bioRxivCell BiologyPreprint
Focal adhesion pathway inhibition is the central axis of macrophage phenotypic responses to monoclonal antibody therapy in aggressive lymphoma via high-throughput screening and high-content imaging
Stuart James BlakemoreiD, Peter ZentisiD, Linda Müller, Reinhild Brinker, Michael MichalikiD, Christian JüngstiD, Anna C. BeielsteiniD, Jing ZhangiD, René NeuhausiD, Alexandra FloriniD, Michael J. HallekiD, Jörn MeineliD, Reinhard Buettner, Astrid C SchaussiD, Christian Philipp PallaschiD
Abstract
High-grade B-cell lymphoma (HGBCL) frequently arises as a refractory or relapsed state of diffuse large B-cell lymphoma (DLBCL) and is associated with poor outcomes due to multi-drug resistance and hallmark oncogenic translocations. To identify novel therapeutic strategies, we developed a dual high-throughput screening (HTS) and high-content imaging (HCI) macrophage-tumour co-culture platform that quantifies antibody-dependent and antibody-independent cellular phagocytosis (ADCP/AICP) across a 1,241-compound library. Using GFP+ HGBCL cells and mCherry+ macrophages, we validated our methodology through time-resolved phenotypic profiling, Euclidean distance-based analysis, and hit compound prioritisation. Pathway interrogation revealed focal adhesion as a central hub of macrophage phenotypic modulation, highlighting focal adhesion kinase (FAK/PTK2) as a candidate therapeutic target. Pharmacological inhibition with PF-562271 enhanced phagocytic activity, altered macrophage morphology, and synergised with anti-CD20 monoclonal antibodies in vitro and ex vivo. In vivo, Rituximab plus PF-562271 significantly reduced lymphoma burden and prolonged survival in xenograft models. Collectively, our work demonstrates that HTS/HCI-driven phenotypic profiling of tumour-associated macrophages can uncover actionable therapeutic combinations and nominates FAK inhibition as a promising strategy to potentiate antibody immunotherapy in HGBCL.
- organism: human
- assay: flow cytometry
- assay: imaging
- research focus: cell-cell communication
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 0
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