It would be very beneficial if the status of cancers could be determined from a blood specimen. However, peripheral blood leukocytes are very heterogeneous between individuals, and thus high-resolution technologies are likely required. We used cytometry by time-of-flight and next-generation sequencing to ask whether a plasma cell cancer (multiple myeloma) and related precancerous states had any consistent effect on the peripheral blood mononuclear cell phenotypes of patients. Analysis of peripheral blood samples from 13 cancer patients, 9 precancer patients, and 9 healthy individuals revealed significant differences in the frequencies of the T-cell, B-cell, and natural killer–cell compartments. Most strikingly, we identified a novel B-cell population that normally accounts for 4.0% ± 0.7% (mean ± SD) of total B cells and is up to 13-fold expanded in multiple myeloma patients with active disease. This population expressed markers previously associated with both memory (CD27+) and naïve (CD24loCD38+) phenotypes. Single-cell immunoglobulin gene sequencing showed polyclonality, indicating that these cells are not precursors to the myeloma, and somatic mutations, a characteristic of memory cells. SYK, ERK, and p38 phosphorylation responses, and the fact that most of these cells expressed isotypes other than IgM or IgD, confirmed the memory character of this population, defining it as a novel type of memory B cells. Cancer Immunol Res; 3(6); 650–60. ©2015 AACR.
The human immune system has a unique role in cancer pathogenesis, modulating therapeutic responses, and disease outcome. Especially with emerging immune therapeutics and treatment regimens, the characterization of cancer-associated immunologic signatures has been of considerable interest. However, most studies of tumor “immunomes” rely on gene expression data and focus on the tumor microenvironment (1–3). To some extent, this may be due to the limited resolving power of standard cell analysis methods such as flow cytometry and the heterogeneity of peripheral blood cells. However, recent advances in cell phenotyping technology, particularly cytometry by time-of-flight (CyTOF), which supports 40-label analysis (4–6), and next-generation sequencing of immunoglobulin (Ig) genes (7, 8), have greatly increased the possible depth of analysis at the single-cell level and expanded the resolving power of immune phenotyping tremendously. This suggests that it may be possible to detect disease-related immune signatures in human peripheral blood using these methods. As peripheral blood is readily and safely obtainable from almost any patient, finding useful signatures of disease in this type of specimen could be very valuable both clinically and in understanding the immune response to particular cancers at a systems biology level.
For this reason, we analyzed the peripheral blood of patients with hematopoietic cancer (multiple myeloma) and its corresponding precancer [asymptomatic myeloma and monoclonal gammopathy of undetermined significance (MGUS)]. Multiple myeloma, a malignancy of the B lineage, is characterized by the accumulation of clonal plasma cells in the bone marrow and the production of monoclonal Ig. The corresponding precancer diseases, MGUS and asymptomatic myeloma, are characterized by the production of monoclonal Ig and the lack of symptoms in often undiagnosed patients. They are not considered malignant because they do not always progress into multiple myeloma; however, patients have an approximate risk of 1% per year to develop multiple myeloma (9), and many, if not all, multiple myeloma cases develop from a preceding MGUS or asymptomatic myeloma (10, 11).
Multiple myeloma represents a prototypical disease model for tumor–microenvironment interactions (12, 13), and recent studies of the multiple myeloma microenvironment have underlined its role in promoting tumor growth and survival (14), as well as protection from T-cell responses (15) and chemotherapeutics (16, 17). The myeloma cells “reprogram” their environment, influencing many chemokine and cytokine levels, including IL6, IL1α, IL1β, MIP-1α, TNFα, M-CSF, and VEGF (13). With this profound effect on the bone marrow—the organ in which hematopoiesis takes place in adults—we reasoned that multiple myeloma and perhaps its precursor diseases might have a significant impact on immune cells in the peripheral blood that could be detectable with these new technologies.
Here, we report that we have found major effects of multiple myeloma, but not MGUS or asymptomatic myeloma, that are detectable in peripheral blood cells of patients with active disease, in particular significant changes in the T-cell, B-cell, and natural killer (NK)–cell compartments, and most notably, the expansion of a novel, polyclonal B-cell subset.
