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An integrative approach to identifying cancer chemoresistance-associated pathways
© Chao et al; licensee BioMed Central Ltd. 2011
Received: 23 August 2010
Accepted: 24 March 2011
Published: 24 March 2011
Resistance to chemotherapy severely limits the effectiveness of chemotherapy drugs in treating cancer. Still, the mechanisms and critical pathways that contribute to chemotherapy resistance are relatively unknown. This study elucidates the chemoresistance-associated pathways retrieved from the integrated biological interaction networks and identifies signature genes relevant for chemotherapy resistance.
An integrated network was constructed by collecting multiple metabolic interactions from public databases and the k-shortest path algorithm was implemented to identify chemoresistant related pathways. The identified pathways were then scored using differential expression values from microarray data in chemosensitive and chemoresistant ovarian and lung cancers. Finally, another pathway database, Reactome, was used to evaluate the significance of genes within each filtered pathway based on topological characteristics.
By this method, we discovered pathways specific to chemoresistance. Many of these pathways were consistent with or supported by known involvement in chemotherapy. Experimental results also indicated that integration of pathway structure information with gene differential expression analysis can identify dissimilar modes of gene reactions between chemosensitivity and chemoresistance. Several identified pathways can increase the development of chemotherapeutic resistance and the predicted signature genes are involved in drug resistant during chemotherapy. In particular, we observed that some genes were key factors for joining two or more metabolic pathways and passing down signals, which may be potential key targets for treatment.
This study is expected to identify targets for chemoresistant issues and highlights the interconnectivity of chemoresistant mechanisms. The experimental results not only offer insights into the mode of biological action of drug resistance but also provide information on potential key targets (new biological hypothesis) for further drug-development efforts.
The development of chemotherapy resistance is of tremendous significance to patients, researchers, and care providers who rely on conventional cytotoxic agents for the treatment of cancer. Still, the mechanisms and related biological pathways that contribute to chemotherapy resistance are relatively poorly understood. Numerous attempts have been made to mitigate or eliminate chemotherapy resistance, based-on certain assumptions about the various mechanisms, but low response rates and poor clinical outcomes for patients can be attributed to our inability to identify and subsequently target major molecular interactions associated with such resistance. Many genes have recently been reported to determine sensitivity to multiple drugs include drug transporters and metabolizing enzymes [1–4], and certain genes have also been demonstrated to determine sensitivity to specific chemotherapy drugs [5–7]. Other studies have attempted to estimate the chemosensitivity of cancers using genome-wide expression profile analyses, such as cDNA microarray and single nucleotide polymorphisms [8–10]. Although these studies have described genes as being capable of determining the sensitivity to chemotherapy drugs, the interactions between such genes have not been addressed, and considerable attention has focused on identifying molecular interactions associated with chemotherapy resistance. Cabusora et al. reported particular response sub-networks in the M. tuberculosis network after treatment with unspecific stress-inducers and comparison with antibacterial drugs . To identify rational targets for combination therapy, Riedel et al. attempted to identify the biological networks implicated by differential gene expression between sensitive and resistant cell lines .
However these studies did not take into account the drug active pathways, including the regulatory interactivities of genes influenced by the drug. The drug active pathway plays an important role in the drug responses of the cellular system affected by the drug and the prediction of side-effects, which is also a very important issue for identifying and validating drug target genes through their regulatory relationships. Moreover, considerations should be taken of drug resistance mechanisms, including reduced intracellular drug accumulation, increased detoxification of the drug by thiol-containing molecules, increased DNA damage repair, and altered cell signaling pathways and apoptosis mediators . In addition, chemotherapy drugs can be categorized based on their function, chemical structure and interaction with other drugs. Cisplatin and carboplatin, classified as DNA alkylating agents, are platinum-based chemotherapy drugs used to treat various cancers, including sarcomas, small cell lung cancer, ovarian cancer, lymphomas and germ cell tumors. These platinum-based chemotherapy drugs react with DNA in vivo by binding to and causing cross-linking of DNA which ultimately triggers apoptosis . For example, cisplatin forms highly reactive, charged, platinum complexes which bind to nucleophilic groups (such as GC-rich sites) in DNA, inducing intra-strand and inter-strand DNA cross-links, as well as DNA-protein cross-links. These cross-links result in apoptosis and cell growth inhibition. When cells become resistant to cisplatin, the doses must be increased, and a large dose escalation can lead to severe multi-organ toxicities and intractable vomiting. The mechanisms of cisplatin drug resistance may include decreased intracellular accumulation of cisplatin and increased DNA repair, which also are drug resistance related pathways considered in this approach. Hence, a large biological interaction network was re-constructed by collecting from public databases DNA damage-related pathways, cell signalling-related pathways and the regulatory relationships between genes.
