microarrays Microarrays Microarrays Microarrays 2076-3905 MDPI 10.3390/microarrays1010044 microarrays-01-00044 Article In Silico Analysis of Microarray-Based Gene Expression Profiles Predicts Tumor Cell Response to Withanolides Efferth Thomas 1 * Greten Henry Johannes 2 3 Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Staudinger Weg 5, Mainz 55128, Germany Heidelberg School of Chinese Medicine, Karlsruher Straße 12, Heidelberg 69126, Germany; Email: heidelbergschool@aol.com Biomedical Sciences Institute Abel Salazar, University of Porto, Porto 4050-313, Portugal Author to whom correspondence should be addressed; Email: efferth@uni-mainz.de; Tel.: +49-6131-392-5751; Fax: +49-6131-392-3752. 22 05 2012 2012 1 1 44 63 19 04 2012 09 05 2012 15 05 2012 © 2012 by the authors; licensee MDPI, Basel, Switzerland. 2012

This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

Withania somnifera (L.) Dunal (Indian ginseng, winter cherry, Solanaceae) is widely used in traditional medicine. Roots are either chewed or used to prepare beverages (aqueous decocts). The major secondary metabolites of Withania somnifera are the withanolides, which are C-28-steroidal lactone triterpenoids. Withania somnifera extracts exert chemopreventive and anticancer activities in vitro and in vivo. The aims of the present in silico study were, firstly, to investigate whether tumor cells develop cross-resistance between standard anticancer drugs and withanolides and, secondly, to elucidate the molecular determinants of sensitivity and resistance of tumor cells towards withanolides. Using IC50 concentrations of eight different withanolides (withaferin A, withaferin A diacetate, 3-azerininylwithaferin A, withafastuosin D diacetate, 4-B-hydroxy-withanolide E, isowithanololide E, withafastuosin E, and withaperuvin) and 19 established anticancer drugs, we analyzed the cross-resistance profile of 60 tumor cell lines. The cell lines revealed cross-resistance between the eight withanolides. Consistent cross-resistance between withanolides and nitrosoureas (carmustin, lomustin, and semimustin) was also observed. Then, we performed transcriptomic microarray-based COMPARE and hierarchical cluster analyses of mRNA expression to identify mRNA expression profiles predicting sensitivity or resistance towards withanolides. Genes from diverse functional groups were significantly associated with response of tumor cells to withaferin A diacetate, e.g. genes functioning in DNA damage and repair, stress response, cell growth regulation, extracellular matrix components, cell adhesion and cell migration, constituents of the ribosome, cytoskeletal organization and regulation, signal transduction, transcription factors, and others.

cancer drug development medicinal food microarray pharmacogenomics natural products 1. Introduction

Drug resistance and severe adverse side-effects are major obstacles to cancer chemotherapy. Therefore, new therapy options with improved efficacy are urgently required. Nutritional sources such as marine and terrestrial plants are fertile grounds in which to find bioactive constituents with anti-tumor activity. The long-lasting experience of traditional phytotherapy may facilitate the identification of novel treatment strategies. In India, herbs have been used as foods and medicine for millennia. In recent years, the active principles of food and medicinal herbs have been increasingly elucidated, making the active chemical compounds accessible to molecular biological and biochemical research [1,2,3,4,5,6,7,8]. As compounds of Ayurveda (Sankskrit: knowledge of life) may have molecular targets different from those of standard anti-cancer drugs, they are attractive candidates in the search for novel drugs suitable to treat otherwise drug-resistant tumors. These natural compounds may also show reduced side effects on normal organs [9].

Withania somnifera (L.) Dunal (Indian ginseng, winter cherry, Sanskrit: Ashwagandha) belongs to the family of Solanaceae (nightshade plants). In Ayurveda, roots of the plant are either chewed or used to prepare beverages (aqueous decocts). Withania is widely known for its aphrodisiacal, liver tonic, anti-inflammatory, immune-stimulatory, and astringent activities. Furthermore, Withania somnifera is used to treat asthma, ulcers, emaciation, insomnia, senile dementia, tumors, diabetes, neurodegenerative disorders and numerous other symptoms and disorders [10,11,12,13,14].

The major phytochemicals in Withania somnifera and other Withania species are the withanolides, C-28-steroidal lactone triterpenoids built on an intact or rearranged ergostane framework. Within this framework, C-22 and C-26 are appropriately oxidized forming a six-membered lactone ring [15]. Other bioactive constituents in Withania somnifera are alkaloids, such as isopelletierine and anaferine [16].

In addition, Withania somnifera’s use in traditional medicine, Withania somnifera extracts as well as isolated withaferin A can exert chemopreventive effects [17,18,19,20]. Both substances have been shown to inhibit tumor cell growth in cell lines in vitro, in mouse tumors in vivo, and in human xenograft tumors transplanted into nude mice [21,22,23,24,25,26].

As is frequently observed with natural products in general, withaferin A exerts its anti-cancer effect by targeting multiple pathways rather than a single target in tumor cells. Withaferin A induces cell cycle arrest in the G2M phase [23,24,27,28,29,30]. Furthermore, withaferin A inhibits angiogenesis [31,32]. The full range of mechanisms contributing to the anti-cancer activity of withaferin A is incompletely understood at present. In addition, the cytotoxic and anti-cancer effects of other withanolides have not yet been thoroughly investigated either [33,34,35,36,37,38,39].

The aims of the present study were, firstly, to investigate whether tumor cells develop cross-resistance between standard anticancer drugs and withanolides and, secondly, to elucidate the molecular determinants of sensitivity and resistance of tumor cells to withanolides. Using IC50 concentrations of withanolides and the most frequently used standard clinical anticancer drugs, we analyzed the cross-resistance profile of 60 cell lines of the National Cancer Institute (NCI), USA [40]. Next, we performed transcriptomic microarray-based COMPARE analyses of mRNA expressions and then subjected the candidate genes to hierarchical cluster analyses to identify mRNA expression profiles, which predict sensitivity and resistance of tumor cell withanolides. Our microarray-based investigation resulted in novel candidate genes associated with the response of cancer cells to withaferin A diacetate. We found that tumor cell response was associated with genes from diverse functional groups (DNA damage and repair, stress response, cell growth regulation, extracellular matrix components, cell adhesion and cell migration, constituents of the ribosome, cytoskeletal organization and regulation, signal transduction, transcription factors, and others), indicating that resistance and sensitivity may be determined by multiple mechanisms.

