Deep profiling of apoptotic pathways with mass cytometry identifies a synergistic drug combination for killing myeloma cells

Abstract

Multiple myeloma is an incurable and fatal cancer of immunoglobulin-secreting plasma cells. Most conventional therapies aim to induce apoptosis in myeloma cells but resistance to these drugs often arises and drives relapse. In this study, we sought to identify the best adjunct targets to kill myeloma cells resistant to conventional therapies using deep profiling by mass cytometry (CyTOF). We validated probes to simultaneously detect 26 regulators of cell death, mitosis, cell signaling, and cancer-related pathways at the single-cell level following treatment of myeloma cells with dexamethasone or bortezomib. Time-resolved visualization algorithms and machine learning random forest models (RFMs) delineated putative cell death trajectories and a hierarchy of parameters that specified myeloma cell survival versus apoptosis following treatment. Among these parameters, increased amounts of phosphorylated cAMP response element-binding protein (CREB) and the pro-survival protein, MCL-1, were defining features of cells surviving drug treatment. Importantly, the RFM prediction that the combination of an MCL-1 inhibitor with dexamethasone would elicit potent, synergistic killing of myeloma cells was validated in other cell lines, in vivo preclinical models and primary myeloma samples from patients. Furthermore, CyTOF analysis of patient bone marrow cells clearly identified myeloma cells and their key cell survival features. This study demonstrates the utility of CyTOF profiling at the single-cell level to identify clinically relevant drug combinations and tracking of patient responses for future clinical trials.

Fig. 1. CyTOF probes for detection of BCL-2 family members.

a Representative histograms of the expression of the pro-survival proteins MCL-1, BCL-2, and BCL-XL in U266B1 cells (shaded gray histograms) versus isogenic CRISPR/Cas9-knockout control cells lacking these pro-survival proteins (black lines) analyzed by intracellular flow cytometry. b Representative histograms of the expression of pro-apoptotic BIM and BAX in untreated KMS-12-PE cells (shaded gray histograms) or BAK in KMS-12-PE cells treated with 100 nM tBid (aBAK) (shaded gray histograms) versus isogenic CRISPR/Cas9-knockout controls (black lines) analyzed by intracellular flow cytometry. c Immunoblotting of cell lysates from 12 different myeloma cell lines for the indicated BCL-2 family proteins and the loading control HSP70. d Histograms of the expression of pro-survival and pro-apoptotic proteins in the same myeloma cell lines assayed by CyTOF. e Dot-plot comparisons of western blot values quantified using densitometry and median expression value by CyTOF for each BCL-2 family protein. Pearson correlation values (r) are calculated for each marker using all 12 multiple myeloma cell lines. Data from c, d, and e are representative of two independent experiments.

Fig. 2. Substantial changes in cell cycle and signaling states accompany apoptotic cell death following treatment of multiple myeloma cells with cytotoxic drugs.

a Representative histograms of cisplatin levels (upper panels) and dot plots of cPARP versus aCASP3 levels (lower panels) in MM.1S cells over the time course of bortezomib or dexamethasone treatment. b Heatmap summary of relative protein expression levels measured by mass cytometry in MM.1S cells over time with the indicated treatments. The color scale indicates z-score values after z-score normalization across rows of the asinh ratio of change in expression levels for each marker relative to untreated cells. c Representative histograms of protein expression or phosphorylation states in MM.1S cells analyzed by mass cytometry before and after treatment with dexamethasone or bortezomib. The levels of the indicated parameters in MM.1S cells are shown following drug treatment at early (yellow histograms) or late (pink histograms) timepoints following drug treatment, or in untreated cells (blue histograms). Data are representative of three independent experiments.

Fig. 3. FLOW-MAP visualizations demonstrate the cell state dynamics of multiple myeloma cells following cytotoxic drug treatment.

a FLOW-MAP visualization of the response of live (i.e., cisplatin^neg) MM.1S cells to bortezomib treatment at 0, 6, and 24 h, colored according to timepoint, or b expression of activated Caspase-3 (aCASP3) and cleaved PARP (cPARP), or c Cyclin A, Cyclin B, pH3, and pRB. d FLOW-MAP visualization of the response of MM.1S cells to dexamethasone treatment at 0, 24, and 72 h, colored according to timepoint, or e expression of activated Caspase-3 (aCASP3) and cleaved PARP (cPARP), or f Cyclin A, Cyclin B, pH3, and pRB. FLOW-MAPs used 10,000 cells randomly subsampled and merged into 2000 clusters from each timepoint of treatment.

