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Pharmacogenomic and molecular docking studies on the cytotoxicity of the natural steroid wortmannin against multidrug-resistant tumor cells.

ABSTRACT

Wortmannin is a cytotoxic compound derived from the endophytic fungi Fusarium oxysporum, Penicillium wortmarmii and Penicillium funiculosum that occurs in many plants, including medicinal herbs. The rationale to develop novel anticancer drugs is the frequent development of tumor resistance to the existing antineoplasic agents. Therefore, it is mandatory to analyze resistance mechanisms of novel drug candidates such as wortmannin as well to bring effective drugs into the clinic that have the potential to bypass or overcome resistance to established drugs and to substantially increase life span of cancer patients. In the present project, we found that P-glycoprotein-overexpressing tumor cells displaying the classical multidrug resistance phenotype toward standard anticancer drugs were not cross-resistant to wortmannin. Furthermore, three point-mutated PIK3CA protein structures revealed similar binding energies to wortmannin than wildtype PIK3CA. This protein is the primary target of wortmannin and part of the PI3K/AKT/mTOR signaling pathway. PIK3CA mutations are known to be associated with worse response to therapy and shortened its activity toward wild-type and mutant PIK3CA with similar efficacy.

Keywords:

Wortmannin

Fusarium oxysporum

ABC-transporter

Cluster analysis

Drug resistance

Kinase inhibitor

Microarrays

Molecular docking

Introduction

Despite tremendous successes in basic cancer research and clinical oncology in the past few decades, many cancer patients still cannot be cured. In addition to surgery, radiotherapy, and antibody-based immunotherapy, chemotherapy is a mainstay of cancer treatment. However, drug therapy frequently leads to non-satisfactory treatment results with fatal consequences for patients because of severe side effects and development of drug resistance. Therefore, drug research constantly attempts to improve treatment results by the preclinical development of new drugs and the optimization of therapy regimens in the clinic.

In addition to synthetic small molecules derived from combinatorial chemistry, natural products always played an important role in cancer pharmacology (Newman and Cragg 2007). Natural products are not only well-established cytotoxic anticancer drugs (e.g. anthracyclines, Vinca alkaloids, taxanes, camptothecins etc.), but also valuable lead compounds for the development of novel targeted chemotherapy approaches. Some examples are geldanamycin (heat shock protein 90 inhibitor), trichostatin A (histone deacetylase inhibitor), flavopiridol (cyclin-dependent kinase inhibitor) and many others (Walkinshaw and Yang 2008; Gallorini et al. 2012; Garcia-Carbonero et al. 2013).

Signal transduction pathways are crucial elements in cancer cells, whose abnormal activation lead to carcinogenesis, proliferation, invasion and metastasis of tumors (Leber and Efferth 2009; Spano et al. 2012). Among others, signaling routes related to the epidermal growth factor receptor (EGFR) play a key role in cancer biology (Efferth 2012). One important pathway is the EGFR/PI3K/AKT-mTOR pathway (Efferth 2012). Therefore, there have been tremendous efforts to identify inhibitors of EGFR, PI3K, ART or mTOR as novel cancer therapeutics. Erlotinib and gefitinib (EGFR inhibitors), LY294002 (PI3K inhibitor), peritosine (Akt inhibitor), as well as rapamycin and sirolimus (mTOR inhibitors) are just a few examples that attracted the field of cancer drug discovery and development during the past years.

Previously, we described natural products as EGFR inhibitors (e.g. dicentrine, bis(helenalinyl(glutarate))) and as inhibitors of EGFR-downstream signaling (i.e. artesunate) (Konkimalla et al. 2009, 2010; Konkimalla and Efferth 2010), indicating that inhibition of this signaling route can also be approached by natural products.

Wortmannin is an antifungal mycotoxin from Fusarium oxysporum and other fungi species (Abbas and Mirocha 1988). It also exerts profound cytotoxic activity against cancer cells by inhibiting PI3K and also MAPK and mTOR (Workman et al. 2010).

Fusarium oxysporum is an endophytic fungus, which produces several pharmacologically interesting compounds (Musavi et al. 2015; Kumara et al. 2014; Wang et al. 2011; Cui et al. 2012; Liu et al. 2012; Kumar et al. 2013). This fungus can be found in many plant species, including those used as medicinal plants (Raviraja 2005; Abdou et al. 2010; Mishra et al. 2012; Tiwari et al. 2014). Endocytic fungi reside in host plants in a symbiotic fashion. They obtain shelter by the host and produce highly bioactive compounds, which are advantageous for the host. Several clinical established anticancer drugs are produced by endocytic fungi such as Fusarium oxysporum, e.g. camptothecin, vinblastine, vincristine, taxol and others (Kumar et al. 2013; Musavi et al. 2015). Therefore, wortmannin is a phytotherapeutically interesting compound with considerable potential as lead compound for the development of new anticancer drugs.

