Related Experiment Video
Updated: Aug 30, 2025

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells
Published on: July 15, 2015
Transcriptional profiles define drug refractory disease in myeloma
Yuan Xiao Zhu1, Laura A Bruins1, Xianfeng Chen2
1Division of Hematology-Oncology Mayo Clinic Phoenix Arizona USA.
Abstract:
Identifying biomarkers associated with disease progression and drug resistance are important for personalized care. We investigated the expression of 121 curated genes, related to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs) responsiveness. We analyzed 28 human multiple myeloma (MM) cell lines with known drug sensitivities and 130 primary MM patient samples collected at different disease stages, including newly diagnosed (ND), on therapy (OT), and relapsed and refractory (RR, collected within 12 months before the patients' death) timepoints. Our findings led to the identification of a subset of genes linked to clinical drug resistance, poor survival, and disease progression following combination treatment containing IMIDs and/or PIs. Finally, we built a seven-gene model (MM-IMiD and PI sensitivity-7 genes [IP-7]) using digital gene expression profiling data that significantly separates ND patients from IMiD- and PI-refractory RR patients. Using this model, we retrospectively analyzed RNA sequcencing (RNAseq) data from the Mulltiple Myeloma Research Foundation (MMRF) CoMMpass (n = 578) and Mayo Clinic MM patient registry (n = 487) to divide patients into probabilities of responder and nonresponder, which subsequently correlated with overall survival, disease stage, and number of prior treatments. Our findings suggest that this model may be useful in predicting acquired resistance to treatments containing IMiDs and/or PIs.
Insights
Researchers identified a seven-gene biomarker model (IP-7) to predict treatment resistance in multiple myeloma (MM). This model helps distinguish between newly diagnosed and refractory patients, aiding personalized care for immunomodulatory drug (IMiD) and proteasome inhibitor (PI) therapies.
Area of Science:
- Oncology
- Genetics
- Pharmacology
Background:
- Personalized medicine in multiple myeloma (MM) requires biomarkers for disease progression and drug resistance.
- Immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs) are key MM treatments, but resistance limits efficacy.
Purpose of the Study:
- To identify gene expression biomarkers associated with IMiD and PI responsiveness in MM.
- To develop a predictive model for acquired resistance to IMiD- and PI-based therapies in MM patients.
Main Methods:
- Analyzed gene expression of 121 curated genes in 28 MM cell lines and 130 primary MM patient samples.
- Utilized digital gene expression profiling to build a seven-gene model (IP-7).
- Retrospectively validated the IP-7 model using RNA sequencing data from two large MM patient cohorts (MMRF CoMMpass and Mayo Clinic).
Main Results:
- Identified a subset of genes linked to clinical drug resistance, poor survival, and disease progression.
- The IP-7 model effectively differentiated newly diagnosed (ND) MM patients from refractory relapsed (RR) patients.
- IP-7 model-based responder/non-responder stratification correlated with overall survival, disease stage, and prior treatment history.
Conclusions:
- The developed seven-gene (IP-7) model shows potential for predicting acquired resistance to IMiD and PI treatments in MM.
- This biomarker model could aid in tailoring treatment strategies and improving patient outcomes in multiple myeloma.

