Tumour-intrinsic features shape T cell differentiation through precursor to symptomatic multiple myeloma

Domain: neuroscience
Published: 2026-02-05
Editorial outcome: minor_revision

Abstract
Multiple myeloma (MM) is associated with skewed T cell activation and function which is present in asymptomatic myeloma precursor conditions, but underlying mechanisms of progression remain undefined. Here, we assemble a large single-cell RNA sequencing dataset of the bone marrow and blood from patients with MM, precursor conditions, and non-cancer controls. We demonstrate that, unlike solid cancers, MM is not characterized by T cell exhaustion, but by antigen-driven terminal memory differentiation. This is influenced by tumour-intrinsic features including tumour burden and expression of antigen-presentation genes. Expanded TCR clones accumulating in MM are not enriched with viral specificities but accumulate in effector states in highly-infiltrated marrows. Additionally, we identify a role for T cell dynamics in patients treated with autologous stem cell transplantation and demonstrate T cell features predict progression from precursor to symptomatic MM. Together, these results suggest that anti-tumour immunity drives a distinctive form of cancer-associated T cell differentiation in MM.

Introduction
T cells are polyfunctional immune cells and fundamental players in anti-tumour immunity1. In solid cancers, evidence suggests that early in carcinogenesis tumour growth can be curtailed by tumour-reactive T cells. However, persistent activation drives these cells away from functional memory states towards a hypo-responsive state of terminal differentiation termed exhaustion1,2, characterised by the expression of immune checkpoint molecules like programmed cell death protein 1 (PD-1), contributing to cancer progression in solid cancers. This complex interaction is believed to shape tumours from the early precursor stages to relapsed and refractory disease1. Understanding these insights has refined the treatment of solid tumours through the development of immunotherapies targeting exhausted T cells3.

Multiple myeloma (MM) is a haematological malignancy of bone marrow (BM) plasma cells (PC) that is largely incurable4, but is invariably preceded by precursor states of increased risk of progression: monoclonal gammopathy of undetermined significance (MGUS) and smouldering multiple myeloma (SMM)5. While not every MGUS or SMM patient will progress, virtually every MM patient has transitioned through these stages6. Thus, there is a pressing clinical need to understand the determinants of disease progression, so as to accurately identify individuals who may benefit from monitoring and intervention. Current risk factors rely largely on measures of tumour bulk5, namely the levels of plasma cell infiltration in the bone marrow (BMPC%) and serum concentrations of paraprotein (malignant clone-derived immunoglobulin) and beta2-microglobulin (B2m). However, the role of the BM immune microenvironment, particularly T cells, in progression remains poor understood. Available reports of altered immune cell function and phenotype suggest that, like solid tumours, the progression from precursor state to frank MM is accompanied by loss of immune control7. Understanding how MM drives alterations in T cell state and function is complicated by the influence of patient advanced age and marrow homoeostatic T cell differentiation8,9. This, together with high inter-individual immune heterogeneity, confounds attempts to identify associations between T cells and tumour biology or progression.

To solve this, we combined newly-generated single-cell RNA sequencing (scRNA-seq) and T cell receptor (TCR) sequencing (scTCR-seq) data with 12 published studies (see “Methods”), compiling an atlas of over a million single cells from 255 individuals to interrogate T cell dynamics while controlling for natural and tumour-associated sources of inter-individual variation. We show for the first time that the T cell landscape in MM displays features of antigen-driven terminal memory differentiation and highlight the features of tumour biology that drive this. We identify a role for T cell dynamics in the response to autologous stem cell transplantation (ASCT) and observe that alterations in T cells predict progression from precursor SMM to active MM. Our findings provide a framework for understanding tumour-immune interactions in disease evolution and therapy.

Results
Effective integration of scRNA-seq datasets allows a detailed classification of immune cell populations

To study immune differentiation through myeloma disease evolution we generated a large scRNA-seq map of BM (n=240 samples, 77.6%) and peripheral blood (PB; n=68, 22.4%) cells from untreated MGUS (n=23, 9%), SMM (n=60, 25%) and MM (n=67, 25%) patients alongside non-cancer controls (n=105, 41%; Fig. 1, Supplementary Fig. 1A, B and Supplementary Data 1). Together, we collated 1,079,979 cells from 255 individuals with RNA and clinical data, 109 with additional scTCR-seq data, and 1 with cellular indexing of transcriptomes and epitopes (CITE-seq) data. Patients were on average older than controls (controls median 55 range 21–87, MGUS median 62 range: 41–81, SMM median 62 range: 29–81, MM median 62 range: 38–77; Supplementary Fig. 1C). As expected, BMPC% and serum paraprotein levels were higher in MM compared to SMM (PC% p=0.001, paraprotein p<0.05, Wilcoxon test; Supplementary Fig. 1D).

Following quality control and correcting for batch effects (see “Method”; Supplementary Fig. 2A, B), cells were clustered to nine major cell types and phenotyped using RNA expression, protein expression via CITE-seq, and de novo label prediction tools (Fig. 2A, B and Supplementary Fig. 2C–E). T cells (defined as CD3D+CD3E+CD3G+CD8A+ or CD4+ RNA and CD3+ protein) comprised roughly half (50.2%) of the cells in the dataset, with another quarter occupied by similar proportions of myeloid cells (FCN1+FCER1G+CD14+; 15%) and haematogenic progenitors (CD34+MPO+TYMS+; 11%). The remainder of the dataset was largely comprised of equivalent numbers of NK cells (KLRD1+FCG3RA+CD56+; 8%), B cells (CD79A+CD19+; 7.5%), and plasma cells (MZB1+SDC1+; 6.6%).

The use of different sorting strategies across studies precluded in-depth analysis of overall immune composition. However, plasma cells and progenitors were enriched in BM-derived samples relative to PB (p<0.001 and p<0.001, respectively, Wilcoxon test; Fig. 2B), suggesting a relative lack of haemodilution in BM aspirates. As expected, plasma cells were most highly enriched in the BM of patients relative to controls (p<0.001, Wilcoxon test). However, the global distribution of cell types was otherwise similar in diseased and control marrows, suggesting progression to myeloma may be associated with more granular alterations to immune composition.

In-depth T cell phenotyping reveals diverse early and late differentiated CD4+ and CD8+ T cell populations in the marrow and blood

To further probe immune perturbations in disease evolution, T cells were isolated, re-integrated and re-clustered to 19 discrete phenotypes and transcriptional states based on expression of RNA and protein markers (Fig. 2C, D, Supplementary Fig. 3A–C and Supplementary Data 2 and 3)2,10.

Naïve and central memory T cells (defined by SELL and CCR7, Tn and Tcm) composed the majority of CD4+ cells (Tn 49% of CD4+ T cells, Tcm: 21%), while the remainder were composed of diverse regulatory (FOXP3+ Treg, 8.4%), T helper 17 (CCR6+ Th17 7.2%), effector memory (KLRG1+GZMK+ Tem 6.1%) and Tem re-expressing CD45RA (GZMB+GNLY+CD45RA+ Temra, 8.7%) states. CD8+ cells were classified by differentiation stage including early differentiated Tn (20% of CD8+ T cells) and Tcm (4.6%), intermediate Tem (37%), and GZMB and GNLY-expressing terminally differentiated terminal effector memory (Tte, 18%) and Temra (15%) states. CD8+ Tem were further sub-divided by the expression of early differentiation markers like TCF1 (TCF7; Tem.TCF7+) or S1PR1 (Tem.S1PR1+) alongside activation markers like CD69 (Tem.CD69+). The two terminally differentiated CD8+ subsets were distinguished based on the expression of specific markers like Hobit (ZNF683; T terminal effector, Tte) or NK cells genes such as NKG2C (KLRC) and CD45RA protein (Supplementary Fig. 3C; Temra) and were enriched in senescence-associated genes (SenMayo; CD8+ Tte: p=0.03, NES=1.6; CD8+ Temra: p=0.03, NES=1.5, GSEA; Supplementary Data 4). Additionally, rare clusters of tissue resident (Trm, 5.1%) and exhausted (Tex, 0.7%) CD8+ T cells were identified by the expression of ITGA1 and PD-1 (PDCD1), respectively. Additional T cell subsets were identified by the expression of specific markers, including IFN-stimulated cells by ISG15, invariant subsets by CD161 (KLRB1), proliferating T cells by MKI67, and effector T cells (Teff) by effector molecules like TNF-α (TNF) and IFNG alongside the enrichment of a Teff gene signature (Effector versus memory11; p=0.005, NES=3.5, GSEA). We also explored heterogeneity among CD4+ Treg and invariant T cells by performing additional sub-clusterings of these subsets (see “Methods”; Supplementary Fig. 4A).

This functional annotation was consistent with patterns of TCR expansion (Fig. 2E and Supplementary Fig. 3D), with clonally expanded clusters expressing late differentiation markers. The majority (79.8%) of invariant MAIT TCRs were found in cells of invariant cluster, of which 97% were MAITs (Supplementary Fig. 4A). Our phenotypes showed high concordance with published and predicted cluster labels (Supplementary Fig. 3E), arguing for a faithful representation of T cell phenotypes in our integrated dataset.

The marrow of myeloma patients is not enriched in exhausted T cells

Exhausted CD8+ Tex were identified via high expression of PDCD1 and TIGIT, alongside other RNA markers of exhaustion like CXCL13 and LAYN10 (Fig. 2F and Supplementary Fig. 3B). Importantly, CD8+ Tex were distinguished from PDCD1-expressing CD8+ Tem.CD69+. While CD8+ Tem.CD69+ cells did express higher levels of exhaustion markers like PDCD1 and TOX than non-exhausted bone marrow T cells (p<0.001 and p<0.001, one-way ANOVA; PDCD1: adjusted p<0.001, marker gene testing; Fig. 2G and Supplementary Fig. 3F), it was at lower levels than CD8+ Tex (p<0.001 and p<0.001, Wilcoxon test) and further lacked other markers of exhaustion like LAYN and CXCL13 (Fig. 2G and Supplementary Fig. 3B). A dedicated sub-clustering of CD8+ Tem.CD69+ did not identify a subset of these cells with features of CD8+ Tex (Supplementary Fig. 3G). Therefore, despite expressing some exhaustion-associated markers at low levels, CD8+ Tem.CD69+ are not phenotypically exhausted T cells.

We extended this phenotypic description by characterising the function of CD8+ Tem.CD69+. CD69+ and CD69– fractions of memory (CCR7–) CD8+ T cells were isolated from 3 MM patient marrows and activated cells in vitro (see “Methods”). After stimulation, CD69+ cells possessed no significant differences in 4-1BB and effector cytokine expression (Supplementary Fig. 4C), suggesting these cells are not functionally impaired relative to other CD8+ Tem.

Interestingly, the CD8+ Tex cluster was almost entirely composed of cells from a single myeloma patient (1181 of 1222 cells, 97%; Fig. 2H) who contributed the majority of exhaustion marker-expressing cells (Supplementary Fig. 3H), suggesting CD8+ Tex were a donor-specific phenomenon. Furthermore, when excluding this patient, CD8+ Tex were not significantly enriched in the BM of MGUS, SMM or MM patients relative to controls (p=0.76, one-way ANOVA; Fig. 2I). In contrast, CD8+ Tem.CD69+ cells were seen in a much higher number of patients (Fig. 2H), although, like CD8+ Tex, these cells were not enriched in patients relative to controls (p=0.86, one-way ANOVA; Fig. 2I).

We also explored the contribution of bone marrow tissue on T cell composition. Similar T cell phenotypes were observed in the BM and PB (Supplementary Fig. 4D), but the proportion of T cell clusters differed (Supplementary Fig. 4E). Notably, CD8+ Tem.CD69+ were enriched in the BM of both patients and controls (p<0.001 and p<0.001, Supplementary Fig. 4F), suggesting intrinsic marrow biology regulates the abundance of this subset.

These data lead us to suggest that exhausted T cells are rarely seen in myeloma, with the abundant and functional CD8+ Tem.CD69+ being phenotypically distinct from exhausted cells. Neither subset was associated with disease progression, although CD8+ Tem.CD69+ were enriched in the marrow in health and disease.

T cells in diseased marrow of both pre-malignant SMM and overt MM display skewed differentiation resembling exaggerated age-related changes

We next asked how the relative abundance of T cell subsets in the BM was altered across disease stages. BM T cell composition was strikingly similar in patients and controls (Fig. 2A and Supplementary Fig. 5A, B). Therefore, we compared the T cell composition of controls with each myeloma disease stage in turn, statistically controlling for age. The most prominent difference in BM T cell composition between health and disease was the loss of naïve, CD4+ Th17 and Invariant cells and an enrichment of CD4+ and CD8+ Temra, CD8+ Tte, and IFN-stimulated cells (adjusted p<0.1 for all, linear regression; Fig. 3B and Supplementary Fig. 5C). When removing the one patient who contributed the majority of CD8+ Tex cells (97% cells; Fig. 2H), this cluster was not enriched in MM relative to controls independent of age (p=0.54, linear regression). As non-cancer controls included hip replacement and deceased donors, we repeated our analysis with only healthy donors and obtained the same results (Supplementary Fig. 5D).

Low-risk MGUS possessed a T cell composition with the fewest differences from non-diseased control marrows (Supplementary Fig. 5C). Conversely, T cell composition was similar between the higher-risk but pre-cancerous SMM and symptomatic MM (Supplementary Fig. 5E). While the normalised abundance of CD8+ Tex was lower in SMM than MM independent of age (p<0.001, linear regression), in terms of unnormalized counts this only represented 8 MM patients with a median of 1 CD8+ Tex cells each, suggesting a lack of sufficient cell numbers to properly perform this comparison. Next, we collated available risk data for patients in our dataset, and compared T cell composition between international staging system (ISS) and SMM Mayo risk groups5,12 (Supplementary Fig. 5F). While this analysis found no differences between risk groups, with the exception for an increased abundance of CD4+ Tem in ISS groups II and III, these comparisons were limited by risk annotations and may not accurately reflect differences between these groups. However, taken together these results suggest that smouldering and overt myeloma are associated with similar T cell alterations independent of patient age.

To quantify global changes to T cell composition, we ran principal component analysis (PCA) on patients’ T cell cluster abundance data. The first principal component explaining the highest fraction of variance in T cell composition (PC1, 21.9%; Supplementary Fig. 5H) described a compositional shift from naïve and early subsets towards terminal memory cytotoxic clusters (Fig. 3C, D). Therefore, we termed PC1 “T cell skewing”. T cell skewing was greatest (meaning an enrichment of terminal memory clusters) in SMM and MM relative to controls independent of age (p<0.004 and p<0.001, respectively, linear regression; Fig. 3E), demonstrating this metric captured the major alterations to T cells in myeloma. Conversely, T cell skewing was associated with age independent of patient group (p=0.013, linear regression, R=0.29, Pearson correlation; Fig. 3E, F and Supplementary Fig. 5I). Therefore, T cell skewing is a feature of both age and disease, meaning myeloma-associated T cell differentiation directly resembles T cell alterations seen during ageing, or, patients possess an exaggerated form of T cell ageing. Using linear regression, we calculated the degree of exaggerated T cell ageing and excess T cell years (see “Methods”) and found both trended to rise with advanced disease (MGUS versus MM p=0.13 and p<0.07 respectively, one-way ANOVA; Fig. 3G and Supplementary Fig. 5J) suggesting this effect scales linearly with disease severity.