The aim of the present study was to identify molecular factors that determine the responsiveness of tumor cells to wortmannin. For this reason, we have addressed three questions;

(1) Does P-glycoprotein which confers multidrug resistance also confer resistance to wortmannin?

(2) Are there other determinants predicting sensitivity or resistance of cancer cells to wortmannin? To address this question, we performed COMPARE-and hierarchical cluster analyses of transcriptome-wide mRNA expression profiles of cancer cells.

(3) Do mutations in PIK3CA as primary target of wortmannin affect the binding of wortmannin to this protein? Binding affinities of drugs to their target influence tumor killing efficacies, and point mutations in the target protein often lead to decreased drug binding and consequently to drug resistance. Frequently occurring mutations in PIK3CA have been reported to affect drug response of tumor cells (Berns et al. 2007; De Roock et al. 2010; Janku et al. 2013). Therefore, we compared the binding affinities of wortmannin to wild-type PIK3CA and three mutant forms to get hints, whether PIK3CA mutants might cause resistance to wortmannin.

Material and methods

Cell lines

Leukemic CCRF-CEM cells were cultured as previously described (Efferth et al. 2003). Drug resistance of P-glycoprotein/MDRI/ABCB1-overexpressing CEM/ADR5000 cells was maintained in 5000 ng/ml doxorubicin (Kimmig et al. 1990). The mRNA expression of MDR1 in the resistant cell line has been reported (Efferth et al. 2003; Gillet et al. 2004).

A panel of 46 human tumor cell lines of the Developmental Therapeutics Program of the National Cancer Institute (NCI, USA) consisted of leukemia, melanoma, non-small cell lung cancer, colon cancer, renal cancer, ovarian cancer, breast cancer, and prostate carcinoma cells as well as tumor cells of the central nervous system (Alley et al. 1988). Cells were assayed by means of a sulforhodamine B assay (Rubinstein et al. 1990).

Resazurin cell growth inhibition assay

The resazurin (Promega, Mannheim, Germany) reduction assay (O'Brien et al. 2000) was used to assess the cytotoxicity as previously described (Kuete and Efferth 2013). Each assay was conducted at least three times, with two replicates each. Cell viability was evaluated based on a comparison with untreated cells. [IC.sub.50] values were determined as concentrations required to inhibit 50% of cell proliferation and were calculated from a calibration curve by linear regression using Microsoft Excel.

Statistical analysis

mRNA microarray data of the NCI tumor cell line panel are available (Scherf et al. 2000; Staunton et al. 2001) through the NCI website (http://dtp.nci.nih.gov). For hierarchical cluster analysis, objects were classified into dendrograms by calculating distance according to the closeness of between-individual distances by means of the Ward method (WinSTAT program, Kalmia, Cambridge, MA, USA). Cluster models have previously been validated for gene expression profiling and for approaching molecular pharmacology of cancer (Efferth et al. 1997; Scherf et al. 2000). The application of this method for pharmacogenomics of phytochemicals has been described in detail (Efferth et al. 2003).

COMPARE analyses were performed to produce rank-ordered lists of genes expressed in the NCI cell lines as previously described (Pauli et al. 1989; Wosikowski et al. 1997). Briefly, every gene of the NCI microarray database was ranked for similarity of its mRNA expression to the [log.sub.10][IC.sub.50] values for wortmannin. To derive COMPARE rankings, a scale index of correlation coefficients (R-values) was created.

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 (WinSTAT, Kalmia). The chi-squared test was applied to bivariate frequency distributions of pairs of nominal scaled variables (WinSTAT, Kalmia). It was used to calculate significance values (P-values) and rank correlation coefficients (R-values) as a relative measure of the linear dependency of two variables.

