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Molecular Cancer Therapeutics
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Models and Technologies

Target Identification in Small Cell Lung Cancer via Integrated Phenotypic Screening and Activity-Based Protein Profiling

Jiannong Li, Bin Fang, Fumi Kinose, Yun Bai, Jae-Young Kim, Yian A. Chen, Uwe Rix, John M. Koomen and Eric B. Haura
Jiannong Li
1Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
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Bin Fang
2Proteomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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Fumi Kinose
1Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
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Yun Bai
1Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
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Jae-Young Kim
1Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
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Yian A. Chen
3Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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Uwe Rix
4Department of Drug Discovery, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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John M. Koomen
5Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
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Eric B. Haura
1Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
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  • For correspondence: eric.haura@moffitt.org
DOI: 10.1158/1535-7163.MCT-15-0444 Published February 2016
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    Figure 1.

    Workflow for therapeutic target identification in SCLC via integrated phenotypic screen with ABPP ATP probe and LC/MS-MS. A compound library (A) was used for cell viability screen in SCLC cell lines (B), with paired active and inactive compounds based on screen results chosen for further drug profiling using desthiobiotin-ATP probe (C) LC/MS-MS. The identified peptides were quantified by MaxQuant, and the candidate hits (orange nodes) of active drug were determined on the basis of those that inhibited ATP binding by >60% compared with DMSO treatment and filtered by potential hits (blue nodes) of inactive compound (D). Potential targets were validated in SCLC cells using relevant small RNA interference and drugs (E).

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    Figure 2.

    Compound screen across SCLC cell lines. We investigated 235 compounds for the cell viability screen in the 21 SCLC cell lines; 1,000 cells/well were seeded in 384-well plate and treated with individual compounds at 1 μmol/L concentration for 3 days. Cell viability was performed by CellTiter Glo. A heatmap of the average percent cell survival from duplicates compared with DMSO control was plotted by the “imagesc” function in MatLab. Blue indicates less cell survival; red indicates more cell survival. Bottom right (color key legend) shows range of average values. Left, the chemical structures of the active and inactive compound pairs (E08 vs. N09 and K14 vs. B13).

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    Figure 3.

    AURKB is identified by combining drug screen with ABPP ATP probe in H82 cells and drives cell survival in MYC-amplified SCLC cells. A, binding proteins of E08 identified by ABPP ATP probe in H82 cells. Drug profiling of compounds E08 and N09 was performed in H82 cells. The compound's candidate binding proteins (shown on the human kinome tree) were defined as those that inhibited binding of the ATP peptide by >60% compared with DMSO treatment. The orange nodes represent the final candidate targets of active compound E08 and the blue nodes represent the targets of both active and inactive compounds. B, AURKB knockdown by siRNA dramatically inhibited cell viability in H82 cells. H82 cells were transfected with pooled siRNA targeting 15 indicated genes as described in Materials and Methods. Cell viability was performed by CellTiter Glo 5 days after transfection. Cell viability changes were determined for each target gene after normalization on ON-TARGET plus Non-Targeting pool control siRNA and plotted by GraphPad Prism 6. C, MYC-amplified SCLC is more sensitive to depletion of AURKB by siRNA. SCLC cell lines with MYC amplification (n = 6) and no MYC amplification (n = 14) were transfected with pooled siRNA-targeting AURKB. Cell viability was performed by CellTiter Glo 5 days after transfection. The box plot between MYC-amplified (MYC+_SCLC) and lacking MYC amplification (MYC−_SCLC) SCLC cell lines based on cell viability changes was constructed by GraphPad Prism 6 (P based on the Wilcoxon rank-sum test). D, MYC-amplified SCLC is more sensitive to AURKB inhibitors. SCLC cell lines with MYC amplification (n = 6) and lacking MYC amplification (n = 11) were treated with 0.5 μmol/L of indicated AURKB inhibitors. Cell viability was performed by CellTiter Glo 3 days after treatment. A heatmap of average percent cell survival from duplicates compared with DMSO control was plotted by the “imagesc” function in MatLab. Bottom right (color key legend) shows range of average values. P value shown at left bottom was based on the Wilcoxon rank-sum test between MYC amplification (MYC+) and MYC lacking amplification (MYC−) groups.

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    Figure 4.

    TBK1 identified by ABPP ATP probe in SW210.5 cells plays an important role in cell survival in a subset of SCLC cells lacking MYC amplification. A, depletion of TBK1 by siRNA-affected cell growth in SCLC cells. Ten SCLC cell lines with no MYC amplification were transfected with pooled siRNA-targeting TBK1 and lung cancer cell line A549 as the positive control. Cell viability was performed by CellTiter Glo 5 days after transfection. Cell viability changes were constructed by GraphPad Prism 6 after normalizing with ON-TARGET plus Non-Targeting pool control siRNA. B, TBK1 inhibitors affected cell viability in SW210.5 and H1607 cells. Four SCLC cell lines with no MYC amplification were treated with TBK1 inhibitors compound II and BX-795 at the titration concentration. Cell viability was performed by CellTiter Glo 4 days after treatment. The growth curve was constructed by GraphPad Prism 6. C, TBK1 inhibitors induced cell apoptosis in SW210.5 and H1607 cells. SW210.5 and H1607 cells were treated with indicated concentrations of TBK1 inhibitors compound II and BX-795 for 48 hours, and PARP cleavage was evaluated by Western blotting. Equal protein loading was confirmed by β-actin evaluation. D, TBK1-mediated activation of the mitotic kinase PLK1. SW210.5 cells were treated with indicated TBK1 inhibitors for 1 hour and then exposed to 50 ng/mL of nocodazole for 18 hours. Western blot analysis detected the signaling change using indicated antibodies, and β-actin evaluation was confirmed the equal protein loading.

Additional Files

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    • Supplemental Methods and Legends - Supplemental methods and legends for supplemental figures S1 to S3 and supplemental tables S1 to S3
    • Supplementary Table S1. - Cell viability screen in SCLC cells against Roche published kinase inhibitor set
    • Supplementary Table S2 - Hits of E08 and N09 identified by ATP probe-based drug profiling in H82 cells
    • Supplementary Table S3 - Hits of K14 and B13 identified by ATP probe-based drug profiling in SW210.5 cells
    • Supplementary figures S1 to S3 - Figure S1. Compounds screen across SCLC cell lines Figure S2. Aurora kinase B identified by drug profiling in H82 cells and drive cell survival in MYC amplified SCLC Figure S3. TBK1 identified by drug profiling in SW210.5 cells
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Molecular Cancer Therapeutics: 15 (2)
February 2016
Volume 15, Issue 2
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Target Identification in Small Cell Lung Cancer via Integrated Phenotypic Screening and Activity-Based Protein Profiling
Jiannong Li, Bin Fang, Fumi Kinose, Yun Bai, Jae-Young Kim, Yian A. Chen, Uwe Rix, John M. Koomen and Eric B. Haura
Mol Cancer Ther February 1 2016 (15) (2) 334-342; DOI: 10.1158/1535-7163.MCT-15-0444

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Target Identification in Small Cell Lung Cancer via Integrated Phenotypic Screening and Activity-Based Protein Profiling
Jiannong Li, Bin Fang, Fumi Kinose, Yun Bai, Jae-Young Kim, Yian A. Chen, Uwe Rix, John M. Koomen and Eric B. Haura
Mol Cancer Ther February 1 2016 (15) (2) 334-342; DOI: 10.1158/1535-7163.MCT-15-0444
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