Identification of Novel Protein Expression Changes Following

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Identification of novel protein expression changes following cisplatin treatment and application to combination therapy Amy L. Stark, Ashraf G Madian, Sawyer W. Williams, Vincent Chen, Claudia Wing, Ronald J. Hause Jr, Lida Anita To, Amy L. Gill, Jamie L. Myers, Lidija K. Gorsic, Mark F. Ciaccio, Kevin P. White, Richard B. Jones, and M. Eileen Dolan J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00576 • Publication Date (Web): 13 Sep 2017 Downloaded from http://pubs.acs.org on September 14, 2017

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Journal of Proteome Research

Identification of novel protein expression changes following cisplatin treatment and application to combination therapy Amy L. Stark1+, Ashraf G. Madian2+, Sawyer W. Williams3, Vincent Chen4, Claudia Wing1, Ronald J. Hause Jr4,5,6, Lida Anita To6, Amy L. Gill7, Jamie L. Myers1, Lidija K. Gorsic1, Mark F Ciaccio4,6, Kevin P. White5,6,8, Richard B. Jones2,4,6,7 and M. Eileen Dolan1,2,5,7* 1

Department of Medicine; 2Committee on Clinical Pharmacology and

Pharmacogenomics; 3University of Notre Dame, Notre Dame, IN, USA; 4Ben May Department for Cancer Research; 5Committee on Genetics, Genomics and Systems Biology; 6The Institute for Genomics and Systems Biology; 7Committee on Cancer Biology, 8Department of Human Genetics, The University of Chicago, Chicago, IL, USA +

These authors contributed equally to the work

*Corresponding author: M. Eileen Dolan, PhD, 900 E 57th St, KCBD 7100, University of Chicago, Chicago, IL 60637; ph: 773-702-4441 email: [email protected];

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Abstract Determining the effect of chemotherapeutic treatment on changes in protein expression can provide important targets for overcoming resistance. Due to challenges in simultaneously measuring large numbers of proteins, a paucity of data exists on global changes. To overcome these challenges, we utilized microwestern arrays that allowed us to measure the abundance and modification state of hundreds of cell signaling and transcription factor proteins in cells following drug exposure. HapMap lymphoblastoid cell lines (LCLs) were exposed to cisplatin, a chemotherapeutic agent commonly used to treat testicular, head and neck, non-small cell lung, and gynecological cancers. We evaluated the expression of 259 proteins following 2, 6, and 12 hours of cisplatin treatment in two LCLs with discordant sensitivity to cisplatin. Of these 259 proteins, 66 displayed significantly different protein expression changes (p 1). The CI was then used to classify the antagonistic, additive, or synergistic effects according to the CalcuSyn™ software and was defined as follows: 0.1–0.3 very strong synergy; 0.3–0.7 strong synergy; 0.7–0.85 moderate synergy; 0.85–0.9 low synergy; 0.9–1.1 additivity; 1.1–1.2 low antagonism; 1.2–1.45 moderate antagonism Statistical Modeling. A two-step regression strategy was used to identify proteins with significant expression changes using the maSigPro package from Bioconductor 12. The model used was: yij = β0 + β1C1ij+δ0Tij+δ1TijCij + εij in which yij was the log2-transformed, β-actin-normalized protein expression fold change relative to time 0 for a given protein, i was experimental group (either control vs. DMSO or sensitive vs. resistant cell line after DMSO normalization), j was time point (2, 6, or 12 hours), T was time, C was experimental group, β was the linear regression coefficient, δ was quadratic regression coefficient, and εij was random variation. This approach allowed us to test for significant linear and quadratic effects in response to cisplatin treatment that were either shared or unique between experimental groups. The first step of this strategy was to fit the model using least squares to estimate parameters, derive P-values from their associated F-statistics with each coefficient, and control the FDR with the Benjamini Hochberg approach. In the second step, model reduction for each protein was performed using backward stepwise regression to retain only variables that significantly fit the protein data to the model at a nominal P < 0.05.

