|
Advertisement |
|
|
||
 |
|
|||||
|
|||||
|
 |
||||||
|
||||||
|
||||||
|
Journal of Clinical Oncology, Vol 25, No 28 (October 1), 2007: pp. 4350-4357 © 2007 American Society of Clinical Oncology. DOI: 10.1200/JCO.2007.11.0593 Pharmacogenomic Strategies Provide a Rational Approach to the Treatment of Cisplatin-Resistant Patients With Advanced Cancer
From the Division of Medical Oncology, Department of Medicine; Institute for Genome Sciences and Policy; Department of Surgery, Duke University, Durham, NC; and the Division of Gynecologic Oncology, H. Lee Moffitt Cancer Center, Tampa, FL Address reprint requests to Anil Potti, MD, Duke University, Department of Medicine, Box 3841 Medical Center, Durham, NC 27710; e-mail: anil.potti{at}duke.edu
Purpose Standard treatment for advanced non–small-cell lung cancer (NSCLC) includes the use of a platinum-based chemotherapy regimen. However, response rates are highly variable. Newer agents, such as pemetrexed, have shown significant activity as second-line therapy and are currently being evaluated in the front-line setting. We utilized a genomic strategy to develop signatures predictive of chemotherapeutic response to both cisplatin and pemetrexed to provide a rational approach to effective individualized medicine. Methods Using in vitro drug sensitivity data, coupled with microarray data, we developed gene expression signatures predicting sensitivity to cisplatin and pemetrexed. Signatures were validated with response data from 32 independent ovarian and lung cancer cell lines as well as 59 samples from patients previously treated with cisplatin. Results Genomic-derived signatures of cisplatin and pemetrexed sensitivity were shown to accurately predict sensitivity in vitro and, in the case of cisplatin, to predict treatment response in patients treated with cisplatin. The accuracy of the cisplatin predictor, based on available clinical data, was 83.1% (sensitivity, 100%; specificity 57%; positive predictive value, 78%; negative predictive value, 100%). Interestingly, an inverse correlation was seen between in vitro cisplatin and pemetrexed sensitivity, and importantly, between the likelihood of cisplatin and pemetrexed response in patients. Conclusion The use of genomic predictors of response to cisplatin and pemetrexed can be incorporated into strategies to optimize therapy for advanced solid tumors.
Since the advent of chemotherapy to treat cancer, there have been numerous advances in the development, selection, and application of these agents, sometimes with remarkable successes. In several instances, particularly in early-stage disease, combination chemotherapy in the adjuvant setting has been found to be curative. However, most patients with clinically or pathologically advanced solid tumors will eventually relapse and die as a result of their disease. For example, in advanced non–small-cell lung cancer (NSCLC), third-generation regimens consisting of a platinum analog in combination with a second agent increases overall response and survival when compared with older regimens.1-3 However, overall response is still only 20% to 30%,3 suggesting that a majority of the patients do not respond to a platinum analog. Subsequently, those patients who experience treatment failure with platinum-based therapy typically receive pemetrexed, docetaxel, or targeted therapies as second-line treatment, with response rates of approximately 7% to 10%.4-6 New technologies offer the potential to measure genome-wide gene activity that may serve as a powerful adjunct to currently available clinical and biochemical markers. Such complementary approaches may better characterize the complexity of the disease and identify discrete clinical and biologically relevant phenotypes.7-9 The ability to find structure in the data, in the form of patterns of gene expression, provides snapshots of gene activity in a cell or tissue sample that can then be used to describe a phenotype.10,11 This transforms biology from an observational molecular science to a data-intensive quantitative genomic science.12-14 The dimension and complexity of such data provide an opportunity to uncover patterns and trends that can distinguish subtle phenotypes in ways that traditional methods cannot. Recently, we have described the use of gene expression profiling to develop signatures of drug sensitivity to individual chemotherapeutic drugs.15 These signatures also reliably predicted in vitro and in vivo response to individual cytotoxic drugs. Thus, development of gene expression profiles that can predict response to commonly used cytotoxic agents may provide a unique opportunity to better utilize drugs previously shown to be effective in first- or second-line therapy. Here, we describe a novel approach to rationalized drug therapy in NSCLC, by developing predictors of cisplatin (a first-line agent) and pemetrexed (a second-line agent) sensitivity and demonstrating the clinical value of identifying the most appropriate drug on the basis of sensitivity profile for the treatment regimen of each individual patient, thus moving beyond empirical therapeutic choices that are now in current practice. Such an approach is likely to maximize response to chemotherapeutic drugs and may change the current paradigm of cancer therapy, particularly in NSCLC, and possibly in other advanced cancers.