PC1 values and the abundance of terminal memory subsets in the BM showed a strong correlation with PB (Fig. 3H), indicating a similarity to systemic T cell alterations seen with ageing8.

Taken together, these data suggest SMM and MM are associated with a pattern of T cell differentiation skewing closely resembling alterations observed during T cell ageing.

Features of antigen-specific responses underpin myeloma-associated T cell differentiation

Next, we analysed features of the TCR repertoire. Repertoire clonality was associated with T cell skewing in patients independent of age (p<0.001, linear regression, R=0.68; Fig. 4A). We observed similar results when restricting analysis to CD8-expressing memory clones (p<0.01, linear regression, R=0.46; Fig. 4A). Both the clonality and the abundance of expanded CD8+ memory clones trended for enrichment in MM relative to controls (Fig. 4B). T cell skewing and CD8+ memory diversity did not correlate in controls (p=0.48, linear regression; Supplementary Fig. 6A), suggesting clonal expansion may be a unique feature of T cell differentiation in myeloma.

The accumulation of TCRs possessing similar CDR3 sequences indicates responses against common antigens. We grouped all expanded TCRs in the dataset (11,545 clones) into 279 clusters (composed of 1014 clones, 8.8% of input; see “Methods”; Fig. 4C and Supplementary Fig. 6B, C). To prioritise grouping TCRs recognising antigens in the same human leucocyte antigen (HLA) background, TCR clustering was restricted within individuals. An increasingly large fraction of the TCR repertoire was occupied by clustered clones in MM (median 8.7% range 3–35) relative to SMM (median 3.1% range 0–11.3; p=0.0009) and this fraction correlated with T cell skewing (R=0.4, p=0.02, Pearson correlation; Fig. 4D). T cell skewing was specifically associated with the clustering of GZMB-expressing CD8+ memory cells (R=0.47, p=<0.01; Fig. 4E), suggesting conserved antigen-specific responses drive T cell differentiation in myeloma, specifically among GZMB-expressing subsets.

We next explored the antigen specificity of these T cells. TCR specificity databases are mostly composed of viral antigens, allowing us to ask if viral antigen specificities were involved in T cell differentiation and clonality in myeloma (see “Methods”). In 19 SMM and MM patients BM samples we found a median of 7 (range 2–88) putative HLA-matched viral-reactive TCRs (Supplementary Fig. 6D, E). Putative viral-reactive clones occupied a range of T cell clusters, including naïve and effector memory phenotypes. Comparing gene expression between clones with and without putative viral specificity annotations, we found clones predicted to not be viral-specific expressed genes characteristic of terminal memory such as GZMB, perforin (PRF1) and Hobit (ZNF683; Fig. 4F and Supplementary Data 4). We summarised the expression of these genes into a non-viral specificity gene signature (Supplementary Data 4). Non-viral signature expression mapped to GZMB+ terminal memory clusters (Supplementary Fig. 6F), correlated with T cell skewing, clonality and repertoire clustering (Fig. 4G), and was enriched in MM relative to precursor conditions (Fig. 4H). Taken together, these data show MM-associated T cell differentiation occurs alongside alterations to the TCR repertoire resembling antigen-directed T cell immunity which are distinct from viral-specific TCR clones.

Tumour-intrinsic features drive two clonally-related patterns of T cell differentiation

We next asked if features of tumour biology may drive T cell skewing. We examined serum paraprotein and beta2-microglobulin (B2m) concentrations, plasma cell marrow infiltration, and tumour cell transcriptional state (see “Methods”, Supplementary Fig. 7). We included all patients irrespective of disease stage.

We did not find an association between T cell skewing and tumour transcriptional state, BMPC%, or B2m levels (Supplementary Fig. 8A, B). However, we observed a positive correlation between high serum paraprotein and enhanced T cell skewing (R=0.43, p<0.03, Pearson correlation; Fig. 5A), alongside the abundance of CD8+ Temra (R=0.5, p=0.01) and high TCR clonality (R=0.53, p<0.01; Fig. 5A and Supplementary Fig. 8C). This association was independent of age (p<0.05, linear regression) and exaggerated T cell ageing similarly correlated with paraprotein (Supplementary Fig. 8C). We recapitulated this finding in two additional cohorts. Firstly, 10 SMM and 30 untreated MM patients were profiled by cytometry by time-of-flight (CyTOF; see “Methods”; Supplementary Data 5 and Supplementary Fig. 9A) and the abundance of CD8+ Temra (CD28–CD45RA+) correlated with paraprotein (R=0.38, p=0.053, Pearson; Fig. 5B). Secondly, a large group of 175 precursor patients (n=33 MGUS, n=142 SMM) were profiled at recruitment by flow cytometry (Supplementary Fig. 9B). Using a minimal set of markers, we approximated T cell skewing via the abundance of IL7R– (late differentiated) CD8+ T cells (Supplementary Fig. 8D). In this cohort, the abundance of IL7R– CD8+ T cells significantly correlated with paraprotein independent of age (R=0.24, p<0.001 Pearson correlation; p independent of age p<0.001 linear regression, Fig. 5C). Together, this connects T cell skewing observed in patients with serum paraprotein levels, indicating a relationship with tumour bulk.

We speculated that tumour-intrinsic features were associated with individual T cell clusters. Analysis of T cell cluster abundance with BM PC % and transcriptional pathway activity in tumour cells revealed effector T cells (Teff) were enriched in highly-infiltrated marrows populated by tumour cells expressing stress-associated genes (BMPC%: R=0.55, tumour stress: R=0.6; Fig. 5D and Supplementary Fig. 8D). Several other clusters were significantly correlated with tumour stress, including positive and negative associations with CD8+ Trm and CD4+ Tem, respectively (Supplementary Fig. 8E). The expression of a smaller set of T cell effector genes (CD69, TNF, IFNG) possessed the same correlations (BMPC%: R=0.43, tumour stress: R=0.79, Pearson correlation; Supplementary Fig. 8F), suggesting a T cell effector programme specifically was associated with BMPC% and tumour stress.

The co-occurrence of tumour-associated memory and effector T cells suggested a differentiation process between these two states. We therefore asked if effector T cells were clonally related to terminal memory subsets. Clonally expanded CD8+ effector T cells were significantly enriched among CD8+ Temra (p<0.001, chi-squared test; Fig. 5E). Additionally, clonally expanded Teff cells enriched in marrows containing stressed tumours expressed markers characteristic of CD8+ Temra (Fig. 5F). Therefore, terminally differentiated memory clones accumulating in MM are continuous with effector-like T cells in highly-infiltrated marrows, suggesting these states are linked by a T cell differentiation pathway.

Finally, we asked if tumour genomic features were associated with T cell skewing. t(4;14) (positive n=6, negative n=17) was not significantly associated with T cell skewing (p=0.45, Wilcoxon test) but a 1q gain (positive n=10, negative n=19) trended to enrich for enhanced T cell skewing (Supplementary Fig. 7G; p=0.057, Wilcoxon test), potentially connecting this adverse prognostic factor to T cell differentiation.

Myeloma-associated T cell differentiation possesses features of anti-tumour immunity

To identify molecular drivers of tumour-immune crosstalk, we analysed the expression of intercellular signalling molecules in tumour-associated T cell subsets (see “Methods”). This analysis identified an intercellular signalling network dominated by T cell effector molecules IFN-γ and TNF-α and antigen-presentation pathways (Fig. 5G). Patients with high T cell effector cytokine expression possessed the highest corresponding pathway activity in tumour cells (IFN-γ: R=0.3, p<0.05; TNF-α: R=0.76, p<0.001, Pearson correlation; Supplementary Fig. 8G), mechanically linking these processes. Notably, MHC and antigen processing and presentation genes were significantly enriched (GSEA, adjusted p<0.1) in tumour cells for 6 of 16 (37.5%) patients tested (Fig. 5H). Interestingly, non-viral specificity signature expression was highest in patients whose tumour cells significantly upregulated the MHC pathways (p=0.021, Wilcoxon test; Fig. 5H), suggesting that MM-associated T cell differentiation contains a component of tumour reactivity that was connected to tumour MHC class I expression and potentially antigen presentation.

To explore tumour-reactivity, we utilised a recently-reported gene signature of tumour-reactive BM T cells in MM13 and found significant positive correlation with our non-viral gene signature (p<0.001, R=0.55 Pearson correlation; Supplementary Fig. 8H). We also reasoned that if MM-associated T cell differentiation was linked to tumour reactivity, this might be revealed in the response to immunotherapy. We acquired additional scRNA-seq data from 3 patients who responded to T cell engager (TCE) therapy14 and identified TCRs which significantly expanded post-treatment (see “Methods”; Supplementary Fig. 8I). Interestingly, T cells bearing these TCRs highly expressed the non-viral specificity signature pre-treatment (Fig. 5I). Together, this suggests this phenotype contains tumour-reactive TCR clones with the capability to respond to immunotherapy.

Finally, we asked if T cell-associated pathway activity in tumour cells was related to outcome. In an independent cohort of 599 MM patients, we found patients whose tumours highly expressed either MHC or stress pathway genes had superior outcome (MHC p=0.031 and stress p=0.014, log-rank test; Fig. 5J), suggesting that T cell differentiation dynamics associated with these tumour-intrinsic processes may influence clinical outcomes.

Autologous stem cell transplantation enhances T cell skewing through the expansion of pre-existing clones contributing to poor outcome

We next asked how the T cell dynamics we observed in untreated patients are altered by anti-tumour therapy. Autologous stem cell transplantation (ASCT) is standard of care in fitter patients with newly diagnosed MM. Work in patients and mouse models has suggested that the benefit of ASCT relies on the augmentation of anti-tumour T cell immunity15,16. To explore this hypothesis, we analysed bone marrow samples from newly diagnosed transplant-eligible patients who received ASCT with deep TCR-seq, scRNA-seq and scTCR-seq (Fig. 6A).

Repertoire clonality rose significantly following ASCT (p<0.001, paired Wilcoxon; Fig. 6B). Similarly, the frequency of CDR3 clusters among expanded TCRs rose post-ASCT (Fig. 6C). We modelled the abundance of TCRs pre- and post-treatment with a Poisson framework (see “Methods”) to identify TCRs which significantly increased in abundance post-treatment (Fig. 6D). Among these clones, 29% (range 10.7–48) were present at low abundance pre-treatment. Using pre-treatment samples found in the larger scRNA-seq dataset (Fig. 2), we identified the baseline phenotype of clones which expanded post-ASCT and found they predominantly occupied terminally-differentiated and effector CD8+ T cell clusters (Fig. 6E) and highly expressed the non-viral signature (p<0.001, unpaired Wilcoxon; Fig. 6F). In contrast, clones we annotated as viral-reactive (see “Methods”) did not significantly expand post-ASCT (Corrected p>0.05, Poisson model). We independently performed scRNA-seq on four samples from patients taken post-ASCT and found that ASCT-expanded clones post-treatment expressed terminal differentiation and Teff genes such as GZMB and TNF (Fig. 6G) and expressed significantly higher levels of the non-viral signature (p<0.001, mixed-effect model with patient as random effect). Together, this shows ASCT is associated with alterations resembling antigen-driven CD8+ T cell differentiation.

We next asked if the abundance of clonally expanded terminally differentiated CD8+ T cells was reflective of response to therapy. ASCT patients were followed for a median of 3.9 years (range 1–5.8) after which 12 of 19 (63%) patients relapsed. The presence of detectable residual tumour in the marrow post-ASCT (minimal residual disease positive, MRD-positive) is associated with earlier relapse17. Notably, we found that post-ASCT marrow T cells from MRD-positive patients demonstrated significantly higher TCR clonality (p=0.035, Fig. 6H). As ASCT-expanded clones were terminally-differentiated pre-treatment (Fig. 6E), we asked if T cell skewing prior to ASCT was associated with poor outcome. We identified MM patients in our CyTOF cohort who received ASCT and clustered CD8+ T cells into phenotypes aligned with our scRNA-seq data (Supplementary Fig. 8J). The abundance of CD8+ Tte and CD8+ Temra in pre-treatment samples was significantly associated with MRD-positivity (p=0.02 and p=0.03 respectively, unpaired Wilcoxon test; Supplementary Fig. 6I), suggesting T cell skewing at diagnosis predicts poor outcome following ASCT.

T cell skewing is not associated with rapid SMM progression

Finally, we explored whether T cell dynamics could be used to improved SMM risk stratification. To assess an association between T cell features and progression, 199 precursor disease patients were followed for a median of 1.1 years (range 1–5.8) after which 25 (15%) patients progressed to active disease (Fig. 7A). As expected, we found an association between time-to-progression and known progression-associated clinical features (BMPC%, paraprotein, FLC ratio: p<0001 log-rank test for all).

Firstly, we asked whether T cell skewing identified SMM patients at high risk of progression. T cell skewing was not significantly enhanced in MM relative to SMM in either our scRNA-seq (p=0.53; Fig. 3C) or CyTOF (p=0.37; Supplementary Fig. 10A) cohorts. Similarly, in our flow cytometry cohort the abundance of late-differentiated IL7R– CD8+ T cells was not higher in SMM patients who subsequently progressed relative to non-progressors (p=0.25, one-way ANOVA; Fig. 7B). As such, there was only a weak trend between IL7R– CD8+ T cells and time-to-progression (p=0.13, log-rank test; Fig. 7C). These results demonstrate that T cell skewing presents similarly in asymptomatic SMM and active MM and, in this cohort, does not predict progression.

Regulatory CD4+ T cell loss is a biomarker to predict risk of SMM progression

To explore T cell features which might identify SMM patients at increased risk of progression, we returned to our analysis comparing SMM and MM patients (Supplementary Fig. 5E). We observed a reduction in the abundance of CD4+ Treg in MM relative to SMM in the scRNA-seq (uncorrected p=0.03) and CyTOF (p=0.05) cohorts (Fig. 7D). In the larger flow cytometry cohort, we found CD4+ Treg abundance was reduced in SMM patients that subsequently progressed relative to both non-progressors and MGUS patients (Fig. 7E, left and Supplementary Figs. 9B and 10B). As no individual CD4+ Treg sub-cluster correlated with CD4+ Treg loss (Supplementary Fig. 4A; p>0.1 for all, Pearson correlation) we concluded this represented an overall depletion of CD4+ Treg numbers. As such, this effect was also seen in the reduction of CD4+ Treg among total (non-tumour) cells in progressing SMM patients (Fig. 7E, right). Together, this suggests CD4+ Treg loss defines active myeloma relative to precursor disease.

We explored the ability of CD4+ Treg loss to serve as a marker of SMM progression. We focused on the abundance of CD4+ Treg among non-tumour cells as this quantification was independent of existing risk factors (paraprotein p=0.64 and BMPC% p=0.58, Pearson correlation; Supplementary Fig. 10C). MGUS and SMM patients with low CD4+ Treg abundance (<0.53% non-tumour cells) were significantly more likely to progress to MM (p=0.016 log-rank test, hazard ratio=2.7; Fig. 7F). We obtained the same results when removing MGUS patients (n=167 SMM, p=0.035 log-rank test hazard ratio=2.4). Multivariate analysis showed CD4+ Treg abundance predicted progression independently of existing SMM risk models (Mayo p=0.04, IMWG p=0.08; Fig. 7G). Together, this suggests that CD4+ Treg loss represents a putative biomarker for SMM risk.