Molecular docking

The protocol for molecular docking was previously reported by us (Zeino 2013). An X-ray crystallography-based structure of wild-type PI3K[alpha]-catalytic subunit (PDB ID: 4JPS) and its H1047R mutant (PDB ID : 3HHM) were obtained from Protein Data Bank (http://www.rcsb.org/pdb). Elomology models of two further mutants, H1047L and H1047Y were created by us using MODELLER 9.11 (Fiser and Sali 2003; Venkatachalam et al. 2003) and a Swiss-MODEL structure assessment tool (http://swissmodel.expasy.org/) based on the wild-type structure (PDB ID: 4JPS) as template. A grid box was then constructed to define docking spaces in each protein according to its pharmacophores. Docking parameters were set to 250 runs and 2,500,000 energy evaluations for each cycle. Docking was performed three times independently by Autodock4 and with AutodockTools1.5.7rcl (Morris et al. 2009) using the Lamarckian Algorithm. The corresponding lowest binding energies and predicted inhibition constants were obtained from the docking log files (dig). Mean [+ or -] SD of binding energies were calculated from three independent docking. Visual molecular dynamics (VMD) was used to depict the docking poses of wortmannin and the inhibitors for each target protein.

Results

Tumor-type dependent response toward wortmannin

If the average [log.sub.10][IC.sub.50] values over the entire range of 46 cell lines were diversified regarding their tumor types, ovarian and lung cancer cell lines were most resistant toward wortmannin, whereas prostate cancer cell lines were most sensitive (Fig. 1).

Sensitivity of wortmannin toward P-glycoprotein/MDRI-expresing tumor cells

It can be suggested that the varying sensitivities of the tumor cell lines to wortmannin might be due to differences in the expression of the drug-resistance mediating efflux transporter, P-glycoprotein/MDR1. Therefore, we correlated the [log.sub.10][IC.sub.50] values for wortmannin of the cell line panel with different parameters of P-glycoprotein/MDR1. We used the mRNA expression as determined by microarray hybridization or RT-PCR the P-glycoprotein-encoding MDR1/ABCB1 gene as well as the intracellular accumulation rates of rhodamine 123 (R123). R123is a P-glycoprotein substrate and the flow cytometric determination of R123 uptake can serve as functional assay for P-glycoprotein activity. As shown in Table 1, none of these parameters revealed significant correlations with the [log.sub.10][IC.sub.50] values for wortmannin, indicating that the cellular sensitivity of wortmannin was not related to the expression of MDR1/ABCB1 or the activity of P-glycoprotein. For comparison, the established anticancer drug epirubicin was used as positive control. Epirubicin is a well-known substrate of P-glycoprotein. As expected, the [log.sub.10][IC.sub.50] values for epirubicin significantly correlated with these Pglycoprotein/MDRJ/ABCBJ parameters (Table 1).

To confirm the missing correlation between responsiveness to wortmannin and P-glycoprotein expression, we investigated wortmannin in multidrug-resistant P-glycoprotein (MDR1/ABCB1)-overexpressing CEM/ADR5000 cells and drug-sensitive parental CCRF-CEM cells using a resazurin assay. The degree of resistance of CEM/ADR5000 cells was calculated by dividing the [IC.sub.50] value of this cell line by the [IC.sub.50] value of the parental CCRF-CEM cells. A weak hypersensitivity of the CEM/ADR5000 cells compared to the parental CCRF-CEM cells was observed (0.81-fold, Table 2).

Compare and hierarchical cluster analyses of mRNA microarray data

We mined the NCI database and correlated the microarray-based transcriptome-wide mRNA expression of 46 tumor cell lines with the [log.sub.10][IC.sub.50] values for wortmannin. This is a hypothesis-generating bioinformatical approach to identify novel putative molecular determinants of cellular response to wortmannin. The scale rankings of genes obtained by COMPARE computation were subjected to Pearson's rank correlation tests. The thresholds for correlation coefficients were R > 0.50 for direct correlations and R < -0.50 for inverse correlations (Table 3). The identified genes belong to different functional groups such as proliferation and cell cycle regulation (ANAPC13, HSPA9, TSPAN3, VEGFA). metabolism (B3GALT6, BHMT2, KMO, LIPC, MDH1B, NPL, SMC3), cytoskeleton (SHROOM4, DNASE1, TBCD), signal transduction (ADRBK2, ARF1, CLSTN2, DLGAP2, GPRC5C, GUK1, PTAR1, RABGGTB, WNT10A, ZNF544), transcriptional regulation (HAT1, JMJD5, PCGF6, ZNF599), RNA processing (DDX1, MRPS9, RPL31, SNRNP200,), protein turnover (HSPA9, PSMG4, PSKH1, RNF181, RNGTT, WDR20), as well as other or unknown functions (ABI3BP, C12orf42, CXCL6, DNASE1, LOC401098, LOC729059, LOC727973, PCDHG, REEP2, TF, SAA1, SAA4, SMPD4, STARD7, TSN, VCAM1).

