Multivalent, asymmetric IL-2-Fc fusions provide optimally enhanced regulatory T cell selectivity

Abstract

The common γ-chain receptor cytokines are promising immune therapies due to their central role in coordinating the proliferation and activity of various immune cell populations. One of these cytokines, interleukin (IL)-2, has potential as a therapy in autoimmunity but is limited in effectiveness by its modest specificity toward regulatory T cells (Tregs). Therapeutic ligands are often made dimeric as antibody Fc fusions to confer desirable pharmacokinetic benefits, with under-explored consequences on signaling. Here, we systematically profiled the signaling responses to a panel of wild type and mutein IL-2 molecules in various Fc fusion configurations. We used a tensor-structured dimensionality reduction scheme to decompose the responses of each cell population to each ligand over a range of time points and cytokine concentrations. We found that dimeric muteins are uniquely specific for Tregs at intermediate ligand concentrations. We then compared signaling response across all treatments to a simple, two-step multivalent binding model. Our model was able to predict cellular responses with high accuracy. Bivalent Fc fusions display enhanced specificity and potency for Tregs through avidity effects toward IL-2Rα. We then utilize our model to identify the potential benefits conferred by valency engineering as an additional mechanism for cytokines with optimized therapeutic benefits. In total, these findings represent a comprehensive analysis of how ligand properties, and their consequent effects on surface receptor-ligand interactions, translate to selective activation of immune cell populations. It also identifies a new route toward engineering even more selective therapeutic cytokines.

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2

“…For instance, CPD has been useful in bulk RNA-seq analysis measured across different tissues and patients; the three resulting component association vectors explain variance from each gene, tissue, and patient, respectively 20 . Where these methods are appropriate, we and others have observed several benefits of a tensor decomposition-based analytical approach [20][21][22] . Tensor decompositions can be more effective at removing noise, isolating distinct variation patterns, and imputing missing values compared to matrixbased techniques 20,[23][24][25] .…”

“…Tensor decompositions can be more effective at removing noise, isolating distinct variation patterns, and imputing missing values compared to matrixbased techniques 20,[23][24][25] . Most importantly, associating trends in the dataset to specific dimensions can help interpret the results and therefore derive insights from the underlying experiments 20,26,27 . Dimensionspecific associations also make these methods especially effective in data integration for combining datasets with shared experimental parameters 20,[28][29][30][31][32] .…”