ABSTRACT
Proteasome inhibitors have revolutionized outcomes in multiple myeloma, but they are used empirically, and primary and secondary resistance are emerging problems. We have identified TJP1 as a determinant of plasma cell proteasome inhibitor susceptibility. TJP1 suppressed expression of the catalytically active immunoproteasome subunits LMP7 and LMP2, decreased proteasome activity, and enhanced proteasome inhibitor sensitivity in vitro and in vivo. This occurred through TJP1-mediated suppression of EGFR/JAK1/STAT3 signaling, which modulated LMP7 and LMP2 levels. In the clinic, high TJP1 expression in patient myeloma cells was associated with a significantly higher likelihood of responding to bortezomib and a longer response duration, supporting the use of TJP1 as a biomarker to identify patients most likely to benefit from proteasome inhibitors.
Subject(s)
ErbB Receptors/metabolism , Janus Kinase 1/metabolism , Multiple Myeloma/drug therapy , Proteasome Endopeptidase Complex/metabolism , Proteasome Inhibitors/therapeutic use , STAT3 Transcription Factor/metabolism , Zonula Occludens-1 Protein/metabolism , Animals , Antineoplastic Agents/pharmacology , Biomarkers, Tumor/genetics , Biomarkers, Tumor/metabolism , Blotting, Western , Bortezomib/pharmacology , Bortezomib/therapeutic use , Cell Line, Tumor , Cysteine Endopeptidases/metabolism , Disease-Free Survival , Erlotinib Hydrochloride/pharmacology , Erlotinib Hydrochloride/therapeutic use , Gene Expression Profiling/methods , Gene Expression Regulation, Neoplastic/drug effects , Humans , Mice, SCID , Multiple Myeloma/genetics , Multiple Myeloma/metabolism , Proteasome Inhibitors/pharmacology , RNA Interference , Reverse Transcriptase Polymerase Chain Reaction , Signal Transduction/drug effects , Xenograft Model Antitumor Assays/methods , Zonula Occludens-1 Protein/geneticsABSTRACT
Breast cancer is one of the most common malignant tumours. However, the existing functional knowledge about the proteins related to the breast cancer is too general to promote the following study. To find their finer functions, we suggest using the function-specific interaction sub-networks by integrating the functional knowledge of Gene Ontology and the protein-protein interaction network. The results show that the proposed approach is able to reliably find finer functions for these proteins with high precision. The predicted finer functions are highly valuable for guiding the follow-up wet-lab re-search to study the molecular mechanism of breast cancer.