Then, these genes were subjected to hierarchical cluster analysis. Only the mRNA expression data, but not the [log.sub.10][IC.sub.50] values of wortmannin for the 46 cell lines were included into the cluster analysis. Four main cluster branches appeared (Fig. 2). The distribution of cell lines being either sensitive or resistance to wortmannin was significantly different between the four clusters. As the [log.sub.10][IC.sub.50] values of the compounds were not prior included into the cluster analysis. we now analyzed whether or not the obtained gene expression profile predicted sensitivity or resistance of cell lines to wortmannin. Indeed, a significant relationship was obtained (P = 0.004; Table 4).

Molecular docking

The P13K [alpha]-catalytic subunit (PIK3CA) belongs to the most mutated proteins in human cancer and these mutations are known to cause drug resistance in the clinic (Berns et al. 2007; De Roock et al. 2010; Janku et al. 2013). Therefore, the question arises, whether these mutations are also relevant for resistance of cancer cells to wortmannin. For this reason, we used the crystal structure of PIK3CA (PDB ID: 4JPS) and the H1047R mutant (PDB ID: 3HHM). Two further frequently occurring mutants (H1047L and H1047Y) were constructed by us by homology modeling. Wild-type and mutant PIK3CA models were then used for docking of wortmannin and GDC0941. GDC0941 is a known P1K3CA inhibitor and was used as control drug. The results of the molecular dockings are shown in Fig. 3. In panel (a), it can be seen that wortmannin (displayed in red) and GDC0941 (displayed in yellow) bound with similar affinity to P1K3CA (-9.38 [+ or -] 0.01 and -9.60 [+ or -] 0.04 kcal/mol, respectively).

Remarkably, the binding of wortmannin to the 3 mutant PIK3CA proteins was in a comparable range (from -8.30 [+ or -] 0.01 to -10.20 [+ or -] 0.01 kcal/mol), which indicates that tumor cells carrying these mutant PIK3CA proteins may retain sensitivity to wortmannin.

Discussion

Wortmannin is a natural steroid produced by an endophytic fungus, Fusarium oxysporum, that can be found in many plants. The pharmacological relevance of wortmannin relates not only to its cytotoxic activity toward cancer cells, but to the fact that it is a specific inhibitor of PI3K ([alpha]-subunit (PIK3CA). This kinase is a key molecule of the P13K/Akt/mTOR signaling pathway that is activated in a large fraction of tumors. Therefore, wortmannin has a great potential as lead compound for targeted cancer therapy and it can be expected that wortmannin-derived drugs will pave their way into the clinic.

As the clinical implementation of most--if not all--novel anticancer drugs has been dogged by the development of tumor resistance, it is worth investigating, which mechanisms might account for wortmannin resistance.

The results of our present investigation indicate that the broad spectrum drug transporter, P-glycoprotein/MDR1 does not confer resistance to wortmannin. P-glycoprotein is one of the most well-known drug resistance mechanisms. Its overexpression in tumor cells leads to the efficient extrusion of a large number of established anticancer drugs and cytotoxic natural products out of cancer cells. P-glycoprotein confers a multidrug resistance phenotype that considerably limits the treatment options to kill tumors. Therefore, it was a pleasing result that the expression of P-glycoprotein/MDR1 in the NCI cell line panel did not correlate with cellular response to wortmannin.

Also, multidrug-resistant CEM/ADR5000 cells expressing high levels of P-glycoprotein/MDRl and high degrees of resistance to well-known anticancer drugs such as doxorubicin (1036-fold), vincristine (613-fold), docetaxel (435-fold) and many others (Efferth et al. 2008), were even slightly more sensitive to wortmannin than the parental, wild-type, drug-sensitive CCRF-CEM tumor cells. This indicates that wortmannin may successfully be used to kill otherwise unresponsive, multidrug-resistant tumors.

P-glycoprotein/MDR1 is, however, not the only mechanism of drug resistance and multiple other resistance factors also determine, whether or not tumor treatment is successful. Microarray technology allows a comprehensive insight into the plethora of molecular determinants of drug response. This methodology may be especially helpful to identify potential mechanisms of novel, still incompletely understood cytotoxic compounds. Therefore, we performed COMPARE and hierarchical cluster analyses of transcriptome-wide, microarray-based mRNA expression of the NCI cell line panel.