Results Protein selection and quantification. To identify proteins expressed in LCLs after cisplatin treatment, we screened 1378 antibodies targeting transcription factors and core signaling proteins. Previously, YRI HapMap LCLs were phenotyped for sensitivity 7 ACS Paragon Plus Environment


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to cisplatin 8, 10 and we selected three unrelated LCLs from across the distribution of sensitivity: one resistant cell line GM19102 (IC50 = 10.78 µM), one sensitive cell line GM19210 (IC50 = 2.17 µM), one intermediate cell line GM19143 (IC50 = 6.92 µM) in terms of their sensitivity to cisplatin). These cell lines were treated with 5 µM cisplatin and collected at 2, 6, and 12 hour post treatment and samples at each time point were pooled. The concentration of cisplatin (5 µM) was chosen for 2 reasons: 1) this is a clinically relevant concentration of drug 13 and; 2) this concentration was intermediate between the concentration required to inhibit 50% of cell growth in sensitive and resistant LCLs. The time points were based on previous data in which we showed negligible caspase 3/7 activation at 2, 4, and 6 hour but measurable levels at 24 hour post cisplatin treatment 14. Therefore, the changes in protein expression were measured prior to caspase 3/7 activation leading to apoptosis. Of the 1378 proteins screened, 35% were expressed at a level detected by our antibodies. The remaining 65% were either not expressed in LCLs after 5 µM cisplatin exposure or at a level that was not detectable by our antibodies. Of the expressed proteins that indicated detectable levels in the lysate of the pooled LCL samples, we measured changes in 259 proteins following 0, 2, 6, and 12 hour of cisplatin or DMSO (vehicle) treatment in one sensitive (GM19210) and one resistant (GM19102) LCL. Figure 1 illustrates the workflow of our approach. The frequency distribution of cisplatin-induced cytotoxicity in 68 YRI LCLs was used to select cells to quantify proteins and those results are illustrated in the heatmaps (Figure 1). We previously demonstrated that cellular sensitivity to cisplatin is correlated with cellular proliferation rate 15; therefore we designed the study with cell lines having discordant drug sensitivities despite having similar growth rates, thus minimizing the effect of growth rate as a confounding variable. Intra-individual protein changes mediated by cisplatin. To observe changes in protein expression following cisplatin treatment, we evaluated the fold change in expression over time after 5 µM cisplatin exposure compared to vehicle (Supplementary Figure 1). We modeled the statistical analysis using two-step linear regression. At p < 0.05, the expression of 32 and 49 proteins was found to change significantly compared to vehicle within cell lines GM19210 and GM19102, respectively. Among these proteins,


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15 were commonly different from vehicle control in both the sensitive and resistant cell line (Table 1). Protein differences between cisplatin-sensitive and -resistant LCLs after cisplatin treatment. In addition to comparing protein changes for cisplatin versus control within each cell line, we also identified protein changes comparing the sensitive GM19210 versus resistant GM19102 cell line across the three time points using a twostep regression analysis. Evaluating the protein expression over the three time points, 66 proteins displayed different expression patterns between the sensitive and resistant cell lines (p < 0.05). Of these 66 proteins that were differentially expressed between sensitive and resistance cell lines, 14 proteins’ expression levels were changed significantly following exposure to 5 µM cisplatin within a single cell line relative to vehicle (Table 1). We evaluated a smaller subset of 63 proteins (of which 15 were significantly different between sensitive and resistant in the first set of LCLs) in a second pair of LCLs (sensitive: GM19206, IC50 = 3.94 µM; resistant: GM18912, IC50 = 16.29 µM) (Supplementary Table 3). Of the set evaluated, six consistently displayed significantly different (p < 0.05) protein expression patterns in both pairs of LCLs (Table 2) and 25 proteins displayed non-significant patterns between the sensitive and resistant cell lines for both pairs. Therefore 31/63 (49%) of proteins exhibited concordant significance between the two pairs. Notably, the pattern of ATF2 was significant and similar in both sets of cell lines (Supplementary Figure 1 and 2). When comparing the sensitive GM19210 and the resistant GM19102, different patterns across the time points became apparent and underscored the importance of looking at multiple time points after drug exposure. We observed different patterns as seen in Supplementary Figure 1, but the dynamic nature of protein expression changes highlighted the unique observations that could be observed at only one time point. CREB protein levels underscored the difference in protein changes between the sensitive and resistant LCL that were only observed at 6 hours after treatment (Figure 2, top). Conversely, NR0B2 protein levels followed similar expression patterns in both cell lines through the first 6 hours after exposure, but diverged at 12 hours after treatment (Figure 2, middle). PAX8 protein levels were divergent at the earliest time point