 2. Results 2.1. Cytotoxicity of Withanolides towards Cancer Cells

Eight withanolides were analyzed. Their chemical structures are shown in Figure 1. These substances were investigated over doses ranging from 10−8 to 10−4 M in 60 cell lines of the NCI, and log10IC50 values were calculated for each withanolide for each cell line. The mean log10IC50 values for cell lines of each tumor type are depicted in Figure 2. Withafastuosin D diacetate, withaferin A diacetate, and 4-B-hydroxy-withanolide E were the most cytotoxic compounds of the panel tested, whereas withafastuosin E and withaperuvin showed only minimal inhibitory activity towards the cancer cell lines in the panel. Withaferin A, isowithanololide E, and 3-azerininylwithaferin A demonstrated intermediate cytotoxicity (Figure 2). Among a panel of 60 tumor cell lines, leukemia cell lines were on average most sensitive towards withafastuosin D diacetate, withaferin A diacetate, 4-B-hydroxy-withanolide E, and withaferin A. Colon cancer lines were on average most sensitive among cell lines derived from solid cancers.

 2.2. Cross-Resistance of the NCI Cell Line Panel Towards Withanolides

In order to find out whether the cell lines in the NCI panel exhibit cross-resistance to the eight withanolides investigated, we correlated the corresponding log10IC50 values to each other for each cell line. As shown in Table 1, correlation coefficients of R > 0.6 (Pearson’s correlation test) were found frequently for correlation among the withanolides, indicating that the cell line panel shows significant cross-resistance towards the various withanolides. Next, the log10IC50 values of the NCI cell line panel for the eight withanolides were correlated with those for established anticancer agents. Representative drugs from several major cancer drug classes (alkylating agents, platinum compounds, DNA topoisomerase I and II inhibitors, antimetabolites, and mitotic spindle poisons) were chosen. Significant correlations were consistently observed between withanolides and carmustine (BCNU), lomustine (CCNU), and semustine (methyl-CCNU), indicating that many of the NCI cell lines demonstrate cross-resistance between withanoloides and these nitrosoureas. The correlations between withanolides and other established anticancer drugs were weak (R < 0.6) (Table 1).

Chemical structures of the 8 withanolides tested.

Mean log10IC50 values of 8 withanolides for tumor cell lines from the NCI drug screening panel as assayed by the sulforhodamine B test.

 microarrays-01-00044-t001_Table 1

Cross-resistance of the NCI cell line panel towards withanolides and anticancer drugs.

Compounds R-/P-Values Withafastuosin D diacetate 4-B-hydroxy-withanolide E Withaferin A diacetate Withaferin A Isowithanololide E 3-Azerininyl-withaferin A Withafastuosin E Withaperuvin
Withanolides:
Withafastuosin D diacetate R-value 0.517 0.853 0.67 0.608 0.528 0.413 0.544
P-value 1.00 × 10−4 9.18 × 10−18 4.37 × 10−7 2.08 × 10−7 8.22 × 10−5 8.64 × 10−4 5.11 × 10−6
4-B-hydroxy-withanolide E R-value 0.548 0.548 0.452 0.42 0.492 0.469
P-value 3.29 × 10−5 1.05 × 10−4 7.06 × 10−4 0.003 3.01 × 10−4 4.48 × 10−4
Withaferin A diacetate R-value 0.701 0.7 0.475 0.426 0.623
P-value 5.95 × 10−8 3.47 × 10−10 3.69 × 10−4 5.98 × 10−4 6.99 × 10−8
Withaferin A R-value 0.609 0.549 0.358 0.466
P-value 5.70 × 10−6 8.41 × 10−5 0.001 7.17 × 10−4
Isowithanololide E R-value 0.348 0.439 0.513
P-value 0.008 3.94 × 10−4 1.63 × 10−5
3-Azerininyl-withaferin A R-value 0.28 0.349
P-value 0.033 0.008
Withafastuosin E R-value 0.291
P-value 0.016
Alkylating agents:
Carmustine (BCNU) R-value 0.607 0.466 0.747 0.508 0.59 0.393 0.411 0.721
P-value 1.74 × 10−7 4.21 × 10−4 5.64 × 10−12 2.16 × 10−4 4.47 × 10−7 0.003 8.27 × 10−4 5.79 × 10−11
Lomustine (CCNU) R-value 0.515 0.509 0.579 0.517 0.43 0.304 0.381 0.479
P-value 1.51 × 10−5 1.09 × 10−4 7.78 × 10−7 1.65 × 10−4 3.37 × 10−4 0.019 0.002 6.15 × 10−5
Semustine (me-CCNU) R-value 0.542 0.389 0.653 0.381 0.519 0.305 0.362 0.618
P-value 5.63 × 10−6 0.003 1.36 × 10−8 n.s. 1.48 × 10−5 0.019 0.003 1.2 × 10−7
Melphalan R-value 0.471 0.268 0.496 0.349 0.429 0.186 0.253 0.55
P-value 8.47 × 10−5 0.033 3.25 × 10−5 0.01 3.53 × 10−4 n.s. 0.03 3.24 × 10−6
Ifosfamide R-value 0.162 0.222 0.14 0.265 0.136 0.204 0.199 0.083
P-value n.s. n.s. n.s. 0.041 n.s. n.s. n.s. n.s.
Platin compounds:
Cisplatin R-value 0.199 0.11 0.296 0.191 0.282 0.05 0.131 0.379
P-value n.s. n.s. 0.011 n.s. 0.015 n.s. n.s. 0.002
Carboplatin R-value 0.124 0.22 0.279 0.195 0.308 0.11 0.127 0.391
P-value n.s. n.s. 0.016 n.s. 0.009 n.s. n.s. 0.001
DNA topoisomerase I inhibitors:
Camptothcin R-value 0.205 −0.018 0.131 0.076 0.05 0.125 0.069 0.307
P-value n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.
Topotecan R-value 0.242 0.119 0.178 0.108 0.102 0.104 0.198 0.308
P-value 0.032 n.s. n.s. n.s. n.s. n.s. n.s. 0.009
DNA topoisomerase II inhibitors:
Adriamycin R-value 0.327 0.198 0.362 0.203 0.196 0.267 0.207 0.413
P-value 0.006 n.s. 0.002 n.s. n.s. 0.035 n.s. 5.70 × 10−4
Daunorubicin R-value 0.44 0.213 0.447 0.305 0.21 0.333 0.247 0.46
P-value 2.44 × 10−4 n.s. 1.95 × 10−4 0.022 n.s. 0.011 0.033 1.25 × 10−4
Etoposide R-value 0.237 0.063 0.244 0.256 0.199 0.15 0.109 0.246
P-value 0.036 n.s. 0.031 0.047 n.s. n.s. n.s. 0.03
Teniposide R-value 0.343 0.215 0.358 0.217 0.203 0.264 0.185 0.437
P-value 0.004 n.s. 0.003 n.s. n.s. 0.037 n.s. 2.67 × 10−4
Antimetabolites:
5-Fluorouracil R-value 0.357 0.16 0.365 0.054 0.3 0.25 0.272 0.129
P-value 0.003 n.s. 0.002 n.s. 0.011 0.046 0.021 n.s.
Methotrexate R-value 0.451 0.114 0.418 0.154 0.27 0.299 0.218 0.21
P-value 1.66 × 10−4 n.s. 4.94 × 10−4 n.s. 0.019 0.021 n.s. n.s.
Mitotic spindle poisons:
Vincristine R-value 0.283 0.266 0.307 0.177 0.102 0.446 0.258 0.156
P-value 0.015 0.034 0.009 n.s. n.s. 8.32 × 10−4 0.028 n.s.
Vinblastine R-value 0.033 −0.222 0.041 −0.040 0.017 −0.007 0.085 0.058
P-value n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.
Paclitaxel R-value 0.283 0.193 0.308 0.143 0.048 0.399 0.304 0.315
P-value 0.015 n.s. 0.009 n.s. n.s. 0.003 0.011 0.008
Taxotere R-value 0.202 −0.041 0.158 0.146 0.098 0.337 0.177 0.215
P-value n.s. n.s. n.s. n.s. n.s. 0.01 n.s. n.s.

