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Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP)

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Science Translational Medicine  08 Aug 2018:
Vol. 10, Issue 453, eaan0941
DOI: 10.1126/scitranslmed.aan0941
  • Fig. 1 A high-throughput screen identified four potential drug inhibitors that preferentially targeted Bort-resistant cell lines.

    (A) Top: Bort dose-response curve, IC50 analysis in RPMI 8226 and P100v cells. Bottom: Aggresome formation analysis in RPMI 8226 and P100v cells. Aggresome formation (red) is counterstained with DNA dye, Hoechst (blue). Scale bars, 10 μm. DMSO, dimethyl sulfoxide. (B) Schematic outline of drug screen on both the sensitive RPMI 8226 and resistant P100v. (C and D) Results of the high-throughput screening of the 114 compound FDA-approved oncology drugs set V on both RPMI 8226 and P100v cell lines at 1 μM (C) and 5 μM (D). Highlighted red bars indicate the four drugs (Dac, Dec, Mech, and MitoC) that consistently appeared as top hits for the two concentrations used. (E) Dose-response curves and IC50 of the positive hits from drug screen on RPMI 8226 and P100v. All dose-response curves (A and E) are means ± SD of three independent biological replicates.

  • Fig. 2 Parabolic response surface maps and validation of QPOP-ranked efficacious drug combinations.

    (A) Parabolic response surface maps from top QPOP-ranked two-drug combinations. (B) Parabolic response surface maps from selected low QPOP-ranked two-drug combinations. Axes lengths differ in (A) and (B) to aid visualization. Monotherapy versus combination therapy dose-response curve and IC50 analysis of (C) Bort and Mech; (D) Dec and MitoC; (E) Dec, MitoC, and Mech; and (F) Pano, Bort, and Dex in P100v cells. Data are means ± SD of three independent biological replicates. (G) Bort and Mech and (H) Dec and MitoC were evaluated as single- or dual-drug treatment on relapsed MM patient samples. Data are means ± SD of three independent biological replicates. (I) Combination indices of Dec and MitoC on different patient samples based on the derived IC50.

  • Fig. 3 Dec and MitoC synergistically repressed global DNA methylation and reactivated tumor suppressor genes.

    (A) Mean relative genomic DNA methylation normalized to RPMI 8226 in RPMI 8226 and P100v cell lines. **P < 0.01. Statistical analyses were performed using two-tailed Student’s t test upon confirming normality using Shapiro-Wilk test. (B) Mean relative genomic DNA methylation normalized to control DMSO when P100v was treated with 2.5 μM Dec (D) and/or 5 μM MitoC (M) for 24 hours. **P < 0.01. (C) Unsupervised hierarchical clustering analysis of both RPMI 8226 and P100v together with the corresponding drug treatments for 24 hours. (D) Volcano plots showing difference in mean methylation (x axis) and significance of the difference (y axis) after Dec treatment compared to untreated control and dual-drug treatment. Relative mRNA expression of (E) CDKN1A and (F) PTPN6. *P < 0.05 and **P < 0.01, compared to P100v. Statistical analyses were performed using two-tailed Student’s t test, upon confirming normality using Shapiro-Wilk test. All bar plots represent means ± SD of three independent biological replicates.

  • Fig. 4 Dec and MitoC induced synergistic up-regulation of activated DNA damage response and strand breaks.

    (A) Alkaline comet assay after single- or dual-drug treatment with Dec (153 nM) and MitoC (306 nM), with accompanying quantification of the tail/head length ratio. Scale bars, 50 μm. Data are means ± SD (n = 3). ***P < 0.001, as compared to control DMSO. Experiments were performed in independent triplicate biological repeats. Statistical analyses were performed using one-way ANOVA with Dunn’s correction for multiple comparisons, with two-tailed Student’s t test applied to two independent groups. (B) Representative immunoblots of Chk-1 and H2AX, indicators of activated DNA damage response, when P100v was treated with Dec and MitoC, singly or in combinatorial therapy, for 6 hours. (C and D) Quantification of the protein expression relative to β-actin expression in (B). Data are means ± SD. *P < 0.05 and **P < 0.01, as compared to control DMSO. Experiments were performed in independent triplicate biological repeats. Statistical analyses were performed using two-tailed Student’s t test upon confirming normality using Shapiro-Wilk test.

