Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

A Gene Expression-Based Risk Stratification Model for Newly Diagnosed Multiple Myeloma

ABSTRACT: We recently defined a gene expression-based signature of high-risk multiple myeloma; this predictive signature was developed with and independently validated for newly diagnosed patients treated with high dose therapy and stem cell rescue. Here we use Phase 3 clinical trial data to show that this signature also predicts short survival in relapsed disease treated with single agent bortezomib or high dose dexamethasone. In addition, a survival signature derived with relapsed myeloma samples identified newly diagnosed patients with short survival. Taken together these data suggest that a similar biology underlies poor outcome in both newly diagnosed and relapsed myeloma and provide strong evidence that the high-risk signature is a powerful tool to identify patients who are candidates for new therapeutic regimens. Keywords: Model validation See above (Series_summary)

ORGANISM(S): Homo sapiens

SUBMITTER: Yiming Zhou

PROVIDER: E-GEOD-8991 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Multiple myeloma (MM) is a devastating plasma cell malignancy characterized by the expansion of aberrant monoclonal plasma cells in the bone marrow, leading to severe clinical manifestations and poor prognosis, particularly in relapsed/refractory cases. Identifying novel therapeutic targets is crucial to improve treatment outcomes in these patients. In this study, we investigated the role of protein arginine methyltransferase 1 (PRMT1) in MM pathogenesis and explored its potential as a therapeutic target. We observed that PRMT1, responsible for majority of asymmetric di-methylation in cells, exhibited the highest expression among PRMT family members in MM cell lines and primary MM cells. Importantly, PRMT1 expression was significantly elevated in relapsed/refractory patients compared to newly diagnosed patients. High expression of PRMT1 expression was strongly associated with poor prognosis. We found that genetic or enzymatic inhibition of PRMT1 impaired MM cell growth, induced cell cycle arrest, and triggered cell death. Treatment with MS023, a potent PRMT type I inhibitor, demonstrated a robust inhibitory effect on viability of primary cells isolated from both newly diagnosed and proteasome inhibitor-relapsed/refractory patients in a dose-dependent manner. In vivo studies using a xenograft model revealed that targeting PRMT1 by either CRISPR/Cas9-mediated knockout or MS023 treatment significantly attenuated MM tumor growth and prolonged the survival of tumor-bearing mice. Histological analysis further confirmed increased apoptotic cell death in MS023-treated tumors. Collectively, our findings establish PRMT1 as an indispensable and novel therapeutic vulnerability in MM. The elevated expression of PRMT1 in relapsed/refractory patients underscores its potential as a target for overcoming treatment resistance. Moreover, our results highlight the efficacy of MS023 as a promising therapeutic agent against MM, offering new avenues for therapeutic approaches in relapsed/refractory MM.

Dataset Information

A Gene Expression-Based Risk Stratification Model for Newly Diagnosed Multiple Myeloma

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets