Methods related to waldenström macroglobulinemia and precursors thereof

Abstract

Disclosed herein are methods for determining whether a subject is suffering from Waldenström macroglobulinemia (WM) or a precursor condition thereof, or multiple myeloma (MM) or a precursor condition thereof. These methods comprise determining, in a sample obtained from the subject, data indicative of the proportions of two or more immune cell populations. Methods of monitoring a subject with WM, or a precursor condition thereof, are also disclosed. These methods comprise determining in tumor cells obtained from a sample obtained from the subject and a reference sample expression of two or more gene expression signatures which indicate whether the subject is at risk of disease progression.

Description

METHODS RELATED TO WALDENSTRÖM MACROGLOBULINEMIA AND PRECURSORS THEREOF CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. Provisional Application No.63 / 708,138 filed October 16, 2024, the entire contents of which 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 Institutes of Health. The government has certain rights to the invention. FIELD

[0003] There are provided herein methods for determining whether a subject is suffering from Waldenström macroglobulinemia (WM) or a precursor condition thereof, or multiple myeloma (MM) or a precursor condition thereof. These methods comprise determining, in a sample obtained from the subject, data indicative of the proportions of two or more immune cell populations. Also provided herein are methods of monitoring a subject with WM, or a precursor condition thereof. Such methods comprise determining in tumor cells obtained from a sample obtained from the subject and a reference sample expression of two or more gene expression signatures which indicate whether the subject is at risk of disease progression. BACKGROUND

[0004] Waldenstrom’s Macroglobulinemia (WM) is a rare non-Hodgkin, IgM-secreting lymphoplasmacytic lymphoma (LPL) of the bone marrow (BM) with an incidence of 3.8 per million persons per year in the United States. It is consistently preceded by two precursor conditions, immunoglobulin M monoclonal gammopathy of undetermined significance (IgM MGUS) and smoldering WM (SWM). According to the definition of the 2ndInternational Workshop on WM, asymptomatic patients with a monoclonal IgM protein in their peripheral blood (PB) serum and no morphological evidence of BM infiltration by lymphoma are diagnosed with IgM MGUS, while asymptomatic patients with a monoclonal IgM proteinand any amount of BM infiltration are diagnosed with SWM. Together, IgM MGUS and SWM are referred to as asymptomatic WM (AWM).

[0005] Currently, patients with AWM are not treated until they progress to overt, symptomatic WM due to concerns over the short- and long-term complications of treatment. To improve follow-up recommendations and direct investigations of risk-adapted treatment approaches, a better understanding is needed which patients with AWM are at the greatest risk of disease progression.

[0006] Studies utilizing gene expression profiling (GEP) technology to characterize the WM transcriptome along stages of disease progression have been inconclusive in terms of identifying key gene expression programs associated with progression (Trojani et al. Cancers (Basel) 13(8), 1837, 2021 doi: https: / / doi.org / 10.3390%2Fcancers13081837 and Paiva et al. 2015 Blood 125:2370-80). Even less is known about the role of the BM immune microenvironment in regulating disease progression to overt WM. Changes in the composition and function of T cells, NK cells and monocytes have been reported, with increased cytotoxic T and NK cells, regulatory T cells (Tregs), and non-canonical monocytes, however, it is unclear how these may impact disease progression from early stages to overt WM (Cholujova et al. Int J Cancer 152:1947-1963; Kaushal et al. Blood Cancer Discov 2:600-615; Sacco et al. Blood 141:2615-2628).

[0007] In a retrospective study conducted on 439 patients with AWM, BM infiltration, serum levels of IgM, β2-microglobulin, and albumin were identified as independent predictors for progression (Bustoros et al. 2019 J Clin Oncol 37:1403-1411). Using these variables, a prognostic model to stratify patients into low, intermediate, and high-risk groups was developed, which was validated in multiple external datasets (Bustoros et al.; and Merli et al.2020 Leuk Lymphoma 61:987-989). However, this as well as other prediction algorithms leverage clinical variables only, highlighting the need for studies that dissect the biology of disease progression and identify tumor-intrinsic and extrinsic factors that can inform patient prognostication and therapy selection. SUMMARY

[0008] By comparing changes in immune cell proportions between patients with asymptomatic Waldenstrom’s Macroglobulinemia (AWM) and patients with smolderingmultiple myeloma (SMM), another bone marrow (BM) malignancy arising from antibody- producing cells, it was possible to identify changes in the proportions of immune cell populations specific to Waldenstrom’s Macroglobulinemia (WM) and precursor conditions thereof and multiple myeloma (MM) and precursor conditions thereof. It was found that assessing the compositional differences of these immune cell populations can be used to diagnose the presence of these BM malignancies and also differentiate between (i) WM and precursor conditions thereof and (ii) MM and precursor conditions thereof.

[0009] Accordingly, in one aspect, a method for determining whether a subject is suffering from Waldenström macroglobulinemia (WM) or a precursor condition thereof, or multiple myeloma (MM) or a precursor condition thereof is provided, wherein the method comprises (a) determining, in a sample obtained from the subject, data indicative of the proportions of two or more immune cell populations selected from the group consisting of (i) CD4+ and / or CD8+ central memory T cells (TMCs), (ii) naïve CD4+ T cells, (iii) natural killer (NK) cells, (iv) CD4+ and / or CD8+ effector memory T cells (TEMs), (v) CD14+ and / or CD16+ monocytes, and / or myeloid precursors thereof, and (vi) dendritic cells and / or myeloid precursors thereof; (b) obtaining a statistical model describing corresponding proportions of two or more immune cell populations determined for samples collected from individuals with WM and precursor conditions thereof, MM and precursor conditions thereof, and healthy individuals; and (c) calculating, based on the statistical data, a score, wherein the score indicates the likelihood of the subject having WM or a precursor condition thereof, or MM or a precursor condition thereof.

[0010] In a related aspect, a method for determining whether a subject is suffering from Waldenström macroglobulinemia (WM) or a precursor condition thereof, or multiple myeloma (MM) or a precursor condition thereof is provided, wherein the method comprises (a) receiving data indicative of the proportions of two or more immune cell populations in a sample obtained from the subject, wherein the two or more of immune cell populations are selected from the group consisting of (i) CD4+ and / or CD8+ central memory T cells (TMCs), (ii) naïve CD4+ T cells, (iii) natural killer (NK) cells, (iv) CD4+ and / or CD8+ effector memory T cells (TEMs), (v) CD14+ and / or CD16+ monocytes, and / or myeloid precursors thereof, and (iv) dendritic cells, and / or myeloid precursors thereof; (b) obtaining a statistical model based on corresponding proportions of the two or more immune cell populationsdetermined for samples collected from individuals with WM and precursor conditions thereof, MM and precursor conditions thereof, and healthy individuals; (c) calculating, based on the statistical model and the data, a score indicative of the likelihood of the subject having WM or a precursor condition thereof, or MM or a precursor condition thereof.

[0011] Tumor-intrinsic mechanisms of progression from AWM to WM were also investigated and changes in the BM immune microenvironment with disease progression were comprehensively characterized by performing single-cell RNA sequencing (scRNAseq). These investigations resulted in the discovery of gene expression signatures that can be used to monitor (the risk of) disease progression in a subject having Waldenström macroglobulinemia (WM), or a precursor condition thereof.

[0012] Accordingly, in another aspect, a method for monitoring a subject with Waldenström macroglobulinemia (WM), or a precursor condition thereof, is provided, wherein the method comprises determining in tumor cells obtained from a sample obtained from the subject and a reference sample expression of two or more gene expression signatures selected from the group consisting of (a) a first gene expression signature comprising ACTG1, S100A4, TMSB4X, TMSB10, and GAPDH; (b) a second gene expression signature comprising CXCR4, STK17A, YBX3, FCER2, and LYST; (c) a third gene expression signature comprising AHNAK, ZNF596, TTN, FCRK3, and SELL; (d) a fourth gene expression signature comprising DUSP22, CD9, VPREB3, GSTP1, and H1FX; and (e) a fifth gene expression signature comprising LTB, DSP, NFKB2, BCL7A, and JUP; and wherein: (i) decreased activity of the first gene expression signature, and / or (ii) increased activity of the second, third, fourth, and / or fifth gene expression signature(s), in the tumor cells of the sample relative to tumor cells of the reference sample indicates that the subject is at risk of disease progression.

[0013] In a related aspect, a method for monitoring a subject with Waldenström macroglobulinemia (WM), or a precursor condition thereof, is provided that comprises determining in tumor cells obtained from a sample of the subject and healthy B cells of a reference sample expression of two or more gene expression signatures selected from the group consisting of (a) a first gene expression signature comprising ACTG1, S100A4, TMSB4X, TMSB10, and GAPDH; (b) a second gene expression signature comprising CXCR4, STK17A, YBX3, FCER2, and LYST; (c) a third gene expression signature comprising AHNAK,ZNF596, TTN, FCRK3, and SELL; (d) a fourth gene expression signature comprising DUSP22, CD9, VPREB3, GSTP1, and H1FX; and (e) a fifth gene expression signature comprising LTB, DSP, NFKB2, BCL7A, and JUP; and wherein: (i) decreased activity of the first gene expression signature in the tumor cells of the sample relative to healthy B cells of the reference sample indicates that the subject is suffering from IgM MGUS, and / or (ii) increased activity of the second, third, fourth, and / or fifth gene expression signature(s) in the tumor cells of the sample relative to healthy B cells of the reference sample indicates that the subject is at risk of disease progression.

[0014] The second gene expression signature comprises FCER2, which encodes the cell surface protein CD23, and the fourth expression signature comprises CD9, which encodes the cell surface protein CD9, indicating that CD23 and CD9 may be useful surface markers for monitoring WM, or precursor conditions thereof. Therefore, in a further aspect, a method for monitoring a subject’s Waldenström macroglobulinemia (WM), or precursor condition thereof, is provided that comprises determining in tumor cells in a sample obtained from the subject and a reference sample surface expression of CD9 and / or CD23.

[0015] 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

[0016] Aspects will be described, by way of example, with reference to the following drawings.

[0017] FIG. 1A. Volcano plot of proportional changes of cell types in the bone marrow (BM) of patients with asymptomatic Waldenstrom’s Macroglobulinemia (AWM) (n=25, see right side) compared to healthy donors (HD) (n=18, see left side). Patients with at least 100 immune cells were included. P-values were computed with two-sided Wilcoxon’s rank-sum test and corrected using the Benjamini-Hochberg approach, resulting in a q value. Cell types with q < 0.1 were marked with stars and labeled.

