Biomarkers for predicting the sensitivity of cancer cells to TRAIL-R1 agonistic monoclonal antibody

Cancer Letters 292 (2010) 269–279

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Cancer Letters journal homepage: www.elsevier.com/locate/canlet

Biomarkers for predicting the sensitivity of cancer cells to TRAIL-R1 agonistic monoclonal antibody Shinsuke Araki, Yusuke Nakayama, Akira Hori, Koji Yoshimura * Pharmaceutical Research Laboratories, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, 10 Wadai, Tsukuba, Ibaraki 300-4293, Japan

a r t i c l e

i n f o

Article history: Received 27 October 2009 Received in revised form 10 December 2009 Accepted 11 December 2009

Keywords: TRAIL-R1 antibody Predictive biomarker Apoptosis TRAIL-resistance

a b s t r a c t Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and an agonistic monoclonal antibody to TRAIL-R1 (TRAIL-R1 mAb) induce apoptosis and show anti-proliferative activity in vitro and in vivo. However, some TRAIL-R1-expressing cell lines are not sensitive to either TRAIL-R1 mAb or TRAIL. We have identified four genes (STK17B, SP140L, CASP8, and AIM1) whose expression levels differ significantly between TRAIL-R1 mAb-sensitive and resistant cell lines. Using the expression levels of these genes, we predicted TRAILR1 mAb and TRAIL sensitivity in our test cell lines with 75% (9/12) and 84% (21/25) accuracy, respectively. Knockdown of STK17B in TRAIL-R1 mAb-sensitive cells augmented Bcl-2 expression and suppressed TRAIL-R1 mAb-induced apoptosis. Our results may be useful for predicting the response of cancers to TRAIL-agonistic drugs in the clinic. Ó 2009 Elsevier Ireland Ltd. All rights reserved.

1. Introduction The Food and Drug Administration’s ‘Critical Path Opportunities’ report (http://www.fda.gov/oc/initiatives/ criticalpath/) states that ‘a new generation of predictive biomarkers would dramatically improve the efficiency of product development’. The use of predictive biomarkers to facilitate the development of novel anticancer agents now has clear precedents. The value of predictive biomarkers for both diagnosis and clinical decision-making has been shown for trastuzumab (HerceptinÒ), gefitinib (IressaÒ), and imatinib (GleevecÒ) [1–4]. More recently, multi parameter gene expression profiling has been used to predict therapeutic responses to anticancer compounds, including standard chemotherapeutics [5–7] and novel molecular-targeted anticancer agents [8–10]. These examples demonstrate the significance of the molecular markers for improving cancer treatments. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a member of the TNF superfamily, which

* Corresponding author. Tel.: +81 29 864 6341; fax: +81 29 864 6308. E-mail address: [email protected] (K. Yoshimura). 0304-3835/$ - see front matter Ó 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.canlet.2009.12.007

induces apoptosis in a wide range of human cancer cell types, but not in most normal cells [11–13]. TRAIL transmits an apoptotic signal via two death receptors, TRAIL-R1 (DR4) and TRAIL-R2 (DR5). Although there are additional receptor proteins (TRAIL-R3, TRAIL-R4 and osteoprotegerin) that bind to TRAIL, these receptors lack functional death domains and do not transmit the apoptotic signal [14]. The binding of TRAIL leads to trimerization of TRAIL-R1/R2 and subsequent activation of the receptormediated death pathway [15]. The activated TRAIL-R1/R2 recruits and activates an adaptor protein called Fas-associated death domain (FADD) via interactions between the death domain on the death receptors and FADD. The death effector domain of FADD recruits and activates caspase-8 or -10, leading to the formation of the death-inducing signaling complex [16]. Activated caspase-8 or -10 activate additional caspases (caspase-3 or -7), which then act on the final death substrates for apoptosis in type I cells [17,18]. In type II cells, activated caspase-8 triggers a mitochondria-dependent apoptotic amplification loop by activating Bid, which induces the accumulation of Bax and the release of cytochrome c from mitochondria. This is followed by the activation of caspase-9, caspase-3, and caspase-7, and finally programmed cell death [19].

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Targeting the TRAIL pathway has been considered an attractive strategy for the cancer treatment. In preclinical studies, recombinant TRAIL has been shown to suppress growth in a human tumor xenograft model with no apparent systemic toxicity [12]. Agonistic monoclonal antibodies targeting TRAIL-R1 and TRAIL-R2 also exhibited potent antitumor activity in vivo without toxicity [20,21]. Earlyphase clinical trials have been initiated for the evaluation of soluble TRAIL, and for monoclonal antibodies to both TRAIL-R1 and TRAIL-R2. We have investigated the apoptotic activity of an agonistic monoclonal antibody to TRAIL-R1 (TRAIL-R1 mAb) both in vitro and in vivo, and found that TRAIL-R1 mAb possesses anti-proliferative activity against various cancer cell lines. However, some cell lines (e.g. COLO320DM) are not sensitive to TRAIL-R1 mAb, even though they express TRAIL-R1. This suggests the existence of factors that determine the sensitivity/resistance of cancer cell lines to TRAIL-R1 mAb. Identification of these factors is the key to the reliable prediction of the sensitivity of particular cancer cells to this antibody, and to increasing the probability of a clinically successful treatment. Therefore, we examined the gene expression profiles of a variety of cancer cell lines using microarray technology followed by bioinformatic analysis of the results, and identified a new combination of genes that is able to predict the in vitro sensitivity of cancer cells to both TRAIL-R1 mAb and TRAIL with high reliability.