In Vitro Chemosensitivity Predictors The [–log10(M)] GI50/50% growth inhibitory dose (IC50), total growth inhibition (TGI), and 50% lethal concentrate (LC50) data on the NCI-60 cell line panel for pemetrexed was used to populate a matrix with MATLAB software (MathWorks, Natick, MA) with the relevant expression data for each individual cell line. When multiple entries for a drug screen existed (by National Service Center number), the entry with the largest number of replicates was included. To develop an in vitro gene expression–based predictor of pemetrexed sensitivity from the pharmacologic data used in the NCI-60 drug screen studies, we chose cell lines within the NCI-60 panel that would represent the extremes of sensitivity (http://dtp.nci.nih.gov/docs/cancer/cancer_data.html). Our hypothesis was that such a selection would identify cell lines that represent the extremes of sensitivity to a given drug.15 Relevant expression data (updated data available on the Affymetrix U95A2 GeneChip; Affymetrix, Santa Clara, CA) for the selected NCI-60 cell lines were then used in a supervised analysis using Bayesian regression methodologies to refine the model, as described previously,16 to develop a probit model predictive of sensitivity to pemetrexed. The collection of data in the NCI-60 data occasionally does not represent a significant diversity in resistant and sensitive cell lines to any given drug. Thus, if a drug screening experiment did not result in widely variable GI50/IC50 and/or LC50 data, the generation of a genomic predictor is not possible using our methods, as in the case of cisplatin. Thus, we used data published by Györffy et al,17 where they had determined definitive resistance and sensitivity to cisplatin in 30 cancer cell lines. Importantly, we also had access to corresponding gene expression data to facilitate the generation of a model that would predict sensitivity to cisplatin.
Statistical Analysis Methods
Cell and RNA Preparation Briefly, total RNA was extracted using the Qiashredder and Qiagen RNeasy Mini kit (Qiagen, Hilden, Germany) and the quality of RNA was checked by an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). The targets for Affymetrix DNA microarray analysis were prepared according to the manufacturer's instructions. Biotin-labeled cRNA, produced by in vitro transcription, was fragmented and hybridized to the Affymetrix U133A GeneChip arrays at 45°C for 16 hours and then washed and stained using the GeneChip Fluidics (Affymetrix). The arrays were scanned by a GeneArray Scanner (Affymetrix) and patterns of hybridization were detected as light emitted from the fluorescent reporter groups incorporated into the target and hybridized to oligonucleotide probes. All analyses were performed in a MIAME (minimal information about a microarray experiment)-compliant fashion, as defined in the guidelines established by Microarray Gene Expression Data (MGED).
Classification of Platinum Response in Ovarian Tumors Response to therapy was evaluated using standard criteria for patients with measurable disease, based on WHO guidelines.18 CA-125 was used to classify responses only in the absence of a measurable lesion and based on established guidelines.19 A complete response (CR) was defined as a complete disappearance of all measurable and assessable disease or, in the absence of measurable lesions, a normalization of the CA-125 level after salvage therapy. A partial response (PR) was considered a 50% or greater reduction in the product obtained from measurement of each bidimensional lesion for at least 4 weeks or a drop in the CA-125 by at least 50% for at least 4 weeks. Progressive disease (PD) was defined as a 50% or greater increase in the product from any lesion documented within 8 weeks of initiation of therapy, appearance of any new lesion within 8 weeks of initiation of therapy, or a doubling of CA-125 from baseline. For the purposes of our analysis, a clinically beneficial response (ie, "responder") included CR or PR. A patient who did not demonstrate a CR or PR was considered a "nonresponder."