[0018] FIG. 1B. Volcano plot of proportional changes of cell types in the BM of patients with IgM monoclonal gammopathy of unknown significance (IgM MGUS) (n=6, see right side) compared to HD (n=18, see left side). Patients with at least 100 immune cells were included. P-values were computed with two-sided Wilcoxon’s rank-sum test and corrected using the Benjamini-Hochberg approach, resulting in a q value. Cell types with q < 0.1 were marked with stars and labeled.

[0019] FIG.1C. Heatmap of cell type proportions in patients with IgM MGUS (n=6), SWM (n=19), WM (n=3), and HD (n=18) (x-axis, shown in top bar and labelled according to the right side key). Cell types that changed significantly in proportion between patients with AWM and HD, excluding progenitor cells, are shown on the y-axis. The long bar on the right visualizes the log2 fold-change, according to the key on the right labelled “LFC”. The top bar visualizes whether the patient is a healthy donor (HD), or has IgM MGUS, SWM, or WM, as labelled by the key bearing those indications on the right.

[0020] FIG.1D. Box plots, violin plots, and scatter plots visualizing the proportion of Tregs in patients with IgM MGUS (n=6) compared to patients with SWM (n=19). Violin outline width represents density. Each circle represents a patient. Box: 1st quartile, median, 3rd quartile; whiskers: + / - 1.5*interquartile range (IQR). The p-value was computed with two- sided T-test.

[0021] FIG. 2A. Box plots, violin plots, and scatter plots comparing the proportion of activated and IFN-stimulated T and NK cells in the BM microenvironment of HD (n=18), patients with SMM (n=24), IgM MGUS (n=6), and SWM (n=19). Violin outline width represents density. Each circle represents a patient. Box: 1st quartile, median, 3rd quartile; whiskers: + / - 1.5*IQR. P-values were computed with two-sided Wilcoxon’s rank-sum test and corrected with the Benjamini-Hochberg approach, resulting q values are shown in the figure.

[0022] FIG. 2B. Volcano plot of proportion changes in the BM of patients with AWM (n=25, right) compared to patients with SMM (n=24, left). Patients with at least 100 immune cells were included. P-values were computed with two-sided Wilcoxon’s rank-sum test and corrected using the Benjamini-Hochberg approach, resulting in a q value. Cell types with q<0.1 were marked with stars and labeled.

[0023] FIG. 2C. Volcano plot of proportional changes of cell types in the BM of patients with smoldering multiple myeloma (SMM) (n=24, right) compared to HD (n=18, left). Patients with at least 100 immune cells were included. P-values were computed with two- sided Wilcoxon’s rank-sum test and corrected using the Benjamini-Hochberg approach, resulting in a q value. Cell types with q < 0.1 were marked with stars and labeled.

[0024] FIG. 2D. Scatter plots of genes differentially expressed in patients with AWM compared to HD (x-axis) and in patients with AWM compared to patients with SMM (y-axis) in myeloid cells. Genes whose log2fold-change between patients with AWM and HD is higher than that between patients with AWM and patients with SMM are shown in the top- right quadrant; genes whose log2fold-change is lower are shown in the top-left quadrant. Genes are colored in a darker hue as the difference between the two axes (i.e., the distance from the diagonal line) increases. Genes below the dashed diagonal line represent genes with a higher upregulation in patients with AWM compared to HD than in patients with AWM compared to patients with SMM. Genes above the dashed diagonal line represent genes with a higher upregulation in patients with AWM compared to patients with SMM than in patients with AWM compared to HD.

[0025] FIG. 2E. Scatter plots of genes differentially expressed in patients with AWM compared to HD (x-axis) and in patients with AWM compared to patients with SMM (y-axis) in T cells. Genes whose log2 fold-change between patients with AWM and HD is higher than that between patients with AWM and patients with SMM are shown in the top-right quadrant; genes whose log2fold-change is lower are shown in the top-left quadrant. Genes are colored in a darker hue as the difference between the two axes (i.e., the distance from the diagonal line) increases. Genes below the dashed diagonal line represent genes with a higher upregulation in patients with AWM compared to HD than in patients with AWM compared to patients with SMM. Genes above the dashed diagonal line represent genes with a higher upregulation in patients with AWM compared to patients with SMM than in patients with AWM compared to HD.

[0026] FIG. 3A. Principal component (PC) embedding of BM samples from patients with AWM (n=25), patients with SMM (n=24), and HD (n=18). Patients with at least 100 immune cells were included in this analysis.

[0027] FIG. 3B. Bar plot showing the weight of each cell type (18 features) on predicting a diagnosis of AWM (left) or SMM (right). In this graph, negative numbers indicate that loss or reduction in that cell type is indicative of the disease type and positive numbers indicate that gain or increase in that cell type is indicative of the disease type. The further removed the number from 0, the greater the “importance” in predicting disease type.

[0028] FIG. 3C. Confusion matrix visualizing the accuracy of a Support Vector Machine (SVM) classifier trained using 5-fold cross-validation on BM samples from patients with AWM (n=25), patients with SMM (n=24), and HDs (n=17). The classifier’s performance in the held-out subsets (n=66) is shown.

[0029] FIG. 3D. Box plots, violin plots, and scatter plots of BM infiltration on the BM biopsy (%, y-axis) of patients with AWM who were either correctly classified by the SVM classifier (left, n=21) or misclassified (right, n=3). Violin outline width represents density. Each circle represents a patient. Box: 1st quartile, median, 3rd quartile; whiskers: + / - 1.5*IQR. The p-value was computed with two-sided Wilcoxon’s rank-sum test.

[0030] FIG. 4. Heatmap of marker gene expression (mean Z-score) for gene expression (GEX) signatures active in WM tumor cells. The dashed box outlines the relevant genes for each GEX signature.

[0031] FIG. 5A. Heatmap of scaled expression (Z-score for each gene per patient sample) for 30 GEX signature marker genes in an external gene expression profiling (GEP) dataset of patients with IgM MGUS (n=13) and WM (n=36) (GSE171739, as provided by Trojani et al. supra). Vertical dashed lines delineate distinct signature markers; horizontal dashed lines delineate distinct subtypes of disease, as detected via hierarchical clustering. On the left, bars visualize (from left to right): the patients’ subtype (ACTG / S100A4, DUSP22 / CD9, CXCR4 / AHNAK / BCL7A, or CXCR4 / AHNAK, as labelled in the “Subtype” key on the right) clusters (1, 2, 3, 4, 5, or 6, as labelled in the “Cluster” key on the right), and disease stage (MGUS [abbreviated from “IgM MGUS”] or WM, as labelled in the “Stage” key on the right).

[0032] FIG. 5B. Box plots and scatter plots visualizing the activity of each signature between patients with IgM MGUS (n=13) and WM (n=36) in the GEP dataset. Box: 1st quartile, median, 3rd quartile; whiskers: + / - 1.5*IQR. Each circle represents a patient.Pvalueswere computed with two-sided Wilcoxon’s rank-sum test and corrected with Benjamini- Hochberg, resulting q values are shown in the figure.

[0033] FIG. 5C. Box plots visualizing the log10-scaled activity of GEX-7 (y-axis) in each WM tumor clone (x-axis). Tumors were grouped into two categories: CXCR4-mutant (n=6), CXCR4-WT (n=13). One patient had unknown CXCR4 status and was excluded from this analysis. Secondary WM tumors (n=4) were considered of unknown status and were excluded from this analysis, too. Tumors were sorted by median GEX-7 activity in descending order. Box: 1st quartile, median, 3rd quartile; whiskers: + / - 1.5*IQR. Each circle represents a single cell.

[0034] FIG. 5D. Bar plot visualizing the fraction of tumor cells assigned to GEX-7 signature in an AWM patient with serial samples at an AWM timepoint (T1) and a progression timepoint 5 years later and following 5 cycles of treatment (T2). The p-value was computed with a two-sided Fisher’s exact test. Error bars represent the 95% confidence interval (CI).

[0035] FIG. 6A. Bar plot of genes (n=101, x-axis) that are upregulated in more than 30% of WM tumors compared to memory B cells. The y axis corresponds to the number of tumors showing significant upregulation (two-sided Wilcoxon, q < 0.05 & log2fold-change > 0.5) of the given gene.

[0036] FIG. 6B. Heatmap of the subset of Figure 4E genes (y-axis) that were significantly up- or downregulated (two-sided Wilcoxon, q < 0.05) between patients with IgM MGUS (n=13) and WM (n=36) (x-axis) in the external gene expression profiling (GEP) dataset (GSE171739, as provided by Trojani et al. supra). Top bars visualize (from top to bottom): the patient’s disease stage (IgM MGUS or WM, as labelled in the “Stage” key on the right) and class (ACTG / S100A4, CXCR4 / AHNAK, CXCR4 / AHNAK / BCL7A, or DUSP22 / CD9, as labelled in the “Class” key on the right). Genes were sorted based on the difference of mean expression in patients with IgM MGUS and WM. “Expression” is the Z-score for each gene per patient sample.

[0037] FIG. 7A. Box plots, violin plots, and scatter plots comparing signature activity for signatures GEX-2, GEX-5, and GEX-7 between patients with (n=20) and without CXCR4 mutations (n=32) in an external bulk RNA-seq dataset. Box: 1st quartile, median, 3rdquartile; whiskers: + / - 1.5*IQR. Violin outline width represents density. Each circle represents a patient. P-values were computed with two-sided Wilcoxon’s rank-sum test and corrected with Benjamini-Hochberg, resulting q values are shown in the figure.

[0038] FIG. 7B. Box plots, violin plots, and scatter plots comparing signature activity for signatures GEX-2, GEX-5, and GEX-7 between patients with (n=27) and without lymphadenopathy (LAD) (n=25) in an external bulk RNA-seq dataset. Box: 1st quartile, median, 3rd quartile; whiskers: + / - 1.5*IQR. Violin outline width represents density. Each circle represents a patient. P-values were computed with two-sided Wilcoxon’s rank-sum test and corrected with Benjamini-Hochberg, resulting q values are shown in the figure.

[0039] FIG.7C. Scatter plot of signature activity for signatures GEX-2, GEX-5, and GEX- 7 (y-axis) and BM infiltration percentage (x-axis) in an external bulk RNA-seq dataset (Hunter et al. Blood. 2016 Aug 11;128(6):827-38). Each circle represents a patient. P-values were computed using Pearson’s correlation test and corrected with Benjamini-Hochberg, resulting q values are shown in the figure.

[0040] FIG. 8. Schematic illustration of an exemplary computer system configured to perform statistical analysis as described herein. DETAILED DESCRIPTION

[0041] 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.