Combining pathway structure information mined from the re-constructed large biological interaction network with gene differential expression values, this study elucidates the particular platinum-based chemoresistance-associated pathways. Genes deemed relevant for chemotherapy resistance were also determined. Results of this study demonstrated that the identified pathways can increase chemotherapy resistance. This approach can identify pathways with a response dissimilar to that of known modes of biological action, and these new hypotheses can be used early in the drug development process to avert repeated and costly clinical trails. The major contributions of this approach are: (1) to reveal the phenomenon of chemoresistant mechanisms and related interactions between genes by combining pathway structure information with gene differential expressions; (2) to provide crossing validation candidate signature gene sets by calculating the values of betweenness centrality and degree in large complex networks; and (3) to propose new hypotheses for chemoresistant mechanisms through systems biology.
Materials and databases
This section covers the graph-theoretical properties, biological network constructions, and data sets.
Graphs and networks
Basic graph-theoretical properties and representations used by this study are as follow:
DEFINITION. A graph G = (V, E) = (V(G), E(G)) consists of a vertex set V(G) with vertices (or nodes) v i ∈ V(G), and an edge set E(G) with (v i , v j )∈E(G).
A graph G with biological information yields a biological network N B as follows:
DEFINITION. Let N B = (V, E, δ) be a network with vertices v∈V, edges e∈E, and a function δ: Y → P (Y = V ∪ E) that maps vertices and edges onto their respective properties p∈P.
Depending on the particular network representation, in a biological network vertex properties can include genes, proteins or chemical elements, and edge properties may refer to specific interactions, such as binding or regulating. The mapping δ: Y → P is at least subjective because for all p∈P, there exists a y∈Y with δ(y) = p.
Heterogeneous biological network integration and re-construction
To integrate heterogeneous biological networks, we identified three types of interactions relevant to a network: (i) protein interactions, such as protein-DNA binding or multi-state protein phosphorylation by kinases during signaling, (ii) regulatory reactions including co-expressions in regulons, and positive and negative regulation, and (iii) metabolic reactions. For protein interaction data, we parsed the Pathway Interaction Database (PID) , a highly-structured, curated collection of information about known biomolecular interactions and key cellular processes assembled into signaling pathways. Furthermore, the TRANSFAC  database provided information on regulatory reactions including co-expressions in regulons, and positive and negative regulation. For metabolic reaction data, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) [17, 18] to construct molecular interaction and reaction networks for metabolism. KEGG contains reaction networks of cellular processes, human diseases and drug development. Given this study's focus on identifying differential expression pathways during platinum-based chemotherapy drugs resistance, we determined diversified pathways correlated with cancer diseases, DNA repair, and metabolism for parsing and integration. Pathway selection criteria and the overall pathway sets collected in this study are listed in Additional file 1.
Peters et al. presented the results of a preliminary investigation into the molecular phenotype of patient-derived ovarian tumor cells in the context of sensitivity or resistance to carboplatin . They correlated chemoresponse data with gene expression patterns at the level of transcription. Primary cultures of cells derived from ovarian carcinomas of individual patients (n = 6) were characterized using the ChemoFx assay and classified as either carboplatin sensitive (n = 3) or resistant (n = 3). Three representative cultures of cells from each individual tumor were then subjected to Affymetrix gene chip analysis (n = 18) using U95A human gene chip arrays. They identified numbers of differentially expressed genes that define transcriptional differences between chemosensitive and chemoresistant cells and temporal responses to carboplatin expressed in an ex vivo setting. Gabriela et al. investigated the response to cisplatin of a panel of NSCLC cell lines and found an inverse correlation between sensitivity and damage formation resulting from this agent . Further analysis of multiple alternate cellular end-points including cell cycle analysis, apoptosis and gene expression changes, revealed cisplatin damage tolerance to be a mechanism of chemoresistance in this model system. Both gene expression data sets were available through the Gene Expression Omnibus (GEO) at NCBI  (GEO platform accession number GDS 1381 and GSE 6410, respectively).
