Diagnosis of Plasma Cell Dyscrasias and Monitoring of Minimal Residual Disease by Multiparametric Flow Cytometry

Abstract

Plasma cell dyscrasia (PCD) is a heterogeneous disease that has seen a tremendous change in outcomes due to improved therapies. Over the past few decades, multiparametric flow cytometry has played an important role in the detection and monitoring of PCDs. Flow cytometry is a high-sensitivity assay for early detection of minimal residual disease (MRD) that correlates well with progression-free survival and overall survival. Before flow cytometry can be effectively implemented in the clinical setting, sample preparation, panel configuration, analysis, and gating strategies must be optimized to ensure accurate results. Current consensus methods and reporting guidelines for MRD testing are discussed.

Keywords: High-sensitivity assay; MRD; Minimal residual disease; Multiparametric flow cytometry; Multiple myeloma; Panel optimization; Plasma cell dyscrasia; Plasma cell neoplasm.

Figure 1. Signal-to-noise ratio performance assessment strategy for mAbs

The strategy to assess the performance of each mAb in the PCD panel relies on defining a negatively and positively staining population for each mAb. These populations are defined in Table 3. Next, the median fluorescence intensity (MFI) is determined for each population and the signal-to-noise ratio calculated by dividing the positive population’s MFI by the negative population’s MFI. To qualify as an acceptable mAb, the calculated signal-to-noise value should be greater than the recommend value in Table 4. Panel 1A: For CD45, erythroid precursors are used as the negative population defined as SSC^lo/CD45⁻ events (r1: yellow dots and corresponding histogram) and T cells as the positive population defined as CD45^br/SSC^lo (r2) and CD19⁻/CD56⁻ events (r3: black dots and corresponding histogram). Panel 1B: For CD19, NK cells are used as the negative population defined as CD45^br/CD56⁺ events (r4: yellow dots and corresponding histogram) and B cells as the positive population defined as CD19+/CD45^br events (r5: black dots and corresponding histogram). Panel 1C: For cKappa light chain, cLambda light chain⁺ B cells are used as the negative population defined as CD45^br/SSC-A^lo (r6) and CD19⁺/cLambda light chain⁺ events (r7: yellow dots and corresponding histogram) and cLambda light chain⁻ cells as the positive population defined as CD45^br/SSC-A^lo (r6) and CD19⁺/cLambda light chain⁻ events (r8: black dots and corresponding histogram). Panel 1D: For CD138 a procedural control for immunophenotyping, CD-Chex CD103™ Plus cells which contains a CD138 positive population are spiked into a bone marrow sample. B cells are used as the negative population defined as CD45^br/CD19⁺ events (r9: yellow dots and corresponding histogram) and spiked control cells as the positive population defined by FSC-A and SSC-A (r10) and as CD45^dim/CD81⁺ events (r11: black dots and corresponding histogram). Note these data are all gated on R1 & R2 & R3, ‘Total Leukocytes’ as defined in Fig. 2. In data not shown, for the CD19 negative and positive populations, and CD138 negative population a FSC-A vs SSC-A plot was used to define lymphocytes.

Figure 2. Gating strategy used for the identification of normal and abnormal plasma cells

Panel 2A: A rectangular region (R1) is placed on the bivariate plot of Time vs. SSC-A to circumscribe all events collected in continuity. This dot plot can be used to assess the chronologic heterogeneity of the acquisition by eliminating any invalid events such as air bubbles which occur during the run. Panel 2B: Serial gating is performed by applying the region R1 to a bivariate plot of FSC-A vs. FSC-H. A rhomboid region (R2) is then created to include the singlet cell population. Caution should be exercised not to exclude hyperdiploid or tetraploid plasma cells which may exhibit aberrantly high light scatter characteristics. Panel 2C: Gate a bivariate plot of FSC-A vs. SSC-A on (R1 and R2). An irregular region (R3) is created to circumscribe the cell population of interest and exclude aggregated events, debris, and dead and apoptotic events. Create 3 separate bivariate plots (Panel 2D: CD138 vs. CD38), (Panel 2E: CD45 vs. CD38), and (Panel 2F: CD45 vs. CD138). Gate each of these bivariate plots on ‘Total Leukocytes’ (R1 & R2 & R3). An irregular region (R4) is drawn on Panel 2D circumscribing the CD138⁺/CD38⁺ events; another irregular region (R5) is drawn on Panel 2E circumscribing the CD45^+/-/CD38⁺ events; a third irregular region (R6) is drawn on Panel 2F circumscribing the CD45^+/-/CD138⁺ events. CD45 is helpful for defining PCs and identifying any CD38⁻ or CD138⁻ PC populations. The Boolean gate (R1 & R2 & R3 & R4 & R5 & R6) defines both normal (blue) and abnormal (red) PCs for subsequent immunophenotyping.

Figure 3. Immunophenotypic profiles of normal and abnormal PCs

The immunophenotypic profiles of bone marrow from a patient (Panel 3A) with no obvious hematological disease at testing; (Panel 3B) a patient with PCD; and (Panel 3C) a patient with multiple myeloma MRD are shown. As PCD is a heterogeneous disease, no single marker can be reliably used to identify all abnormal cell populations. Instead the interpretation is based on all the markers included in the analysis. In this example, 6 different bivariate plots that were each separately gated on plasma cells using the strategy defined in Fig. 2 are shown. Blue: normal plasma cells; Red: neoplastic plasma cells.

Figure 3. Immunophenotypic profiles of normal and abnormal PCs

The immunophenotypic profiles of bone marrow from a patient (Panel 3A) with no obvious hematological disease at testing; (Panel 3B) a patient with PCD; and (Panel 3C) a patient with multiple myeloma MRD are shown. As PCD is a heterogeneous disease, no single marker can be reliably used to identify all abnormal cell populations. Instead the interpretation is based on all the markers included in the analysis. In this example, 6 different bivariate plots that were each separately gated on plasma cells using the strategy defined in Fig. 2 are shown. Blue: normal plasma cells; Red: neoplastic plasma cells.

Figure 4. Presence of mast cells, hematogones and erythroid precursors can be used for quality assessment of bone marrow aspirates

Panel 4A: Mast cells are identified by drawing a rectangular region (R7) to circumscribe CD27⁻/CD117⁺ events. Panel 4B – C: B cells are first identified by creating a region (R8) on a bivariate plot of CD56 vs. CD19 to circumscribe CD56⁻/CD19⁺ cells. Then a bivariate plot of CD45 vs. CD81, gated on R8 and Total Leukocytes is used to define mature and immature B cells. Hematogones (immature B cells) are defined by R9 as the CD45^dim/CD81^br population. Panel D: Erythroid precursors are defined by R10 on a bivariate plot of SSC-A vs. CD45 which circumscribes the CD45^−/dim/SSC^lo population. Note: these histograms are all gated on ‘Total Leukocytes’ identified using the Boolean strategy (R1 & R2 & R3) defined in Fig. 2.