Methods related to multiple myeloma and precursors thereof

Abstract

Disclosed herein are differences in the features of immune cell populations in subjects with monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM), compared to subjects with multiple myeloma (MM). These features are useful in methods for monitoring progression from MGUS or SMM to MM in a subject. In some instances, certain features are also useful in methods for predicting the response of a subject's MM to therapy.

Description

METHODS RELATED TO MULTIPLE MYELOMA AND PRECURSORS THEREOF CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. Provisional Application No.63 / 689,201 filed August 30, 2024, U.S. Provisional Application No. 63 / 698,902 filed September 25, 2024, and U.S. Provisional Application No.63 / 715,843 filed November 4, 2024, the entire contents of each is incorporated herein by reference. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under grant number R35CA263817-01A1 awarded by the National Cancer Institute. The government has certain rights to the invention. FIELD

[0003] There are provided herein methods of identifying differences in the features of immune cell populations, said differences being indicative of progression of monoclonal gammopathy of unknown significance (MGUS) and smoldering multiple myeloma (SMM) to multiple myeloma (MM). BACKGROUND

[0004] Multiple myeloma (MM) is a hematological malignancy that arises from plasma cells and accounts for approximately 10% of all hematological malignancies. Almost all cases of MM are preceded by premalignant, asymptomatic stages termed monoclonal gammopathy of unknown significance (MGUS) and smoldering multiple myeloma (SMM). About 1% of MGUS patients and 10% of SMM patients go on to develop MM each year. Some patients appear to be at higher risk for progression to MM than others.

[0005] Patients with MGUS and SMM are monitored to assess whether they are progressing to MM. Conventional monitoring methods include imaging methods to assess the presence of lytic lesions in the bones, and use of blood, urine, or bone marrow samples to monitor kidney function, plasma cell counts, hypercalcemia, anemia, and concentration of myeloma protein.

[0006] MM patients have dysregulated immune systems, causing them to have increased susceptibility to infections. How the immune system becomes dysregulated and whether features of dysregulation can be used to predict disease progression is not yet known. SUMMARY

[0007] Several gene expression signatures in immune cells have now been discovered that accompany the progression from MGUS or SMM to MM. Accordingly, in one aspect, a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject is provided that comprises determining expression of one or more gene expression signatures in a sample obtained from the subject and a reference sample, wherein the one or more gene expression signatures are selected from: a) a myeloid cell-enriched gene expression signature comprising G0S2, THBS1, TIMP1, HIF1A, PLAUR, SRGN, CEBPB, UPP1, NINJ1, and VEGFA, wherein a decrease in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; b) a myeloid cell-enriched gene expression signature comprising IL1B, CXCL8, ATP2B1-AS1, CCL3, SAT1, IER3, CXCL2, KLF4, CCL3L1, and ATF3, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; c) a dendritic cell- and B cell-enriched gene expression signature comprising JCHAIN, PTGDS, LILRA4, ITM2C, PLD4, RASD1, IRF8, TCF4, PPP1R14B, and CCDC50, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; d) an immune cell-associated gene expression signature comprising ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, and OAS3, wherein an increase in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; e) a lymphocyte-enriched (e.g., T cells, for example CD8+ T cells) gene expression signature comprising TNFAIP3, CXCR4, PMAIP1, TSPYL2, NR4A2, FAM177A1, HSPA5, PDE4D, PER1, and RNF125, wherein a decrease of the expression of thegene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; f) a myeloid cell-enriched gene expression signature comprising FOS, FOSB, CITED2, ID1, ARHGEF40, and USP2, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; g) a granzyme B (GZMB)+ CD8+ effector memory T cell (TEM)-specific gene expression signature comprising ZBP1, TPT1, TNFAIP3, TGFB1, SYTL3, SLC7A5, SLA2, RBM38, PPP1R16B, PIK3R1, PDE4D, ODC1, HSPA5, HMGB2, H3F3B, FTH1, FAM177A1, EIF1, and BTG1, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; h) a T cell-specific gene expression signature comprising CISH, TNFRSF1A, CX3CR1, SLAMF6, LPAR6, PIM1, C14orf119, PRDX3, CALHM2, TAGAP, SIT1, LINC01871, KLRB1, SNHG5, S100A11, and EOMES, and one or more of GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, and GIMAP7, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; and i) a tumor cell-specific gene expression signature comprising SERPINB9, MAP3K8, PELI1, CSRNP1, STX11, UBALD2, PER1, ESR1, GADD45A, GADD45B, PNP, KLF9, SIK1B, FAM49A, CYTOR, AREG, SNX9, PMAIP1, MYADM, and C11orf96, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0008] In another aspect, a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject is provided that comprises: a. determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in a sample obtained from the subject and a reference sample; b. determining the proportion of GZMB+ CD8+ TEMs that are LAT1+ TIMAP+ TGFβ+ in the sample obtained from the subject and the reference sample; andc. comparing the proportions determined in steps a. and b. in the sample and the reference sample; wherein an increase in the proportion of GZMB+ CD8+ TEMs and a decrease in the proportion of GZMB+ CD8+ TEMs that are LAT1+ TIMAP+ TGFβ+ in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0009] It has further been discovered that, in the progression from MGUS or SMM to MM, the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) increases.

[0010] Accordingly, in another aspect, provided herein is a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject comprising: a. determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in a sample obtained from the subject and a reference sample (e.g., a sample obtained from one or more healthy donors or one or more subjects with MGUS or SMM who have not progressed to MM or a prior sample from the same individual); and b. comparing the proportion of GZMB+ CD8+ TEMs in the sample and the reference sample wherein an increase in the proportion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0011] It has also been discovered that, in the progression from MGUS or SMM to MM, the proportion of cytokine-expressing myeloid cells (e.g., expressing cytokines comprising one or more of IL1B, CXCL8, CCL3, and CCL4) decreases.

[0012] Accordingly, in another aspect, provided herein is a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject comprising: a. determining the proportion of cytokine-expressing myeloid cells in a sample obtained from the subject and a reference sample (e.g., a sample obtained from one or more healthy donors or one or more subjects with MGUS or SMM who have not progressed to MM or a prior sample from the same individual); andb. comparing the proportion of cytokine-expressing myeloid cells in the sample and the reference sample wherein a decrease in the proportion of cytokine-expressing myeloid cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0013] Also discovered herein is that, in the progression from MGUS or SMM to MM, T cell clonality increases, i.e., T cells clonally expand. These expanded clones were observed to exhibit a terminal differentiation bias. Furthermore, TCR repertoire diversity was observed to decrease.

[0014] Accordingly, in another aspect, provided herein is a method for monitoring progression from MGUS or SMM to MM in a subject comprising: a. determining T cell repertoire diversity in a sample obtained from the subject and a reference sample (e.g., a sample obtained from one or more healthy donors or one or more subjects with MGUS or SMM who have not progressed to MM or a prior sample from the same individual); and b. comparing the T cell repertoire diversity in the sample and the reference sample wherein a decrease in the T cell repertoire diversity in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0015] Provided herein is a method for monitoring progression from MGUS or SMM to MM in a subject comprising: a. determining clonal expansion of T cells in a sample obtained from the subject and a reference sample (e.g., a sample obtained from one or more healthy donors or one or more subjects with MGUS or SMM who have not progressed to MM or a prior sample from the same individual); and b. comparing the clonal expansion of T cells in the sample and the reference sample wherein an increase in the clonal expansion of T cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0016] Also discovered herein is that, in the progression from MGUS or SMM to MM, an interferon (IFN) expression signature increases in CD138+ plasma cells.

[0017] Accordingly, provided herein is a method for monitoring progression from MGUS or SMM to MM in a subject, comprising determining expression of an interferon (IFN) signature in CD138+ plasma cells in a sample obtained from the subject and a reference sample (e.g., a sample obtained from one or more healthy donors or one or more subjects with MGUS or SMM who have not progressed to MM), wherein an increase of the expression of the IFN signature in the CD138+ plasma cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0018] An increased IFN signature was determined to have prognostic value in the progression-free survival (PFS) and overall survival (OS) of subjects with MM.

[0019] Accordingly, provided herein is a method for predicting the response of a subject’s MM to therapy, comprising determining expression of an interferon (IFN) signature in CD138+ plasma cells in a sample obtained from the subject and a reference sample, wherein an increase of the expression of the IFN signature in the CD138+ plasma cells in the sample relative to the reference sample indicates that the subject’s MM may not respond to therapy.

[0020] Subjects with MM and increased T cell clonality were observed to have significantly inferior overall survival (OS) than subjects with MM and decreased T cell clonality.

[0021] Accordingly, provided herein is a method for predicting the response of a subject’s MM to therapy, comprising determining clonal expansion of T cells in a sample obtained from the subject and a reference sample, wherein an increase of the clonal expansion of T cells in the sample relative to the reference sample indicates that the subject’s MM may not respond to therapy.

[0022] Other features, objects, and advantages are apparent in the detailed description, drawings and examples that follow. It should be understood, however, that the detailed description, the drawings, and the examples, are given by way of illustration only, not limitation. Various changes and modifications will become apparent to those skilled in the art. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Drawings are for illustration purposes only.

[0024] FIG. 1A shows a representative volcano plot showing the changes in immune cell proportions between patients with MGUS (left) and patients with newly-diagnosed multiplemyeloma (NDMM, right). MoDC = monocyte derived dendritic cells, cDCs = conventional dendritic cells.

[0025] FIG. 1B shows a representative forest plot showing the coefficient of the disease stage term in a Gaussian linear model fit on each cell type’s proportion using disease stage and chronological age as independent variables.

[0026] FIG. 1C shows a representative volcano plot showing pseudobulk differential expression analysis between CD14+ monocytes in patients with MM (right / top) and patients with MGUS (left / bottom).

[0027] FIG. 1D shows a representative volcano plot showing pseudobulk differential expression analysis between GZMB+ CD8+ TEMs in patients with multiple myeloma (MM) (right / top) and patients with MGUS (left / bottom).

[0028] FIG. 1E shows a representative stacked bar graph showing the proportion of cell types represented in cells in which a particular signature is active (i.e., in the top quartile of the activity distribution). When present, the cell types in the graph are shown according to the key in the bottom right, in the order as they appear in that key. The bar graph inset at the top of the figure shows whether each signature was specific or broad, colored according to the key “specific” or “broad” on the top right of the figure.

[0029] FIG. 1F shows representative boxplot and violin plot overlays of the signatures where signature activity in immune cells was significantly different between patients with MGUS (left / bottom of each graph) and newly diagnosed MM (NDMM, right / top of each graph), i.e., signatures S10, S11, S15, S21, S26, S27 and S28. The p-value for each graph was computed using Wilcoxon’s rank-sum test and adjusted using the Benjamini-Hochberg approach.

[0030] FIG. 2A shows a representative bar graph showing a gene expression signature marked by IFN signaling in tumor cells.

[0031] FIG. 2B shows a representative bar graph showing a gene expression signature marked by IFN signaling in immune cells.

[0032] FIG. 2C shows a representative scatterplot showing correlation between the IFN activity in tumor and immune cells (Pearson correlation coefficient r=0.71).

[0033] FIG.2D shows a representative boxplot and violin plot overlay showing interferon (IFN) signature activity in immune cells of patients with SMM, split based on progression status (NP = Non-progressors, left; P = Progressors, right). The p-value was computed using Wilcoxon’s rank-sum test.

[0034] FIG. 2E shows a representative boxplot and violin plot overlay showing IFN signature activity in patients with MGUS who remained stable over 10 years of follow-up (Stable, left) or progressed to MM (Progressive, right). The p-value was computed using Wilcoxon’s rank-sum test.

[0035] FIG. 2F shows a representative boxplot and violin plot overlay showing IFN signature activity in patients with MGUS, SMM, NDMM, or RRMM. The p-values were computed using Wilcoxon’s rank-sum test and corrected with the Benjamini-Hochberg approach.

[0036] FIG. 2G shows a representative Kaplan-Meier curve showing progression-free survival (PFS) in patients with MM, split based on median IFN activity. The p-value was computed using a log-rank test.

[0037] FIG.3A shows a representative boxplot and violin plot overlay showing of the Chao index of TCR repertoire diversity in GZMB+ CD8+ TEMs (y-axis) in patients with MGUS (left) and NDMM (right). The p-value was computed using Wilcoxon’s rank-sum test.

[0038] FIG. 3B shows a representative boxplot and violin plot overlay showing the proportion of GZMK+ CD8+ TEMs (left) and the proportion of GZMB+ CD8+ TEMs (right) out of clonally expanded T cells in patients with MGUS (left) or NDMM (right). The p- values were computed using Wilcoxon’s rank-sum test.

[0039] FIG. 3C shows a representative boxplot and violin plot overlay showing the proportion of clonally expanded GZMB+ CD8+ TEMs that were cycling (i.e., in S or G2M phase) in patients with MGUS (left) or NDMM (right). The p-values were computed using Wilcoxon’s rank-sum test.

[0040] FIG.3D shows representative scatter plots showing the Chao index of T cell receptor (TCR) repertoire diversity in all T cells (y-axis) and chronological age (x-axis) across patients with MGUS (left), SMM (center), and NDMM (right).

[0041] FIG.3E shows a representative forest plot showing the coefficient for disease stage and chronological age in a Gaussian linear model fit on repertoire diversity Chao index using disease stage and age as covariates.

[0042] FIG. 3F shows a representative boxplot and violin plot overlay showing the Chao index of TCR repertoire diversity (y-axis) in patients with SMM who did not progress (NP, left) and patients with SMM who progressed to MM (P, right). The p-value was computed using Wilcoxon’s rank-sum test.

[0043] FIG.3G shows a representative Kaplan-Meier curve showing overall survival (OS) in patients with MM split into two groups based on their median T cell clonality (high vs low). The p-value was computed using a log-rank test.

[0044] FIG.4A shows a representative boxplot and violin plot overlay showing the activity of the T cell activation gene expression signature (y-axis) in patients with MGUS (left) and NDMM (right). The p-value was computed using Wilcoxon’s rank-sum test.

[0045] FIG. 4B shows a representative heatmap showing the genes that mark the T cell activation gene expression signature, and whether they encode a cytokine or a surface protein (GESP). White boxes indicate that the gene encodes neither a cytokine nor a surface protein. Grey boxes indicate that the gene encodes a cytokine or a surface protein.

[0046] FIG.4C shows a representative boxplot and violin plot overlay showing the activity of the T cell activation signature in rare or expanded T cell clones (i.e., clones with more than one T cell) in patients with MGUS (left) and NDMM (right). The p-values were computed using Wilcoxon’s rank-sum test.

[0047] FIG. 4D shows a representative volcano plot showing the log-fold change in expression of genes between GZMB+ CD8+ TEM populations between SMM patients split into two groups: a first group defined by having low (1stquartile of the distribution) T cell activation gene expression signature activity in their GZMB+ CD8+ TEMs (left) and a second group defined by having high (3rdquartile of the distribution) T cell activation gene expression signature activity in their GZMB+ CD8+ TEMs (right).

[0048] FIG.4E shows a representative boxplot and violin plot overlay showing the T cell activation gene expression signature activity in GZMB+ CD8+ TEMs of patients with SMM, split based on progression status (NP = Non-progressors, left; P = Progressors, right). The p- value was computed using Wilcoxon’s rank-sum test.

[0049] FIG.4F shows representative scatterplots showing the positive correlation of the T cell activation gene expression signature activity in GZMB+ CD8+ TEMs sampled from the peripheral blood (PB) compared to the T cell activation gene expression signature activity in GZMB+ CD8+ TEMs sampled from the bone marrow (BM), in patients with MGUS (left), SMM (middle), and MM (right).

[0050] FIG. 5A shows a representative volcano plot showing the log-fold change in expression between tumor cell populations from SMM patients split into two groups: a first group defined by having low (1stquartile of the distribution) T cell activation gene expression signature activity in their GZMB+ CD8+ TEMs (left) and a second group defined by having high (3rdquartile of the distribution) T cell activation gene expression signature activity in their GZMB+ CD8+ TEMs (right).

[0051] FIG.5B shows a representative boxplot and violin plot overlay showing the tumor cell correlate of T cell functionality in patients with MGUS (left), SMM (center), or NDMM (right). The p-value was computed using Wilcoxon’s rank-sum test.

[0052] FIG. 5C shows a representative boxplot and violin plot overlay showing the mean SERPINB9 levels in tumor cells from patients with MGUS, SMM, or NDMM. The p-value was computed using Wilcoxon’s rank-sum test.

[0053] FIG. 5D shows a representative boxplot and violin plot overlay showing the percentage of cycling tumor cells in SMM patients split into two groups: a first group defined by having low (i.e., less than or equal to the median of the distribution) tumor cell correlate of T cell functionality gene expression signature activity (left) and a second group defined by having high (i.e., greater than the median of the distribution) tumor cell correlate of T cell functionality gene expression signature activity (right). The p-value was computed using Wilcoxon’s rank-sum test.

[0054] FIG. 5E shows a representative boxplot and violin plot overlay showing immune control signature activity in tumor cells of patients (“tumor signature activity”) with (left to right) MGUS, SMM, NDMM, or RRMM. The p-value was computed using Wilcoxon’s rank-sum test and corrected with the Benjamini-Hochberg approach.

[0055] FIG. 5F shows a representative boxplot and violin plot overlay showing immune control signature activity in tumor cells of patients (“tumor signature activity”) with MGUSwho remained stable over 10 years of follow-up (Stable, left) or progressed to MM (Progressive, right). The p-value was computed using Wilcoxon’s rank-sum test.

[0056] FIG.5G shows a representative boxplot and violin plot overlay showing SERPINB9 expression in tumor cells of patients with MGUS who remained stable over 10 years of follow-up (Stable, left) or progressed to MM (Progressive, right). The p-value was computed using Wilcoxon’s rank-sum test.

[0057] FIG. 6A shows a representative volcano plot showing pseudobulk differential expression analysis between GZMB+ CD8+ TEMs in patients with NDMM (right / top) and patients with MGUS (left / bottom). The top 20 genes in each group are labeled.

[0058] FIG. 6B shows a representative bar graph showing a gene expression signature marked by IFN signaling in immune cells.

[0059] FIG. 6C shows a representative volcano plot showing pseudobulk differential expression analysis between GZMB+ CD8+ TEMs in SMM patients with high T cell activation gene expression signature activity (“high”, right / top) and SMM patients with low T cell activation gene expression signature activity (“low”, left / bottom). The top 20 genes in each group are labeled as circles. Genes associated with a T cell activation gene expression signature in GZMB+ CD8+ TEMs including, e.g., SLC7A5, PIK3R1, and TGFB1, are labelled with triangles. Genes associated with cytotoxicity and terminal differentiation e.g., CX3CR1 and PRF1, are labeled with inverted triangles, which can be associated with a T cell dysfunction signature. DETAILED DESCRIPTION

[0060] Unless otherwise defined herein, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs and as commonly used in the art to which this application belongs. Exemplary methods and materials are described below, although methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure. In case of conflict, the present specification, including definitions, will control. To facilitate ready understanding, certain terms used herein are first defined below.

[0061] As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. For example, “a lymphocyte” is understood to represent one or more lymphocyte(s) or a population oflymphocytes. As such, the terms “a” (or “an”), “one or more”, and “at least one” can be used interchangeably herein. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular.

[0062] “And / or” where used herein is to be taken as specific disclosure of each of the two specified features or components with or without the other. Thus, the term “and / or” as used in a phrase such as “A and / or B” herein is intended to include “A and B”, “A or B”, “A” (alone), and “B” (alone). Likewise, the term "and / or" as used in a phrase such as “A, B, and / or C” is used interchangeably with “A and / or B and / or C” and is intended to encompass each of the following aspects: A, B, and C; A, B, or C; A or C; A or B; B or C; A and C; A and B; B and C; A (alone); B (alone); and C (alone).

[0063] Throughout this specification, the words “have” and “comprise”, or variations such as “has”, “having”, “comprises”, or “comprising” will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers. It is further understood that wherever aspects are described herein with the language “comprising” or “having” or grammatical equivalents thereof, otherwise analogous aspects described in terms of “consisting of” and / or “consisting essentially of” are also provided. In other words, if a composition comprising A, B and C is recited, a composition consisting essentially of A, B and C is also contemplated as is a composition consisting of A, B and C.

[0064] The term “staging” refers to the process of determining the extent a cancer has developed by growing or spreading in a subject. Typically, classification of a cancer involves assigning the cancer a number from I to III or IV, as the case may be. Generally speaking, the lower the number, the less the cancer has spread.

[0065] Generally, techniques of cell and tissue culture, molecular biology, virology, immunology, microbiology, genetics, analytical chemistry, synthetic organic chemistry, medicinal and pharmaceutical chemistry, and protein and nucleic acid chemistry and hybridization described herein are those well-known and commonly used in the art. Enzymatic reactions and purification techniques are performed according to manufacturer’s specifications, as commonly accomplished in the art or as described herein. Further, nomenclature used in connection with these technology areas herein is as commonly used in the art as can be seen by reference to, e.g., http: / / www.informatics.jax.org / mgihome / nomen / gene.shtml and https: / / www.genenames.org / .