Systems and implementation
Lists of seed nodes
User interested gene symbols
⟡ CEBPD (CCAAT/enhancer binding protein (C/EBP), delta)
⟡ SOD1(superoxide dismutase 1, soluble)
⟡ XRCC4 (X-ray repair complementing defective repair in Chinese hamster cells 4)
⟡ PTGS2 (prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase), (COX2))
⟡ RBMS3 (RNA binding motif, single stranded interacting protein)
⟡ STK39 (serine threonine kinase 39 (STE20/SPS1 homolog, yeast))
⟡ CUTL1 (cut-like homeobox 1)
⟡ CREG1 (cellular repressor of E1A-stimulated genes 1)
⟡ APBB2 (amyloid beta (A4) precursor protein-binding, family B, member 2)
⟡ ADAMTS1(ADAM metallopeptidase with thrombospondin type 1 motif, 1)
⟡ JAZF1(JAZF zinc finger 1)
⟡ JMJD2C (Jumonji domain 2)
⟡ MSI2 (musashi homolog 2 (Drosophila))
⟡ RABGAP1L (RAB GTPase activating protein 1-like)
⟡ NAV2 (neuron navigator 2)
⟡ ZMIZ1 (zinc finger, MIZ-type containing 1)
⟡ ZNF291 (SCAPER, S-phase cyclin A-associated protein in the ER)
⟡ ZRANB3 (zinc finger, RAN-binding domain containing 3)
⟡ CENTG2 (AGAP1, Homo sapiens ArfGAP with GTPase domain, ankyrin repeat and PH)
⟡ ATXN1 (ataxin 1-like)
⟡ THSD4 (thrombospondin, type I, domain containing 4)
⟡ CYP27C1 (cytochrome P450, family 27, subfamily C, polypeptide 1)
⟡ IL1A (interleukin 1, alpha)
⟡ IL1B (interleukin 1, beta)
⟡ NFKB1 (nuclear factor of kappa light polypeptide gene enhancer in B-cells 1)
⟡ NFKB2 (nuclear factor of kappa light polypeptide gene enhancer in B-cells 2)
⟡ CDK4 (cyclin-dependent kinase 4)
⟡ MCM2 (minichromosome maintenance complex component 2)
⟡ MCM4 (minichromosome maintenance complex component 4)
⟡ CDC45L (CDC45 cell division cycle 45-like (S. cerevisiae))
DNA damage genes
⟡ MYC (v-myc myelocytomatosis viral oncogene homolog (avian))
⟡ TP53 (tumor protein p53)
⟡ PCNA (proliferating cell nuclear antigen)
⟡ TP73 (tumor protein p73)
⟡ ATF4 (activating transcription factor 4 (tax-responsive enhancer element B67))
Scoring and filtering pathways
The main procedure of pathway scoring was calculating the differential expression values for the genes as metrics for weighted edges in the pathway. In this study, genes, proteins and other cellular components were coded as vertices which are connected by their edges to represent the interactions in the integrated biological network. However, the scoring step assumes weights on the edges for summing scores, and such edge weights must be calculated from the vertices' scores. Therefore, the identified pathway was subsequently transformed and represented as a line graph in which the edges represent genes, proteins and other cellular components, and vertices refer to interactions. Edges can then be directly weighted by gene expression values.
REMARK. Give a biological network N B , its line graph L(N B ) is a graph such that each vertex of L(N B ) represents an edge of N B ; and two vertices of L(N B ) are adjacent if and only if their corresponding edges share a common endpoint in N B .
Under this scoring function, the pathways of all sizes can be compared, with a high score indicating a biologically active pathway and pathways were then filtered by an assigned threshold score. In summary, the k-shortest path approach guarantees effective pathway identification through a particular set of seed nodes. The scoring functions (Formulas 1 and 2) contribute an appropriate constraint filtering pathways. Once the top n pathways have been selected, the analysis of pathways process can be performed.
Analyze the pathway signatures
where v 1 ∈V 1, v 2∈V 2 and e 1∈E 1, e 2∈E 2. In other words, the intersection between all v 1 ∈V 1 and v 2∈V 2, and the intersection between corresponding edges e 1∈E 1, e 2∈E 2 under the condition that ∀ v 1 , v 2 : δ(v 1 ) = δ(v 2 ) and ∀ e 1 , e 2 : δ(e 1 ) = δ(e 2 ). An edge e∈ N 1 B is selected if both the originating and terminating vertices have δ-corresponding vertices in N 2 B.
Formula (5) indicates the degree centrality of an undirected graph. As for a vertex representing the gene (or protein) in an undirected graph, the higher the degree, the more reactions it interacts with and the more important the vertex is.