2. Materials and methods 2.1. Cell cultures Human colon cancer cell lines COLO205, HCT15, DLD1, COLO201, and COLO320DM; human lung cancer cell lines NCI-H460, NCI-H2122, NCI-H358, PC14, NCI-H1703, NCIH23, NCI-H838, NCI-H226, NCI-H520, NCI-H522, and NCIH2347; human breast cancer cell lines Zr75-1, BT474, T47D, and BT549; and the SNU-668 human stomach cancer cell line were maintained in RPMI 1640 (Sigma–Aldrich, St. Louis, MO). The HCT116 human colon cancer cell line, A549 human lung cancer cell line, and MCF7 and SKBr3 human breast cancer cell lines were maintained in DMEM (Sigma–Aldrich). The human colon cancer cell lines SW480, SW48, SW620 SW1116, SW1417, SW403, SW837, and SW948, and the human breast cancer cell lines MDA-MB435S, MDA-MB-231, MDA-MB-436, MDA-MB-175VII, MDA-MB-468, MDA-MB-157, and MDA-MB-361 were maintained in Leibovitz’s L-15 medium (Invitrogen, Carlsbad, CA). The WiDr and LS180 human colon cancer cell lines were maintained in minimum essential medium (Invitrogen). All media were supplemented with 10% fetal bovine serum (JRH Biosciences, Andover Hampshire, UK), penicillin (10,000 units/mL; Invitrogen), and streptomycin (10,000 lg/mL; Invitrogen). The cells were seeded and sub-cultured in 100-mm diameter dishes every 3–4 days. HCT15, DLD1, NCI-H460, NCI-H2122, NCI-H358, NCIH1703, NCI-H23, SW480, SW48, SW620, MDA-MB-435S, MDA-MB-231, MDA-MB-436, MDA-MB-175VII, MDA-MB468, WiDr, MCF7, SKBr3, A549, HCT116, Zr75-1, BT474,

NCI-H838, NCI-H226, NCI-H520, NCI-H522, and NCIH2347 were purchased from American Type Culture Collection (Manassas, VA). COLO205, COLO201, COLO320DM, LS180, PC14, SW1116, SW1417, SW403, SW837, SW948, MDA-MB-157, MDA-MB-361 T47D, and BT549 were purchased from Dainippon Sumitomo Pharmaceutical (Osaka, Japan). SNU-668 was purchased from the Korean Cell Line Bank (Seoul, South Korea). MB-231T, a sub-line of MDAMB-231, was established in house by continuous culture for several months in DMEM at 5% CO2 (not in ATCC-recommended Leibovitz’s L-15 medium). 2.2. Cell growth inhibition assay for TRAIL-R1 mAb A fully humanized agonistic antibody to TRAIL-R1 (TRAIL-R1 mAb) was purified from hybridoma cells (ATCC number: PTA-3570). Cancer cells (5  103) were seeded into 96-well plates and incubated overnight. Fifty microliters of diluted TRAIL-R1 mAb was then added to the culture medium in each well. After 48 h of incubation, the WST-8 assay (Dojin, Kumamoto, Japan) or the CellTiterGlo luminescent cell viability assay (Promega, Madison, WI) was performed according to the manufacturers’ protocols. 2.3. RNA preparation and GeneChip microarray analysis Total RNA was extracted using the RNeasy Miniprep kit (Qiagen, Valencia, CA) according to the manufacturers’ protocol. The quality of the total RNA was ascertained by the presence of two distinct peaks at 18S and 28S, with no additional peaks of degradation, using a bioanalyzer (Agilent Technology, Santa Clara, CA). Preparation of cDNA and cRNA, hybridization, and microarray scanning were performed according to the manufacturers’ protocols. cDNA and biotinylated cRNA were synthesized from 10 lg of total RNA using GeneChip expression 3’ amplification reagent kits (one-cycle cDNA synthesis and IVT labeling; Affymetrix, Santa Clara, CA), and the biotinylated cRNA was hybridized to Affymetrix U133 Plus 2.0 human genome arrays. The captured signals were normalized to the median expression level using the GeneSpring software package (Agilent Technology). 2.4. Real-time polymerase chain reaction analysis and calculation of expression index Reverse transcription was performed using the TaqMan Reverse Transcription Reagents kit (Applied Biosystems). A mixture of random hexamers (50 lM) and 7 lL of RNasefree water was incubated with 400 ng of RNA at 65 °C for 5 min to denature the secondary structures of the mRNA and to anneal the hexamer primers. After incubation, the following reagents were added: 2 lL 10 reverse transcriptase (RT) buffer, 4.4 lL 25 mmol/L MgCl2, 4 lL deoxyNTP mixture (2.5 mmol/L of each), 0.4 lL RNase inhibitor (20 U/mL), 0.5 lL Multi Scribe RT (50 U/lL), and 0.7 lL of water. The total mixture (20 lL) was incubated at 25 °C for 10 min for equilibration, at 48 °C for 60 min to allow cDNA synthesis, and finally at 95 °C for 5 min to inactivate the RT enzyme. The resultant mixture was stored at 20 °C

