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ABCC5, ERCC2, XPA and XRCC1 transcript abundance levels correlate with cisplatin chemoresistance in non-small cell lung cancer cell lines

Abstract

Background

Although 40–50% of non-small cell lung cancer (NSCLC) tumors respond to cisplatin chemotherapy, there currently is no way to prospectively identify potential responders. The purpose of this study was to determine whether transcript abundance (TA) levels of twelve selected DNA repair or multi-drug resistance genes (LIG1, ERCC2, ERCC3, DDIT3, ABCC1, ABCC4, ABCC5, ABCC10, GTF2H2, XPA, XPC and XRCC1) were associated with cisplatin chemoresistance and could therefore contribute to the development of a predictive marker. Standardized RT (StaRT)-PCR, was employed to assess these genes in a set of NSCLC cell lines with a previously published range of sensitivity to cisplatin. Data were obtained in the form of target gene molecules relative to 106 β-actin (ACTB) molecules. To cancel the effect of ACTB variation among the different cell lines individual gene expression values were incorporated into ratios of one gene to another. Each two-gene ratio was compared as a single variable to chemoresistance for each of eight NSCLC cell lines using multiple regression. In an effort to validate these results, six additional lines then were evaluated.

Results

Following validation, single variable models best correlated with chemoresistance (p < 0.001), were ERCC2/XPC, ABCC5/GTF2H2, ERCC2/GTF2H2, XPA/XPC and XRCC1/XPC. All single variable models were examined hierarchically to achieve two variable models. The two variable model with the highest correlation was (ABCC5/GTF2H2, ERCC2/GTF2H2) with an R2 value of 0.96 (p < 0.001).

Conclusion

These results provide markers suitable for assessment of small fine needle aspirate biopsies in an effort to prospectively identify cisplatin resistant tumors.

Background

Non-small cell lung cancer (NSCLC) is the most common type of bronchogenic carcinoma. Although chemotherapeutic regimens with greater efficacy continue to be developed, the best regimens presently give an overall response rate of only 30–50%. Lack of response is attributable to resistance that is present de novo or develops in response to treatment. If the resistance to drugs could be surmounted or if the most effective drug candidates for treatment could be better determined, the impact in terms of survival would be substantial. Because mechanisms of chemoresistance likely involve multiple gene products, we hypothesize that patterns of individual gene expression and/or indices comprising the expression values of multiple genes will provide more effective markers of chemoresistant NSCLC tumors than values of individual genes.

Currently, cisplatin and carboplatin are among the most widely used cytotoxic anticancer drugs. However, resistance to these drugs through de novo or induced mechanisms undermines their curative potential [1]. Recently, understanding regarding potential modes of chemoresistance to platinum compounds has been obtained through studies correlating cytotoxicity with nucleotide excision-repair (NER) [2–7] or drug uptake/efflux [7–13]. In this study, we investigated whether de novo gene expression differences are correlated with a predisposition of NSCLC tumors to chemoresistance.

Current advances in technology, including microarrays and quantitative RT-PCR methods, enable classification of cancer types on the basis of TA levels rather than histomorphology [14, 15]. For example, these techniques enable the discovery of predictive markers based on TA profiles. Microarray screening analysis currently is being investigated to predict chemotherapeutic sensitivity based on TA profiles [16–18]. An advantage of microarray analysis is that thousands of genes may be simultaneously evaluated. However, it is generally recognized that, due to lack of standardization, relatively low sensitivity and relatively poor lower thresholds of detection, microarray assessments need to be confirmed with follow-up quantitative methods. StaRT-PCR is a method that enables rapid, sensitive, reproducible, standardized, quantitative measurements for many genes simultaneously [19, 49, 50].

Briefly, in StaRT-PCR, the TA level of each gene is made relative to an internal standard (IS) within a standardized mixture of internal standards (SMIS). Known concentrations of these mixtures are combined with cDNA samples in a master mixture for PCR amplification. This enables quantitative measurement of gene expression while controlling for inter-sample, inter-experimental and loading differences. With StaRT-PCR, due to the presence of the SMIS, the measurements are quantitative and quality-controlled when measured either kinetically or at endpoint [51, 52]. In other words, measurement of each TA value relative to a known quantity of internal standard controls for variation in amplification efficiency in early, log-linear, and plateau phases of PCR [53].

