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Exploiting big data survival information to unify risk-stratification related, adaptive immune receptor parameters for multiple myeloma

Genes & Immunity volume 24, pages 194–199 (2023)Cite this article

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Abstract

With the improvement of treatment options, multiple myeloma related life expectancy has been prolonged, but the disease remains largely incurable. Immunotherapy is a growing field that shows promise in advancements for treatment, and recent work has demonstrated an opportunity to use immune receptor, complementarity determining region-3 (CDR3)-candidate antigen chemical complementarity scores to identify survival distinctions among subgroups of patients. Here, we have applied the complementarity scoring algorithm to identify multiple myeloma related, CDR3-cancer testis antigen (CTA) relationships associated with survival distinctions. Furthermore, we have overlapped these immune receptor features with a previous study that showed a dramatic survival distinction based on T-cell receptor, V- and J-gene segment usage, HLA allele combinations, whereby 100% of the patients in certain combination groups had no mortality related to multiple myeloma, during the study period. This overlap evaluation was consistent with the idea that there are likely considerable constraints on productive TRB-antigen-HLA combinations but more flexibility, and unpredictability, for the TRA-antigen-HLA combinations. Also, the approaches in this reported indicated the potential importance of the CTA, IGSF11, as a multiple myeloma antigen, an antigen previously, independently considered as a vaccine candidate in other settings.

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Fig. 1: Kaplan–Meier (KM) analysis of the upper and lower 50th percentile TRA CDR3-CTA CS groups.
Fig. 2: Kaplan–Meier (KM) analysis of the upper and lower 50th percentile TRB CDR3-CTA CS groups.

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Data availability

As noted above, original data are available in the supporting online material file as well as in the supporting online material for ref. [11].

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Acknowledgements

Authors thank USF research computing and Ms Corinne Walters, for administrative assistance with the NIH approval process for dataset access; and the taxpayers of the State of Florida. Dedicated to Rosario.

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Authors and Affiliations

  1. Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA

    Hope J. Wolmarans, Vayda R. Barker, Taha I. Huda & George Blanck

  2. Department of Pediatrics, Oregon Health and Science University Hospital, Portland, OR, 97239, USA

    Andrea Chobrutskiy

  3. Department of Internal Medicine, Oregon Health and Science University Hospital, Portland, OR, 97239, USA

    Boris I. Chobrutskiy

  4. Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA

    George Blanck

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  1. Hope J. Wolmarans
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Contributions

HJW: Conceptualization; Formal analysis; Methodology; Visualization; Writing—review & editing. VRB: Methodology; Visualization. AC: Conceptualization; Methodology; Visualization. BIC: Conceptualization; Formal analysis; Methodology; Software; Visualization; Writing—review & editing. TIH: Resources; Methodology; Software. GB: Methodology; Project administration; Resources; Supervision; Writing—review & editing.

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Correspondence to George Blanck.

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The authors declare no competing interests.

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The data were accessed via NIH dbGaP project approval number 22594, for George Blanck.

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Wolmarans, H.J., Barker, V.R., Chobrutskiy, A. et al. Exploiting big data survival information to unify risk-stratification related, adaptive immune receptor parameters for multiple myeloma. Genes Immun 24, 194–199 (2023). https://doi.org/10.1038/s41435-023-00212-z

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