Methods
Clinical sample acquisition
Bone marrow aspirates from individuals with myeloma or precursor conditions were obtained from patients included in one of four ongoing clinical trials: (1) Defining risk in smouldering myeloma for early detection of multiple myeloma (COSMOS), a multicentre, observational UK study in smouldering myeloma (NCT05047107, COSMOS study UK Research Ethics Committee reference: 270077); (2) Risk-Adapted therapy Directed According to Response (RADAR), a randomised phrase II/III trial in newly diagnosed patients with multiple myeloma eligible for transplant (UK Research Ethics Committee reference 20:/LO/0238)41; (3) Carfilzomib/Cyclophosphamide/Dexamethasone with Maintenance Carfilzomib in Untreated Transplant-eligible Patients with Symptomatic MM to Evaluate the Benefit of Upfront ASCT (CARDAMON), a phase II trial (UK Research Ethics Committee reference: 148600)42; (4) Biology of Myeloma, an observational study open to all plasma cell disorder patients treated at University College London Hospitals (Research ethics committee reference: 07/Q0502/17). Bone marrow aspirates from non-cancer controls were collected as a by-product of routine elective orthopaedics surgery (hip or knee replacements) via the UCL/UCLH Biobank for Studying Health and Disease (UK Research Ethics Committee no: 272816). Material was obtained following written informed consent in accordance with the Declaration of Helsinki.

Sample acquisition and processing
Bone marrow aspirates were collected in ethylenediamine-tetraacetic acid (EDTA, Cambridge Bioscience, 60-00030-11) and processed within 24 h of collection. Mononuclear cells (MNCs) were isolated by Ficoll Paque density gradient centrifugation, using SepMate tubes (StemCell Technologies, 85420). Freshly isolated BM MNCs were analysed for tumour infiltration (BM PC %) by multi-parameter flow cytometry. Tumour cell marrow infiltration was determined as the frequency of live BM MNCs cells co-expressing CD38 and CD138 (Supplementary Fig. 1E). For CyTOF experiments, MNCs were cryo-preserved in 90% FBS and 10% DMSO prior to use. For flow cytometry experiments, samples were assessed fresh immediately after acquisition.

Multi-parameter flow cytometry antibody staining, data acquisition and analysis
Single cell suspensions from freshly isolated BM mononuclear cells were resuspended in PBS, blocked with 5% mouse and rat serum and stained with the following antibodies: CD138-PE (1:100; MI15, BioLegend, 356504), CD38-PE-Cy7 (1:25; HB7, BioLegend, 356608), CD3-BV785 (1:100; OKT3, BioLegend, 317330), CD56-BV605 (1:100; NCAM16.2, BD Biosciences, 562780), CD4-FITC (1:50, OKT4, BioLegend, 317408), CD8-PB (1:100, RPA-T8 BioLegend, 301033), CD25-BV711 (1:100, M-A251, BioLegend, 356138), CD127-PECy5 (1:100, A019D5 BioLegend, 351324). Fixable Viability Stain-780 (1:250; BD Biosciences, 65-0865-14) was used for dead cell exclusion. Samples were measured by LSRFortessa Cell Analyser (BD Biosciences) and manually gated (Supplementary Fig. 9B) with FlowJo (v10, BD Biosciences).

scRNA-seq and scTCR seq sample and library preparation
For newly-generated “T cell-enriched/depleted” scRNA-seq samples T cells were enriched from freshly isolated BM MNCs by magnetic separation using a Pan T cell Isolation Kit (Miltenyi Biotec, 130-096-535) and CD15 MicroBeads (Miltenyi Biotec, 130-046-601). After sorting, the T cell depleted and enriched compartments were pelleted and resuspended in 0.04% BSA in PBS at 10^6 cells/mL and loaded onto the Chromium Controller (10X Genomics). For newly generated ‘CD8-enriched’ samples T cells were enriched using the same protocol with the addition of CD4 MicroBeads (Miltenyi Biotec, 120-000-440) and only CD8-enriched samples were loaded. This generated a total of 47 libraries. All samples were processed using the Chromium Next GEM Single Cell 5’ Dual Index Kit (10X Genomics, v2) following manufacturer’s protocol. T cell and CD8-enriched samples were additionally processed using the VDJ kit (10x Genomics). The libraries were sequenced by Illumina NovoSeq 6000. We used CellRanger v6.0.0 pipeline (10x Genomics) to align gene expression (GEX) and V(D)J (Immune Profiling) experiments using the GRCh38-2020-A and vdj_GRCh38_alts_ensembl-5.0.0 human reference genomes, respectively. Across samples, a median of 6367 cells with a median proportion of 0.76 cells with productive VJ spanning TRA and TRB pairs per-sample.

Publicly available data
To assemble our large integrated dataset, scRNA-seq data from 12 published studies was acquired and combined with newly generated data (Supplementary Data 6)18,26–28,43–50. Specifically, data shared through the gene expression omnibus (GEO) can be accessed for Maura et al. under the accession GSE161195, Bailur et al. GSE163278, Oetjen et al. GSE120221, Granja et al. GSE139369, Zavidij et al. GSE124310, Kfoury et al. GSE143791, Zheng et al. GSE156728, Botta et al. GSE205393, and Friedrich et al. GSE216571. 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. at covid19cellatlas.org, Conde et al. at tissueimmunecellatlas.org, and Liu et al. at humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81.

Filtering, integration, clustering, and dimensionality reduction of scRNA-seq data
scRNA-seq data were analysed using scanpy (1.8.2)51. Gene-barcode matrices for all newly generated and re-analysed samples were assigned unique sample-specific barcodes, merged, and subset to high-quality cells for integration (minimum unique genes >200, minimum total counts >500, total percentage mitochondrial chromosome-encoding transcripts <10%, total percentage transcripts encoding haemoglobin genes HBB, HBA1 and HBA2 <20%). Cells called as doublets by scrublet (0.2.3)52 were removed. Samples with <100 high-quality cells were removed before integration.

For integration, we utilised single-cell variational inference (scVI) from the scvi-tools package (0.15.2)53. A subset of 7000 highly variable genes across batches were calculated using log(x+1) normalised gene expression with the function scanpy.pp.highly_variable_genes(adata, batch_key=”batch”) to identify genes with consistently high intercellular variation across different batches. Specific gene groups which can vary between cells for technical (mitochondrial, representing cell stress) or irrelevant biological (immunoglobin and TCR genes, representing lymphocyte clonality) reasons were excluded from highly variable genes to prioritise clustering on phenotype-defining genes. The un-normalised expression of these 7000 variable genes was prepared for a scVI model using the function scvi.model.SCVI.setup_anndata() with sample batch as the batch key and sample identifier and 10x chemistry as categorical covariate keys. A scVI model was then initialised with the following non-default parameters: scvi.model.SCVI(n_latent=30, n_layers=2, dropout_rate=0.2, gene_likelihood=”nb”). These parameters (number of variable genes, number of latent dimensions and hidden layers, dropout rate) were selected through a parameter sweep focused on minimising batch influence on integrated latent representation and retaining biological identity (data not shown). Minimisation of batch influence was assessed by linear regression of latent dimensions against batch covariates as implemented by scib (https://github.com/theislab/scib). The retention of biological identity was assessed by analysing the separation of CD4+ and CD8+ T cells (the median log ratio of CD4-expressing and CD8A-expressing cells closest to zero across clusters). This model was trained for a maximum of 400*(20,000*x) epochs where x was the number of input cells. Integration was first performed on all cells then repeated for just T cell clusters using 5000 highly variable genes but otherwise identical parameters.

The latent representation of the trained scVI model was used to create a k-nearest neighbours graph using scanpy.pp.neighbors(adata, n_neighbors=10) for subsequent graph-based clustering using the Leiden algorithm. The size of the local neighbourhood (n_neighbors=10) and Leiden clustering resolutions were selected for optimum granularity of biological clusters. Analysis of the latent representation was used as input for creation of a uniform manifold approximation and projection (UMAP, scanpy default parameters) or Minimum-Distortion Embedding using pymde (0.1.15)54. For visualisation of a large number of cells on either UMAP or MDE, scattermore (1.0) was used to create dot plots.

Differential expression and pathway analysis of scRNA-seq data
Differential expression between specified conditions was performed using scran (1.26.2) function pairwiseTTests() between specified contrasts with batch as the blocking level for each cell to model for batch effects. This restricts differential expression comparisons within individual batches and pools the downstream result, meaning no inter-batch comparisons were performed (which would incur batch effects). Marker genes were combined with supervised analysis of the expression of known RNA and protein markers to phenotype clusters. Genes were identified as significantly differentially expressed with a false discovery rate (FDR, Benjamini and Hochberg-adjusted p value) of <0.1. Pathway analysis of differentially expressed genes was performed using fgsea (1.24.0) with gene set enrichment analysis of gene sets from BIOCARTA, KEGG and REACTOME databases accessed via msigdbr (7.5.1; Supplementary Data 3).

Phenotyping gene expression clusters from scRNA-seq data
Cluster markers genes were calculated using log-normalised expression of all genes in a study-aware fashion using the findMarkers function (scran) specifying test.type=”wilcox” and batch as the blocking level for each cell. Clusters characterised by expression of known stress-associated genes (for example, JUN, FOS)55 or by co-expression of marker genes for independent phenotypes (for example, T and B cells) were removed. For T cell cluster phenotyping, clusters lacking expression of CD3D, CD3E and CD3G or both CD4 and CD8A were removed. Cell type prediction tools were run with default parameters: “Azimuth” (https://azimuth.hubmapconsortium.org/) and “Celltypst” (https://www.celltypist.org/). Manually curated T cell naïve and cytotoxicity gene signatures were taken from Chu et al.56. Gene sets were applied to cells UCell (2.2.0)57.

CD4+ Treg sub-clusters were defined by early markers CCR7 and TCF7, suppressive molecules CD25 (IL2RA) and CTLA4, and activation markers GITR (TNFRSF18) and OX40 (TNFRSF4; Supplementary Fig. 4A. The invariant cluster was composed of γδ T cells (gdT) (defined by TRDV2 and TRDC) alongside KLRB1 and SLC4A10-expressing MAIT cells (Supplementary Fig. 4B). One MAIT sub-cluster was enriched in Th17 RORC and CCR6 possibly corresponding to MAIT17 cells58.

Differential abundance analysis of scRNA-seq data
We normalised cell type abundance following a compositional data framework59. For each sample, cluster counts were derived and zero values replaced by a Bayesian-multiplicative replacement strategy which preserves the ratios between non-zero clusters, implemented using the zCompositions (1.4.0-1) function cmultRepl()60, generating zero-imputed pseudo-counts. The centred log-ratio (CLR) transformation was then used to transform pseudo-counts relative to the geometric mean of all clusters in a given sample, implemented using the compositions package (2.0-6) function clr(). The CLR transformation thus reports cell type abundance relative to the per-sample average seeking to reduce the mutual dependency of proportional data61.

Normalised cluster abundances were used as input for a combination of intercept-only and additive regression models exploring the relationship between cluster abundance and different conditions (for example, patient group, or patient group and age) as described. Selected comparisons were also performed using a mixed-effect model with an additional random effect term (for sample, study of origin) implemented using lmerTest (3.1-3).

For samples from Stephenson et al.46 the median age of each age range was used (for example, 52.5 was used for the 50–55 group). For the model in Fig. 3E, age values were binarized to above and below the median (62 years) but results remained significant when modelling age as a continuous variable in both SMM (disease p<0.001, age p=0.01, linear model) and MM (disease p<0.001, age p=0.02, linear model). T cell skewing was greatest (enrichment of terminal memory clusters) in SMM and MM relative to controls independent of age (p<0.004 and p<0.001, respectively, linear regression; Fig. 3E). In the cases of patients with longitudinal sampling45,48, only the first longitudinal timepoint was analysed unless otherwise specified. For analysis of paraprotein data, only patients with IgG and IgA isotype tumours (which produce heavy chain paraprotein molecules measurable in serum) were analysed.

The abundance of invariant and CD4+ Treg sub-clusters was not significantly different between health and disease (all comparisons p>0.1, linear regression), except for an enrichment of γδ T cells and CD4+ Th17-like MAITs in SMM relative to controls (Supplementary Fig. 5G).

Unsupervised ordination of T cell composition and calculation of exaggerated T cell ageing
Normalised T cell cluster abundance was scaled per-cluster and used as input for PCA using the base R stats function prcomp. For the comparison of T cell skewing with the peripheral blood (Fig. 3H), we first re-scaled normalised peripheral blood T cell cluster abundance to the same centre and range of the scaled BM T cell abundance matrix. Next, we multiplied this scaled PB matrix by the pre-calculated BM PCA feature loadings via feature (cluster)-wise matrix multiplication, yielding the PC1 values for the input PB matrix.

Exaggerated T cell ageing was calculated by first constructing a linear regression model examining the relationship between PC1 and age for non-cancer controls only. The residuals of this model (Supplementary Fig. 5J) were normality distributed as assessed by Shapiro–Wilk test (p=0.89), Kolmogorov–Smirnov test (p=0.97) and visually via a quantile-quantile plot (Supplementary Fig. 5K). Next, the age of cancer patients was used to predict PC1 values for each patient in this model. The difference between predicted and real PC1 values (residuals) for each patient was interpreted as the difference between the T cell skewing expected for each patient’s age versus their observed T cell skewing, respectively. These residual-derived values were termed “exaggerated T cell ageing”. A patient was considered to have exaggerated T cell ageing if their values were greater than zero (meaning skewing was greater than expected for their age). Similarly, “excess T cell years” were calculated as the residuals between a patient’s real age and the age predicted from their PC1 values by a model of PC1 and age in controls.