  • Fig. 5 QPOP identified optimal in vivo dosages of Dec and MitoC.

    (A) Tumor volume analysis after treatment with nine different drug combinations (n = 4 per group) of varying concentrations of Dec and MitoC. Data are means ± SD. (B) Kaplan-Meier analysis of drug treatments. (C) Parabolic response surface map from QPOP analysis, where output is defined by the toxicity (mean change in body weight) subtracted from efficacy (mean survival time of the mice). (D) Tumor progression analysis upon administration of the optimal drug concentration of Dec (1.5 mg/kg) and MitoC (1.5 mg/kg), singly or in combination (Dec/MitoC; n = 6 per group). Data are means ± SD (n = 6). *P < 0.05, as compared to DMSO vehicle and Dec, and **P < 0.01, as compared to MitoC. Statistical analyses were performed using two-tailed Student’s t test at the end point, upon confirming normality using Shapiro-Wilk test. (E) Kaplan-Meier analysis of Dec/MitoC treatment versus Bort/Dex/Melph [Bort (0.5 mg/kg), Dex (1 mg/kg), and Melph (2.5 mg/kg)] and Bort/Dex/Lena [Bort (0.5 mg/kg), Dex (1 mg/kg), and Lena (3 mg/kg)]. *P < 0.05. Statistical analyses were performed using log-rank (Mantel-Cox) test. (F) Tumor progression analysis curves. Data are means ± SD (n = 6 per group). *P < 0.05 and ***P < 0.001, as compared to Dec/MitoC. Statistical analyses were performed using two-tailed Student’s t test at the end point upon confirming normality using Shapiro-Wilk test.

  • Fig. 6 Analyses of in vivo optimized Dec/MitoC drug combination.

    (A) Representative tumor images after treatment with Dec and MitoC at 1.5 mg/kg each, in monotherapy or combination therapy, together with clinically used combinations, Bort/Dex/Melph and Bort/Dex/Lena (n = 6 per group). Scale bars, 1 cm. (B) Representative images of TUNEL and Ki67 staining performed on tumors after drug treatments. All statistical analyses were performed using two-tailed Student’s t test upon confirming normality using Shapiro-Wilk test. Scale bars, 50 μm (TUNEL) and 20 μm (Ki67). Quantification of (C) Ki67 and (D) TUNEL staining. Data are means ± SD (n = 6). *P < 0.05, **P < 0.01, and ***P < 0.001, as compared to DMSO. Relative mRNA expression, normalized to GAPDH, of the perturbed genes, (E) CDKN1A and (F) PTPN6, after drug treatments (n = 4 per group). Data are means ± SD (n = 3). *P < 0.05, **P < 0.01, and ***P < 0.001, as compared to DMSO vehicle. All statistical analyses were performed using two-tailed Student’s t test upon confirming normality using Shapiro-Wilk test.

  • Table 1 Ex vivo sensitivity of Dec/MitoC.

    Parabolic response surface maps of Dec and MitoC on diagnosed MM patient samples (1 to 4) from QPOP analysis, with accompanying R2 values, IC50 values, and CIs. Data are means ± SD (n = 3). Statistical analyses were performed using sum-of-squares F test. NS, not significant.


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  • Table 2 Top 10 two-drug ranked combinations for patient 1.

    Overall rankings are indicated in parentheses.