[0042] Unless otherwise required by context, singular terms shall include pluralities, and plural terms shall include the singular. Thus, 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 T cell” is understood to represent one or more T cell(s) or a population of T cells. As such, the terms “a” (or “an”), “one or more”, and “at least one” can be used interchangeably herein.

[0043] “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).

[0044] 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.

[0045] 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.

[0046] Generally, nomenclature used in connection with, and 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. Methods for differential diagnosis of WM and MM and precursor conditions thereof

[0047] A computer-implemented method (e.g., computer-implemented method) for determining whether a subject is suffering from Waldenström macroglobulinemia (WM) or a precursor condition thereof, or multiple myeloma (MM) or a precursor condition thereof is provided, wherein the method comprises (a) determining, in a sample obtained from the subject, data indicative of the proportions of two or more (e.g., three, four, five or more) immune cell populations selected from the group consisting of (i) CD4+ and / or CD8+ centralmemory T cells (TMCs), (ii) naïve CD4+ T cells, (iii) natural killer (NK) cells, (iv) CD4+ and / or CD8+ effector memory T cells (TEMs), (v) CD14+ and / or CD16+ monocytes, and / or myeloid precursors thereof, and (vi) dendritic cells and / or myeloid precursors thereof; (b) obtaining one or more statistical models describing corresponding proportions of two or more immune cell populations determined for samples collected from individuals with WM and precursor conditions thereof, MM and precursor conditions thereof, and healthy individuals; and (c) calculating, based on the one or more statistical models and the data, a score which indicates the likelihood of the subject having WM or a precursor condition thereof, or MM or a precursor condition thereof. Suitable statistical models may include, e.g., Neural Networks, Support Vector Machines (SVMs), or logistic regression models. For example, the score may be derived from an SVM classifier, a neural network classifier, a logistic regression classifier, or may be a Z-score.

[0048] In a related aspect, a method (e.g., computer-implemented method) for determining whether a subject is suffering from Waldenström macroglobulinemia (WM) or a precursor condition thereof, or multiple myeloma (MM) or a precursor condition thereof is provided, wherein the method comprises (a) receiving data indicative of the proportions of two or more (e.g., three, four, five or more) immune cell populations in a sample obtained from the subject, wherein the two or more of immune cell populations are selected from the group consisting of (i) CD4+ and / or CD8+ central memory T cells (TMCs), (ii) naïve CD4+ T cells, (iii) natural killer (NK) cells, (iv) CD4+ and / or CD8+ effector memory T cells (TEMs), (v) CD14+ and / or CD16+ monocytes, and / or myeloid precursors thereof, and (iv) dendritic cells, and / or myeloid precursors thereof; (b) obtaining one or more statistical models describing corresponding proportions of the two or more immune cell populations determined for samples collected from individuals with WM and precursor conditions thereof, MM and precursor conditions thereof, and healthy individuals; (c) calculating, based on the one or more statistical models and the data, a score indicative of the likelihood of the subject having WM or a precursor condition thereof, or MM or a precursor condition thereof. Suitable statistical models may include, e.g., Neural Networks, Support Vector Machines (SVMs), or logistic regression models. For example, the score may be derived from an SVM classifier, a neural network classifier, a logistic regression classifier, or may be a Z-score.

[0049] According to the definition of the 2ndInternational Workshop on WM, asymptomatic patients with a monoclonal IgM protein in their peripheral blood (PB) serum and no morphological evidence of BM infiltration by lymphoma are diagnosed with IgM Monoclonal Gammopathy of Undetermined Significance (IgM MGUS), while asymptomatic patients with a monoclonal IgM protein and any amount of BM infiltration are diagnosed with smoldering WM (SWM). IgM MGUS and SWM may be referred to as asymptomatic WM (AWM). Accordingly, the precursor condition of WM can be IgM MGUS or SWM. Traditionally, precursor conditions of WM such as smoldering WM and IgM MGUS are defined as shown in Table 1. Table 1. Classification of Waldenström’s macroglobulinemia (WM) IgM monoclonal Bone marrow Symptoms Symptoms due to protein infiltration attributable to tumor infiltration IgM Symptomatic + + + + WM Smoldering + + − − WM IgM-related + − + − disorders IgM MGUS + − − −

[0050] The precursor condition of MM can be Monoclonal Gammopathy of Undetermined Significance (MGUS) or smoldering MM (SMM). In the context of MM, MGUS is typically IgG MGUS or IgA MGUS.

[0051] For example, the methods described in this section are capable of differentiating between (i) IgM MGUS / smoldering WM, and (ii) MGUS / SMM, which can be challenging and typically requires a battery of diagnostic test using conventional diagnostic methods.

[0052] The data indicative of the proportions of the two or more immune cell populations can be determined using single-cell RNA sequencing, flow cytometry, Cytometry by Time of Flight (CyTOF), immunohistochemistry, spatial transcriptomics, or spatial proteomics.

[0053] NK cells may comprise activated NK cells and CD56dim NK cells, and / or S100B+ CD56dim NK cells. CD14+ monocytes may comprise IL1β+ CD14+ monocytes (or IL1β+ CD14+ myeloid precursors thereof), RETN+ CD14+ monocytes (or RETN+ CD14+ myeloidprecursor thereof), S100A+ CD14+ monocytes (or S100A+ CD14+ myeloid precursor thereof), and / or HLA-DR++ CD14+ monocytes (or HLA-DR++ CD14+ myeloid precursors thereof). Dendritic cells may comprise conventional type 1 dendritic (cDC1) cells, or myeloid precursors thereof; and / or plasmacytoid dendritic cells (pDC), or precursors thereof. CD4+ central memory T cells (TCMs) may be activated (“aCD4+ TCMs”). CD8+ TMCs may be activated (“aCD8+ TCMs”). The two or more of immune cell populations may further comprise regulatory T cells (Tregs). Training a statistical model

[0054] The statistical model or one or more statistical models may be trained using training input data obtained from gene expression data for two or more (e.g., three, four, five or more) immune cell populations determined for samples collected from individuals with WM and precursor conditions thereof, MM and precursor conditions thereof, and healthy individuals, and using disease state data of the individuals from which the gene expression data was obtained as ground truth data. The training input data is data indicative of the proportions of two or more immune cell populations in the samples, and the disease state data is indicative of whether the individuals providing the samples have WM and precursor conditions thereof, MM and precursor conditions thereof, or neither disease.

[0055] 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 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.

[0056] For instance, the training input data may be gene expression data generated by single cell RNA sequencing (scRNAseq) or spatial transcriptomics. In some instances, the training input data may be protein expression data obtained from flow cytometry, Cytometry by Time of Flight (CyTOF), immunohistochemistry, or spatial proteomics determined for samplescollected from individuals with WM and precursor conditions thereof, MM and precursor conditions thereof, and healthy individuals.

[0057] An exemplary computer implemented method of training the one or more statistical models to predict whether a subject is suffering from WM or a precursor condition thereof, or MM or a precursor condition thereof, comprises: (a) obtaining scRNAseq data comprising gene expression data for two or more (e.g., three, four, five or more) immune cell populations for samples collected from individuals with WM and precursor conditions thereof, individuals with MM and precursor conditions thereof, and healthy individuals; (b) determining, from the scRNAseq data, data indicative of the proportions of the two or more (e.g., three, four, five or more) immune cell populations for each sample, wherein the data indicative of the proportions of the two or more (e.g., three, four, five or more) immune cell populations is training input data for the one or more statistical models; (c) obtaining disease state data for the individual from which each sample was obtained, wherein the disease state data indicates whether the individual has WM or precursor conditions thereof, MM or precursor conditions thereof, or whether the individual has neither disease, wherein the disease state data is ground truth data used to validate the output of the statistical model; and (d) training the one or more statistical models, using the training input data and the ground truth data, to calculate a score indicative of the likelihood that the subject has WM or a precursor condition thereof, or MM or a precursor condition thereof. The score indicates whether a subject is suffering from WM or a precursor condition thereof, or MM or a precursor condition thereof, and optionally whether the subject is suffering from neither condition or their precursors. Suitable statistical models may include, e.g., Neural Networks, Support Vector Machines (SVMs), or logistic regression models. For example, the score may be derived from an SVM classifier, a neural network classifier, a logistic regression classifier, or may be a Z-score.

[0058] The data indicative of the proportions of the two or more (e.g., three, four, five or more) immune cell populations may be a ratio between the two or more populations (for example, 1:1 or 2:1). The data indicative of the proportions of the two or more (e.g., three, four, five or more) immune cell populations may be, for each of the two or more immune cell populations, the percentage of the overall immune cell population made up by the respective immune cell population. For example, a proportion of 2:1 could be expressed as the firstimmune cell population making up 66.6% of the overall population and the second immune cell population making up 33.3% of the overall population.

[0059] Training of the one or more statistical models may involve K-fold cross validation, wherein the gene expression data for two or more (e.g., three, four, five or more) immune cell populations determined for samples collected from individuals with WM and precursor conditions thereof, individuals with MM and precursor conditions thereof, and healthy individuals are split into K subsets, wherein K-1 subsets are used for training and 1 subset is used for validation. K may be 4, 5, 6, or 7. The chosen numerical value may depend on the size of data sets available for training. The performance of the model can be assessed by determining a correlation coefficient between predicted disease categorization and observed categorization for a given subject in the validation fold. The process may be repeated by changing the validation fold. Once every fold has been used for validation, the correlation coefficients may be averaged across the K models. The model may be retrained on the entire set of gene expression data for the two or more immune cell populations prior to deployment.

[0060] For a computational method that can provide robust predictions whether a subject is suffering from WM or a precursor condition thereof, or MM or a precursor condition thereof, a large set of training data is typically used to train the model. For example, gene expression data for 5 or more (e.g., 10 or more, or 15 or more) immune cell populations for samples collected from individuals with WM and precursor conditions thereof, MM and precursor conditions thereof, and healthy individuals, may result in more robust predictions. For example, using a panel of 17 different immune cell populations comprising activated NK cells, CD56dim NK cells, S100B+ CD56dim NK cells, IL1β+ CD14+ monocytes or myeloid precursors thereof, RETN+ CD14+ monocytes or myeloid precursor thereof, S100A+ CD14+ monocytes or myeloid precursor thereof, HLA-DR++ CD14+ monocytes myeloid precursors thereof, cDC1 cells, pDC cells, activated CD4+ TMCs, activated CD8+ TCMs, and Tregs was found to provide a statistical model that can determine with high accuracy whether a subject is suffering from WM or a precursor condition thereof, or MM or a precursor condition thereof.