n.s., not significant (P > 0.05).

 2.3. COMPARE and Cluster Analyses of Microarray-Based mRNA Hybridization:

COMPARE analyses were performed to obtain a gene expression profile and identify the most up- or down-regulated genes correlated with the IC50 values for the withanolides. We performed COMPARE analyses of log10IC50 values for the three most cytotoxic withanolides (withafastuosin D diacetate, withaferin A diacetate, and 4-B-hydroxy-withanolide E) and the transcriptomic mRNA-based expression profiles of the NCI cell lines to produce scale indices of correlation coefficients. The microarray data from the NCI website [40] was used to perform further in silico analysis. The mRNA expression levels were determined by microarray analysis [41,42,43]. We performed a standard COMPARE analysis in which cell lines most inhibited by withanolides (lowest log10IC50 values) were correlated with the lowest mRNA expression levels of genes. These genes can be considered possible candidate genes in determining cellular resistance to withanolides. Furthermore, reverse COMPARE analysis was carried out, correlating the most inhibited cell lines with the highest gene expression levels. Considering a COMPARE coefficient of R > 0.6 as cut-off value, only two genes each fulfilled this criterion in connection with withafastuosin D diacetate and 4-B-hydroxy-withanolide E. These two compounds were, therefore, excluded from further analyses. The genes whose mRNA expression correlated with withaferin A diacetate are shown in Table 2. Table 2 differs from Table 1 in that it is rearranged in such a way that genes are grouped according to their order in the cluster analysis. This allows one to see which genes were clustered together and which ones were separated. Three main clusters were observed; however, a pattern of genes with similar functions was not seen among the clusters.

Among the genes were genes from diverse functional groups, such as DNA damage response and repair (RAD54L), stress response (ANXA2, PPIH, UACA), cell growth regulation (BCAR3, CD53, NASP, TRIM3), extracellular matrix components, cell adhesion and migration (ADAM9, ASAP2, ITGB1, LAMB1), ribosomal proteins (RPS23, RPL5, LOC440055, LOC729362), cytoskeletal organization and regulation (CORO1A, LCP1, PLS3, WAS), signal transduction (ASAP2, BCAR3, DLG2, GNA11, PTPN7, RGS12, RNF138, SH3BP4, TJP1), transcription factors (IFZF1, HCLS1, TRIM3, ZNF112, ZNF228),and others(ALDH7A1, LSM2, MANBAL, NACA, STMN4).

Next, the 40 genes identified by standard and reverse COMPARE analyses were subjected to hierarchical cluster analysis. The dendrogram obtained by this procedure can be divided into three major branches (Figure 3). The distribution of cell lines sensitive or resistant to withaferin A diacetate varies significantly between the branches of the dendrogram. The distribution of cell lines among the dendrogram predicts resistance to withaferin A diacetate with statistical significance (P = 0.00208 × 10−6; χ2-test; Table 3).

microarrays-01-00044-t002_Table 2

Correlation of constitutive mRNA expression of genes identified by compare analyses with IC50 values for withaferin A diacetate of 60 tumor cell lines.