Materials and Methods
Thirteen multiple myeloma, 9 MGUS/asymptomatic myeloma patients (either untreated or without cancer-specific treatment during the last 6 months; Table 1 and Supplementary Table S1), and 5 treatment-naïve colorectal cancer patients (Supplementary Fig. S10) were recruited in accordance with federal and local human subjects regulations (Institutional Review Board protocol ID 25310). Nine age- and sex-matched healthy individuals were recruited through the Stanford Blood Center. The median ages of the multiple myeloma, precancer, and healthy control groups were 67, 69, and 68 years, respectively. Male subjects accounted for 54% in the cancer cohort, 56% in the precancer cohort, and 56% in the healthy control group.
Peripheral blood mononuclear cells (PBMC) were isolated from up to 40 mL of freshly drawn heparin anticoagulated blood using Ficoll-Paque PLUS (GE Healthcare Bio-Sciences AB) centrifugation and resuspended in cell culture medium (Supplementary Table S2). Freshly isolated PBMCs were immediately used for in vitro stimulation and CyTOF staining; remaining cells were frozen after the addition of an equal volume of FBS containing 20% DMSO (both Sigma Aldrich).
For CyTOF, PBMCs were stimulated in 1 mL of cell culture medium containing 20 ng/mL phorbol 12-myristate 13-acetate (PMA), 1 μmol/L ionomycin (Sigma Aldrich), 5 μg/mL R848, or 3 μg/mL CpG ODN2216 (both InvivoGen) or left unstimulated for 6 hours. GolgiPlug (1 μL) and 0.7 μL GolgiStop (both BD Biosciences) were added at the beginning of the stimulation for PMA/ionomycin or to unstimulated samples, or added after 2 hours for R848 or after 3 hours for CpG ODN2216/DOTAP. DOTAP liposomal transfection reagent (Roche) was added at 1 μL/mL for CpG ODN2216 stimulation. Stimulation was done at 37°C and 5% CO2.
For phosphorylation analysis, stimulation was done in reverse time order in 250 μL prewarmed cell culture medium containing 50 μmol/L CpG ODN2006 (InvivoGen), or 10 μg/mL goat anti-human IgM (Life Technologies) and 10 μg/mL goat F(ab)2 anti-human IgG (AbD Serotec). For B-cell receptor (BCR) stimulation, H2O2 (MP Biomedicals) was added within 10 seconds after addition of the stimulating antibodies to a final concentration of 3.3 mmol/L.
CyTOF antibody labeling and staining
Purified antibodies were labeled using MaxPar DN3 kits (Fluidigm) and stored at 4°C at 0.2 mg/mL in W buffer (Fluidigm) containing antibody stabilizer (Candor).
For staining, 1 to 10 × 106 cells were washed in CyFACS buffer (Supplementary Table S2) and stained in 50 μL CyFACS buffer containing a surface antibody cocktail (Supplementary Table S3) for 30 minutes. The γδ T-cell antibody stain was done separately, and the metal-labeled anti-PE antibody was added to the surface antibody cocktail. Cells were washed in CyPBS, PBS (Ambion), and stained with maleimide-DOTA loaded with 115 In for 20 minutes at room temperature. After washing in CyFACS and CyPBS, cells were fixed in 150 μL of 2% paraformaldehyde (PFA; Electron Microscopy Sciences) over night. Cells were washed twice in permeabilization buffer (eBioscience) and stained in 50 μL intracellular antibody cocktail (Supplementary Table S3) for 30 minutes on ice. After another wash in permeabilization buffer and CyPBS, cells were stained with iridium DNA intercalator (Fluidigm) for 20 minutes at room temperature, washed 2× in CyFACS, 2× in CyPBS, 2× in H2O, and resuspended in H2O for analysis on a CyTOF instrument (Fluidigm).
Cell signaling analysis
Cells were thawed, washed twice in prewarmed cell culture medium, and rested for 2 hours at 37°C with 5% CO2. Cells were washed in pure PBS and stained with zombie aqua (BioLegend), washed once in pure PBS and stained with CD24 and CD38 antibodies (Supplementary Table S4) for 15 minutes at 4°C. After washing in prewarmed cell culture medium, cells were resuspended in 250 μL of warm cell culture medium and immediately stimulated as described in the cell stimulation section. Stimulation was stopped by adding 150 μL of 4% PFA and incubated for 15 minutes at room temperature. Cells were washed with pure PBS and permeabilized in methanol at −80°C overnight. After washing twice in pure PBS, cells were stained with an intracellular staining cocktail of antibodies specific for phosphorylated signaling molecules and additional phenotyping markers (Supplementary Table S4), washed, and finally stained with AF488-labeled goat anti-rabbit antibody (Supplementary Table S4) before analysis on an LSRII flow cytometer (BD Biosciences).
