Abstract
Despite improving outcomes, 40% of patients with newly diagnosed multiple myeloma treated with regimens containing daratumumab, a CD38-targeted monoclonal antibody, progress prematurely. By integrating tumor whole-genome and microenvironment single-cell RNA sequencing from upfront phase 2 trials using carfilzomib, lenalidomide and dexamethasone with daratumumab (NCT03290950), we show how distinct genomic drivers including high APOBEC mutational activity, IKZF3 and RPL5 deletions and 8q gain affect clinical outcomes. Furthermore, evaluation of paired bone marrow profiles, taken before and after eight cycles of carfilzomib, lenalidomide and dexamethasone with daratumumab, shows that numbers of natural killer cells before treatment, high T cell receptor diversity before treatment, the disappearance of sustained immune activation (that is, B cells and T cells) and monocyte expansion over time are all predictive of sustained minimal residual disease negativity. Overall, this study provides strong evidence of a complex interplay between tumor cells and the immune microenvironment that is predictive of clinical outcome and depth of treatment response in patients with newly diagnosed multiple myeloma treated with highly effective combinations containing anti-CD38 antibodies.
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Data availability
WGS and scRNA-seq data have been uploaded to EGA: EGAD00001011132. The Kydar scRNA-seq data are publicly available at the National Center for Biotechnology Information’s Gene Expression Omnibus (accession no. GSE161195). The CoMMpass data were downloaded from the MMRF researcher gateway portal (https://research.themmrf.org). CoMMpass raw data are accessible in dbgap: phs000748.v1.p1. Further information on research design is available in the Nature Research Reporting Summary linked to this article. Source data are provided with this paper.
Code availability
All the code used for the genomic and single-cell analyses has been uploaded to https://github.com/UM-Myeloma-Genomics and https://github.com/Eileen-Boyle/CITE-DKRD.
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Acknowledgements
This work was supported by the Myeloma Solutions Fund (MSF), Paula and Rodger Riney Multiple Myeloma Research Program Fund, the Multiple Myeloma Research Foundation (MMRF), the Perelman Family Foundation, the Memorial Sloan Kettering Cancer Center National Cancer Institute (NCI) Core Grant (P30 CA 008748) and the Sylvester Comprehensive Cancer Center NCI Core Grant (P30 CA 240139). F.M. is supported by the American Society of Hematology, Leukemia & Lymphoma Society (LLS) and International Myeloma Society. K.M. is supported by the American Society of Hematology, the MMRF and the International Myeloma Society. G.J.M. received grant support through a Translational Research Program award from the Leukemia & Lymphoma Society (6020-20) and Myeloma Solution Fund. O.L. received grant support from the Paula and Rodger Riney Multiple Myeloma Research Program Fund, Myeloma Solutions Fund, the MMRF, the Perelman Family Foundation and the Sylvester Comprehensive Cancer Center NCI Core Grant (P30 CA 240139). Cell sorting and flow cytometry technologies were provided by NYU Langone’s Cytometry and Cell Sorting Laboratory, which is supported in part by grant P30CA016087 from the NCI. Single-cell sequencing was performed in NYU’s Genome Technology Center shared resource, partially supported by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center. We particularly thank P. Meyn, Y. Zhang, C. Marier and E. Guzman. Cartoon figures were designed using BioRender.com. U.A.S. is supported by the American Society of Hematology Scholar Award, International Myeloma Society Career Development Award and NCI MSK Paul Calabresi Career Development Award for Clinical Oncology K12 CA184746.
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F.M., O.L. and G.J.M. designed and supervised the study, collected and analyzed the data, and contributed to the writing of the paper. E.M.B. designed the single-cell experiment, collected and analyzed the data, and contributed to the writing of the paper. D.C. and K.M. collected and analyzed the data and contributed to the writing of the paper. D.G., B.D., H.G., P.B., B.Z., A.C. and M.C. analyzed the data. Y.W., A.S., J.E.H., D.K., H.H., E.G., S.M., M.R., Q.G., U.S., C.T., M.H., M.S., G.S., H.L., D.J.C., S.G., Y.Z., A.A., A.D., A.M.L., F.E.D., S.U. and N.K. collected the data.