In an initial survey, StaRT-PCR was used to measure expression of 35 genes involved in DNA repair, multi-drug resistance, cell cycling and apoptosis in two cell lines previously reported to be the least (H460) and most (H1435) chemoresistant among 20 NSCLC cell lines [20]. It was determined that genes involved in DNA repair (ERCC2, XRCC1) and drug influx/efflux (ABCC5) were associated with chemoresistance. The number of genes from each of these two categories was expanded to include additional representative genes associated with generalized DNA damage recognition and repair (DDIT3), associated specifically with NER (LIG1, ERCC3, GTF2H2, XPA, XPC), or associated with drug transport (ABCC1, ABCC4, ABCC10). Expression of these twelve genes was measured in eight NSCLC cell lines with variable cisplatin resistance [20]. StaRT-PCR data were obtained using ACTB as a reference gene. Thus, data were reported in the form of mRNA molecules/106 ACTB molecules. These data then were combined into interactive transcript abundance indices (ITAI) by placing one or more genes directly associated with the phenotype on the numerator and one or more genes negatively associated with the phenotype on the denominator [19, 21]. It is reasonable to expect that optimal predictors of phenotypes are more likely to be discovered among ITAI than among expression levels of individual genes. This has been demonstrated for certain cancer-related phenotypes [19, 21–23]. A further advantage of ITAI is that they control for previously observed variation in the reference gene value (in this case, ACTB) from one cell line to another [19, 21]. When a single gene in the numerator is divided by another single gene in the denominator, the reference value mathematically cancels out. The ITAI values were compared to cisplatin chemoresistance among the eight NSCLC cell lines with variable resistance. Results then were validated in an additional six NSCLC cell lines.

Results

Reproducibility

Among the gene expression measurements for which three or more replicate values were obtained, the mean coefficient of variation was 38.5% (see Additional file 1). This is similar to the reproducibility observed in other gene expression studies using the StaRT-PCR method [19, 22]. Recently, through implementation of robotic liquid handlers, automation software, and standard operating procedures in the NCI funded (CA95806) Standardized Expression Measurement (SEM) Center, variation among replicates has been reduced to a CV of less than 10% [50].

Individual gene expression measurements and chemoresistance

The results of the direct comparison of individual gene expression mean values versus cisplatin chemoresistance are presented in Table 1. All StaRT-PCR data values were in the form of molecules/106 ACTB molecules (see Additional file 1). For 8/12 genes assessed, the correlation was significant (p < 0.05).

Table 1 Correlation between each of twelve putative chemoresistance transcript abundance values and chemoresistance among NSCLC cell lines. Eight of the 12 selected multi-drug resistance and DNA repair genes were significantly correlated with chemoresistance among the first group of 8 NSCLC cell lines evaluated (Group 1). In order to validate these results, an additional six lines were evaluated (Group 2).

Establishment of inter-active transcript abundance indices

ITAI were established as balanced ratios comprising every possible combination with one gene divided by the TA value of another gene for data obtained from each of the initial eight NSCLC cell lines (Group 1). Each TA value was calculated as molecules/106 ACTB molecules. Thus, in these ITAI, the effect of the reference gene, ACTB, is cancelled. For example: ERCC2 molecules/106 ACTB molecules ÷ XPC molecules/106 ACTB molecules = ERCC2 molecules/XPC molecules. Bivariate analysis of each two-gene ratio versus corresponding cisplatin IC50 chemoresistance value was conducted among the eight cell lines (see Additional file 2). There were 12 genes assessed and 11 sets of ratios for each gene as the numerator resulting in 132 ratios. The data from bivariate analyses then were ranked in descending order such that the ratio set listed first was that for which the mean value for correlation with chemoresistance was highest, and the ratio set listed last was that for which the mean r value for correlation with chemoresistance was lowest. Thus, the ratio set with ERCC2 in the numerator is listed first because the mean r value for the ratios between ERCC2 and each of the other eleven genes was the most positive among the twelve genes evaluated. In contrast, the ratio set with XPC in the numerator is listed last because the ratios between XPC and each of the other 11 genes had the most negative correlation with chemoresistance.

Modelling of gene expression with chemoresistance

The ratios ERCC2/XPC, ABCC5/GTF2H2, ERCC2/XRCC1, ERCC2/GTF2H2, XPA/XPC, XRCC1/XPC, and ABCC5/XPC were the best (i.e. those single variable models with highest R2 identified in the initial eight NSCLC cell lines by simple linear regression (see Additional file 2). The effect of adding a second variable into the model was then assessed. The best two variable model was (ABCC5/GTF2H2, ERCC2/GTF2H2) with an R2 value of 0.96.

Validation of Models

We tested our single and two variable models in an additional six NSCLC cell lines (Table 2). In statistical analysis of the combined data for all 14 NSCLC cell lines, the p value improved or stayed the same for three of the single variable models (ERCC2/XPC, ABCC5/GTF2H2, XRCC1/XPC), as well as the two variable model. The decline in p value for ERCC2/GTF2H2 and XPA/XPC was not significant. In contrast, ERCC2/XRCC1 was no longer significantly associated with chemoresistance, and the p value declined substantially for ABCC5/XPC.

Table 2 Bivariate correlation between two-transcript abundance ratios and chemoresistance in a validation set of NSCLC cell lines. The ratios best correlated with chemoresistance from Additional file 2 were evaluated in an additional six lines. The effect of adding a second variable into the model was assessed. ERCC2/XPC was no longer correlated with chemoresistance and ABCC5/XPC had substantially lower p value. The other single variable models and the two variable model were validated.