[0061] The trained statistical model is configured to receive, as input data, data indicative of the proportions of the two or more (e.g., three, four, five or more) immune cell populations for a subject. The model is configured to calculate, based on the input data, a score indicativeof the likelihood of the subject having WM or a precursor condition thereof, or MM or a precursor condition thereof, or optionally the likelihood of the subject having neither WM or a precursor condition thereof, nor MM or a precursor condition thereof.

[0062] Also provided is a computer program comprising computer program code configured to cause one or more physical computing devices to perform the computer- implemented method described above when the code is run. Also provided is a computer readable storage medium (optionally non-transitory) having stored thereon the computer program.

[0063] FIG. 8 of the accompanying drawings schematically illustrates an exemplary computer system 100 upon which a computer program for implementing all or part of the methods described herein may run. The exemplary computer system 100 comprises a computer-readable storage medium 102, a memory 104, a processor 106 and one or more interfaces 108, which are all linked together over one or more communication busses 110. The exemplary computer system 100 may take the form of a conventional computer system, such as, for example, a desktop computer, a personal computer, a laptop, a tablet, a smart phone, a server, a mainframe computer, and so on.

[0064] The computer-readable storage medium 102 and / or the memory 104 may store one or more computer programs (or software or code) and / or data (including but not limited to: the scRNAseq data, ground truth disease state data, training input data, and the statistical model(s)). The computer programs stored in the computer-readable storage medium 102 and / or the memory 104 may include computer programs that, when executed by the processor 106, cause the processor 106 to carry out a computational method described herein. The computer-readable storage medium 102 and / or the memory 104 may be a non-transitory computer readable storage medium. The computer-readable storage medium 102 and / or the memory 104 may be one or more memory chips or memory blocks implemented within the processor, magnetic media such as hard disk or floppy disks, or optical media such as for example DVD and the data variants thereof, e.g., CD.

[0065] The processor 106 may be any data processing unit suitable for executing one or more computer readable program instructions, such as those belonging to computer programs stored in the computer-readable storage medium 102 and / or the memory 104. The processor 106 may include one or more of: general purpose computers, special purpose computers,microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASIC), gate level circuits and processors based on multi-core processor architecture, as nonlimiting examples. As part of the execution of one or more computer-readable program instructions, the processor 106 may store data to and / or read data from the computer-readable storage medium 102 and / or the memory 104. The processor 106 may comprise a single data processing unit or multiple data processing units operating in parallel or in cooperation with each other. The processor 106 may, as part of the execution of one or more computer readable program instructions, store data to and / or read data from the computer-readable storage medium 102 and / or the memory 104.

[0066] The one or more interfaces 108 may comprise a network interface enabling the computer system 100 to communicate with other computer systems across a network. The computer system 100 may obtain the first data, ground truth label data, training input data, and / or the statistical model(s) via the network. The network may be any kind of network suitable for transmitting or communicating data from one computer system to another. For example, the network could comprise one or more of a local area network, a wide area network, a metropolitan area network, the internet, a wireless communications network, and so on. The computer system 100 may communicate with other computer systems over the network via any suitable communication mechanism / protocol. The processor 106 may communicate with the network interface via the one or more communication busses 110 to cause the network interface to send data and / or commands to another computer system over the network. Similarly, the one or more communication busses 110 enable the processor 106 to operate on data and / or commands received by the computer system 100 via the network interface from other computer systems over the network.

[0067] The interface 108 may alternatively or additionally comprise a user input interface and / or a user output interface. The user input interface may be arranged to receive input from a user, or operator, of the system 100. The user may provide this input via one or more user input devices (not shown), such as a mouse (or other pointing device, track-ball, or keyboard). The user output interface may be arranged to provide a graphical / visual output to a user or operator of the system 100 on a display (or monitor or screen) (not shown). The processor 106 may instruct the user output interface to form an image / video signal which causes thedisplay to show a desired graphical output. The display may be touch-sensitive enabling the user to provide an input by touching or pressing the display.

[0068] The interface 108 may alternatively or additionally comprise an interface to a measurement system (e.g., a sequencing machine or flow cytometer) for determining the proportions of the two or more (e.g., three, four, five or more) immune cell populations.

[0069] In general, the various examples may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although these are not limiting examples. While various aspects may be illustrated as block diagrams, it is well understood that these blocks may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0070] A single processor or other unit may fulfil the functions of several items recited in the claims. The functions may be performed in a single integrated electronic device, or the functions may be distributed across different discrete devices. For example, some functions may be performed by a remote service accessed via a wired or wireless network connection. A computer program may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Methods of monitoring disease progression Gene expression-based methods

[0071] In another aspect, a method of monitoring a subject with Waldenström macroglobulinemia (WM), or a precursor condition thereof, is provided, wherein the method comprises determining in tumor cells obtained from a sample obtained from the subject and a reference sample expression of two or more gene expression signatures selected from the group consisting of (a) a first gene expression signature comprising ACTG1, S100A4, TMSB4X, TMSB10, and GAPDH; (b) a second gene expression signature comprising CXCR4, STK17A, YBX3, FCER2, and LYST; (c) a third gene expression signature comprising AHNAK,ZNF596, TTN, FCRK3, and SELL; (d) a fourth gene expression signature comprising DUSP22, CD9, VPREB3, GSTP1, and H1FX; and (e) a fifth gene expression signature comprising LTB, DSP, NFKB2, BCL7A, and JUP. The reference sample may be a sample obtained from the subject at an earlier timepoint. Alternatively, the reference sample is a representative sample from one or more different subjects with IgM MGUS or smoldering WM.

[0072] For example, the first gene expression signature was found to be associated with precursor conditions of WM such as IgM MGUS. Decreased activity of the first gene expression signature in the tumor cells of the sample relative to tumor cells of the reference sample (e.g., an IgM MGUS sample) may therefore indicate that the subject is at risk of disease progression.

[0073] The second, third, fourth, and fifth gene expression signatures were found to be associated with different subtypes of WM. Increased activity of the second, third, fourth, and / or fifth gene expression signature(s), in the tumor cells of the sample relative to tumor cells of the reference sample (e.g., an IgM MGUS sample) may indicate that the subject is at risk of disease progression.

[0074] In some instances, additional reference samples may be included as comparators. For example, reference samples of representative groups of individuals suffering from each of the subtypes represented by the second, third, fourth, and fifth gene expression signatures may additionally be included to determine which subtype of WM the subject has progressed to.

[0075] Additionally or alternatively, healthy B cells may be used as a reference sample in the monitoring methods described herein. Accordingly, in a related aspect, a method of monitoring a subject with Waldenström macroglobulinemia (WM), or a precursor condition thereof, is provided that comprises determining in tumor cells obtained from a sample of the subject and healthy B cells of a reference sample expression of two or more gene expression signatures selected from the group consisting of (a) a first gene expression signature comprising ACTG1, S100A4, TMSB4X, TMSB10, and GAPDH; (b) a second gene expression signature comprising CXCR4, STK17A, YBX3, FCER2, and LYST; (c) a third gene expression signature comprising AHNAK, ZNF596, TTN, FCRK3, and SELL; (d) a fourth gene expression signature comprising DUSP22, CD9, VPREB3, GSTP1, and H1FX; and (e) a fifthgene expression signature comprising LTB, DSP, NFKB2, BCL7A, and JUP. The reference sample of healthy B cells may be obtained from the subject’s sample or from one or more healthy individuals.

[0076] As noted above, the first gene expression signature was found to be associated with precursor conditions of WM such IgM MGUS. Thus, relative to healthy B cells, the activity of the first gene expression signature is increased in a subject suffering from IgM MGUS. A decrease in activity of the first gene expression signature in the tumor cells of the sample over time to a level of activity similar to healthy B cells of the reference sample (which serve as baseline in this context) may indicate that the subject is at risk of disease progression, or that the subject’s disease is progressing.

[0077] Increased activity of the second, third, fourth, and / or fifth gene expression signature(s) in the tumor cells of the sample relative to healthy B cells of the reference sample may indicate that the subject is at risk of disease progression.

[0078] Increase activity of the fourth gene expression signature in tumor cells in the sample relative to tumor cells / healthy B cells of the reference sample may indicate that the tumor cells are more likely to be CXCR4 wild-type. Alternatively or additionally, increased activity of the fourth gene expression signature in tumor cells in the sample relative to tumor cells / healthy B cells of the reference sample may indicate that the subject is at risk of developing lymphadenopathy (LAD). Moreover, increased activity of the fourth gene expression signature in tumor cells in the sample relative to tumor cells / healthy B cells of the reference sample can further indicate that the subject may benefit from treatment with a Bruton’s tyrosine kinase (BTK) inhibitor.

[0079] The genes comprised within a gene expression signature identified herein are typically evaluated together. For single-cell based methods, e.g., scRNA seq, not all genes of a gene expression signature may be captured in each individual cell. In some instances, Z- scores are calculated for each gene in the signature, and a mean of the Z-scores obtained for each gene comprised in the gene expression signature is then calculated.

[0080] Typically, the identified gene expression signatures, or the genes comprised in each gene expression signature, are expressed to at least some extent in tumor cells and / or healthy B cells. The activity of a gene expression signature (or genes comprised in the geneexpression signature) is (are) increased in tumor cells of the subject’s sample relative to tumor cells / healthy B cells of the reference sample if the level(s) of expression (or mean of the Z- scores of the genes comprised in a given gene expression signature) is (are) at least 5% higher (e.g., at least 10% or at least 15% higher) in tumor cells of the subject’s sample relative to tumor cells / healthy B cells of the reference sample. The activity of a gene expression signature (or genes comprised in the gene expression signature) is (are) decreased in tumor cells of the subject’s sample relative to tumor cells / healthy B cells of the reference sample if the level(s) of expression (or mean of the Z-scores of the genes comprised in a given gene expression signature) is (are) at least 5% lower (e.g., at least 10% or at least 15% lower) in tumor cells of the subject’s sample relative to tumor cells / healthy B cells of the reference sample.

[0081] Tumor cells can be identified (e.g., in scRNAseq data) by expression of one or more (e.g., two, three, four, five, six, or all) of IGHM, CD79B, CD27, TNFRSF13B, ITM2C, JCHAIN, and CD1C. In some instances, the identification of tumor cells also includes determining expression of one or more (e.g., two, three, four, or all) of ITM2B, SYK, BLNK, CD52, and CD53. Surface protein-based methods

[0082] In a further aspect, a method for monitoring a subject with Waldenström macroglobulinemia (WM), or a precursor condition thereof, is provided that comprises determining in tumor cells in a sample obtained from the subject and in a reference sample, surface expression of CD9 and / or CD23.