[0066] All publications and other reference materials referenced herein are hereby incorporated by reference in their entirety. Although a number of documents are cited herein, this citation does not constitute an admission that any one of these documents forms part of the common general knowledge in the art. Methods of monitoring disease progression

[0067] Identified herein are differences in the features of immune cell populations in subjects with plasma cell premalignancies, such as monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM), compared to subjects with multiple myeloma (MM). In particular, changing proportions of various immune cell populations, decreased number of T cell clones or increased size of T cell clones, and increased interferon (IFN) signaling are identified in subjects with MM compared to subjects with MGUS or SMM. These insights provide the basis for methods for monitoring disease progression from MGUS or SMM to MM, e.g., to determine whether a subject diagnosed with MGUS or SMM is progressing to, or has progressed to, MM. Accordingly, the methods can be used to inform patient treatment strategies and may assist in the earlier diagnosis and treatment of MM than use of conventional monitoring methods alone. Sample

[0068] In the methods described herein, a sample obtained from a subject is compared to a reference sample. Accordingly, the sample obtained from the subject will be of the same type (e.g., blood or bone marrow) as the reference sample. For instance, the sample obtained from the subject and the reference sample may be a blood sample. Alternatively, the sample obtained from the subject and the reference sample may be a bone marrow sample. The bone marrow sample may be a bone marrow biopsy or a bone marrow aspirate. Typically, the sample obtained from the subject and the reference sample are bone marrow samples.

[0069] The reference sample can be a sample obtained from the same subject at an earlier timepoint. The relative change in the feature of interest between the subject’s current sample and the subject’s earlier sample may be assessed as a percentage change. For example, for a feature that increases with progression to MM, the increase may be at least about 10% compared to the reference sample, e.g., about 15% or more, about 20% or more, about 25% or more, about 30% or more, about 35% or more, about 40% or more, about 45% or more, or about 50% or more compared to the reference sample. For a feature that decreases withprogression to MM, the decrease may be at least about 10% compared to the reference sample, e.g., about 15% or more, about 20% or more, about 25% or more, about 30% or more, about 35% or more, about 40% or more, about 45% or more, or about 50% or more compared to the reference sample.

[0070] Alternatively, the reference sample may be a representative sample obtained from one or more (e.g., multiple) different subjects (e.g., a panel of samples obtained from different subjects).

[0071] For example, the reference sample may be a representative sample obtained from one or more (e.g., multiple) different subjects with MGUS or SMM who have not progressed to MM. Or the reference sample may be a representative sample obtained from one or more (e.g., multiple) healthy donors (e.g., a panel of samples obtained from different healthy donors). It will be appreciated that the immune cell features being used to monitor a subject with MGUS or SMM will become increasingly dissimilar to the reference sample as the subject progresses to MM.

[0072] Alternatively, a reference sample may be provided that is a representative sample obtained from one or more (e.g., multiple) different subjects with MGUS or SMM who progressed to MM, or from one or more (e.g., multiple) different subjects with MM. It will be appreciated that the immune cell features being used to monitor a subject with MGUS or SMM will become increasingly similar to the reference sample as the subject progresses to MM.

[0073] In some instances, the reference sample may comprise samples obtained from multiple healthy donors, multiple patients with MGUS, multiple patients with SMM, or multiple patients with MM. In another instance, the reference sample may comprise samples from two or more of a panel of healthy donors, a panel of patients with MGUS, a panel of patients with SMM, and a panel of patients with MM. For example, the reference sample may comprise samples obtained from multiple healthy donors, multiple patients with MGUS, multiple patients with SMM, and multiple patients with MM.

[0074] In these instances, the reference sample may provide a distribution of values of a given feature of interest (e.g., a gene or gene expression signature, a proportion of a cell type, T cell clonality, etc.). For example, the increase or decrease of the value of the feature of interest in the subject’s sample relative to the reference sample distribution may beexpressed as being in a percentile range of the reference sample distribution. For instance, the increase may be higher than the median, higher than the 75thpercentile, higher than the 80thpercentile, higher than the 85thpercentile, higher than the 90thpercentile, or higher than the 95thpercentile of the reference sample distribution. For example, the decrease may be lower than the median, lower than the 25thpercentile, lower than the 20thpercentile, lower than the 15thpercentile, lower than the 10thpercentile, or lower than the 5thpercentile of the reference sample distribution. Alternatively, the increase or decrease of the value of the feature of interest in the subject’s sample relative to the reference sample distribution may be expressed as one or more standard deviations away from the mean of the reference sample. For example, the increase may be 1 standard deviation or more above the mean, 1.5 standard deviations or more above the mean, or 2 standard deviations or more above the mean. For example, the decrease may be 1 standard deviation or less below the mean, 1.5 standard deviations or less below the mean, or 2 standard deviations or less below the mean.

[0075] Methods described herein may utilize two or more reference samples. For example, a first reference sample from one or more (e.g., multiple) healthy donors or from one or more (e.g., multiple) different subjects with MGUS or SMM who have not progressed to MM and a second reference sample from one or more (e.g., multiple) different subjects with MGUS or SMM who progressed to MM, or from one or more (e.g., multiple) different subjects with MM. Gene expression signatures

[0076] In one aspect, methods for monitoring progression from MGUS or SMM to MM are provided that comprise determining one or more gene expression signatures in a sample obtained from the subject and a reference sample. Typically, the sample and reference sample are each bone marrow samples, e.g., are each bone marrow aspirates or bone marrow biopsies.

[0077] The one or more gene expression signatures may comprise an immune cell activation gene expression signature. For instance, a method for monitoring progression from MGUS or SMM to MM in a subject is provided that comprises determining expression of an immune cell activation gene expression signature comprising SLC7A5 in a sample obtained from the subject and a reference sample, wherein a decrease in the expression of the immune cell activation gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The immune cell activationgene expression signature may further comprise TGFB1, PIK3R1, and optionally SYTL3, FTH1, and one or more NF-κB pathway genes (e.g., RELB).

[0078] The immune cell activation gene expression signature may be determined in CD8+ T cells in the sample and the reference sample, so may, in such instances, be referred to as a T cell activation gene expression signature. For example, the CD8+ T cells may comprise, or consist of, granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs). Alternatively or in addition, the CD8+ T cells may comprise, or consist of, granzyme K (GZMK)+ CD8+ effector memory T cells (TEMs).

[0079] The immune cell activation gene expression signature may further be determined in CD56dim natural killer (NK) cells in the sample and the reference sample.

[0080] In some instances, the immune cell activation gene expression signature may further be determined in central memory CD4+ T cells (CD4+ TCMs) in the sample and the reference sample.

[0081] The exact gene composition of the immune cell activation gene expression signature comprising SLC7A5 may vary depending on the type of immune cell in which it is determined. For example, in GZMB+ CD8+ TEMs, the immune cell activation gene expression signature may comprise one or more (e.g., two, three, four, five, six or all) of the following genes: FTH1, EIF1, TGFB1, H3F3B, PIK3R1, RBM38, HSPA5, SYTL3, and ZBP1. In some instances, in GZMB+ CD8+ TEMs, the immune cell activation gene expression signature may comprise one or more of the named genes shown in FIG.4D that have a log- fold change of 1.5 or more, e.g., 2 or more.

[0082] As an alternative or in addition to determining the immune cell activation gene expression signature, a method for monitoring progression from MGUS or SMM to MM may comprise determining expression of a T cell dysfunction signature in CD8+ T cells in the sample obtained from the subject and the reference sample. An increase in the expression of the T cell dysfunction signature in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM. The T cell dysfunction signature may comprise one or more (e.g., two, three, four, five, six or all) of CISH, TNFRSF1A, CX3CR1, SLAMF6, LPAR6, PIM1, and EOMES. The T cell dysfunction signature may further comprise BATF and / or PRF1. Alternatively or in addition, the T cell dysfunction signature comprises one or more of genes in the GIMAP family (e.g., GIMAP4, GIMAP7,GIMAP6, GIMAP1, and GIMAP2). For example, the T cell dysfunction signature may comprise one or more of the named genes shown in FIG.4D that have a log-fold change of minus (-) 0.5 or less.

[0083] As an alternative or in addition to determining the immune cell activation gene expression signature, a method for monitoring progression from MGUS or SMM to MM may comprise determining expression of an immune control signature, or the tumor cell correlate of T cell functionality and activation, comprising SERPINB9 in tumor cells in the sample obtained from the subject and the reference sample. In addition to SERPINB9, the immune control signature, or the tumor cell correlate of T cell functionality and activation, may comprise one or more (e.g., two, three, four, five, six or all) of MAP3K8, PELI1, CSRNP1, STX11, UBALD2, PER1, ESR1, GADD45A, GADD45B, PNP, KLF9, SIK1B, FAM49A, CYTOR, AREG, SNX9, PMAIP1, MYADMC11orf96. A decrease in the expression of the immune control signature in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM. For example, the immune control signature may comprise one or more of the named genes shown in FIG.5A that have a log-fold change of 0.5 or more, 1 or more, 1.5 or more, e.g., 2 or more.

[0084] As an alternative or in addition to determining the immune cell activation gene expression signature, a method for monitoring progression from MGUS or SMM to MM may comprise determining expression of an interferon (IFN) signature in immune cells and / or in CD138+ plasma cells in the sample obtained from the subject and the reference sample, wherein an increase of the expression of the IFN signature in the immune cells and / or the CD138+ plasma cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The IFN signature may comprise one or more (e.g., two, three, four, five, six, seven, or all) of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, and XAF1. The IFN signature may further comprise one or more (e.g., two, three, four, five, six, or all) of MX1, IFI27, IFI44, HERC5, IFIT1, OAS1, CMPK2. For example, the IFN signature may comprise ISG15, IFI6, OAS1, and XAF1. In immune cells, the IFN signature may comprise ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, and OAS3. For example, in immune cells, the IFN signature may comprise or consist of ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, MX1, IFI27, IFI44, HERC5, IFIT1, OAS1, CMPK2, and OAS3. In some instances, the IFN signature may comprise one or more of the named genes shown in FIG. 5A that have a log-fold change of minus (-) 1 or less. Or, theIFN signature may comprise one or more of the named genes shown in FIG.6B that have a log-fold change of minus (-) 1 or less.

[0085] As an alternative or in addition to determining the immune cell activation gene expression signature, a method for monitoring progression from MGUS or SMM to MM may comprise determining expression of a hypoxia gene expression signature comprising HIF1A in monocytes in the sample obtained from the subject and the reference sample. A decrease in the expression of the hypoxia gene expression signature in the monocytes in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM. The hypoxia gene expression signature may further comprise TIMP1 and optionally one or more of UPP1, TLE3, LINC01578, and CXXC5. Alternatively or in addition, the hypoxia gene expression signature may further comprise one or more of THBS1, TGFB1, PLAUR, and VEGFA. In some instances, the hypoxia gene expression signature may comprise HIF1A, TIMP1, and two, three, or all of UPP1, TLE3, LINC01578, and CXXC5. In other instances, the hypoxia gene expression signature may comprise HIF1A, TIMP1, and two or more (e.g., three, four, five, six, seven, or all) of UPP1, TLE3, LINC01578, CXXC5, THBS1, TGFB1, PLAUR, and VEGFA. For example, the hypoxia gene expression signature may comprise or consist of HIF1A, TIMP1, UPP1, TLE3, LINC01578, CXXC5, THBS1, TGFB1, PLAUR, and VEGFA. The monocytes comprise or consist of CD14+ monocytes and / or CD16+ monocytes.

[0086] The exact gene composition of the hypoxia gene expression signature comprising HIF1A may vary depending on the type of monocyte in which it is determined. For example, in CD14+ monocytes, the hypoxia gene expression signature may comprise one or more (e.g., two, three, four, five, six or all) of the following genes: TIMP1, ARL4C, UPP1, NINJ1, TLE3, LINC01578, HSPA5, CXXC5, and SLC25A37 (in addition to HIF1A). In CD16+ monocytes, the hypoxia gene expression signature may comprise one or more (e.g., two, three, four, five, six or all) of the following genes: PTGES, GLUL, NRIP1, LINC01578, CTNNB1, UPP1, ABCA1, CXXC5, and TLE3.

[0087] A decrease in expression of the hypoxia gene expression signature may be accompanied by an increase in a (cytokine-positive) myeloid cell-enriched signature comprising one or more (e.g., two, three, four, or all) of IL1B, CCL3, CCL3L1, CXCL8, and CXCL2.

[0088] In some aspects, a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject is provided that comprises determining expression of one or more (e.g., two, three, four, five, six, seven, eight, nine, or ten) gene expression signatures in a sample obtained from the subject and a reference sample, wherein the one or more gene expression signatures are selected from: a. a myeloid cell-enriched (e.g., monocytes and / or dendritic cells) gene expression signature (e.g., S15 in Table 1) comprising THBS1, PLAUR, TIMP1, VEGFA, and HIF1A and one or more (e.g., two, three, four, or all) of G0S2, SRGN, CEBPB, UPP1, and NINJ1, wherein a decrease in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; b. a myeloid cell-enriched (e.g., monocytes and / or dendritic cells) gene expression signature (e.g., S26 in Table 1) comprising IL1B, CCL3, CCL3L1, CXCL8, and CXCL2 and one or more (e.g., two, three, four, or all) of ATP2B1- AS1, SAT1, IER3, KLF4, and ATF3, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; c. an immune cell-associated gene expression signature (e.g., S10 in Table 1) comprising B2M and one or more (e.g., two, three, four, five, six, or all) of TUBA1B, CSTB, PRDX1, ATP6V0B, CTSC, VAMP5, SPPL2A, ATOX1, and ADI1, wherein an increase in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; d. a dendritic cell- and B cell-enriched gene expression signature (e.g., S11 in Table 1) comprising IRF8, LILRA4, and JCHAIN and one or more (e.g., two, three, four, five, six, or all) of PTGDS, ITM2C, PLD4, RASD1, TCF4, PPP1R14B, and CCDC50, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; e. an immune cell-associated gene expression signature (e.g., S27 in Table 1) comprising one or more (e.g., two, three, four, five, six, or all) of ISG15, IFI6,IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, and OAS3, wherein an increase in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; f. a lymphocyte-enriched (e.g., T cells, for example CD8+ T cells) gene expression signature (e.g., S28 in Table 1) comprising CXCR4, TNFAIP3, NR4A2, and FAM177A1 and one or more (e.g., two, three, four, five, or all) of PMAIP1, TSPYL2, HSPA5, PDE4D, PER1, and RNF125, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; g. a myeloid cell-enriched (e.g., monocytes and / or dendritic cells) gene expression signature (e.g., S21 in Table 1) comprising FOS and FOSB and one or more (e.g., two, three, four, or all) of CITED2, ID1, ARHGEF40, USP2, and optionally AP000692.2, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; h. a granzyme B (GZMB)+ CD8+ effector memory T cell (TEM)-specific gene expression signature comprising TGFB1, PIK3R1, SLC7A5, and SYTL3 and one or more (e.g., two, three, four, five, six, or all) of ZBP1, TPT1, TNFAIP3, SLA2, RBM38, PPP1R16B, PDE4D, ODC1, HSPA5, HMGB2, H3F3B, FTH1, FAM177A1, EIF1, and BTG1, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; i. a T cell-specific gene expression signature comprising CISH, TNFRSF1A, CX3CR1, SLAMF6, EOMES, GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, and GIMAP7 and one or more (e.g., two, three, four, five, six, or all) of LPAR6, PIM1, C14orf119, PRDX3, CALHM2, TAGAP, SIT1, LINC01871, KLRB1, SNHG5, and S100A11, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; andj. a tumor cell-specific gene expression signature comprising SERPINB9 and one or more (e.g., two, three, four, five, six, or all) of MAP3K8, PELI1, CSRNP1, STX11, UBALD2, PER1, ESR1, GADD45A, GADD45B, PNP, KLF9, SIK1B, FAM49A, CYTOR, AREG, SNX9, PMAIP1, MYADM, and C11orf96, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0089] In some instances, the method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject is provided wherein the method comprises determining expression of one or more (e.g., two, three, four, five, six, seven, eight, nine, or ten) gene expression signatures in a bone marrow sample obtained from the subject and a reference sample of bone marrow samples obtained from multiple healthy donors and / or multiple patients with MGUS, and / or multiple patients with SMM, wherein the one or more gene expression signatures are selected from: a) a myeloid cell-enriched gene expression signature comprising G0S2, THBS1, TIMP1, HIF1A, PLAUR, SRGN, CEBPB, MAP3K8, UPP1, NINJ1, and VEGFA, wherein a decrease in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; b) a myeloid cell-enriched gene expression signature comprising IL1B, CXCL8, ATP2B1-AS1, CCL3, SAT1, IER2, IER3, CXCL2, KLF4, KLF6, CCL3L1, and ATF3, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; c) a dendritic cell- and B cell-enriched gene expression signature comprising JCHAIN, PTGDS, LILRA4, ITM2C, PLD4, RASD1, IRF8, SERPINF1, IRF7, TCF4, CLEC4C, MZB1, PPP1R14B, and CCDC50, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; d) an immune cell-associated gene expression signature comprising ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, MX1, IFI27, IFI44, HERC5,IFIT1, OAS1, CMPK2, and OAS3, wherein an increase in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; e) a lymphocyte-enriched (e.g., T cells, for example CD8+ T cells) gene expression signature comprising TNFAIP3, CXCR4, PMAIP1, TSPYL2, TGFB1, NR4A2, FAM177A1, HSPA5, ZNF331, PIK3R1, SLC7A5, PDE4D, PER1, and RNF125, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; f) a myeloid cell-enriched gene expression signature comprising FOS, FOSB, DUSP1, CITED2, ID1, ARHGEF40, and USP2, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; g) a granzyme B (GZMB)+ CD8+ effector memory T cell (TEM)-specific gene expression signature comprising ZBP1, TPT1, TNFAIP3, TGFB1, SYTL3, SLC7A5, SLA2, RBM38, PPP1R16B, PIK3R1, PDE4D, ODC1, HSPA5, HMGB2, H3F3B, FTH1, FAM177A1, EIF1, and BTG1, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; h) a granzyme B (GZMB)+ CD8+ effector memory T cell (TEM)-specific gene expression signature comprising CISH, TNFRSF1A, CX3CR1, SLAMF6, LPAR6, PIM1, C14orf119, PRDX3, CALHM2, TAGAP, SIT1, PRF1, LINC01871, KLRB1, SNHG5, S100A11, BATF, and EOMES, and one or more of GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, and GIMAP7, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; and i) a tumor cell-specific gene expression signature comprising SERPINB9, MAP3K8, PELI1, CSRNP1, STX11, UBALD2, PER1, ESR1, GADD45A, GADD45B, PNP, KLF9, SIK1B, FAM49A, CYTOR, AREG, SNX9, PMAIP1, MYADM, FOXP1, PIM3, OTUD1, and C11orf96, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0090] In some instances, the reference sample is a sample obtained from the same subject at an earlier timepoint. For gene expression signatures that increase with progression to MM, expression of the gene expression signature in the sample may be increased relative to a reference sample (e.g., a sample obtained from the same subject at an earlier timepoint) by about 10% or more, e.g., about 15% or more, 20% or more, 25% or more, 30% or more, 35% or more, 40% or more, 45% or more, or 50% or more. Conversely, for gene expression signatures that decrease with progression to MM, expression of the gene expression signature in the sample may be decreased relative to the reference sample (e.g., a sample obtained from the same subject at an earlier timepoint) by about 10% or more, e.g., about 15% or more, 20% or more, 25% or more, 30% or more, 35% or more, 40% or more, 45% or more, or 50% or less.

[0091] Alternatively or additionally, the reference sample may comprise a panel of samples obtained from a cohort comprising multiple healthy donors, multiple patients with MGUS, multiple patients with SMM, or multiple patients with MM and include a distribution of values, each representing the expression of a gene expression signature provided herein for a given individual within the reference sample cohort. In some instances, the reference sample comprises a panel of samples obtained from multiple healthy donors, multiple patients with MGUS, and multiple patients with SMM.

[0092] Where a panel of samples with a distribution of values is provided as reference sample, a provided method may indicate a change relative to the distribution of the values within the reference sample (e.g., a change from MGUS towards MM). For example, for gene expression signatures that increase with progression to MM, the increase of expression of a given gene expression signature in the sample may be indicated as being higher than the median, higher than the 75thpercentile, higher than the 80thpercentile, higher than the 85thpercentile, higher than the 90thpercentile, or higher than the 95thpercentile of the reference sample distribution. Or, the increase of expression of the gene expression signature in the sample may be indicated as being, e.g., 1 standard deviation above, 1.5 standard deviations above, 2 standard deviations above, or 2.5 standard deviations above the mean of the reference sample.

[0093] For gene expression signatures that decrease with progression to MM, the decrease of expression of a given gene expression signature in the sample may be indicated as being lower than the median, lower than the 25thpercentile, lower than the 20thpercentile, lowerthan the 15thpercentile, lower than the 10thpercentile, or lower than the 5thpercentile of the reference sample distribution. Or, the decrease of expression of the gene expression signature in the sample may be indicated as being, e.g., 1 standard deviation below, 1.5 standard deviations below, 2 standard deviations below, or 2.5 standard deviations below the mean of the reference sample.