Discussion

The results obtained by measuring gene expression with StaRT-PCR, incorporating values for individual genes into ITAI, and correlating ITAI with chemoresistance led us to propose several models as potential predictors of cisplatin chemoresistance in cultured NSCLC cells. These models comprise genes that have been associated with cisplatin chemoresistance in previous studies including ABCC5 [13], and XPA [4, 24].

Experimental results suggest that increased expression of ABCC5, also known as MRP5, is associated with exposure to platinum drugs in lung cancer in vivo and/or the chronic stress response to xenobiotics [13]. Thus, increased resistance to platinum drugs with increased ABCC5 levels may be due to glutathione S-platinum complex efflux. Increased efflux of platinum drugs could result in lower levels of drug available to form damaging DNA-platinum drug adducts.

XPA and ERCC2 are components of the nucleotide excision repair (NER) mechanism, which generally is recognized as the major repair response to DNA damage induced by chemotherapeutic agents such as cisplatin [1, 3, 7]. In NER, XPA is the main DNA lesion recognition protein [25], is the key element in assembly of the NER complex by recruiting several other proteins to the lesion site [26] and XPA levels are rate-limiting for NER [4, 27]. Enhanced NER gene expression is a major cause of resistance to cisplatin and other DNA-damaging chemotherapeutic agents [3, 28] and over expression of the XPA gene component of NER has been associated with resistance to cisplatin in human ovarian cancer [4, 24]. ERCC2 specifically is a component of the transcription factor IIH (TFIIH) that consists of seven polypeptides [29, 30] and in its entirety is a repair factor [31–33]. In NER, ERCC2 (or XPD) is essential for TFIIH helicase activity [34] and it has been demonstrated more recently that ERCC2 interacts specifically with GTF2H2 (or p44) and that this interaction results in the stimulation of the 5' to 3' helicase activity [35]. In at least some other tissues, ERCC1 is associated with cisplatin resistance, while ERCC2 is not [36, 37]. Thus, our data support the importance of excision repair in cisplatin resistance, but suggest that there is inter-tissue variation in the excision repair genes that are responsible for de novo cisplatin resistance.

XRCC1 has long been recognized as a key component of the base excision repair (BER) pathway, acting as a "scaffold" for the coordination of other BER proteins at the sites of base damage during repair [38–40]. It has been shown that polymorphisms in XRCC1, while in themselves are not associated with increased risk of lung cancer, have shown an increased risk of lung cancer in a supermultiplicative manner when associated with polymorphisms in another component of BER, poly (ADP-ribose) polymerase family, member 1 transfersase (PARP1) [41]. XRCC1 has also recently been proposed as a component of an alternative nonhomologous end-joining route of DNA double-stranded breaks (DSBs), that complements the predominant repair pathway of DNA-dependent protein kinase (DNA-PK) and X-ray repair complementing defective repair in Chinese hamster cells 4 (XRCC4)-DNA ligase IV complex [42]. Although the NER pathway is the major repair mechanism for cisplatin-DNA adducts, our data supports the proposal of overlapping repair pathways involved in alternative repair of cisplatin adducts, such as the BER pathway. XRCC1 may also be involved in the repair of other types of DNA damage caused by cisplatin including DSBs.

Selection of a stable reference for the amount of sample loaded for each gene expression measurement is important to ensure measurement accuracy and reproducibility. With microarray analysis, because thousands of genes are assessed simultaneously, an index of all genes measured provides a stable reference for the amount of sample loaded from one microarray to another. In quantitative RT-PCR studies, typically, a single non-regulated gene is used as a loading reference, such as ACTB, GAPD, cyclophilin or ribosomal RNA. However, all of these genes have been reported to vary among multiple samples. One way to assess inter-sample variation in reference gene expression among multiple samples is to compare variation between two reference genes. In our experience, ACTB and GAPD vary 50-fold relative to each other among bronchial epithelial cells (BEC) and even more between BEC and other cell types [19, 44]. In situations where limited numbers of genes are measured (< 200), an index of all genes for the normalization of data is not sufficiently stable. In order to eliminate the effect of unknown variation in the reference gene expression among samples, we analyzed balanced ratios of one gene expression value obtained by StaRT-PCR to another. These balanced ratios did not represent actual cellular concentration changes of the individual genes comprising the ratio, but related the expression of one gene to another and could be used for comparison with phenotypic determinants such as chemoresistance. In this study, ITAI analysis (Table 2) confirmed most of the results obtained by analysis of individual gene expression values relative to chemoresistance (Table 1). This suggests that variation in ACTB among this group of cDNA samples was not significant. However, in our experience inter-sample variation in ACTB expression is greater among primary samples. Thus, we will continue to use ITAI to remove doubt regarding potential effect of variation in reference gene expression whenever possible.