Symbol COMPARE Coefficient ID No. Genebank Name Function
Cluster 1:
CORO1A 0.65 GC9728 AA047478 Coronin, actin binding protein, 1A Cytoskeleton component
LSM2 0.646 GC31813 AJ245416 LSM2 homolog, U6 small nuclear RNA associated (S. cerevisiae) Pre-mRNA splicing
HCLS1 0.676 GC34797 X16663 Hematopoietic cell-specific Lyn substrate 1 Transcription factor
Cluster 2:
unknown 0.643 GC34785 X79234 unknown Unknown
PLS3 −0.606 GC37799 M22299 Plastin 3 Actin-bundling protein
RAD54L 0.654 GC32858 X97795 RAD54-like (S. cerevisiae) DNA repair and mitotic recombination
RPL5 0.648 GC36655 U14966 Ribosomal protein L5 Structural constituent of ribosome
IKZF1 0.648 GC61547 AI247840 IKAROS family zinc finger 1 (Ikaros) Transcriptional regulator
DLG2 −0.612 GC10718 R41930 Discs, large homolog 2 (Drosophila) Signal transducer, required for perception of chronic pain through NMDA receptor signaling
RPS23 0.669 GC37806 D14530 Ribosomal protein S23 Structural constituent of ribosome
unknown 0.674 GC33814 D11327 unknown Unknown
RNF138 0.688 GC67595 AI608790 Ring finger protein 138 ubiquitin-protein ligase, ubiquitinylation
unknown 0.663 GC31615 X79234 unknown Unknown
LCP1 0.649 GC27422 J02923 Lymphocyte cytosolic protein 1 (L-plastin) Actin-binding protein, T-cell activation
LAMB1 −0.610 GC18026 AA004918 Laminin, beta-1 Extracellular matrix structural constituent
SH3BP4 −0.608 GC16071 W72796 SH3-domain binding protein Signal transducer, functions in transferrin receptor internalization at the plasma membrane
UACA −0.624 GC14684 N66980 Uveal autoantigen with coiled-coil domains and ankyrin repeats Regulation of stress-induced apoptosis
BCAR3 −0.636 GC14433 N48319 Breast cancer anti-estrogen resistance 3 Adapter protein for activated growth factor receptors to signaling pathways that regulate cell proliferation
ZNF112 −0.640 GC15668 W15410 Zinc finger protein 112 homolog (mouse) DNA binding, transcriptional regulator
LOC440055 0.721 GC36107 AA977163 Similar to ribosomal protein S12 Unknown
Unknown −0.601 GC14769 N92652 unknown Unknown
ALDH7A1 −0.642 GC16889 AA024918 Aldehyde dehydrogenase 7 family, member A1 Aldehyde dehydrogenase (NAD), oxidoreductase
ADAM9 −0.611 GC15762 W47533 ADAM metallopeptidase domain 9 Mediates cell-cell or cell-matrix interactions
TRIM3 −0.616 GC14991 N71362 Tripartite motif-containing 3 Transcriptional repressor, control of cell proliferation
ITGB1 −0.638 GC19072 AA044261 Integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 includes MDF2, MSK12) integrin binding
RGS12 −0.639 GC15931 W67134 Regulator of G-protein signaling 12 Signal transducer, regulator of G proteins
TJP1 −0.612 GC12455 R79560 Tight junction protein 1 (zona occludens 1) Signal transduction for tight junction assembly and stabilizing junctions
ASAP2 −0.638 GC15131 N70773 ArfGAP with SH3 domain, ankyrin repeat and PH domain 2 GTPase activator, Regulates the formation of post-Golgi vesicles, modulates cell migration
STMN4 −0.650 GC11515 H29581 Stathmin-like 4 Unknown
WAS 0.654 GC69113 AI655719 Wiskott-Aldrich syndrome (eczema-thrombocytopenia) effector for Rho-type GTPases, regulates structure and dynamics of the actin cytoskeleton
NACA 0.652 GC30164 AF054187 Nascent polypeptide-associated complex alpha subunit Prevents inappropriate targeting of non-secretory polypeptides to the endoplasmic reticulum (ER)
NASP 0.657 GC83792 AW003362 Nuclear autoantigenic sperm protein (histone-binding) Involved in DNA replication, normal cell cycle progression and cell proliferation
LOC729362 0.679 GC31589 T89651 Similar to ribosomal protein L36a Unknown
GNA11 −0.639 GC31915 N36926 Guanine nucleotide binding protein (G protein), alpha 11 (Gq class) Signal transducer, activator of phospholipase C, GTPase
unknown −0.658 GC32458 M69013 unknown Unknown
Cluster 3:
CD53 0.666 GC89937 M37033 cell differentiation antigen 53,32-40kDa Growth regulation in hematopoietic cells
PPIH 0.662 GC28763 AF016371 Peptidylprolyl isomerase H (cyclophilin H) Chaperone
PTPN7 0.707 GC90165 M64322 Protein tyrosine phosphatase, non-receptor type 7 Regulation of T and B-lymphocyte development and signal transduction
ANXA2P3 −0.652 GC90123 M62895 Annexin A2 pseudogene 3 Unknown
ANXA2 −0.659 GC85483 D00017 Annexin A2 Phospholipase inhibitor, involved in heat-stress response

Only genes with correlation coefficients of >0.6 or <−0.6 were considered. Positive correlation coefficients indicate direct correlations to log10IC50 values; negative ones indicate inverse correlations. Information on gene functions was taken from the OMIM database, NCI, USA [44] and from the GeneCard database of the Weizman Institute of Science, Rehovot, Israel [45].

Dendrogram of hierarchical cluster analysis (complete linkage method) obtained from mRNA expression of 40 genes correlated with log10IC50 values for withaferin A diacetate. The dendrogram shows the clustering of 60 cell lines according to the mRNA expression profile for genes identified by COMPARE analyses (Table 3).

 microarrays-01-00044-t003_Table 3

Separation of clusters of 60 NCI cell lines obtained by hierarchical cluster analysis shown in Figure 3 in comparison to drug sensitivity. The median log10IC50 value (−6.5 m) for each compound was used as cut-off to separate tumor cell lines as being “sensitive” or “resistant”.

Partition Cluster 1 Cluster 2 Cluster 3 Cluster 4
sensitive <−6.5 3 9 4 6
resistant >−6.5 2 17 18 0
χ2-test P = 0.00208

In the present investigation, we showed that six out of the eight withanolides tested exerted profound cytotoxic activity towards tumor cell lines. The cytotoxicity of the withanolides was compared among tumor cell lines belonging to nine different tumor types. We found that leukemic cell lines were particularly sensitive against withanolides. This observation is consistent with previous results obtained by our group that indicate phytochemicals and standard anticancer drugs are frequently more cytotoxic to leukemia cells than to cell lines derived from solid tumors [46,47,48]. Among the cell lines of solid tumors, colon cancer cells were most sensitive to withaferin A diacetate. A possible explanation might be that the Notch-1 signaling pathway plays an important role in colon carcinogenesis and that withanolides inhibit Notch-1 signaling [49].

Our investigation confirms previous reports showing the cytotoxic activity of withanolides against cancer cells [21,22,24,25,26,28]. Furthermore, we have analyzed the cross-resistance profile of the cell line panel to different withanolides and between withanolides and established anticancer drugs. By means of Pearson’s correlation test, we found that the 60 cell lines exerted significant cross-resistance among withanolides. We observed that cell lines sensitive to one withanolide are likely to be sensitive to other withanolides and vice versa.

We extended this analysis to standard anticancer drugs, and found consistent cross-resistance between withanolides and nitrosoureas. Cross-resistance to other drug classes (platin compounds, DNA topoisomerase I or II inhibitors, antimetabolites, mitotic spindle poisons) was less frequent or not observed. This suggests that withanolides and nitrosoureas may share similar mechanisms of action, e.g., DNA damage of tumor cells. Interestingly, withaferin A has been described as exerting chemo- and radiosensitizing effects on tumors in vitro and in vivo [9,50,51,52]. It is possible that the interaction of the activity of two treatment substances on DNA may lead to synergistic and sensitizing effects.

As a next step, we correlated the IC50 values of withaferin A diacetate on 60 tumor cell lines with the microarray-based transcriptomic mRNA expression levels of the cell line panel [53] by COMPARE analysis. This approach has been successfully used to unravel the mode of action of novel compounds [53]. Cluster and COMPARE analyses are also useful for comparing gene expression profiles with IC50 values for investigational drugs to identify candidate genes causing drug resistance [54] and to identify prognostic expression profiles in clinical oncology [55].

We identified genes from diverse functional groups that were significantly associated with the response of tumor cells to withaferin A diacetate. These genes were related to DNA damage and repair, stress response, cell growth regulation, extracellular matrix components, cell adhesion and cell migration, constituents of the ribosome, cytoskeletal organization and regulation, signal transduction, transcription factors, and others.

The fact that genes associated with sensitivity or resistance against withaferin A diacetate were from diverse functional groups speaks for the multiplicity of mechanisms by which withaferin A diacetate inhibits cancer cells. This so-called “multiplicity of mechanisms” can refer to multiple targets that together lead to multiple effects or it can refer to one target leading to activation or inactivation of multiple downstream pathways. Multi-specificity is a general feature of many natural products; rather than acting on one single target, they affect multiple targets and pathways [8].