Label transfer
We validated our phenotypes in an independent scRNA-seq dataset of BM T cells from healthy donors (n=3) alongside precursor disease (n=7) and MM patients (n=10) from Botta et al. (19). The scANVI model from the scvi package was used for label transfer, using the Botta et al. dataset as the query and the full T cell dataset as the reference. The query dataset underwent the same pre-processing and gene filtering as the full T cell dataset. This processed query dataset was then used as input for a scANVI model. For the input reference model of this scANVI model, the full T cell dataset integration SCVI model was used. This scANVI model was then trained with the same hyperparameters as this scVI model. Next, the trained scANVI model was used to predict the T cell phenotype of unlabelled query cells based on labelled reference cells following the scArches semi-supervised surgery pipeline62. Briefly, a k-nearest neighbours’ graph was constructed from the joint query and reference latent space generated by the trained scANVI model. Then, based on the abundance of labelled reference cells near unlabelled reference cells, each reference cell was assigned a weighted prediction for each possible reference label. High confidence predicted labels (uncertainty value >0.2) were determined to be the T cell phenotype of each query cell and taken forward for downstream analysis. This approach made accurate predictions of cell type identity matching the same expression profile as the larger dataset (Supplementary Fig. 4G).

scTCR seq pre-processing, clonal expansion calculation, T cell subset identification and clustering
TCR paired alpha and beta clones (clonotypes) were defined by CellRanger VDJ (raw_clonotype_id, clonotype_id) by matching shared V and J gene and nucleotide CDR3 sequences for alpha and beta TCR chains. Clonotypes were appended to single cells by matching cell barcodes. For scTCR-seq derived from published data, we utilised published clonotype identifiers. Clonal expansion was calculated as the abundance of cells labelled with each clonotype identified in each sample. CD8+ and CD4+ clones were identified by the presence of >75% of a clone’s cells within CD4+ or CD8+ clusters (otherwise, were removed). Repertoire clonality was calculated among each specific subset of cells (such as all T cells or CD8+ memory cells) with a minimum of 100 cells using Simpson’s diversity index63. Clusters of TCRs with similar sequence features were identified within a single patient’s alpha or beta chain repertoire using tcrdist3 (0.2.2)64 using default parameters. TCR clustering networks were constructed and visualised using igraph (R, 1.4.2). CDR3 sequence logos were created with Logomaker (https://logomaker.readthedocs.io/en/latest/).

For analysis of scTCR-seq from Friedrich et al.14 (Supplementary Fig. 8I), clones which significantly expanded post-treatment was identified with a Poisson framework, modelling the abundance of each clone pre- and post-treatment as the number of events and the total number of cells in each sample as the rate of Poisson sampling (to account for differences in sample size).

Annotation of HLA-matched viral reactivity-annotated TCR clones
HLA genotypes for 19 patients were derived using arcasHLA65 ran on Cellranger output bam files (possessorted_genome_bam). All 19 patients were newly sequenced for this study and therefore a combination of T cell-enriched/depleted and CD8-enriched (Supplementary Fig. 1B) samples were available. arcasHLA was ran on every sample for each patient. HLA genotype for class I and class II HLA was almost entirely identical across samples for an individual. In the rare cases of two different samples of the same patient possessed different HLA genotypes, both predicted genotypes were ignored.

Each donor’s repertoire was then compared against the annotated TCR reactivity database VDJdb, IEDB and CEDAR66–68 subset to TCRs with annotated reactivity against an epitope from a single human virus: cytomegalovirus (CMV), Epstein Bar virus (EBV), Influenza A, or severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). TCRs annotated as reactive against more than one human virus were also removed. The viral dataset set was further subset to HLA-matched sequences for each patient’s HLA genotype. A query TCR clone was annotated as putatively viral-reactive if at least one alpha or one beta chain CDR3 sequence perfectly matched a CDR3 annotated against the same virus in the database, and this clone’s paired chain also perfectly matched or possessed a highly similar CDR3 sequence to the same virus in the same HLA background. CDR3 similarity was performed as described previously69. Briefly, each TCR chain’s CDR3 amino acid sequence was deconstructed into a series of overlapping triplets. Pairwise similarity between two CDR3 was defined as the number of shared triplets normalised to the number of triplets per comparison.

Analysis of tumour cell transcriptional state
To analysed tumour cell transcriptional features, we identified putative malignant clones via clonal immunoglobulin usage. We then scored 67,656 plasma cells from 46 patients with a set of pan-cancer transcriptional pathways (Supplementary Fig. 7E and Supplementary Data 3) using UCell. To compare the expression of individual pathways between patients, we calculated the abundance of cancer cells highly expressing a given pathway as the percentage of cells with expression greater than one standard deviation above the median across all patients’ tumour cells.

Identification of malignant plasma cell clones in scRNA-seq
Patient plasma cells were isolated from the clustering of all cells (Fig. 2A) and patients with <50 plasma cells were removed. This generated 67,656 plasma cells from 46 patients with a median of 467 plasma cells each (range: 76–13,638). To identify tumour cells among plasma cells, we leveraged the clonal plasma cell origin of myeloma. First, we attached the expression of all available immunoglobulin genes to each cell, after removing any gene filtering performed earlier. Then, for each plasma cell, we identified the most highly expressed light variable (kappa or lambda), heavy variable, and heavy constant chains. We next quantified and ranked the abundance of every gene for each chain among an individual’s plasma cells (Supplementary Fig. 7A). Most individuals possessed a single gene for each chain which was expressed by the majority of plasma cells (light variable median 95% range: 31–100%, heavy constant median 96% range: 35–100%, heavy variable median 91% range: 24–100%). The frequency of different light and heavy variable genes among tumour cells matched previously reported frequencies in myeloma70, including IGHV3-30 in 3 (6.5%) and IGKV1-39 in 2 (4.3%) patients. We inferred that clonal immunoglobulin expression corresponded to clonal plasma cells and labelled any plasma cell expressing the most highly abundant gene for each chain in that donor as a tumour cell. This method yielded 67,048 predicted tumour cells. Predicted tumour cells uniquely co-expressed clonal immunoglobulin genes (Supplementary Fig. 7B) and expressed genes characteristic of their translocation subgroups (Supplementary Fig. 7C)71, suggesting they did represent malignant cells. These cells composed the majority of plasma cells in all patients but were most abundant in MM (Supplementary Fig. 7D).

Transcriptional pathway analysis of tumour cells in scRNA-seq data
To analyse tumour cell transcriptomes, we scored tumour cells using a set of pan-cancer transcriptional pathways72 (Supplementary Data 3) using UCell. To compare the expression of individual pathways between patients, we calculated the abundance of cancer cells highly expressing a given pathway as the percentage of cells with expression greater than one standard deviation above the median across all patients’ tumour cells.

Identification of novel pathways enriched in malignant cells
To identify novel sets of genes enriched in malignant relative to normal plasma cells, we first isolated each the tumour cells from each patient in turn. Next, we performed differential expression between each patient’s tumour cells only and all other plasma cells not classified as malignant. Differential expression was only performed between cells from the same sequencing batch. This yielded a set of malignant-enriched genes for each patient. Pathway analysis was then performed as described. The pathways significantly enriched among malignant-associated genes in four or more patients were identified (Supplementary Fig. 7F).

Cell-cell interaction analysis
Differentially-expressed genes from T and tumour cells were screened for cognate ligand-receptor interactions in the OmniPath database73 via OmnipathR (3.15.1). From this set of interactions, a cell-cell interaction graph was created with igraph and visualised with ggraph (2.2.1).

Transcriptional pathway expression in CoMMpass
We analysed an association between the expression of the stress and MHC pathways (Supplementary Data 3) with overall survival in bulk RNA sequencing samples from the CoMMpass cohort of newly diagnosed MM patients, with RNA sequencing data processed and normalised as described in Bauer et al.74. We calculated the expression of the pathway by taking the expression of each constituent gene, scaling expression between 0 and 1, and taking the average.

Patient outcome analysis
We assessed the predictive power of (1) transcriptional pathway expression for overall survival in patients in the CoMMpass dataset (Fig. 5J); (2) T cell subset abundance and time-to-progression (Fig. 7C, F). Covariate thresholds (such as expression level) for outcome were selected using the maximally selected rank statistic as implemented by maxstat (0.7-25). The association between this threshold and outcome was assessed using univariate or multivariate (as in Fig. 7G) Cox proportional hazards regression models using the “survival” (3.5-5) with default parameters.

Deep TCR sequencing
Before the RNA extractions samples were T cell enriched, by performing Pan T cell isolation (Miltenyi Biotec), following manufacturer protocol. RNA was extracted using Ralia Prep RNA Cell Miniprep System (Promega), following the manufacturer’s instructions. RNA integrity was assessed by TapeStation (Agilent Technologies). TCR α-chain and β-chain sequencing was performed by utilising whole RNA extracted from CD138-depleted cells from 19 patients with matched pre- and post-ASCT samples, by using a quantitative experimental and computational TCR sequencing pipeline described69. Clonality and clustering of deep TCR-seq samples was calculated identically to scTCR-seq.

CD69+ T cell subset functional assessment
Bone marrow MNCs were obtained from patients enrolled on the RADAR study via density gradient centrifugation with Ficoll-Paque (Cytivia 17144003). Samples were then enriched for T cells using magnetic-activated cell sorting (Pan T cell isolation kit, Miltenyi Biotec, 130-096-535) and stained for flow-activated cell sorting with CD8-eFluor450 (1:100, SK1, Invitrogen 48-0087-42), CD69-BV605 (1:100, FN50, Biolegend 310938), CCR7–BV785 (1:100, G043H7, BioLegend 353229), and Fixable Viability Dye eFluor 780 (1:250, eBioscience, 65-0865-14). An aliquot of unsorted cells was saved for staining (stim–). Cells were sorted into CD8+ CCR7– CD69+ and CD8+ CCR7– CD69– fractions and both were collected. Cells were then resuspended at 1×10^6/ml in RPMI (Gibco 12027599) 10% FBS (Gibco 10500-064) 2mM L-Glutamine and stimulated with 25uL/ml ImmunoCult (STEMCELL, 100-0785) or 10uL/ml TransAct (Miltenyi, 120-111-160) overnight at 37°C (5% CO2). Brefeldin A (Biolegend, 420601) was added 4h prior to harvesting of cells. Cells were then harvested, washed, and extracellular antibodies stained for 30 min in the dark at 4°C; CD57-FITC (1:100, HNK-1, Biolegend 359603), PD-1-BB700 (1:100, EH12.1, Biolegend 566460), Fixable Viability Dye eFluor 780 (1:250, eBioscience, 65-0865-14), CD3-BV421 (1:100, SK7, Biolegend 344834), CD8-V500 (1:100, RPA-T8, BD Horizon, 560774), CD69-BV605 (1:100, FN50, Biolegend 310938), CCR7-BV785 (1:100, G043H7, Biolegend 353229), and 4-1BB-PE-Dazzle (1:100, 4B4-1, Biolegend 309825). Cells were then washed, permeabilised, and intracellular markers stained (FoxP3/Transcription Factor Staining Buffer Set, eBioscience 00-8333-56) for 30 min in the dark at 4°C: IFN-G-PE, (1:100, 4S.B3, Invitrogen, 12-7319-41) and TNF-a-APC (1:100, MAb11, Biolegend, 502912). Cells were then washed, and data acquired on NovoCyte Quanteon.

CyTOF antibody staining, data acquisition and analysis
Details on antibodies are listed in Supplementary Data 5. Conjugation of the purified antibodies with metal reporters was performed with the MaxPar X8 and MaxPar MCP9 antibody labelling kits (Fluidigm Sciences) according to the manufacturer’s instructions. Frozen bone marrow MNCs or the CD138-negative populations were thawed rapidly at 37°C and resuspended into pre warmed thawing media of RPMI (Sigma-Aldrich) containing 20% FBS, 2mM EDTA (pluriSelect) and 5mg DNase (Sigma-Aldrich). Cell suspensions were washed and filtered to form a single cell suspension. Cells were incubated with 5mM Cell-ID cisplatin (Fluidigm Sciences) in serum free RPMI for 3 min at room temperature (rT) to identify dead cells. Cells were then washed and incubated with human Fc block (BioLegend) for 10 min at rT before being barcoded using 6-choose-3 Cadmium CD45 Live Barcoding (Fluidigm Sciences). All samples were stained in the same batch. After live cell barcoding, the combined samples were then stained with metal-conjugated antibodies for surface antigens for 30 min at rT. After staining, cells were washed with MaxPar Cell Staining Buffer (Fluidigm Sciences, 201068) and permeabilised with MaxPar nuclear antigen staining buffer before staining with metal-conjugated antibodies for intracellular antigens. Cells were again washed and fixed using 1.6% paraformaldehyde. Cells were then incubated with Cell-ID intercalator-Ir (Fluidigm Sciences) to stain all cells in MaxPar Fix and Perm Buffer (Fluidigm Sciences, 201067) and aliquoted and frozen in cryovials. Stained samples were thawed and washed on the day of acquisition.

Cells were acquired on the Helios mass cytometer (Fluidigm Sciences). Data from different days were normalised by using EQ Four Element Calibration Beads (Fluidigm Sciences). Data was debarcoded using the Fluidigm CyTOF software and patient sample fcs files run from different days were concatenated. Before downstream analysis, initial data clean-up was carried out using FlowJo. Live CD3+ cells were exported by manual gating on Event_length, Residual, Offset, DNA (191Ir and 193Ir), live cells (195Ir) and CD3 expression (89Y). Further manual CyTOF gating of T cell phenotypes (Supplementary Fig. 9A) was performed with FlowJo (v10, BD Biosciences).

CyTOF clustering
CyTOF data were analysed using a custom R pipeline using gated CD8+ T cells as input. Batch effects were normalised using the R package CytofIn (0.0.0.9000) using technical replicates (multiple aliquots of the same sample) in each batch. Batch correction was assessed using expression distribution and UMAP plots (not shown). Batch-corrected expression values were arcsine-transformed and clustered using FlowSOM (2.6.0) on a 12×12 node self-organising map. Clusters were assigned to phenotypes based on known CD8+ T cell markers (Supplementary Fig. 8J) including CD8+ Temra (CD27–CD28–GZMB+Tbet+CD45RA+) and CD8+ Tte (CD27–CD28–GZMB+CD57+).

