Polymeric immunoglobulin receptor (pIgR) in cancer.

BACKGROUND: The polymeric immunoglobulin receptor (pIgR) is a transmembrane transporter of polymeric IgA through the intestinal epithelium. Its overexpression has been reported in several cancers, but its role as a diagnostic and prognostic biomarker of oncogenesis is currently unclear. METHOD: A literature search was conducted to summarize the functions of pIgR, its expression levels, and its clinical implications. RESULTS: pIgR expression has previously been investigated by proteomic analysis, RNA sequencing, and tissue microarray at the level of both RNA and protein in various cancers including pancreatic, esophageal, gastric, lung, and liver. However, studies have reported inconsistent results on how pIgR levels affect clinical outcomes such as survival rate and chemotherapy resistance. Possible explanations include pIgR mRNA levels being minimally correlated with the rate of downstream pIgR protein synthesis, and the diversity of antibodies used in immunohistochemistry studies further magnifying this ambiguity. In ovarian cancer cells, the transcytosis of IgA accompanied a series of transcriptional changes in intracellular inflammatory pathways that inhibit the progression of cancer, including the upregulation of IFN-gamma and downregulation of tumor-promoting ephrins. These findings suggest that both the levels of pIgR and secreted IgA from tumor-infiltrating B cells affect clinical outcomes. CONCLUSION: Overall, no direct correlation was observed between the levels of pIgR inside tumor tissue and the clinical features in cancer patients. Measuring pIgR protein levels with a more specific and possibly chemically defined antibody, along with tumoral IgA, is a potential solution to better understand the pathways and consequences of pIgR overexpression in cancer cells.

Specific ablation of PDGFRß-overexpressing pericytes with antibody-drug conjugate potently inhibits pathologic ocular neovascularization in mouse models.

Background: Crosstalk between pericytes and endothelial cells is critical for ocular neovascularization. Endothelial cells secrete platelet-derived growth factor (PDGF)-BB and recruit PDGF receptor ß (PDGFRß)-overexpressing pericytes, which in turn cover and stabilize neovessels, independent of vascular endothelial growth factor (VEGF). Therapeutic agents inhibiting PDGF-BB/PDGFRß signaling were tested in clinical trials but failed to provide additional benefits over anti-VEGF agents. We tested whether an antibody-drug conjugate (ADC) - an engineered monoclonal antibody linked to a cytotoxic agent - could selectively ablate pericytes and suppress retinal and choroidal neovascularization. Methods: Immunoblotting, flow cytometry, cell viability test, and confocal microscopy were conducted to assess the internalization and cytotoxic effect of ADC targeting mPDGFRß in an in vitro setting. Immunofluorescence staining of whole-mount retinas and retinal pigment epithelium-choroid-scleral complexes, electroretinography, and OptoMotry test were used to evaluate the effect and safety of ADC targeting mPDGFRß in the mouse models of pathologic ocular neovascularization. Results: ADC targeting mPDGFRß is effectively internalized into mouse brain vascular pericytes and showed significant cytotoxicity compared with the control ADC. We also show that specific ablation of PDGFRß-overexpressing pericytes using an ADC potently inhibits pathologic ocular neovascularization in mouse models of oxygen-induced retinopathy and laser-induced choroidal neovascularization, while not provoking generalized retinal toxicity. Conclusion: Our results suggest that removing PDGFRß-expressing pericytes by an ADC targeting PDGFRß could be a potential therapeutic strategy for pathologic ocular neovascularization.

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning.

c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors available in the clinic. Antibodies blocking the interaction with its only known ligand, hepatocyte growth factor, and/or inducing receptor internalization have been clinically tested. To explore other therapeutic antibody mechanisms like Fc-mediated effector function, bispecific T cell engagement, and chimeric antigen T cell receptors, a diverse panel of antibodies is essential. We prepared a chicken immune scFv library, performed four rounds of bio-panning, obtained 641 clones using a high-throughput clonal retrieval system (TrueRepertoireTM, TR), and found 149 antigen-reactive scFv clones. We also prepared phagemid DNA before the start of bio-panning (round 0) and, after each round of bio-panning (round 1-4), performed next-generation sequencing of these five sets of phagemid DNA, and identified 860,207 HCDR3 clonotypes and 443,292 LCDR3 clonotypes along with their clonal abundance data. We then established a TR data set consisting of antigen reactivity for scFv clones found in TR analysis and the clonal abundance of their HCDR3 and LCDR3 clonotypes in five sets of phagemid DNA. Using the TR data set, a random forest machine learning algorithm was trained to predict the binding properties of in silico HCDR3 and LCDR3 clonotypes. Subsequently, we synthesized 40 HCDR3 and 40 LCDR3 clonotypes predicted to be antigen reactive (AR) and constructed a phage-displayed scFv library called the AR library. In parallel, we also prepared an antigen non-reactive (NR) library using 10 HCDR3 and 10 LCDR3 clonotypes predicted to be NR. After a single round of bio-panning, we screened 96 randomly-selected phage clones from the AR library and found out 14 AR scFv clones consisting of 5 HCDR3 and 11 LCDR3 AR clonotypes. We also screened 96 randomly-selected phage clones from the NR library, but did not identify any AR clones. In summary, machine learning algorithms can provide a method for identifying AR antibodies, which allows for the characterization of diverse antibody libraries inaccessible by traditional methods.