As is presented in Table 2, by evaluating an empirically derived set of balanced ratios (ITAI) derived from expression values for all of the genes measured, it is possible to establish a hierarchy regarding the strength of association between a set of genes and a phenotype.

Conclusion

In summary, the association of ERCC2, ABCC5, XPA, and XRCC1 with chemoresistance was established through a sequential process involving a) screening genes representing many different functional classes, b) evaluating an expanded group of genes represented by those that were positively associated in the first round, c) identification of outliers (see Additional file 2), d) model building and e) validation (Table 2). Although only two of the 35 genes assessed in the first round were correlated with chemoresistance, 8/12 of the selected DNA repair and MDR genes were correlated. The models established in this study demonstrate the importance of evaluating the interaction among multiple genes representing multiple pathways involved in cisplatin chemoresistance. These models will be tested through a blinded study of gene expression levels of the identified potential markers in samples consisting of fine needle aspirate (FNA) biopsies from patients with various treatment outcomes.

Methods

Cell culture

Non-small cell lung cancer (NSCLC) cell lines with varying levels of cisplatin chemoresistance, H460, H1155, H23, H838, H1334, H1437, H1355, H1435, H358, H322, H441, H522, H226 and H647, were obtained from the American Type Culture Collection (Rockville, MD). The previously reported [20] cisplatin IC50 concentration for each line is provided in Table 3. All cells were incubated in RPMI-1640 medium (Biofluids, Inc., Rockville, MD) containing 10% fetal bovine serum (FBS) and 1 mM glutamine at 37°C in the presence of 5% CO2. Proliferative, subconfluent cultures were obtained for RNA extractions and subsequent analyses.

Table 3 Cisplatin chemoresistance in 14 non-small cell cancer cell lines

Reagents

10X PCR buffer for the Rapidcycler (500 mM Tris, pH 8.3; 2.5 mg/μl BSA; 30 mM MgCl2) was obtained from Idaho Technology, Inc. (Idaho Falls, ID). Taq polymerase (5 U/μl), oligo dT primers, RNasin (25 U/μl) and dNTPs were obtained from Promega (Madison, WI). M-MLV reverse transcriptase (200 U/μl) and 5X first strand buffer (250 mM Tris-HCl, pH 8.3; 375 mM KCl; 15 mM MgCl2; 50 mM DTT) were obtained from Gibco BRL (Gaithersburg, MD). DNA 7500 Assay kits containing dye, matrix and standards were obtained from Agilent Technologies, Inc. (Palo Alto, CA). All other chemicals and reagents were molecular biology grade.

RNA extraction and reverse transcription

Total RNA was isolated from cell cultures by a TriReagent protocol (Molecular Research Center, Inc., Cincinnati, OH) [43]. Following extraction, approximately 1 μg of total RNA for each cell line was reverse-transcribed using M-MLV reverse-transcriptase and an oligo dT primer as previously described [44].

Quantitative standardized RT (StaRT)-PCR

Gene expression was determined using previously published quantitative StaRT-PCR protocols [19, 44–50]. Briefly, a master mixture containing buffer, MgCl2, dNTPs, sample cDNA, Taq polymerase and SMIS was prepared and 9 μl aliquots dispensed into 0.6 ml microfuge tubes containing 1 μl of gene-specific primers. A SMIS comprises gene-specific IS's for each gene at defined concentrations relative to one another. The mixture includes IS's for reference (or housekeeping genes) to control for cDNA loading and to simplify normalization of all gene data. All primers used for PCR and those used in the construction of the CTs, are listed in Additional file 3. PCR reactions mixtures were subjected to 35 cycles of PCR with 5 seconds of denaturation at 94°C, 10 seconds of annealing at 58°C and 15 seconds of elongation at 72°C in a Rapidcycler (Idaho Technology, Inc.). PCR products were electrophoretically separated and quantified in an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.) with the DNA 7500 Assay kit. The area under the curve (as calculated by Agilent software) for each native template (NT) and IS peak was used in all calculations. Representative electropherograms of each gene assessed are presented in Additional file 4. The NT/IS ratio for a reference gene, ACTB, and the NT/IS ratios for each target gene were calculated. The initial number of NT molecules for each gene then could be determined from these ratios because the initial number of IS molecules added into the PCR reaction was known. To normalize measurements and control for sample-to-sample variation and inter-experimental loading, the calculated number of target gene molecules was divided by the calculated number of ACTB molecules. A size correction was employed to correct for fluorescence intensity differences affecting the measured area under the curve [19, 48].

Statistical analyses

Ratios of one gene to another, from each of the initial eight NSCLC cell lines, were subjected to multiple regression analysis using SAS 6.12 (SAS Institute Inc., Cary, NC) to determine the combination of genes that best predict cisplatin resistance. Each ratio was compared separately to chemoresistance and ratios with significant correlation to resistance (R2 ≥ 0.88, p < 0.001) then were examined hierarchically to achieve two variable models based on the highest R2 values. Following assessment of an additional 6 cell lines, results for all 14 NSCLC cell lines were combined and also subjected to analysis as described.