Statistical analyses
Box plots represent the first and third quartiles around the median with error bars extending 1.5 times the interquartile range (IQR). For comparison of means in box plots, p values were calculated by either two-sided unpaired Wilcoxon test or one-way ANOVA followed by Tukey’s test for pairwise comparison evaluation as indicated. Correlation coefficient (R) and p values for correlations were calculated by Pearson correlation. In linear regression slopes, shaded regions represent the 95% confidence interval. For hierarchal clustering on heatmaps, Euclidean distance was used as the default distance measure.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In the manuscript Foster et al., the authors aimed to identify changes in T cell subsets driven by MM progression from precursor states and understand how these alterations to T cell differentiation influence disease progression. They demonstrate using single cell RNA and TCR sequencing methods that altered T cells in MM patients possess features of terminal memory differentiation, not exhaustion, and resemble T cell skewing observed in immune ageing. This is correlated with disease stage and driven by tumor intrinsic features. Since T cells were found to not be exhausted, this may explain why checkpoint inhibitors are largely unsuccessful in MM. The authors also identify Treg loss as a novel biomarker for SMM progression, contradictory to the reported increase of immunosuppressive Tregs. The degree of T cell characterization and immune dysfunction through disease stages is extensive, providing a framework for future studies that is immensely valuable to the field. The data analysis is excellent, and the interpretation and conclusions are well-supported.\n1. MAIT cell abundance is repeatedly found to be different between healthy and disease. Can the authors comment on this cell type and possible function/absence in disease?\n2. The authors discuss the “accumulation of TCRs possessing similar CDR3 sequences indicates responses against common antigens” and were not found to be viral reactive. Is there a way to show/model that the increased non-viral TCR repertoire are tumor antigen-specific (antitumor immunity)? Is tumor MHC-I expression increase a good enough correlate? More evidence to support this association?\n3. Have the authors identified any shared tumor neoantigens among patients? If tumor-reactive T cells are not exhausted, why can they not control tumor growth (propose mechanism of tumor escape)?\n4. The authors conclude overall Treg loss “may reflect early reshaping of the BM microenvironment towards a state favoring progression.” This is contradictory to reports of increased Tregs contribute to immunosuppression and favor tumor progression. Could the authors discuss how Treg loss may contribute to active MM progression mechanistically?\n5. Were senescent T cells (characterized by CD28-, CD57+, KLRG-1+, CD160+) not found? TEMRA cells can exhibit cellular senescence. Include more references to T cell senescence in MM?\nMinor comments:\nLine 61: typo “increase h” risk\nLine 119: “show diverse” or “diversify”\nLine 191: “BM T-cell composition was strikingly similar in patients and controls” refers to Fig.3A\nLine 249: …unique feature “of” T cell differentiation in myeloma.\nLine 370: “frequency of CDR3 clusters among expanded TCRs rose post-ASCT” refers to fig.6C?\nFigure 5D: typo: graph x-axis “% tumour stess”\nSupplemental Figures\nLine 87: typo: “pver” donor\nLine 300: “exaggerated T-cell ageing similarly correlated with paraprotein” refers to fig. S8B or C?", "reviewer_2": "In this manuscript, Foster et al. performed a complex and valuable work aiming to understand the T-cell differentiation in multiple myeloma and its precursor stages in response to tumor-intrinsic features by integrating a large collection of single-cell data, including healthy samples, MGUS, SMM, and MM from both bone marrow and peripheral blood. Through accurate sample integration and annotation they thoroughly annotate T-cells, showing a T-cell skewing that moves from naive and early T-cell subsets towards terminal memory cytotoxic and was associated with disease stages and age independently. They further characterize T cells clonality and the relation with T cell skewing further differentiating this tumor associated clonality with viral specific clones. By analyzing also tumor plasma cells authors establish a correlation between clonality and specific T cell populations and MRD positivity.\nThere is no question this manuscript is a valuable resource and also provides some interesting hypothesis for understanding the interaction between the immune microenvironment and MM. However, there are a number of limitations that need to be addressed\nMain\n- Integration of scRNA-seq datasets: When presenting the datasets used in the study, authors referred to 12 public + UCL studies (if I’m not wrong, sometimes referred to as “This study”, “Foster 2024” or “Foster 2025”), however, there are inconsistent numbers between Fig.1, Fig.S1A, B, Table S1 and S6. Seems that Botta 2023 study is not included in the integrated dataset or used independently. Please, clarify and correct the descriptions and figures accordingly. Authors also reach some conclusions based on the cell type composition analysis in the integrated dataset. Due to the divergencies in the sorting strategies followed between studies, these approaches are biologically meaningless, as the differences observed may point to technical biases. Importantly, plasma cells represent only 6.6% of the integrated dataset, a consequence of the different sorting strategies applied, where several removed the plasma cell fraction, and others enriched for the T-cell fraction.\n- Authors did a complete annotation of the different T cell phenotypes. Also, they deeply annotated the CD4+T-reg cells and the Invariant, identifying gdT cells, MAIT cells, etc. Why is this deep annotation not integrated into the general annotation of the dataset?\n- According to the study, BM of myeloma patients is not enriched in exhausted T-cells which is unlike other reported studies so several comments are raised: 1) Why do authors focus on the comparison of CD8+Tex with CD8+Tem-CD69+ cells? What about the other CD8+Tem cells?; 2) Authors stated: “Importantly, we distinguish CD8+Tex from PDCD1-expressing CD8+Tem.CD69+.” However, the increased expression of PDCD1 in CD8+Tem.CD69+ cells is not evident (Fig.2F, G, and Fig.S3B). Could the authors clarify that?; 3) Similar to Fig.2B, it will be nice to see the distribution of cell populations per disease group. The way to show the distribution in Fig.S5A, although informative, provides different messages; 4) Have the authors tried to subcluster the CD8+Tem.CD69+ cells? Could the subclustering reveal CD8+Tem.CD69+ subclusters related to T-cell exhaustion? Or within the overall CD8+Tem cells, could you identify a subcluster with an exhausted phenotype? 5) When comparing T-cell composition between marrow and peripheral blood, the sample donor was included as a random effect in the analysis. If the analysis is performed only with paired marrow and PB samples, are the conclusions the same?\n- Authors stated: “BM T-cell composition was strikingly similar in patients and controls”; however, to support that, a representation similar to Fig.2B must be provided for the T-cell phenotypes. Fig.S5A shows relevant information, but does not support the stated message.\n- In the T-cell composition analysis of controls with each myeloma disease stage (Fig.3B and Fig.S5B), why is the number of individuals per group different between Fig.3B and Fig.S5B?\n- The authors define the T-cell skewing based on the PCA of T-cell phenotypes' abundances, showing association with disease (SMM and MM) and age independently (Fig.3C, Fig.S5H, and Fig3.E). However, the correlation between MM age and T-cell skewing was not significant (Fig.3F, p=0.27). Moreover, the age included in the regression model was binarized (Fig.3E). Why did the authors do that? How was the threshold (62 years old) defined? Is the age still significant if the variable introduced in the model is continuous instead of categorical?\n- The authors performed linear regression models to calculate the “exaggerated T-cell aging” and “T-cell years” (Fig.3G). How good are the defined models? Could the authors show the linear regression model performance? Or include the residuals for the non-cancer donors in the plots? To note, the differences between real age and predicted age are surprisingly high. Model predicts patients' age higher than 100? Could you explain how you can get a residual of 80 or >100?\n- Within the characterization of T-cells in disease marrow, authors stated: “PC1 values and the abundance of terminal memory subsets in the BM showed a strong correlation with PB (fig.3H), indicating a similarity to systemic T-cell alterations seen with ageing (8).” As initially this section was focused on bone marrow data, now I am wondering how PC1 (T-cell skewing) was computed, in order to allow the comparative analysis between PB and BM samples. Are the T-cell skewing derived only from BM samples or both, marrow and PB?\nThis needs to be clarified and should align with the title of the section.\n- Regarding features of antigen specific clones why restrict the general analysis to CD8-expressing memory clones? Have you checked the CD4+T memory cells?\n- In Fig.5G, the result of cell-to-cell interaction analysis is shown, providing nice results. As the pathways identified in Fig.S7F are patient-specific, it would be interesting to explore what happens if this analysis is performed per patient. Detailed information on the analysis should be provided in the methods section.\n- When exploring the implications in the response to immunotherapy, specifically TCE therapy, why are only 3 patients used? And only patients who respond? What about those that not respond? Behave differently?\n- Regarding TCE therapy, authors stated that, “Interestingly, T-cells bearing these TCRs highly expressed the non-viral specificity signature pre-treatment (fig.5I), suggesting this phenotype contains TCR clones with the capability to respond to immunotherapy and may be tumour-reactive.”, pointing out that having these clones with increased non-viral specificity signature can help to fight against the tumor (tumour-reactive). However, within the ASCT cohort, the ASCT-expanded clones, also associated with non-viral signature, with high clonality associated with MDR+. If there are tumor-reactive clones expanded, how do the authors explain the association with MRD+? Is it expected?\n- Authors reference Fig.S5D to compare SMM and MM, pointing to a CD4+Treg reduction in MM; however, this reduction is not identified in Fig.S5D, only CD4+Tem is highlighted. Could you comment on that? How do you identify CD4+Treg?\n- Interestingly, CD4+Treg loss is presented as a biomarker of SMM risk. Although “Multivariate analysis showed CD4+Treg abundance predicted progression independently of existing SMM risk models (Fig. 7G)”, how much predictive power are we winning with the CD4+ Treg information?? Is this multivariate model better or significantly more powerful than the one performed only with the Mayo or IMWG risk models?\nMinor\n- Define \"decreased\" in Fig.S1B.\n- Include in Table S1 the information regarding sorting, sequencing strategy, and tissue.\n- \"B\" is missed in Figure S3.\n- Fig.3C; check the legend. A variance of 24.27% is indicated in the legend, differing from the one shown in the plot and text (21.9%). What do the blue and red represent in the figure? Define in the legend.\n- In Fig.S6B, if 279 clusters have been defined, why are only 248 shown according to the figure legend?\n- Check the reference of figures, in line 273, Fig.4H refers to Fig.4G?; in line 274 Fig.4I refers to Fig.4H?\n- In line 300, authors refer to Fig.S8D, please check that is the correct reference.\n- Fig.5B shows a p=0.053 but in the text says p<0.05.\n- Check the reference of figures, in line 370, Fig.4D refers to Fig.4C?\n- Clonality: In some plots shown as log10(Simpson’s diversity Index), in others, as the Simpson’s diversity Index. Please specify the value used in the legends and/or methods sections.\n- Really interesting the identification of CD4+T-reg loss as a putative biomarker of SMM risk, however, the authors should show if the addition of this information improve significantly the prediction of existing SMM risk models.", "reviewer_3": "The manuscript by Foster et al. presents an ambitious and comprehensive analysis of T-cell differentiation across the spectrum of multiple myeloma (MM) and its precursor states (MGUS and SMM). The authors integrate more than one million single cells from 259 individuals across 12 datasets, including their own newly generated scRNA-seq and scTCR-seq data, to provide an in-depth characterization of T-cell states and dynamics. They uncover a pattern of skewed differentiation not attributable to classical exhaustion, but instead driven by antigen-specific terminal memory features.\nThe manuscript is well written, the analyses are rigorous, and the figures clearly communicate key findings despite the large volume of data. Public release of the dataset adds further value. This study significantly contributes to the understanding of immune surveillance and dysfunction in MM and its progression.\nAmong the important findings presented, the most noteworthy are:\n1. Absence of classical exhaustion: Contrary to prior assumptions, the bone marrow of MM patients does not exhibit widespread enrichment of exhausted CD8⁺ T cells (Tex) (Except for one patient described as outlier). Instead, a more abundant population (CD8⁺Tem.CD69⁺) appears to be activated rather than exhausted. Neither subset is associated with disease progression, suggesting immune dysfunction in MM may not follow classical exhaustion pathways.\n2. T-cell skewing mirrors immune aging: The most prominent change in T-cell differentiation during disease evolution is the enrichment of terminal memory CD4⁺ and CD8⁺ subsets (Temra, Tte), termed \"T-cell skewing.\" This pattern strongly resembles immune aging and is more pronounced in SMM and MM compared to MGUS and healthy controls.\n3. TCR repertoire changes suggest antigen-driven responses: Expanded TCR clones in MM show clonal enrichment, terminal memory phenotypes, and lack viral-specific signatures. These data suggest a tumor-driven, antigen-specific T-cell response, especially given the correlation between T-cell skewing, paraprotein levels, and TCR clonality.\n4. Clinical correlations and prognostic implications: T-cell skewing is associated with tumor burden (serum paraprotein levels) and MRD positivity after ASCT, but it does not predict progression from SMM to MM. In contrast, CD4⁺Treg loss emerges as a novel and independent biomarker of progression risk.\nWhile the results are promising, further validation and clarification of certain aspects would strengthen the study. I look forward to reviewing a revised version.\nSuggestions for Improvement:\n1. Survival Analysis Beyond CoMMpass: Survival analysis is currently limited to the CoMMpass cohort. Given the size of the integrated dataset and available clinical metadata, a survival analysis incorporating T-cell skewing or CD4⁺Treg abundance in their own cohort would significantly increase the translational value of the findings.\n2. Correlation with Cytogenetics: While the authors note the inability to analyze tumor genomic classification, the lack of correlation between key T-cell findings and cytogenetic abnormalities (e.g., t(4;14), del17p, gain1q) is a limitation. If feasible, integrating cytogenetic risk information from a subset of patients or datasets would provide valuable context.\n3. Beyond Correlation. Causation and Risk Modeling: The multivariate analysis demonstrating that CD4⁺Treg loss predicts SMM progression independently of standard risk models is compelling. Extending similar modeling to T-cell skewing, particularly in the context of post-ASCT outcomes or disease aggressiveness, could strengthen the argument for a causal or predictive role.\n4. Clarify the functional role of CD8⁺Tem.CD69⁺ cells in myeloma:\nWhile the manuscript clearly distinguishes CD8⁺Tem.CD69⁺ from exhausted T cells, their functional significance remains somewhat speculative. Since these cells are abundant and express partial exhaustion markers, further clarification, using approaches like by functional assays, cytokine profiling, or external dataset validation, could help determine whether they contribute to anti-tumor immunity, immune suppression, or bystander activation. This would help to clarify the concept of “pseudo-exhausted” CD8⁺ T cells in the marrow.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Foster et al.\nPoint-by point response to reviewers\nReviewer #1 (Remarks to the Author):\nPlease note, we include a list of references specific to this rebuttal at the end of the\ndocument.