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until required for PCR. Real-time PCR was performed at a final reaction volume of 20 lL using a 384-well optical tray fitted with caps. The samples contained 10 lL of real-time PCR Master Mix (Applied Biosystems), 1 lL of cDNA solution, and 2 lL of TaqMan Gene Expression Assays (Applied Biosystems), including a FAM-labeled probe and primers, for each gene. DNA was initially heated at 50 °C for 2 min and at 95 °C for 10 min, followed by 40 cycles at 95 °C for 15 s and 60 °C for 1 min. The amount of FAM fluorescence released from each tube was measured as a function of the PCR cycle number (CT) using an ABI7900 real-time PCR system (Applied Biosystems). Relative quantitation of gene expression was performed using the comparative CT (DDCT) method [22]. Briefly, data normalization was carried out by subtracting the CT value of the housekeeping gene encoding glyceraldehyde 3phosphate dehydrogenase (GAPDH) from that of the target gene. The DDCT was calculated as the difference of the normalized CT values (DCT) for each cell line and a control cell line (COLO205): DDCT = (CT Gene X in cell Y CT GAPDH in cell (CT Gene X in COLO205 CT GAPDH in COLO205). Finally, DDCT Y) was converted to an expression index (i.e. fold of change) using the following formula: expression index = 2 DDCT. 2.5. Statistical analysis Signal-to-noise ratio (S2N) is the index used to explore genes that are strongly correlated with the class distinction to be predicted [23,24]. It is represented by the following equation: S2N = (m1–m2)/(s1 + s2), where m1 and m2 represent the mean value of gene expression for the samples in class1 and class2, and s1 and s2 represent the standard deviation (SD) of the gene expression values for samples in class1 and class2. Large S2N values indicate a strong correlation between gene expression and class distinction, and a positive or negative corresponds to the gene being more highly expressed in class1 or class2. A permutation test was used to calculate whether the correlation was statistically and significantly higher than expected. The permutation test was performed as follows: the sample classification labels were permuted and the S2N was recomputed to compare the true gene correlations to what would have been expected by chance. Genes were selected that met the following criteria: a P-value obtained from the Student’s ttest of <0.005, and an S2N P-value < 0.005 between TRAIL-R1 mAb-sensitive cells and TRAIL-R1 mAb-resistant cells. Decision tree algorithms classify samples by suitable genes for prediction through a tree-like structure [25]. Selection of predictive genes by decision tree algorithms was performed using the ‘R’ statistical software system (www.cran.r-project.org). 2.6. Transfection of siRNAs H460, HCT116, and SW480 cells were each seeded at a density of 2.5  103 cells/well into 96-well plates and incubated overnight in RPMI 1640 containing 10% FBS. STK17B siRNA (ON TARGETplus SMARTpool siRNA; Dharmacon,

271

Boulder, CO) or a control non-silencing siRNA (ON TARGETplus SMARTpool siRNA; Dharmacon) was combined with Lipofectamine RNAiMAX transfection reagent (Invitrogen), and the cells were transfected according to the recommended protocol for the siRNA (final concentration = 50 nmol/L). After 24 h, the medium was replaced with fresh medium. After 72 h, the cell lysates were prepared for examination using the Cell Death Detection enzyme-linked immunosorbent assay (ELISA) (Roche, Basel, Switzerland) and by immunoblot analysis. 2.7. Immunoblot analysis Cells were lysed using radioimmunoprecipitation assay buffer containing 50 mmol/L Tris–HCl (pH 7.6), 150 mmol/ L NaCl, 1% TritonX-100, and protease inhibitor cocktail tablets (Roche). After removing insoluble materials by centrifugation at 15,000 rpm for 15 min, the protein concentration in each supernatant was normalized using the BCA Protein Assay kit (Pierce Biotechnology, Rockford, IL), and the lysates were directly added to a quarter volume of a 5 sample buffer (25 mmol/L Tris–HCl pH 6.8, 10% glycerol, 0.8% sodium dodecyl sulfate (SDS), and 0.004% bromophenol blue) and boiled at 100 °C for 5 min. The samples were subjected to 10% SDS–polyacrylamide gel electrophoresis, and the separated proteins were electrophoretically transferred onto polyvinylidene difluoride membranes (Atto, Tokyo, Japan). The membrane was incubated with antibodies against STK17B (Santa Cruz Biotechnology, Santa Cruz, CA) and GAPDH (Santa Cruz) and visualized using enhanced chemiluminescence reagents (GE Healthcare Bioscience, Piscataway, NJ). 3. Results 3.1. Identification of differentially expressed genes Initially, TRAIL-R1 mAb-sensitive and -resistant cell lines expressing TRAIL-R1 were chosen based on data obtained from previous in vitro experiments. The first analysis was conducted using three TRAIL-R1 mAb-sensitive cell lines (COLO205, NCI-H2122, and SNU-668; IC50 < 1 nmol/L) and three resistant cell lines (COLO320DM, SW620, and NCI-H23; IC50 > 240 nmol/L) as a training set. A comprehensive gene expression analysis of these cells was conducted using microarray analysis. Consequently, 272 differentially expressed genes meeting the criteria described in Section 2 were identified (Fig. 1A). Next, we rechecked the expression of the 272 genes using the Gene Logic database against five TRAIL-R1 mAb-sensitive cell lines (COLO205, HCT15, HCT116, NCI-H460, and MDA-MB-231) and four resistant cell lines (A549, SW620, NCI-H23, and MCF7) to enhance the probability. Eighteen genes out of the 272 genes identified by microarray analysis were again confirmed to be differentially expressed (data not shown). The expression levels of these 18 genes (shown in Table 1) were further confirmed using real-time PCR analysis, and four genes (STK17B, SP140L, CASP8, and AIM1) were found to show significantly different expression using decision tree algorithms and the Student’s t-test. Higher expression levels of the STK17B (DAPK2, deathassociated protein kinase family), SP140L (SAND domain-containing family), and CASP8 (caspase-8) genes, and lower expression levels of the AIM1 (bc-crystallin superfamily) gene were identified in the sensitive cell lines (Fig. 1B and Table 1). 3.2. Determination of TRAIL-R1 mAb sensitivity criteria in the training set We tried to establish arithmetically the ‘TRAIL-R1 mAb-sensitive selection criteria’ using the expression indices of these four genes. If the expression index of AIM1 was >2.2, the cell line was predicted to be