Single-cell immunoglobulin sequencing
PBMCs were thawed and stained with fluorochromes according to Supplementary Table S4. Single B cells were identified by their forward scatter/side scatter (FSC/SSC) characteristics, CD19, and CD20 expression and sorted into RT-PCR buffer in 96-well plates according to the gating strategy in Fig. 4A and Supplementary Fig. S8. Ig genes were amplified and sequenced as previously described (7, 8).
Cells were stained as described for single-cell Ig sequencing and bulk sorted according to the gating scheme in Supplementary Fig. S8. Cells were stained with Vysis LSI CCND1 and Vysis LSI IGH probes (both Abbott Molecular) according to the standard protocols, and 200 cells per population (105 for CD19+CD20+CD24hiCD38−CD27−IgD−IgM− B cells) were analyzed.
Cytometry data analysis and statistics
All two-dimensional gating analysis was done using FlowJo (Treestar). For heatmaps (Figs. 1A and 5; Supplementary Figs. S2 and S9), data in FlowJo-exported tables were trimmed to the 95th and 5th percentiles per gated population and visualized using heatmap.2 (gplots version 2.11.0). Principal component analysis (PCA) was done using prcomp (stats version 2.15.3) and visualized with ggplot2 (version 0.9.3.1) and scatterplot3d (version 0.3–34).
For statistics in scatterplots (Fig. 2B and Supplementary Fig. S3), we used ANOVA. P values were adjusted using the Benjamini–Hochberg correction, and all differences that reached q values below 0.05 were followed up with TukeyHSD, the results of which are indicated as P values in the respective plots. All statistics were done in R version 2.15.3 (18).
Citrus version 0.05 (https://github.com/nolanlab/citrus; ref. 19) was used on all healthy control and all multiple myeloma samples. Each file was pregated on live CD45+ cells in FlowJo. The file sample size was set to 10,000 events and CD33, CD4, CD20, CD8, CD16, CD19, CD11c, CD24, CD10, CD1c, CD14, CCR7, CD7, CD28, CD5, CD38, CD45RA, CD123, CD45RO, CD56, HLA-DR, CD25, TCR-αβ, and CD127 were used as clustering markers. Median differences (median columns) were calculated for IgM, CD57, IgD, CD27, CCR7, CD28, TNFα, IL17A, and HLA-DR. The minimum cluster size was set to 0.02%, and Significance Analysis of Microarrays (SAM) was used as model type.
Phenotypic diversity of PBMC subsets in multiple myeloma
We used CyTOF with a 39-marker panel (Supplementary Table S3) to analyze the human immune system in freshly isolated PBMCs from multiple myeloma patients, from precancer MGUS and asymptomatic myeloma patients (Table 1 and Supplementary Table S1), and from healthy individuals. Frequencies of all major PBMC subsets and their cytokine expression patterns were determined by two-dimensional gating as percentages of the corresponding parent populations in four different stimulus conditions (see Supplementary Fig. S1 and Supplementary Table S5 for typical gating and a list of all applied gates).
We visualized cell frequencies in each of the 244 two-dimensional gates in a heatmap representing the immune phenotypic diversity in the peripheral blood of patients with multiple myeloma, precancer patients, and healthy controls (Fig. 1A). Samples were arranged by disease group, and no clustering was applied to the columns. Emphasizing the phenotypic heterogeneity of the dataset, sample clustering by Euclidian distances of relative cell frequencies did not lead to the formation of clear clusters for cancer patients, precancer patients, and healthy individuals (Supplementary Fig. S2).
The direct comparison of individual population frequencies between disease groups revealed significant differences in the T (CD57+CD8+CD45RA+ αβ and regulatory) and B lymphocyte (total, CD27+, and transitional) compartments in different stimulus conditions as summarized in Supplementary Fig. S3.