The multifaceted nature of withanolides has been previously recognized, e.g., induction of G2M cell cycle arrest [27] and apoptosis [34], inhibition of metastasis [56] and angiogenesis [57], inhibition of the transcription factor NFκB [58] and heat shock protein HSP90 [25,59] , and immunomodulation [20]. It is interesting that our transcriptomic approach pointed to additional mechanisms, whose role for response of tumor cells against withanolides have not been considered so far.

For example, RAD54L is involved in recombinatorial DNA repair (via the RAD52 pathway) and dissociates RAD51 from nucleoprotein filaments formed on double-stranded DNA [45,60]. The cross-resistance profile of withanolides and nitrosoureas for the 60 cancer cell lines may be explained at least in part by RAD54L. This speculation deserves further investigation in the future.

The significant correlation of the expression of stress response genes to the log10IC50 values for withanolides indicates that cell lines with high expression of these genes better resist the cytotoxic effects of withaferin A diacetate than cell lines with low expression levels. ANXA2 is a phospholipase inhibitor, which is involved in the heat stress response [45]. Heat shock proteins and chaperones are known to mediate resistance to conventional anticancer drugs [61]. The possibility of ANXA2 playing a role in multidrug resistance and gemcitabine resistance has been suggested [62,63]. PPIH accelerates protein-folding and may act as chaperone, and UACA regulates stress-induced apoptosis by NFκB inhibition [45]. PPIH and UACA have not yet been linked to cellular response to established drugs or withanolides.

Several genes involved in cell growth regulation were significant in our analysis, namely, CD53, BCAR3, TRIM3, and NASP. Although these genes do not belong to the set of classical cell cycle genes, they suggest that withaferin A diacetate may play a role in inhibiting cancer cell proliferation. Many established anticancer drugs also act against cell proliferation. However, most classical anticancer drugs damage not only cancer cells, but normal proliferating cells, which leads to the severe side effects often observed with chemotherapy, e.g., myelosuppression, sterility, gastrointestinal mucosa damage, and alopecia. Whether withaferin A diacetate also exerts detrimental effects on normal tissues is not known and requires further exploration.

Interestingly, a number of genes encoding extracellular matrix (ECM) components and genes involved in cell adhesion and migration correlated with log10IC50 values for withaferin A diacetate including ADAM9, LAMB1, ITGB1, ASAP2. The ECM, cell adhesion and migration are important components in cancer metastasis and progression. Withanolides are known to inhibit metastasis [56]. We suggest that these four genes identified in our analysis may contribute to the anti-metastatic activity of withaferin A diacetate.

Another interesting finding is that cellular response to withaferin A diacetate was correlated with the expression of genes encoding several constituents of the ribosome. Whereas the role of ribosomal proteins in resistance towards established anticancer drugs has not yet been intensively investigated [64], our study indicates that the ribosomal genes RPS23 and RPL5 and the still poorly characterized ribosome-associated genes LOC440055 and LOC729362 affect resistance towards withaferin A diacetate. Ribosomal proteins are often involved in antibiotic resistance. For example, Streptomycin resistance is based on the modification of an aspartic acid moiety in the ribosomal protein S12 [65]. RPS6 is thought to play an important role in controlling cell growth and proliferation by selective translation of particular classes of mRNA [66]. The RPL6 gene plays an important role in the development of drug resistance in leukemia and gastric cancer cells by suppressing drug-induced apoptosis [64,67]. In previous investigations, we observed that genes encoding ribosomal proteins correlated to cellular sensitivity or resistance towards several cytotoxic phytochemicals, including shikonin, resibufogenin, and artesunate [6,68,69].

4. Experimental Section

Statistical Analyses: The panel of human tumor cell lines of the Developmental Therapeutics Program of NCI and their testing by sulforhodamine B assay and mRNA microarray hybridization have been described [42,70,71]. The data from these assays can be found at the NCI website [40]. For hierarchical cluster analysis, objects were classified by calculation of distances between individuals 445rfG, by means of the complete linkage method. All objects were assembled into cluster trees (dendrograms). Previously, cluster models were validated for gene expression profiling and for approaching molecular pharmacology of cancer [70,72]. Hierarchical cluster analyses applying the complete linkage method were performed with the WinSTAT program (Kalmia, Cambridge, MA, USA). Missing values were automatically omitted, and the closeness of any two joined objects was calculated by the number of data points they contained. In order to calculate distances between all variables included in the analysis, the program automatically standardizes the variables by transforming the data to a set of values with mean = 0 and variance = 1.

For COMPARE analysis, the mRNA expression values of genes of interest and log10IC50 values for the withanolides were selected from the NCI database [40]. mRNA expression was determined by microarray analyses as reported [70]. COMPARE analysis was performed to produce rank-ordered lists of genes expressed in the NCI cell lines. The methodology has been described previously in detail [73]. Briefly, every gene of the NCI microarray database was ranked for similarity of its mRNA expression to those under the log10IC50 concentration for the corresponding compound. To derive COMPARE rankings, a scale index of correlation coefficients (R-values) was created. In the standard COMPARE approach, greater mRNA expression in cell lines correlates with enhanced drug resistance, whereas in reverse COMPARE analyses, greater mRNA expression in cell lines indicates drug sensitivity.

Pearson’s correlation test was used to calculate significance values and rank correlation coefficients as a relative measure of the linear dependency of two variables. This test was implemented into the WinSTAT Program (Kalmia). The Pearson correlation test was used as a measure for interval-scaled linear correlations. We used the Pearson test rather than the Spearman’s Rank correlation test because Spearman’s test is based on the equidistance of values, and the values used for our analysis were not equidistant.

The χ2-test was applied to bivariate frequency distributions of pairs of nominal scaled variables. This test was also implemented into the WinSTAT program (Kalmia Co.). The χ2-test determines the difference between each observed and theoretical frequency for each possible outcome, squares them, divides each by the theoretical frequency, and takes the sum of the results. Performing the χ2-test necessitated defining cell lines as being sensitive or resistant to withaferin A diacetate. This was done by taking the median IC50 value log10 = -6.5 M for withaferin A diacetate as a cut-off threshold.

 5. Conclusions

In summary, our microarray-based investigation delivered novel candidate genes that were associated with the response of cancer cells to withaferin A diacetate. These results merit further investigation to prove the causative contribution of these genes to withaferin A diacetate resistance and sensitivity.