Results and Discussion
Lists of # of genes and relations in integrated database
Statistics information of integrated databases
# of gene
# of relation
Statistics information on each of the three databases
# of TFs
# of target gene parsed
# of pairing regulate relation parsed
# of pathways
# of gene, protein, enzyme parsed
# of relation parsed
PID + KEGG
Significant pathways in ovarian cancer
Genes identified in figure 3 with p-value < 0.05 by t-test
(mean = 3.8E-4)
(mean = 9.71E-4)
(v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog, also called KIT or C-kit receptor)
(growth factor receptor-bound protein 2)
(v-akt murine thymoma viral oncogene homolog 2)
(phosphoinositide-3-kinase, catalytic, gamma polypeptide)
As indicated in Figure 3, the PI3K (Phosphatidylinositol 3-kinas)/AKT gene family are involved as well. The PI3K pathway is stimulated as a physiological consequence of many growth factors and regulators. In addition, the activation of the PI3K pathway results in disturbances of cell growth and survival control, which contributes to a competitive growth advantage, metastatic competence and, frequently, therapy resistance . Therefore, this pathway is an attractive target for the development of novel anticancer agents. The PI3K/Akt cascade plays an important role in the resistance of ovarian cancer cells to cisplatin in vitro. Ohta et al. investigated whether the inhibition of PI3K increased the efficacy of cisplatin in an in vivo ovarian cancer model . Blocking the PI3K/Akt cascade with a PI3K inhibitor (wortmannin) increased the efficacy of cisplatin-induced inhibition of intra-abdominal dissemination and production of ascites in athymic nude mice inoculated ip with the Caov-3 human ovarian cancer cell line. In addition, wortmannin increased the efficacy of cisplatin-induced apoptosis in tumors cells. Ohta et al. also confirmed that wortmannin blocked Akt phosphorylation and the downstream targets of the PI3K/Akt cascade, such as BAD (Bcl-2-associated death protein) and nuclear factor-kB in vivo by immunohistochemical staining and Western blotting. Moreover, Lee et al. used human ovarian cancer cell OVCAR-3 and cisplatin-resistant subclone OVCAR-3/CDDP cells to study the roles of PIK3CA (alias name PI3K) and PTEN on the resistance of human ovarian cancer cells to cisplatin-induced apoptosis . They systematically examined the expressions of apoptosis regulating proteins and PI3K/Akt signaling proteins, finding that OVCAR-3/CDDP cells were 4.8-fold more resistant to cisplatin than OVCAR-3 cells following 72 h exposure to the drug. This resistance correlated with reduced susceptibility to cisplatin-induced apoptosis. Apoptotic proteins were differentially expressed in the OVCAR-3/CDDP cells, resulting in the inhibition of Bax translocalization. Their experimental results indicate that the development of resistance in OVCAR-3 cells is derived from increasing PIK3CA transcription and reducing of PTEN expression. These alterations confer resistance to cisplatin through the activation of PI3K. These in vivo results support the proposition that our algorithm can identify chemoresistance-associated pathways.
In Figure 3, genes are represented by red squares indicating the connected nodes; that is, these genes connect two pathways. Connected nodes are key factors for joining two or more metabolic pathways or passing down signals. Taking GRB2 (Growth factor receptor-bound protein 2) as an example, L'Esperance et al.  found that upregulated genes in post chemotherapy ovarian tumors included a substantial number of genes with previously implicated in mechanisms of chemoresistance including COX2 and tumorigenesis, GRB2. As seen in Figure 3, AKT was also identified as a connected gene, and had significant betweenness centrality and degree values (shown in Table 3), indicating that AKT has potential to act as a "hub node" in biological interaction networks and be involved in chemoresistant mechanisms as well .
Significant results following pathway intersections
The main analysis of this experiment focused on whether different cancers identical chemoresistant mechanisms and whether these chemoresistant mechanisms share some genes in common. After performing intersection by Formula (3), 88 pathways remained (the Additional file 3). The following sections include further analysis.
The major goals of this analysis were: (1) to explore pathways or genes involved in chemoresistant mechanisms; (2) to delineate how these genes or pathways interact with each other; (3) to test whether the p-values of the genes in this pathway are significantly differentially expressed; (4) to analyze the betweenness centrality (Formula 4) and degree (Formula 5) values of genes in this pathway; and (5) to identify the chemoresistance-associated genes.
Genes identified in figure 4 with p-value < 0.05 by t-test
(mean = 3.8E-4)
(mean = 9.71E-4)
(v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog)
(tumor protein p53)
(v-akt murine thymoma viral oncogene homolog)
(glycogen synthase kinase 3 beta)
(wingless-type MMTV integration site family)
(phosphatase and tensin homolog)
(dishevelled, dsh homolog 1 (Drosophila))
(hairy and enhancer of split 1, (Drosophila))
As indicated in Figure 4, the connected gene DVL (disheveled, dsh homolog) connects two critical pathways: the WNT signaling pathway and the Notch signaling pathway. Gatcliffe et al. suggested that WNT signaling plays a role in ovarian tumorigenesis . The WNT pathway participates in many physiologic events in embryogenesis and adult homeostasis including cell fate specification, control of proliferation, and migration. WNT signaling has a significant influence on the embryonic development of the ovary and is also involved in normal follicular development and ovarian function [46, 47]. The WNT signaling pathway is involved in ovarian cancer development via multiple, diverse mechanisms, including gene mutations and changes in pathway components such as extracellular inhibitors and intranuclear transcription cofactors. According to Wang et al., the WNT signaling pathway passes signals to the Notch signaling pathway . The Notch signaling pathway is known to be responsible for maintaining a balance between cell proliferation and death and, as such, plays an important role in the formation of many types of human tumors. In our computational results, WNT signaling connects the Notch signaling pathway through DVL gene, which indicates DVL is a critical gene for passing signals through pathways. In addition, the computational evidence provided by the values of betweenness centrality, degree and p-value indicate that DVL may be involved in platinum-based chemoresistance.