[0083] The characteristic immunophenotypic features of WM tumor cells include surface expression of pan-B-cell antigens such as CD19 and CD20, together with CD22+dim, CD25+, CD27+ and IgM+. A progressively higher percentage of light-chain-isotype plasma cells is observed during progression from IgM MGUS to smoldering WM and to symptomatic WM. Plasma cells are also clonally restricted, and express CD38, CD138, variable CD45, CD79A, and low levels of CD19 and CD20. A minimum panel of antibodies for tumor cell characterization by flow cytometry may include antibodies that specifically bind to IgM, CD25, CD22, CD19, CD27, CD38, CD20, and CD45, respectively.

[0084] The presence of CD9+ and / or CD23+ tumor cells in the sample obtained from the subject may indicate that the subject is at risk of disease progression. Conversely, the absence of CD9+ and / or CD23+ tumor cells in the sample may indicate that the subject may have a low risk of disease progression. Similarly, if substantially all tumor cells in the sample are CD9- and / or CD23-, the subject may have a low risk of disease progression. Alternatively, increased expression of CD9 (CD9high) and / or CD23 (CD23high) on the tumor cells of the sample relative to the cells of the reference sample may indicate that the subject is at high risk of disease progression.

[0085] The reference sample may be tumor cells from a sample obtained from the subject at an earlier timepoint. Alternatively, the reference sample is a representative sample of tumor cells from one or more different subjects with IgM MGUS or smoldering WM. Or the reference sample may be a representative B cell population from one or more healthy donors or healthy B cells of the subject.

[0086] The presence or increased expression of CD9 on tumor cells in the sample relative to the reference sample may further indicate that the tumor cells are more likely to be CXCR4 wild-type. For example, if the majority (e.g., at least 70%, or 80% or more) or substantially all tumor cells are CD9+ in the sample, the tumor cells may be CXCR4 wild-type.

[0087] Relatedly, the presence or increased expression of CD9 on tumor cells in the sample relative to the reference sample (e.g., a sample obtained from the subject at an earlier timepoint) may further indicate that the subject may benefit from treatment with a Bruton’s tyrosine kinase (BTK) inhibitor. For instance, if the majority (e.g., at least 70%, or 80% or more) or substantially all tumor cells are CD9+ in the sample, the subject may respond to treatment with a BTK inhibitor such as ibrutinib.

[0088] Alternatively or additionally, the presence or increased expression of CD9 on tumor cells in the sample relative to the reference sample (e.g., a sample obtained from the subject at an earlier timepoint) may indicate that the subject is at risk of developing lymphadenopathy. For example, if the majority (e.g., at least 70%, or 80% or more) or substantially all tumor cells are CD9+ in the sample, the subject may be at risk of developing lymphadenopathy.

[0089] Presence or increased expression of CD23 on tumor cells in the sample relative to the reference sample may indicate that tumor cells are infiltrating or have infiltrated the subject’s bone marrow.

[0090] The expression of CD9 and / or CD23 is (are) increased on the surface of tumor cells of the subject’s sample relative to healthy B cells of the reference sample if the level(s) of expression is (are) at least 5% higher (e.g., at least 10% or at least 15% higher) in tumor cells of the subject’s sample relative to healthy B cells of the reference sample.

[0091] Surface expression of CD9 and / or CD23 may be determined using flow cytometry, Cytometry by Time of Flight (CyTOF), immunohistochemistry, or spatial proteomics. Sample

[0092] In the methods of monitoring a subject with WM or a precursor condition thereof described herein, a sample obtained from a subject is compared to a reference sample. The reference sample and the sample obtained from the subject will be of the same type (e.g., blood or bone marrow).

[0093] 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 is a bone marrow sample.

[0094] 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 WM, 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 with progression to WM, 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.

[0095] 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).

[0096] For example, the reference sample may be a representative sample obtained from one or more (e.g., multiple) different subjects with a precursor condition of WM who have not progressed to WM. 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 features being used to monitor a subject with a precursor condition of WM will become increasingly dissimilar to the reference sample as the subject progresses to WM.

[0097] Alternatively, a reference sample may be provided that is a representative sample obtained from one or more (e.g., multiple) different subjects with a precursor condition of WM who progressed to WM, or from one or more (e.g., multiple) different subjects with WM. It will be appreciated that the features being used to monitor a subject with a precursor condition of WM will become increasingly similar to the reference sample as the subject progresses to WM.

[0098] In some instances, the reference sample may comprise samples obtained from multiple healthy donors, multiple patients with a precursor condition of WM, or multiple patients with WM. In another instance, the reference sample may comprise samples from two or more of a panel of healthy donors, a panel of patients with a precursor condition of WM, and a panel of patients with WM. For example, the reference sample may comprise samples obtained from multiple healthy donors, multiple patients with a precursor condition of WM, and multiple patients with WM.

[0099] 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, 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 be expressed 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.

[0100] 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 a precursor condition of WM who have not progressed to WM and a second reference sample from one or more (e.g., multiple) different subjects with a precursor condition of WM who progressed to WM, or from one or more (e.g., multiple) different subjects with WM. Monitoring interval

[0101] Typically, the subject is monitored in intervals of 3-4 months, or 6-12 months. A change in the two or more gene expression signatures, or the surface expression of CD9 and / or CD23, may be determined by analyzing the gene expression signature over several time points. For example, only if a change persists over several time points (e.g., at least two or three time points), such change indicates that the subject is progressing or has progressed from, e.g., MGUS or AWM, to WM.

[0102] In some instances, a significant change across two or more gene expression signatures, or the surface expression of CD9 and / or CD23, between a first and a second time point may indicate that a subject is progressing or has progressed from, e.g., MGUS or AWM to WM, in particular if the change persists at a subsequent third time point. Combinations of monitoring methods

[0103] Conventionally, monitoring subjects with IgM MGUS or AWM comprises assessment of different variables including, but not limited to, IgM monoclonal protein levels, albumin levels, β2-microglobulin levels, and bone marrow infiltration. Such variablesare assessed using methods including, but not limited to, imaging methods, and tests performed on blood, and / or bone marrow samples.

[0104] It will therefore be appreciated that the methods of monitoring a subject with WM, or a precursor condition thereof, described herein may be used alongside such conventional methods to assist in the determination and / or provision of a diagnosis. EXAMPLES

[0105] 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. Single-cell RNA sequencing

[0106] To characterize changes in the BM immune microenvironment of patients with asymptomatic Waldenström macroglobulinemia (AWM) and overt WM, single-cell RNA sequencing (scRNAseq) was performed on 28 BM immune cell samples (IgM MGUS, n=6; SWM, n=19; WM, n=3) obtained from 27 patients, including one patient sampled at both the SWM and WM stages. In-house data from BM immune cell samples obtained from healthy donors (HD; n=23) and patients with SMM (n=26) was also integrated (Sklavenitis-Pistofidis et al. Cancer Cell 40:1358-1373 e8, 2022; Zavidij O, et al. Nat Cancer 1:493-506, 2020).

[0107] Patients with a monoclonal IgM protein in the serum and no morphological evidence of lymphoma infiltration in the BM were considered to have IgM MGUS, while patients with any degree of infiltration were considered to have SWM, according to the recommendations of the 2nd International Workshop on WM. The patients’ MYD88 and CXCR4 mutation status were assessed clinically using a clinical-grade deep targeted sequencing assay developed at the Dana-Farber Cancer Institute (Rapid Heme Panel) or, for MYD88 alone, allele-specific PCR. Fluorescence in situ hybridization (FISH) was used clinically to detect the presence of copy number abnormalities.

[0108] 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 or Red Blood Cell lysis buffer (ThermoFisher). Bone marrow mononuclear cells were then subjected to magnetic bead enrichment (MiltenyiBiotec) for CD138 and / or CD19, according to the manufacturer’s instructions, and cryopreserved in Fetal Bovine Serum (FBS) with 10% Dimethylsulfoxide (DMSO). PBMCs were cryopreserved in FBS with 10% DMSO without further selection.

[0109] Cells were thawed in a 37°C water bath. Subsequently, they were centrifuged at 330 g for 5 mins and washed twice with an ice-cold 0.04% Ultrapure Bovine Serum Albumin (BSA) / Phosphate-Buffered Saline (PBS) wash buffer, before being loaded onto a Chromium Controller (10X Genomics) for single cell encapsulation. Libraries were prepared using the Chromium Next GEM Single Cell 5’ Reagent Kit v2 (Dual Index), the Chromium Single Cell Human BCR Amplification Kit, and Library Construction Kits (10X Genomics), according to the manufacturer’s instructions. Libraries were sequenced on a NovaSeq 6000 S4 flow cell at the Genomics Platform of the Broad Institute of MIT and Harvard (Cambridge, MA). CellRanger (v6.0.1) was used to demultiplex FASTQ files and produce count matrices and R (v4.1.3) and Seurat (v4.1.0) were used for downstream analyses. Ambient RNA correction was performed with SoupX (v1.5.2), doublet detection with Scrublet (v0.2.3), scDblFinder (v1.8.0) and SCDS (v1.10.0), and normalization with Scran (v1.22.1)

[0110] Overall, 209,727 immune cells were annotated (T, n=95,754; NK, n=24,219; Myeloid, n=53,172; progenitors, n=36,582). 28,991 normal B cells were excluded and processed separately. The total number of samples with at least 100 immune cells, considered for downstream analyses, was 70 (HD: 18; IgM MGUS: 6; SWM: 19; WM: 3; SMM: 24). Example 2. Altered BM immune cell composition marks progression from AWM to WM

[0111] This example illustrates that progression from IgM monoclonal gammopathy of unknown significance (IgM MGUS) or smoldering Waldenström macroglobulinemia (SWM) to Waldenström macroglobulinemia (SWM) is accompanied by alterations of immune cell composition in the bone marrow (BM).

[0112] Analysis of the scRNAseq data generated in Example 1 revealed that, despite their early stage, patients with AWM showed significantly altered BM immune cell composition. The results are summarized FIG. 1A. Proportions of CD56dimnatural killer (NK) cells, S100B+CD56dimNK cells, CD56brNK cells, CD16+Monocytes, CD4+and CD8+central memory T cells (TCM), Th1 cells, regulatory T cells (Tregs), GZMB+CD8+TEMs, KIR+CD8+TEMs, and Tgd were increased. In contrast, proportions of cytokine-expressing (IL1β+)CD14+monocytes, plasmacytoid dendritic cells (pDCs), canonical dendritic cells type 1 (cDC1), activated and interferon-stimulated T and NK cells (two-sided Wilcoxon, q<0.1) were decreased.