[0094] In some instances, it may be desirable to determine the expression of gene expression signatures that are enriched in or specific to different cell types (e.g., myeloid cells, T cells, and myeloma cells).

[0095] A gene expression signature enriched for lymphocytes may be detected in one or more (or all) or the following cell types: T cells including CD4+ T cells and CD8+ T cells, natural killer (NK) cells, and B cells.

[0096] A gene expression signature enriched for myeloid cells may be detected in one or more (or all) or the following cell types: CD14+ monocytes, CD16+ monocytes macrophages, and dendritic cells including plasmacytoid DCs (pDCs), AS-DCs, conventional type 1 DCs (cDC1s), conventional type 2 dendritic cells (cDC2s), and Monocyte-Derived Dendritic Cells (moDCs).

[0097] A gene expression signature enriched for the immune cells may be detected in one or more (or all) or the following cell types: T cells including CD4+ T cells and CD8+ T cells, natural killer (NK) cells, B cells, monocytes, and dendritic cells.

[0098] A gene expression signature enriched for the T cells may be detected in one or more (or all) or the following cell types: CD4+ T cells (e.g., CD4+ naïve T cells), central memory T cells (including T regulatory cells (T regs)), and CD8+ T cells (e.g., TCF7+ CD8+ memory effector T cells (TEMs)).

[0099] The gene expression signature(s) may be determined with any suitable technique known to the skilled person, such as any suitable method of gene expression profiling. For example, the gene expression signature(s) may be determined using one or more of quantitative PCR (qPCR), bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics.

[0100] In some instances, instead of detecting the expression of the genes of a gene expression signature, the expression of the translated proteins may be determined. For example, the immune cell activation gene expression signature includes gene-encodedsurface proteins (GESP) such as LAT1 (encoded by SLC7A5) and TIMAP (encoded by PPP1R16B) and cytokines such as TGFβ (encoded by TGFB1), which can be detected by the protein-based detection assays disclosed herein, such as flow cytometry, mass spectrometry (e.g., Cytometry by Time of Flight (CyTOF)), immunohistochemistry, or spatial proteomics.

[0101] Methods for monitoring progression from MGUS or SMM to MM that comprise determining one or more gene expression signatures in a sample obtained from the subject and a reference sample described herein may be combined with methods of determining the proportion of one or more immune cell populations in the sample and the reference sample.

[0102] For example, a method for monitoring progression from MGUS or SMM to MM that comprise determining one or more gene expression signatures in a particular cell population (e.g., T cells, (e.g., CD4+ T cells, CD8+ T cells, GZMB+ CD8+ TEMs)), B cells, CD138+ plasma cells, tumor cells, or myeloid cells (e.g., monocytes or dendritic cells) may be combined with a method of determining the proportion of that cell population in the sample and the reference sample. Exemplary gene expression signatures

[0103] The myeloid cell-enriched gene expression signature comprising THBS1, PLAUR, TIMP1, VEGFA, and HIF1A and one or more (e.g., two, three, four, or all) of G0S2, SRGN, CEBPB, UPP1, and NINJ1 may further comprise MAP3K8. For example, the myeloid cell- enriched gene expression signature may comprise G0S2, THBS1, TIMP1, HIF1A, PLAUR, SRGN, CEBPB, MAP3K8, UPP1, NINJ1, and VEGFA.

[0104] The myeloid cell-enriched gene expression signature comprising IL1B, CCL3, CCL3L1, CXCL8, and CXCL2 and one or more (e.g., two, three, four, or all) of ATP2B1-AS1, SAT1, IER3, KLF4, and ATF3 may further comprise IER2 and / or KLF6. For example, the myeloid cell-enriched gene expression signature may comprise IL1B, CXCL8, ATP2B1-AS1, CCL3, SAT1, IER2, IER3, CXCL2, KLF4, KLF6, CCL3L1, and ATF3.

[0105] The dendritic cell- and B cell-enriched gene expression signature comprising IRF8, LILRA4, and JCHAIN and one or more (e.g., two, three, four, five, six, or all) of PTGDS, ITM2C, PLD4, RASD1, TCF4, PPP1R14B, and CCDC50 may further comprise one or more (e.g., two, three, or all) of SERPINF1, IRF7, CLEC4C, and MZB1. For example, the dendritic cell- and B cell-enriched gene expression signature may comprise JCHAIN, PTGDS,LILRA4, ITM2C, PLD4, RASD1, IRF8, SERPINF1, IRF7, TCF4, CLEC4C, MZB1, PPP1R14B, and CCDC50.

[0106] The immune cell-associated gene expression signature comprising one or more (e.g., two, three, four, five, six, or all) of ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, and OAS3 may further comprise one or more (e.g., two, three, four, five, six, or all) of MX1, IFI27, HERC5, IFIT1, OAS1, and CMPK2. For example, the immune cell- associated gene expression signature may comprise ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, OAS3, MX1, IFI27, IFI44, HERC5, IFIT1, OAS1, and CMPK2.

[0107] The lymphocyte-enriched gene expression signature comprising CXCR4, TNFAIP3, NR4A2, and FAM177A1 and one or more (e.g., two, three, four, five, or all) of PMAIP1, TSPYL2, HSPA5, PDE4D, PER1, and RNF125 may further comprise one or more (e.g., two, three, or all) of TGFB1, ZNF331, PIK3R1, and SLC7A5. For example, the lymphocyte- enriched gene expression signature may comprise CXCR4, TNFAIP3, NR4A2, FAM177A1, PMAIP1, TSPYL2, HSPA5, PDE4D, PER1, RNF125, TGFB1, ZNF331, PIK3R1, and SLC7A5.

[0108] The myeloid cell-enriched gene expression signature comprising FOS and FOSB and one or more (e.g., two, three, four, or all) of CITED2, ID1, ARHGEF40, and USP2 may further comprise DUSP1 and / or AP000692.2. For example, the myeloid cell-enriched gene expression signature may comprise FOS, FOSB, DUSP1, CITED2, ID1, ARHGEF40, and USP2.

[0109] The T cell-specific gene expression signature comprising CISH, TNFRSF1A, CX3CR1, SLAMF6, EOMES, GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, and GIMAP7 and one or more (e.g., two, three, four, five, six, or all) of LPAR6, PIM1, C14orf119, PRDX3, CALHM2, TAGAP, SIT1, LINC01871, KLRB1, SNHG5, and S100A11 may be a granzyme B (GZMB)+ CD8+ effector memory T cell (TEM)-specific gene expression signature. The granzyme B (GZMB)+ CD8+ effector memory T cell (TEM)-specific gene expression signature comprising CISH, TNFRSF1A, CX3CR1, SLAMF6, EOMES, GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, and GIMAP7 and one or more (e.g., two, three, four, five, six, or all) of LPAR6, PIM1, C14orf119, PRDX3, CALHM2, TAGAP, SIT1, LINC01871, KLRB1, SNHG5, and S100A11 may further comprise PRF1 and / or BATF. For example, the granzyme B (GZMB)+ CD8+ effector memory T cell (TEM)-specific gene expressionsignature may comprise CISH, TNFRSF1A, CX3CR1, SLAMF6, EOMES, GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, GIMAP7, LPAR6, PIM1, C14orf119, PRDX3, CALHM2, TAGAP, SIT1, LINC01871, KLRB1, SNHG5, S100A11, PRF1 and BATF.

[0110] The tumor cell-specific gene expression signature comprising SERPINB9, MAP3K8, PELI1, CSRNP1, STX11, UBALD2, PER1, ESR1, GADD45A, GADD45B, PNP, KLF9, SIK1B, FAM49A, CYTOR, AREG, SNX9, PMAIP1, MYADM, and C11orf96 may further comprise one or more (e.g., two or all) of FOXP1, PIM3, and OTUD1. For example, the tumor cell-specific gene expression signature may comprise SERPINB9, MAP3K8, PELI1, CSRNP1, STX11, UBALD2, PER1, ESR1, GADD45A, GADD45B, PNP, KLF9, SIK1B, FAM49A, CYTOR, AREG, SNX9, PMAIP1, MYADM, C11orf96, FOXP1, PIM3, and OTUD1. Determining proportions of immune cell populations

[0111] Methods of monitoring disease progression described herein may comprise determining the proportion of one or more immune cell populations (e.g., CD8+ effector memory T cells (TEMs) and / or cytokine-expressing myeloid cells) in a sample obtained from a subject.

[0112] Typically, the subject is monitored in intervals of 3-4 months, or 6-12 months. A change in the proportion of one or more immune cell populations may be determined by analyzing the proportions obtained over several time points. For example, only if a change (e.g., an increase in CD8+ TEMs and / or a decrease in cytokine-expressing myeloid cells) persists over several time points (e.g., at least two, three, four, or five time points), may this indicate that the subject is progressing or has progressed from MGUS or SMM to MM.

[0113] In some instances, a significant change across multiple immune cell populations between a first and a second time point may indicate that a subject is progressing or has progressed from MGUS or SMM to MM. For example, an at least 1%, 2%, 5%, 10% or 15% increase in CD8+ TEMs and an at least 1%, 2%, 5%, 10% or 15% decrease in cytokine- expressing myeloid cells may indicate progression, in particular if the change persists at a subsequent third time point.

[0114] In some instances, an increase in the proportions of CD4+ naïve T cells, central memory T cells (including T regulatory cells (T regs)), TCF7+ CD8+ memory effector T cells (TEMs), CD56br / CD56dim natural killer (NK) cells, CD16+ monocytes, and / or memoryB cells in a sample obtained from a subject with MGUS or SMM relative to a reference sample indicates that the subject is progressing or has progressed to MM. In some instances, a decrease in the proportions of myeloid cells including dendritic cells (DCs, e.g., plasmacytoid DCs (pDCs), AS-DCs, and conventional type 1 DCs (cDC1s)) as well as CD14+ monocytes and macrophages in a sample obtained from a subject with MGUS or SMM relative to a reference sample indicates that the subject is progressing or has progressed to MM.

[0115] Methods to measure proportion of immune cell populations in a sample are known in the art. The methods may be protein-based or RNA-based, or may comprise combinations of both. A protein-based method may comprise one or more of fluorescent-activated cell sorting (FACS), spatial proteomics, single-cell proteomics, immunofluorescence imaging, immunohistochemistry, and immunoblotting. An RNA-based method may comprise gene expression profiling. For instance, the RNA-based method may comprise one or more of quantitative PCR (qPCR), bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics. For example, single-cell RNA sequencing data can be used to analyze the proportions of immune cell populations and gene expression signatures associated with such populations or particular subsets of immune cells as well as to determine T cell clonality and / or T cell receptor (TCR) repertoire diversity.

[0116] Protein-based and RNA-based methods may also be combined. For example, flow cytometry may be used to isolate or enrich one or more cell population(s) from a sample. The one or more cell population(s) may then be analyzed with an RNA-based gene expression profiling method such as bulk RNA sequencing or single-cell RNA sequencing. Alternatively or additionally, the sample obtained from the subject and, if required, the reference sample may be first depleted of tumor cells, e.g., before a RNA-based gene expression profiling method is applied. Granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs)

[0117] The proportion of GZMB+ CD8+ TEMs has been shown herein to increase in the progression from MGUS or SMM to MM. Accordingly, in one aspect, provided herein is a method for monitoring progression from MGUS or SMM to MM in a subject comprising (a) determining the proportion of GZMB+ CD8+ TEMs in a sample obtained from the subject and a reference sample (e.g., a sample obtained from one or more healthy donors or one ormore subjects with MGUS or SMM who have not progressed to MM); and (b) comparing the proportion of GZMB+ CD8+ TEMs in the sample and the reference sample.

[0118] An increase in the proportion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. For example, the proportion of GZMB+ CD8+ TEMs in the sample obtained from the subject may be increased by at least 1%, 2%, or 5%, e.g., at least 10% or at least 15%, compared to the proportion of GZMB+ CD8+ TEMs in the reference sample. In some instances, the increase in the proportion of GZMB+ CD8+ TEMs is determined in the CD138- subset of cells in the sample obtained from the subject. Alternatively, the proportion of GZMB+ CD8+ TEMs is determined in the CD8+ subset of cells in the sample obtained from the subject. In some instances, the proportion of GZMB+ CD8+ TEMs is determined in the immune cells in the sample obtained from the subject. In other instances, the proportion of GZMB+ CD8+ TEMs is determined in the T cells in the sample obtained from the subject. Or, the proportion of GZMB+ CD8+ TEMs is determined in the CD8+ TEMs subset of cells in the sample obtained from the subject.

[0119] Analysis of the proportion of GZMB+ CD8+ TEMs in a sample may comprise determining the presence and / or expression of one or more genes, as described herein. For example, lymphoid cells in the sample may be assessed for the expression of (i) CD8A and / or CD8B, (ii) GZMB and optionally one or more of (iii) GZMK, GZMH, NKG7, GNLY, PRF1, FGFBP2, FCGR3A, CX3CR1, ZNG683, KLRG1, TYROBP, and KIR receptor transcripts. In addition, lymphoid cells (e.g., CD8+ cells) in the sample may also be assessed for the expression of CCL5 and / or KLRG1. For example, CCL5 and KLRG1, a marker of immune senescence, was found to be most active in cytotoxic CD8+ TEMs.

[0120] In some instances, the expression of one or more MHC-II-encoding genes, one or more NF-κB pathway genes (e.g., RELB), BATF and / or BCL3 is determined in CD8+ T cells (e.g., the GZMB+ CD8+ TEMs). For example, the expression of BATF, BCL3 RELB and optionally one or more MHC-II-encoding genes may be determined in the CD8+ T cells (e.g., GZMB+ CD8+ TEMs). An increase in the expression of MHC-II-encoding genes and / or BATF in the CD8+ T cells (e.g., GZMB+ CD8+ TEMs) in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM. In some instances, an increase in the expression of one or more (or all) of HLA-DPB1, HLA-DPA1, NDUFB3, BATF, C14orf119, FBXO6, XAF1, IFI6, HLA-DRA, TYMP, DYNLL1, ISG15,PALM2-AKAP2, SAMD9L, BCL2, S100A11, HCP5, IRF7, and ORMDL2 in GZMB+ CD8+ TEMs may indicate that the subject is progressing or has progressed to MM.

[0121] Conversely, a decrease in the expression of BCL3, and / or one or more of the NF-κB pathway genes (e.g., RELB) in the CD8+ T cells (e.g., GZMB+ CD8+ TEMs) in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM.

[0122] Analysis of the proportion of GZMB+ CD8+ TEMs in a sample may be combined with determining T cell clonality and / or T cell receptor (TCR) repertoire diversity as described herein.

[0123] In some instances, analysis of the proportion of GZMB+ CD8+ TEMs in a sample may further be combined with determining the presence of an interferon (IFN) signature as described herein.

[0124] In some instances, monitoring an increase in the population of GZMB+ CD8+ TEMs in a sample may also be accompanied by monitoring a decrease in the LAT1+ TIMAP+ TGFβ+ subpopulation of GZMB+ CD8+ TEMs. Accordingly, in some aspects, a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject is provided that comprises (a) determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in a sample obtained from the subject and a reference sample; (b) determining the proportion of GZMB+ CD8+ TEMs that are LAT1+ TIMAP+ TGFβ+ in the sample obtained from the subject and the reference sample; and (c) comparing the proportions determined in steps (a) and (b) in the sample and the reference sample. An increase in the proportion of GZMB+ CD8+ TEMs and a decrease in the proportion of GZMB+ CD8+ TEMs that are LAT1+ TIMAP+ TGFβ+ in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM.

[0125] In other instances, monitoring an increase in the population of GZMB+ CD8+ TEMs in a sample may be accompanied by monitoring a decrease in the GZMB+ CD8+ TEMs expressing a gene expression signature comprising one or more (e.g., five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, or all) of ZBP1, TPT1, TNFAIP3, TGFB1, SYTL3, SLC7A5, SLA2, RBM38, PPP1R16B, PIK3R1, PDE4D, ODC1, HSPA5, HMGB2, H3F3B, FTH1, FAM177A1, EIF1, and BTG1.Accordingly, in some aspects, a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject is provided that comprises (a) determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in a sample obtained from the subject and a reference sample; (b) determining the proportion of GZMB+ CD8+ TEMs that express the gene expression signature comprising one or more (e.g., five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, or all) of ZBP1, TPT1, TNFAIP3, TGFB1, SYTL3, SLC7A5, SLA2, RBM38, PPP1R16B, PIK3R1, PDE4D, ODC1, HSPA5, HMGB2, H3F3B, FTH1, FAM177A1, EIF1, and BTG1 in the sample obtained from the subject and the reference sample; and (c) comparing the proportions determined in steps (a) and (b) in the sample and the reference sample. An increase in the proportion of GZMB+ CD8+ TEMs and a decrease in the proportion of GZMB+ CD8+ TEMs that express the gene expression signature comprising one or more (e.g., five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, or all) of ZBP1, TPT1, TNFAIP3, TGFB1, SYTL3, SLC7A5, SLA2, RBM38, PPP1R16B, PIK3R1, PDE4D, ODC1, HSPA5, HMGB2, H3F3B, FTH1, FAM177A1, EIF1, and BTG1 in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM. Cytokine-expressing myeloid cells

[0126] The proportion of cytokine-expressing myeloid cells (e.g., CD14+ monocytes and / or dendritic cells) has been shown herein to decrease in the progression from MGUS or SMM to MM. Accordingly, in one aspect, provided herein is a method for monitoring progression from MGUS or SMM to MM in a subject comprising: determining the proportion of myeloid cells (e.g., cytokine-expressing myeloid cells) in a sample obtained from the subject and a reference sample (e.g., a sample obtained from one or more healthy donors or one or more subjects with MGUS or SMM who have not progressed to MM); and comparing the proportion of myeloid cells (e.g., cytokine-expressing myeloid cells) in the sample and the reference sample; wherein a decrease in the proportion of myeloid cells (e.g., cytokine- expressing myeloid cells) in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0127] A decrease in the proportion of myeloid cells (e.g., cytokine-expressing myeloid cells) in the sample relative to the reference sample indicates that the subject is progressingor has progressed to MM. For example, the proportion of myeloid cells (e.g., cytokine- expressing myeloid cells) in the sample obtained from the subject may be decreased by at least 1%, 2%, 5%, e.g., at least 10% or at least 15%, compared to the proportion of myeloid cells (e.g., cytokine-expressing myeloid cells) in the reference sample. In some instances, the decrease in the proportion of myeloid cells (e.g., cytokine-expressing myeloid cells) is determined in the CD138- subset of the cells in the sample obtained from the subject. In some instances, the decrease in the proportion of myeloid cells (e.g., cytokine-expressing myeloid cells) is determined in immune cells of the sample obtained from the subject. Alternatively, the proportion of myeloid cells (e.g., cytokine-expressing myeloid cells) is determined in the monocyte subset of cells in the sample obtained from the subject. In other instances, the proportion of myeloid cells (e.g., cytokine-expressing myeloid cells) is determined in the dendritic cell subset of the cells in the sample obtained from the subject. Or, the proportion of cytokine-expressing myeloid cells is determined in the myeloid cells of the sample obtained from the subject.

[0128] Myeloid cells may include dendritic cells (DCs, e.g., plasmacytoid DCs (pDCs), AS-DCs, conventional type 1 DCs (cDC1s), canonical dendritic cells type 2 (cDC2), and monocyte-derived dendritic cells (MoDCs), for example cDC2s and MoDCs) as well as CD14+ monocytes and macrophages. Analysis of the proportion of cytokine-expressing myeloid cells in a sample may comprise determining the expression of one or more genes in the population of myeloid cells, as described herein. For instance, the population of myeloid cells may be assessed for the expression of one or more cytokines selected from IL1B, CXCL8, CCL3, and CCL4. In some instances, upregulation of CCL3 and CXCL8 (and optionally one or more of TNFSF13, TNFSF13B, and TNFSF14) in the population of myeloid cells (e.g., CD14+ monocytes) in a sample obtained from a subject with MGUS or SMM relative to a reference sample may indicate that the subject is progressing or has progressed to MM. Furthermore, other genes may also be upregulated, e.g., one or more of TNSF10, GRN, CCL3L1, SIGLEC1, and VSTM1, and upregulation of one or more of these genes in CD14+ monocytes may indicate that the subject is progressing or has progressed to MM.

[0129] The population of myeloid cells may also be assessed for the expression of one or more of THBS1, PLAUR, TIMP1, VEGFA, and HIF1A (as described in more detail elsewhere herein). For instance, determining the expression of HIF1A may be useful as HIF1A was previously shown to bind the promoter and regulate the expression of THBS1, PLAUR,VEGFA, and TIMP1. The population of myeloid cells (e.g., CD14+ monocytes) may show decreased expression of at least two, three, or all of THBS1, PLAUR, TIMP1, VEGFA, and HIF1A. For example, a decrease in a gene expression signature marked by HIF1A and HIF1A-regulated genes (e.g., THBS1, PLAUR, TIMP1, and VEGFA) in the population of myeloid cells (e.g., CD14+ monocytes, cDC2, and / or MoDCs) in a sample obtained from a subject with MGUS or SMM relative to a reference sample may reflect the decrease in the proportion of cytokine-expressing myeloid cells in the sample and indicate that the subject is progressing or has progressed to MM.