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measured, but converged at 6 hours and followed similar expression patterns through 12 hours (Figure 2, bottom). Proteins that changed following cisplatin treatment and whose baseline expression levels correlated with cellular sensitivity to cisplatin. Previous work by our group evaluated 441 protein levels at baseline in 68 Yoruba (YRI) population LCLs from the International HapMap project 8. Of the 441 proteins evaluated, 43 were significantly associated with cisplatin IC50 at p < 0.05 using mixed effect modeling 8. Sixteen of the 43 proteins were evaluated in this study by looking for alterations following 5 µM cisplatin treatment, however only 14 proteins displayed expression in all replicates that passed quality control. Three of the 14 proteins examined after 5 µM cisplatin exposure displayed no significant differences in protein expression in either cell line as compared to vehicle or between the sensitive and resistant LCLs. At significance of p < 0.05, five proteins, SMC1A (Pearson r2 = 0.14; p = 0.0017), STAT3 (Pearson r2 = 0.11; p = 0.0058), TUB (Pearson r2 = 0.13; p = 0.0042), RPS6 (Pearson r2 = 0.18; p = 0.0004), and ZNF266 (Pearson r2 = 0.09; p = 0.0136), exhibited differences in expression between the sensitive and resistant LCL following treatment and the protein expression of these five proteins at baseline correlated with cellular sensitivity to cisplatin in 68 LCLs (Figure 3). Of the proteins displayed in Figure 2, only CREB was also evaluated at baseline and did show association with 5 µM cisplatin induced apoptosis 8. Evaluation of a functional candidate, PDK1, in lung cancer cell lines. Although identification of proteins associated with cisplatin response in LCLs provided insight into the biological understanding of drug resistance, our ultimate goal is to identify new therapeutic targets that will aid in the development of novel combination therapeutic strategies. For this reason, we focused on protein candidates with available small molecule inhibitors. We selected a candidate for functional evaluation (PDK1) that had a strong p-value in the initial panel of two cell lines (p = 0.000015). GSK2334470, a highly specific inhibitor of PDK1, was combined with cisplatin to determine whether the combination was synergistic as would be expected based on the protein analysis because the sensitive LCL showed a decrease in PDK1 protein levels over time after exposure to 5 µM cisplatin (Figure 4A). Therefore, we hypothesized that adding a 10 ACS Paragon Plus Environment