To analyze whether individual markers or combinations of those could distinguish cancer samples from precancer or healthy samples, we applied PCA to cell frequencies in the same 244 two-dimensional gates. The first four principal components described close to 50% of the variance in our dataset (Fig. 1B), and plotting principal components one to three confirmed phenotypic heterogeneity in samples from the cancer group, whereas the precancer and healthy control samples tended to cluster (Fig. 1C and D). Nevertheless, PC1 separated a subset of cancer samples from precancer and healthy donor samples. The loadings for PC1 were dominated by αβ T-cell gates, including costimulatory molecules (CD28), and markers that have been associated with T-cell exhaustion (CD57), or homing (CCR7), both on CD4+ and CD8+ T cells (Fig. 1E). Surprisingly, CD19+CD20+ B cells with a distinct marker combination (CD24loCD38+) and expression of CD27 contributed to PC1 across different stimuli conditions (unstimulated and after PMA/ionomycin stimulation, Fig. 1E, in red). This population was particularly interesting because it combined two markers (CD24loCD38+) that have been associated with the naïve stage of B-cell differentiation (20), and CD27, which is associated with memory B cells (21). This is a novel phenotype that has not been reported previously.
CD24loCD38+CD27+ B cells are uniquely expanded in the peripheral blood of multiple myeloma patients and associated with active disease
On human peripheral blood B cells, CD24 and CD38 expression levels can be measured to identify memory (CD24+CD38−), naïve (CD24loCD38+), and transitional (CD24+CD38hi) subsets (20).
As expected, the majority of CD24+CD38− B cells expressed CD27 in the peripheral blood of all three groups (multiple myeloma, precancer, and healthy individuals), and we detected only minor amounts of CD24loCD38+ B cells expressing CD27 in precancer patients and healthy individuals (Fig. 2A and B). Strikingly, in the multiple myeloma samples, we identified an up to 13-fold expanded CD27+ population among the CD24loCD38+ B cells when compared with that in healthy controls (Fig. 2A and B). Notably, the expression of CD27 on CD24loCD38+ B cells in multiple myeloma samples showed a very broad range (5.3%–66.6%) with samples clustering at the high and low ends of this distribution, suggesting the existence of two patient populations (Fig. 2B, shaded in gray). This dichotomy was already noted in the PCA, where PC1 separated only a subset of multiple myeloma samples from precancer and healthy control samples (Fig. 1C). Nevertheless, the frequency of CD24loCD38+CD27+ B cells was significantly higher in multiple myeloma samples than in precancer samples (P = 0.005) and never exceeded 10% in healthy controls (P = 0.004; Fig. 2B, P values for unstimulated samples). In vitro stimulation showed only minor influences on CD27 expression on CD24loCD38+ B cells, suggesting that the observed phenotype is not due to transient activation. CD24loCD38+CD27+ B cells in multiple myeloma samples showed mixed characteristics of other B-cell development-associated markers: 15.3% ± 9.5% expressed CD10; 27.5% ± 18.3% were IgD+, and 63.1% ± 27.3% expressed TNFα after PMA/ionomycin stimulation (all mean ± SD; Supplementary Fig. S4). Notably, none of the patients were diagnosed with plasma cell leukemia or showed phenotypic characteristics of malignant or de-differentiated plasma cells in the CD24loCD38+ gate (Fig. 2A and Supplementary Fig. S5).
Given the 39-dimensional space of our CyTOF dataset, two-dimensional gating fails to capture the richness that comes from including all possible marker combinations. We therefore used a new analysis method, Citrus (19), for the identification of significantly different marker expressions on cell populations from high-dimensional data from multiple myeloma patients and healthy individuals (Fig. 2C and Supplementary Fig. S6). Among the different expression levels of CD57 on T-cell and NK-cell subsets (Supplementary Fig. S6B and S6C), Citrus detected significantly higher CD27 levels in two B-cell subsets (Fig. 2C). Cluster A (Fig. 2C) showed lower CD24 expression and therefore most likely represents the CD38+CD24lo B-cell subset identified with two-dimensional gating and PCA.
Taken together, CyTOF data analyzed using two different methods enabled us to identify a uniquely expanded B-cell population with aberrant expression levels of CD24, CD38, and CD27 in the peripheral blood of many multiple myeloma patients (Fig. 2B). This population was not evident in MGUS or asymptomatic myeloma patients and healthy individuals.
We then verified these findings with flow cytometry, analyzing PBMCs from all multiple myeloma samples, which confirmed the expansion of the CD24loCD38+CD27+ B cells (Supplementary Fig. S7). In addition, we also analyzed IgA, IgM, and IgD expression levels on B-lineage cells, and serum levels of IgG, IgA, IgM, kappa, and lambda immunoglobulin light chains (Fig. 3 and Table 2).