[0130] In some instances, the population of myeloid cells may be assessed for the expression of IL1B, CXCL8, CCL3, CCL4, THBS1, and PLAUR. The population of myeloid cells (e.g., CD14+ monocytes) may show decreased expression of at least two, three, or all of THBS1, PLAUR, TIMP1, VEGFA, and HIF1A. For example, a decrease in a gene expression signature comprising IL1B, CXCL8, CCL3, CCL4, THBS1, and PLAUR in the population of myeloid cells (e.g., CD14+ monocytes, cDC2, and / or MoDCs) in a sample obtained from a subject with MGUS or SMM relative to a reference sample may reflect the decrease in the proportion of cytokine-expressing myeloid cells in the sample and indicate that the subject is progressing or has progressed to MM.

[0131] Similarly, an increase of a myeloid cell-enriched gene expression signature marked by one or more of IL1B, CCL3, and CXCL8 in a sample obtained from a subject with MGUS or SMM relative to a reference sample may indicate that the subject is progressing or has progressed to MM (as described in more detail elsewhere herein). The cytokine-expressing myeloid cells are typically identifiable as dendritic cells and / or CD14+ monocytes. Methods for identifying CD14+ monocytes and / or dendritic cells are well-known in the art.

[0132] In some aspects, provided herein is a method for monitoring progression from MGUS or SMM to MM in a subject comprising: determining expression of HIF1A and one or more (e.g., all) of THBS1, PLAUR, TIMP1 and VEGFA in myeloid cells (e.g., CD14+ monocytes) in a sample obtained from the subject and a reference sample (e.g., a sample obtained from one or more healthy donors or one or more subjects with MGUS or SMM who have not progressed to MM); and comparing the expression of HIF1A and one or more of THBS1, PLAUR, TIMP1 and VEGFA in myeloid cells in the sample and the reference sample. A decrease in the expression of HIF1A and one or more (e.g., all) of THBS1, PLAUR, TIMP1and VEGFA in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM.

[0133] In some instances, the method further comprises determining expression of one or more (e.g., all) of IL1B, CCL3, and CXCL8 in myeloid cells in the sample obtained from the subject and the reference sample. An increase of the expression of one or more (e.g., all) of IL1B, CCL3, and CXCL8 in myeloid cells in the sample obtained from the subject with MGUS or SMM relative to the reference sample may indicate that the subject is progressing or has progressed to MM. Granzyme K (GZMK)+ CD8+ TEMs

[0134] The methods described herein may further comprise determining the proportion of granzyme K (GZMK)+ CD8+ TEMs in the sample and the reference sample; and comparing the proportion of GZMK+ CD8+ TEMs in the sample and the reference sample; wherein an increase in the proportion of GZMK+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

[0135] The proportion of GZMK+ CD8+ TEMs has been shown herein to increase in the progression from MGUS or SMM to MM. Therefore, in some instances, methods that determine the proportions of CD8+ TEMs and / or cytokine-expressing myeloid cells may further comprise determining the proportion of GZMK+ CD8+ TEMs.

[0136] An increase in the proportion of GZMK+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. For example, the proportion of GZMK+ CD8+ TEMs in the sample obtained from the subject may be increased by 1%, 2%, or 5%, e.g., at least 10% or at least 15% compared to the proportion of GZMK+ CD8+ TEMs in the reference sample. In some instances, the increase in the proportion of GZMK+ CD8+ TEMs is determined in the CD138- subset of cells in the sample obtained from the subject. Alternatively, the proportion of GZMK+ CD8+ TEMs is determined in the CD8+ subset of cells in the sample obtained from the subject.

[0137] Analysis of the proportion of GZMK+ CD8+ TEMs in a sample may comprise determining the presence of one or more genes, as described herein. For example, lymphoid cells in the sample may be assessed for the expression of GZMK as well as CD8A and / or CD8B. In addition, lymphoid cells (e.g., CD8+ cells) in the sample may also be assessed for the expression of CCL5 and / or KLRG1.T cell clonality and T cell receptor (TCR) repertoire diversity

[0138] In some aspects, a method for monitoring progression from MGUS or SMM to MM in a subject is provided that comprises determining T cell receptor (TCR) repertoire diversity in a sample obtained from the subject and a reference sample, and comparing the TCR repertoire diversity in the sample and the reference sample. A decrease in the TCR repertoire diversity in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. For example, TCR repertoire diversity in the sample obtained from the subject may decrease by 1%, 2%, or 5%, e.g., at least 10% or at least 15% compared to the TCR repertoire diversity in the reference sample. The T cells may comprise, consist substantially of, or consist of GZMB+ CD8+ TEMs.

[0139] In other aspects, a method for monitoring progression from MGUS or SMM to MM in a subject is provided that comprises determining clonality (specifically, clonal expansion) of T cells in a sample obtained from the subject and a reference sample, and comparing the clonality of T cells in the sample and the reference sample. An increase in the clonality of T cells in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM. For example, T cell clonality in the sample obtained from the subject may increase by at least 1%, 2%, or 5%, e.g., at least 10% or at least 15% compared to the T cell clonality in the reference sample.

[0140] In other aspects, a method for staging MM in a subject is provided, comprising determining clonal expansion of T cells in a sample obtained from the subject and a reference sample, and determining the stage of the subject’s MM based upon the clonal expansion of T cells in the sample. Appropriate and accurate staging of MM can be used to select an appropriate treatment. For example, an increase of T cell clonality in the sample relative to the reference sample indicates that the subject’s MM may not respond to therapy. The T cells may comprise or consist of GZMB+ CD8+ TEMs.

[0141] TCR repertoire diversity takes the clonal composition into account, specifically the number of unique TCR sequences (richness) and the relative abundance of these sequences (evenness). T cell clonality relates to the frequency of observed TCRs within a sample. TCR repertoire diversity and T cell clonality are inversely related. Accordingly, methods that determine T cell repertoire diversity and clonality of T cells may be combined.

[0142] TCR repertoire diversity has been shown herein to decrease in the progression from MGUS or SMM to MM. In particular, GZMB+ CD8+ TEM TCR repertoire diversity has been shown herein to decrease in the progression from MGUS or SMM to MM.

[0143] Accordingly, the repertoire diversity of the T cell population may be determined in a method disclosed herein. Alternatively, the TCR repertoire diversity of a subset of T cells may be determined in a method disclosed herein, e.g., CD8+ TEMs or GZMB+ CD8+ TEMs.

[0144] It may be also advantageous to combine a method that determines TCR repertoire diversity and / or a method that determines clonality of T cells with other methods disclosed herein. For example, TCR repertoire diversity and / or clonality of T cells may be assessed as part of a method that determines the proportion of one or more immune cell populations (e.g., CD8+ GZMB+ TEMs). Accordingly, the T cell population for which TCR repertoire diversity and T cell clonality is determined may comprise or consist of GZMB+ CD8+ TEMs.

[0145] Accordingly, a method for monitoring progression from MGUS or SMM to MM in a subject is provided that comprises (a) determining in a sample obtained from the subject and a reference sample (i) the proportion of GZMB+ CD8+ TEMs; and (ii) clonal expansion of GZMB+ CD8+ TEMs relative to other T cell populations; and (b) comparing the proportion and clonal expansion of GZMB+ CD8+ TEMs in the sample and the reference sample. An increase in the proportion and clonal expansion of GZMB+ CD8+ TEMs in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM. For example, the proportion of GZMB+ CD8+ TEMs and clonal expansion of GZMB+ CD8+ TEMs in the sample obtained from the subject may both increase by at least 1%, 2%, or 5%, e.g., at least 10% or at least 15% compared to the proportion of GZMB+ CD8+ TEMs and clonal expansion of GZMB+ CD8+ TEMs in the reference sample. In addition, TCR repertoire diversity may be determined. For example, T cell repertoire diversity in the sample obtained from the subject may decrease, e.g., by at least 1%, 2%, or 5%, e.g., at least 10% or at least 15%, compared to the TCR repertoire diversity in the reference sample, and thus may provide a further indicator that the subject is progressing or has progressed to MM.

[0146] Furthermore, it is shown herein that expanded GZMB+ CD8+ TEM clones exhibit a terminal differentiation bias. Accordingly, method for monitoring progression from MGUS or SMM to MM in a subject described herein may further comprise determining in a sample obtained from the subject and a reference sample the extent of terminal differentiation ofGZMB+ CD8+ TEM cells or clones; and comparing the extent of terminal differentiation of GZMB+ CD8+ TEM cells or clones in the sample and the reference sample. An increase in the extent of terminal differentiation of GZMB+ CD8+ TEM cells or clones in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM.

[0147] Methods to measure TCR repertoire diversity are known in the art, for example as described in Robins et al. Blood (2009) 114(19):4099–4107 and Li et al. Nature Protocols (2019) 14(8):2571-2594. For example, TCR repertoire diversity may be measured using bulk TCR sequencing, single cell TCR sequencing, or spatial TCR sequencing.

[0148] Prior to TCR sequencing, T cells may first be enriched. Methods of enriching for T cells are known in the art, for example fluorescent activated cell sorting (FACS) or magnetic bead sorting. Interferon (IFN) signaling

[0149] Furthermore, a method for monitoring progression from MGUS or SMM to MM in a subject is provided that comprises determining expression of an interferon (IFN) signature in one or more immune cells (e.g., lymphoid cells such as CD8+ T cells and / or myeloid cells) in a sample obtained from the subject and a reference sample. An increase of the expression of the IFN signature in the one or more immune cells in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM.

[0150] Depending on the type of sample, expression of the IFN signature may be assessed in a particular subset of cells. For instance, expression of the IFN signature may be assessed in T cells, NK cells, B cells, monocytes, and / or dendritic cells. For example, expression may be assessed in the CD138+ plasma cells, GZMB+ CD8+ TEMs, and / or CD14+ monocytes, as described above, e.g., when assessing the proportion of one or more cell type in a sample of subject suffering from MGUS or SMM.

[0151] In some instances, expression of the IFN signature may be assessed in CD138+ plasma cells. CD138+ plasma cells may include but are not limited to tumor cells. Tumor cells may be identified within bulk CD138+ cells by CD19-, CD45dim, CD27-, CD56+, CD117+ expression. Alternatively, tumor cells may be identified using single-cell BCR sequencing, e.g., as explained in US provisional application no.63 / 607,961. Accordingly, in some aspects, a method for monitoring progression from MGUS or SMM to MM in a subjectis provided that comprises determining expression of an IFN signature in CD138+ plasma cells in a sample obtained from the subject and a reference sample. An increase of the expression of the IFN signature in the CD138+ plasma cells in the sample relative to the reference sample may indicate that the subject is progressing or has progressed to MM. In other aspects, a method for predicting the response of a subject’s MM to therapy is provided that comprises determining expression of an IFN signature in CD138+ plasma cells in a sample obtained from the subject and a reference sample. An increase of the expression of the IFN signature in the CD138+ plasma cells in the sample relative to the reference sample may indicate that the subject’s MM may not respond to therapy. In other aspects, a method for staging MM in a subject is provided that comprises determining expression of an IFN signature in CD138+ plasma cells in a sample obtained from the subject and a reference sample, and using this determination in order to stage the subject’s MM.

[0152] The expression of an IFN signature can be determined using methods known in the art. Such methods may comprise immunoblotting, quantitative PCR (qPCR), bulk RNA sequencing, or single-cell RNA sequencing. CD138+ cells can be identified using well- known methods such as fluorescent-activated cell sorting (FACS).

[0153] Exemplary genes whose expression may be determined may include, e.g., one or more (for instance, at least 4, 5, or 6) of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, XAF1 MX1, OAS1, OASL, TYMP, and IRF7 (as described in more detail elsewhere herein). For example, the expression of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, and XAF1I may be determined. Alternatively, the expression of STAT1, IFI6, IFI44, ISG15, IFIT2, IFIT3, and XAF1 may be determined. In some instances, expression of one or more (e.g., two, three, four, five, six, or all) of MX1, IFI27, IFI44, HERC5, IFIT1, OAS1, CMPK2 may additionally determined. For example, expression of at least 10, or all, of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, XAF1 MX1, OAS1, OASL, TYMP, and IRF7 is assessed to determine whether the expression of the IFN signature is increased. The expression of IFITM1 and LY6E may also be determined.

[0154] Depending on the selected cell-type, different sets of IFN genes may be assessed. For example, in immune cells, expression of ISG15, IFI6, and MX1, and optionally one or more (e.g., two, three, four, five, six, seven, eight, nine, ten, or all) of the following genes may be assessed when determining the IFN signature: IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, OAS3, HERC5, IFI27, GBP1, CMPK2, OAS1, IFIT1, RSAD2, IRF7, andEIF2AK2. In tumor cells, expression of IFITM1, IFI6, and ISG15, and optionally one or more (e.g., two, three, four, or all) of the following genes may be assessed when determining the IFN signature: IFI27, LY6E, MX1, XAF1, and STAT1. An exemplary IFN signature in immune cells may comprise ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, MX1, IFI27, IFI44, HERC5, IFIT1, OAS1, CMPK2, and OAS3.

[0155] In some aspects, a method for monitoring progression from MGUS or SMM to MM in a subject is provided that comprises determining expression of more than one gene expression signature in at least two types of cells (e.g., at least two types of immune cells, for instance, lymphoid cells such as CD8+ T cells and myeloid cells) in a sample obtained from the subject and a reference sample. For example, determining the expression of an IFN signature in lymphoid cells or CD138+ plasma cells may be combined with determining the expression of HIF1A and one or more of THBS1, PLAUR, TIMP1 and VEGFA in myeloid cells. Combinations of monitoring methods

[0156] Conventionally, monitoring subjects with MGUS or SMM comprises assessment of different variables including, but not limited to, presence of lytic lesions in the bones, kidney function, plasma cell counts, hypercalcemia, anemia, and concentration of myeloma protein, degree of BM infiltration by plasma cells, levels of monoclonal immunoglobulin in the blood (M-spike), free light chain (FLC) ratio, cytogenetic variables that can be measured using methods such as fluorescence in situ hybridization (FISH) e.g. gain or amplification of chr1q and deletion of chr13q. Incorporating analysis of further genomic variables such as KRAS mutations, Myc translocations, Del17p / TP53 mutations, chromothripsis, and APOBEC mutagenesis have also been demonstrated to improve risk stratification models (Bustoros et al. (2020) Journal of Clinical Oncology 38(21):2380-2389 and Mateos et al. (2020) Blood Cancer Journal 10(102), 11 pages). Such variables are assessed using methods including, but not limited to, imaging methods, and tests performed on blood, urine, or bone marrow samples.

[0157] It will therefore be appreciated that one or more of the methods for monitoring progression of MGUS or SMM to MM described herein may be used alongside such conventional methods to assist in the determination and / or provision of a diagnosis.

[0158] Methods of monitoring progression of subjects with MGUS or SMM to MM described herein, e.g., methods comprising determining one or more gene expression signatures and methods of determining the proportion of one or more immune cell populations may be used in combination. Further, such combinations may be utilized in combination with conventional monitoring methods known in the art and described herein.

[0159] Typically, the methods described here employ single-cell RNA sequencing using one or more bone marrow samples obtained from the subject. Accordingly, in such instances the reference sample(s) is / are (a) bone marrow sample(s) as well.. Exemplary methods for monitoring disease progression

[0160] Also provided herein is a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject is provided that comprises determining expression of a myeloid- cell enriched gene expression signature comprising G0S2, THBS1, TIMP1, HIF1A, PLAUR, SRGN, CEBPB, UPP1, NINJ1, and VEGFA in a sample obtained from the subject and a reference sample , wherein a decrease in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The myeloid-cell enriched gene expression signature may further comprise MAP3K8. For example, the myeloid-cell enriched gene expression signature may comprise G0S2, THBS1, TIMP1, HIF1A, PLAUR, SRGN, CEBPB, UPP1, NINJ1, VEGFA, and MAP3K8.

[0161] Also provided herein is a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject is provided that comprises determining expression of a myeloid- cell enriched gene expression signature comprising IL1B, CXCL8, ATP2B1-AS1, CCL3, SAT1, IER3, CXCL2, KLF4, CCL3L1, and ATF3 in a sample obtained from the subject and a reference sample, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The myeloid-cell enriched gene expression signature may further comprise IER2 and / or KLF6. For example, the myeloid-cell enriched gene expression signature may comprise IL1B, CXCL8, ATP2B1-AS1, CCL3, SAT1, IER3, CXCL2, KLF4, CCL3L1, ATF3, IER2, and KLF6.

[0162] Also provided herein is a method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject is provided that comprises determining expression of a myeloid- cell enriched gene expression signature comprising FOS, FOSB, CITED2, ID1, ARHGEF40, and USP2 in a sample obtained from the subject and a reference sample, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The gene expression signature may further comprise DUSP1 and / or AP000692.2. For example, the myeloid-cell enriched gene expression signature may comprise FOS, FOSB, CITED2, ID1, ARHGEF40, USP2, and DUSP1.

[0163] The methods of monitoring progression comprising determining the proportion of myeloid cells may be combined with one or both methods of monitoring progression comprising determining expression of a myeloid-cell enriched gene expression signature provided herein, for example, by utilizing single-cell RNA sequencing. NUMBERED EMBODIMENTS

