Publications by authors named "M O'Connor-McCourt"

The architectural complexity and heterogeneity of the tumor microenvironment (TME) remains a substantial obstacle in the successful treatment of cancer. Hypoxia, caused by insufficient oxygen supply, and acidosis, resulting from the expulsion of acidic metabolites, are prominent features of the TME. To mitigate the consequences of the hostile TME, cancer cells metabolically rewire themselves and express a series of specific transporters and enzymes instrumental to this adaptation.

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Myelofibrosis (MF) is a progressive chronic myeloproliferative neoplasm characterized by hyperactivation of JAK/STAT signaling and dysregulation of the transcription factor GATA1 in megakaryocytes (MKs). TGF-β plays a pivotal role in the pathobiology of MF by promoting BM fibrosis and collagen deposition and by enhancing the dormancy of normal hematopoietic stem cells (HSCs). In this study, we show that MF-MKs elaborated significantly greater levels of TGF-β1 than TGF-β2 and TGF-β3 to a varying degree, and we evaluated the ability of AVID200, a potent TGF-β1/TGF-β3 protein trap, to block the excessive TGF-β signaling.

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An increasingly appreciated conundrum in the discovery of antibody drug conjugates (ADCs) is that an antibody that was selected primarily for strong binding to its cancer target may not serve as an optimal ADC. In this study, we performed mechanistic cell-based experiments to determine the correlation between antibody affinity, avidity, internalization and ADC efficacy. We used structure-guided design to assemble a panel of antibody mutants with predicted Her2 affinities ranging from higher to lower relative to the parent antibody, Herceptin.

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Effective biologic therapeutics require binding affinities that are fine-tuned to their disease-related molecular target. The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform aids in the selection of mutants that improve/modulate the affinity of antibodies and other biologics. It uses a consensus z-score from three scoring functions and interleaves computational predictions with experimental validation, significantly enhancing the robustness of the design and selection of mutants.

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