We found that only a minority of this novel B-cell population expressed IgD (29.4% ± 17.2% IgD+) or IgM (32.1% ± 16.0% IgM+, both mean ± SD), indicating that they largely represent a differentiation stage after class-switch recombination (Table 2). With 20.8% ± 10.1% of this population expressing IgA, the majority can be assumed to be IgG+. Strikingly, the expansion of CD27+ B cells among CD24loCD38+ cells was associated with high serum levels of the patient's predominant Ig light chain and therefore might be useful as a measure of disease activity. Disease activity was also reflected in an either very high or very low K/Λ ratio depending on the M protein type of the disease (Table 2).
Immunoglobulin genes of single CD24loCD38+CD27+ B cells from multiple myeloma patients are polyclonal and show somatic mutations
To analyze clonality and whether the CD24loCD38+CD27+ population is a memory or naïve B-cell population, we sequenced the Ig genes from single CD24loCD38+CD27+IgD−IgM−IgA− B cells from 2 multiple myeloma patients (sort gates in Fig. 4A).
In total, light chain sequences from 93 individual cells were sequenced and further analyzed. Both patients used a variety of different V region genes that partly overlapped between patients (Fig. 4B and C). The direct comparison of individual cells' Ig sequences within the same sample revealed 8 cells in subject 7 and 7 cells in subject 11 whose sequences occurred twice (Fig. 4D). Nevertheless, based on different Ig sequences and the variety of V regions used, the CD24loCD38+CD27+ B-cell compartment has to be considered polyclonal, although an underlying antigen-driven process responsible for the expansion of this B-cell subset is possible. Notably, there were no identical sequences across both patients.
We detected an average of 18 (subject 7) or 14 (subject 11) somatic mutations per V region, 5 (subject 7) and 3 (subject 11) of which were silent and 13 (subject 7) or 11 (subject 11) were nonsilent mutations. None of the V region sequences were germline in either patient (Fig. 4E), a further indication that this B-cell subset is a memory population.
To further strengthen the case that the investigated B-cell populations are not directly related to the malignant plasma-cell clone, we FACS-sorted bulk CD24loCD38+CD27+, CD24loCD38+CD27−IgM+, CD24hiCD38−CD27+, and CD24hiCD38−CD27−IgM−IgD− B cells (Supplementary Fig. S8) from 1 patient whose malignant plasma-cell population contained the t(11;14). Sorted cells were analyzed with FISH, and the t(11;14) could not be detected in any of these populations (Supplementary Fig. S8).
CD24loCD38+CD27+ B cells show phosphorylation patterns similar to total CD27+ memory B cells after BCR and TLR9 stimulation
Aberrant intracellular signaling has already been demonstrated in cancer, especially in lymphoma B cells (22). We measured the phosphorylation of one BCR proximal (SYK) and two downstream (ERK and p38) signaling molecules in response to BCR stimulation or BCR-bypassing TLR9 stimulation. The BCR was stimulated with polyclonal anti-human IgG and IgM in the presence of H2O2. The addition of H2O2 can amplify early signaling events by inhibiting protein tyrosine phosphatase activity (23), thereby increasing signal strength. Phosphorylation levels in either unstimulated cells or after the addition of H2O2 will be referred to as baseline phosphorylation.
The quantification of signaling molecule phosphorylation with flow cytometry requires intracellular staining after PFA fixation and methanol permeabilization, which particularly affects CD38 PE-Cy7 staining. Because of this decrease in the CD38 signal, CD24loCD38+ and CD24hiCD38− B cells will be referred to as CD24lo and CD24hi B cells (Fig. 5A). CD24hi and CD24lo populations corresponded to CD24+CD38− and CD24loCD38+ populations respectively after staining without fixation/permeabilization, as shown by the predominant expression of CD27 on CD24hi (memory) B cells in healthy individuals after methanol permeabilization (Supplementary Fig. S9A). Phosphorylation responses to anti-human IgG/IgM or TLR9 ligand CpG ODN2006 stimulation were clearly distinguishable from baseline phosphorylation, as shown in Fig. 5B.