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B

A

STK17B

SP140L

7.00

2.00 1.80

6.00

5

EXpression index

EXpression index

1.60 5.00 4.00 3.00 2.00

1.40 1.20 1.00 0.80 0.60 0.40

1.00 0.20 0.00

0.00

S1

0

2 R

3

0

CASP8

2 R

AIM1

3.50

4.00

3.00

3.50

2.50

EXpression index

EXpression index

COLO205 NCI-H2122 SNU-668 COLO320DM NCI-H23 SW620

0

S1

2.00 1.50 1.00

3.00 2.50 2.00 1.50 1.00

0.50

0.50

0.00

0.00

S

R

S

R

Fig. 1. Differential gene expression of STK17B, SP140L, CASP8, and AIM1 between TRAIL-R1 mAb-sensitive and TRAIL-R1 mAb-resistant cell lines. (A) Heat map showing the relative expression levels of 272 genes differentially expressed between TRAIL-R1 mAb-sensitive and -resistant cell lines. Red denotes relatively high expression and green denotes relatively low expression. (B) The expression indices of STK17B, SP140L, CASP8, and AIM1 in 16 TRAIL-R1 mAbsensitive (S) cell lines and 12 resistant (R) cell lines were measured using real-time PCR, and the expression index was calculated as described in Section 2. The blue points indicate the expression index in each cell, and the red bars indicate the mean value.

TRAIL-R1 mAb resistant, regardless of the expression levels of the other genes. Second, for all other situations, we adopted a scoring system concerning the expression index of STK17B, SP140L, and CASP8 genes. For each gene, points were assigned to a certain cell line according to its expression index (+1 for expression index > 0.7, 0 for expression index of 0.5– 0.7, 1 for expression index < 0.5). In this way, the points for each of the three genes were determined, and the sum of the points from all three genes represented the total score. If the total score was positive, the cell line was regarded as TRAIL-R1 mAb sensitive. If the total score was negative, the cell line was regarded as TRAIL-R1 mAb resistant. Finally, if the total score was zero, the sensitivity of the cell line was considered indeterminate. Using these criteria, we were able to estimate TRAIL-R1 mAb sensitivity in 26/28 cell lines in the training set; 25 of these predictions successfully matched the actual sensitivities (Table 1).

3.3. Validation of TRAIL-R1 mAb sensitivity criteria in the test set To validate our prediction criteria, the TRAIL-R1 mAb sensitivity and the mRNA expression indices of the four genes were investigated using a test set of 14 cell lines (Table 2). Cell lines exhibiting >30% growth inhibition in the presence of 40 nmol/L of TRAIL-R1 mAb was regarded as sensitive. Five cell lines were sensitive. All the others were resistant. Next,

the expression levels of the STK17B, SP140L, CASP8, and AIM1 genes were measured using real-time PCR, and TRAIL-R1 mAb sensitivity estimated using our prediction criteria. The calculation predicted that two cell lines would be sensitive and ten cell lines would be resistant. Predictions for the remaining two cell lines were inconclusive. As a result, we were able to predict TRAIL-R1 mAb sensitivity in 9/14 cell lines. The predictions matched the actual in vitro results obtained for 7/10 of the cell lines predicted to be resistant, and 2/2 of the cell lines predicted to be sensitive (Table 2). We then further validated the prediction criteria using a resistant cell line derived from a sensitive cell line. The MDA-MB-231 human breast cancer cell line is sensitive to TRAIL-R1 mAb (IC50: 1.8 nmol/L), but an MDA-MB-231 sub-line (MB-231T) was resistant to TRAIL-R1 mAb (IC50 > 30 nmol/L). The expression levels of the STK17B, SP140L, CASP8, and AIM1 genes were measured using real-time PCR in MB-231T cells. The expression levels of STK17B, SP140L, and CASP8 were down-regulated in MB-231T cells compared with those in MDA-MB-231 cells. In contrast, the expression level of AIM1 in MB-231T cells was up-regulated compared with MDA-MB-231 cells (Table 3). Our established criteria successfully predicted the actual sensitivity of MDA-MB-231 and MB-231T cell lines to TRAIL-R1 mAb seen in our in vitro experiments, confirming the reliability of our criteria and suggesting the critical involvement of these four genes in TRAIL-R1-mediated apoptotic signaling.

