Multiple myeloma is a hematologic malignancy characterized by the proliferation of neoplastic plasma cells in the bone marrow. Although the first-to-market proteasome inhibitor bortezomib (Velcade) has been successfully used to treat patients with myeloma, drug resistance remains an emerging problem. In this study, we identify signatures of bortezomib sensitivity and resistance by gene expression profiling (GEP) using pairs of bortezomib-sensitive (BzS) and bortezomib-resistant (BzR) cell lines created from the Bcl-XL/Myc double-transgenic mouse model of multiple myeloma. Notably, these BzR cell lines show cross-resistance to the next-generation proteasome inhibitors, MLN2238 and carfilzomib (Kyprolis) but not to other antimyeloma drugs. We further characterized the response to bortezomib using the Connectivity Map database, revealing a differential response between these cell lines to histone deacetylase (HDAC) inhibitors. Furthermore, in vivo experiments using the HDAC inhibitor panobinostat confirmed that the predicted responder showed increased sensitivity to HDAC inhibitors in the BzR line. These findings show that GEP may be used to document bortezomib resistance in myeloma cells and predict individual sensitivity to other drug classes. Finally, these data reveal complex heterogeneity within multiple myeloma and suggest that resistance to one drug class reprograms resistant clones for increased sensitivity to a distinct class of drugs. This study represents an important next step in translating pharmacogenomic profiling and may be useful for understanding personalized pharmacotherapy for patients with multiple myeloma.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4076840 | PMC |
http://dx.doi.org/10.1158/1535-7163.MCT-12-1151 | DOI Listing |
Target Oncol
January 2025
Berenson Cancer Center, West Hollywood, CA, USA.
Multiple myeloma (MM) is a bone-marrow-based cancer of plasma cells. Over the last 2 decades, marked treatment advances have led to improvements in the overall survival (OS) of patients with this disease. Key developments include the use of chemotherapy, immunomodulatory drugs, proteasome inhibitors, and monoclonal antibodies.
View Article and Find Full Text PDFEur J Prev Cardiol
January 2025
Department of Medicine, Mount Auburn Hospital, Harvard Medical School, 330 Mt Auburn St, Cambridge, MA 02138, USA.
J Immunother Cancer
January 2025
Rapa Therapeutics, Rockville, Maryland, USA.
Background: Polyclonal autologous T cells that are epigenetically reprogrammed through mTOR inhibition and IFN-α polarization (RAPA-201) represent a novel approach to the adoptive T cell therapy of cancer. Ex vivo inhibition of mTOR results causes a shift towards T central memory (T) whereas ex vivo IFN-α promotes type I cytokines, with each of these functions known to enhance the adoptive T cell therapy of cancer. Rapamycin-resistant T cells polarized for a type II cytokine phenotype were previously evaluated in the allogeneic transplantation context.
View Article and Find Full Text PDFPLoS One
January 2025
Division of Pharmacology, Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Toll-like receptor (TLRs) activation in multiple myeloma (MM) cells induces heterogeneous functional responses including cell growth and proliferation, survival or apoptosis. These effects have been suggested to be partly due to increase in secretion of cytokines such as IL-6 or IFNα among others from MM cells following TLR activation. However, whether triggering of these receptors also modulates production of immunoglobulin free light chains (FLCs), which largely contribute to MM pathology, has not been investigated in MM cells before.
View Article and Find Full Text PDFHematology
December 2025
Department of Hematology, XuChang Central Hospital, XuChang, People's Republic of China.
Introduction: Mitochondria and angiogenesis play key roles in multiple myeloma (MM) development, but their interrelated genes affecting MM prognosis are under-studied.
Methods: We analyzed TCGA_MMRF and GSE4581 datasets to identify four genes - CCNB1, CDC25C, HSP90AA1, and PARP1 - that significantly correlate with MM prognosis, with high expression indicating poor outcomes.
Results: A prognostic signature based on these genes stratified patients into high- and low-risk groups, with the latter showing better survival.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!