The haematological malignancy multiple myeloma is associated with skewed T-cell activation and function. T-cell alterations are detectable in asymptomatic myeloma precursor conditions and have the potential to identify precursor patients at imminent risk of progression. However, what myeloma-associated T-cells alterations represent mechanistically, how they relate to tumour burden and gene expression, and what influences high inter-patient variability in immune composition remains unknown. Here, we assembled the largest ever dataset of published and newly-generated single-cell RNA and TCR sequencing of the marrow and blood from patients with myeloma, precursor conditions, and age-matched non-cancer controls. We show myeloma is not associated with T-cell exhaustion and instead defined by a pattern of T-cell differentiation resembling antigen-driven terminal memory differentiation. Myeloma-associated T-cell differentiation was dependent on tumour-intrinsic features including tumour burden and tumour expression of antigen-presentation genes. Expanded TCR clones accumulating in myeloma were not enriched for viral specificity and were detected in effector states in highly infiltrated marrows. Together, these results suggest anti-tumour immunity drives a novel form of cancer-associated T-cell memory differentiation in myeloma.

The authors have declared no competing interest.

This work was funded by CRUK, the Medical Research Council, and the UCL/UCLH Biomedical Research Centre.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee of Health Research Authority UK (Research ethics committee references: 07/Q0502/17, 07/Q0502/17) gave ethical approval for this work. Ethics committee of the London Central Research Ethics Committee (20/LO/0238) gave ethical approval for this work. Ethics committee of the London City and East Research Ethics Committee (London, UK; date of favourable ethical opinion Feb 23, 2015) gave ethical approval for this work.

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Published datasets were acquired following the instructions in each original publication. Specifically, data shared through the gene expression omnibus (GEO) can be accessed for Maura et al. under accession GSE161195, Bailur et al. under accession GSE163278, Oetjen et al. under accession GSE120221, Granja et al. under accession GSE139369, Zavidij et al. under accession GSE124310, Kfoury et al. under accession GSE143791, and Zheng et al. under accession GSE156728. Data shared via dbGaP for Sklavenitis-Pistofidis et al. can be accessed under accession phs002476.v1.p1. Data shared online can be accessed for Stephenson et al. (via https://covid19cellatlas.org/), Conde et al. (via https://www.tissueimmunecellatlas.org/), and Liu et al. (via https://explore.data.humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81). The integrated single-cell RNA and TCR datasets and cohort information are available online (https://zenodo.org/doi/10.5281/zenodo.11047959). CoMMpass data were downloaded from the MMRF researcher gateway (https://research.themmrf.org). Newly-generated raw sequencing data will be made publicly available and uploaded to the GEO upon peer-reviewed publication.

https://zenodo.org/doi/10.5281/zenodo.11047959