    Top-ranked two-drug combinations
    RankDecBortMitoCPanoMelphDexOutput
    1 (208)3000.633000−2.17Dec + MitoC
    2 (262)3001.831000−2.09Dec + MitoC
    3 (280)6000.633000−2.05Dec + MitoC
    4 (307)6001.831000−1.98Dec + MitoC
    5 (361)00.2340.633000−1.87
    6 (388)00.0740.633000−1.77
    7 (406)300.0740000−1.74
    8 (415)300.2340000−1.72
    9 (460)00.0741.831000−1.61
    10 (478)00.2341.831000−1.53
    (562)00.23400.00300.006−1.36Bort + Pano + Dex
    (654)00.234007.50.006−1.01Bort + Melph + Dex
  • Table 3 Top 10 two-drug ranked combinations for patient 2.

    Overall rankings are indicated in parentheses.

    Top-ranked two-drug combinations
    RankDecBortMitoCPanoMelphDexOutput
    1 (199)0020.0028000.345
    2 (232)60000.0028000.359
    3 (262)30000.0028000.381
    4 (310)0020.0014000.430
    5 (346)0010.0028000.449
    6 (382)60000.0014000.486
    7 (391)60020000.490Dec + MitoC
    8 (394)0000.00280.800.494
    9 (412)30020000.508Dec + MitoC
    10 (427)30000.0014000.519
    (461)00.00200.002800.0080.545Bort + Pano + Dex
    (707)00.001000.80.0080.979Bort + Melph + Dex
  • Table 4 Top 10 two-drug ranked combinations for patient 3.

    Overall rankings are indicated in parentheses.

    Top-ranked two-drug combinations
    RankDecBortMitoCPanoMelphDexOutput
    1 (181)60000.0028000.147
    2 (226)30000.0028000.156
    3 (262)0020.0028000.162
    4 (334)0020.0014000.200
    5 (361)0010.0028000.228
    6 (370)60000.0014000.244
    7 (397)30000.0014000.252
    8 (424)00.00200.0014000.274
    9 (434)0000.00280.400.294
    10 (435)0000.00280.800.294
    24 (568)30020000.501Dec + MitoC
    (427)00.00200.001400.0080.274Bort + Pano + Dex
    (662)00.002000.40.0080.612Bort + Melph + Dex
  • Table 5 Top 10 two-drug ranked combinations for patient 4.

    Overall rankings are indicated in parentheses.

    Top-ranked two-drug combinations
    RankDecBortMitoCPanoMelphDexOutput
    1 (90)300.0020000−0.117
    2 (96)00.002000.80−0.112
    3 (213)00.002000.400.005
    4 (319)00.00220000.051
    5 (334)600.00200000.059
    6 (345)00.00200.0028000.063
    7 (371)00.00210000.087
    8 (386)00.00200.0014000.092
    9 (427)00.0020000.0080.125
    10 (431)00.0020000.0160.128
    13 (530)30020000.226Dec + MitoC
    (98)00.002000.80.008−0.109Bort + Melph + Dex
    (347)00.00200.002800.0080.0654Bort + Pano + Dex

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/10/453/eaan0941/DC1

    Materials and Methods

    Fig. S1. Validation of QPOP-optimized combinations on P100v cells.

    Fig. S2. IC50 comparison of QPOP-ranked efficacious drug combinations across different cell lines.

    Fig. S3. QPOP-optimized drug combinations are efficacious against 7 nM Bort-resistant ALL cells.

    Fig. S4. QPOP-optimized drug combinations are efficacious against 200 nM Bort-resistant ALL cells.

    Fig. S5. Venn diagram showing common significant probes between three treatment groups.

    Fig. S6. Mean weight of tumor-bearing mice for identification of optimal in vivo drug concentrations via QPOP analysis.

    Fig. S7. Analysis of QPOP-optimal drug combination in a Bort-resistant ALL in vivo model.

    Table S1. Top hits from the high-content screening of the FDA-approved oncology drug set at 1 μM on P100v.

    Table S2. Top hits from the high-content screening of the FDA-approved oncology drug set at 5 μM on P100v.

    Table S3. QPOP analysis using a design consisting of 128 combinations for 14 drugs at two levels (−1 and 1).

    Table S4. Concentrations of drugs used for first iteration consisting of 14 drugs at two levels (1 and −1).

    Table S5. Estimates and significance of QPOP analysis for first iteration (14 drugs at two levels).