 References Koehn F.E. Carter G.T. The evolving role of natural products in drug discovery Nat. Rev. Drug Discov. 2005 4 206 220 10.1038/nrd1657 Efferth T. Li P. Kaina B. From traditional Chinese medicine to rational cancer therapy Trends Mol. Med. 2007 13 353 361 10.1016/j.molmed.2007.07.001 Schmidt B.M. Ribnicky D.M. Lipsky P.E. Raskin I. Revisiting the ancient concept of botanical therapeutics Nat. Chem. Biol. 2007 3 360 366 10.1038/nchembio0707-360 Efferth T. Kahl S. Paulus K. Adams M. Rauh R. Boechzelt H. Hao X. Kaina B. Bauer R. Phytochemistry and pharmacogenomics of natural products derived from traditional Chinese medicine and Chinese Materia Medica with activity against tumour cells Mol. Cancer Therap. 2008 7 152 161 10.1158/1535-7163.MCT-07-0073 Li J.W.H. Vederas J.C. Drug discovery and natural products: End of an era or an endless frontier? Science 2009 325 161 165 10.1126/science.116824319589993 Mahringer A. Karamustafa S. Klotz D. Kahl S. Konkimalla V.B. Wang Y.F. Wang J.S. Liu H.Y. Boechzelt H. Hao X.J. Bauer R. Fricker G. Efferth T. Inhibition of P-glycoprotein at the blood brain barrier by phytochemicals derived from traditional Chinese medicine Cancer Genomics Proteomics 2010 7 191 205 20656985 Efferth T. Personalized cancer medicine: From molecular diagnostics to targeted therapy with natural products Planta Med. 2010 76 1 12 10.1055/s-0029-1240671 Efferth T. Koch E. Complex interactions between phytochemicals. The multi-target therapeutic concept of phytotherapy Curr. Drug Targ. 2011 12 122 132 10.2174/138945011793591626 Konkimalla V.B. Efferth T. Molecular mechanisms and interactions responsible for radio- and chemoresistance of tumors and their modulation by natural products from Ayurveda Herbal Medicine. A Cancer Chemopreventive and Therapeutic Perspective Arora R. Jaypee Brothers Medical Publishers Pvt. Ltd. New Delhi, India; St. Louis, MI, USA 2010 513 530 Mishra L.C. Singh B.B. Dagenais S. Scientific basis for the therapeutic use of Withania somnifera (ashwagandha): A review Altern. Med. Rev. 2000 5 334 336 10956379 Modak M. Dixit P. Londhe J. Ghaskadbi S. Paul A. Devasagayam T. Indian herbs and herbal drugs used for the treatment of diabetes J. Clin. Biochem. Nutr. 2007 40 163 173 10.3164/jcbn.40.163 Lu L. Liu Y. Zhu W. Shi J. Liu Y. Ling W. Kosten T.R. Traditional medicine in the treatment of drug addiction Am. J. Drug Alcohol. Abuse 2009 35 1 11 10.1080/00952990802455469 Maurya R. Chemistry and pharmacology of Withania coagulans: An Ayurvedic remedy J. Pharm. Pharmacol. 2010 62 153 160 10.1211/jpp.62.02.0001 Ven Murthy M.R. Ranjekar P.K. Ramassamy C. Deshpande M. Scientific basis for the use of Indian ayurvedic medicinal plants in the treatment of neurodegenerative disorders: Ashwagandha Cent. Nerv. Syst. Agents Med. Chem. 2010 10 238 236 10.2174/187152491100603023820528765 Mirjalili M.H. Moyano E. Bonfill M. Cusido R.M. Palazón J. Steroidal lactones from Withania somnifera, an ancient plant for novel medicine Molecules 2009 14 2373 2393 10.3390/molecules14072373 Rastogi R.P. Mehrotra B.N. Compendium of Indian Medicinal Plants Central Drug Research Institute New Delhi, India; St. Louis, MI, USA 1998 Davis L. Kuttan G. Effect of Withania somnifera on 20-methylcholanthrene induced fibrosarcoma J. Exp. Clin. Cancer Res. 2000 19 165 167 10965813 Davis L. Kuttan G. Effect of Withania somnifera on DMBA induced carcinogenesis J. Ethnopharmacol. 2001 75 165 168 10.1016/S0378-8741(00)00404-9 Prakash J. Gupta S.K. Kochupillai V. Singh N. Gupta Y.K. Joshi S. Chemopreventive activity of Withania somnifera in experimentally induced fibrosarcoma tumours in Swiss albino mice Phytother. Res. 2001 15 240 244 10.1002/ptr.779 Muralikrishnan G. Dinda A.K. Shakeel F. Immunomodulatory effects of Withania somnifera on azoxymethane induced experimental colon cancer in mice Immunol. Invest. 2010 39 688 698 10.3109/08820139.2010.487083 Sharada A.C. Solomon F.E. Devi P.U. Udupa N. Srinivasan K.K. Antitumor and radiosensitizing effects of withaferin A on mouse Ehrlich ascites carcinoma in vivo Acta Oncol. 1996 35 95 100 10.3109/02841869609098486 Srinivasan S. Ranga R.S. Burikhanov R. Han S.S. Chendil D. Par-4-dependent apoptosis by the dietary compound withaferin A in prostate cancer cells Cancer Res. 2007 67 246 243 10.1158/0008-5472.CAN-06-243017185378 Stan S.D. Zeng Y. Singh S.V. Ayurvedic medicine constituent withaferin a causes G2 and M phase cell cycle arrest in human breast cancer cells Nutr. Cancer. 60 51 60 Malik F. Kumar A. Bhushan S. Mondhe D.M. Pal H.C. Sharma R. Khajuria A. Singh S. Singh G. Saxena A.K. Suri K.A. Qazi G.N. Singh J. Immune modulation and apoptosis induction: Two sides of antitumoural activity of a standardised herbal formulation of Withania somnifera Eur. J. Cancer. 2009 45 1494 1509 10.1016/j.ejca.2009.01.03419269163 Yu Y. Hamza A. Zhang T. Gu M. Zou P. Newman B. Li Y. Gunatilaka A.A. Zhan C.G. Sun D. Withaferin A targets heat shock protein 90 in pancreatic cancer cells Biochem. Pharmacol. 2010 79 542 551 10.1016/j.bcp.2009.09.017 Hahm E.R. Lee J. Huang Y. Singh S.V. Withaferin a suppresses estrogen receptor-α expression in human breast cancer cells Mol. Carcinog. 