\nReview comments:\nIn the manuscript Foster et al., the authors aimed to identify changes in T cell subsets\ndriven by MM progression from precursor states and understand how these alterations\nto T cell differentiation influence disease progression. They demonstrate using single\ncell RNA and TCR sequencing methods that altered T cells in MM patients possess\nfeatures of terminal memory differentiation, not exhaustion, and resemble T cell\nskewing observed in immune ageing. This is correlated with disease stage and driven by\ntumor intrinsic features. Since T cells were found to not be exhausted, this may explain\nwhy checkpoint inhibitors are largely unsuccessful in MM. The authors also identify Treg\nloss as a novel biomarker for SMM progression, contradictory to the reported increase\nof immunosuppressive Tregs. The degree of T cell characterization and immune\ndysfunction through disease stages is extensive, providing a framework for future\nstudies that is immensely valuable to the field. The data analysis is excellent, and the\ninterpretation and conclusions are well-supported.\nWe thank the reviewer for their generous and insightful comments, and we are glad they\nfound our study to be effective and informative.\n1. MAIT cell abundance is repeatedly found to be different between healthy and disease.\nCan the authors comment on this cell type and possible function/absence in disease?\nWe find a myeloma-associated loss of naïve T-cells subsets and MAIT cells in our data\n(Fig.S5C). From this, we infer that MAIT loss represents an additional component of the\nT-cell immune aging-like diRerentiation which we found in the marrow and blood of\npatients relative to controls (Fig.3). Supporting this hypothesis, several studies have\ndescribed a reduction of MAIT cell abundance in the peripheral blood with advancing age\n(Walker et al., 2014; van der Geest et al., 2018) (similar to naïve T cells), suggesting MAIT\nloss may also a feature of T-cell aging. We also note that the MAIT cluster with the most\ninflammatory transcriptional profile, MAIT17 cells (MAIT.3; Fig.S4B) was not enriched in\ndisease (Fig.S5G), suggesting that inflammatory MAIT cells may not play an active role in\nprogression. However, MAIT cells were relatively rare in our dataset (<3% cells) precluding\nfurther analysis of this subset.\n2. The authors discuss the “accumulation of TCRs possessing similar CDR3 sequences\nindicates responses against common antigens” and were not found to be viral reactive.\nIs there a way to show/model that the increased non-viral TCR repertoire are tumor\nantigen-specific (antitumor immunity)? Is tumor MHC-I expression increase a good\nenough correlate? More evidence to support this association?\nFoster et al.\nWe screened patient TCR repertoires for shared tumour antigen-reactive TCRs (from\nVDJdb, IEDB and CEDAR accessed 06/2023, N=13 unique human cancer epitopes, N =\nHowever, these consisted of single alpha or beta chain matches (unlike the higher-\nconfidence paired chain matches we made to viral reactive-TCRs) and all these TCRs\npossess additional annotated reactivities against at least one viral antigen. This suggests\nthese TCRs may be commonly observed across patients due to their frequent probability\nof generation (Chernigovskaya et al., 2025), versus possessing a shared reactivity. We\nadditionally speculated the poor coverage of tumour antigens, none of them notably\nmyeloma-specific (with the majority of annotated tumour epitopes being the melanoma\nantigens MART1 and PMEL, 4034/5261, 77%) led to the failure to annotate tumour\nantigens in our data. For this reason, we did not report this analysis.\nTo explore the role of tumour-reactivity in the T-cell dynamics we describe, we take\nadvantage of a recent pre-print which has reported a gene signature of CD8+ myeloma-\nreactive T-cells (Wagner et al., 2025). This phenotype largely overlaps with the cells we\ndescribe as terminal memory cells (expressing GZMB, ZNF683, ZEB2) and possessed a\nsignificant positive correlation with our reported non-viral signature (R=0.55, P<0.001,\nPearson correlation), which we report in fig.S8H (inset below, lines 341-343 in\nmanuscript). This supports our hypothesis that myeloma-associated T-cell\ndifferentiation represents the activity of tumour-reactive clones.\nFig.S8H. Dot plot showing the correlation between the expression of the non-viral gene\nsignature and the Tumor-reactive Features in T cells gene signature from Wagnar et al.\n(2025). R and P-values were calculated by Pearson correlation\nFinally, while we agree MHC-I expression at the RNA level is only a proxy for\nimmunogenicity, we point to several other studies in solid tumours that have connected\nMHC-I expression at the RNA level to proxies of immunogenicity such as T-cell infiltration\n(immune “heat”) and the response to immunotherapy (Dhatchinamoorthy et al., 2021;\nYang et al., 2023).\n3. Have the authors identified any shared tumor neoantigens among patients? If tumor-\nreactive T cells are not exhausted, why can they not control tumor growth (propose\nmechanism of tumor escape)?\nFoster et al.\nRegarding shared neoantigens, genome sequencing was unfortunately unavailable for\nthe majority of patients in our single-cell atlas, precluding this likely informative analysis.\nWe would like to suggest several credible tumour-extrinsic and -intrinsic features which\nmay impede the ability of T-cells to control tumour growth in the absence of exhaustion.\nPrincipally, while the T-cell subsets we find enriched in SMM and MM are not\nphenotypically exhausted (Fig.3A-B), they do possess hallmarks of TEMRA-like\ndifferentiation including the expression of T-bet (TBX21) and ZEB2 (Fig.S3B) (Kay Chung\net al., 2021). TEMRA cells possess blunted effector capacity relative to less differentiated\neffector memory T-cells. Therefore, even in the absence of canonical exhaustion, a loss\nof effector function may underpin myeloma association T-cell differentiation,\ncontributing to immune escape. Alternatively, other immune cell types may interact with\nnon-exhausted T-cells to curtail their ability to control tumour growth, such as\nimmunosuppressive myeloid populations (Costa et al., 2021). Regarding tumour-\nintrinsic features, the high level of genomic sub-clonality seen in myeloma cells at\ndiagnosis (Dutta et al., 2019) may present too wide a range of antigenic targets for T-cells.\nThis means that while some tumour sub-clones are effectively targeted by non-\nexhausted T-cells, other clones escape and continue to grow, leading to tumour escape.\nFurther studies can be built on our findings to better explore this mechanism.\nWe have included these points in our Discussion section (lines 486-492).\n4. The authors conclude overall Treg loss “may reflect early reshaping of the BM\nmicroenvironment towards a state favoring progression.” This is contradictory to reports\nof increased Tregs contribute to immunosuppression and favor tumor progression.\nCould the authors discuss how Treg loss may contribute to active MM progression\nmechanistically?\nWe agree these results appear at odds with previous work studying Tregs in the MM\nmicroenvironment. However, CD4+Treg are a known constituent cell type of the\nhomeostatic bone marrow niche where they support haematopoiesis and normal B-\nlymphopoiesis (Méndez-Ferrer et al., 2020; Camacho et al., 2020; Pierini et al., 2017).\nTherefore, CD4+Treg loss may reflect the reshaping of the marrow microenvironment\nand disruption of the haemopoietic niche, with a healthy homeostatic state giving way to\none favouring tumour growth. Additionally, we only report Treg loss relative to untreated\nprecursor patients (Fig.7), where the microenvironment is likely most similar to healthy\nmarrow. On the other hand, an accumulation of immunosuppressive Tregs may be a\nfeature of late-stage disease or treated patients, where their presence promotes\ntherapeutic resistance.\n5. Were senescent T cells (characterized by CD28-, CD57+, KLRG-1+, CD160+) not\nfound? TEMRA cells can exhibit cellular senescence. Include more references to T cell\nsenescence in MM?\nWe apologise for the confusion regarding T-cell phenotype terminology. The terminally\ndiRerentiated GZMB+ CD4+ and CD8+ subsets did possess the features of cellular\nsenescence at the RNA level: CD28-, KLRG1+, CD160+. To further illustrate this point, we\nFoster et al.\nhave performed a GSEA using a senescence gene signatures (SenMayo) (Saul et al., 2022)\nand found them to be significantly enriched in terminally diRerentiated subsets\n(CD8+Tte: P=0.03, NES=1.6; CD8+Temra: P=0.03, NES=1.). We have reworded our\ndescription of this phenotype in the results section to highlight the senescent and TEMRA\nfeatures of these subsets in our Results (lines 138-139 in manuscript) and Discussion\nsections (lines 486-492).\nFurthermore, we have performed additional CD8+T-cell phenotyping at the protein level\nby performing a clustering analysis of our CyTOF data (Fig.S8J, inset below). This analysis\nrevealed the presence of CD8+ subsets in MM patients defined as CD28-KLRG1+ and\neither CD45RA–CD57HI or CD45RA+CD57–, which we term CD8+Tte and CD8+Temra,\nrespectively, as in our scRNA-seq data.\nFig.S8J. Heatmap showing the scaled expression of T-cell marker proteins in CD8+ T-cell\nclusters identified by Cytometry by Time-of-Flight\nReviewer #2 (Remarks to the Author):\nPlease note, we include a list of references specific to this rebuttal at the end of the\ndocument.\nIn this manuscript, Foster et al. performed a complex and valuable work aiming to\nunderstand the T-cell differentiation in multiple myeloma and its precursor stages in\nresponse to tumor-intrinsic features by integrating a large collection of single-cell data,\nincluding healthy samples, MGUS, SMM, and MM from both bone marrow and\nperipheral blood. Through accurate sample integration and annotation they thoroughly\nannotate T-cells, showing a T-cell skewing that moves from naive and early T-cell\nsubsets towards terminal memory cytotoxic and was associated with disease stages\nand age independently. They further characterize T cells clonality and the relation with T\ncell skewing further differentiating this tumor associated clonality with viral specific\nclones. By analyzing also tumor plasma cells authors establish a correlation between\nclonality and specific T cell populations and MRD positivity.\nThere is no question this manuscript is a valuable resource and also provides some\ninteresting hypothesis for understanding the interaction between the immune\nmicroenvironment and MM. However, there are a number of limitations that need to be\naddressed\nFoster et al.\nWe are glad this reviewer found our manuscript to be interesting, and we extend our\nsincere thanks for their insightful feedback.\nMain\n- Integration of scRNA-seq datasets: When presenting the datasets used in the study,\nauthors referred to 12 public + UCL studies (if I’m not wrong, sometimes referred to as\n“This study”, “Foster 2024” or “Foster 2025”), however, there are inconsistent numbers\nbetween Fig.1, Fig.S1A, B, Table S1 and S6. Seems that Botta 2023 study is not included\nin the integrated dataset or used independently. Please, clarify and correct the\ndescriptions and figures accordingly.\nWe apologise for the confusion regarding the number and naming of individual datasets\nused in this study. We have updated Figures S1, S2, S3 and S5 and the Zenodo\nrepository (https://zenodo.org/records/17418275) to more accurately describe the\ncells generated in this manuscript solely as “This study”.\nWe re-analyse data from 12 published studies in this manuscript: Oetjen et al. (2018),\nBailur et al. (2019), Granja et al. (2019), Zavidij et al. (2020), Kfoury et al. (2021), Zheng et\nal. (2021), Liu et al. (2021), Stephenson et al. (2021), Sklavenitis-Pistofidis et al. (2022),\nBotta et al. (2023), Conde et al. (2023), and Maura et al. (2023). We additionally analysed\ndata from Friedrich et al., (2023; Fig.S8I) – however, as this study not included in our\noverall integrated dataset, we omitted it from these calculations.\nThis publicly-available data comprised 261 samples from 230 patients. We combined\nthese data with 47 samples from 25 patients we generated ourselves. Together, this\ntotalled 1,079,979 single cells from 308 samples from 255 individuals (n=105 non-cancer\ncontrol, n=23 MGUS, n=60 SMM, n=67 MM). Of these 308 samples, 240 were derived from\nthe bone marrow and 68 from the peripheral blood. Additionally, 109 patients had scTCR-\nseq data and 1 had CITE-seq. We have made these changes into most recent Zenodo\ndataset version (https://zenodo.org/records/17418275) have updated the text and\nfigures accordingly (Fig.1, Fig.S1 inset below, lines 91-96 in manuscript).\nFoster et al.\nFig.S1A. Sample collection for scRNA-seq cohort.\nThese calculations include the data from Botta et al. (2023), which was analysed by\nscANVI re-projection (Fig.S4G) and omitted from the original (v4) Zenodo repository. This\ndataset (with predicted cluster label annotations) is now available on the Zenodo\nresource (https://zenodo.org/records/17418275).\nPlease note, the actual number of cells, patients, and samples in the Zenodo repository\nis less than listed here, as data from Sklavenitis-Pistofidis et al. (n=253,239 cells, n=73\nsamples, n=51 individuals) are omitted as per our data sharing agreement with these\nauthors and must be acquired through direct contact with them.\nAuthors also reach some conclusions based on the cell type composition analysis in the\nintegrated dataset. Due to the divergencies in the sorting strategies followed between\nstudies, these approaches are biologically meaningless, as the differences observed\nmay point to technical biases. Importantly, plasma cells represent only 6.6% of the\nintegrated dataset, a consequence of the different sorting strategies applied, where\nseveral removed the plasma cell fraction, and others enriched for the T-cell fraction.\nWe agree that diRerent sorting strategies will make comparison of cell type composition\nacross studies invalid for biological insight. We chose to describe cell type composition\nacross studies in the results to help provide an unbiased overview of the dataset,\nparticularly for readers who may be interested in utilising our scRNA-seq resource. We\nhave updated the results text to reflect that this comparison is purely for descriptive\npurposes, and not meant to reflect biological insight (lines 112-113, “the use of different\nsorting strategies across studies precluded in-depth analysis of overall immune composition”).\nFurthermore, we were careful to only make biological inferences using T-cell cluster\ncomposition as a proportion of T-cells, which we calculated through in silico T-cell\nselection (Fig.S2C-E) as is widely performed in immunological studies including other\nlarge scRNA-seq atlas studies (Chu et al., 2023; Zheng et al., 2021).\n- Authors did a complete annotation of the different T cell phenotypes. Also, they deeply\nannotated the CD4+T-reg cells and the Invariant, identifying gdT cells, MAIT cells, etc.\nWhy is this deep annotation not integrated into the general annotation of the dataset?\nWe have updated the uploaded dataset to include these cell type labels\n(https://zenodo.org/records/17418275). We chose to report the phenotype and\nabundance of these rarer T-cell subsets in separate supplemental figures (Fig.S4A,\nFig.S5G) to enhance clarity and allow for deeper interpretation of all T-cell subsets in\nFig.2.\n- According to the study, BM of myeloma patients is not enriched in exhausted T-cells\nwhich is unlike other reported studies so several comments are raised: 1) Why do authors\nfocus on the comparison of CD8+Tex with CD8+Tem-CD69+ cells? What about the other\nCD8+Tem cells?;\nFoster et al.\nWe focused the comparison of CD8+Tex with CD8+Tem.CD69+ as both expressed\nexhaustion-related genes (TIGIT and TOX and, to a lesser, extent PDCD1 – please see\nbelow) relative to other CD8+ subsets. We inferred from this that CD8+Tem.CD69+ cells\nare the prevalent subset termed “exhausted” in previous myeloma studies. Thus, we\nchose to focus on this subset, in order to show that they are actually distinct from cells\nwith a phenotypic profile more closely resembling exhausted T-cell (CD8+Tex).\nFig.2F-G and Fig.S3B compares CD8+Tex with other CD8+ subsets, revealing the\nenrichment of exhaustion-associated genes in the CD8+Tex cluster. Finally, we would like\nto point to a recent publication which similarly fails to identify an enrichment of\nphenotypically exhausted T-cells in myeloma (Shasha et al., 2025). This would suggest\nour findings are shared with other emerging studies and we include this point in our\nDiscussion (lines 455-456).\n2) Authors stated: “Importantly, we distinguish CD8+Tex from PDCD1-expressing\nCD8+Tem.CD69+.” However, the increased expression of PDCD1 in CD8+Tem.CD69+\ncells is not evident (Fig.2F, G, and Fig.S3B). Could the authors clarify that?;\nWe apologise for the lack of clarity here. PDCD1 was most highly expressed in CD8+Tex.