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O.L. has received research funding from the National Institutes of Health (NIH), NCI, U.S. Food and Drug Administration, MMRF, International Myeloma Foundation, Leukemia and Lymphoma Society, Paula and Rodger Riney Myeloma Foundation, Tow Foundation, Myeloma Solutions Fund, Perelman Family Foundation, Rising Tide Foundation, Amgen, Celgene, Janssen, Takeda, Glenmark, Pfizer, Seattle Genetics and Karyopharm; has received honoraria from and/or served on advisory boards for Adaptive, Amgen, Binding Site, BMS, Celgene, Cellectis, Glenmark, Janssen, Juno and Pfizer; and serves on independent data monitoring committees for clinical trials lead by Takeda, Merck, Janssen and Theradex. G.J.M. has received funding from the NIH, NCI, MMRF, Leukemia and Lymphoma Society, Perelman Family Foundation, Amgen, Celgene, Janssen and Takeda; has received honoraria from and/or served on advisory boards for Adaptive, Amgen, BMS, Celgene and Janssen; and serves on independent data monitoring committees for clinical trials lead by Takeda, Karyopharm and Sanofi. M.S. has received clinical trial research support to the institution from Angiocrine Bioscience, Inc., Omeros Corporation and Amgen, Inc.; has provided consultancy services to Omeros Corporation, Angiocrine Bioscience, Inc. (past) and McKinsey & Company (past); has served on an ad hoc advisory board for Kite – A Gilead Company (past); and has received honoraria for CME activity from i3Health (past), Medscape, LLC. (past) and CancerNetwork. G.S. has received research funding to the institution from Janssen, Amgen, BMS, and Beyond Spring. DSMB for Arcellx. S.G. receives research funding from Miltenyi Biotec, Takeda Pharmaceutical Co., Celgene Corp., Amgen Inc., Sanofi, Johnson and Johnson, Inc. and Actinium Pharmaceuticals, Inc. and is on advisory boards for Kite Pharmaceuticals, Inc., Celgene Corp., Sanofi, Novartis, Johnson and Johnson, Inc., Amgen Inc., Takeda Pharmaceutical Co., Jazz Pharmaceuticals, Inc. and Actinium Pharmaceuticals, Inc. U.A.S. reports research funding support from Celgene/BMS and Janssen to the institution and nonfinancial research support; and personal fees from ACCC, MashUp MD, Janssen Biotech, Sanofi, BMS, MJH LifeSciences, Intellisphere, Phillips Gilmore Oncology Communications, i3 Health and RedMedEd outside the submitted work. S.U. has received research funding from Abbvie, Amgen, Array Biopharma, BMS, Celgene, Gilead, GSK, Janssen, Merck, Pharmacyclics, Sanofi, Seattle Genetics, SkylineDX and Takeda; and has had advisory and/or consulting roles with Abbvie, Amgen, BMS, Celgene, EdoPharma, Genentech, Gilead, GSK, Janssen, K36 Therapeutics, Moderna, Novartis, Oncopeptides, Sanofi, Seattle Genetics, SecuraBio, SkylineDX, Takeda and TeneoBio. A.D. has served as a consultant for Incyte, EUSA Pharma and Loxo and receives research support from Roche and Takeda. B.D. has received honoraria from and served on advisory boards for Janssen and Sanofi. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Clinical features and outcomes.
Clinical impact of sex (a, Female n = 31, Male n = 18), Race (b, African Americans n = 4, White n = 38), age (c, < 65 years n = 37; >65 years n = 12), Isotype (d, IgG n = 30; IgG 30; Light chain 9), and ISS (e, ISS1 n = 22, ISS2 n = 24, ISS3 n = 3). PFS: progression free survival; ISS: international scoring system.
Extended Data Fig. 2 Focal and large copy number aberrations associated with poor prognosis and non-sustained MRD-negativity.
a) Copy number cumulative plot for XBP1 deletions across the CoMMpass trial (n = 752) and the (D)KRd study (n = 60). B) Correlation between XBP1 and CD38 expression using CoMMpass newly diagnosed MM patients with available RNAseq data (n = 591). R2 and p-value were estimated using linear regression (lm R package). c-f) Copy number cumulative plot for CCSER1 deletion, large gains on 18q24, 17q22 and 8q. In a) and c-d) vertical black lines represent the GISTIC peak positions. g-h) Clinical impact of large gains on 4q (g) and 17q (h). In g-h) dark green always reflect the wild type (WT), while the dark yellow reflect the mutated cases.
