Pharmacogenomic and molecular docking studies on the cytotoxicity of the natural steroid wortmannin against multidrug-resistant tumor cells.
ABSTRACTWortmannin 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 |
| Words: | 6850 |
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