[0164] Numbered embodiments are provided herein. 1. A method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject, comprising determining expression of one or more gene expression signatures in a sample obtained from the subject and a reference sample, wherein the one or more gene expression signatures are selected from: a. a myeloid cell-enriched gene expression signature comprising G0S2, THBS1, TIMP1, HIF1A, PLAUR, SRGN, CEBPB, UPP1, NINJ1, and VEGFA, wherein a decrease in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; b. a myeloid cell- enriched gene expression signature comprising IL1B, CXCL8, ATP2B1-AS1, CCL3, SAT1, IER3, CXCL2, KLF4, CCL3L1, and ATF3, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM;c. an immune cell-associated gene expression signature comprising B2M, TUBA1B, CSTB, PRDX1, ATP6V0B, CTSC, VAMP5, SPPL2A, ATOX1, and ADI1, wherein an increase in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; d. a dendritic cell- and B cell-enriched gene expression signature comprising JCHAIN, PTGDS, LILRA4, ITM2C, PLD4, RASD1, IRF8, TCF4, PPP1R14B, and CCDC50, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; e. an immune cell-associated gene expression signature comprising ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, and OAS3, wherein an increase in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; f. a lymphocyte-enriched gene expression signature comprising TNFAIP3, CXCR4, PMAIP1, TSPYL2, NR4A2, FAM177A1, HSPA5, PDE4D, PER1, and RNF125, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; g. a myeloid cell-enriched gene expression signature comprising FOS, FOSB, CITED2, ID1, ARHGEF40, USP2, and AP000692.2, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; h. a granzyme B (GZMB)+ CD8+ effector memory T cell (TEM)-specific gene expression signature comprising ZBP1, TPT1, TNFAIP3, TGFB1, SYTL3, SLC7A5, SLA2, RBM38, PPP1R16B, PIK3R1, PDE4D, ODC1, HSPA5, HMGB2, H3F3B, FTH1, FAM177A1, EIF1, and BTG1, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM;i. a T cell-specific gene expression signature comprising CISH, TNFRSF1A, CX3CR1, SLAMF6, LPAR6, PIM1, C14orf119, PRDX3, CALHM2, TAGAP, SIT1, LINC01871, KLRB1, SNHG5, S100A11, and EOMES, and one or more of GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, and GIMAP7, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; and j. a tumor cell-specific gene expression signature comprising SERPINB9, MAP3K8, PELI1, CSRNP1, STX11, UBALD2, PER1, ESR1, GADD45A, GADD45B, PNP, KLF9, SIK1B, FAM49A, CYTOR, AREG, SNX9, PMAIP1, MYADM, and C11orf96, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The method of embodiment 1, further comprising determining clonal expansion of T cells in the sample obtained from the subject and the reference sample, wherein an increase of the clonal expansion of T cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The method of any one of the preceding embodiments, further comprising determining clonal expansion of GZMB+ CD8+ TEMs relative to other T cell populations, wherein an increase in the clonal expansion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The method of any one of the preceding embodiments, further comprising determining the proportions of GZMB+ CD8+ TEMs in the sample and the reference sample, wherein an increase in the proportion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The method of any one of the preceding embodiments, further comprising determining the proportions of cytokine-expressing myeloid cells in the sample and the reference sample, wherein a decrease in the proportion of cytokine-expressing myeloid cells inthe sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. The method of any one of the preceding embodiments, wherein: i. the lymphocytes comprise T cells including CD4+ T cells and CD8+ T cells, natural killer (NK) cells, B cells; and / or ii. myeloid cells comprise CD14+ monocytes, CD16+ monocytes macrophages, and dendritic cells including plasmacytoid DCs (pDCs), AS-DCs, conventional type 1 DCs (cDC1s), conventional type 2 dendritic cells (cDC2s), and Monocyte-Derived Dendritic Cells (moDCs). The method of any one of the preceding embodiments, wherein: i. the immune cells comprise T cells including CD4+ T cells and CD8+ T cells, natural killer (NK) cells, B cells, monocytes and dendritic cells; ii. T cells comprise CD4+ naïve T cells, central memory T cells (including T regulatory cells (T regs)), and TCF7+ CD8+ memory effector T cells (TEMs). The method of any one of the preceding embodiments, comprising one or more of quantitative PCR (qPCR), bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics. A method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject comprising: a. determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in a sample obtained from the subject and a reference sample; b. determining the proportion of GZMB+ CD8+ TEMs that are LAT1+ TIMAP+ TGFβ+ in the sample obtained from the subject and the reference sample; and c. comparing the proportions determined in steps a. and b. in the sample and the reference sample; wherein an increase in the proportion of GZMB+ CD8+ TEMs and a decrease in the proportion of GZMB+ CD8+ TEMs that are LAT1+ TIMAP+ TGFβ+ in the samplerelative to the reference sample indicates that the subject is progressing or has progressed to MM. 0. The method of embodiment 9, wherein the proportions in steps a. and b. are determined using flow cytometry, Cytometry by Time of Flight (CyTOF), immunohistochemistry, or spatial proteomics 1. The method of any one of embodiments 1-10, wherein the reference sample was obtained from the subject at an earlier timepoint. 2. The method of any one of embodiments 1-10, wherein the reference sample is a representative sample from one or more different subjects with MGUS or SMM who have or have not progressed to MM or patients with MM. 3. The method of any one of embodiments 1-10, wherein the reference sample is a representative sample from one or more healthy donors. 4. The method of any one of the preceding embodiments, wherein the sample obtained from the subject and the reference sample is a bone marrow sample or a blood sample. 5. A method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject comprising: a. determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in a sample obtained from the subject and a reference sample; and b. comparing the proportion of GZMB+ CD8+ TEMs in the sample and the reference sample wherein an increase in the proportion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.6. The method of embodiment 15, wherein the method further comprises determining the proportion of cytokine-expressing myeloid cells in the sample and the reference sample; and comparing the proportion of cytokine-expressing myeloid cells in the sample and the reference sample; wherein a decrease in the proportion of cytokine- expressing myeloid cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. 7. A method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject comprising: a. determining the proportion of cytokine-expressing myeloid cells in a sample obtained from the subject and a reference sample; and b. comparing the proportion of cytokine-expressing myeloid cells in the sample and the reference sample wherein a decrease in the proportion of cytokine-expressing myeloid cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. 8. The method of embodiment 17, wherein the method further comprises determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in the sample and the reference sample, and comparing the proportion of GZMB+ CD8+ TEMs in the sample and the reference sample; wherein an increase in the proportion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. 9. The method of any one of embodiments 15-17, wherein the method further comprises determining the proportion of granzyme K (GZMK)+ CD8+ TEMs in the sample and the reference sample; and comparing the proportion of GZMK+ CD8+ TEMs in the sample and the reference sample; wherein an increase in the proportion of GZMK+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.The method of any one of embodiments 16-19, wherein the cytokine-expressing myeloid cells express one or more cytokines selected from IL1B, CXCL8, CCL3, CCL3L1, and CCL4. The method of any one of embodiments 16-20, wherein the cytokine-expressing myeloid cells are CD14+ monocytes or dendritic cells. The method of any one of embodiments 16-21, wherein the cytokine-expressing myeloid cells further express one or more of THBS1, PLAUR, TIMP1, VEGFA, and HIF1A. The method of any one of embodiments 15, 16, or 18-22, wherein the GZMB+ CD8+ TEMs express an interferon (IFN) signature, e.g., one or more of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, and XAF1. The method of any one of embodiments 15, 16, or 18-23, wherein the method comprises determining in lymphoid cells the expression of: a. CD8A and / or CD8B; b. GZMK and / or GZMB; and optionally c. CCL5; and / or d. KLRG1. The method of embodiment 24, wherein the method further comprises determining the proportion of lymphoid cells that expresses an interferon (IFN) signature, e.g., one or more of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, and XAF1. The method of embodiment 15, 16, or 18-25, wherein the method further comprises determining clonal expansion of GZMB+ CD8+ TEMs relative to other T cell populations, e.g., using single-cell T cell receptor (TCR) sequencing. A method for monitoring progression from MGUS or SMM to MM in a subject comprising:a. determining T cell repertoire diversity in a sample obtained from the subject and a reference sample; and b. comparing the T cell repertoire diversity in the sample and the reference sample wherein a decrease in the T cell repertoire diversity in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. 8. A method for monitoring progression from MGUS or SMM to MM in a subject comprising: a. determining clonal expansion of T cells in a sample obtained from the subject and a reference sample; and b. comparing the clonal expansion of T cells in the sample and the reference sample wherein an increase in the clonal expansion of T cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. 9. The method of embodiment 27 or embodiment 28, wherein the T cells comprise or consist of GZMB+ CD8+ TEMs. 0. A method for monitoring progression from MGUS or SMM to MM in a subject, comprising determining expression of an interferon (IFN) signature in CD138+ plasma cells in a sample obtained from the subject and a reference sample, wherein an increase of the expression of the IFN signature in the CD138+ plasma cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM. 1. A method for predicting the response of a subject’s MM to therapy, comprising determining expression of an interferon (IFN) signature in CD138+ plasma cells in a sample obtained from the subject and a reference sample, wherein an increase of the expression of the IFN signature in the CD138+ plasma cells in the sample relative to the reference sample indicates that the subject’s MM may not respond to therapy.The method of embodiment 30 or 31, further comprising determining clonal expansion of GZMB+ CD8+ TEMs relative to other T cell populations e.g., using single-cell TCR sequencing. The method of any one of embodiments 30-32, further comprising determining the proportions of GZMB+ CD8+ TEMs and cytokine-expressing myeloid cells in the sample and the reference sample, e.g., as set out in embodiments 20-24. The method of any one of embodiments 31-33, wherein the IFN signature comprises at least one or more of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, and XAF1. A method for predicting the response of a subject’s MM to therapy, comprising determining clonal expansion of T cells in a sample obtained from the subject and a reference sample, wherein an increase of the clonal expansion of T cells in the sample relative to the reference sample indicates that the subject’s MM may not respond to therapy. The method of embodiment 35, wherein the T cells comprise or consist of GZMB+ CD8+ TEMs. The method of any one of embodiments 15-36, wherein the reference sample was obtained from the subject at an earlier timepoint. The method of any one of embodiments 15-36, wherein the reference sample is a representative sample from one or more different subjects with MGUS or SMM who have or have not progressed to MM or patients with MM. The method of any one of embodiments 15-36, wherein the reference sample is a representative sample from one or more healthy donors. The method of any one of embodiments 15-39, wherein the sample obtained from the subject and the reference sample is a bone marrow sample or a blood sample.EXAMPLES

[0165] The following examples are included for illustrative purposes only and are not intended to limit the scope of the claims. Those of ordinary skill in the art may be aware of materials and methods similar or equivalent to those described below and any of these can be used to practice or test the provided methods and compositions. Example 1. Increase in CD8+ memory T cells and decrease in cytokine-expressing myeloid cells marks progression from MGUS to MM

[0166] This example illustrates that progression from monoclonal gammopathy of unknown significance (MGUS) to multiple myeloma (MM) is accompanied by an increase in CD8+ memory T cells (TEMs) and a decrease in cytokine-expressing myeloid cells.

[0167] Immune cell proportions between patients with MGUS (n=36) and healthy donors (n=32) were compared, considering only individuals with at least 300 immune cells detected across fractions. A significant increase was observed in the abundance of CD4+ naïve T cells and central memory T cells, including regulatory T cells (Tregs); TCF7+ CD8+ effector memory T cells (TEMs) and CD56br / CD56dimnatural killer (NK) cells; CD16+ monocytes; and memory B cells. A significant decrease in the abundance of plasmacytoid dendritic cells (pDCs), AS-DCs and conventional type 1 dendritic cells (cDC1s) as well as CD14+ monocytes and macrophages was observed. These changes were similar to those seen in SMM and MM and suggest that immune dysregulation is established early.

[0168] Single-cell RNA-seq coupled with single-cell B cell receptor (BCR) and T cell receptor (TCR) sequencing (10X Genomics) was performed on approximately 6 million tumor and immune cells from 533 bone marrow (BM) and peripheral blood (PB) samples of 365 patients with plasma cell dyscrasias and healthy donors (HD). For 241 individuals, both the CD138+ (i.e., plasma cells) and the CD138- (i.e., immune cells) fractions of the BM were sequenced to enable integrative analyses of tumor biology and immune dysregulation. Specifically, mononuclear cells were isolated with Ficoll and subjected to magnetic bead enrichment for CD138. CD138+ and CD138- fractions were sequenced separately. Tumor cells were detected using BCR sequencing.

[0169] To identify immune cell populations that change significantly in abundance with disease progression, BM immune cell proportions were compared between patients with MGUS and patients with MM using Wilcoxon’s rank-sum test. The changing abundance ofdifferent cell types is shown in FIG.1A. The proportions were calculated out of all immune cell captured, excluding tumor cells. Notably, a significant decrease in the abundance of cytokine-expressing myeloid cells, expressing such cytokines as IL1B, CXCL8, CCL3, and CCL4, as well as THBS1, and PLAUR, was observed in the BM of patients with MM compared to patients with MGUS. Standard single-cell RNA-seq processing workflows to cluster cells followed by differential expression analysis comparing each cluster to the remaining clusters was performed to identify markers. A significant increase in the abundance of granzyme B-expressing (GZMB+) and granzyme K-expressing (GZMK+) CD8+ effector memory T cells (TEMs) (q < 0.1) was also observed (FIG.1A). In FIG, 1A, the log2 fold-change of the mean proportion in patients with NDMM vs MGUS is shown on the x-axis and the –log10 of the q-value, computed using Wilcoxon’s rank-sum test and corrected using the Benjamini-Hochberg approach is shown on the y-axis. As well as expressing higher levels of GZMK, GZMK+ CD8+ TEMs typically express higher levels of CD27, CD28, CXCR4, CCL3, and CCL4 transcripts. As well as expressing higher levels of GZMB, GZMB+ CD8+ TEMs typically express higher levels of GZMH, NKG7, GNLY, PRF1, FGFBP2, FCGR3A, CX3CR1, ZNG683, KLRG1, TYROBP, and KIR receptor transcripts.

[0170] These changes were independent of chronological age, which is typically higher in patients with later disease stages (FIG.1B). A Gaussian linear model was fit on the cell type’s proportion using disease stage and chronological age as independent variables. The figure shows the coefficient for the disease stage variable.

[0171] This example illustrates that progression from MGUS to MM is accompanied by an increase in GZMB+ CD8+ TEMs and GZMK+ CD8+ TEMs as well as a decrease in cytokine-expressing myeloid cells. Example 2. Gene expression changes in CD14+ monocytes during progression to MM

[0172] This example illustrates that cytokine-expressing myeloid cells including CD14+ monocytes change not only in abundance, but also in their gene expression profile with progression from MGUS to MM.

[0173] Gene expression profiles between CD14+ monocytes (including cytokine- expressing ones) from patients with NDMM and those from patients with MGUS werecompared using a pseudobulk approach. The results of this comparison are shown in FIG. 1C.

[0174] A significant upregulation of CXCL8 and CCL3 in patients with NDMM compared to MGUS was observed, indicating that the observed decrease in the proportion of cytokine- expressing myeloid cells may reflect changes in the activity of cytokine programs that are active in those cells. In addition to CCL3, which encodes MIP1a, a key factor in osteoclastogenesis, TNFSF14, which encodes LIGHT another cytokine that promotes osteoclastogenesis, was also upregulated in NDMM, together with TNFSF13 (encoding APRIL) and TNFSF13B (encoding BAFF), two key plasma cell maintenance factors. On the other hand, THBS1 and the pro-angiogenic VEGFA and VEGFB, as well as TIMP1 and CXCL16 were downregulated.

[0175] This example illustrates upregulation of CXCL8, CCL3, TNFSF13, TNFSF13B, and TNFSF14, and downregulation of THBS1, VEGFA, VEGFB, TIMP1 and CXCL16 in myeloid cells including CD14+ monocytes. Example 3. Changes in cytotoxic CD8+ TEM functionality during progression to MM

[0176] This example illustrates that cytotoxic CD8+ TEMs change not only in abundance, but also in functionality with progression from MGUS to MM.

[0177] Gene expression profiles between GZMB+ CD8+ TEMs from patients with newly diagnosed multiple myeloma (NDMM) and patients with MGUS were compared. A significant upregulation of interferon (IFN) stimulation genes as well as MHC-II-encoding genes was observed. In addition, a significant upregulation of BATF was observed. BATF is a transcription factor that is important for CD8+ T cell effector differentiation and may be associated with short-lived responses and induction of exhaustion programs FIG.1D.

[0178] A significant downregulation of the following genes was also observed: BCL3, a transcription factor that restricts terminal differentiation; ABCB1, an ATP-binding cassette transporter that are important for CD8+ T cell activation and response to cognate antigen; and members of the NF-κB pathway, such as RELB, which is important for mounting successful CD8+ T cell responses.

[0179] These results indicate that cytotoxic CD8+ TEMs change not only in abundance, but also in functionality with disease progression to MM. In particular, this example illustratesupregulation of IFN signaling in GZMB+ CD8+ TEMs as patients progress from MGUS to MM. Example 4. Changes in gene expression signatures associated with progression to MM

[0180] This example illustrates that changes in at least seven gene expression signatures accompany the progression from MGUS to MM. These include an increase in a gene expression signature (S27) marked by IFN signaling genes (e.g., STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, XAF1, MX1, IRF7, OAS1, OASL, IFI27, and TYMP) and a decrease in a gene expression signature (S15) marked by HIF1A and HIF1A-regulated genes (e.g., THBS1, PLAUR, TIMP1, and VEGFA) that may be driven by the hypoxic conditions of the bone marrow.

[0181] While classifying cell types into discrete subtypes based on hard clustering approaches (for example, GZMK+ vs GZMB+ CD8+ TEMs) is a useful way to simplify and categorize immune biology, immune cell phenotypes exist on a continuum and so-called subtypes can share expression of certain gene programs to different degrees. To quantify such broad subtype programs in the BM immune microenvironment, SignatureAnalyzer (SA), which is based on Bayesian non-negative matrix factorization (NMF), was used across both myeloid and lymphoid populations and median signature activity was systematically compared between patients with MM and MGUS. SignatureAnalyzer (SA) is described in Kim, J. et al., (2016)Nat. Genet., 48: 600–606 and Kasar, S. et al. (2015)Nat. Commun., 6: 8866.

[0182] To ensure signature reproducibility, SA was run ten times. For each signature in each run, the percentage of runs was computed in which it was detected. To consider a signature detected in a run, graphs of cosine similarity (>= 0.7) computed on the W matrix (gene x signature) were constructed, and the number of runs in the corresponding signature’s neighborhood was counted. The run with the highest average reproducibility was selected (in this case, 94.3%), and signatures were filtered out that were supported by < 50% of runs (in this case, all signatures met this criterion and no signatures were filtered). Overall, 37 signatures were detected, of which seven were filtered out for having < 100 cells assigned to them. One was filtered out as it represented erythrocytic ambient contamination, resulting in a final number of 29 signatures.

[0183] Each signature was considered to be active in cells with activity in the top quartile of the respective signature’s distribution. The proportion of each cell type within that subgroup of cells was computed, and the normalized Shannon diversity index was computed on the distribution of cell type proportions per signature. Signatures with a diversity index in the top quartile of the distribution were considered to be “broad”; the remaining were considered to be “specific” to individual populations. This analysis is summarized in FIG 1E, which shows the relative contribution of different immune cell types to the group of cells in which a gene expression signature is active in the >75th percentile. Even though most signatures showed some degree of enrichment in certain cell types, they still had activity in other immune cell populations (FIG.1E).

[0184] To identify gene expression signatures that are associated with disease progression from MGUS to MM, signature activity was systematically compared between patients with NDMM and MGUS. For signatures with immune cell type-“specific” activity (n=22), median signature activity was computed per patient considering cell types contributing > 20% of high activity to the signature, while for signatures with broad activity (n=7), median signature activity was computed per patient considering all immune cells.

[0185] In an initial analysis, four gene expression signatures were identified with significantly different activity in patients with MM compared to MGUS. A significant increase was observed in a gene expression signature marked by CD8A, CD8B, CCL5, granzyme-encoding genes, and KLRG1, a marker of immune senescence, which was most active in cytotoxic CD8+ TEMs (S12). Furthermore, a significant increase was observed in a signature marked by IFN signaling genes (S27), such as STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, VEGFA, and XAF1. In addition, a significant decrease of activity in a signature (S15) marked by THBS1, PLAUR, TIMP1, VEGFA, and HIF1A, which was most active in cytokine-expressing myeloid cells. Moreover, a cytokine-positive myeloid cell-enriched signature (S26) marked by IL1B, CCL3, CCL3L1, CXCL8, and CXCL2 increased.

[0186] Further analysis revealed (i) a significant decrease of activity in a lymphocyte- enriched gene expression signature (S28) marked by CXCR4, TNFAIP3, NR4A2, and FAM177A1; (ii) a significant increase of activity in a myeloid cell-enriched gene expression signature (S21) marked by FOS and FOSB; (iii) a significant increase of activity in a broadly active gene expression signature (S10), which was marked by B2M; and (iv) a significant increase of activity in a dendritic cell (DC) and B cell-enriched gene expressionsignature (S11) comprising genes such as IRF8, LILRA4, and JCHAIN. Overall, as illustrated in FIG.1F and Table 1, seven gene expression signatures (S10, S11, S15, S21, S26, S27, and S28) were identified with significantly different activity in the immune cells of patients with NDMM compared to MGUS (Wilcoxon, q < 0.1). Specifically, FIG. 1F shows the median activity of signatures that are significantly (q < 0.1) different between patients with MGUS and NDMM. The gene expression signature marked by CD8A, CD8B, CCL5, granzyme- encoding genes, and KLRG1 (S12) did not meet the cut-off requirements.Table 1. Gene expression signatures associated with progression from MGUS to MM S10 S11 S15 S21 S26 S27 S28 B2M JCHAIN G0S2 FOS IL1B ISG15 TNFAIP3 TUBA1B PTGDS THBS1 FOSB CXCL8 IFI6 CXCR4 CSTB LILRA4 TIMP1 CITED2 ATP2B1-AS1 IFI44L PMAIP1 PRDX1 ITM2C HIF1A ID1 CCL3 IFIT2 TSPYL2 ATP6V0B PLD4 PLAUR ARHGEF40 SAT1 XAF1 NR4A2 CTSC RASD1 SRGN USP2 IER3 IFIT3 FAM177A1 VAMP5 IRF8 CEBPB AP000692.2 CXCL2 EPSTI1 HSPA5 SPPL2A TCF4 UPP1 KLF4 STAT1 PDE4D ATOX1 PPP1R14B NINJ1 CCL3L1 IFI44 PER1 ADI1 CCDC50 VEGFA ATF3 OAS3 RNF125

[0187] The results are in line with the changes observed in immune cell composition and – for example – help to explain the observed decrease in cytokine-expressing myeloid cells, by identifying the specific cytokine program that changes with progression within those cells. Notably, hypoxia-inducible factor 1A (HIF1A) was previously shown to bind the promoter and regulate the expression of THBS1, PLAUR, VEGFA, and TIMP1, suggesting that this cytokine program may be driven by the hypoxic conditions of the bone marrow. The results also help to qualify the observed decrease in cytokine-expressing myeloid cells, by uncoupling specific cytokine programs within those cells and demonstrating that one gene expression signature (S26) marked by pro-inflammatory neutrophil chemotaxis factors (IL1B, CCL3, CCL3L1, CXCL8, and CXCL2) increases.

[0188] FIG. 1E shows that the relative contribution of different immune cell types to S10 and S27 is “broad”, including CD4+ T cells (“CD4+ T”), CD8+ T cells (“CD8+ T”), other T cells (“Other T”), natural killer (NK) cells, B cells, monocytes (“Mono”), and dendritic cells (DC). FIG. 1E also shows that each of the immune cell types (i.e., CD4+ T cells, CD8+ T cells, other T cells, NK cells, B cells, monocytes and DCs) contribute to the group of cells in which signatures S11, S15, S21, S26 and S28 are active. Nevertheless, the contribution is classified as “specific”. In particular, analysis in FIG.1E shows that relative contribution of different immune cell types to signatures S11, S15, S21, S26 and S28 is more specific to particular immune cell types, as follows: (i) for S15, S21, and S26, monocytes and DCs show the highest relative contribution (ii) for S28, T cell populations show the highest relativecontribution, mostly by CD8+ T cells and other T cells and (iii) for S11, DCs and B cells show the highest relative contribution.

