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Publications

Journal Article Epublish

PubMed: 40665393 DOI: 10.1186/s13073-025-01503-y

Utilizing genomics to identify novel immunotherapeutic targets in multiple myeloma high-risk subgroups.

Genome Med 17(1):79

Authors: Enze Liu, Oumaima Jaouadi, Riya Sharma, Nathan Becker, Travis S Johnson, Parvathi Sudha, Vivek S Chopra, Faiza Zafar, Habib Hamidi, Charlotte Pawlyn, Attaya Suvannasankha, Rafat Abonour, Brian A Walker

Affiliations

  • Melvin and Bren Simon Comprehensive Cancer Center, Division of Hematology and Oncology, School of Medicine, Indiana University, Indianapolis, IN, USA.
  • Myeloma Institute, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA.
  • Department of Biostatistics and Health Data Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.
  • Genentech Inc, South San Francisco, CA, USA.
  • Institute of Cancer Research, 15 Cotswold Road, London, UK.
  • Melvin and Bren Simon Comprehensive Cancer Center, Division of Hematology and Oncology, School of Medicine, Indiana University, Indianapolis, IN, USA.
  • Myeloma Institute, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA.

Abstract

BACKGROUND

Immunotherapy is now standard of care for multiple myeloma (MM), where the most common targets are B cell maturation antigen, CD38, and G protein-coupled receptor class C group 5 member D (GPRC5D). However, additional novel targets are needed to counter tumor heterogeneity, therefore new strategies to identify additional targets are also required.

METHODS

We utilized multi-omics data from two large datasets A framework that utilized prior knowledge of cell surface potential, expression in healthy organs, and expression level in MM cells was established to define novel immunotherapeutic targets. High confidence targets were prioritized for myeloma populations and subgroups, validated with flow cytometry and immunoblotting.

RESULTS

Novel population-level candidate targets such as ITGA4 and LAX1, as well as subtype-specific targets including ROBO3 in t(4;14), CD109 in t(14;16), CD20 in t(11;14), CD180 in hyperdiploidy, GPRC5D in 1q gain, and ADAM28 in biallelic TP53 samples were identified. Candidate target surface expression was validated by flow cytometry and CRISPR-Cas9 knock-out models. Sub-clonal differences in expression were noted, using single-cell RNA-seq data. Additionally, alternative splicing of existing immunotherapy targets, such as FCRL5, was noted as a potential mechanism of antigen loss.

CONCLUSIONS

Our study presents a methodology to identify novel candidate immunotherapy targets. We also use known genomic data to identify subtype-specific targets that could be used either as complementary or alternative targets to existing treatments. We show that immunotherapy targets can have heterogenous expression within a patient, which can affect treatment efficacy. Taken together, our study establishes a robust methodology to identify novel therapeutic targets in MM, revealing critical insights that will inform the development of current and next-generation immunotherapies.

MeSH

Cells, Flow Cytometry, Genomics, Multiple Chronic Conditions, Immunotherapy, Multiple Myeloma, Therapeutics, Rna