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S. Araki et al. / Cancer Letters 292 (2010) 269–279 Table 1 Prediction of TRAIL-R1 mAb sensitivity in the training set. Cancer cell type

Cell line

Sensitivity

IC50 (nmol/L)

STK17B

SP140L

CASP8

AIM1

Score

Prediction accuracy

Colon

COLO205 HCT15 HCT116 DLD1 SW480 COLO201 SW48 COLO320DM SW620 WiDr LS180

S S S S S S S R R R R

1.5 2.9 3.5 8.3 0.9 29.9 13.4 >238 >238 >238 >238

1.00 3.46 0.52 2.34 0.66 1.44 1.54 0.32 0.23 1.03 0.61

1.00 0.53 0.96 0.48 0.23 1.14 0.72 0.00 0.22 1.33 0.63

1.00 1.38 0.71 1.28 0.78 1.26 0.39 0.15 0.23 1.60 0.66

1.00 0.52 0.18 1.93 0.03 1.86 0.66 0.26 0.03 2.97 2.44

+3 +2 +2 +1 0 +3 +1 3 3

S S S S N/D S S R R R R

10/10

Lung

NCI-H2122 NCI-H460 NCI-H358 PC-14 NCI-H23 A549 NCI-H1703

S S S S R R R

2.1 0.29 4.2 26.5 >238 >238 >238

1.71 0.89 1.44 0.72 0.23 0.31 0.46

0.95 1.03 1.10 0.29 0.00 0.14 0.35

1.45 1.08 1.23 0.15 0.10 0.45 0.21

0.54 0.07 2.23 0.97 0.02 0.03 0.00

+3 +3 +3 1 3 3 3

S S S R R R R

6/7

Breast

MDA-MB-231 MDA-MB-435S MDA-MB-436 MDA-MB-175VII MCF7 BT474 SKBr3 MDA-MB-468 Zr75-1

S S S S R R R R R

1.8 8 2.1 19 >238 >238 >238 >238 >238

1.15 1.14 1.13 5.78 0.84 1.13 0.36 0.39 0.69

0.61 1.66 0.25 1.89 0.44 0.02 0.03 0.76 0.02

0.58 1.11 0.81 3.15 0.51 1.40 0.42 0.73 0.23

0.42 1.56 0.14 1.93 1.11 3.78 1.68 3.37 2.27

+1 +3 +1 +3 0

S S S S N/D R R R R

8/8

SNU-668

S

1.62

0.72

0.48

0.36

+1

Gastric

3

S

1/1

Total

25/26

S = sensitivity, R = resistance, N/D = not determined.

Table 2 Prediction of TRAIL-R1 mAb sensitivity in the test set. Cancer cell type

Cell line

STK17B

SP140L

Colon

SW1116 SW1417 SW403 SW837 SW948

0.38 1.82 1.81 3.41 3.12

0.31 0.17 0.66 0.83 0.18

0.56 0.58 1.13 1.29 2.79

1.12 0.66 2.24 2.23 1.89

+1

Lung

NCI-H838 NCI-H226 NCI-H520 NCI-H522 NCI-H2347

0.49 0.21 0.30 0.00 0.94

0.39 1.01 0.52 0.01 0.14

0.59 0.10 0.11 0.10 0.19

0.69 0.01 1.52 0.00 1.14

l 1 2 3 1

T47D BT-549 MDA-MB-157 MDA-MB-361

0.92 1.24 6.08 0.70

1.18 0.08 1.11 0.09

038 0.45 1.95 0.59

5.78 0.52 0.00 1.73

1 +3 0

Breast

CASP8

AIM1

Score 2 0

Prediction

Assay

GI rate(%) (40 nmol/L)

Accuracy

R N/D R R S

S R R R S

56.6 6.2 1.8 3.9 99.2

3/4

R R R R R

S R R R S

56.1 22.1 25.1 14.0 34.5

3/5

R R S N/D

R R S R

3.4 0.0 38.3 0.0

3/3

Total

9/12

S = sensitivity, R = resistance, N/D = not determined.

Table 3 Comparison of MDA-MB-231 and MB-231T cell lines. Cell line

IC50 (nmol/L)

STK17B

SP140L

CASP8

AIM1

Score

Prediction

Sensitivity

MDA-MB-231 MB-231T

1.8 >30

1.15 0.12

0.61 0.46

0.58 0.19

0.42 0.60

+1 3

S R

S R

S = sensitivity, R = resistance.

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S. Araki et al. / Cancer Letters 292 (2010) 269–279

Table 4 Prediction of TRAIL Sensitivity. Cancer cell type

Cell line

TRAIL-Rl mAb

TRAIL sensitivity

IC50 (nmol/L)

Prediction

Accuracy

Colon

COLO205 HCT15 HCT116 DLD1 SW480 COLO201 SW48 COLO320DM SW620 WiDr LS180

S S S S S S S R R R R

S S S S S S S R S S R

0.005 0.042 0.027 0.058 0.075 0.12 0.031 >26 0.13 0.067 5.6

S S S S N/D S S R R R R

8/10

Lung

NCI-H2122 NCI-H460 NCI-H358 PC-14 NCI-H23 A549 NCI-H1703

S S S S R R R

S S S S R R S

0.03 0.004 0.046 0.085 >26 4.6 0.11

S S S R R R R

5/7

MDA-MB-231 MDA-MB-435S MDA-MB-436 MCF7 BT474 SKBr3 MDA-MB-468 Zr75-1

S S S R R R R R

S S S R R R R R

0.83 0.13 0.27 >26 >26 >26 7.4 7.6

S S S N/D R R R R

SNU-668

S

S

0.024

S

1/1

Total

21/25

Breast

Gastric

7/7

S = sensitivity, R = resistance, N/D = not determined.