No significant phosphorylation was detectable at baseline in any subpopulations of unstimulated B cells. The addition of H2O2 resulted in slightly increased phosphorylation over time that was most pronounced in ERK and p38 (Fig. 5C). Interestingly, total CD27+ and CD24loCD27+ B cells showed very similar phosphorylation levels in both healthy individuals and PBMCs from multiple myeloma patients. Stimulation with CpG ODN2006 or anti-human IgG/IgM elicited up to 21-fold higher phosphorylation when compared with that of H2O2 controls (Fig. 5D and E). The unique phosphorylation patterns of CD27+, CD27−, and total B-cell populations were consistent at baseline and after stimulation, suggesting essentially different intracellular signaling responses in these subsets; however, significant differences between healthy and multiple myeloma B-cell phosphorylation levels could be detected neither at baseline nor after stimulation. Overall, CpG ODN2006 stimulation caused mostly ERK phosphorylation, whereas BCR stimulation elicited predominantly SYK and p38 phosphorylation in the analyzed populations (Fig. 5E). The unique phosphorylation patterns of CD27+, CD27−, and total B-cell subsets at baseline and after BCR stimulation were confirmed in two additional sample pairs (Supplementary Fig. S9).
In this study, we present high-dimensional cytometry data on the human immunologic landscape of peripheral blood cells across most of the known developmental stages of multiple myeloma (multiple myeloma, asymptomatic myeloma, MGUS, and healthy individuals). We manually determined the frequencies of all major PBMC subsets (αβ T, γδ T, B, NK, myeloid dendritic cells, plasmacytoid dendritic cells, and monocytes) and the expression of additional phenotypic and functional markers in four different stimulus conditions. With 434 two-dimensional initial gates per sample, to the best of our knowledge, this is the most detailed phenotypic and functional analysis of PBMC subsets in this disease. After removing mutually exclusive gates (e.g., from CD38+CD4+ and CD38−CD4+ T-cell gates, only the CD38+CD4+ was kept), 244 of these gates (listed in Supplementary Table S5) were used for statistical analyses and data visualization. Cell frequencies in each of these gates were compared between disease groups, one at a time, and using PCA. The manual comparisons allowed a very detailed view of particular cell subsets. Elevated numbers of CD57+CD45RA+CD8+ T cells (Supplementary Fig. S3A) could be expected given the previous work of Sze and colleagues (24); cells sharing this phenotype are considered to be clonally restricted and associated with a favorable disease outcome. In addition, we detected lower numbers of regulatory T cells (Treg) in the peripheral blood of multiple myeloma patients (Supplementary Fig. S3A). In multiple myeloma, the Treg/Th17 balance correlates with disease outcome (25, 26); however, differences in frequencies of IL17-producing cells between patients with multiple myeloma, precancer patients, and healthy individuals did not reach statistical significance, and Treg frequencies in the peripheral blood of multiple myeloma patients are a matter of debate (26, 27).
We detected significantly lower B-cell frequencies in total leukocytes of multiple myeloma patients when compared with those in healthy individuals (Supplementary Fig. S3A). In a study involving hundreds of patients, low CD19+ cell frequencies have been associated with an advanced disease stage and higher B-cell numbers with a better prognosis of multiple myeloma (28). The low percentages in our study may therefore reflect an advanced stage of disease in our patients, most of whom were in International Staging System (ISS) stadium 3 (Table 1), and/or the limited sample size. Memory (CD20+CD27+) B-cell frequencies were also elevated (Supplementary Fig. S3A) as shown along with all other direct subset comparisons in Supplementary Fig. S3B.
The most striking finding was the selective expansion of CD24loCD38+CD27+ B cells in the peripheral blood of multiple myeloma patients with active disease as measured by serum immunoglobulin light chain levels. This B-cell phenotype has not been described before and attracted our attention for two reasons. First, because of a curiously mixed phenotype, in which, although CD24loCD38+ expression is a characteristic of naïve B cells, CD27 is currently one of the most well-established memory B cell markers and has been associated with the presence of somatic mutations in Ig genes (21, 29, 30). Second, this phenotype was stable even after 6 hours of in vitro stimulation. CD24 expression on CD24loCD38+CD27+ B cells in multiple myeloma patients appeared to be higher than in their healthy counterparts, but was still lower than in CD24+CD38− memory B cells (Fig. 2A). Even though this population shows variable CD24 expression (Supplementary Fig. S7), CD38 expression was clearly present while (CD27+) memory B cells have been shown to be CD38− in various studies (20, 31).
