Proteomics

Dataset Information

0

Mapping the Multiple Myeloma cell surface proteome for immune target discovery


ABSTRACT: Multiple myeloma (MM) is an incurable malignancy of plasma cells. Immunotherapy is a promising treatment option that relies on the identification of biologically and therapeutically relevant cell surface targets. We unbiasedly mapped the surfaceome of 7 MM cell lines and 904 primary MM patients bearing high-risk cytogenetics integrating Mass-Spectrometry and RNA-seq analyses. We developed an integrated database for cell surface molecule annotation and identified 326 candidates including immune-related proteins. By selecting the proteins with a favorable expression in normal tissues we validated 26 candidates in 30 primary relapsed/refractory MM patients and 11 targets resulted most highly and frequently expressed. We defined their protein abundance in normal hematopoietic stem cells and T-cells by flow-cytometry, narrowing the list to 6 top targets. By using the CRISPR/Cas9 system in MM cells, we found that knock-out (KO) of CCR1, LRRC8D and SEMA4A individually reduced cell growth in vitro and in vivo and blocked cell migration. Further, KO MM cells resulted more sensitive to treatment with Bortezomib or Venetoclax and increased T-cell proliferation when in co-culture with healthy T-cells compared to controls. Finally, by combining a CCR1 blocking antibody and SEMA4A and LRRC8D KO MM cell proliferation further decreased. This study provides a compelling target discovery pipeline that led to the identification and validation of novel immunotherapeutic targets with favorable expression in malignant and normal cells and highly likely playing critical functions in MM biology, suggesting potential novel immunotherapeutic approaches.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Perna Fabian  

PROVIDER: MSV000088419 | MassIVE | Fri Nov 19 17:44:00 GMT 2021

REPOSITORIES: MassIVE

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

| phs001819 | dbGaP
2022-12-12 | GSE207471 | GEO
2024-01-19 | PXD040171 | Pride
2020-05-07 | GSE141170 | GEO
2022-07-07 | GSE180207 | GEO
2011-03-08 | E-GEOD-27838 | biostudies-arrayexpress
2011-03-08 | GSE27838 | GEO
2024-08-22 | PXD051789 | Pride
2023-06-01 | GSE223650 | GEO
2021-04-12 | GSE171837 | GEO