCL cell lines. Incorporating NF-kappa B fingerprints and mutational information into computational models enables the prediction of heterogeneous DLBCL cell populations' responses to microenvironmental stimuli, and these predictions are experimentally validated.