The signature chemoresistance-associated genes
As shown in Figure 4, CEBPD interacts with KRAS as well and led to a domino effect that may cause chemoresistance. It was found that mutations in this candidate gene, KRAS, are one of the most frequent genetic abnormalities in ovarian carcinoma . In other words, KRAS mutation is a common event in ovarian cancer primarily in carcinomas characterized by lower grade, lower FIGO stage, and mucinous histotype. The KRAS mutational status is not a prognostic factor for patients treated with standard therapy. However, in line with experience from colorectal cancer and NSCLC, it may prove important for predicting the response to EGFR-targeted therapies . Thus far, there is no biological evidence directly indicating KRAS gene is involved in platinum-based chemoresistance but, from the computational experiment results, we may infer that KRAS plays a critical role in chemoresistance. More computational results with high scores of intersected pathways are provided in Additional file 4, and analysis of these data may reveal new chemoresistant mechanisms.
Although platinum-based chemotherapeutic agents are widely used for the treatment of endometrial, cervical and breast cancers, chemoresistance caused by molecular mechanisms still remains a major therapeutic problem. The platinum-based anti-tumor agent is a DNA-reactive reagent which causes cell cycle arrest at various phases in the cell cycle and induces apoptosis. Hence, the drug active pathway plays an important role in drug resistance in the cellular system. It is also a very important issue in the identification and validation of drug target genes by supplying their interactive relationships. This approach elucidated the particular chemoresistance-associated pathways in large biological interaction networks. Genes deemed relevant for chemotherapy resistance were also likewise determined. After identifying the chemoresistance-associated pathways, the scoring procedure filtered the significant pathways according to the genes' differential expressions. Consequently, this allowed for the identification of dissimilarities between the responses of chemosensitivity to the chemoresistance expression cancer data. In particular, we identified genes and pathway components such as the Hedgehog signalling pathway, the WNT signalling pathway, and the notch signalling pathway, that are relevant to chemoresistance for ovarian and lung cancer. The advantage of comparison analysis is in recognizing the divergent and convergent mechanisms of chemoresistance between cancers. Through systems biology methods, biologists can perform a comprehensive survey to upon which to base hypothetical assumptions.
The advantages of pathway intersections analysis include: revealing whether different cancers have same chemoresistant mechanisms, and determining whether some common genes involved in these chemoresistant mechanisms. As expected, we observed a great deal of correspondence between the response interactions of ovarian and lung cancer expression data by intersecting pathways. The analysis of platinum-based chemotherapeutic agents revealed insights into common responses among the chemoresistant mechanisms as well as the candidate genes such as Bcl-2, AHR and, most importantly, SOD1. The results also indicate that the WNT signaling pathway, the Notch signaling pathway and the FAK pathway are involved in ovarian and lung chemoresistance. Therefore, further analysis of our computational experiment results may reveal additional chemoresistance mechanisms, which indicates this approach can anticipate target identification and chemoresistance in the future development of cancer drugs.
Pathways with a dissimilar response to that of known modes of biological action can be easily identified early in the drug development process to avert repeated and costly clinical trails. This approach reveals chemoresistance-associated pathways in scilicon and enables easier comparisons with the generated graphs. Furthermore, by exploring signature genes involved in chemoresistance mechanisms, this approach sheds light on how these genes or pathways interact with each other, and provides analysis of the betweenness centrality and degree values of genes in pathways. In summary, this method is sufficiently flexible to accommodate various types of biological network information and experimental data, and offers not only insights into the mechanisms of chemoresistance but also provides information on potential candidate target genes for future drug-development efforts.
This research work was supported in part by Research Grant NSC98-2218-E-231-002 from the National Science Council, Taiwan. We thank Dr. Lan Chun Tu for the advice and critical reading of the manuscript.