COMPARE analyses of the mRNA microarray data revealed a set of genes that were significantly associated with sensitivity or resistance of the cell lines to wortmannin. These genes represent candidate genes determining cellular response to this drug, whose functional relevance for wortmannin needs to be verified in the future. These genes have not been described to confer resistance to clinically established anticancer drugs. As genes relevant for wortmannin resistance may be unrelated to resistance to standard chemotherapy, wortmannin may be still active in otherwise drug-resistant tumors.

It is remarkable that the cell lines of the NCI panel have been grouped by hierarchical cluster analysis according to their sensitivity or resistance to wortmannin, although the [log.sub.10][IC.sub.50] values for wortmannin have not been included into the cluster analysis. This indicates that the resistance of tumor cells could be predicted solely on the basis of their gene expression profile. The prediction of drug resistance of tumors has been a hot topic in cancer biology for decades. The basic idea is that it would be desirable to know the sensitivity or resistance of individual tumors prior to chemotherapy. Based on the sensitivity resistance profile, the most appropriate therapy regimen could be chosen to optimize treatment outcome of each individual patient. While several cellular assays have been thoroughly investigated in the past, none of them entered clinical routine diagnostics (Bertelsen et al. 1984; Mattern et al. 1986; Van Nguyen et al. 1991; Kubota and Weisenthal 2006; Blumenthal and Goldenberg 2007; Cree 2009). More recently, microarray technology has been suggested to predict chemosensitivity of individual tumors and gene expression profiling reached great attention for personalized medicine of tumors (Volm et al. 2002; Bonnefoi et al. 2009; Tan and Lee 2012). Commercial assays for gene expression profiling are now on the market to predict tumor sensitivity of individual cancer patients. While such tests have been developed for standard chemotherapy and targeted antibodies and small molecules, less is known about the prediction of sensitivity resistance of tumors to cytotoxic natural products. In this case, the sensitivity of a tumor to such as natural product has also to be tested. Our results indicate that microarray-based gene expression profiling might indeed be a suitable tool to predict tumor responsiveness to natural products. Further investigations are warranted to analyze this in more detail.

Another important mechanism of drug resistance represents the occurrence of point mutations in the target of wortmannin, PIK3CA. Mutations in this protein have clinically been shown to be associated with unfavorable response to treatment and shorter survival times of patients. In breast cancer patients, PIK3CA mutations were correlated with resistance to HER2-targeting drugs such as trastuzumab, alone or in combination with lapatinib or pertuzumab (Berns et al. 2007; Hanker et al. 2013). Colorectal carcinoma patients responded worse to EGFR-targeting antibodies such as cetuximab or panitumumab, if their tumors harbored PIK3CA mutations compared to wild-type proteins (Sartore-Bianchi et al. 2009; Siena et al. 2009). Comparable results have been obtained for combination therapy of cetuximab plus chemotherapy in colon cancer patients (Wee et al. 2009). After treatment with tyrosine kinase inhibitors targeting EGFR, patients suffering from non-small cell lung cancer revealed shorter times to progression and shorter overall-survival times, if their tumors carried mutated rather than wild-type P1K3CA (Ludovini et al. 2011). Advanced cancers of diverse tumor type carrying PIK3CA mutations responded less to treatment with inhibitors of the P13K/Akt/mTOR pathway and had higher progression rates than tumors without PIK3CA mutations (Ganesan et al. 2013; Janku et al. 2013). All these clinical observations demonstrate that PIK3CA mutations render tumor cells resistant to tumor therapy. Hence, it can be questioned, whether or not wortmannin's inhibitory activity toward PIK3CA may also be affected by the occurrence of mutations in this protein, which may hamper the binding of this compound to its target protein. Our molecular docking approach did not indicate large differences in the binding affinities of wortmannin between wild-type and mutant forms of PIK3CA. This may open the favorable perspective that wortmannin inhibits PIK3CA independent of the mutational status and that tumor cells may not develop wortmannin resistance due to PIK3CA mutations.