Abbreviations

LIG1 – DNA ligase I:

ATP-independent

ERCC2 – excision repair cross-complementing rodent repair deficiency (ERCC):

complementation group 2

ERCC3 – ERCC:

complementation group 3

DDIT3 :

DNA-damage-inducible transcript 3

ABCC1 – ATP-binding cassette:

subfamily C (ABCC), member 1

ABCC4 – ABCC:

member 4

ABCC5 – ABCC:

member 5

ABCC10 – ABCC:

member 10

GTF2H2 – general transcription factor IIH:

polypeptide 2

XPA – xeroderma pigmentosum (XP):

complementation group A

XPC – XP:

complementation group C and XRCC1 – X-ray repair complementing defective repair in Chinese hamster cells 1.

References

  1. Perez RP: Cellular and molecular determinants of cisplatin resistance. Eur J Cancer. 1998, 34: 1535-1542. 10.1016/S0959-8049(98)00227-5

    Article  CAS  PubMed  Google Scholar 

  2. Dijt F, Fitchinger-Schepman AM, Berends F, Reedijk J: Formation and repair of cisplatin-induced adducts to DNA in cultured normal and repair-deficient human fibroblasts. Cancer Res. 1988, 48: 6058-6062.

    CAS  PubMed  Google Scholar 

  3. Zamble DB, Lippard SJ: Cisplatin and DNA repair in cancer chemotherapy. Trends Biochem Sci. 1995, 20: 435-439. 10.1016/S0968-0004(00)89095-7

    Article  CAS  PubMed  Google Scholar 

  4. States JC, Reed E: Enhanced XPA mRNA levels in cisplatin-resistant human ovarian cancer are not associated with XPA mutations or gene amplifications. Cancer Lett. 1996, 108: 233-237. 10.1016/S0304-3835(96)04428-X

    Article  CAS  PubMed  Google Scholar 

  5. Ferry KV, Fink D, Johnson SW, Hamilton TC, Howell SB: Quantitation of platinum-DNA adduct repair in mismatch repair deficient and proficient human colorectal cancer cell lines using an in vitro DNA repair assay [abstract]. Proc Am Assoc Cancer Res. 1997, 38: 359-

    Google Scholar 

  6. Jordan P, Carmo-Fonseca M: Molecular mechanisms involved in cisplatin cytotoxicity. Cell Mol Life Sci. 2000, 57: 1229-1235.

    Article  CAS  PubMed  Google Scholar 

  7. Kartalou M, Essigmann JM: Mechanisms of resistance to cisplatin. Mutat Res. 2001, 478: 23-43.

    Article  CAS  PubMed  Google Scholar 

  8. Berger W, Elbling L, Hauptmann E, Micksche M: Expression of the multidrug resistance-associated protein (MRP) and chemoresistance of human non-small-cell lung cancer cells. Int J Cancer. 1997, 73: 84-93. 10.1002/(SICI)1097-0215(19970926)73:1<84::AID-IJC14>3.0.CO;2-5

    Article  CAS  PubMed  Google Scholar 

  9. Borst P, Kool M, Evers R: Do cMOAT (MRP2), other MRP homologues, and LRP play a role in MDR?. Cancer Biol. 1997, 8: 205-213. 10.1006/scbi.1997.0071.

    Article  CAS  Google Scholar 

  10. Young LC, Campling BG, Voskoglou-Nomikos T, Cole SPC, Deeley RG, Gerlach JH: Expression of multidrug resistance protein-related genes in lung cancer: correlation with drug response. Clin Cancer Res. 1999, 5: 673-680.

    CAS  PubMed  Google Scholar 

  11. Berger W, Elbling L, Micksche M: Expression of the major vault protein LRP in human non-small-cell lung cancer cells: activation by short-term exposure to antineoplastic drugs. Int J Cancer. 2000, 88: 293-300. 10.1002/1097-0215(20001015)88:2<293::AID-IJC23>3.3.CO;2-J

    Article  CAS  PubMed  Google Scholar 

  12. Borst P, Evers R, Kool M, Wijnholds J: A family of drug transporters: the multidrug resistance-associated proteins. J Nat Cancer Inst. 2000, 92: 1295-1302. 10.1093/jnci/92.16.1295

    Article  CAS  PubMed  Google Scholar 

  13. Oguri T, Isobe T, Suzuki T, Nishio K, Fujiwara Y, Katoh O, Yamakido M: Increased expression of the MRP5 gene is associated with exposure to platinum drugs in lung cancer. Int J Cancer. 2000, 86: 95-100. 10.1002/(SICI)1097-0215(20000401)86:1<95::AID-IJC15>3.0.CO;2-G

    Article  CAS  PubMed  Google Scholar 

  14. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999, 286: 531-537. 10.1126/science.286.5439.531