3.4. Examination of TRAIL sensitivity using TRAIL-R1 mAb sensitivity criteria Since TRAIL-R1 mAb is known to stimulate the TRAIL-mediated signaling pathway, it is reasonable to use the prediction criteria established for TRAIL-R1 mAb sensitivity to predict TRAIL sensitivity. Therefore, the TRAIL sensitivity of 27 cell lines (Table 1) was also examined. Cell lines with IC50 < 1 nmol/L to TRAIL were regarded as sensitive (Table 4). TRAIL sensitivity matched TRAIL-R1 mAb sensitivity in 24/27 cell lines, and again we were able to predict TRAIL sensitivity in 21/25 cell lines (84% accuracy) using the TRAIL-R1 mAb prediction criteria (Table 4).

3.5. Involvement of STK17B in TRAIL-R1 mAb sensitivity The expression levels of the STK17B, SP140L, and CASP8 genes were higher in TRAIL-R1 mAb sensitive cell lines (Fig. 1). Since it is well-known that CASP8 plays a critical role in TRAIL signaling, the up-regulation of CASP8 in sensitive cells is considered quite reasonable. On the other hand, the involvement of STK17B and SP140L in TRAIL-mediated apoptotic signaling has not been established. Therefore, we examined whether repression of the STK17B and SP140L genes reduced the sensitivity of TRAIL-R1 mAb-sensitive cell lines. First, we analyzed the effect of siRNA-mediated STK17B knockdown on TRAIL-R1 mAb-induced apoptosis. Immunoblot analysis confirmed that the transfection of STK17B siRNAs led to >95% reduction of the STK17B protein level compared with that seen with control siRNAs. STK17B knockdown had no effect on the expression of the control protein, GAPDH (Fig. 2A). Knockdown of STK17B without TRAILR1 mAb treatment also had no effect on the cell growth rate (data not shown). Transfection of STK17B siRNA clearly reduced DNA fragmentation induced by TRAIL-R1 mAb exposure compared with the control siRNA transfection (Fig. 2B). Knockdown of SP140L did not affect apoptosis and TRAIL-R1 mAb sensitivity under these conditions (data not shown). Also, caspase-3/7 activity and cleavage of PARP (a hallmark biochemical feature of apoptosis induced by TRAIL-R1 mAb exposure) were also reduced by STK17B siRNA transfection compared with control siRNA transfection (Fig. 2A and C). We also found that an anti-apoptotic protein, Bcl-

2, was induced 72 h after transfection of STK17B siRNA (Fig. 3). These results suggested that STK17B contributes significantly to TRAIL-R1 mAb sensitivity, and is one of the critical proteins for TRAIL signaling. STK17B is a member of death-associated protein (DAP) kinase family, which contains DAP [26], DRP-1 [27], ZIP kinase [28], DAPK2 [29], STK17A, and STK17B [30]. STK17B and STK17A share the highest homology in the DAP kinase family [30], suggesting that the expression of these genes might be regulated in a similar manner. Indeed, differential expression of STK17B and STK17A between TRAIL-R1 mAb-sensitive and -resistant cell lines was statistically significant (Fig. 4A), although the other DAP kinase family genes were not differentially expressed. Phylogenetic analysis showed that STK17A and STK17B were closely related to each other (Fig. 4B), and overexpression of STK17A has been reported to induce cell death in prostate cancer cells [31]. However, a reduction in TRAIL-R1 mAb-induced apoptosis mediated by STK17A knockdown was not observed in our experiments (data not shown). These results suggest a difference in apoptotic signaling between STK17A and STK17B, and identify STK17B as one of the predictive markers for TRAIL-R1 mAb sensitivity.

4. Discussion Using gene expression profiles, we have looked for biomarkers that may be useful for predicting the sensitivity of cancer cells to TRAIL-R1 mAb. Although there are many reports on the mechanisms of resistance of tumor cells to TRAIL [32–34], predictive biomarkers for TRAIL-agonistic drugs have not been clearly identified. Therefore, we carried out a comprehensive analysis of the gene expression profiles of TRAIL-R1 mAb-sensitive and -resistant cancer cell lines, and identified several genes whose expression profiles differed significantly between these cell lines. We also established criteria for predicting the response of indi-

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A

B (+)

(-)

NS

NS

1.2 DNA fragmentation

TRAIL-R1 mAb

1 0.8

NS STK17B si

0.6 0.4 0.2 0 0 3 30 TRAIL-R1 mAb (nM)

DNA fragmentation

PARP

STK17B GAPDH

C

NS STK17B si

0 6 30 TRAIL-R1 mAb (nM)

250000

1.2

200000

DNA fragmenation

Caspase-3/7 activity

300000

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

150000 100000 50000 NS

STK17B si

1 0.8

NS STK17B si

0.6 0.4 0.2 0 0

6

30

TRAIL-R1 mAb (nM) Fig. 2. Involvement of STK17B in TRAIL-R1 mAb sensitivity. (A) H460 cells were transfected with STK17B siRNA (STK17B si) or non-silencing control siRNA (NS). After 72 h, TRAIL-R1 mAb (30 nmol/L) was added. After a further 3 h, cell lysates were prepared. PARP, STK17B and GAPDH were detected using immunoblotting with an antibody to each protein. (B) H460 (top), HCT116 (middle), and SW480 (bottom) cells were transfected using STK17B siRNA (STK17B si) or non-silencing control siRNA (NS). After 72 h, a cell death detection ELISA assay was performed. Necrosis and histone-complexed DNA fragments were likewise detected. The error bars represent the standard deviation (SD) of triplicate assays. (C) H460 cells were transfected with STK17B siRNA (STK17B si) or non-silencing control siRNA (NS). After 72 h, TRAIL-R1 mAb (30 nmol/L) was added. After a further 3 h, a caspase-3/7 assay was performed. The error bars represent the SD of triplicate assays.