\nPDCD1 was also expressed in CD69+CD8+Tem at higher levels than other CD8+ subsets,\nexcluding CD8+Tex. However, this was not suRiciently evident in Fig.2G due to the much\nhigher absolute expression of PDCD1 in CD8+Tex. Therefore, we have plotted the\nexpression with CD8+Tex omitted to better highlight this diRerence in the new Fig.S3F\n(inset below), referred to in line 161. Additionally, marker gene testing showed PDCD1\nwas significantly enriched in CD69+CD8+Tem relative to other clusters (adjusted\nP<0.001, unpaired Wilcoxon). We include this finding in the results section to further\nenhance clarity line 160).\nFig.S3F. Ridge plot comparing exhaustion-associated gene expression CD8+Tem.CD69+\nand other CD8+ clusters.\n3) Similar to Fig.2B, it will be nice to see the distribution of cell populations per disease\ngroup. The way to show the distribution in Fig.S5A, although informative, provides\ndifferent messages;\n- Authors stated: “BM T-cell composition was strikingly similar in patients and controls”;\nhowever, to support that, a representation similar to Fig.2B must be provided for the T-\nFoster et al.\ncell phenotypes. Fig.S5A shows relevant information, but does not support the stated\nmessage.\nWe agree such a figure would be informative and have provided this in fig.S5A.\nFig.S5A. Bar chart comparing T-cell cluster composition in non-cancer controls (Non),\nMGUS, SMM and MM patients (left) and in the peripheral blood (PB) and bone marrow\n(BM) of all controls and patients (Pt.; right).\n4) Have the authors tried to subcluster the CD8+Tem.CD69+ cells? Could the\nsubclustering reveal CD8+Tem.CD69+ subclusters related to T-cell exhaustion? Or\nwithin the overall CD8+Tem cells, could you identify a subcluster with an exhausted\nphenotype?\nThis is a compelling suggestion which we agree would help advance our findings. We\nisolated, re-integrated and re-clustered CD8+Tem.CD69+ cells (Fig.S3G, inset below).\nWe refer to this new analysis in the manuscript lines 163-164: “A dedicated sub-\nclustering of CD8+Tem.CD69+ did not identify a subset of these cells with features of\nCD8+Tex”. This suggests the exhausted phenotype is unique to the CD8+Tex cluster, and\nnot to be identified within CD8+Tem.CD69+ cells.\nFoster et al.\nFig.S3G. Heatmap showing scaled average RNA expression of exhaustion-associated\nmarkers in CD8+Tem.CD69+ sub-clusters and CD8+Tex.\n5) When comparing T-cell composition between marrow and peripheral blood, the\nsample donor was included as a random effect in the analysis. If the analysis is\nperformed only with paired marrow and PB samples, are the conclusions the same?\nThis is a good suggestion. We have repeated our marrow and blood T-cell composition\ncomparison using paired samples (Fig.S4F, inset below). These results are similar to our\nrandom eRect testing, notably showing CD69+CD8+Tem are enriched in the marrow in\nboth controls (P=0.06, paired Wilcoxon) and patients (P<0.001, paired Wilcoxon).\nBox plot showing the abundance of CD8+Tem.CD69+ in PB and BM samples from all\ncontrols and patients (left, P-value calculated by unpaired Wilcoxon test) and controls\nand patients with paired PB and BM samples (right, P-value calculated by paired\nWilcoxon test).\n- In the T-cell composition analysis of controls with each myeloma disease stage (Fig.3B\nFoster et al.\nand Fig.S5B), why is the number of individuals per group different between Fig.3B and\nFig.S5B?\nThe reported patient numbers diRer between figures because the boxplots in Fig.3B show\nthe abundance of T-cell subsets among all patients in the cohort, whereas Fig.S5C\nreports the results of a diRerential abundance analysis analysing solely patients who had\navailable age data (which was used in the linear model to calculate the reported P-\nvalues). The lack of age information for all patients in the cohort is an unfortunate\nlimitation but perhaps unavoidable for a large meta-analysis such as this (we reached\nout to all original authors for age data, but it was not always available). We have updated\nthe Fig.S5 legend to reflect this diRerence for enhanced clarity: “Only individuals with age\ninformation are included in this comparison”.\n- The authors define the T-cell skewing based on the PCA of T-cell phenotypes' abundances,\nshowing association with disease (SMM and MM) and age independently (Fig.3C, Fig.S5H,\nand Fig3.E). However, the correlation between MM age and T-cell skewing was not\nsignificant (Fig.3F, p=0.27).\nWe apologise for the confusion regarding Fig.3F. We have redesigned this figure to better-\ncommunicate our findings (inset below). This figure serves as a visualisation for the\neRects modelled statistically in Fig.3E. We report the presence of similar age versus\nskewing slopes in controls (Non) and MM patients. This suggests an equivalent eRect of\nage on T-cell skewing in both controls (Non) and MM patients. We quantify this by\ncalculating the individual R values (R=0.27 in Non, R=0.19 in MM) and the significance of\nthe age versus skewing association independent of disease stage (P=0.02, Non & MM\nonly; in the full cohort R=0.29 and P<0.002, Fig.S5I). However, the two slopes possess\ndiRerent y-intercept values. This suggests that, independent of age, MM patients possess\na higher degree of T-cell skewing relative to controls (or MM patients tend to have\nenhanced T-cell skewing across a range of ages). We quantify this statistically in the\nlinear model in Fig.3E (P<0.001) and the same model for controls and MM only in Fig.3F\n(P=0.001). Taken together, this shows the joint, independent eRects of age and disease\nstage on T-cell skewing.\nFoster et al.\nFig.3F. Dot plot showing the absolute diberence between T-cell skewing in controls and\nMM patients across the range of ages. Inset P-values indicate the significance of the\nassociation between T-cell skewing and age independent of disease (age | disease) and\ndisease independent of age (disease | age). P-values derived from linear regression. R\nvalue calculated by Pearson correlation.\nWe have updated the text to include this interpretive detail (lines 225-235): “T-cell\nskewing was greatest (meaning an enrichment of terminal memory clusters) in SMM and\nMM relative to controls independent of age (P<0.004 and P<0.001, respectively, linear\nregression; fig.3E), demonstrating this metric captured the major alterations to T-cells in\nmyeloma. Conversely, T-cell skewing was associated with age independent of patient\ngroup (P=0.013, linear regression, R=0.29, Pearson correlation; fig.3E and F; fig.S5I).”\nWe also update the legend for fig.3F for enhanced clarity (lines 1355-1359): “Dot plot\nshowing the absolute diRerence between T-cell skewing in controls and MM patients\nacross the range of ages. Inset P¬-values indicate the significance of the association\nbetween T-cell skewing and age independent of disease (age | disease) and disease\nindependent of age (disease | age).”\nMoreover, the age included in the regression model was binarized (Fig.3E). Why did the\nauthors do that? How was the threshold (62 years old) defined? Is the age still significant\nif the variable introduced in the model is continuous instead of categorical?\nWe binarized age by the median value in the cohort (62 years) so as to reduce the\ninfluence of outliers of non-linearity on the association. However, we repeated our\nmodelling with age as a continuous variable. As in our original median-binarized age\nmodel, we found statistically significantly independent associations between T-cell\nskewing and both age and disease relative to controls in both SMM (disease P<0.001, age\nP=0.01, linear model) and MM (disease P<0.001, age P=0.02, linear model). We include\nFoster et al.\nthe results of this test in our updated methods section (lines 656-656): “age values were\nbinarized to above and below the median (62 years) but results remained significant when\nmodelling age as a continuous variable in both SMM (disease P<0.001, age P=0.01, linear\nmodel) and MM (disease P<0.001, age P=0.02, linear model)”.\n- The authors performed linear regression models to calculate the “exaggerated T-cell\naging” and “T-cell years” (Fig.3G). How good are the defined models? Could the authors\nshow the linear regression model performance? Or include the residuals for the non-\ncancer donors in the plots?\nWe thank the reviewer for these insightful comments on our linear modelling. The\ninclusion of non-cancer control residuals was an instructive suggestion, and we provide\nthis in a new Fig.S5J (inset below). We chose to plot the non-cancer donor residuals on a\nseparate plot so as to not complicate interpretation of Fig.3G). This analysis reveals a\nwide distribution of values in control samples residuals. This suggests additional\nunmodelled sources of variation in T-cell skewing, highly likely given this analysis ignores\nknown correlates of immune aging (such as history of infection). However, the average\nvalues in patient groups are higher than the average in controls (excess T-cell years: SMM\nvs controls 25.1 years, MM vs controls 38.1 years). Therefore, there remains an eRect of\ndisease on exaggerated T-cell aging and excess T-cell years.\nFig.S5J. Box plot showing exaggerated T-cell ageing and excess T-cell years (see Methods)\nin non-cancer control patients.\nWe assessed our T-cell skewing ~ age model (used in Fig.3G) fit by analysing the normality\nof residuals. Residuals were normally distributed as assessed by a Shapiro-Wilk test\nFoster et al.\n(P=0.89) and Kolmogorov-Smirnov test (P=0.97). We also confirmed this with a Q-Q plot\n(inset below). From this, we infer the suitability of our model. We include this additional\ndata in our methods section (lines 677-679): “the residuals of this model (Fig.S5J) were\nnormality distributed as assessed by a Shapiro-Wilk test (P=0.89) and Kolmogorov-\nSmirnov test (P=0.97)”.\nTo note, the differences between real age and predicted age are surprisingly high.\nModel predicts patients' age higher than 100? Could you explain how you can get a\nresidual of 80 or >100?\nAs noted, there are large diRerences between real and predicted T-cell age (>100 years,\nFig.3G). We calculated “excess T-cell years” as an interpretable metric for accelerated\nimmune aging. However, we agree that >100 years of immune aging may not be accurate.\nThe inclusion of control residuals (Fig.S5J) helps contextualise these data and shows a\nhigh degree of variation around the mean, likely due to other unmodelled variables (such\nas history of infection or ethnicity, unfortunately such data was not available). However,\nother investigators have conducted similar immune aging analyses in other disease\nsettings and found equivalent results.\nAlpert et al. (2019) compared individuals with and without cardiovascular disease and\nfound age- and gender-adjusted “IMM-AGE” values of in excess of 50 and up to 100\n(Alpert et al. 2019, Extended Data Fig. 10c), where IMM-AGE is an immune-based metric\nof immune aging directly re-scaled to age in years.\n[FIGURE REDACTED]\nc. Age- and gender-adjusted IMM-AGE score of individuals stratified based on\ncardiovascular disease (dots); bold lines denote mean values (P= 0.0023, n= 2,292, two-\ntailed t-test). Taken unedited from Alpert et al. (2019) Extended Data Figure 10.\nFoster et al.\nA second study by Sparks et al. (2024) calculated an immune health metric (IHM) where\na reduction of an IHC value by 0.1 roughly corresponded to aging 20 years in controls\n(Sparks et al. 2024: Fig.3g). Patients with X-linked chronic granulomatous disease (X-\nCGD) possessed extreme reductions of IHM values of up to 0.3 relative to the average\nIMH in controls (Sparks et al. 2024: Fig.3e), corresponding to roughly 60 years of\naccelerated immune aging.\n[FIGURE REDACTED]\ng. Scatterplot with trendline showing the age dependence of the IHM in healthy\nindividuals only (Pearson correlation and P values shown; n = 34 healthy participants in\nthe training set with serum protein, whole blood transcriptomic and CBC/TBNK data). e.\nIHM scores of individual participants grouped by condition. Unadjusted P values from\ntwo-sided Wilcoxon test comparing each disease to the healthy group: * P < 0.0001.\nTaken unedited from Sparks et al. (2024) Fig. 3.\nFrom this, we conclude that the current approaches to estimate immune age in health\nand disease employed by us and others will predict extreme degrees of immune aging up\nto 50-100 years. Future work, with more specific immune metrics and greater clinical\nannotation, may derive more precise measures. Please note both metrics presented by\nthese articles require data types not available in our scRNA-seq atlas, hence we were\nunable to calculate these metrics for comparison with our own.\nWe also tentatively speculate that, if accurate, excess T-cell years of several decades\nmay explain the loss of functional capacity leading to tumour immune escape and\nprogression.\n- Within the characterization of T-cells in disease marrow, authors stated: “PC1 values\nand the abundance of terminal memory subsets in the BM showed a strong correlation\nwith PB (fig.3H), indicating a similarity to systemic T-cell alterations seen with ageing (8).”\nAs initially this section was focused on bone marrow data, now I am wondering how PC1\n(T-cell skewing) was computed, in order to allow the comparative analysis between PB\nand BM samples. Are the T-cell skewing derived only from BM samples or both, marrow\nand PB? This needs to be clarified and should align with the title of the section.\nFoster et al.\nWe apologised for omitting this from our methodology section. We initially calculated\nPCs on scaled normalised bone marrow T-cell cluster abundance. This is reported in\nFig.3C and Fig.S5H. We have updated the methods section to include our methodology\nfor this step: “For the comparison of T-cell skewing with the peripheral blood (Fig.3H), we\nfirst re-scaled normalised peripheral blood T-cell cluster abundance to the same centre\nand range of the scaled BM T-cell abundance matrix. Next, we multiplied this scaled PB\nmatrix by the pre-calculated BM PCA feature loadings via feature (cluster)-wise matrix\nmultiplication, yielding the PC1 values for the input PB matrix” (lines 671-675). We report\nthese PB PC1 values in Fig.3H.\n- Regarding features of antigen specific clones why restrict the general analysis to CD8-\nexpressing memory clones? Have you checked the CD4+T memory cells?\nWe share this reviewer’s curiosity about the specificity of CD4+ T-cells. However, the\nvast majority of annotated TCR specificities are against MHC-I epitopes, thus CD8+T-cell\nTCRs. In the subset of the VDJdb we utilised, only 1,297 of 62,091 (2.1%) of TCRs\nannotated as viral-reactive were against MHC-II epitopes. Therefore, we reasoned we\nwere far less likely to find shared CD4+ specifies and omitted this analysis in favour of\nCD8+ T-cells.\n- In Fig.5G, the result of cell-to-cell interaction analysis is shown, providing nice results.\nAs the pathways identified in Fig.S7F are patient-specific, it would be interesting to\nexplore what happens if this analysis is performed per patient. Detailed information on\nthe analysis should be provided in the methods section.\nThis is interesting suggestion and we have pursued this line of reasoning with the\nfollowing analysis. Noting their node centrality in the inter-cellular signalling network, we\nfocused on TNF-α and IFN-γ-mediated pathways. We asked if the expression of these\neffector molecules by Teff was correlated with the expression of associated pathways in\ntumour cells on a per-patient basis. This analysis revealed the expression of these\neRector cytokine in T-cells was significantly correlated with the association downstream\npathway activity in tumour cells (fig.S8G; IFN-γ: R=0.3, P<0.05; TNF-α: R=0.76, P<0.001,\nPearson correlation). This suggests that the functional profile of effector T-cells is directly\nrelated to tumour transcriptional state in a subset of patients. We include this analysis\nin our discussion section (lines 331-334): “Patients with high T-cell effector cytokine\nexpression possessed the highest corresponding pathway activity in tumour cells (IFN-γ:\nR=0.3, P<0.05; TNF-α: R=0.76, P<0.001, Pearson correlation; fig.S8G), mechanistically\nlinking these processes”.\nFoster et al.\nFig.S8G. Dot plots showing the correlation between the indicated effector molecules (IFNG\nand TNF) in T-cells and the abundance of cancer cells highly expressing downstream\nsignalling pathways for each effector molecule.\n- When exploring the implications in the response to immunotherapy, specifically TCE\ntherapy, why are only 3 patients used? And only patients who respond? What about those\nthat not respond? Behave differently?