Abbreviations

ABC             ATP-binding cassette
AKT             v-akt murine thymoma viral oncogene homolog
CCRF-CEM        acute T-Iymphoblastic leukemia cells derived from
                a 4-year old female Caucasian
CEM/ADR5000     adriamycin-resistant human acute T-lymphoblastic
                CCRF-CEM leukemia cells
H1047L mutant   histidine 1047 leucine mutant
H1047R mutant   histidine 1047 arginine mutant
H1047Y mutant   histidine 1047 tyrosine mutant
MDR             multidrug resistance
mTOR            mammalian target of rapamycin
NCI             National Cancer Institute
PDB             Protein Data Bank
PI3K            phosphoinositide-3-kinase
RT-PCR          reverse transcriptase polymerase chain reaction


ARTICLE INFO

Article history: Received 1 October 2014

Revised 23 October 2014

Accepted 15 November 2014

Acknowledgment

We are grateful for stipends of the Alexander-von-Humboldt-Foundation, Germany to V.K. and the National Research Council, Khartoum, Sudan to M.E.M.S.

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Victor Kuete (a,b), Mohamed E.M. Saeed (a), Onat Kadioglu (a), Jonas Bortzler (a), Hassan Khalid (c), Henry Johannes Greten (d,e), Thomas Efferth (a), *

(a) Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Mainz, Germany

(b) Department of Biochemistry, Faculty of Science, University of Dschang, Dschang, Cameroon

(c) Department of Pharmacognosy, University of Khartoum, Khartoum, Sudan

(d) Abel Salazar Biomedical Sciences Institute, University of Porto, Porto, Portugal

(e) Heidelberg School of Chinese Medicine, Heidelberg, Germany

* Corresponding author at: Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Mainz, Staudinger Weg 5, 55128 Mainz, Germany. Tel.: +49 6131 3925751; fax: +49 6131 3923752.

E-mail address: efferth@uni-mainz.de (T. Efferth).

http://dx.doi.org/10.1016/j.phymed.2014.11.011

Table 1
Correlation of [log.sub.10][IC.sub.50] values for wortmannin to
expression of MDR1/ABCB1 mRNA by RT-PCR, and microarray analyses,
and P-glycoprotein function (cellular rhodamine 123 accumulation) in
the NCI cell line panel. Epirubicin was used as positive control, as
it is a well-known substrate of P-glycoprotein. The analysis was
performed by means of Pearson's rank correlation test.

                                     Wortmannin   Epirubicin

ABCB1/MDR1 (microarrays)   R-value    0.168       0.529
                           P-value    0.132       8.28 x [10.sup.-6]
ABCB1/MDR1 (RT-PCR)        R-value   -0.011       0.459
                           P-value    0.471       1.26 x [10.sup.-4]
R123 accumulation          R-value   -0.003       0.525
                           P-value    0.492       1.15 x [10.sup.-5]

Table 2
Cytotoxicity of wortmannin toward sensitive and
multidrug-resistant P-glycoprotein/MDR1-overexpressing
cancer cell lines as determined by the resazurin reduction
assay.

Cell Line     [IC.sub.50] ([micro]M)   Degree of resistance (a)

CCRF-CEM      3.87 [+ or -] 0.35
CEM/ADR5000   3.15 [+ or -] 0.44       0.81

(a) The degree of resistance was determined as the ratio of
[IC.sub.50] value of the resistant/[IC.sub.50] sensitive cell line.

Table 3
Correlation of constitutive mRNA expression of genes identified by
COMPARE analyses with [log.sub.10][IC.sub.50] values of wortmannin
for 46 tumor cell lines.

Compare       Compare      GenBank     Gene
Coefficient   Pattern ID   Accession   Symbol