    Article  CAS  PubMed  Google Scholar 

  15. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000, 403: 503-511. 10.1038/35000501

    Article  CAS  PubMed  Google Scholar 

  16. Scherf U, Ross DT, Waltham M, Smith LH, Lee JK, Tanabe L, Kohn KW, Reinhold WC, Myers TG, Andrews DT, Scudiero DA, Eisen MB, Sausville EA, Pommier Y, Botstein D, Brown PO, Weinstein JN: A gene expression database for the molecular pharmacology of cancer. Nat Genet. 2000, 24: 236-244. 10.1038/73439

    Article  CAS  PubMed  Google Scholar 

  17. Kihara C, Tsunoda T, Tanaka T, Yamana H, Furukawa Y, Ono K, Kitahara O, Zembutsu H, Yanagawa R, Hirata K, Takagi T, Nakamura Y: Prediction of sensitivity of esophageal tumors to adjuvant chemotherapy by cDNA microarray analysis of gene-expression profiles. Cancer Res. 2001, 61: 6474-6479.

    CAS  PubMed  Google Scholar 

  18. Zembutsu H, Ohnishi Y, Tsunoda T, Furukawa Y, Katagiri T, Ueyama Y, Tamaoki N, Nomura T, Kitahara O, Yanagawa R, Hirata K, Nakamura Y: Genome-wide cDNA microarray screening to correlate gene expression profiles with sensitivity of 85 human cancer xenografts to anticancer drugs. Cancer Res. 2002, 62: 518-527.

    CAS  PubMed  Google Scholar 

  19. Willey JC, Crawford EL, Jackson CM, Weaver DA, Hoban JC, Khuder SA, DeMuth JP: Expression measurement of many genes simultaneously by quantitative RT-PCR using standardized mixtures of competitive templates. Am J Respir Cell Mol Biol. 1998, 19: 6-17.

    Article  CAS  PubMed  Google Scholar 

  20. Tsai CM, Chang KT, Wu LH, Chen JY, Gazdar AF, Mitsudomi T, Chen MH, Perng RP: Correlations between intrinsic chemoresistance and HER-2/neu gene expression, p53 mutations, and cell proliferation characteristics in non-small cell lung cancer cell lines. Cancer Res. 1996, 56: 206-209.

    CAS  PubMed  Google Scholar 

  21. DeMuth JP, Jackson CM, Weaver DA, Crawford EL, Durzinsky DS, Durham SJ, Zaher A, Phillips ER, Khuder SA, Willey JC: The gene expression index c-myc x E2F1/p21 is highly predictive of malignant phenotype in human bronchial epithelial cells. Am J Respir Cell Mol Biol. 1998, 19: 18-24.

    Article  CAS  PubMed  Google Scholar 

  22. Crawford EL, Khuder SA, Durham SJ, Frampton M, Utell M, Thilly WG, Weaver DA, Ferencak WJ, Jennings CA, Hammersley JR, Olson DA, Willey JC: Normal bronchial epithelial cell expression of glutathione transferase P1, glutathione transferase M3, and glutathione peroxidase is low in subjects with bronchogenic carcinoma. Cancer Res. 2000, 60: 1609-1618.

    CAS  PubMed  Google Scholar 

  23. Rots JG, Willey JC, Jansen G, Van Zantwijk CH, Noordhuis P, DeMuth JP, Kuiper E, Veerman AJ, Pieters R, Peters GJ: mRNA expression levels of methotrexate resistance-related proteins in childhood leukemia as determined by a standardized competitive template-based RT-PCR method. Leukemia. 2000, 14: 2166-2175. 10.1038/sj.leu.2401943

    Article  CAS  PubMed  Google Scholar 

  24. Dabholkar M, Vionnet J, Bostick-Bruton F, Yu JJ, Reed E: Messenger RNA levels of XPAC and ERCC1 in ovarian cancer tissue correlate with response to platinum-based chemotherapy. J Clin Invest. 1994, 94: 703-708.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Asahina H, Kuraoka I, Shirakawa M, Morita EH, Miura N, Miyamoto I, Ohtsuka E, Okada Y, Tanaka K: The XPA protein is a zinc metalloprotein with an ability to recognize various kinds of DNA damage. Mutat Res DNA Repair. 1994, 315: 229-237. 10.1016/0921-8777(94)90034-5

    Article  CAS  PubMed  Google Scholar 

  26. Li L, Peterson CA, Lu X, Legerski RJ: Mutations in XPA that prevent association with ERCC1 are defective in nucleotide excision repair. Mol Cell Biol. 1995, 15: 1993-1998.