[0113] Strikingly, patients with IgM MGUS, who had no morphological evidence of BM infiltration but had monoclonal IgM protein in their serum and / or a MYD88 L265P mutation detected, already showed significantly higher proportion of CD56dim NK cells, S100B+CD56dimNK cells, Th1 cells, and KIR+CD8+TEMs (see FIG. 1B). They also showed significantly lower proportion of cytokine-expressing CD14+monocytes, pDCs, and activated and interferon-stimulated T and NK cells compared to HD (two-sided Wilcoxon, q<0.1; see FIG.1B). These data indicate that certain alterations in immune cell composition may even precede morphological evidence of BM infiltration. In fact, most changes in immune cell composition could be observed as early as at the IgM MGUS stage (see FIG. 1C), indicating that immune dysregulation is established early in the course of disease.

[0114] Notably, the proportion of Tregs was significantly increased in patients with SWM compared to those with IgM MGUS (two-sided T-test, p=0.013) (see FIG. 1D). This finding is in line with a prior study showing that WM tumor cells are capable of inducing Treg differentiation and expansion. These data suggest that Tregs may play a role in disease progression from IgM MGUS to overt WM.

[0115] This example illustrates that illustrates that progression from IgM MGUS or SWM to overt SWM is accompanied by early-stage alterations of immune cell composition in the BM that may be exploited for diagnostic purposes. Example 3. Disease-specific immune hallmarks of AWM and SMM

[0116] This example illustrates AWM- and SMM-specific changes in the immune cell compositions in the BM.

[0117] Differences in the composition of the BM immune microenvironment between patients with AWM and SMM were explored to determine whether some changes may reflect a general immune response against clonal expansion in the BM and others may be specific to the tumor type.

[0118] In both AWM and SMM, patients showed significantly higher proportions of S100B+CD56dimNK cells and CD16+ monocytes. This increase was more pronounced inpatients with AWM compared to SMM (two-sided Wilcoxon, q=9.9e-04 and q=1.5e-02, respectively). Patients with AWM had significantly fewer activated and interferon-stimulated T and NK cells compared to patients with SMM and healthy donors (two-sided Wilcoxon, q<0.1), suggesting this may be an immune hallmark of WM (FIG.2A). Furthermore, patients with SMM had significantly fewer CD14+monocytes and significantly more Tregs and naïve CD4+T cells compared to both patients with AWM (FIG.2B) and HDs FIG.2C, suggesting these changes may be immune hallmarks of Multiple Myeloma (MM).

[0119] Disease-specific changes were also observed at the gene expression level. For example, myeloid cells from patients with SMM showed a disease-specific decrease in the expression of CEBPD (two-sided Wilcoxon compared to HD, q=8.98e-07), a transcription factor that regulates the expression of the alarmins S100A8 and S100A9, which also showed disease-specific downregulation (q=0.012 and q=0.022, respectively) (FIG. 2D). On the other hand, myeloid cells from patients with AWM showed a disease-specific increase in the expression of MNDA (two-sided Wilcoxon compared to HD, q=5.39e-10), a key regulator of interferon response (FIG. 2D). This change may be associated with the observed depletion of interferon-stimulated T and NK cells in patients with AWM.

[0120] Furthermore, T cells from patients with AWM showed consistently higher levels of expression of genes important for receptor-mediated activation, such as PTPRC, CD2, LCK, IL2RG, IL7R, GIMAP4, and RAC2, along with lower levels of common activation markers, such as CD69, CXCR4, and members of the AP-1 and NFkB pathway (FIG. 2E). This expression profile is consistent with the observed depletion of activated T and NK cells in patients with AWM and suggests that patient T cells may present altered activation potential compared to both HD and patients with SMM.

[0121] This example illustrates AWM- and SMM-specific changes in the immune cell compositions in the BM that may be exploited for the differential diagnosis of these conditions at an early stage. Example 4. Using immune profiling to differentiate AWM from SMM

[0122] This example illustrates a method for determining whether a subject is suffering from a precursor condition of WM or MM that comprises determining in a sample obtained from the subject the proportions of immune cells comprising: CD4+ and / or CD8+ centralmemory T cells (TMCs), CD4+ TN, natural killer (NK) cells, CD8+ effector memory T cells (TEMs), CD14+ monocytes, dendritic cells, and Th1 cells.

[0123] Since results described in Example 3 indicated that WM and MM and their precursor conditions presented with different changes in immune cell composition, it was hypothesized that immune profiling alone may be able to diagnose and differentiate the two tumor types. Indeed, in a principal component analysis of immune cell composition, samples from patients with AWM clustered separately from both samples from patients with SMM and samples from HD (FIG. 3A).

[0124] For the purposes of this analysis, progenitor cells were removed, and the cohort used for analysis was restricted to individuals with at least 100 immune cells excluding progenitor cells (HD: n=17; SMM: n=24; AWM: n=25). Next, features for each comparison (SMM vs HD, AWM vs HD) were ranked based on the heuristic described by Golub et al. (Science 286:531-7, 1999). The union of the top 10 cell types were retained across both comparisons for a total of 17 features (FIG. 3B). The cohort was randomly split into 5 subsets, and a 5- fold cross-validation was performed, each time training a Support Vector Machine (SVM) classifier on the 4 training subsets and testing it on the held-out subset. For each sample, the SVM classifier determined the diagnosis (i.e., HD, SMM, or AWM). The function svm() from the R package e1071 (v1.7.11) was used to train the classifier with cost set to 0.1 and a linear kernel.

[0125] Across all testing subsets (n=66), the SVM classifier was able to correctly diagnose the presence of malignancy in all cases (n=49) with a sensitivity of 100% (95% confidence interval [CI]: 91-100) and a specificity of 88% (95% CI: 62-98) (FIG. 3C). Furthermore, it correctly diagnosed AWM in 22 out of 25 cases with a sensitivity of 88% (95% CI: 68-97) and a specificity of 90% (95% CI: 76-97) and correctly diagnosed SMM in 20 out of 24 cases with a sensitivity of 83% (95% CI: 62-95) and a specificity of 88% (95% CI: 74-96) (FIG. 3C). Notably, misclassification of AWM cases was not associated with the degree of BM infiltration (two-sided Wilcoxon, p=0.74) (FIG. 3D).

[0126] These results indicate that it is possible to use immune profiling to differentially diagnose AWM from SMM in patients. Considering that 32% (n=8 / 25) of the AWM cohort had little to no morphological evidence of BM infiltration by lymphoma (<10% infiltration)without a discernible effect on the classifier’s performance, this approach may represent a viable strategy in the early detection of AWM.

[0127] This example illustrates that analysis of the proportions of immune cells including CD4+ and / or CD8+ TMCs, CD4+ TN, NK cells, CD8+ TEMs, CD14+ monocytes, dendritic cells, and Th1 cells in a sample obtained from a subject can be used to determine whether the subject suffers from a precursor condition of WM or MM at an early disease stage. This information can be used to guide treatment decisions. Example 5. Identification of gene expression signatures associated with WM progression

[0128] This example describes the identification of gene expression signatures in WM tumor cells characterized by marker genes such as CXCR4, AHNAK, ACTG1, S100A4, DUSP22, and CD9.

[0129] To identify malignant cells, B cells and plasma cells from each patient were considered separately. For each patient, B cell receptor clonotypes were sorted by their frequency, and clonotypes whose frequency was at least double that of the next clonotype in order and which had at least 30 cells, were nominated as expanded. Expanded clonotypes which clustered separately from normal B cells and whose immunoglobulin isotype matched the one detected clinically by immunofixation were considered malignant. Cells residing in the same clusters as the malignant clonotypes were considered malignant. A panel of established markers was then used to determine the type of the lymphoid clone. WM clones were identified based on the expression of IGHM, CD79B, CD27, TNFRSF13B, ITM2C, JCHAIN, and CD1C.

[0130] SignatureAnalyzer-GPU (v0.0.8) was used for signature discovery on a matrix of WM tumor cells and the top 2000 highly variable genes, excluding immunoglobulin, ribosomal, and mitochondrial genes. The tool was run 30 times directly on the raw count matrix with a Poisson objective and exponential (L1) priors for both the H and the W matrices. The run 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. 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 ofW (i.e., how strongly each gene contributes to the signature) and F (i.e., how strongly each signature contributes to the gene).

[0131] Overall, eight gene expression signatures (GEX) were extracted, which showed variable activity across tumors (FIG. 4). Signatures GEX-1 and GEX-4 were removed from further consideration, as they corresponded to either a generic immune cell activation module (GEX-1) or monocytic contamination (GEX-4). For the remaining six signatures, key marker genes included CXCR4; the tumor suppressor RASSF6; AHNAK, a gene involved in cytokinesis; the transcription factor ZNF595; thymosins beta 4 (TMSB4X) and beta 10 (TMSB10); DUSP22, which is located next to IRF4 on chromosome 6 and is involved in structural variants in lymphoma; CD9, a gene associated with B cell apoptosis, dependence on follicular dendritic cells, and disease progression in lymphoma; and BCL7A, a gene frequently mutated in lymphoma and myeloma.

[0132] This example describes the identification of gene expression signatures in WM tumor cells characterized by markers genes such as CXCR4, AHNAK, ACTG1, S100A4, DUSP22, and CD9 that may be used to monitor disease progression to WM in patients suffering from AWM. Example 6. Validation of gene expression signatures associated with progression to WM

[0133] This example illustrates that gene expression signatures in WM tumor cells characterized by markers genes such as (i) CXCR4 and AHNAK, (ii) ACTG1 and S100A4, and (iii) DUSP22 and CD9 differentiate patients with IgM MGUS from patients with overt WM and thus can be used to monitor progression from AWM to WM.

[0134] To validate the gene expression signatures identified in Example 5, an external gene expression profiling (GEP) dataset of 13 patients with IgM MGUS and 36 patients with overt WM (GSE171739, as provided by Trojani et al. supra) was used. The dataset was downloaded from the Gene Expression Omnibus (GEO) using the getGEO() function from the GEOquery (v2.68.0) R package. CD19+patient samples (IgM MGUS: n=13; WM: n=36) were selected for downstream analysis, and gene-level data were generated by averaging across each gene’s probes. Normalized expression values for 30 marker genes (5 markers per signature) of six gene expression signatures (GEX-2, GEX-3, GEX-5, GEX-6, GEX-7, and GEX-8) were z-scored and a Euclidean distance matrix was constructed for agglomerativehierarchical clustering with complete linkage. The number of clusters was set to 6, one per signature, and the final number of subtypes was determined to be 4 based on manual curation for ease of classification. The Z-score for each gene per patient sample, where each patient sample is further defined by subtype, cluster, and stage, is shown in FIG. 5A.

