Open AccessTranscriptional profiles define drug refractory disease in myeloma
Author(s) -
Zhu Yuan Xiao,
Bruins Laura A.,
Chen Xianfeng,
Shi ChangXin,
Bonolo De Campos Cecilia,
Meurice Nathalie,
Wang Xuewei,
Ahmann Greg J.,
Ramsower Colleen A.,
Braggio Esteban,
Rimsza Lisa M.,
Stewart A. Keith
Publication year - 2022
Publication title -
ejhaem
Language(s) - English
Resource type - Journals
ISSN - 2688-6146
DOI - 10.1002/jha2.455
Subject(s) - multiple myeloma , lenalidomide , medicine , refractory (planetary science) , oncology , drug resistance , disease , gene expression profiling , drug , carfilzomib , gene , gene expression , cancer research , biology , pharmacology , genetics , astrobiology
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.