- Bosch TM: Pharmacogenomics of drug-metabolizing enzymes and drug transporters in chemotherapy. Methods Mol Biol. 2008, 448: 63-76. full_text.View ArticlePubMedGoogle Scholar
- Meisel P: Arylamine N-acetyltransferases and drug response. Pharmacogenomics. 2002, 3 (3): 349-366. 10.1517/14622418.104.22.1689.View ArticlePubMedGoogle Scholar
- Gottesman MM, Fojo T, Bates SE: Multidrug resistance in cancer: role of ATP-dependent transporters. Nat Rev Cancer. 2002, 2 (1): 48-58. 10.1038/nrc706.View ArticlePubMedGoogle Scholar
- Hembruff SL, Laberge ML, Villeneuve DJ, Guo B, Veitch Z, Cecchetto M, Parissenti AM: Role of drug transporters and drug accumulation in the temporal acquisition of drug resistance. BMC Cancer. 2008, 8: 318-10.1186/1471-2407-8-318.View ArticlePubMedPubMed CentralGoogle Scholar
- Torigoe T, Izumi H, Ishiguchi H, Yoshida Y, Tanabe M, Yoshida T, Igarashi T, Niina I, Wakasugi T, Imaizumi T, et al: Cisplatin resistance and transcription factors. Curr Med Chem Anticancer Agents. 2005, 5 (1): 15-27. 10.2174/1568011053352587.View ArticlePubMedGoogle Scholar
- Liu LZ, Zhou XD, Qian G, Shi X, Fang J, Jiang BH: AKT1 amplification regulates cisplatin resistance in human lung cancer cells through the mammalian target of rapamycin/p70S6K1 pathway. Cancer Res. 2007, 67 (13): 6325-6332. 10.1158/0008-5472.CAN-06-4261.View ArticlePubMedGoogle Scholar
- Wu C, Wangpaichitr M, Feun L, Kuo MT, Robles C, Lampidis T, Savaraj N: Overcoming cisplatin resistance by mTOR inhibitor in lung cancer. Mol Cancer. 2005, 4 (1): 25-10.1186/1476-4598-4-25.View ArticlePubMedPubMed CentralGoogle Scholar
- Kang HC, Kim IJ, Park JH, Shin Y, Ku JL, Jung MS, Yoo BC, Kim HK, Park JG: Identification of genes with differential expression in acquired drug-resistant gastric cancer cells using high-density oligonucleotide microarrays. Clin Cancer Res. 2004, 10 (1 Pt 1): 272-284. 10.1158/1078-0432.CCR-1025-3.View ArticlePubMedGoogle Scholar
- Tanaka T, Tanimoto K, Otani K, Satoh K, Ohtaki M, Yoshida K, Toge T, Yahata H, Tanaka S, Chayama K, et al: Concise prediction models of anticancer efficacy of 8 drugs using expression data from 12 selected genes. Int J Cancer. 2004, 111 (4): 617-626. 10.1002/ijc.20289.View ArticlePubMedGoogle Scholar
- Nakatsu N, Yoshida Y, Yamazaki K, Nakamura T, Dan S, Fukui Y, Yamori T: Chemosensitivity profile of cancer cell lines and identification of genes determining chemosensitivity by an integrated bioinformatical approach using cDNA arrays. Mol Cancer Ther. 2005, 4 (3): 399-412.PubMedGoogle Scholar
- Cabusora L, Sutton E, Fulmer A, Forst CV: Differential network expression during drug and stress response. Bioinformatics. 2005, 21 (12): 2898-2905. 10.1093/bioinformatics/bti440.View ArticlePubMedGoogle Scholar
- Riedel RF, Porrello A, Pontzer E, Chenette EJ, Hsu DS, Balakumaran B, Potti A, Nevins J, Febbo PG: A genomic approach to identify molecular pathways associated with chemotherapy resistance. Mol Cancer Ther. 2008, 7 (10): 3141-3149. 10.1158/1535-7163.MCT-08-0642.View ArticlePubMedGoogle Scholar
- Rabik CA, Dolan ME: Molecular mechanisms of resistance and toxicity associated with platinating agents. Cancer Treat Rev. 2007, 33 (1): 9-23. 10.1016/j.ctrv.2006.09.006.View ArticlePubMedGoogle Scholar
- Kelland L: The resurgence of platinum-based cancer chemotherapy. Nat Rev Cancer. 2007, 7 (8): 573-584. 10.1038/nrc2167.View ArticlePubMedGoogle Scholar
- Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH: PID: the Pathway Interaction Database. Nucleic Acids Res. 2009, D674-679. 10.1093/nar/gkn653. 37 DatabaseGoogle Scholar
- Matys V, Fricke E, Geffers R, Gossling E, Haubrock M, Hehl R, Hornischer K, Karas D, Kel AE, Kel-Margoulis OV, et al: TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 2003, 31 (1): 374-378. 10.1093/nar/gkg108.View ArticlePubMedPubMed CentralGoogle Scholar
- Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, et al: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, D480-484. 36 DatabaseGoogle Scholar
- Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006, D354-357. 10.1093/nar/gkj102. 34 DatabaseGoogle Scholar
- Peters D, Freund J, Ochs RL: Genome-wide transcriptional analysis of carboplatin response in chemosensitive and chemoresistant ovarian cancer cells. Mol Cancer Ther. 2005, 4 (10): 1605-1616. 10.1158/1535-7163.MCT-04-0311.View ArticlePubMedGoogle Scholar
- Almeida GM, Duarte TL, Farmer PB, Steward WP, Jones GD: Multiple end-point analysis reveals cisplatin damage tolerance to be a chemoresistance mechanism in a NSCLC model: implications for predictive testing. Int J Cancer. 2008, 122 (8): 1810-1819. 10.1002/ijc.23188.View ArticlePubMedGoogle Scholar
- Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, et al: NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009, D885-890. 10.1093/nar/gkn764. 37 DatabaseGoogle Scholar
- Yen JY: Finding the K Shortest Loopless Paths in a Network. Management Science. 1971, 17 (11): 712-716. 10.1287/mnsc.17.11.712.View ArticleGoogle Scholar
- Hour TC, Lai YL, Kuan CI, Chou CK, Wang JM, Tu HY, Hu HT, Lin CS, Wu WJ, Pu YS, et al: Transcriptional up-regulation of SOD1 by CEBPD: a potential target for cisplatin resistant human urothelial carcinoma cells. Biochem Pharmacol. 2010, 80 (3): 325-334. 10.1016/j.bcp.2010.04.007.View ArticlePubMedPubMed CentralGoogle Scholar
- Wood RD, Mitchell M, Lindahl T: Human DNA repair genes, 2005. Mutat Res. 2005, 577 (1-2): 275-283. 10.1016/j.mrfmmm.2005.03.007.View ArticlePubMedGoogle Scholar
- Kohno K, Uchiumi T, Niina I, Wakasugi T, Igarashi T, Momii Y, Yoshida T, Matsuo K, Miyamoto N, Izumi H: Transcription factors and drug resistance. Eur J Cancer. 2005, 41 (16): 2577-2586. 10.1016/j.ejca.2005.08.007.View ArticlePubMedGoogle Scholar
- Gery S, Tanosaki S, Hofmann WK, Koppel A, Koeffler HP: C/EBPdelta expression in a BCR-ABL-positive cell line induces growth arrest and myeloid differentiation. Oncogene. 2005, 24 (9): 1589-1597. 10.1038/sj.onc.1208393.View ArticlePubMedGoogle Scholar
- Huang AM, Montagna C, Sharan S, Ni Y, Ried T, Sterneck E: Loss of CCAAT/enhancer binding protein delta promotes chromosomal instability. Oncogene. 2004, 23 (8): 1549-1557. 10.1038/sj.onc.1207285.View ArticlePubMedGoogle Scholar
- Sanford DC, DeWille JW: C/EBPdelta is a downstream mediator of IL-6 induced growth inhibition of prostate cancer cells. Prostate. 2005, 63 (2): 143-154. 10.1002/pros.20159.View ArticlePubMedGoogle Scholar
- Thangaraju M, Rudelius M, Bierie B, Raffeld M, Sharan S, Hennighausen L, Huang AM, Sterneck E: C/EBPdelta is a crucial regulator of pro-apoptotic gene expression during mammary gland involution. Development. 2005, 132 (21): 4675-4685. 10.1242/dev.02050.View ArticlePubMedGoogle Scholar
- Ideker T, Ozier O, Schwikowski B, Siegel AF: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 2002, 18 (Suppl 1): S233-240.View ArticlePubMedGoogle Scholar
- Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B, Garapati P, Hemish J, Hermjakob H, Jassal B, et al: Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 2009, D619-622. 10.1093/nar/gkn863. 37 DatabaseGoogle Scholar
- Eltabbakh GH, Awtrey CS: Current treatment for ovarian cancer. Expert Opin Pharmacother. 2001, 2 (1): 109-124. 10.1517/14656522.214.171.124.View ArticlePubMedGoogle Scholar
- Hibi K, Takahashi T, Sekido Y, Ueda R, Hida T, Ariyoshi Y, Takagi H: Coexpression of the stem cell factor and the c-kit genes in small-cell lung cancer. Oncogene. 1991, 6 (12): 2291-2296.PubMedGoogle Scholar
- Krystal GW, Hines SJ, Organ CP: Autocrine growth of small cell lung cancer mediated by coexpression of c-kit and stem cell factor. Cancer Res. 1996, 56 (2): 370-376.PubMedGoogle Scholar