\nFor the TCE analysis, we simply selected clinically responding patients with a suRicient\nnumber of T-cells present in pre-treatment samples that subsequently expanded\nfollowing TCE therapy. We adopted this approach based on the assumption that T-cell\nexpansion in clinical responding patients likely involves (at least in part) TCR tumour-\nreactivity, as was argued by Friedrich et al. (2023). This allows us to connect our non-viral\nsignature to the phenotype of putative tumour-reactive T-cells. Regarding responders and\nnon-responders, we discussed this analysis among co-authors and agreed that this\ncomparison, whilst interesting, was not the aim of our project. Hence, we chose not to\npursue this. However, our scRNA-seq resource and non-viral signature may prove helpful\nin investigating this area in the future.\n- Regarding TCE therapy, authors stated that, “Interestingly, T-cells bearing these TCRs\nhighly expressed the non-viral specificity signature pre-treatment (fig.5I), suggesting this\nphenotype contains TCR clones with the capability to respond to immunotherapy and\nmay be tumour-reactive.”, pointing out that having these clones with increased non-viral\nspecificity signature can help to fight against the tumor (tumour-reactive). However,\nwithin the ASCT cohort, the ASCT-expanded clones, also associated with non-viral\nsignature, with high clonality associated with MDR+. If there are tumor-reactive clones\nexpanded, how do the authors explain the association with MRD+? Is it expected?\nWe are pleased this reviewer shares our interest in this compelling paradoxical finding.\nWe have discussed this at length internally, and share our opinions below:\nFirstly, we would suggest that TCE therapy is unique in that it relies (in part) on\nendogenous T-cell immunity and specificity, as showed by Friedrich et al. (2023) and\nWagner et al. (2025). Therefore, a high abundance of endogenous tumour-reactive T-\ncells is likely to be associated with superior outcome in this treatment modality.\nHowever, a high number of tumour-reactive T-cells, whilst superficially beneficial, may\nbe indicative of negative patient features such higher tumour burden or higher tumour\nmutational burden (giving rise to neoantigens, thus tumour-reactive T-cells). Therefore,\nthere is likely to be a balance of positive and negative clinical outcomes associated with\nsuch T-cells, contextually dependent on treatment modality and time of sampling (such\nas pre- or post-therapy).\nIn our analysis of patient outcome post-ASCT, we find the clonal expansion of non-viral\nsignature-expressing T-cells is associated with MRD+. As this is day 100 post-treatment,\nthe presence of these cells may be indicative of ongoing T-cell responses insufficient to\nclear residual tumour cells (hence, MRD+). It is also possible the high levels of clonal\nexpansion we see post-ASCT represents an excessive narrowing of the TCR repertoire\nagainst a small set of tumour antigens (“original antigenic sin”), meaning these T-cells\nwould be unable to control tumour subclones that emerge post-therapy. The protective\nfunction of these T-cells may not be suRicient to eradicate tumour, and longer-term\nclinical outcomes may be dependent on post-ASCT therapy to further enhance T cell\nfunction with maintenance agents that stimulate T cell function such as IMiDs and/or\nCD38 monoclonal antibodies such as is now standard of care. Additionally, post-ASCT\nresponses deepen with time in some patients, in whom such expanded clones may\nindeed be functional and eradicate tumour.\nFurther studies of diRerent treatments in the context of consolidation and maintenance\ntherapy are required to clarify these points. However, we believe our work makes the first\nstep towards understanding how frequency and expansion behaviour of tumour-specific\nT cells relates to outcomes of different types of therapies in myeloma.\n- Authors reference Fig.S5D to compare SMM and MM, pointing to a CD4+Treg reduction\nin MM; however, this reduction is not identified in Fig.S5D, only CD4+Tem is highlighted.\nCould you comment on that? How do you identify CD4+Treg?\nWe apologise for insufficiently explaining this result. Fig.S5E shows the corrected P-\nvalues for the comparison of T-cell composition between SMM and MM. In this analysis,\nthe difference in CD4+Treg abundance was not significant following P-value correction\n(corrected P > 0.1). However, the uncorrected P-value was 0.03, which we reported when\nsubsequently analysing CD4+Treg abundance in more detail in Fig.7. We have updated\nthe results text to better reflect this difference in reported P-values (lines 414-415): “We\nobserved a reduction in the abundance of CD4+Treg in MM relative to SMM in the scRNA-\nseq (uncorrected P=0.03)”.\n- Interestingly, CD4+Treg loss is presented as a biomarker of SMM risk. Although\n“Multivariate analysis showed CD4+Treg abundance predicted progression\nindependently of existing SMM risk models (Fig. 7G)”, how much predictive power are we\nwinning with the CD4+ Treg information?? Is this multivariate model better or\nFoster et al.\nsignificantly more powerful than the one performed only with the Mayo or IMWG risk\nmodels?\n- Really interesting the identification of CD4+T-reg loss as a putative biomarker of SMM\nrisk, however, the authors should show if the addition of this information improve\nsignificantly the prediction of existing SMM risk models.\nThis is valid concern, and we agree an extended analysis including statistical power and\nan assessment of predictive ability would enhance this analysis. However, we are unable\nto sufficiently answer this point due to lack of data from the relevant cohorts. We simply\nwished to highlight initial observations that provide an insight into T cell biology in the\ndevelopment of MM and connect these findings to clinical outcomes as a “proof of\nprinciple” for future of immune-based disease monitoring. Subsequent work in larger\nimmunologically annotated cohorts will be required to fully address this important point.\nInclude in Table S1 the information regarding sorting, sequencing strategy, and tissue.\nAs table S1 shows clinical metadata at the level of individual donors, this information is\nnot directly applicable here. However, this metadata is directly available in the data\nobject shared on Zenedo (https://zenodo.org/records/17418275).\nReviewer #3 (Remarks to the Author):\nPlease note, we include a list of references specific to this rebuttal at the end of the\ndocument.\nThe manuscript by Foster et al. presents an ambitious and comprehensive analysis of T-\ncell differentiation across the spectrum of multiple myeloma (MM) and its precursor\nstates (MGUS and SMM). The authors integrate more than one million single cells from\n259 individuals across 12 datasets, including their own newly generated scRNA-seq\nand scTCR-seq data, to provide an in-depth characterization of T-cell states and\ndynamics. They uncover a pattern of skewed differentiation not attributable to classical\nexhaustion, but instead driven by antigen-specific terminal memory features.\nThe manuscript is well written, the analyses are rigorous, and the figures clearly\ncommunicate key findings despite the large volume of data. Public release of the\ndataset adds further value. This study significantly contributes to the understanding of\nimmune surveillance and dysfunction in MM and its progression.\nAmong the important findings presented, the most noteworthy are:\n1. Absence of classical exhaustion: Contrary to prior assumptions, the bone marrow of\nMM patients does not exhibit widespread enrichment of exhausted CD8⁺ T cells (Tex)\n(Except for one patient described as outlier). Instead, a more abundant population\n(CD8⁺Tem.CD69⁺) appears to be activated rather than exhausted. Neither subset is\nassociated with disease progression, suggesting immune dysfunction in MM may not\nfollow classical exhaustion pathways.\n2. T-cell skewing mirrors immune aging: The most prominent change in T-cell\nFoster et al.\ndifferentiation during disease evolution is the enrichment of terminal memory CD4⁺ and\nCD8⁺ subsets (Temra, Tte), termed \"T-cell skewing.\" This pattern strongly resembles\nimmune aging and is more pronounced in SMM and MM compared to MGUS and\nhealthy controls.\n3. TCR repertoire changes suggest antigen-driven responses: Expanded TCR clones in\nMM show clonal enrichment, terminal memory phenotypes, and lack viral-specific\nsignatures. These data suggest a tumor-driven, antigen-specific T-cell response,\nespecially given the correlation between T-cell skewing, paraprotein levels, and TCR\nclonality.\n4. Clinical correlations and prognostic implications: T-cell skewing is associated with\ntumor burden (serum paraprotein levels) and MRD positivity after ASCT, but it does not\npredict progression from SMM to MM. In contrast, CD4⁺Treg loss emerges as a novel\nand independent biomarker of progression risk.\nWhile the results are promising, further validation and clarification of certain aspects\nwould strengthen the study. I look forward to reviewing a revised version.\nWe thank the reviewer for their positive comments and helpful suggestions that we feel\nhave greatly advanced our manuscript.\nSuggestions for Improvement:\n1. Survival Analysis Beyond CoMMpass: Survival analysis is currently limited to the\nCoMMpass cohort. Given the size of the integrated dataset and available clinical\nmetadata, a survival analysis incorporating T-cell skewing or CD4Treg abundance in their\nown cohort would significantly increase the translational value of the findings.\nUnfortunately, outcome data was not available for majority of the scRNA-seq studies we\nanalysed. Outcome data was available for Maura et al. (2023) but of the patients present\nin our scRNA-seq, only 1/16 progressed. Additionally, our scRNA-seq cohort is derived\nfrom numerous centres and countries-of-origin, which would have created an extremely\nclinically diverse cohort in which to analyse outcome. Taken together, this precluded\nfurther survival analysis in our scRNA-seq cohort. We hope future scRNA-seq datasets\nwill include more complete clinical annotation to enhance future meta-analysis such as\nthis.\n2. Correlation with Cytogenetics: While the authors note the inability to analyze tumor\ngenomic classification, the lack of correlation between key T-cell findings and\ncytogenetic abnormalities (e.g., t(4;14), del17p, gain1q) is a limitation. If feasible,\nintegrating cytogenetic risk information from a subset of patients or datasets would\nprovide valuable context.\nWe share this reviewer’s curiosity regarding an association between T-cell diRerentiation\nand tumour genomic features. Due to incomplete clinical annotation in published\ndatasets, the only tumour genomic features with suRicient data across studies were 1q\ngain (positive n=10, negative n=19) and t(4;14) (positive n=6, negative n=17). For del17p,\npositive n=2, negative n=10. t(4;14) was not significantly associated with T-cell skewing\n(P=0.45, Wilcoxon test) but 1q gain showed a trend for enhanced T-cell skewing (Fig.S7G;\nFoster et al.\nP=0.06, Wilcoxon test), potentially suggesting this adverse prognostic factor may also\ninfluence T-cell diRerentiation. Future studies with more complete clinical data or\nmatched genomic sequencing will help answer this question. We included this analysis\nin our results (lines 321-325).\nFig.S7G. Box plot showing T-cell skewing in patients with confirmed 1q gain positivity (+)\nand negativity (–). For (C), (D), and (G) P-values derived by Wilcoxon test.\n3. Beyond Correlation. Causation and Risk Modeling: The multivariate analysis\ndemonstrating that CD4⁺Treg loss predicts SMM progression independently of standard\nrisk models is compelling. Extending similar modeling to T-cell skewing, particularly in\nthe context of post-ASCT outcomes or disease aggressiveness, could strengthen the\nargument for a causal or predictive role.\nFollowing the suggestion of this reviewer, we sought to strengthen the clinical associated\nwe describe between T-cell skewing and depth of response following ASCT in newly-\ndiagnosed patients, defined as MRD negative response.\nFollowing this, we asked if T-cell skewing in pre-treatment samples may have the ability\nto predict outcome following ASCT. We carried out CyTOF analysis of pre-treatment bone\nmarrow samples in newly-diagnosed MM patients and acquired information on MRD\nstatus at day 100 post-ASCT. To examine the same CD8+T-cell phenotypes we identified\nin scRNA-seq, we performed additionally clustering analysis, identifying 6 CD8+T-cell\nclusters equivalent to the phenotype in our scRNA-seq data (Fig.S8J, inset below). When\nwe correlate pre-treatment CD8+T-cell subset abundance with subsequent MRD at day\n100 post-ASCT, we found both CD8+Temra (CD27-CD28-GZMB+Tbet+CD45RA+) and\nCD8+Tte (CD27-CD28-GZMB+CD57+) were enriched in MRD+ patients (Fig.6I, inset\nbelow; Temra P=0.02, Tte P=0.03, unpaired Wilcoxon test). Therefore, T-cell skewing in\nnewly diagnosis patient pre-treatment is also associated with incomplete tumour\neradication.\nFoster et al.\nFig.S8I. Heatmap showing the scaled expression of T-cell marker proteins in CD8+ T-cell\nclusters identified by Cytometry by Time-of-Flight (CyTOF\nFig.6I. Box plot comparing CD8+T-cell CyTOF cluster (see Methods) abundance in pre-\ntreatment samples from patients who achieved MRD+ and MRD- at D100 post-ASCT. For\n(B), (C), (F) and (H) P-values derived by Wilcoxon test.\nWe include these findings in the Results section (lines 388-393). Together, these results\nfurther support the association between T-cell diRerentiation state and patient response\nto treatment and demonstrates the clinical utility of immune-based disease assessment\nin myeloma.\n4. Clarify the functional role of CD8Tem.CD69 cells in myeloma:\nWhile the manuscript clearly distinguishes CD8Tem.CD69 from exhausted T cells, their\nfunctional significance remains somewhat speculative. Since these cells are abundant\nand express partial exhaustion markers, further clarification, using approaches like by\nfunctional assays, cytokine profiling, or external dataset validation, could help\ndetermine whether they contribute to anti-tumour immunity, immune suppression, or\nbystander activation. This would help to clarify the concept of “pseudo-exhausted” CD8\nT cells in the marrow.\nFoster et al.\nWe appreciate this reviewer’s interest in the CD8 Tem.CD69+ subset. As suggested, we\nexplored whether the expression of exhaustion-associated markers TIGIT and TOX\n(fig.2G) in this subset reflected a loss of functional capacity. To assess this, we isolated\nCD69+ and CD69- fractions of CD8+ memory (CCR7-) CD3+T-cells from the bone\nmarrow of MM patients (n=3) and activated cells in vitro with anti-CD3/anti-CD28\nantibodies. After overnight stimulation, CD69+ cells demonstrated no significant\ndifferences in expression of activation markers and effector cytokines (fig.S4C, inset\nbelow). Therefore, CD69+ cells have a similar functional profile to other CD8+ memory\nT-cells, reflecting their “pseudo-exhausted” phenotype. We include this analysis in the\nResults (lines 167-171), Methods (lines 823-839) and Discussion (line 460) sections.\nLeft, box plots comparing the expression of activation markers and effector cytokines in\nCD69+ and CD69- fractions of MM patient BM CD8+ memory (CCR7-) T-cells under\nconditions of no stimulation (stim–) and after overnight in vitro activation with anti-\nCD3/anti-CD28 antibodies (stim+). A set of three independent experiments is shown. P-\nvalue calculated by paired Wilcoxon test. Right, representative histograms from one\nexperiment showing the expression of 4-1BB, TNF-α, and IFN-γ in stimulated (red) and\nunstimulated (blue) CD69- (upper) and CD69+ (lower) cells.\nTo summarise, we provide evidence that CD8 Tem.CD69+ are a marrow-enriched\npopulations (in both health and myeloma, Fig.S4E-F) which are not enriched in tumour-\nspecific TCRs (Fig.S6F). The dominant T-cell differentiation axis we describe (T-cell\nskewing) which was significantly related to disease severity (Fig.3E) and paraprotein\n(Fig.5A-B) did not involve CD8 Tem.CD69+. This suggests CD8 Tem.CD69+ may\npredominantly be a bystander population, and not directly involved in the anti-myeloma\nimmune response.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nFoster et al.\nReferences\nAlpert, A., Y. Pickman, M. Leipold, Y. Rosenberg-Hasson, X. Ji, R. Gaujoux, H. Rabani, E.\nStarosvetsky, K. Kveler, S. SchaRert, D. Furman, O. Caspi, U. Rosenschein, P.\nKhatri, C.L. Dekker, H.T. Maecker, M.M. Davis, and S.S. Shen-Orr. 2019. 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Science (80-. ). 374.\ndoi:10.1126/SCIENCE.ABE6474/SUPPL_FILE/SCIENCE.ABE6474_TABLES_S1_TO_\nS4.ZIP."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have responded appropriately to all critiques, strengthening their manuscript. I have no further suggestions or concerns. Leif Bergsagel.", "reviewer_2": "The auhtors have answered my comments adequately and I have no further issues", "reviewer_3": "I commend the authors on their thorough responses to the prior critiques. I now find the manuscript acceptable for publication.\nArun Wiita", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter