基因签名
多发性骨髓瘤
医学
比例危险模型
队列
肿瘤科
转录组
基因
基因表达
达拉图穆马
癌症研究
计算生物学
内科学
生物
硼替佐米
遗传学
作者
Paula Restrepo,Sherry Bhalla,Yogita Ghodke‐Puranik,Adolfo Aleman,Violetta V. Leshchenko,David T. Melnekoff,Sarita Agte,Joy Jiang,Deepu Madduri,Joshua Richter,Shambavi Richard,Ajai Chari,Hearn Jay Cho,Sundar Jagannath,Christopher J. Walker,Yosef Landesman,Alessandro Laganà,Samir Parekh
摘要
Selinexor is the first selective inhibitor of nuclear export to be approved for the treatment of relapsed or refractory multiple myeloma (MM). Currently, there are no known genomic biomarkers or assays to help select MM patients at higher likelihood of response to selinexor. Here, we aimed to characterize the transcriptomic correlates of response to selinexor-based therapy.We performed RNA sequencing on CD138+ cells from the bone marrow of 100 patients with MM who participated in the BOSTON study, followed by differential gene expression and pathway analysis. Using the differentially expressed genes, we used cox proportional hazard models to identify a gene signature predictive of response to selinexor, followed by validation in external cohorts.The three-gene signature predicts response to selinexor-based therapy in patients with MM in the BOSTON cohort. Then, we validated this gene signature in 64 patients from the STORM cohort of triple-class refractory MM and additionally in an external cohort of 35 patients treated in a real-world setting outside of clinical trials. We found that the signature tracks with both depth and duration of response, and it also validates in a different tumor type using a cohort of pretreatment tumors from patients with recurrent glioblastoma. Furthermore, the genes involved in the signature, WNT10A, DUSP1, and ETV7, reveal a potential mechanism through upregulated interferon-mediated apoptotic signaling that may prime tumors to respond to selinexor-based therapy.In this study, we present a present a novel, three-gene expression signature that predicts selinexor response in MM. This signature has important clinical relevance as it could identify patients with cancer who are most likely to benefit from treatment with selinexor-based therapy.
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