    Table S6. QPOP analysis using a design consisting of 155 combinations for nine drugs at three different levels (−1, 0, and 1).

    Table S7. Concentrations of drugs used for second iteration consisting of nine drugs at three levels (1, 0, and −1).

    Table S8. Estimates and significance of QPOP analysis for second iteration (nine drugs at three levels).

    Table S9. Top 10 ranked QPOP-optimized two-drug combinations.

    Table S10. Top 8 ranked QPOP-optimized three-drug combinations.

    Table S11. Validation of Dec/MitoC on MM patient samples.

    Table S12. Drugs and corresponding concentrations used for ex vivo QPOP.

    Table S13. RNA sequencing pathway analysis upon treatment with 2.5 μM Dec versus DMSO control.

    Table S14. RNA sequencing pathway analysis comparing Dec-treated and Dec/MitoC-treated P100v cells.

    Table S15. Concentrations of Dec and MitoC used for in vivo QPOP analysis at five levels.

    Table S16. Output used for in vivo QPOP analysis.

    Table S17. Ranked list of in vivo QPOP drug dosage optimizations.

    Table S18. Estimates and significance of in vivo QPOP analysis (four drugs at five levels).

    Table S19. Primary data from figures shown (Excel file).

    Data file S1. MATLAB code for quadratic series.

  • The PDF file includes:

    • Materials and Methods
    • Fig. S1. Validation of QPOP-optimized combinations on P100v cells.
    • Fig. S2. IC50 comparison of QPOP-ranked efficacious drug combinations across different cell lines.
    • Fig. S3. QPOP-optimized drug combinations are efficacious against 7 nM Bort-resistant ALL cells.
    • Fig. S4. QPOP-optimized drug combinations are efficacious against 200 nM Bort-resistant ALL cells.
    • Fig. S5. Venn diagram showing common significant probes between three treatment groups.
    • Fig. S6. Mean weight of tumor-bearing mice for identification of optimal in vivo drug concentrations via QPOP analysis.
    • Fig. S7. Analysis of QPOP-optimal drug combination in a Bort-resistant ALL in vivo model.
    • Table S1. Top hits from the high-content screening of the FDA-approved oncology drug set at 1 μM on P100v.
    • Table S2. Top hits from the high-content screening of the FDA-approved oncology drug set at 5 μM on P100v.
    • Table S3. QPOP analysis using a design consisting of 128 combinations for 14 drugs at two levels (−1 and 1).
    • Table S4. Concentrations of drugs used for first iteration consisting of 14 drugs at two levels (1 and −1).
    • Table S5. Estimates and significance of QPOP analysis for first iteration (14 drugs at two levels).
    • Table S6. QPOP analysis using a design consisting of 155 combinations for nine drugs at three different levels (−1, 0, and 1).
    • Table S7. Concentrations of drugs used for second iteration consisting of nine drugs at three levels (1, 0, and −1).
    • Table S8. Estimates and significance of QPOP analysis for second iteration (nine drugs at three levels).
    • Table S9. Top 10 ranked QPOP-optimized two-drug combinations.
    • Table S10. Top 8 ranked QPOP-optimized three-drug combinations.
    • Table S11. Validation of Dec/MitoC on MM patient samples.
    • Table S12. Drugs and corresponding concentrations used for ex vivo QPOP.
    • Table S13. RNA sequencing pathway analysis upon treatment with 2.5 μM Dec versus DMSO control.
    • Table S14. RNA sequencing pathway analysis comparing Dec-treated and Dec/MitoC-treated P100v cells.
    • Table S15. Concentrations of Dec and MitoC used for in vivo QPOP analysis at five levels.
    • Table S16. Output used for in vivo QPOP analysis.
    • Table S17. Ranked list of in vivo QPOP drug dosage optimizations.
    • Table S18. Estimates and significance of in vivo QPOP analysis (four drugs at five levels).

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Table S19. Primary data from figures shown (Excel file).
    • Data file S1 (Microsoft Word format). MATLAB code for quadratic series.

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