-0.696        GC158028     AI652913    LOC729059
-0.684        GC153378     AF152526    PCDHG
-0.669        GC180726     NM_001078   VCAM1
-0.667        GC182174     NM_002993   CXCL6
-0.665        GC99372      W78050      AB13BP
-0.664        GC188864     NM_022036   GPRC5C
-0.663        GC65277      AI435157    ZNF599
-0.657        GC170839     AW873556    WNT10A
-0.647        GC91235      N36417      PTAR1
-0.629        GC155089     AI073407    TF
-0.627        GC179106     M55983      DNASE1
-0.610        GC188934     NM_022131   CLSTN2
-0.608        GC56724      AI005420    SHROOM4
-0.605        GC155102     A1074145    KMO
-0.605        GC173886     BE677493    ANAPC13
-0.600        GC167520     AV723710    RNF181
-0.599        GC60865      AI217352    LOC727973
-0.598        GC36263      AA829286    SAA1
-0.586        GC35351      M81349      SAA4
-0.585        GC187285     NM_017614   BHMT2
-0.583        GC180076     NM_000236   UPC
-0.583        GC172511     BC006249    GUK1
-0.579        GC89923      M36340      ARF1
-0.576        GC147667     AA012848    TBCD
-0.572        GC66199      AI480245    DLGAP2
-0.571        GC156639     A1356758    B3GALT6
-0.570        GC49421      AA758779    C12orf42
 0.611        GC74858      AI803587    NPL
 0.603        GC166265     AL582429    MRPS9
 0.585                     R62547      TSPAN3
 0.579        GC57373      AI028424    PSMG4
 0.576        GC66375      AI494519    ZNF544
 0.565        GC17901      W90239      RABGGTB
 0.565        GC188360     NM_020151   STARD7
 0.552        GC55444      AF025654    RNGTT
 0.546        GC98337      W25248      PSKH1
 0.542        GC58224      AI076012    MDH1B
 0.538        GC183595     NM_004939   DDX1
 0.536        GC78020      AI910868    LOC401098
 0.523        GC17256      AA057617    ADRBK2
 0.522        GC18836      AA037748    RPL31
 0.513        GC152019     AF020043    SMC3
 0.510        GC183342     NM_004622   TSN
 0.509        GC17378      AA054130    SNRNP200
 0.507        GC15550      W19225      VEGFA
 0.506        GC13205      H58736      WDR20
 0.506        GC151565     AB047006    PCGF6
 0.503        GC14419      N46426      REEP2
 0.503        GC58967      AI122764    JMJD5
 0.502        GC182652     NM_003642   HAT1
 0.501        GC55935      AF052134    SMPD4
 0.498        GC88967      L15189      HSPA9