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specific inhibitor would result in an increase in the sensitivity of cancer cell lines to cisplatin. GSK2334470 had previously been shown to inhibit PDK1 at an IC50 of 10nM with no inhibition of 93 other protein kinases at 500 fold higher concentrations in HEK cells 16. The inhibitor was also tested for specificity in U87 glioblastoma cell lines and fibroblasts 16. We verified GSK2334470’s successful inhibition of PDK1 both independent and in the presence of cisplatin using Western blots. Because cisplatin is used to treat lung cancer, we chose a set of human nonsmall lung cancer cell lines harboring clinically relevant mutations. Human non-small cell lung carcinoma cell lines A549 (KRAS mutant), NCI-H1437 (p53 mutant), and NCIH2126 (p53 and STK11 mutant) were evaluated with GSK2334470 alone, cisplatin alone, and GSK2334470 for 24 hours prior to cisplatin. We selected a 24 hour pretreatment with the GSK2334470 so that PDK1 inhibition would occur at the time cisplatin was added to the cells. At 100 nM GSK2334470 alone, minimal cellular growth inhibition occurred (percent survival > 80%) for all lines (Figure 4B). However, when 100 nM of the inhibitor was combined with increasing concentrations of cisplatin, synergy occurred in all cell lines (15-40 µM). NCI-H2126 demonstrated the strongest synergism with the lowest combination indexes, ranging between 0.25 and 0.7 for different doses of cisplatin with the percent survival decreasing an average of 25% with the largest effect seen at 15 µM (40% reduction) (Supplemental Figure 3). Both A549 and H1437 displayed moderate synergism with cisplatin treatment, with combination indexes ranging between 0.57-0.70 and 0.59-0.84, respectively (Figure 4C).

Discussion In this study, we measured changes in the abundance and modification state of cell signaling and transcription factor proteins in LCLs over time following treatment with cisplatin. Microwestern arrays were employed for the simultaneous measurement of hundreds of proteins to allow for a more comprehensive analysis of protein changes following cisplatin treatment. We demonstrated the dynamic nature of protein expression changes following exposure to cisplatin. We targeted one identified protein (PDK1) with a known inhibitor (GSK2334470) in non-small cell lung cancer cell lines (A549, NCI-H1437, and NCI-H2126) and demonstrated synergy with cisplatin treatment. 11 ACS Paragon Plus Environment


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This dataset provides a broad assessment of protein changes that may play a role for varying degrees of cisplatin resistance. This approach is likely to be similarly useful for the rapid evaluation of proteins and combination strategies for other chemotherapeutics. Our protein dataset is focused on the examination of transcription factors and cell signaling proteins. Despite the biological importance of transcription factors, few studies have examined their abundances 17, predominantly because their expression tends to be low which makes their quantification by mass spectrometry a major challenge 18. In a recent study, a targeted mass spectrometry method was used to determine the absolute abundances of 10 transcription factors in mouse 3T3-L1 pre-adipocytes 19. In contrast, in our study we quantified over a hundred (~140) transcription factors in cisplatin resistant versus sensitive LCLs following cisplatin treatment using an antibody-based methodology therefore providing new knowledge regarding proteins important in cellular sensitivity to cisplatin. Previous studies (8) evaluated the contribution of baseline protein levels to cisplatin response. Most baseline protein studies utilized western blot technology. For example, ubiquitin carboxyl terminal hydrolase (UCHL1) and fibroblast growth factor 13 (FGF13) were shown to contribute to cisplatin resistance in ovarian cancer cells 20 and cervical cancer cells 21, respectively. Recently, mass spectrometry-based proteomic methods have been applied to identify proteins relevant to cisplatin at baseline (prior to exposure). It was shown that secreted protein dihydrodiol dehydrogenase 2 (AKR1C2) is a potential biomarker for predicting the efficacy of cisplatin in non-small cell lung cancer 22. Paulitschke et al., have demonstrated that cisplatin resistant cells display an increase of expression of calcium ion binding, lysosomal, and cell adhesion proteins in melanoma cells 23. In contrast, this study investigated collections of proteins across multiple time points after drug exposure. We also leveraged previously generated data consisting of 441 proteins at baseline across 68 genetically defined YRI 8 to identify SMC1A, STAT3, TUB, RPS6, and ZNF266 as proteins whose baseline expression correlated significantly to sensitivity to cisplatin. Both STAT3 and SMC1A have been implicated previously for their role in cisplatin response 8, 24. In contrast, TUB has not been previously implicated with cisplatin therapy response, but is important in body composition and obesity development 25. Recent work has suggested that cisplatin 12 ACS Paragon Plus Environment