Extended Data Fig. 3 Landscape of the immune environment according to time point and response group.
Projected cells on reference UMAP by time-point and response. Each UMAP has a comparable number of cells to the normal bone marrow reference. T1=time-point 1, T2=timepoint 2, n=number of cells, ref_UMAP= reference UMAP, WNN_UMAP= Weighted Nearest Neighbor Analysis derived UMAP, Mono= monocytes, NK=natural killer, DC=dendritic-cells. Visual differences can be appreciated such as the significant reduction in B and T-cell at T2 in patients who achieve MRD-negativity. n=represent number of cells.
Extended Data Fig. 4 Impact of number of cell to the cell type annotation.
Random sampling of a maximum of 500, 1000, 1500, 2000, 2500, 3000, and 3500 cells per sample (samples, n = 37). Two-sided p-values were estimated using Kruskal-Wallis test. Boxplots represent quartiles centered around the median.
Extended Data Fig. 5 Changes in cellular population across time and response.
Boxplot showing the differences in cellular populations between T1 (baseline) and T2 (post-therapy) in patients that achieve or fail to achieve sustained MRD-negativity. N1=sustained MRD-negativity group at T1 (n = 9), N2=sustained MRD-negativity group at T2 (n = 10), P1=no sustained MRD-negativity group at T1 (n = 8), P2=no sustained MRD-negativity group at T2 (n = 10). Two-sided p-values were estimated using Kruskal-Wallis test. Boxplots represent quartiles centered around the median. a. Memory B-cells b. Naïve B-cells. c. B1 Progenitor cells d. B2 progenitor cells e. Dendritic cell Progenitor. f. megakaryocytic progenitor g. lympho-myeloid progenitor. h. red blood cell progenitor, i. granulocyte-monocyte progenitor j. hematopoietic stem-cell. k. gamma/delta T-cell l. mucosae-associated T-cells m. Regulatory T-cells, n. CD4 effector 1, o. CD4 effector 2, p. CD8 memory 1, q. CD8 memory 2, r. CD8 naïve, s. CD4 memory. t. CD4 naïve u CD4/CD8 ratio, v. classical dendritic cells 2, w. Plasmacytoid dendritic cells.
Extended Data Fig. 6 Heatmap representation of the NK clusters.
Three NK clusters were identified: #0, #1, #2. T1=timepoint 1, T2=timepoint 2.
Extended Data Fig. 7 Shannon diversity and number of T-cell clones per sample identified.
a-b) Number of clones(a) and Shannon diversity (b) index across different groups and time pointes. N1=sustained MRD-negativity group at T1 (n = 9), N2=sustained MRD-negativity group at T2 (n = 10), P1=no sustained MRD-negativity group at T1 (n = 8), P2=no sustained MRD-negativity group at T2 (n = 10). Two-sided p-values were estimated using Kruskal-Wallis test. Boxplots represent quartiles centered around the median.
Extended Data Fig. 8 Monocytes and dendritic cell clustering highlight phenotypic changes.
a) Heatmap representation of the monocyte and dendritic cells. b) Differential expression between P1 and N1 monocytes. c) Pathway enrichment between P1 and N1 monocytes.
Extended Data Fig. 9 Pairwise comparison of overall populations (L1) and subpopulations (L2) of samples with both timepoints available.
P-values were estimated using Wilcoxon-paired test.
Extended Data Fig. 10 Identification of response associated immune-signatures.
Partition Around Medoids (PAM) bi-plot representation. Blue-lines indicate loading vectors (NK = NK cells, Mono=Monocytes, T = T-cells), Confidence ellipse. T1=first timepoint, T2=second timepoint).
Supplementary information
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Flow sorting gating strategy.
Supplementary Table 1
Supplementary Tables 1–15.
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Maura, F., Boyle, E.M., Coffey, D. et al. Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens. Nat Cancer 4, 1660–1674 (2023). https://doi.org/10.1038/s43018-023-00657-1
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DOI: https://doi.org/10.1038/s43018-023-00657-1