K1 ST

NS

7B

si

vidual tumors to the TRAIL-R1 mAb based on the expression patterns of four genes (STK17B, SP140L, CASP8, and

Bcl-2

GAPDH Fig. 3. Induced Bcl-2 expression by STK17B siRNA. (A) H460 cells were transfected with STK17B siRNA (STK17B si) or non-silencing control siRNA (NS). After 72 h, cell lysates were prepared. Bcl-2 and GAPDH were detected using immunoblotting with an antibody to each protein.

AIM1) that successfully predicted TRAIL-R1 mAb and TRAIL sensitivity with an accuracy of 75.0% (9/12) and 84% (21/ 25), respectively, in our test set of cell lines. Recent studies indicate that the genes GALNT14 and GALNT3 (encoding the enzymes responsible for O-glycosylation) play a critical role in TRAIL sensitivity [34,35]. Wagner et al. identified these genes using microarray analysis of both TRAIL-sensitive and TRAIL-resistant cell lines. To see whether our marker genes are statistically significant in their panel of cell lines, we re-examined their data set (available from Gene Expression Omnibus with accession number GSE8332) in light of our results. We found that the expression levels of SP140L and CASP8 genes were significant, with P-values of 0.0075 and 0.0009 (Student’s ttest), respectively, while the expression levels of STK17B and AIM1 genes were not. We next examined our data set to see if the ‘hit’ genes identified by Wegener et al. , were significant in the cell lines we tested, and found that

S. Araki et al. / Cancer Letters 292 (2010) 269–279

B

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

P = 0.0081

20 18 16 14 12 10 8 6 4 2 0

P = 0.0147

COLO201 HCT15 MDA-MB-175VII DLD1 NCI-H2122 SNU668 MDA-MB-231 SW48 MDA-MB-436 COLO205 NCI-H358 MDA-MB-435S NCI-H460 SW480 HCT116 PC-14 WiDr MDA-MB-468 LS180 BT474 MCF7 Zr75-1 NCI-H1703 A549 SKBr3 COLO320DM SW620 NCI-H23

A

STK17A expression index STK17B expression index

276

DAPK DRP-1 ZIP kinase STK17B STK17A

Fig. 4. Differential gene expression of STK17B family between TRAIL-R1 mAb-sensitive and TRAIL-R1 mAb-resistant cell lines. (A) The expression indices of STK17B and STK17A. Sixteen TRAIL-R1 mAb sensitive cell lines (open bars) and twelve resistant cell lines (black bars) were measured using real-time PCR, and the expression index was calculated as described in Section 2. (B) Phylogenetic analysis of DAPK family sequences. Amino acid sequences of STK17B, STK17A, DAPK, DRP-1, and ZIP kinase were aligned using CLUSTAL W.

the expression levels of the GALNT14 and GALNT3 genes were not (P = 0.17 and P = 0.42, respectively). We speculate that these differences in outcome may be due the different origin tissues of the cell lines tested. Further evaluation using larger data sets for individual cancer cell types will be of interest. Since it was difficult to identify a single marker gene with high predictive accuracy, we looked for a set of genes that can be used as predictive biomarkers, and which may provide further insight to the complicated mechanism of TRAIL resistance. Mechanisms of TRAIL resistance are known to relate either to increased expression or activation of anti-apoptotic molecules such as c-FLIP, NF-jB, Bcl-2, Akt, survivin, and XIAP, or to the defective expression of apoptotic molecules such as TRAIL-R1, FADD, and CASP8 [32,33]. However, contrary to previous reports, these genes (with the exception of CASP8) did not show significantly different expression profiles between TRAIL-R1 mAb-sensitive and -resistant cells in this study. CASP8 is a critical molecule in TRAIL-induced (as well as Fas L- and TNF-a-induced)

apoptosis. Knockdown of CASP8 by siRNA in TRAIL-sensitive cells completely abolishes their sensitivity to TRAIL [34]. Therefore, CASP8 has been suggested to be an important predictive biomarker for TRAIL-R1 mAb sensitivity, and our data are consistent with this. We observed that the knockdown of STK17B expression by siRNA partially suppressed TRAIL-R1 mAb-induced apoptosis in human cancer cells. In addition, the STK17A and STK17B genes were differentially expressed between TRAIL-R1 mAb-sensitive and -resistant cells, although the other DAP kinase family genes were not differentially expressed. Based on their homology and phylogenetic analysis, it is reasonable to suppose that STK17A and STK17B are expressed differentially in TRAIL-R1 mAb-sensitive and -resistant cells, and that they may have functional redundancy. We found that Bcl-2 expression was significantly induced after transfection of STK17B siRNA. It has been reported that Bcl-2, Bcl-xL, and c-FLIP are down-regulated in the activated T cells of STK17B transgenic mice [36], suggesting that STK17B has a critical func-