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therapy increases risk for obesity and other metabolic/body composition phenotypes 26 thus suggesting that common protein mechanisms may underlie affects of body composition and cisplatin response. Little is known regarding ZNF266 (also referred to as HZF1), but evidence suggests that it may play a role in cardiac development 27. RPS6 is a subunit of the ribosome and is important in cellular growth 28 and could be correlated with cisplatin response through its effect on cellular proliferation. This study provided information on protein levels over time and across cells with different sensitivity to cisplatin. One of the most significantly changed transcription factors between the discordant LCLs was SIM2. Interestingly, a previous study of mRNA expression changes after cisplatin in A549 lung cancer cell line also identified SIM2 as down-regulated following treatment with cisplatin 29. In our study, we observed SIM2 protein level staying relatively constant in the resistant LCL, but displaying a fourfold decrease in expression in the sensitive LCL within two hours. This pattern suggests that it could be used as a biomarker for sensitivity to cisplatin. Interestingly, our results highlight the dynamic nature of protein expression levels after exposure to cisplatin. We selected three early time points (2, 6, and 12 hours) and observed different protein changes at each time point. Our primary analysis in this study was to use a two-step regression to evaluate change over the three time points; however, we cannot exclude the possibility of an important observation occurring at a single time point. We identified 3-phosphoinositide-dependent protein kinase 1 (PDK1) as a highly significant protein that conferred resistance to cisplatin. PDK1 is a master regulator protein kinase that controls the activity of other kinases in the AGC (protein kinase A (PKA), protein kinase G (PKG), protein kinase C (PKC)) kinase family including AKT 30. PDK’s mechanism involves the phosphorylation of specific threonine or serine residues within the activation loop that is required for kinase activation. The activated kinases control cellular processes which include cell differentiation, growth, and survival 31. These processes may explain why higher PDK1 expression was observed in the cell line resistant to cisplatin. Beyond cisplatin, the knockdown of PDK1 expression sensitized MCF7 cells to gemcitabine-induced apoptosis 32. Additionally, inhibition of PDK1 increased the effect of tamoxifen in MCF7 breast cancer cell line 33. In this study 13 ACS Paragon Plus Environment


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we demonstrated that GSK2334470, a PDK1 inhibitor, was synergistic with cisplatin treatment in several distinct lung cancer cell lines: A549, NCI-H1437 and NCI-H2126. Higher concentrations of the inhibitor tested had either additive or antagonistic effects likely due to the fact that PDK1 is important for cell growth and survival. This work represents an exciting new direction for PDK1 inhibitors in combination with cisplatin treatment. Further work will be required to evaluate potential adverse effects, but our study suggested that a low dose of 100 nM GSK2334470 alone had minimal effects on cell viability at levels that resulted in synergistic effects with cisplatin treatment in multiple lung cancer cell lines. High-throughput protein quantification on a large number of samples remains challenging. This study demonstrates the utility of examining a large number of proteins and also the importance of examining multiple samples. For example, in a second pair of sensitive and resistant LCLs, approximately half of the proteins indicated the same significant change (p < 0.05) and the other half had discordant findings (i.e. significant in one pair, but not the other). This suggests the variation between individuals in drugresponse may begin at the protein level and warrants larger sampling in future studies.

Conclusions In summary, we demonstrated the utility of our protein-centered approach for understanding cisplatin response. With this approach, we observed novel changes in protein expression and/or modification over time after exposure to cisplatin in LCLs with discordant sensitivity. We also demonstrated that PDK1 inhibition was synergistic with cisplatin in growth inhibition of relevant tumor cell lines. These observations warrant further investigation, potentially in animal models. Importantly, our approach has broad applicability to understand the role of proteins in chemotherapeutic resistance and find modulators to overcome resistance to chemotherapy.