[0135] Relatively consistent covariation of expression was observed for the key markers of signatures GEX-2, GEX-5, GEX-6, GEX-7, and GEX-8, delineating four subsets of tumors: a CXCR4-high subset, which aligns with the AHNAK-high subset and can be further broken down to a BCL7A-high subset; a DUSP22 / CD9-high subset, which only partly overlaps with the CXCR4 / AHNAK-high subset; and an ACTG1 / S100A4-high subset, which co-expressed thymosin beta 4 (TMSB4X) and thymosin beta 10 (TMSB10) (see FIG. 5A).

[0136] Interestingly, the expression of FCER2, which encodes CD23, appeared to co- segregate with the CXCR4 / AHNAK-high subset, suggesting that CD23 and CD9 may be useful surface markers for WM subclassification. CD23 was previously shown to be expressed in a subset of patients with WM who showed higher levels of serum IgM, however an association with CXCR4 mutations has not been described before. Patients with IgM MGUS mostly co-segregated with the ACTG1 / S100A4-high and DUSP22 / CD9-high subsets (FIG. 5A).

[0137] Samples were then scored for each signature by taking the mean of the expression levels of marker genes of the gene expression signature. Each signature’s activity was compared between patients with IgM MGUS and patients with WM using two-sided Wilcoxon’s rank-sum tests. P-values were adjusted using the Benjamini-Hochberg approach. Comparison of signature activity between patients with IgM MGUS and WM revealed significantly lower activity of GEX-2 (two-sided Wilcoxon, q=0.01) (corresponding to the ACTG1 / S100A4-high subset) and significantly higher activity of GEX-6 / GEX-7 / GEX-8 (q=6e-04, q=1e-04, and q=0.06, respectively) (corresponding to the CXCR4 / AHNAK-high subset) and GEX-5 (corresponding to the DUSP22 / CD9-high subset) (q=0.07) in patients with overt disease (FIG. 5B).

[0138] A prior study reported a higher risk of progression in patients with CXCR4 mutations. Notably, however, signature GEX-7 was active in tumors with and without reported CXCR4 mutations, thus it may capture risk of progression in both CXCR4-mutant as well as WT tumors (FIG.5C). In one patient, who was CXCR4-WT and who was sampledat the AWM stage and then again at the WM stage five years later and following 5 cycles of treatment, the proportion of cells assigned to GEX-7 increased significantly from timepoint 1 to timepoint 2 (two-sided Fisher’s exact, p=5.1e-08) (FIG. 5D).

[0139] This example illustrates that gene expression signatures in WM tumor cells characterized by markers genes such as (i) CXCR4 and AHNAK, (ii) ACTG1 and S100A4, and (iii) DUSP22 and CD9 differentiate patients with IgM MGUS from patients with overt WM and thus can be used to monitor progression from AWM to WM. The example also illustrates that these gene expression signatures may be used for risk stratification of patients suffering from AWM. Example 7. Differential gene expression analysis of genes dysregulated in WM tumors

[0140] This example illustrates that gene expression signatures characterized by markers genes such as (i) CXCR4 and AHNAK, and (ii) DUSP22 and CD9, respectively, co-segregate with other genes frequently upregulated in WM tumors, whereas a gene signature characterized by markers genes such as ACTG1 and S100A4 aligned with genes downregulated in WM tumors.

[0141] The data generated in Example 6 was used to identify genes that are frequently dysregulated in WM tumors. Differential gene expression analysis was performed on a per- patient level each time comparing a patient’s tumor cells to normal memory B cells. A list of 101 genes that were shown to be consistently upregulated (two-sided Wilcoxon, q<0.05; log2 fold-change>0.5) in more than 30% of tumors in the scRNA seq dataset generated in Example 1 were tested for differential expression between patients with IgM MGUS and patients with overt WM in GSE171739 using two-sided Wilcoxon’s rank-sum tests. P-values were adjusted using the Benjamini-Hochberg approach. Genes with a q-value < 0.05 (n=53) are shown in FIG. 6A.

[0142] As expected, IGHM (encoding the constant region of IgM) was the most frequently upregulated gene, compared to memory B cells which contain a mixture of IgM+and class- switched B cells. Similarly, CD79B, which is frequently mutated in patients with WM and regulates BCR signaling, was significantly upregulated in most patients, as were proximal BCR components, SYK and BLNK. Genes that are typically expressed in marginal zone B cells, such as ITM2C, ITM2B, JCHAIN, and CD1C, were frequently upregulated, as weregenes that we discovered in the previous signature analysis, such as DUSP22, RASSF6, and VPREB3. In line with these results, JCHAIN and ITM2B, as well as RASSF6 were previously shown to be upregulated in tumor cells from patients with overt WM.

[0143] Most tumors showed significantly higher expression of CD52, the target of alemtuzumab, which can lead to therapeutic responses in patients with WM, and CD53, a surface tetraspanin that interacts with CXCR4 to facilitate downstream signaling.

[0144] Notably, one of the top hits was RAC2, a gene that interacts with phospholipase C Gamma 2 and can bypass Bruton’s Tyrosine Kinase (BTK) in downstream BCR signaling, driving resistance to the BTK inhibitor ibrutinib in patients with Diffuse Large B Cell Lymphoma (DLBCL) and Mantle Cell Lymphoma (MCL). It is thus possible that the high levels of RAC2 expression in most WM tumors may be related to the development of resistance to ibrutinib in patients with WM.

[0145] Approximately half of the genes (n=53 / 101) were significantly (two-sided Wilcoxon, q<0.05) up- / downregulated between patients with IgM MGUS (n=13) and overt WM (n=36) in the external GEP dataset, indicating that they may be relevant for prognostication. To illustrate these results, the 53 genes were Z-scored for visualization as shown in FIG. 6B. Strikingly, genes upregulated in patients with WM appeared to segregate patients into two broad classes, which largely overlapped with the CXCR4 / AHNAK-high and DUSP22 / CD9-high subsets, while genes downregulated in patients with WM aligned with our ACTG1 / S100A4-high subset, which was enriched for patients with IgM MGUS. Genes like ITGB1 (encoding CD29), CLECL1, and SYNE2 appeared to be upregulated primarily in the CXCR4 / AHNAK-high subset of WM cases.

[0146] This example illustrates that gene expression signatures characterized by markers genes such as (i) CXCR4 and AHNAK, and (ii) DUSP22 and CD9, respectively, co-segregate with other genes frequently upregulated in WM tumors, whereas a gene signature characterized by markers genes such as ACTG1 and S100A4 aligned with genes downregulated in WM tumors. The identified set of additional marker genes may be useful when monitoring AWM patients and allow earlier risk stratification and identification of suitable treatment options for patients suffering from AWM.Example 8. Validation of expression signatures associated with progression

[0147] This example confirms that gene expression signatures characterized by markers genes such as (i) CXCR4 and AHNAK, and (ii) DUSP22 and CD9, respectively, may be used for risk stratification of patients suffering from AWM and WM.

[0148] To further validate the gene expression signatures identified in Example 5 and assess their clinical relevance, an external bulk RNA-seq cohort of 52 patients with MYD88-mutant WM and available genotypic information for CXCR4 mutations (Hunter et al., supra) was analyzed. Samples were scored for signatures GEX-2, GEX-5, and GEX-7 by taking the mean of the expression levels of marker genes in each gene expression signature. Signature activity was compared between patients of different genotypic status, patients with or without the lymphadenopathy (LAD), and patients with or without splenomegaly using Wilcoxon’s rank-sum tests. Gene expression signature activity was compared to BM infiltration and serum IgM levels using Pearson’s correlation tests. P-values were corrected using the Benjamini-Hochberg approach.

[0149] In line with the results in Example 6, no significant association between the presence of a CXCR4 mutation (n=20) and the activity of signature GEX-7 (two-sided Wilcoxon, q=0.23), which is marked by CXCR4, was observed (FIG. 7A). CXCR4-mutant patients showed significantly lower activity of signature GEX-5 (q=1.3e-05), suggesting that patients with high activity of GEX-5 may be more likely to be CXCR4-WT and therefore more likely to respond better to therapy with Ibrutinib. Indeed, DUSP22 was previously shown to be significantly downregulated in CXCR4-mutant patients along with other dual specificity phosphatases.

[0150] Interestingly, the presence of LAD (n=27) was associated with significantly higher GEX-5 activity (two-sided Wilcoxon, q=0.023; see FIG. 7B). This is in line with signature GEX-5 being marked by CD9, a gene shown to denote the dependency of lymphoma cells on follicular dendritic cells, making it more likely that these cells will remain in the lymph node. This association may also explain why patients with WM showed higher activity of GEX-5 compared to patients with IgM MGUS, as the presence of LAD is a criterion for the diagnosis of overt disease. No signature was associated with the presence of splenomegaly or serum level of IgM.

[0151] Lastly, a significant positive correlation was observed between the activity of the high-risk signature, GEX-7, and the degree of BM infiltration (Pearson’s correlation test, q=0.027), suggesting that this signature may be prognostically relevant even in the setting of overt WM (FIG. 7C). This example confirms that gene expression signatures characterized by markers genes such as (i) CXCR4 and AHNAK, and (ii) DUSP22 and CD9, respectively, may be used for risk stratification of patients suffering from AWM and WM.

[0152] 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.

[0153] 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 determining whether a subject is suffering from Waldenström macroglobulinemia (WM) or a precursor condition thereof, or multiple myeloma (MM) or a precursor condition thereof comprising: a. determining, in a sample obtained from the subject, data indicative of the proportions of two or more immune cell populations selected from the group consisting of: i. CD4+ and / or CD8+ central memory T cells (TMCs), ii. naïve CD4+ T cells, iii. natural killer (NK) cells, iv. CD4+ and / or CD8+ effector memory T cells (TEMs), v. CD14+ and / or CD16+ monocytes, and / or myeloid precursors thereof, and vi. dendritic cells and / or myeloid precursors thereof b. obtaining a statistical model describing corresponding proportions of the two or more immune cell populations determined for samples collected from individuals with WM and precursor conditions thereof, MM and precursor conditions thereof, and healthy individuals; c. calculating, based on the statistical model and the data, a score; wherein the score indicates the likelihood of the subject having WM or a precursor condition thereof, or MM or a precursor condition thereof.