Coefficient   Name                           Function

-0.696        Uncharacterized protein        Unknown
                LOC7290592
-0.684        Protocadherin [gamma]          Cell-cell connections in
                cluster                        the brain
-0.669        Vascular cell adhesion         Cell-cell recognition;
                molecule 1                     leukocyte-endothelial
                                               cell adhesion
-0.667        Chemokine (C-X-C motif)        Chemotactic for
                ligand 6                       neutrophil granulocytes
-0.665        ABI family, member 3           Collagen binding;
                (NESH) binding protein         glycosaminoglycan
                                               binding; heparin
                                               binding
-0.664        G protein-coupled              Retinoic acid-inducible
                receptor, family C,            G-protein
                group 5, member C
-0.663        Zinc finger protein 599        Transcriptional
                                               regulation
-0.657        Wingless-type MMTV             Ligand for frizzled
                integration site family,       transmembrane receptor
                member 10A                     family members
-0.647        Protein prenyltransferase      Protein prenyltransferase
                alpha subunit repeat
                containing 1
-0.629        Transferrin                    Iron transport; ion
                                               storage and utilization
-0.627        Deoxyribonuclease 1            Apoptosis inducer;
                                               inhibitor of actin
                                               polymerization
-0.610        Calsyntenin 2                  Modulator of
                                               calcium-mediated
                                               postsynaptic signals
-0.608        Shroom family member 4         Regulation of
                                               cytoskeletal
                                               architecture
-0.605        Kynurenine 3-monooxygenase     L-Kynurenine (L-Kyn)
                (kynurenine                    hydroxylation
                3-hydroxylase)
-0.605        Anaphase promoting complex     Component of cell
               subunit 13                      cycle-regulated E3
                                               ubiquitin ligase
-0.600        Ring finger protein 181        E3 ubiquitin-protein
                                              ligase
-0.599                                       Unknown
-0.598        Serum amyloid A1               Major acute phase
                                               reactant.
                                               Apolipoprotein of HDL
                                               complex
-0.586        Serum amyloid A4,              Major acute phase
                constitutive                   reactant.
                                               Apolipoprotein of HDL
                                               complex
-0.585        Betaine-homocysteine           Regulation of
                S-methyltransferase 2          homocysteine metabolism
-0.583        Hepatic lipase                 Hydrolysis of
                                               phospholipids, mono-,
                                               di-, and triglycerides,
                                               and acyl-CoA thioesters
-0.583        Guanylate kinase 1             GMP and cGMP recycling
-0.579        ADP-ribosylation factor 1      GTP-binding protein and
                                               protein trafficking
-0.576        Tubulin folding cofactor D     Tubulin-folding protein
-0.572        Discs, large (Drosophila)      Molecular organization
                homologue-associated           of synapses and
                protein 2                      neuronal cell signaling
-0.571        UDP-Gal:[beta]-Gal             Galactose transfer
                [beta]-1,
                3-galactosyltransferase
                polypeptide 6
-0.570        Chromosome 12 open reading     Unknown
                frame 42
 0.611        N-Acetylneuraminate            Formation of pyruvate
                pyruvate lyase                 and N-acetylmannosamine
                (dihydrodipicolinate
                synthase)
 0.603        Mitochondrial ribosomal        Structural constituent
                protein S9                     of ribosome
 0.585        Tetraspanin 3                  Regulating proliferation
                                               and migration of
                                               oligodendrocytes
 0.579        Proteasome (prosome,           Chaperone protein
                macropain) assembly            promoting assembly of
                chaperone 4                    20S proteasome
 0.576        Zinc finger protein 544        Transcriptional
                                               regulation
 0.565        Rab                            Catalyzes transfer of a
                geranylgeranyltransferase,     geranyl-geranyl moiety
                [beta] subunit                 to both cysteines in
                                               Rab proteins
 0.565        StAR-related lipid transfer    Unknown
                (START) domain
                containing 7
 0.552        RNA guanylyltransferase and    RNA 5'-triphosphatase
                5'-phosphatase                 activity; catalyzing
                                               the first two steps of
                                               cap formation
 0.546        Protein serine kinase HI       Intranuclear SR protein
                                               trafficking and
                                               pre-mRNA processing
 0.542        Malate dehydrogenase IB,       Malate dehydrogenase
                NAD (soluble)                  activity;
                                               oxidoreductase activity
 0.538        DEAD (Asp-Glu-Ala-Asp) box     ATP-dependent RNA
                helicase 1                     helicase
 0.536        Uncharacterized LOC401098      Unknown
 0.523        Adrenergic, [beta],            Phosphorylates
                receptor kinase 2              [beta]-adrenergic
                                               receptors
 0.522        Ribosomal protein L31          RNA binding; structural
                                               constituent of ribosome
 0.513        Structural maintenance of      Central component of
                chromosomes 3                  cohesin; chromosome
                                               cohesion during cell
                                               cycle
 0.510        Translin                       Endoribonuclease activity
 0.509        Small nuclear                  RNA helicase; pre-mRNA
                ribonucleoprotein 200 kDa      splicing
                (U5)
 0.507        Vascular endothelial growth    Growth factor
                factor A
 0.506        WD repeat domain 20            Regulation of USP12-UAF1
                                               deubiquitinating
                                               enzyme activity
 0.506        Polycomb group ring finger 6   Transcriptional repressor
 0.503        Receptor accessory protein 2   Enhancer of odorant
                                               receptor expression
 0.503        JmjC domain-containing         Histone demethylase
                protein 5
 0.502        Histone acetyltransferase 1    Acetylates soluble
                                               histone H4
 0.501        Sphingomyelin                  Hydrolysis of membrane
                phosphodiesterase 4,           sphingomyelin
                neutral membrane (neutral
                sphingomyelinase-3)
 0.498        Heat shock 70 kDa protein 9    Cell proliferation and
                (mortalin)                     cellular aging;
                                               chaperone

Positive correlation coefficients indicate direct correlations to
[log.sub.10][IC.sub.50] values, negative ones indicate inverse
correlations. Information on gene functions was taken from the OMIM
database, NCI, USA (http://www.ncbi.nlm.nih.gov/Omim/) and from the
GeneCard database of the Weizman Institute of Science, Rehovot,
Israel (http://bioinfo.weizmann.ac.il/cards/index.html).

Table 4
Separation of clusters of 46 NCI cell lines obtained by hierarchical
cluster analysis shown in Fig. 2 in comparison to drug sensitivity.

                    Partition   Cluster   Cluster   Cluster   Cluster
                                1         2         3         4

Sensitive           <-5.29      0         5         12         5
Resistant           >-5.29      4         0          7        12
[chi square]-test   P = 0.004

The median [log.sub.10][IC.sub.50] value (-5.29 M) for each compound
was used as cut-off to separate tumor cell lines as being "sensitive"
or resistant".
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Author:Kuete, Victor; Saeed, Mohamed E.M.; Kadioglu, Onat; Bortzler, Jonas; Khalid, Hassan; Greten, Henry J
Publication:Phytomedicine: International Journal of Phytotherapy & Phytopharmacology
Geographic Code:4EUGE
Date:Jan 15, 2015
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