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tion not only in T cell apoptosis but also in cancer apoptosis. Although the detailed mechanism of action and regulation of STK17B in TRAIL signaling is unknown, knockdown of STK17B expression does not suppress apoptosis induced by chemotherapeutic drugs such as 5-FU and cisplatin, but does suppress UV-induced apoptosis in a rat colon cancer cell line [37]. Thus, it is conceivable that the function of STK17B is limited to particular apoptosis signaling pathways, and that the expression level of STK17B is one of the decisive factors for TRAIL-R1 mAb sensitivity, but not for chemotherapeutic drug sensitivity. SP140L shares 30–50% homology with members of the SP100 family that consists of SP100, SP110, SP140, and SP140L. We also analyzed the expression levels of these SP100 family genes. We found a statistically significant difference in the differential expression of SP140L, SP100, and SP110 in TRAIL-R1 mAb-sensitive and -resistant cell lines (data not shown). SP140 was expressed at very low levels in the cell lines examined. These genes are located in close proximity to each other on chromosome 2q37. Although the biological function of SP140L is not well defined, the SP140L family contains a SP100 homology domain and a SAND domain with DNA binding activity. SP100 is a wellknown constitutive component of polymorphonuclear (PML) nuclear bodies; macromolecular structures considered to play a role in DNA damage response and apoptosis signaling [38–41]. Cells from PML-deficient mice show severe defects in apoptosis triggered by genotoxic stress and death receptors, as well as Fas and TNF activation [40]. Taken together, it is possible that SP140L is also a constituent of PML nuclear bodies and plays a role in the induction and/or reinforcement of programmed cell death through the selective and dynamic regulation of proapoptotic transcriptional events. Therefore, although we did not observe the effects of siRNA knockdown of SP140L on TRAIL-R1 mAb-induced apoptosis, the present results imply that the SP140L family has functional redundancy, and is a potential TRAIL-R1 mAb predictive biomarker. AIM1 is member of the bc-crystallin superfamily of proteins and has been reported to function as a melanoma tumor suppressor [42,43]. However, its biological function is not well established. In our preliminary experiments, overexpression of the AIM1 gene partially reduced the TRAIL-R1 mAb sensitivity in the TRAIL-R1 mAb-sensitive cancer cell lines (data not shown). We are currently undertaking more a detailed analysis of the function of AIM1 in TRAIL signaling. In this study, gene expression profiling was used to predict the sensitivity of cancer cells to TRAIL-R1 mAb. The relevance of the use of gene expression profiling in cancer prognosis has gained recognition. Two gene expression profiling-based diagnostic tests have been developed recently: MammaPrint™ (Agendia, Amsterdam, The Netherlands) and Oncotype DX™ (Genomic Health Inc., Redwood City, CA) [44,45]. MammaPrint measures the expression levels of 70 genes from a patient’s tumor tissue, while Oncotype DX measures 21 genes. Both tests estimate the risk of tumor recurrence in a subset of breast cancer patients. The FDA approved MammaPrint as the first medical device in the category of ‘In Vitro Diagnostic Multivariate Index Assay (IVDMIA)’. Unlike MammaPrint, Oncotype

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DX has also been used to estimate whether a patient is likely to benefit from chemotherapy. Both the American Society of Clinical Oncology and the National Comprehensive Cancer Network recommended Oncotype DX for the analysis of tumor markers in breast cancer [46,47]. A large-scale clinical study to assess the application of Oncotype DX to clinical decision-making is ongoing. In contrast to MammaPrint, which uses microarray-based analysis, Oncotype DX uses real-time PCR. Amongst a number of methods to measure gene expression, real-time PCR has some advantages. First, real-time PCR is considered to have a broader clinical application as it can be performed on formalin-fixed and paraffin embedded (FFPE) tumor tissue blocks. Microarray-based analysis is rather limited since it requires fresh or unpreserved tumor tissues. Second, real-time PCR is widely accepted as a rapid, cost-effective, and highly sensitive diagnostic method compared with microarray analysis. Therefore, it is of particular interest to examine whether real-time PCR can be used for the clinical validation of the predictive marker genes established in this study. Retrospective analysis to assess the validity of the candidate marker genes by real-time PCR can be designed using FFPE samples from cancer patients treated with TRAIL-R1 mAb during the course of clinical studies. Although we initially developed and used our original prediction criteria, a Weighted Voting system will be applied for future studies of TRAIL-R1 mAb predictive biomarkers, with a larger number of clinical samples. The Weighted Voting system is a widely accepted method that has been used to classify samples according to their therapeutic response [9,23]. Based on the data presented in this study, we propose that the STK17B, SP140L, CASP8, and AIM1 genes are an important set of biomarkers for predicting the sensitivity of cells derived from solid cancers to TRAIL-R1 mAb and other TRAIL-agonistic drugs. Further validation of the accuracy of this method in a clinical setting is awaited. Conflict of interest No potential conflicts of interest were disclosed. Acknowledgments We thank Shuichi Furuya and Osamu Nakanishi for great encouragement and support for our research. We also thank Ryujiro Hara and Juran Kato-Stankiewicz for critical reading of the manuscript and helpful discussion, Akiko Asakawa for technical support, and Hiroshi Tanaka and Yuichi Hikichi for providing the MB-231T cell line. References [1] J. Baselga, D. Tripathy, J. Mendelsohn, S. Baughman, C.C. Benz, L. Dantis, N.T. Sklarin, A.D. Seidman, C.A. Hudis, J. Moore, P.P. Rosen, T. Twaddell, I.C. Henderson, L. Norton, Phase II study of weekly intravenous recombinant humanized anti-p185HER2 monoclonal antibody in patients with HER2/neu-overexpressing metastatic breast cancer, J. Clin. Oncol. 14 (1996) 737–744. [2] B.J. Druker, M. Talpaz, D.J. Resta, B. Peng, E. Buchdunger, J.M. Ford, N.B. Lydon, H. Kantarjian, R. Capdeville, S. Ohno-Jones, C.L. Sawyers, Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine

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