[0189] This example illustrates that progression from MGUS to MM is associated with an increase and decrease in at least seven different gene expression signatures associated with different immune cell types. The progression-associated changes include an increase in gene expression signatures marked by IFN signaling and T cell cytotoxicity respectively, and a decrease in a gene expression signature associated with hypoxia that is most active in cytokine-expressing myeloid cells. Example 5. Increased IFN signaling as marker for progression to MM

[0190] This example confirms that a gene expression signature marked by IFN signaling is associated with progression from MGUS to MM.

[0191] As shown in Example 4, the gene expression signature marked by IFN signaling was active in both myeloid and lymphoid cells. SignatureAnalyzer (SA) was used on matched plasma cell samples from the same patients to test whether IFN signaling spanned both tumor and immune cells. Indeed, a signature of IFN signaling was discovered within both normal and malignant plasma cells. Gene expression signatures marked by IFN signaling were uncovered in both tumor cells (FIG.2A) and immune cells (FIG. 2B). The tumor signature included IFITM1, IFI6, ISG15, IFI27, LY6E, MX1, XAF1, and STAT1. The immune signature was more pronounced and similarly included ISG15, IFI6, MX1, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, OAS3, HERC5, IFI27, GBP1, OAS1, CMPK2, IFIT1, RSAD2, IRF7, and EIF2AK2. Patients with high IFN signaling activity in plasma cells also had high IFN signaling activity in immune cells (Pearson’s r=0.76, p=2e-32), suggesting a coordinated increase in IFN signaling across tumor and immune cells. As shown in FIG.2C, high IFN signaling activity indeed correlated between tumor cells and immune cells (Pearson’s r=07.1, p=7.3e-31)

[0192] Malignant plasma cells are capable of secreting IFN type I, which can help promote Treg differentiation and activation and ultimately, tumor growth. Within patients with smoldering multiple myeloma (SMM), patients who later progressed to overt disease showed significantly higher IFN signaling activity in their immune cells (FIG. 2D), suggesting that IFN signaling may be a risk factor of progression to overt MM.

[0193] Two external gene expression datasets with tumor cell data were employed to validate the role of IFN signaling in disease progression. The first dataset comprised tumor samples from bone marrow aspirates of patients with MGUS (n=22), SMM (n=24), MM (n=73), and relapsed-refractory MM (RRMM) (n=28). In this first dataset, IFN activity was found to be significantly higher in patients with RRMM compared to MGUS (Wilcoxon, p=0.04, q=0.076) with a similar trend for patients with MM compared to MGUS (p=0.05, q=0.076) (FIG.2E). The second dataset comprised tumor samples from patients with MGUS who did not progress over 10 years of follow-up (so-called stable) (n=319) and patients with MGUS who went on to progress (n=39). In this second dataset, IFN activity was significantly higher in progressors (p=0.04) (FIG.2F).

[0194] It was also observed that patients with overt MM in the CoMMpass cohort and higher IFN activity showed significantly shorter progression-free survival (PFS), suggesting that IFN activity may portend inferior response to treatment as well and a higher risk of relapse (FIG.2G).

[0195] This example illustrates that a gene expression signature marked by IFN signaling is associated with progression from MGUS to MM and also has prognostic value with respect to PFS of MM patients and their responsiveness to therapy. Example 6. Reduced repertoire diversity, and clonal expansion, of GZMB+ CD8+ TEMs indicates progression to MM

[0196] This example illustrates that GZMB+ CD8+ TEMs are significantly more clonally expanded than other T cells in patients with MM compared to MGUS.

[0197] As shown in Example 1, GZMB+ CD8+ TEMs increased significantly in abundance with disease progression to MM. Using the matched single-cell TCR-seq data provided in Example, the repertoire diversity of GZMB+ CD8+ TEMs in patients with MM and MGUS was compared. To account for differential cell numbers, 50 GZMB+ CD8+ TEMs per individual were sampled repeatedly with 1,000 iterations. The average diversity across iterations per individual was then computed.

[0198] It was observed that GZMB+ CD8+ TEMs were significantly more clonally expanded (hence showed lower repertoire diversity) in patients with overt MM compared to MGUS (FIG.3A). Specifically, 50 cells were down sampled from each sample and the Chao index of diversity was computed. This procedure was repeated 1,000 times and the meandiversity estimate across iterations was computed per sample. Mean estimates were compared between patients of different stages using Wilcoxon’s rank-sum test. Each point in FIG. 3A represents a patient. As both GZMK+ and GZMB+ CD8+ TEMs appeared to increase in proportion with disease progression, it was tested whether clonally expanded T cells were equally likely to exhibit less mature, more memory-like GZMK+ phenotype or the more mature, more terminally differentiated GZMB+ phenotype. A significant increase in the proportion of clonally expanded T cells exhibiting the more mature GZMB-expressing phenotype was observed in patients with MM, whereas this was not observed for the GZMK- expressing phenotype, suggesting that CD8+ TEMs both clonally expand and exhibit a terminal differentiation bias (FIG. 3B). Specifically, “expanded clones” were defined as clones comprising > 1% of the sample’s repertoire. Only samples with at least 100 T cells were included in this analysis. For each patient, the proportion of T cells exhibiting GZMK+ or GZMB+ phenotype within the expanded clones was computed. These proportions were then compared between patients with MGUS and MM using Wilcoxon’s rank-sum test. Each point in FIG.3B represents a patient.

[0199] This example illustrates that MM patients have a decreased GZMB+ CD8+ TEM repertoire diversity compared to MGUS patients. This example also illustrates that the GZMB+ CD8+ TEM clones of MM patients are expanded, i.e., comprise more cells per clone, compared to the GZMB+ CD8+ TEM clones of MGUS patients. Furthermore, these expanded clones exhibited a terminal differentiation bias. Example 7. Reduced repertoire diversity, and clonal expansion, of T cells indicates progression to MM

[0200] This example illustrates that MM patients with increased clonal T cell expansion have reduced overall survival.

[0201] To assess whether the phenomenon identified in Example 5 happens during disease progression or alternatively reflects a predisposition to progression in patients with these T cell features, the cycling frequency of clonally expanded T cells was compared between patients with MGUS and MM. Specifically, cells were scored for the expression of S phase and G2M phase genes and assigned to cell cycle phases. For each patient, clonally expanded (>1% of the repertoire) T cells were isolated and the proportion of clonal expanded T cells in S or G2M phase (i.e., cycling cells) was computed. Cycling proportions were compared between patients with MGUS and MM using Wilcoxon’s rank-sum test. It was observed thatclonally expanded CD8+ TEMs from patients with MM showed significantly higher frequency of cycling cells compared to those from patients with MGUS, suggesting that these T cells are actively clonally expanding during disease progression (FIG.3C).

[0202] Indeed, the overall TCR repertoire diversity significantly decreased with disease progression from MGUS to SMM and MM, and this phenomenon was independent of chronological age, which is typically higher in patients of later stages and is itself associated with increased clonal expansion (FIG.3D and FIG.3E). In the test cohort, patients with SMM who went on to develop MM showed significantly lower repertoire diversity compared to non-progressors (Wilcoxon, p=0.0033), suggesting that clonal T cell expansion is a risk factor of progression (FIG.3F).

[0203] The degree of clonal T cell expansion at baseline may impact response to therapy, including immunotherapy, suggesting that this feature may be relevant for prognostication and therapy selection in patients with overt MM. To examine whether progressive clonal expansion impacts response to therapy and patient outcome in patients with MM, bulk TCR- seq on PB-derived immune cells from 100 patients with MM from the CoMMpass cohort was performed, using the ImmunoSeq platform (Adaptive Biotechnologies). Patients were split into two groups, based on the median degree of T cell clonality, and compared their overall survival (OS) using Kaplan-Meier analysis. Specifically, T cell clonality was measured using standard indices and patients were split into two groups based on the median, i.e., grouped as either higher than the median “high” or lower than the median “low”. It was found that patients with MM and increased T cell clonality showed significantly inferior OS and this effect was independent of chronological age, which is a predictor of inferior OS in its own right (FIG.3G).

[0204] This example illustrates that MM patients have a decreased T cell repertoire diversity, compared to MGUS patients. This example also illustrates that the T cell clones of MM patients are expanded, i.e., comprise more cells per clone, compared to the T cell clones of MGUS patients. Surprisingly, MM patients with increased T cell clonality showed significantly inferior overall survival compared to MM patients with low T cell clonality.Example 8. Gene expression signature of GZMB+ CD8+ TEMs with decreased activity in patients with MM compared to MGUS

[0205] This example identifies a gene expression signature comprising TGFB1, PIK3R1, SLC7A5, and SYTL3 in GZMB+ CD8+ TEMs that decreases in activity as patients progress from MGUS to MM. This gene expression signature may be alternatively referred to as an “immune cell activation gene expression signature” or a “T cell activation gene expression signature”.

[0206] To quantify gene expression programs that pertain to GZMB+ CD8+ TEM functionality and assess how those change with disease progression from MGUS to MM, SignatureAnalyzer was deployed on a subset of 50,000 GZMB+ CD8+ TEMs. The resulting signatures were projected on the entirety of the GZMB+ CD8+ TEMs, and median signature activity was compared across patients with MGUS and NDMM. One signature was identified that decreased significantly in activity in patients with MM compared to MGUS (see FIG.4A). This signature (S20), i.e., a “T cell activation gene expression signature”, was marked by genes such as TGFB1, PIK3R1, SLC7A5, and SYTL3, which have been reported to regulate cytotoxic T cell responses to cognate antigen.

[0207] To assess the robustness of signature discovery, the procedure was repeated ten times and the cosine similarity across signatures and runs based on gene weights obtained from the W matrix was computed. While some signatures were only discovered in some of the runs, the T cell activation gene expression signature marked by genes such as TGFB1, PIK3R1, SLC7A5, and SYTL3 was discovered in all ten repeats, demonstrating that it is a robust signature. This gene expression signature is shown in FIG.4B. Notably, the signature includes gene-encoded surface proteins (GESP) such as LAT1 (encoded by SLC7A5) and TIMAP (encoded by PPP1R16B) and cytokines such as TGFβ (encoded by TGFB1). Therefore, monitoring an increase in the population of GZMB+ CD8+ TEMs may be accompanied by monitoring a decrease in the subpopulation of LAT1+ TIMAP+ TGFβ+ subpopulation.

[0208] Importantly, the T cell activation gene expression signature showed significantly lower activity in clonally expanded CD8+ TEMs, compared to singletons in patients with MGUS and patients with MM, suggesting that decreased expression of the signature may coincide with the progressive clonal expansion observed with disease progression (FIG.4C).

[0209] This example indicates that clonal expansion of GZMB+ CD8+ TEMs is associated with decrease in a T cell activation gene expression signature comprising TGFB1, PIK3R1, SLC7A5, and SYTL3, marking the progression from MGUS to MM. Example 9. GZMB+ CD8+ TEM gene expression signature is associated functional CD8+ T cells

[0210] This example illustrates that patients with SMM who go on to develop MM show significantly lower activity of a T cell activation gene expression signature comprising SLC7A5 in their GZMB+ CD8+ TEMs. Moreover, the activity of the gene expression signature in the peripheral blood (PB) was positively correlated with that in the bone marrow (BM).

[0211] To understand whether the gene expression signature identified in Example 8 may reflect T cell functionality, patients with SMM were split into two groups: a first group with low T cell activation gene expression signature activity (n=31) and a second group with high T cell activation gene expression signature activity (n=31). The split was based on the 1stand 3rdquartile of the distribution, respectively. The gene expression profiles of GZMB+ CD8+ TEMs were then compared between these two groups (FIG.4D).

[0212] As can be seen on the righthand side of FIG.4D, genes marking the signature, such as SLC7A5, were upregulated in the second group with high T cell activation gene expression signature activity, along with CD69, a canonical T cell activation marker (not all genes of the T cell activation gene expression signature identified in Example 8 are labelled). As can be seen on the lefthand side of FIG. 4D, The first group with low gene expression signature activity showed higher expression levels of a gene expression signature associated with T cell dysfunction including CISH, a negative regulator of TCR signaling; several genes of the GIMAP family (of GIMAP1, GIMAP2, GIMAP4, GIMAP6, and GIMAP7), which function downstream of the TCR and provide important survival and death signals; TNFRSF1A, a gene encoding TNFR1, which is thought to activate the caspase cascade and lead to apoptosis post-activation; CX3CR1, a marker of short-lived exhausted effector CD8+ T cells; SLAMF6, a marker of progenitor exhausted T cells; and EOMES, a transcription factor regulating T cell exhaustion.

[0213] These results indicate that the gene expression signature identified in Example 8 captures functional CD8+ T cells, which decrease with disease progression. In accordancewith this hypothesis, patients with SMM who went on to develop MM showed significantly lower activity of the T cell activation gene expression signature in their GZMB+ CD8+ TEMs (Wilcoxon, p=0.0034) (FIG. 4E). Importantly, the activity of the signature in the PB was positively correlated with that in the BM, indicating that the changes in T cell functionality are systemic (FIG.4F).

[0214] The data in this example indicate that PB may be used in place of BM to detect a T cell activation gene expression signature comprising SLC7A5 to monitor the progression from SMM to MM. Example 10. Gene expression signature of GZMB+ CD8+ TEMs indicates improved disease control

[0215] This example illustrates that patients with higher T cell functionality (as indicated by a T cell activation gene expression signature comprising SLC7A5) and immune control (as indicated by an “immune control signature” in their tumor cells comprising SERPINB9) have improved disease control even after progression to MM.

[0216] If the gene expression signature identified in Example 8 reflects higher T cell functionality, a transcriptional footprint of immune control could be present in tumor cells from patients with high activity of the T cell activation gene expression signature in their GZMB+ CD8+ TEMs. Tumor cells were detected using BCR sequencing. To test this hypothesis, the gene expression profiles of tumor cells (i.e., CD138+ plasma cells that were identified as tumor cells by BCR sequencing) from SMM patients with high T cell activation gene expression signature activity in their GZMB+ CD8+ TEMs (n=31) was compared to those from SMM patients with low T cell activation gene expression signature activity in their GZMB+ CD8+ TEMs (n=31). The results of this analysis are shown in FIG.5A.

[0217] The analysis identified an immune control signature in tumor cells in SMM patients with high T cell activation gene expression signature activity in their GZMB+ CD8+ TEMs. As shown in FIG.5A, the top upregulated gene was SERPINB9, a gene encoding a protease tasked with neutralizing granzyme B to evade immune-mediated cell death. CD138+ plasma cells from patients with low T cell activation gene expression signature activity in their GZMB+ CD8+ TEMs, presumed to have T cells of lower functionality, showed higher levels of IFN signaling and CCR2, a receptor that mediates tumor cell interactions with stroma cells which support tumor growth. The observed IFN signaling was as described in Example 5.

[0218] The immune control signature including SERPINB9 decreased significantly with disease progression, mirroring the progressive decrease of the T cell activation gene expression signature in GZMB+ CD8+ TEMs (FIG. 5B and FIG. 5C). FIG.5B shows the median score per tumor sample for the marker genes of the immune control signature in patients with MGUS, SMM, and NDMM. FIG.5C illustrates the mean SERPINB9 expression levels in the same samples. Importantly, as can be seen from FIG. 5D, patients with SMM whose tumor cells showed higher activity of the immune control signature showed significantly lower tumor cycling, which is an established biomarker of aggressive biology and outcome in patients with MM. Cycling tumor cells were identified on the basis of expression of established gene expression signatures for the S and G2M phases of the cell cycle (Tirosh et al. Science, (2016) 352(6282):189-196.

[0219] Overall, the data in this example indicate that patients with higher T cell functionality and immune control have improved disease control even after progression to MM. Example 11. T cell functionality is highest at the earliest stages of progression to MM

[0220] This example confirms that T cell functionality is highest at the earliest stages of disease progression to MM and that tumor cells use SERPINB9 to evade killing by cytotoxic T cells.

[0221] Tumor genomic evolution could be to blame for the decrease in activity of the immune control signature in tumor cells associated with disease progression, which was identified in Example 10. To test this hypothesis, Numbat was used to infer copy number variants (CNVs). Phylogenetic trees in tumors from the test cohort were reconstructed, and the evolution of the immune control signature was traced across branches.

[0222] The immune control signature was validated in two external cohorts, showing that it indeed decreases with disease progression from MGUS to RRMM and that patients with MGUS who will go on to progress to MM show significantly lower activity of the signature (FIG.5E, FIG.5F, FIG.5G).

[0223] Collectively, the results in this example indicate that T cell functionality is highest at the earliest stages of disease progression to MM and that tumor cells use SERPINB9 to evade killing by cytotoxic T cells when those are functional.Example 12. Progression from MGUS to NDMM is associated with loss of functionality in GZMB+ CD8+ TEMs

[0224] This example illustrates that progression from MGUS to NDMM is associated with a loss of functionality in GZMB+ CD8+ TEMs.

[0225] To gain further insight into phenotypic changes in immune cell populations, differential expression analysis was performed in pseudo-bulk manner, comparing GZMB+ CD8+ TEMs between patients with NDMM (n=45) and patients with MGUS (n=33). Patients were filtered out if they had fewer than 30 cells of the respective cell type.

[0226] FIG. 6A illustrates the results of this analysis. Within GZMB+ CD8+ TEMs in NDMM patients, significant upregulation of IFN signaling genes was observed. Moreover, GZMB+ CD8+ TEMs in these patients also significantly upregulated MHC-II-encoding genes, which are associated with T cell activation and immunoregulatory activity, as well as BATF, a transcription factor that is important for CD8+ T cell effector differentiation and may be associated with short-lived responses and induction of exhaustion programs. A significant downregulation of BCL3, a transcription factor that restricts terminal differentiation, was also observed in GZMB+ CD8+ TEMs in NDMM patients. In addition, GZMB+ CD8+ TEMs in these patients also significantly downregulated SLC7A5, an amino acid transporter, and ABCB1, an ATP-binding cassette transporter, which are both important for CD8+ T cell activation and response to cognate antigen, as well as members of the NFkB pathway, such as RELB, which is important for mounting successful CD8+ T cell responses. These results, along with the results presented in Example 11, indicate that cytotoxic CD8+ TEMs change not only in abundance, but also in functionality with disease progression from MGUS to NDMM.

[0227] Given the observed upregulation of IFN signaling genes in GZMB+ CD8+ TEMs of NDMM patient and the fact that cytotoxic T cells release IFN-γ in a leaky synaptic manner that can impact cells beyond the synapse, it was hypothesized that broad increases in an IFN signaling program across multiple cell types in the BM microenvironment may be observed with progression from MGUS to NDMM. To quantify such broadly active programs, SignatureAnalyzer was used on both myeloid and lymphoid populations and employed a signature robustness algorithm was employed, which in this case resulted in a mean reproducibility of ~92% (Boiarsky R et al.: Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis. Nat Commun13:7040, 2022; Kim J et al.: Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat Genet 48:600-606, 2016). Overall, 37 signatures were detected, of which 9 were filtered out for having <100 cells assigned to them, for a final number of 28 signatures. Broadly active signatures (n=7) were identified based on high diversity in cell types (normalized Shannon index in the top quartile) across cells they were highly active in (top activity quartile). While most signatures showed some degree of enrichment in certain cell types, signatures corresponding to immune cell activation and IFN signaling, were broadly active and showed balanced activity across multiple populations.

[0228] As shown in Example 5, the IFN signaling signature is active in both myeloid and lymphoid cells. Further analysis with SignatureAnalyzer was used to identify signatures in tumor cells from the same NDMM patients, and test whether IFN stimulation spanned both tumor and immune cells. A signature of IFN stimulation was confirmed within malignant plasma cells. Importantly, NDMM patients with high IFN signaling activity in tumor cells also had high IFN signaling activity in immune cells (Pearson’s r=0.72, p=2.8e-31), confirming a coordinated niche-wide increase in IFN stimulation across tumor and immune cells. The IFN signature observed in immune cells is shown in FIG.6B and is consistent with the findings in Example 5. This figure shows a barplot of signature weight or R score for the top 20 gene markers of the IFN signaling signatures in immune cells, with genes being sorted by signature weight in decreasing order.

[0229] To validate the role of IFN signaling in disease progression, two external gene expression datasets with tumor cell data were analyzed to determine whether the same coordinated increase in tumor and immune cells could be observed in these datasets (Chng WJ et al.: Molecular dissection of hyperdiploid multiple myeloma by gene expression profiling. Cancer Res 67:2982-9, 2007; Sun F et al.: A gene signature can predict risk of MGUS progressing to multiple myeloma. J Hematol Oncol 16:70, 2023). The dataset published by Chng WJ et al. comprised tumor samples from patients with MGUS (n=22), SMM (n=24), NDMM (n=73), and relapsed-refractory MM (RRMM, n=28) (GSE6477). In this dataset, IFN stimulation signature activity was significantly higher in patients with NDMM and RRMM compared to MGUS (two-sided Wilcoxon, q=0.024 & q=0.0053, respectively). The dataset published by Sun F et al. comprised tumor samples from patients with MGUS who did not progress over 10 years of follow-up (so-called “stable”) (n=319) and patients with MGUS who went on to progress during that time (n=39) (GSE235356). Inthis dataset, IFN stimulation signature activity was significantly higher in progressors than patients with stable disease (two-sided Wilcoxon, p=0.009).