SUPPORTING INFORMATION:

The following files are available free of charge at ACS website http://pubs.acs.org: Supplemental Figure 1. Time course fold change protein levels 14 ACS Paragon Plus Environment

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Supplemental Figure 2. Second subset of time course fold change protein levels in a second set of LCLs. Supplemental Figure 3. Lung cancer line H2126 is more sensitive to cisplatin in the presence of PDK1 inhibitor. Supplemental Table 1. Details regarding the antibodies screened in this study Supplemental Table 2. Average log2 fold change for cisplatin and vehicle across three time points in GM19102 and GM19210. Supplemental Table 3. Average log2 fold change for cisplatin and vehicle across three time points in GM18912 and GM19206.

Author Contributions ALS and AGM designed experiments, performed experiments, analyzed data, created figures, and drafted the manuscript. SWW analyzed data and edited manuscript. VC, CW, LAT, ALG, JLM and LKG performed experiments and compiled figures. RJH analyzed data. MFC KPW designed experiments. RBJ and MED designed experiments, analyzed data, and drafted the manuscript. All authors reviewed the manuscript.

Acknowledgements Cell lines were provided by the Lymphoblastoid Cell Line Core of the Pharmacogenomics of Anticancer Agents Research Group (http://pharmacogenetics.org). This study was supported by NIH NIGMS GM61393 (MED), NCI CA125183 (MED), NCI CA136765 (MED), T32 GM07197 (RJH), ACS, IL (RBJ) NIH P50-MH094267 (RBJ), Chicago Center for Systems Biology (RBJ), the Women’s Board of the University of Chicago Cancer Center (AGM) and the Biological Sciences Division of the University of Chicago (AGM).

Additional Information The authors declare no competing financial interests.



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Table 1. Proteins whose expression patterns are significantly (p < 0.05) different than vehicle in both a sensitive and resistant LCL. Italicized font indicates that the protein expression patterns were not significantly different (p > 0.05) between the cell lines. The genes that have previous literature evidence linking them to cisplatin (as of July 2016 as determined by querying PubMed with the gene name and cisplatin) are displayed with boldfaced font. Gene Name

19102 p-value

19210 p-value

Protein Name

19102 vs 19210 p-value

PDK1

3.82E-02

2.69E-04

1.46E-05

BCL2

4.89E-02

4.61E-04

6.18E-03

ZNF238

ZNF238

1.45E-05

9.66E-04

5.62E-06

ETS2

ETS2

1.04E-02

1.48E-03

4.14E-05

PKC

2.86E-02

1.70E-03

2.99E-02

ZNF219

ZNF219

9.64E-03

2.61E-03

1.97E-02

GAB1Y423 (~50 kDa)

GAB1

4.27E-04

5.51E-03

4.87E-03

STAT3

2.07E-05

5.85E-03

1.20E-05

HIF1AN (~40 kDa)

HIF1AN

3.53E-02

1.22E-02

8.18E-02

CECR6

CECR6

3.11E-02

1.92E-02

2.52E-03

PKC

1.13E-02

1.97E-02

2.69E-04

RPS6

1.39E-03

2.52E-02

1.49E-04

PDK1

BCL2

pPKC(PAN)betallS66 0 (~40 kDA)

STAT3 (~80 kDa)

pPKC(PAN)betallS66 0 (~80 kDa)

RPS6(S240244)


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NR0B2

7.70E-03

3.60E-02

3.16E-04

KLHL41

2.78E-03

3.90E-02

0.07

APTX

4.52E-03

3.98E-02

2.35E-02

NR0B2

KBTBD10 APTX



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Table 2. Proteins whose expression patterns are significantly (p < 0.05) different between two sets of sensitive and resistant LCLs. The genes that have previous literature evidence linking them to cisplatin (as of July 2016 as determined by querying PubMed database with the gene name and cisplatin) are displayed with boldfaced font.