    PubMed Central  CAS  PubMed  Google Scholar 

  27. Mu D, Park CH, Matsunaga T, Hsu DS, Reardon JT, Sancar A: Reconstitution of human DNA repair excision nuclease in a highly defined system. J Biol Chem. 1995, 270: 2415-2418. 10.1074/jbc.270.6.2415

    Article  CAS  PubMed  Google Scholar 

  28. Reed E: Anticancer drugs: platinum analogs. Principles and Practice of Oncology. Edited by: Devita VT Jr, Hellman S, Rosenberg SA. 1993, 390-399. Philadelphia, PA: Lippincott

    Google Scholar 

  29. Mu D, Hsu DS, Sancar A: Reaction mechanism of human DNA repair excision nuclease. J Biol Chem. 1996, 271: 8285-8294. 10.1074/jbc.271.32.19451

    Article  CAS  PubMed  Google Scholar 

  30. Schaeffer L, Moncollin V, Roy R, Staub A, Mezzina M, Sarasin A, Weeda G, Hoeijmakers JH, Egly JM: The ERCC2/DNA repair protein is associated with the class II BTF2/TFIIH transcription factor. EMBO J. 1994, 13: 2388-2392.

    PubMed Central  CAS  PubMed  Google Scholar 

  31. Drapkin R, Reardon JT, Ansari A, Huang JC, Zawel L, Ahn K, Sancar A, Reinberg D: Dual role of TFIIH in DNA excision repair and in transcription by RNA polymerase II. Nature. 1994, 368: 769-772. 10.1038/368769a0

    Article  CAS  PubMed  Google Scholar 

  32. Wang Z, Svejstrup JQ, Feaver WJ, Wu X, Kornberg RD, Friedberg EC: Transcription factor b (TFIIH) is required during nucleotide-excision repair in yeast. Nature. 1994, 368: 74-76. 10.1038/368074a0

    Article  CAS  PubMed  Google Scholar 

  33. Prakash S, Sung P, Prakash L: DNA repair genes and proteins of Saccharomyces cerevisiae. Annu Rev Genet. 1993, 27: 33-70. 10.1146/annurev.ge.27.120193.000341

    Article  CAS  PubMed  Google Scholar 

  34. Coin F, Marinoni JC, Rodolfo C, Fribourg S, Pedrinin AM, Egly JM: Mutations in the XPD helicase gene result in XP and TTD phenotypes, preventing interaction between XPD and the p44 subunit of TFIIH. Nature Genet. 1998, 20: 184-188. 10.1038/2491

    Article  CAS  PubMed  Google Scholar 

  35. Yen L, Woo A, Christopoulopoulos G, Batist G, Panasci L, Roy R, Mitra S, Alaoui-Jamili MA: Enhanced host cell reactivation capacity and expression of DNA repair genes in human breast cancer cells resistant to bi-functional alkylating agents. Mutation Research. 1995, 337: 179-189. 10.1016/0921-8777(95)00022-C

    Article  CAS  PubMed  Google Scholar 

  36. Damia G, Guidi G, D'Incalci M: Expression of genes involved in nucleotide excision repair and sensitivity to cisplatin and melphalan in human cancer cell lines. European Journal of Cancer. 1998, 34: 1783-1788. 10.1016/S0959-8049(98)00190-7

    Article  CAS  PubMed  Google Scholar 

  37. Dabholkar M, Bostick-Bruton F, Weber C, Bohr VA, Egwuagu C, Reed E: ERCC1 and ERCC2 expression in malignant tissues from ovarian cancer patients. Journal of the National Cancer Institute. 1992, 84: 1512-1517.

    Article  CAS  PubMed  Google Scholar 

  38. Hoeijmakers JHJ: Genome maintenance mechanisms for preventing cancer. Nature. 2001, 411: 366-374. 10.1038/35077232

    Article  CAS  PubMed  Google Scholar 

  39. Wood RD, Mitchell M, Sgouros J, Lindahl T: Human DNA Repair Genes. Science. 2001, 291: 1284-1289. 10.1126/science.1056154

    Article  CAS  PubMed  Google Scholar 

  40. Sancar A, Lindsey-Boltz LA, Ünsal-KaÇmaz K, Linn S: Molecular mechanisms of mammalian DNA repair and the DNA damage checkpoints. Annu Rev Biochem. 2004, 73: 39-85. 10.1146/annurev.biochem.73.011303.073723

    Article  CAS  PubMed  Google Scholar 

  41. Zhang X, Xiaoping M, Liang G, Hao B, Wang Y, Tan W, Li Y, Guo Y, He F, Wei Q, Lin D: Polymorphisms in DNA base excision repair genes ADPRT and XRCC1 and risk of lung cancer. Cancer Res. 2005, 65: 722-726.

    CAS  PubMed  Google Scholar 

  42. Audebert M, Salles B, Calsou P: Involvement of Poly (ADP-ribose) Polymerase-1 and XRCC1/DNA Ligase III in an alternative route for DNA double-strand breaks rejoining. J Biol Chem. 2004, 279: 55117-55126. 10.1074/jbc.M404524200

    Article  CAS  PubMed  Google Scholar 

  43. Chomczynski P: A reagent for the single-step simultaneous isolation of RNA, DNA and proteins from cell and tissue samples. Biotechniques. 1993, 15: 536-537.