[0230] To better understand how T cell functionality changes with progression, an analysis similar to that described in Example 10 was performed. Specifically, patients with SMM were split into a low- T cell activation group (“low”) and a high-T cell activation group (“high”) based on the lowest and highest quartiles, respectively, of the signature described in Examples 9 and 10, and the gene expression profiles of GZMB+ CD8+ TEMs between these two groups was compared, after filtering for genes expressed in more than 10% of cells (n=4,702) and patients with more than 30 cells (n=32 and n=32, respectively). The results of this analysis are shown in FIG. 6C. As expected, genes marking the T cell activation gene expression signature identified in Example 8 such as SLC7A5, PIK3R1, and TGFB1 were upregulated in the high-activity group, along with CD69, a canonical T cell activation marker, and CXCR4, which is critical for T cell homing to the BM and memory homeostasis. The low-activity group showed significantly higher levels of genes associated with cytotoxicity and terminal differentiation including, e.g.,CXCR3R1 (a marker of short-lived exhausted effector CD8+ T cells) and PRF1. Consistent with the findings in Example 9, the low-activity group showed higher expression levels of CISH, a negative regulator of TCR signaling; several genes of the GIMAP family, which function downstream of the TCR and provide important survival and death signals; SLAMF6, a marker of progenitor exhausted T cells; and EOMES, a transcription factor regulating T cell exhaustion. These results indicate that T cell activation gene expression signature captures functional CD8+ TEMs, which decrease with disease progression.

[0231] The analyses in this example confirm that progression from MGUS to NDMM is associated with a loss of functionality in GZMB+ CD8+ TEMs. Specifically, this example confirms that a gene expression signature comprising TGFB1, PIK3R1, and SLC7A5 in GZMB+ CD8+ TEMs decreases in activity as patients progress from MGUS to MM and can be used to monitor the progression from SMM to MM. In addition, this example also confirms that an immune cell-associated gene expression signature comprising ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, and OAS3 and a T cell dysfunction signature comprising CISH, CX3CR1, SLAMF6, EOMES, BATF and one or more of genes in the GIMAP family increases in activity as patients progress from MGUS to MM.Example 13. Materials and methods

[0232] This example provides the materials and methods that were used to generate the data described in the preceding examples. Single-cell RNA-seq

[0233] Single-cell RNA-seq was performed on 879 samples from 517 bone marrow (BM) aspirate and peripheral blood (PB) specimens and 362 patients with plasma cell dyscrasias and healthy donors (HD). Bone marrow aspirates were subjected to magnetic bead enrichment for CD138pos tumor cells and for 237 individuals, both the CD138pos and the CD138neg fraction (i.e., immune cells) were sequenced to enable integrative analyses of tumor biology and immune dysregulation. In total, following quality control and annotation, sequenced ~4.7 million cells were sequenced, including ~2.2M malignant and normal plasma cells, ~1.1M T cells, ~498K Monocytes, ~210K B cells, ~231K NK cells, ~361K progenitor cells, ~47K Dendritic cells, and ~1.8K stroma cells. Progenitor and stroma cells were excluded from downstream analyses, and plasma cells were processed separately from other immune cells to distinguish normal from malignant cells. Malignant cells were identified using a combination of single-cell BCR clonotypic data (n=377) and gene expression-based approaches. Immune cells were subclustered and characterized with greater granularity, yielding a total of 47 subpopulations. Patient samples and processing

[0234] Participants were enrolled on either the PCROWD study, an observational prospective cohort study of plasma cell pre-malignancies (IRB #14-174), or the PROMISE study (IRB #18-370), a national screening study for individuals who are at high risk for myeloma, namely people with a strong family history of MM and / or people who self-identify as Black or African American; all participants are over the age of 30. For PCROWD, patients are recruited at Dana-Farber Cancer Institute and satellite sites in the Boston area or through referral, and word of mouth. The PROMISE study also participates in community outreach efforts across the United States by attending health fairs and patient webinars. All participants provided informed consent prior to the collection of data and specimens. The institutional review board of the Dana-Farber Cancer Institute approved the studies in accordance with the Declaration of Helsinki. Electronic health records were reviewed for clinical data extraction and collected data were reviewed by two independent collectors. Data collectionon progression to MM for patients with precursor conditions was cut off in August of 2023. A total of 24 out of patients with SMM and BM samples at baseline (n=179) went on to develop overt MM; 17 of them received therapy for SMM in the context of a clinical trial prior to progression, while 7 progressed on observation. All 24 patients were considered progressors for the purposes of this study and compared to non-progressors at baseline, prior to treatment administration. Clinical-grade FISH testing was conducted on first-pull bone marrow aspirate samples at the Mayo Clinic pathology laboratories in Rochester, MN.

[0235] Bone marrow aspirates and peripheral blood were collected in EDTA tubes, and bone marrow mononuclear cells (BMMCs) and peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll separation and / or Red Blood Cell lysis buffer (ThermoFisher). Bone marrow mononuclear cells were then subjected to magnetic bead enrichment (Miltenyi Biotec) for CD138, according to the manufacturer’s instructions, and cryopreserved in heat- inactivated Fetal Bovine Serum (FBS) supplemented with 10% Dimethylsulfoxide (DMSO). PBMCs were either cryopreserved in FBS with 10% DMSO without further selection or underwent CD138 magnetic bead enrichment for Circulating Tumor Cell (CTC) isolation. Single-cell RNA / V(D)J-seq library preparation

[0236] Enriched samples were thawed or freshly prepared and loaded onto the Chromium Controller instrument for single-cell encapsulation (10X Genomics). All GEM generation / barcoding, post GEM RT clean-up / cDNA amplification and 5’ gene expression (GEX) library construction steps were completed using the Chromium Next GEM Single Cell 5’ Reagent Kit v2 (Dual Index) and Library Construction Kit, according to the manufacturer’s instructions. In addition to GEX libraries, cDNA was also subjected to V(D)J amplification using Chromium Single Cell Human BCR Amplification Kit and Library Construction Kit, according to the manufacturer’s instructions. Library quality was assessed using a High-Sensitivity DNA Kit and the Bioanalyzer 2100 instrument (Agilent Technologies). Final GEX and V(D)J library quantification was performed using Quant-iT Picogreen dsDNA Assay Kit (Invitrogen). Pooled libraries were sequenced on NovaSeq S4 flow cells at the Genomics Platform of the Broad Institute of MIT and Harvard (Cambridge, MA). A total of 879 GEX, 377 BCR, and 354 TCR libraries were sequenced. For 75 specimens, replicate libraries were prepared by thawing an additional aliquot of cells to assess the reproducibility of immune cell proportion estimates.Patient sample single-cell RNA / V(D)J-seq data processing

[0237] CellRanger mkfastq (v5.0.1) was used to generate FASTQ files. Gene expression matrices were generated by CellRanger count (v6.0.1) with the genome reference (refdata- gex-GRCh38-2020-A) provided by 10X Genomics114. To remove ambient RNA, CellBender (v0.2.0) was run on the gene expression matrices with the target false positive rate cutoff of 0.01. CD138pos libraries were processed separately from CD138neg ones (“plasma cell object”) (Fleming SJ et al: Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat Methods, 2023). Immune cells identified in the CD138pos libraries were processed together with the CD138neg library cells (“immune cell object”). In both objects, poor quality cells with >15% mitochondrial gene expression, either <200 detected genes, >5,000 detected genes, <400 UMIs, or >50,000 UMIs were filtered out. Three doublet tools, Scrublet (v0.2.3), scDblFinder (v1.8.0), and SCDS (v1.10.0) were used to calculate multiplet scores (Wolock SL et al.: Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. Cell Syst 8:281-291 e9, 2019; Germain PL et al: Doublet identification in single-cell sequencing data using scDblFinder. F1000Res 10:979, 2021; Bais AS, Kostka D: scds: computational annotation of doublets in single-cell RNA sequencing data. Bioinformatics 36:1150-1158, 2020). For immune cells, integration was performed using Harmony (v0.1.0) and correcting for sample ID and assay type (3’ vs 5’) (Korsunsky I et al: Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 16:1289-1296, 2019). Broad cell types (T, NK, Monocytes, DCs, Progenitor cells etc.) were determined on the basis of expressing established lineage markers and were subclustered separately for cell annotation. Cells were then annotated based on a list of established expression markers, as well as cluster-specific markers obtained through differential expression analysis (using Wilcoxon rank-sum tests), as previously described (Sklavenitis-Pistofidis R et al: Immune biomarkers of response to immunotherapy in patients with high-risk smoldering myeloma. Cancer Cell 40:1358-1373 e8, 2022). Clusters of cells enriched for cells that were deemed to be doublets by at least 2 out of three methods, which co-expressed markers of multiple cell types (heterotypic doublets) were removed from consideration (n=371,290). Clusters with higher mitochondrial and ribosomal gene expression that clearly separated from well-annotated clusters and lacked interpretable expression markers were also removed downstream (n=579,159).

[0238] CellRanger vdj (v6.0.1) was used with the flag of “--chain=IG” on FASTQ files with the VDJ reference file (refdata-cellranger-vdj-GRCh38-alts-ensembl-5.0.0) (Zheng GX, Terry JM, Belgrader P, et al: Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049, 2017). All V(D)J contigs detected by CellRanger were then processed to identify a single alpha and beta chain per cell barcode, based on UMI support. Cells annotated as T cells and which had V(D)J data were considered for TCR repertoire analyses. For each patient, clonotypic data from multiple samples (BM or PB), aliquots, and timepoints were combined. Harmonized clonotypes were identified anew based on the frequency of unique CDR3 amino-acid (AA) sequences across samples. Repertoire diversitywas assessed based on the Chao1 index: ^^^^^^^^ + ((^^^^^^^^^^^^^^^^^^^^ ∗ (^^^^^^^^^^^^^^^^^^^^ − 1)) / (2 ∗(^^^^^^^^^^^^^^^^^^^^ + 1))), where Sobs is the number of unique clones, NSingleton is the number ofclones with a frequency of 1 and NDoubletonis the number of clones with a frequency of 2 (Deng Y et al.: Nonparametric richness estimators Chao1 and ACE must not be used with amplicon sequence variant data. ISME J 18, 2024). To account for differential T cell numbers across samples, T cells were downsampled (n=100 for overall T cell diversity; n=50 for GZMB+ CD8+ TEM diversity) per sample with 10,000 iterations, and computed the average Chao index across iterations. Within each sample, a clone was considered rare if its frequency was ≤1%; small, if its frequency was >1% and <5%; medium, if its frequency was ≥5% and <10%; and large, if its frequency was ≥10%. Small, medium, and large clones were considered expanded. Bulk TCR-seq sample selection, processing & analysis

[0239] A total of 98 PBMC samples from patients with NDMM in the CoMMpass cohort were selected for this experiment based on sample availability (Skerget S et al: Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes. Nat Genet 56:1878-1889, 2024). DNA was extracted from viably frozen PBMCs (90% FBS / 10% DMSO) using QIAmp DNA Mini kits (Qiagen) and quantified using Qubit (ThermoFisher). Libraries were prepared using ImmunoSeq hsTCRB kits (Deep Survey) by Adaptive Biotechnologies and sequenced on a NextSeq (High Output) at the Genomics Platform at the Broad Institute of MIT and Harvard (Cambridge, MA). BCL directories were transferred to Adaptive Biotechnologies for processing and processed matrices with T cell clonality estimates were received for analysis. Patients were split into high (> median) or low clonality subgroups and survival analysis was performed usingsurvival data downloaded from the Multiple Myeloma Research Foundation’s Researcher Gateway (IA22 release). Survival analysis was performed using the survival (v3.5-5) and survminer (v0.4.9) R packages. Identification of malignant plasma cells

[0240] Plasma cells were identified within CD138pos libraries on the basis of expressing key lineage markers, such as SDC1 (encoding CD138), CD38, XBP1, PRDM1, IRF4, and TNFRSF17 (encoding BCMA). Cell barcodes that were determined to correspond to plasma cells were considered for downstream analysis. Plasma cells identified within CD138neg libraries (n=273,967), which did not have scBCR-seq performed, were removed from consideration. All V(D)J contigs detected by CellRanger (v6.0.1) were processed to identify a single heavy and light chain per cell barcode, based on UMI support (Zheng GX et al: Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049, 2017). For each patient, clonotypic data from multiple samples (BM or PB), aliquots, and timepoints were combined. Harmonized clonotypes were identified anew based on the frequency of unique CDR3 amino-acid (AA) sequences across samples. Clones were sorted based on their cell count and candidate expanded clones were identified as clones with more than 20 cells which were at least twice the size of the next clone in order of frequency. Candidate expanded clones were then manually reviewed to determine whether they were normal or malignant. For a candidate expanded clone to be considered malignant, it was asked that: (i) its immunoglobulin isotype matched that reported clinically through serum protein immunofixation (IFX), (ii) key MM oncogenes (for example, CCND1, MAF, NSD2, etc.) were expressed in accordance with the tumor’s cytogenetics by Fluorescence in situ hybridization (FISH) (when that was available), and (iii) key immunophenotypic markers of plasma cell malignancy (NCAM1, CD19, PTPRC, etc.) were up- or down-regulated in the population accordingly (Boiarsky R et al: Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis. Nat Commun 13:7040, 2022; Ledergor G et al: Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat Med 24:1867-1876, 2018). Because a single tumor can present with variants of a malignant clonotype (for example, a light chain only variant of the main clonotype), consensus clonotypes were determined for each tumor by sorting malignant clonotypes based on the number of tumor cells using them and selecting the top clonotype. For each consensus clonotype, the Levenshtein distance between eachclonotype detected in the sample and the consensus clonotype was computed on the CDR3 AA sequence and normalized by the length of the longest of the two sequences. Clonotypes with at least 90% similarity to the consensus clonotype were considered variants of the malignant clonotype and were annotated as malignant, whereas clonotypes with at least 60% but less than 90% similarity were considered suspicious and excluded from the normal plasma cell compartment. This approach was used for 231 patients who had V(D)J data available; for samples without V(D)J data available (n=38), malignant cells were identified on the basis of their clustering separately from normal plasma cells in the RNA space, demonstrating near-monoclonal expression of the patient’s tumor’s immunoglobulin isotype (detected via serum IFX) and consistent expression pattern of key expression markers of plasma cell malignancy (see above), and / or presenting somatic copy number variants, as inferred using Numbat (see below) (Gao T et al: Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nat Biotechnol 41:417-426, 2023). A total of 205 patients in this cohort had tumor cells detected, and 4 patients had two distinct tumors detected each, with different malignant clonotype for each tumor, for a total of 209 tumors. Identification of cycling cells

[0241] Cell cycle scores were obtained on plasma cells and immune cells separately using S phase and G2M phase gene lists by Tirosh et al. and the score_genes_cell_cycle function in Scanpy (v1.8.2) (Wolf FA, Angerer P, Theis FJ: SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15, 2018). For plasma cells, to determine appropriate S and G2M score thresholds to assign cells into cell cycle phases, the S score threshold was first set at the 99.9thpercentile of the respective distribution for normal plasma cells from healthy donors (n=180,790), which were assumed to show little proliferation (1 in 1000). Then, the G2M score threshold was set at the 99.9thpercentile of the respective distribution for normal plasma cells from healthy donors that did not pass the S phase threshold. Subsequently, the S score threshold was iteratively updated by setting it to the 99.9thpercentile of S scores in non-G2M normal plasma cells until the updated threshold is <0.01 different from the prior threshold. This approach resulted in an S score threshold of ~0.1106 and a G2M score threshold of ~0.1060. Cells were assigned to the S phase if their S score was > ~0.1106, and cells that were not assigned to the S phase were assigned to the G2M phase if their G2M score was > ~0.1060. Cells in the S and G2M phases wereconsidered to be cycling. For immune cells, cells with an S score > 0.1 and an S score higher than the G2M score were assigned to the S phase; cells with a G2M score > 0.1 and a G2M score higher than the S score were assigned to the G2M phase. Inference of copy number variants

[0242] Copy number variants (CNVs) were inferred using Numbat (v1.1.0) (Gao T et al: Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nat Biotechnol 41:417-426, 2023). Specifically, allelic data was collected from malignant and normal plasma cells as well as B cells to ensure that the frequency of CNVs and particularly, clonal deletions, would not negatively impact genotyping and phasing in samples with high tumor purity. Furthermore, a custom panel of 1.2K healthy donor plasma cells was used as expression reference to correct for cell type-specific expression patterns, such as immunoglobulin expression, which otherwise result in artifactual CNV inference on chromosomes 2, 14, and 22, as previously described (Sklavenitis- Pistofidis R et al: Single-cell RNA sequencing defines distinct disease subtypes and reveals hypo-responsiveness to interferon in asymptomatic Waldenstrom's Macroglobulinemia. Nat Commun 16:1480, 2025). Ultimately, CNVs were inferred on tumor cells only for each tumor to increase detection sensitivity. Patients with >15K tumor cells (n=14) were downsampled to 15K prior to running Numbat for efficiency. Numbat was run with max_iter = 2, tau = 0.1, min_cell = 10, to further increase detection sensitivity.

[0243] Out of 209 tumors profiled, a total of 37 had no CNVs detected. One tumor had a single Trisomy 12 detected which is not compatible with an MM diagnosis and was removed from consideration. A total of 171 tumors had CNVs detected. Numbat clones with <100 cells were filtered out. Clone-level CNV probabilities were then computed as the median joint probability for each CNV across cells in the respective clone and CNVs were called if the median probability for the event in that clone was > 0.65; if the event was deemed to be present (> 0.65) in the clone immediately prior to the given clone, the event was called if the median probability in the given clone was > 0.55. Variants not called in any clone were removed from consideration and clones with identical CNV profiles were collapsed together.

[0244] Numbat CNV calls were translated into focal, arm-level, or chromosome-level calls as follows: for each segment, its overlap with both the p and q arms of the corresponding chromosome was computed; if the overlap was > 0.85, the event was called an arm-level event for that particular arm, otherwise it was called a focal event; if both arms of a particularchromosome were involved by a single event at the arm level, the event was called a chromosome-level event. If a tumor had at least two trisomies or one tetrasomy, the tumor was considered to be hyperdiploid (HRD). Three tumors with multiple arm-level gains, but which did not meet the above criteria, and had no IgH translocation, were force-called hyperdiploid. Phylogenetic tree construction and subclone-level analyses

[0245] Clone-level CNV profiles were used for phylogenetic tree inference via maximum parsimony analysis, as implemented in the R package phanghorn (v2.11.1) (Schliep KP: phangorn: phylogenetic analysis in R. Bioinformatics 27:592-3, 2011). Out of 171 tumors with CNV data detected, 117 tumors presented a linear evolution pattern, 26 presented a branching evolution pattern, and 28 were filtered out as no subclones remained post-filtering for clones with at least 100 cells. In MGUS / SMM / NDMM tumors with linear evolution pattern and BM tumor cells at baseline (n=112), cells without Numbat data were removed; subclones with <100 tumor cells and subclone ranks represented in <10 tumors were filtered out; following filtering, tumors without subclones were removed from consideration for a total of 105 tumors remaining, including 65 SMM tumors. The top 50 (ordered by log-fold change) upregulated genes in tumor cells from patients with SMM and high S10 score (>3rdquartile) in their GZMB+ CD8+ TEMs were used to score the plasma cell object using Scanpy’s (v1.8.2) score_genes function (Wolf FA et al.: SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15, 2018). Mean log10(S17 + 1) scores (S17 corresponding to the IFN stimulation signature extracted from the baseline BM tumor cell object) were computed per subclone. In SMM tumors, Gaussian linear models were fit on subclone-level IFN stimulation scores using subclone rank and tumor ID as covariates with default parameters. Cytogenetic classification of tumors

[0246] All cells were scored for key MM signatures (CD-1, CD-2, MS, MF, HY) reported by Zhan et al. using Scanpy’s (v1.8.2) score_genes function (Skerget S et al: Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes. Nat Genet 56:1878-1889, 2024; Wolf FA et al.: SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15, 2018; Zhan F et al: The molecular classification of multiple myeloma. Blood 108:2020-8, 2006; Barwick BG, Neri P, Bahlis NJ, et al: Multiple myeloma immunoglobulin lambda translocations portend poor prognosis.Nat Commun 10:1911, 2019). After min-max normalizing each signature across cells, median signature scores were calculated within tumor cells from each compartment in each patient. Median signature scores as well as mean log1p-transformed gene expression values for key MM oncogenes (CCND1, CCND2, CCND3, NSD2, FGFR3, MAF, MAFA, MAFB, ITGB7) within tumor cells per patient and compartment were used as features in a multivariate logistic regression model to detect IgH translocations. These features were selected based on established knowledge about their association with underlying IgH translocations in MM (Skerget S et al: Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes. Nat Genet 56:1878-1889, 2024; Zhan F et al: The molecular classification of multiple myeloma. Blood 108:2020-8, 2006; Barwick BG, Neri P, Bahlis NJ, et al: Multiple myeloma immunoglobulin lambda translocations portend poor prognosis. Nat Commun 10:1911, 2019). Mean gene expression at the tumor level was used as a feature to overcome the drop-out phenomenon, whereby a given gene and particularly a lowly-expressed gene, such as the transcription factors that are upregulated in myeloma IgH translocations, may not be detected in all cells that actually express it. Translocation partner genes were complemented by median signature levels of key cytogenetic signatures to further reduce the reliance on single genes. For translocations of MAF transcription factors, such as t(14;16), t(14;20), and t(8;14), ITGB7 mean expression levels were included in the model, as there are cases where little to no expression is detected for the underlying MAF transcription factor, yet ITGB7 is still highly expressed (Zhan F et al: The molecular classification of multiple myeloma. Blood 108:2020-8, 2006). For tumors with t(4;14) translocation, both NSD2 and FGFR3 levels were included in the model, as well as median levels of the MS signature, as FGFR3 expression is lost in a fraction of t(4;14) tumors (Santra M, Zhan F, Tian E, et al: A subset of multiple myeloma harboring the t(4;14)(p16;q32) translocation lacks FGFR3 expression but maintains an IGH / MMSET fusion transcript. Blood 101:2374-6, 2003; Chesi M, Nardini E, Lim RS, et al: The t(4;14) translocation in myeloma dysregulates both FGFR3 and a novel gene, MMSET, resulting in IgH / MMSET hybrid transcripts. Blood 92:3025-34, 1998).