2011 50 614 624 10.1002/mc.20760 Devi P.U. Akagi K. Ostapenko V. Tanaka Y. Sugahara T. Withaferin A: A new radiosensitizer from the Indian medicinal plant Withania somnifera Int. J. Radiat. Biol. 1996 69 193 197 10.1080/095530096146020 Stan S.D. Hahm E.R. Warin R. Singh S.V. Withaferin A causes FOXO3a- and Bim-dependent apoptosis and inhibits growth of human breast cancer cells in vivo Cancer Res. 68 7661 7669 Mandal C. Dutta A. Mallick A. Chandra S. Misra L. Sangwan R.S. Mandal C. Withaferin A induces apoptosis by activating p38 mitogen-activated protein kinase signaling cascade in leukemic cells of lymphoid and myeloid origin through mitochondrial death cascade Apoptosis 2008 13 1450 1464 10.1007/s10495-008-0271-0 Choi M.J. Park E.J. Min K.J. Park J.W. Kwon T.K. Endoplasmic reticulum stress mediates withaferin A-induced apoptosis in human renal carcinoma cells Toxicol. In Vitro 2011 25 692 698 10.1016/j.tiv.2011.01.010 Mohan R. Hammers H.J. Bargagna-Mohan P. Zhan X.H. Herbstritt C.J. Ruiz A. Zhang L. Hanson A.D. Conner B.P. Rougas J. Pribluda V.S. Withaferin A is a potent inhibitor of angiogenesis Angiogenesis. 2004 7 115 122 10.1007/s10456-004-1026-3 Mathur R. Gupta S.K. Singh N. Mathur S. Kochupillai V. Velpandian T. Evaluation of the effect of Withania somnifera root extracts on cell cycle and angiogenesis J. Ethnopharmacol. 2006 105 336 341 10.1016/j.jep.2005.11.020 Choudhary M.I. Hussain S. Yousuf S. Dar A. Mudassar A.-R. Chlorinated and diepoxy withanolides from Withania somnifera and their cytotoxic effects against human lung cancer cell line Phytochemistry 2010 71 2205 2209 10.1016/j.phytochem.2010.08.01921044792 Mondal S. Mandal C. Sangwan R. Chandra S. Mandal C. Withanolide D induces apoptosis in leukemia by targeting the activation of neutral sphingomyelinase-ceramide cascade mediated by synergistic activation of c-Jun N-terminal kinase and p38 mitogen-activated protein kinase Mol. Cancer. 2010 9 239 20836852 Abdeljebbar L.H. Benjouad A. Morjani H. Merghoub N. El Haddar S. Humam M. Christen P. Hostettmann K. Bekkouche K. Amzazi S. Antiproliferative effects of withanolides from Withania adpressa Therapie 2009 64 121 127 10.2515/therapie/200901519664406 Xu Y.M. Marron M.T. Seddon E. McLaughlin S.P. Ray D.T. Whitesell L. Gunatilaka A.A. 2,3-Dihydrowithaferin A-3beta-O-sulfate, a new potential prodrug of withaferin A from aeroponically grown Withania somnifera Bioorg. Med. Chem. 2009 17 2210 2214 10.1016/j.bmc.2008.10.09119056281 He Q.P. Ma L. Luo J.Y. He F.Y. Lou L.G. Hu L.H. Cytotoxic withanolides from Physalis angulata L Chem. Biodivers. 2007 4 443 449 10.1002/cbdv.200790036 Subbaraju G.V. Vanisree M. Rao C.V. Sivaramakrishna C. Sridhar P. Jayaprakasam B. Nair M.G. Ashwagandhanolide, a bioactive dimeric thiowithanolide isolated from the roots of Withania somnifera J. Nat. Prod. 2006 69 1790 1792 10.1021/np060147p Mathur S. Kaur P. Sharma M. Katyal A. Singh B. Tiwari M. Chandra R. The treatment of skin carcinoma, induced by UV B radiation, using 1-oxo-5beta, 6beta-epoxy-witha-2-enolide, isolated from the roots of Withania somnifera, in a rat model Phytomedicine 2004 11 452 460 10.1016/j.phymed.2003.05.004 Developmental Therapeutics Program NCI/NIH Hompage (accessed on 21 May 2012) Available online:http://dtp.nci.nih.gov Alley M.C. Scudiero D.A. Monks A. Hursey M.L. Czerwinski M.J. Fine D.L. Abbott B.J. Mayo J.G. Shoemaker R.H. Boyd M.R. Feasibility of drug screening with panels of human tumor cell lines using a microculture tetrazolium assay Cancer Res. 1988 48 589 601 3335022 Monk A. Scudiero D. Skehan P. Shoemaker R. Paull K. Vistica D. Hose C. Langley J. Cronise P. Vaigro-Wolff A. Feasibility of a high-flux anticancer drug screen using a diverse panel of cultured human tumor cell lines J. Natl. Cancer Inst. 1991 83 757 756 10.1093/jnci/83.11.757 Staunton J.E. Slonim D.K. Coller H.A. Tamayo P. Angelo M.J. Park J. Scherf U. Lee J.K. Reinhold W.O. Weinstein J.N. Mesirov J.P. Lander E.S. Golub T.R. Chemosensitivity prediction by transcriptional profiling Proc. Natl. Acad. Sci. USA 2001 98 10787 10792 10.1073/pnas.19136859811553813 OMIM®—nline Mendelian Inheritance in Man® Hompage (accessed on 21 May 2012) Available online:http://www.ncbi.nlm.nih.gov/Omim GeneCard database. Weizman Institute of Science: Rehovot, Israel 2012 (accessed on 21 May 2012) Available online:http://bioinfo.weizmann.ac.il/cards/index.html Efferth T. Dunstan H. Sauerbrey A. Miyachi H. Chitambar C.R. The antimalarial artesunate is also a potent anticancer drug Int. J. Oncol. 2001 18 767 773 11251172 Efferth T. Kahl S. Paulus K. Adams M. Rauh R. Boechzelt H. Hao X. Kaina B. Bauer R. Phytochemistry and pharmacogenomics of natural products derived from traditional Chinese medicine and chinese materia medica with activity against tumor cells Mol. Cancer Ther. 2008 7 152 161 18202018 Sertel S. Eichhorn T. Bauer J. Hock K. Plinkert P.K. Efferth T. Pharmacogenomic determination of genes associated with sensitivity or resistance of tumor cells to curcumin and curcumin derivatives J. Nutr. Biochem. 2011 in press Koduru S. Kumar R. Srinivasan S. Evers M.B. Damodaran C. Notch-1 inhibition by Withaferin-A: A therapeutic target against colon carcinogenesis Mol. Cancer Ther. 2010 9 202 210 20053782 Senthilnathan P. Padmavathi R. Magesh V. Sakthisekaran D. Chemotherapeutic efficacy of paclitaxel in combination with Withania somnifera on benzo(a)pyrene-induced experimental lung cancer Cancer Sci. 2006 97 658 664 10.1111/j.1349-7006.2006.00224.x Kalthur G. Pathirissery U.D. Enhancement of the response of B16F1 melanoma to fractionated radiotherapy and prolongation of survival by withaferin A and/or hyperthermia Integr. Cancer Ther. 2010 9 370 377 10.1177/1534735410378664 Yang E.S. Choi M.J. Kim J.H. Choi K.S. Kwon T.K. Withaferin A enhances radiation-induced apoptosis in Caki cells through induction of reactive oxygen species, Bcl-2 downregulation and Akt inhibition Chem. Biol. Interact. 