- Raspollini MR, Amunni G, Villanucci A, Baroni G, Taddei A, Taddei GL: c-KIT expression and correlation with chemotherapy resistance in ovarian carcinoma: an immunocytochemical study. Ann Oncol. 2004, 15 (4): 594-597. 10.1093/annonc/mdh139.View ArticlePubMedGoogle Scholar
- Hennessy BT, Smith DL, Ram PT, Lu Y, Mills GB: Exploiting the PI3K/AKT pathway for cancer drug discovery. Nat Rev Drug Discov. 2005, 4 (12): 988-1004. 10.1038/nrd1902.View ArticlePubMedGoogle Scholar
- Ohta T, Ohmichi M, Hayasaka T, Mabuchi S, Saitoh M, Kawagoe J, Takahashi K, Igarashi H, Du B, Doshida M, et al: Inhibition of phosphatidylinositol 3-kinase increases efficacy of cisplatin in in vivo ovarian cancer models. Endocrinology. 2006, 147 (4): 1761-1769. 10.1210/en.2005-1450.View ArticlePubMedGoogle Scholar
- Lee S, Choi EJ, Jin C, Kim DH: Activation of PI3K/Akt pathway by PTEN reduction and PIK3CA mRNA amplification contributes to cisplatin resistance in an ovarian cancer cell line. Gynecol Oncol. 2005, 97 (1): 26-34. 10.1016/j.ygyno.2004.11.051.View ArticlePubMedGoogle Scholar
- L'Esperance S, Popa I, Bachvarova M, Plante M, Patten N, Wu L, Tetu B, Bachvarov D: Gene expression profiling of paired ovarian tumors obtained prior to and following adjuvant chemotherapy: molecular signatures of chemoresistant tumors. Int J Oncol. 2006, 29 (1): 5-24.PubMedGoogle Scholar
- Hintze A, Adami C: Evolution of complex modular biological networks. PLoS Comput Biol. 2008, 4 (2): e23-10.1371/journal.pcbi.0040023.View ArticlePubMedPubMed CentralGoogle Scholar
- Siddik ZH: Cisplatin: mode of cytotoxic action and molecular basis of resistance. Oncogene. 2003, 22 (47): 7265-7279. 10.1038/sj.onc.1206933.View ArticlePubMedGoogle Scholar
- Gagnon V, Mathieu I, Sexton E, Leblanc K, Asselin E: AKT involvement in cisplatin chemoresistance of human uterine cancer cells. Gynecol Oncol. 2004, 94 (3): 785-795. 10.1016/j.ygyno.2004.06.023.View ArticlePubMedGoogle Scholar
- Ali IU, Schriml LM, Dean M: Mutational spectra of PTEN/MMAC1 gene: a tumor suppressor with lipid phosphatase activity. J Natl Cancer Inst. 1999, 91 (22): 1922-1932. 10.1093/jnci/91.22.1922.View ArticlePubMedGoogle Scholar
- Myers MP, Pass I, Batty IH, Van der Kaay J, Stolarov JP, Hemmings BA, Wigler MH, Downes CP, Tonks NK: The lipid phosphatase activity of PTEN is critical for its tumor supressor function. Proc Natl Acad Sci USA. 1998, 95 (23): 13513-13518. 10.1073/pnas.95.23.13513.View ArticlePubMedPubMed CentralGoogle Scholar
- Gatcliffe TA, Monk BJ, Planutis K, Holcombe RF: Wnt signaling in ovarian tumorigenesis. Int J Gynecol Cancer. 2008, 18 (5): 954-962. 10.1111/j.1525-1438.2007.01127.x.View ArticlePubMedGoogle Scholar
- Ricken A, Lochhead P, Kontogiannea M, Farookhi R: Wnt signaling in the ovary: identification and compartmentalized expression of wnt-2, wnt-2b, and frizzled-4 mRNAs. Endocrinology. 2002, 143 (7): 2741-2749. 10.1210/en.143.7.2741.View ArticlePubMedGoogle Scholar
- Yao HH, Matzuk MM, Jorgez CJ, Menke DB, Page DC, Swain A, Capel B: Follistatin operates downstream of Wnt4 in mammalian ovary organogenesis. Dev Dyn. 2004, 230 (2): 210-215. 10.1002/dvdy.20042.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang Z, Li Y, Banerjee S, Sarkar FH: Emerging role of Notch in stem cells and cancer. Cancer Lett. 2009, 279 (1): 8-12. 10.1016/j.canlet.2008.09.030.View ArticlePubMedGoogle Scholar
- Dobrzycka B, Terlikowski SJ, Kowalczuk O, Niklinska W, Chyczewski L, Kulikowski M: Mutations in the KRAS gene in ovarian tumors. Folia Histochem Cytobiol. 2009, 47 (2): 221-224. 10.2478/v10042-009-0039-6.View ArticlePubMedGoogle Scholar
- Auner V, Kriegshauser G, Tong D, Horvat R, Reinthaller A, Mustea A, Zeillinger R: KRAS mutation analysis in ovarian samples using a high sensitivity biochip assay. BMC Cancer. 2009, 9: 111-10.1186/1471-2407-9-111.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/4/23/prepub
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