    Google Scholar 

  44. Willey JC, Coy E, Brolly C, Utell MJ, Frampton MW, Hammersley J, Thilly WG, Olson D, Cairns K: Xenobiotic metabolism enzyme gene expression in human bronchial epithelial and alveolar macrophage cells. Am J Respir Cell Mol Biol. 1996, 14: 262-271.

    Article  CAS  PubMed  Google Scholar 

  45. Apostolakos MJ, Schuermann WH, Frampton MW, Utell MJ, Willey JC: Measurement of gene expression by multiplex competitive polymerase chain reaction. Anal Biochem. 1993, 213: 277-284. 10.1006/abio.1993.1421

    Article  CAS  PubMed  Google Scholar 

  46. Willey JC, Coy EL, Frampton MW, Torres A, Apostolakos MJ, Hoehn G, Schuermann WH, Thilly WG, Olson DE, Hammersley JR, Crespi CL, Utell MJ: Quantitative RT-PCR measurement of cytochromes p450 1A1, 1B1, and 2B7, microsomal epoxide hydrolase, and NADPH oxidoreductase expression in lung cells of smokers and non-smokers. Am J Respir Cell Mol Biol. 1997, 17: 114-124.

    Article  CAS  PubMed  Google Scholar 

  47. Celi FS, Zenilman ME, Shuldiner AR: A rapid and versatile method to synthesize internal standards for competitive PCR. Nucleic Acids Research. 1993, 21: 1047-

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  48. Crawford EL, Peters GJ, Noordhuis P, Rots MG, Vondracek M, Grafström RC, Lieuallen K, Lennon G, Zahorchak RJ, Georgeson MJ, Wali A, Lechner JF, Fan PS, Kahaleh MB, Khuder SA, Warner KA, Weaver DA, Willey JC: Reproducible gene expression measurement among multiple laboratories obtained in a blinded study using Standardized RT (StaRT)-PCR. Molecular Diagnosis. 2001, 6: 217-225. 10.1054/modi.2001.29789

    Article  CAS  PubMed  Google Scholar 

  49. Willey JC, Crawford EL, Knight CA, Warner KA, Motten CR, Herness Peters E, Zahorchak RJ, Graves TG, Bergman JR, Weaver DA, Vondracek M, Grafström RC: Use of standardized mixtures of internal standards in quantitative RT-PCR to ensure quality control and develop a standardized gene expression database. A-Z of Quantitative PCR. Edited by: Bustin SA. 2004, 545-576. International University Line, La Jolla, CA

    Google Scholar 

  50. Willey JC, Crawford EL, Knight CR, Warner KA, Motten CA, Herness EA, Zahorchak RJ, Graves TG: Standardized RT-PCR and the standardized expression measurement center. Methods Mol Biol. 2004, 258: 13-41.

    CAS  PubMed  Google Scholar 

  51. Pagliarulo V, George B, Beil SJ, Groshen S, Laird PW, Cai J, Willey J, Cote RJ, Datar RH: Sensitivity and reproducibility of standardized-competitive RT-PCR for transcript quantification and its comparison with real time RT-PCR. Mol Cancer. 2004, 3: 5- 10.1186/1476-4598-3-5

    Article  PubMed Central  PubMed  Google Scholar 

  52. Millson A, Suli A, Hartung L, Kunitake S, Bennett A, Nordberg MCL, Hanna W, Wittwer CT, Arun S, Lyon E: Comparison of two quantitative polymerase chain reaction methods for detecting HER2/neu amplification. J Mol Diagn. 2003, 184-90:

    Google Scholar 

  53. Gilliland G, Perrin S, Blanchard K, Bunn HF: Analysis of cytokine mRNA and DNA: Detection and quantification by competitive polymerase chain reaction. Proc Natl Acad Sci USA. 1990, 87: 2725-2729.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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Correspondence to James C Willey.

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Authors' contributions

DAW participated in study design, cell culture, transcript abundance analysis and data interpretation and drafted the manuscript. ELC participated in data interpretation and manuscript preparation. KAW participated in cell culture and study design. FE conducted gene expression experiments. SAK conducted statistical analyses and participated in study design and data interpretation. JCW conceived of the study, participated in study design and data interpretation, and critically reviewed the manuscript.

Competing interests

DAW, ELC, KAW and JCW each have significant equity interest in Gene Express, Inc. which produces and markets StaRT-PCR reagents used in these studies.

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Weaver, D.A., Crawford, E.L., Warner, K.A. et al. ABCC5, ERCC2, XPA and XRCC1 transcript abundance levels correlate with cisplatin chemoresistance in non-small cell lung cancer cell lines. Mol Cancer 4, 18 (2005). https://doi.org/10.1186/1476-4598-4-18

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