Fig. 4. Random forest models trained on mass cytometry time course data identify the key features of cytotoxic drug-induced apoptosis.

a Schematic representation of random forest model-learning using single-cell CyTOF data. MM.1S cells at late timepoints after drug treatment are separated into “apoptotic” or “viable” cells based on aCASP3 levels. Random forest models are trained to correctly classify cells into these two groups based on other features, through tenfold cross-validation on training data. Markers that consistently improve the purity of the two populations when used are reflected by a larger mean decrease in Gini impurity index. b Mean decreases in Gini impurity index for all markers included in random forest models, using “most features” measured by CyTOF in single cells, in the bortezomib-specific and dexamethasone-specific models. c Mean decreases in Gini impurity index for “BCL-2 family” proteins included in random forest models for bortezomib- or dexamethasone-treated multiple myeloma cells. d Schematic diagram of the assessment of random forest models using set-aside test data or data from independent CyTOF analyses. Similar to the data used to train the models, MM.1S cells from the last timepoint of drug treatment are gated on aCASP3 levels and designated as “viable” or “apoptotic.” The drug-specific random forest models are then tested against these states and accuracy is assessed across the cell population. e Receiving operating characteristic (ROC) curves illustrating the accuracy of each drug-specific model (bortezomib or dexamethasone) using “most features” or restricted to only the “BCL-2 family” parameters. Results are shown for two independent runs (technical replicates Run 1 in red and Run 2 in green) as well as the set-aside test data (Run 3 in blue). The area under the curve (AUC) is shown for each model and each separate dataset.

Fig. 5. FLOW-MAP comparison of key model features following bortezomib or dexamethasone treatment.

FLOW-MAP visualization comparing response with (a) bortezomib or (b) dexamethasone treatment in MM.1S cells. A single FLOW-MAP was produced with data from both treatment time courses using the same markers as clustering variables as listed in Fig. 3, colored by time and levels of aCASP3, pCREB, MCL-1, IκBα, pS6, BIM, and p53. c ROC curve showing the accuracy of cross-testing drug-specific models, with random forest models using “most features” or restricted to only the “BCL-2 family” proteins. The AUC is shown for each model and each pair of drug-specific model and drug data.

Fig. 6. FLOW-MAP visualization of the key features in the apoptotic response to dexamethasone in multiple myeloma cells reveals a transitional population.

FLOW-MAP visualization of response to dexamethasone treatment comparing (a) parental MM.1 S and (b) BAK^−/−BAX^−/− MM.1S cell line. A single FLOW-MAP was produced with data from both cell lines, using 6000 cells randomly subsampled and merged into 1200 clusters from each timepoint of treatment in each cell line. The FLOW-MAP was constructed using the same markers as clustering variables as listed in Fig. 3, and colored according to time or levels of aCASP3, pCREB, pS6, MCL-1, BIM, and IκBα. c Dot plot of cPARP and aCASP3 levels for WT MM.1S and BAK^−/−BAX^−/− MM.1S cells.

Fig. 7. Synergistic killing of multiple myeloma cells with MCL-1 inhibition combined with dexamethasone.

a Mean (±SEM) viability of the MM.1S cell line as measured by flow cytometric analysis of propidium iodide (PI) versus Annexin V staining following treatment with the combination of 1.25 μM of BH3-mimetic drugs as inhibitors (i) of iBCL-2, iBCLX_L, or iMCL-1 (S63845) and titration of dexamethasone (0–10 μM) compared with each treatment alone for 24 h. The dotted horizontal line represents 50% loss of viability. b The interaction landscapes identified from combination treatments in a. For each combination, the landscapes are shown in 3D where the BLISS scores represent the excess percentage inhibition beyond that expected from additive interaction. c The interaction landscapes of OPM2, AMO1, U266, KMS-12-BM, and H929 cell lines after combination treatments with S63845 and dexamethasone for 24 h. Data from a and b are representative of three independent experiments. Data from c are representative of two independent experiments.

Fig. 8. Synergistic killing of myeloma cells from patients by combining MCL-1 inhibition with dexamethasone.

a Viability of bone marrow cells isolated from multiple myeloma patients assessed after 24 h of treatment with dexamethasone, the combination of dexamethasone and S63845, and S63845 alone, relative to DMSO control. b Box and whisker plot showing BLISS scores for ex vivo drug synergy in primary myeloma cells from patients. c tSNE analysis of treatment naive multiple myeloma patients 11 and 12 to represent the higher dimensional relationships among bone marrow cells. Ellipse highlights putative myeloma populations with the phenotype CD45^lowCD38^posCD138^pos/negIRF4^pos. d MCL-1 protein levels measured by CyTOF gated on CD45^lowCD38^highCD138^pos/neg and IRF4^pos treatment naive myeloma cells from patients 11 and 12.