2. A method for determining whether a subject is suffering from Waldenström macroglobulinemia (WM) or a precursor condition thereof, or multiple myeloma (MM) or a precursor condition thereof comprising: a. receiving data indicative of the proportions of two or more immune cell populations in a sample obtained from the subject, wherein the two or more of immune cell populations are selected from the group consisting of: i. CD4+ and / or CD8+ central memory T cells (TMCs), ii. naïve CD4+ T cells, iii. natural killer (NK) cells,iv. CD4+ and / or CD8+ effector memory T cells (TEMs), v. CD14+ and / or CD16+ monocytes, and / or myeloid precursors thereof, and vi. dendritic cells, and / or myeloid precursors thereof; b. obtaining a statistical model describing corresponding proportions of the two or more immune cell populations determined for samples collected from individuals with WM and precursor conditions thereof, MM and precursor conditions thereof, and healthy individuals; c. calculating, based on the statistical model and the data, a score indicative of the likelihood of the subject having WM or a precursor condition thereof, or MM or a precursor condition thereof.

3. The method of any one of the preceding claims, wherein NK cells comprise activated NK cells and CD56dimnatural killer cells, and / or S100B+ CD56dimnatural killer cells.

4. The method of any one of the preceding claims, wherein CD14+ monocytes comprise a. IL1β+ CD14+ monocytes, or IL1β+ CD14+ myeloid precursors thereof, b. RETN+ CD14+ monocytes, or RETN+ CD14+ myeloid precursor thereof, c. S100A+ CD14+ monocytes, or S100A+ CD14+ myeloid precursor thereof, and / or d. HLA-DR++ CD14+ monocytes, or HLA-DR++ CD14+ myeloid precursors thereof.

5. The method of any one of the preceding claims, wherein dendritic cells comprise a. conventional type 1 dendritic (cDC1) cells, or myeloid precursors thereof; and / or b. plasmacytoid dendritic cells (pDC), or precursors thereof.

6. The method of any one of the preceding claims, wherein CD4+ and / or CD8+ TCMs are activated.

7. The method of any one of the preceding claims, wherein the immune cells further comprise Tregs.

8. The method of any one of the preceding claims, wherein the precursor condition of WM is asymptomatic WM (AWM), e.g., IgM Monoclonal Gammopathy of Undetermined Significance (IgM MGUS) or smoldering WM (SWM).

9. The method of any one of the preceding claims, wherein the precursor condition of MM is Monoclonal Gammopathy of Undetermined Significance (MGUS) or smoldering MM (SMM).

10. The method of any one of the preceding claims, wherein the data indicative of the proportions of the two or more immune cell populations are determined using single-cell RNA sequencing, flow cytometry, Cytometry by Time of Flight (CyTOF), immunohistochemistry, spatial transcriptomics, or spatial proteomics.

11. A method for monitoring a subject with Waldenström macroglobulinemia (WM), or a precursor condition thereof, comprising determining in tumor cells obtained from a sample obtained from the subject and a reference sample expression of two or more gene expression signatures selected from the group consisting of: a. a first gene expression signature comprising ACTG1, S100A4, TMSB4X, TMSB10, and GAPDH; b. a second gene expression signature comprising CXCR4, STK17A, YBX3, FCER2, and LYST; c. a third gene expression signature comprising AHNAK, ZNF596, TTN, FCRK3, and SELL; d. a fourth gene expression signature comprising DUSP22, CD9, VPREB3, GSTP1, and H1FX; and e. a fifth gene expression signature comprising LTB, DSP, NFKB2, BCL7A, and JUP; and wherein: (i) a decrease in activity of the first gene expression signature, and / or(ii) an increase in activity of the second, third, fourth, and / or fifth gene expression signature(s), in the tumor cells of the sample relative to tumor cells of the reference sample indicates that the subject is at risk of disease progression.

12. The method of claim 11, wherein the reference sample was obtained from the subject at an earlier timepoint or is a representative sample from one or more different individuals with IgM MGUS who have or have not progressed to WM.

13. The method of claim 11 or 12, wherein the activity of the first gene expression signature is decreased in the tumor cells of the sample relative to the tumor cells of the reference sample when the activity in the tumor cells of the sample is: a. lower than the median or the 25th percentile of the distribution of the activity in the tumor cells of the reference sample; or b. 1 standard deviation or less, 1.5 standard deviations or less, or 2 standard deviations or less below the mean activity in tumor cells of the reference sample.

14. The method of claim 11 or 12, wherein the activity of the first gene expression signature is decreased in the tumor cells of the sample relative to the tumor cells of the reference sample by about 10% or more, about 20% or more, or about 50% or more, wherein the reference sample was obtained from the subject at an earlier timepoint.

15. The method of any one of claims 11-14, wherein the activity of the second, third, fourth, and / or fifth gene expression signature(s) is increased in the tumor cells of the sample relative to the tumor cells of the reference sample when the activity in the tumor cells of the sample is: a. higher than the median or the 75th percentile of the distribution of the activity in the tumor cells of the reference sample; or b. 1 standard deviation or more, 1.5 standard deviations or more, or 2 standard deviations or more above the mean activity in the tumor cells of the reference sample.

16. The method of any one of claims 11-14, wherein the activity of the first gene expression signature is increased in the tumor cells of the sample relative to the tumor cells of the reference sample by about 10% or more, about 20% or more, or about 50% or more, wherein the reference sample was obtained from the subject at an earlier timepoint.

17. A method for monitoring a subject with Waldenström macroglobulinemia (WM), or a precursor condition thereof, comprising determining in tumor cells obtained from a sample obtained from the subject and healthy B cells of a reference sample expression of two or more gene expression signatures selected from the group consisting of: a. a first gene expression signature comprising ACTG1, S100A4, TMSB4X, TMSB10, and GAPDH; b. a second gene expression signature comprising CXCR4, STK17A, YBX3, FCER2, and LYST; c. a third gene expression signature comprising AHNAK, ZNF596, TTN, FCRK3, and SELL; d. a fourth gene expression signature comprising DUSP22, CD9, VPREB3, GSTP1, and H1FX; and e. a fifth gene expression signature comprising LTB, DSP, NFKB2, BCL7A, and JUP; and wherein: (i) decreased activity of the first gene expression signature in the tumor cells of the sample relative to healthy B cells of the reference sample indicates that the subject is suffering from IgM MGUS, and / or (ii) increased activity of the second, third, fourth, and / or fifth gene expression signature(s) in the tumor cells of the sample relative to healthy B cells of the reference sample indicates that the subject is at risk of disease progression.

18. The method of claim 17, wherein the healthy B cells of the reference sample are obtained from the subject’s sample or from one or more healthy individuals.

19. The method of claim 17 or 18, wherein the activity of the first gene expression signature is decreased in the tumor cells of the sample relative to the healthy B cells of the reference sample when the activity in the tumor cells of the sample is: a. lower than the median or the 25th percentile of the distribution of the activity in the healthy B cells of the reference sample; or b. 1 standard deviation or less, 1.5 standard deviations or less, or 2 standard deviations or less below the mean activity in healthy B cells of the reference sample.

20. The method of claim 17 or 18, wherein the activity of the first gene expression signature is decreased in the tumor cells of the sample relative to the healthy B cells of the reference sample by about 10% or more, about 20% or more, or about 50% or more, wherein the reference sample was obtained from the subject at an earlier timepoint.

21. The method of any one of claims 17-20, wherein the activity of the second, third, fourth, and / or fifth gene expression signature(s) is increased in the tumor cells obtained from the sample relative to the healthy B cells of the reference sample when the activity in the tumor cells of the sample is: a. higher than the median or the 75th percentile of the distribution of the activity in the healthy B cells of the reference sample; or b. 1 standard deviation or more, 1.5 standard deviations or more, or 2 standard deviations or more above the mean activity in the healthy B cells of the reference sample.

22. The method of any one of claims 17-20, wherein the activity of the second, third, fourth, and / or fifth gene expression signature(s) is increased in the tumor cells obtained from the sample relative to the healthy B cells of the reference sample when the activity in the tumor cells of the sample is about 10% or more, about 20% or more, or about 50% or more, wherein the reference sample was obtained from the subject at an earlier timepoint.

23. The method of any one of claims 11-22, wherein increased activity of the fourth gene expression signature in tumor cells in the sample relative to the reference sample indicates that a. the tumor cells are more likely to be CXCR4 wild-type; b. the subject is at risk of developing lymphadenopathy; and / or c. the subject may benefit from treatment with a Bruton’s tyrosine kinase (BTK) inhibitor.

24. A method for monitoring a subject with Waldenström macroglobulinemia (WM), or a precursor condition thereof, comprising determining in tumor cells in a sample obtained from the subject and a reference sample surface expression of CD9 and / or CD23.

25. The method of claim 24, wherein presence or increased expression of CD9 and / or CD23 on the tumor cells of the sample relative to tumor cells of the reference sample indicates that the subject is at risk of disease progression.

26. The method of claim 25, wherein expression of CD9 and / or CD23 is increased in the tumor cells of the sample relative to the tumor cells of the reference sample when expression of CD9 and / or CD23 on the tumor cells of the sample is about 10% or more, about 20% or more, or about 50% or more than expression of CD9 and / or CD23 on the tumor cells of the reference sample, wherein the reference sample was obtained from the subject at an earlier timepoint.

27. The method of claim 24, wherein presence or increased expression of CD9 and / or CD23 on the tumor cells of the sample relative to cells of the reference sample indicates that the subject is at risk of disease progression.

28. The method of claim 27, wherein expression of CD9 and / or CD23 is increased in the tumor cells of the sample relative to the cells of the reference sample when expression on the tumor cells of the sample is: a. higher than the median or the 75th percentile of the distribution of expression on the cells of the reference sample; orb. 1 standard deviation or more, 1.5 standard deviations or more, or 2 standard deviations or more above the mean expression on the cells of the reference sample.

29. The method of any one of claims 24, 27, or 28, wherein the reference sample is a representative B cell population from one or more healthy donors.

30. The method of any one of claims 24-29, wherein presence or increased expression of CD9 on tumor cells in the sample relative to the reference sample indicates that a. the tumor cells are more likely to be CXCR4 wild-type; b. the subject is at risk of developing lymphadenopathy; and / or c. the subject may benefit from treatment with a Bruton’s tyrosine kinase (BTK) inhibitor.

31. The method of any one of claims 24-30, wherein presence or increased expression of CD23 on tumor cells in the sample relative to the reference sample indicates that tumor cells are infiltrating or have infiltrated the subject’s bone marrow.

32. The method of any one of claims 24-31, wherein surface expression of CD9 and / or CD23 is determined using flow cytometry, Cytometry by Time of Flight (CyTOF), immunohistochemistry, or spatial proteomics.

33. 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.