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137 Genomics of multiple myeloma influences the expression of CAR T-cell targets
  1. Christina Yu1,
  2. Brian Walker2,
  3. G David Roodman2,
  4. Kun Huang3,
  5. Michel Sadelain4 and
  6. Fabiana Perna2
  1. 1Indiana University School of Medicine and Ohio State University, Indianapolis, IN, USA
  2. 2Indiana University School of Medicine, Indianapolis, IN, USA
  3. 3Indiana University School of Medicine and Regenstrief Institute, Indianapolis, IN, USA
  4. 4Memorial Sloan Kettering Cancer Center, New York, NY, USA


Background Multiple Myeloma (MM) is an incurable disease, with a particularly poor prognosis for patients with refractory/relapsed MM or high-risk cytogenetics. Chimeric Antigen Receptor (CAR) T-cell therapy targeting BCMA can induce deep responses in highly pretreated RRMM; however, remissions are not sustained, and the majority of patients eventually relapse. We hypothesized that genomic determinants of MM play a role in dictating the expression of surface targets that can be of use for immune targeting.

Methods We analyzed the gene expression of 24 immunotherapeutic targets in a combined dataset of 1900 MM patients from three independent expression datasets obtained from the Multiple Myeloma Research Foundation CoMMpass study and Gene Expression Omnibus. Given that CAR T-cell therapy may be especially important for patients with high-risk myeloma, we defined the expression of each target in high-risk MM patients by stratifying patients based on several genomic features impacting prognosis. Additionally, we conducted a gene co-expression network analysis and identified 30 gene modules highly correlated with 16 cell surface targets from our panel, further suggesting that genetic determinants of MM may shape a targetable cell surfaceome. In order to determine whether targeting any of these candidate antigens might cause major toxicity to normal cells, we utilized several repositories providing protein data1 to annotate their expression in several normal cell types.

Results We determined that a number of genomic factors could stratify the 24 targets into three general groups: 1) targets that show consistent overexpression in high-risk patients: IGF1R, ITGB7, GPRC5D and CD70, and are thus suitable for most high-risk patients; 2) targets that are down-regulated in patients with high-risk genomic features: CD200, CD19, CD40, CD1D and IGKC, perhaps playing a role in cancer immune escape; and 3) targets associated with one specific genetic abnormality, i.e. t(4;14): FUT3, SLAMF7, CD56, CD138 and BCMA, thus of use for precision CAR therapy in this high-risk patient subset.

Conclusions Our work provides a means of target selection for precision CAR therapy, by considering both patient genomic backgrounds and cancer cell surface profiles. Furthermore, our results provide a roadmap for immunotherapy of MM by unbiasedly comparing the expression of top MM cell surface targets in patient data and normal cells and suggest that the genetic landscape of MM may predict the expression of specific targets for precision immunotherapy. The quest for novel MM targets for immunotherapies remains open, and CAR target discovery driven by specific genetic events remains an active area of investigation.


  1. Perna F, Berman SH, Soni RK, et al. Integrating proteomics and transcriptomics for systematic combinatorial chimeric antigen receptor therapy of AML. Cancer Cell 2017;32(4):506–19.

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