Citrus is a new analysis tool that compares marker expression in individual multidimensional cell clusters between sample groups in a statistically rigorous fashion (19). Besides differences in CD27 expression on B-cell subsets (Fig. 2C), Citrus confirmed differences in CD57 expression on T-cell and NK-cell subsets (Supplementary Fig. S6B and S6C). Automated gating/clustering strategies confirmed our findings from data analyst–influenced manual gating with high statistical significance (false discovery rate < 0.01), but did not add any categorically new findings. Thus, the two-dimensional analysis approach even in this 39-dimensional dataset was still able to reveal most of the significant differences between sample groups, although it could not address all marker combinations on every single cell. The advantages of Citrus are represented in the “unsupervised” nature of the approach and the enormous reduction of analysis time.
Multiple myeloma plasma cells are usually not highly proliferative, and the dividing multiple myeloma precursor pool is assumed to comprise post–germinal-center B cells (31–34), which could match the phenotype of CD24loCD38+CD27+ B cells (Supplementary Fig. S4). Yet, the existence and phenotype of clonotypic B cells in the peripheral blood of multiple myeloma patients is still a matter of debate (33, 35–37). Taking into account the phenotype of CD24loCD38+CD27+ B cells (Fig. 2A and Supplementary Fig. S5), the polyclonality of immunoglobulin genes, and the absence of multiple myeloma clone-specific translocations (Supplementary Fig. S8), this B-cell population is unlikely to be directly derived from or preceding the multiple myeloma plasma-cell population. The presence of somatic mutations in all single cells analyzed characterized this population as a memory-cell population, raising a question about the exclusive use of CD24 and CD38 for the identification of naïve and memory B cells. To determine whether CD24loCD38+CD27+ B cells can also be expanded in malignancies other than multiple myeloma, we analyzed peripheral blood samples from 5 colorectal cancer patients (Supplementary Fig. S10) and could not detect any significant expansion of this population. Therefore, we suggest that the polyclonal expansion of CD24loCD38+CD27+ B cells in multiple myeloma is most likely a phenomenon driven by active disease that results in some cues to which this B-cell type is responsive.
Earlier studies have shown aberrant intracellular signaling in different types of lymphoma B cells and that BCR signaling can be phenotype dependent (22, 38). Our intracellular signaling data indicated that the multiple myeloma B-cell compartments are not obviously different from their counterparts in healthy individuals on a functional level. In this context, CD24loCD38+CD27+ B cells behaved very similarly to “conventional” CD27+ B cells, further indicating that these cells are not in some unusual state. The trigger for their expansion in multiple myeloma can be assumed in some cues exposed during active disease.
Disclosure of Potential Conflicts of Interest
W.H. Robinson is the founder, has ownership interest (including patents), and is a consultant/advisory board member for Atreca, Inc. No potential conflicts of interest were disclosed by the other authors.
Conception and design: L. Hansmann, C.-H. Ju, W.H. Robinson, M.M. Davis
Development of methodology: L. Hansmann, C.-H. Ju, W.H. Robinson
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): L. Hansmann, L. Blum, M. Liedtke
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): L. Hansmann, L. Blum, W.H. Robinson, M.M. Davis
Writing, review, and/or revision of the manuscript: L. Hansmann, L. Blum, M. Liedtke, W.H. Robinson, M.M. Davis
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): L. Hansmann, M.M. Davis
Study supervision: L. Hansmann, W.H. Robinson, M.M. Davis
Other (generation of figures): L. Hansmann
Single-cell sorting was performed in the Stanford Shared FACS Facility using NIH S10 Shared Instrument Grant (S10RR025518-01). L. Hansmann was supported by a research fellowship from the German Research Foundation (DFG, HA 6772/1-1). This research was funded by grants (to M.M. Davis) from the NIH (U19 AI00019) and the Howard Hughes Medical Institute.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
The authors thank Jonathan M. Irish (Vanderbilt University), William O'Gorman (now at Genentech), and Michael Leipold and Henrik Mei (at the Human Immune Monitoring Center, Stanford) for very helpful phosphoflow and CyTOF advice, Dana Bangs at Stanford Cytogenetics for help with FISH analyses, Cindy Kin for help with consenting patients, as well as Alessandra Aquilanti and Murad Mamedov for critically reading the article. CyTOF analysis was performed in the Human Immune Monitoring Center at Stanford, and single-cell sorting was performed in the Stanford Shared FACS Facility.
Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/).
- Received December 12, 2014.
- Revision received February 9, 2015.
- Accepted February 12, 2015.
- ©2015 American Association for Cancer Research.