Protein Name

Gene Name

19102-19210 p-value

1920618912 p-value

ZNF238

ZNF238

5.62E-06

2.49E-03

p-EF2K

EEF2K

4.38E-02

8.73E-04

ATF2

ATF2

1.49E-04

2.53E-03

GTF3C1

GTF3C1

3.13E-03

3.53E-03

CSDA

YBX3

3.56E-02

8.69E-03

FGD1

FGD1

4.58E-02

1.20E-02


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Figure Legends

Figure 1. Quantification of 259 proteins after exposure to 5 µM cisplatin. The frequency distribution of cisplatin-induced cytotoxicity in 68 YRI LCLs was used to select lymphoblastoid cell lines (with similar growth characteristics) that had discordant sensitivity were exposed to 5 µM cisplatin or vehicle and harvested at 2, 6, and 12 hours. The cells were evaluated for protein expression after cisplatin exposure relative to vehicle 1) within each LCL quantified; 2) between the two discordant LCLs; 3) overlap with the baseline protein correlation. Furthermore, a subset of proteins was evaluated in a second set of LCLs and one protein, PDK1, was evaluated with a small molecule inhibitor.

Figure 2. Protein expression patterns vary across time after being exposed to 5 µM cisplatin. Protein levels 2, 6, and 12 hours after 5 µM cisplatin exposure were calculated relative to vehicle and the fold change is displayed for both GM19210 (sensitive to cisplatin) in squares and GM19102 (resistant to cisplatin) in circles. Error across the triplicates for each time point is shown in whiskers. CREB proteins levels are displayed on the top panel, NR0B2 protein levels are displayed in the middle panel, and PAX8 protein levels are displayed on the bottom panel.

Figure 3. Baseline protein expression level correlates cisplatin IC50 and also expression patterns change in response to 5 µM cisplatin. At p < 0.05, 43 proteins were correlated with cisplatin IC50 in 68 unrelated YRI LCLs. Of the 43 proteins, 14 were also evaluated after 5 µM cisplatin treatment in a sensitive and resistant LCL with five displaying significantly (p < 0.05) different expression between sensitive and resistant LCLs. At baseline, the Pearson correlation coefficients were 0.14, 0.13, 0.18, 0.11, and 0.09 for SMC1A, TUB, RPS6, STAT3, and ZNF266, respectively.

Figure 4. PDK1 inhibitor enhances cisplatin effectiveness in non-small cell lung cancer cell lines. The synergy (an increase in the efficacy of a combination of 21 ACS Paragon Plus Environment


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treatments in comparison to any of the individual treatments) between PDK1 inhibitor and cisplatin was evaluated. PDK1 protein expression significantly differed between a sensitive (top) and resistant (bottom) LCL (p = 0.0000146)(A). The solid line indicates protein levels after 5 µM cisplatin and the dotted line indicates protein levels after exposure to vehicle, DMSO. GSK2334470, a specific inhibitor to PDK1, displayed minimal growth inhibition alone across three non-small cell lung cancer cell lines, A549, H1437, and H2126 (B). Using 100 nM of the inhibitor, we observed synergy with cisplatin at a range of concentrations 15 µM through 40 µM (C). Synergy was assessed using a combination index calculated with CalcuSyn software.

Figure 5. Phosporylated PDK1 levels are reduced in H2126 cell by GSK2334470. Phosphorylated PDK1 levels were assessed with Bradford protein quantification after exposure of 100 nM GSK2334470 alone (A) or with 20 µM cisplatin (B). Protein levels were quantified using the Odyssey imaging system and each band was normalized against beta-actin. PDK1 inhibition was observed under both conditions.


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Figure 1



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Figure 2


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Figure 3



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Figure 4


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Figure 5 27 ACS Paragon Plus Environment


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Table of content graphic


Identification of Novel Protein Expression Changes Following

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