2011 190 9 15 10.1016/j.cbi.2011.01.015 Leteurtre F. Kohlhagen G. Paull K.D. Pommier Y. Topoisomerase II inhibition and cytotoxicity of the anthrapyrazoles DuP 937 and DuP 941 (Losoxantrone) in the National Cancer Institute preclinical antitumor drug discovery screen J. Natl. Cancer Inst. 1994 86 1239 1244 10.1093/jnci/86.16.1239 Efferth T. Gebhart E. Ross D.D. Sauerbrey A. Identification of gene expression profiles predicting tumor cell response to l-alanosine Biochem. Pharmacol. 2003 66 613 621 10.1016/S0006-2952(03)00341-1 Efferth T. Olbrich A. Bauer R. mRNA expression profiles for the response of human tumor cell lines to the antimalarial drugs artesunate, arteether, and artemether Biochem. Pharmacol. 2002 64 617 623 10.1016/S0006-2952(02)01221-2 Thaiparambil J.T. Bender L. Ganesh T. Kline E. Patel P. Liu Y. Tighiouart M. Vertino P.M. Harvey R.D. Garcia A. Marcus A.I. Withaferin A inhibits breast cancer invasion and metastasis at sub-cytotoxic doses by inducing vimentin disassembly and serine 56 phosphorylation Int. J. Cancer. 2011 129 2744 2755 10.1002/ijc.2593821538350 Conner B.P. Rougas J. Pribluda V.S. Withaferin A is a potent inhibitor of angiogenesis Angiogenesis 2004 7 115 122 10.1007/s10456-004-1026-3 Grover A. Shandilya A. Punetha A. Bisaria V.S. Sundar D. Inhibition of the NEMO/IKKβ association complex formation, a novel mechanism associated with the NF-κB activation suppression by Withania somnifera’s key metabolite withaferin A BMC Genomics. 2010 11 25 10.1186/1471-2164-11-2520067632 Grover A. Shandilya A. Agrawal V. Pratik P. Bhasme D. Bisaria V.S. Sundar D. Hsp90/Cdc37 chaperone/co-chaperone complex, a novel junction anticancer target elucidated by the mode of action of herbal drug Withaferin A BMC Bioinformatics 2011 12 30 10.1186/1471-2105-12-3021261969 Swagemakers S.M. Essers J. de Wit J. Hoeijmakers J.H. Kanaar R. The human RAD54 recombinational DNA repair protein is a double-stranded DNA-dependent ATPase J. Biol. Chem. 1998 273 28292 28297 10.1074/jbc.273.43.282929774452 Fuqua S.A. Oesterreich S. Hilsenbeck S.G. Von Hoff D.D. Eckardt J. Osborne C.K. Heat shock proteins and drug resistance Breast Cancer Res. Treat. 1994 32 67 71 10.1007/BF00666207 Zhang F. Zhang L. Zhang B. Wei X. Yang Y. Qi R.Z. Ying G. Zhang N. Niu R. Anxa2 plays a critical role in enhanced invasiveness of the multidrug resistant human breast cancer cells J. Proteome Res. 2009 8 5041 5047 10.1021/pr900461c Gong X.G. Lv Y.F. Li X. Q. Xu F.G. Ma Q.Y. Gemcitabine resistance induced by interaction between alternatively spliced segment of tenascin-C and annexin A2 in pancreatic cancer cells Biol. Pharm. Bull. 2010 33 1261 1267 10.1248/bpb.33.1261 Chen H. Xie Z.X. Jiang H. Zhang Z.W. Wang G.P. Effect of ribosomal protein L6 on drug resistance and apoptosis in K562/A02 cells Zhongguo Shi Yan Xue Ye Xue Za Zhi 2007 15 292 295 17493334 Harris E.H. Burkhart B.D. Gillham N.W. Boynton J.E. Antibiotic resistance mutations in the chloroplast 16S and 23S rRNA genes of Chlamydomonas reinhardtii: Correlation of genetic and physical maps of the chloroplast genome Genetics. 1989 123 281 292 2583478 Stolovich M. Tang H. Hornstein E. Levy G. Cohen R. Bae S.S. Birnbaum M.J. Meyuhas O. Transduction of growth or mitogenic signals into translational activation of TOP mRNAs is fully reliant on the phosphatidylinositol 3-kinasemediated pathway but requires neither S6K1 nor rpS6 phosphorylation Mol. Cell Biol. 2002 22 8101 8113 10.1128/MCB.22.23.8101-8113.200212417714 Du J. Shi Y. Pan Y. Jin X. Liu C. Liu N. Han Q. Lu Y. Qiao T. Fan D. Regulation of multidrug resistance by ribosomal protein l6 in gastric cancer cells Cancer Biol. Ther. 2005 4 242 247 10.4161/cbt.4.2.1477 Efferth T. Miyachi H. Bartsch H. Pharmacogenomics of a traditional Japanese herbal medicine (Kampo) for cancer therapy Cancer Genomics Proteomics 2007 4 81 92 17804870 Sertel S. Eichhorn T. Sieber S. Sauer A. Weiss J. Plinkert P.K. Efferth T. Efferth, T. Factors determining sensitivity or resistance of tumor cell lines towards artesunate Chem-Biol. Interact. 2010 185 42 52 10.1016/j.cbi.2010.02.002 Scherf U. Ross D.T. Waltham M. Smith L.H. Lee J.K. Tanabe L. Kohn K.W. Reinhold W.C. Myers T.G. Andrews D.T. A gene expression database for the molecular pharmacology of cancer Nat. Genet. 2000 24 236 244 10.1038/73439 Amundson S.A. Do K.T. Vinikoor L.C. Lee R.A. Koch-Paiz C.A. Ahn J. Reimers M. Chen Y. Scudiero D.A. Weinstein J.N. Integrating global gene expression and radiation survival parameters across the 60 cell lines of the National Cancer Institute Anticancer Drug Screen Cancer Res. 2008 68 415 424 10.1158/0008-5472.CAN-07-212018199535 Efferth T. Fabry U. Osieka R. Apoptosis and resistance to daunorubicin in human leukemic cells Leukemia 1997 11 1180 1186 10.1038/sj.leu.24006699205009 Wosikowski K. Schuurhuis D. Johnso K. Paull K.D. Myers T.G. Weinstein J.N. Bates S.E. Identification of epidermal growth factor receptor and erbB2 pathway inhibitors by correlation with gene expression patterns J. Natl. Cancer Inst. 1997 89 1505 1515 10.1093/jnci/89.20.1505