[0247] Bone marrow tumor cells from tumors classified by FISH (n=105) or research-level WGS (n=85) were used to train the model iteratively following a leave-one-out approach (n=134) (Alberge JB et al: Genomic landscape of multiple myeloma and its precursor conditions. Nat Genet 57:1493-1503, 2025). Hyperdiploid cases were used merely as controls for the model to detect the presence of one of six key IgH translocations: t(11;14), t(4;14),t(14;16), t(14;20), t(6;14), or t(8;14). Coefficients from the 134 leave-one-out models were aggregated by taking the median and generated an aggregate model, whereby the probability exp(^^ +^^ ∗^^ +⋯+^^ ∗ ^^ ) for each class is computed using the equation: ^^ =0,^^ 1,^^ 1 ^^,^^ ^^^^where jcorresponds to a particular class and i corresponds to a particular feature. Subsequently, the tumor was classified as the class with the maximum probability, so long as that probability was larger than 0.5; alternatively, it was classified as “No Trx”. Following manual review, two tumors with exceedingly high CCND2 expression but without any features of t(14;16), t(14;20), or t(8;14) translocation were force-called t(12;14); one tumor was force-called t(6;14) due to high CCND3 expression; one tumor with high ITGB7 expression but no MAF, MAFB, or MAFA expression, was force-called “No Trx”; and three tumors with discordant results in the bone marrow and peripheral blood were reconciliated by following either the bone marrow-based classification (n=2) or the blood-based one (n=1). Tumors classified as having no IgH translocation (“No Trx”) were classified as hyperdiploid (HRD) when Numbat detected at least two extra chromosomal copies in any of the autosomes. Tumors without an IgH translocation according to the classifier or HRD according to Numbat were classified as “Other” (n=16) (Gao T et al: Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nat Biotechnol 41:417-426, 2023). Patients were ultimately classified based on this information, as well as FISH results extracted from medical records, and research-level WGS or whole-exome sequencing (WES) (Bustoros M et al: Genomic Profiling of Smoldering Multiple Myeloma Identifies Patients at a High Risk of Disease Progression. J Clin Oncol 38:2380-2389, 2020; Bustoros M et al: Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes. Nat Commun 13:3449, 2022; Alberge JB et al: Genomic landscape of multiple myeloma and its precursor conditions. Nat Genet 57:1493-1503, 2025). Extraction of gene expression signatures & robustness analysis

[0248] SignatureAnalyzer-GPU (v0.0.8) was used for signature discovery on matrices of up to 50,000 cells at a time and the top 2000 highly variable genes, excluding immunoglobulin, ribosomal, and mitochondrial genes (Boiarsky R et al: Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis. Nat Commun 13:7040, 2022; Kim J et al: Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat Genet 48:600-606, 2016; Sklavenitis-Pistofidis R et al: Single-cell RNA sequencing defines distinct diseasesubtypes and reveals hypo-responsiveness to interferon in asymptomatic Waldenstrom's Macroglobulinemia. Nat Commun 16:1480, 2025). The tool was deployed on the raw count matrix 10 times with 30 runs every time, and a maximum of 10,000 iterations, using a Poisson objective and exponential (L1) prior for the W matrix and half-normal prior (L2) for the H matrix. For each run, the iteration that resulted in a K (i.e., number of signatures) equal to the mode of the K distribution and had the lowest objective was selected for downstream analysis. To assess the reproducibility of signatures across runs, cosine similarity scores on the W matrix (gene x signature) were computed and the connectivity between signatures in a network graph was visualized, where each signature was represented by a vertex. For each signature, one matched signature in every other run was selected with the highest cosine similarity score. If the similarity was at least 70%, an edge was drawn between two signatures. A signature was considered detected in the runs represented in the connected component it belonged to within the network graph. The proportion of runs in which a signature was detected represented its reproducibility. The run with the highest mean reproducibility across signatures was selected for downstream analysis, and signatures with <50% reproducibility were removed from consideration. When two or more runs had similar (i.e., <1% different) mean reproducibility scores, the run with the lower number of signatures was selected. The W matrix of the selected run was then projected onto the rest of the cells in a semi-supervised SignatureAnalyzer run to obtain signature activities across all relevant cells. Gene expression signature markers were nominated by (i) multiplying the W matrix by the sum of signature activity across all cells in the H matrix, (ii) calculating the fraction of each signature’s activity for each gene (matrix F) and (iii) ranking genes based on the product of W (i.e., how strongly each gene contributes to the signature) and F (i.e., how strongly each signature contributes to the gene) (“R” score) (Sklavenitis-Pistofidis R et al: Immune biomarkers of response to immunotherapy in patients with high-risk smoldering myeloma. Cancer Cell 40:1358-1373 e8, 2022; Sklavenitis-Pistofidis R et al: Single-cell RNA sequencing defines distinct disease subtypes and reveals hypo-responsiveness to interferon in asymptomatic Waldenstrom's Macroglobulinemia. Nat Commun 16:1480, 2025). Each gene was then assigned to the signature with the maximum R score, and the top 10 genes for each signature were considered its markers. Signatures representing erythrocytic contamination were removed from downstream analyses. Signatures dominated by single genes were identified based on the R score of their top gene marker being more than two times higher than that of the next gene in order.

[0249] In the overall immune cell run, immune cell signatures were distinguished into those with broad or cell type-specific activity, by (i) identifying cells in which a given signature was active (> 3rdquartile of the signature’s distribution), (ii) computing the proportion of cells in which the given signature was active per cell type, (iii) normalizing those proportions by dividing them by the sum of proportions for the given signature across cell types, and (iv) computing the normalized Shannon index across normalized proportions. Signatures with an index above the 3rd quartile of the distribution were considered broadly active; the remaining signatures were considered cell type-specific. For broadly active signatures, each patient was scored by the median activity of the signature across all immune cells in the object; for cell type-specific signatures, each patient was scored by the median activity of the signature in populations contributing more than 20% to the signature’s activity. In the GZMB+ CD8+ TEM and tumor cell runs, each patient was scored by the median activity of a given signature across all cells of that type. Pseudo-bulk differential expression analysis

[0250] Genes expressed (>0 counts) in <10% of cells were removed from consideration. Pseudo-bulk profiles were generated by adding cell counts per cell type and individual, and individuals with less than 30 cells for the given cell type were excluded from downstream analyses. Pseudo-bulk counts were size-factor normalized and differential expression analysis was performed using DESeq2 (v1.32.0) (Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550, 2014). Analysis of external gene expression profiling datasets

[0251] Normalized data and metadata from two external gene expression profiling datasets, GSE6477 and GSE235356, were downloaded using the getGEO() function of the R package GEOquery (v2.68.0) (Chng WJ et al: Molecular dissection of hyperdiploid multiple myeloma by gene expression profiling. Cancer Res 67:2982-9, 2007; Sun F et al: A gene signature can predict risk of MGUS progressing to multiple myeloma. J Hematol Oncol 16:70, 2023; Davis S, Meltzer PS: GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23:1846-7, 2007). Unannotated probes (GSE6477, n=1,058; GSE235356, n=8,841) were removed from downstream analyses. Normalized expression estimates were averaged across probes mapping to a given gene to obtain gene-level estimates, which were then log2-transformed. Gene-level estimates were subsequently mix-max normalized and each patient sample was scored for a given signature by taking the average across the signature’s gene markers. Groups of patient samples were then compared using Wilcoxon’s rank-sum test and p-values were corrected using the Benjamini-Hochberg approach.

[0252] It should be understood that the details provided herein are given by way of illustration only, not limitation. Other features, objects, and advantages are apparent from the above detailed description, drawings and examples. Various changes and modifications will be apparent to those skilled in the art.

[0253] All patents, patent publications, and non-patent publications referenced herein are indicative of the level of skill of those skilled in the art to which this invention pertains. All these publications are herein incorporated by reference to the same extent as if each individual publication were specifically and individually indicated as being incorporated by reference.

Claims

CLAIMS 1. A method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject, comprising determining expression of one or more gene expression signatures in a sample obtained from the subject and a reference sample, wherein the one or more gene expression signatures are selected from: a. a myeloid cell-enriched gene expression signature comprising G0S2, THBS1, TIMP1, HIF1A, PLAUR, SRGN, CEBPB, UPP1, NINJ1, and VEGFA, wherein a decrease in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; b. a myeloid cell- enriched gene expression signature comprising IL1B, CXCL8, ATP2B1-AS1, CCL3, SAT1, IER3, CXCL2, KLF4, CCL3L1, and ATF3, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; c. a dendritic cell- and B cell-enriched gene expression signature comprising JCHAIN, PTGDS, LILRA4, ITM2C, PLD4, RASD1, IRF8, TCF4, PPP1R14B, and CCDC50, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; d. an immune cell-associated gene expression signature comprising ISG15, IFI6, IFI44L, IFIT2, XAF1, IFIT3, EPSTI1, STAT1, IFI44, and OAS3, wherein an increase in the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; e. a lymphocyte-enriched gene expression signature comprising TNFAIP3, CXCR4, PMAIP1, TSPYL2, NR4A2, FAM177A1, HSPA5, PDE4D, PER1, and RNF125, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM;f. a myeloid cell-enriched gene expression signature comprising FOS, FOSB, CITED2, ID1, ARHGEF40, and USP2, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; g. a granzyme B (GZMB)+ CD8+ effector memory T cell (TEM)-specific gene expression signature comprising ZBP1, TPT1, TNFAIP3, TGFB1, SYTL3, SLC7A5, SLA2, RBM38, PPP1R16B, PIK3R1, PDE4D, ODC1, HSPA5, HMGB2, H3F3B, FTH1, FAM177A1, EIF1, and BTG1, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; h. a T cell-specific gene expression signature comprising CISH, TNFRSF1A, CX3CR1, SLAMF6, LPAR6, PIM1, C14orf119, PRDX3, CALHM2, TAGAP, SIT1, LINC01871, KLRB1, SNHG5, S100A11, and EOMES, and one or more of GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, and GIMAP7, wherein an increase of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM; and i. a tumor cell-specific gene expression signature comprising SERPINB9, MAP3K8, PELI1, CSRNP1, STX11, UBALD2, PER1, ESR1, GADD45A, GADD45B, PNP, KLF9, SIK1B, FAM49A, CYTOR, AREG, SNX9, PMAIP1, MYADM, and C11orf96, wherein a decrease of the expression of the gene expression signature in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

2. The method of claim 1, further comprising determining clonal expansion of T cells in the sample obtained from the subject and the reference sample, wherein an increase of the clonal expansion of T cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

3. The method of any one of the preceding claims, further comprising determining clonal expansion of GZMB+ CD8+ TEMs relative to other T cell populations, wherein anincrease in the clonal expansion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

4. The method of any one of the preceding claims, further comprising determining the proportions of GZMB+ CD8+ TEMs in the sample and the reference sample, wherein an increase in the proportion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

5. The method of any one of the preceding claims, further comprising determining the proportions of cytokine-expressing myeloid cells in the sample and the reference sample, wherein a decrease in the proportion of cytokine-expressing myeloid cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

6. The method of any one of the preceding claims, wherein: i. the lymphocytes comprise T cells including CD4+ T cells and CD8+ T cells, natural killer (NK) cells, B cells; and / or ii. myeloid cells comprise CD14+ monocytes, CD16+ monocytes macrophages, and dendritic cells including plasmacytoid DCs (pDCs), AS-DCs, conventional type 1 DCs (cDC1s), conventional type 2 dendritic cells (cDC2s), and Monocyte-Derived Dendritic Cells (moDCs).

7. The method of any one of the preceding claims, wherein: i. the immune cells comprise T cells including CD4+ T cells and CD8+ T cells, natural killer (NK) cells, B cells, monocytes and dendritic cells; ii. T cells comprise CD4+ naïve T cells, central memory T cells (including T regulatory cells (T regs)), and TCF7+ CD8+ memory effector T cells (TEMs).

8. The method of any one of the preceding claims, comprising gene expression profiling.

9. The method of claim 8, wherein gene expression profiling comprises one or more of quantitative PCR (qPCR), bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics.

10. A method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject comprising: a. determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in a sample obtained from the subject and a reference sample; b. determining the proportion of GZMB+ CD8+ TEMs that are LAT1+ TIMAP+ TGFβ+ in the sample obtained from the subject and the reference sample; and c. comparing the proportions determined in steps a. and b. in the sample and the reference sample; wherein an increase in the proportion of GZMB+ CD8+ TEMs and a decrease in the proportion of GZMB+ CD8+ TEMs that are LAT1+ TIMAP+ TGFβ+ in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

11. The method of claim 10, wherein the proportions in steps a. and b. are determined using flow cytometry, Cytometry by Time of Flight (CyTOF), immunohistochemistry, or spatial proteomics 12. The method of any one of claims 1-11, wherein the reference sample was obtained from the subject at an earlier timepoint.

13. The method of any one of claims 1-11, wherein the reference sample is a representative sample from one or more different subjects with MGUS or SMM who have or have not progressed to MM or patients with MM.

14. The method of any one of claims 1-11, wherein the reference sample is a representative sample from one or more healthy donors.

15. The method of any one of the preceding claims, wherein the sample obtained from the subject and the reference sample is a bone marrow sample or a blood sample.

16. A method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject comprising: a. determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in a sample obtained from the subject and a reference sample; and b. comparing the proportion of GZMB+ CD8+ TEMs in the sample and the reference sample wherein an increase in the proportion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

17. The method of claim 16, wherein the method further comprises determining the proportion of cytokine-expressing myeloid cells in the sample and the reference sample; and comparing the proportion of cytokine-expressing myeloid cells in the sample and the reference sample; wherein a decrease in the proportion of cytokine-expressing myeloid cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

18. A method for monitoring progression from monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma (SMM) to multiple myeloma (MM) in a subject comprising: a. determining the proportion of cytokine-expressing myeloid cells in a sample obtained from the subject and a reference sample; and b. comparing the proportion of cytokine-expressing myeloid cells in the sample and the reference sample wherein a decrease in the proportion of cytokine-expressing myeloid cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

19. The method of claim 18, wherein the method further comprises determining the proportion of granzyme B (GZMB)+ CD8+ effector memory T cells (TEMs) in thesample and the reference sample, and comparing the proportion of GZMB+ CD8+ TEMs in the sample and the reference sample; wherein an increase in the proportion of GZMB+ CD8+ TEMs in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

20. The method of any one of claims 16-19, wherein the cytokine-expressing myeloid cells express one or more cytokines selected from IL1B, CXCL8, CCL3, CCL3L1, and CCL4.

21. The method of any one of claims 17-20, wherein the cytokine-expressing myeloid cells are CD14+ monocytes or dendritic cells.

22. The method of any one of claims 17-21, wherein the cytokine-expressing myeloid cells further express one or more of THBS1, PLAUR, TIMP1, VEGFA, and HIF1A.

23. The method of any one of claims 16, 17, or 19-22, wherein the GZMB+ CD8+ TEMs express an interferon (IFN) signature, e.g., one or more of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, and XAF1.

24. The method of any one of claims 16, 17, or 19-23, wherein the method comprises determining in lymphoid cells the expression of: a. CD8A and / or CD8B; b. GZMK and / or GZMB; and optionally c. CCL5; and / or d. KLRG1.

25. The method of claim 24, wherein the method further comprises determining the proportion of lymphoid cells that expresses an interferon (IFN) signature, e.g., one or more of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, and XAF1.

26. The method of claim 16, 17, or 19-25, wherein the method further comprises determining clonal expansion of GZMB+ CD8+ TEMs relative to other T cell populations, e.g., using single-cell T cell receptor (TCR) sequencing.

27. A method for monitoring progression from MGUS or SMM to MM in a subject comprising: a. determining T cell repertoire diversity in a sample obtained from the subject and a reference sample; and b. comparing the T cell repertoire diversity in the sample and the reference sample wherein a decrease in the T cell repertoire diversity in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

28. A method for monitoring progression from MGUS or SMM to MM in a subject comprising: a. determining clonal expansion of T cells in a sample obtained from the subject and a reference sample; and b. comparing the clonal expansion of T cells in the sample and the reference sample wherein an increase in the clonal expansion of T cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

29. The method of claim 27 or claim 28, wherein the T cells comprise or consist of GZMB+ CD8+ TEMs.

30. A method for monitoring progression from MGUS or SMM to MM in a subject, comprising determining expression of an interferon (IFN) signature in CD138+ plasma cells in a sample obtained from the subject and a reference sample, wherein an increase of the expression of the IFN signature in the CD138+ plasma cells in the sample relative to the reference sample indicates that the subject is progressing or has progressed to MM.

31. A method for predicting the response of a subject’s MM to therapy, comprising determining expression of an interferon (IFN) signature in CD138+ plasma cells in a sample obtained from the subject and a reference sample, wherein an increase of the expression of the IFN signature in the CD138+ plasma cells in the sample relative to the reference sample indicates that the subject’s MM may not respond to therapy.

32. The method of claim 30 or 31, further comprising determining clonal expansion of GZMB+ CD8+ TEMs relative to other T cell populations e.g., using single-cell TCR sequencing.

33. The method of any one of claims 30-32, further comprising determining the proportions of GZMB+ CD8+ TEMs and cytokine-expressing myeloid cells in the sample and the reference sample.

34. The method of 33, wherein the cytokine-expressing myeloid cells express one or more cytokines selected from IL1B, CXCL8, CCL3, CCL3L1, and CCL4.

35. The method of claim 33 or 34, wherein the cytokine-expressing myeloid cells are CD14+ monocytes or dendritic cells.

36. The method of any one of claims 33-35, wherein the cytokine-expressing myeloid cells further express one or more of THBS1, PLAUR, TIMP1, VEGFA, and HIF1A.

37. The method of any one of claims 33-36, wherein the GZMB+ CD8+ TEMs express an interferon (IFN) signature, e.g., one or more of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, and XAF1.

38. The method of any one of claims 33-37, wherein the method comprises determining in lymphoid cells the expression of: a. CD8A and / or CD8B; b. GZMK and / or GZMB; and optionally c. CCL5; and / or d. KLRG1.

39. The method of any one of claims 31-38, wherein the IFN signature comprises at least one or more of STAT1, IFI6, IFI44L, ISG15, IFIT2, IFIT3, and XAF1.

40. A method for predicting the response of a subject’s MM to therapy, comprising determining clonal expansion of T cells in a sample obtained from the subject and areference sample, wherein an increase of the clonal expansion of T cells in the sample relative to the reference sample indicates that the subject’s MM may not respond to therapy.

41. The method of claim 40, wherein the T cells comprise or consist of GZMB+ CD8+ TEMs.

42. The method of any one of claims 16-41, wherein the reference sample was obtained from the subject at an earlier timepoint.

43. The method of any one of claims 16-41, wherein the reference sample is a representative sample from one or more different subjects with MGUS or SMM who have or have not progressed to MM or patients with MM.

44. The method of any one of claims 16-41, wherein the reference sample is a representative sample from one or more healthy donors.

45. The method of any one of claims 16-44, wherein the sample obtained from the subject and the reference sample is a bone marrow sample or a blood sample.

46. The method of any one of the preceding claims, wherein the increase is selected from: a. higher than the median of the distribution or higher than the 75th percentile of the distribution of the reference sample; b. 1 standard deviation or more above the mean, 1.5 standard deviations or more above the mean, or 2 standard deviations or more above the mean of the reference sample; or c. about 10% or more, about 20% or more, or about 50% or more relative to the reference sample, wherein the reference sample was obtained from the subject at an earlier timepoint.

47. The method of any one of the preceding claims, wherein the decrease is selected from: a. lower than the median of the distribution or lower than the 25th percentile of the distribution;b. 1 standard deviation or less below the mean, 1.5 standard deviations or less below the mean, or 2 standard deviations or less below the mean; or c. about 10% or less, about 20% or less, or about 50% or less relative to the reference sample, wherein the reference sample was obtained from the subject at an earlier timepoint.

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