Cross-Platform Affymetrix Gene Chip Comparison
Lung and Ovarian Cancer Cell Culture
Cell Proliferation and Drug Sensitivity Assays
Developing a Gene Expression–Based Predictor of Cisplatin Sensitivity The experimental strategy for analysis employed in this study is similar to that used for the development of oncogenic pathway and chemotherapy sensitivity signatures as described previously.9,15 Samples representing extreme cases are used to train the expression data to develop a genomic signature that can predict drug sensitivity. A predictor of cisplatin sensitivity was developed by analyzing cell lines described by Györffy et al.17 Using Bayesian binary regression analysis, genes highly correlated with drug sensitivity were identified and used to develop a model that could differentiate between cisplatin sensitivity and resistance. The developed model consisting of 45 genes based on cisplatin sensitivity (Fig 1A) was validated in a leave-one-out cross-validation. The cisplatin sensitivity predictor includes DNA repair genes such as ERCC1 and ERCC4, among others, that had altered expression in the list of cisplatin sensitivity predictor genes. Interestingly, one previously described mechanism of resistance to cisplatin therapy results from the increased capacity of cancer cells to repair DNA damage incurred, by activation of DNA repair genes.22,23
Developing a Gene Expression–Based Predictor of Pemetrexed Sensitivity In NSCLC, where platinum-based therapy is the standard of care, response rates are only 30%. One approach to identifying potential drugs effective in cisplatin-resistant patients is to examine the NCI-60 data set for agents whose IC50 profile showed an inverse relationship with cisplatin, focusing on those known to be effective in NSCLC. Of these drugs, an inverse correlation with cisplatin sensitivity was identified with docetaxel, abraxane, and pemetrexed. The strongest inverse correlation was found between cisplatin and pemetrexed sensitivity (P < .001; Pearson r value, 0.1;  = 0.05). Using methods previously described,15 a predictor of pemetrexed sensitivity was developed by identifying NCI-60 cell lines that were most resistant or sensitive to pemetrexed. Using Bayesian binary regression analysis, genes whose expression was most highly correlated with drug sensitivity were used to develop a predictive model that could differentiate between pemetrexed sensitivity and resistance. The developed model consisting of 85 genes based on pemetrexed sensitivity (Fig 1B) was validated in a leave-one-out cross-validation. Interestingly, multiple genes involved in nucleotide and cellular metabolism constituted the pemetrexed sensitivity predictor and is biologically consistent with the known mechanism of pemetrexed sensitivity, which involves interference with cell-cycle progression by reducing the pool of substrates necessary for DNA replication.24
In Vitro Validation of the Cisplatin and Pemetrexed Predictor
Similar to the independent validation of the cisplatin sensitivity predictor, the pemetrexed predictor was validated using gene expression data from an independent cohort of 17 NSCLC cell lines with respective in vitro drug sensitivity assays. As shown in Figure 2B, the correlation between the predicted probability of sensitivity to pemetrexed in the 17 NSCLC cell lines and the respective IC50 for pemetrexed validated the ability of the pemetrexed predictor to predict sensitivity to the drug in an independent cohort of cancer cell lines.
In Vivo Validation of the Cisplatin Sensitivity Predictor
Patterns of Predicted Chemotherapy Response to Cisplatin and Pemetrexed in NSCLC The cisplatin and pemetrexed predictors were utilized to profile the potential options of using these two drugs in a collection of 91 NSCLC described previously25 (GEO accession number: GSE3141). These samples were first sorted according to the patterns of predicted sensitivity to cisplatin (Fig 4A, left panel). The pattern observed indicated that those patients resistant to cisplatin (red) were more sensitive to pemetrexed (blue). Although the data points in the scatter plot do not appear to be perfectly correlated, this analysis suggests that the relationship was statistically significant (P = .004, log-rank; Fig 4A, right panel). A similar relationship was also demonstrated in the independent cohort of NSCLC cell lines (Fig 4B), suggesting the possibility of an alternative therapy for treatment of advanced or metastatic NSCLC patients who would be predicted to be platinum resistant. As a comparison, the pemetrexed signature was also applied to the ovarian cancer patient data set. In this analysis, however, only two (< 4%) of 59 patients were identified to have greater than 50% probability of being sensitive to pemetrexed.
The Sequence of Chemotherapy May Be Critical in Optimizing Responses Currently, first-line treatment with a platinum-based regimen is the standard of care for advanced NSCLC. Those patients developing resistance to cisplatin are treated with a taxane, pemetrexed, or erlotinib as second-line options. To explore the effect of cisplatin resistance, as well as prior treatment with potentially ineffective therapies, the IC50 of various lung cancer cell lines to cisplatin and pemetrexed were analyzed and revealed an inverse relationship (Fig 5A). Thereafter, one NSCLC cell line (H2030) that is resistant to cisplatin, paclitaxel, and docetaxel, but sensitive to pemetrexed on the basis of cell proliferation assays (IC50), was treated with pemetrexed, docetaxel, or paclitaxel in a systematic fashion. Interestingly, when H2030 was first treated for 4days with a taxane (docetaxel or paclitaxel), resistance to subsequent pemetrexed exposure was induced (Fig 5B). In contrast, when H2030 was first treated with pemetrexed, H2030 was sensitive, as expected (Fig 5B). Although these in vitro observations are only hypothesis generating at this time, this proof of principle experiment suggests that the sequence of second-line chemotherapy in NSCLC may prove to be important in determining clinical outcomes. Specifically, in tumors from cisplatin-refractory patients who are also predicted to be resistant to a taxane, treatment with a taxane (docetaxel or paclitaxel) before pemetrexed therapy may induce resistance to subsequent pemetrexed therapy. This preliminary observation, pending further validation, suggests the importance of including genomic-based, disease-specific, treatment prioritization in clinical practice.
Multiple randomized controlled trials during the last two decades have established combination chemotherapy (cisplatin-based) to be the standard of care as first-line treatment in recurrent or metastatic NSCLC.1,26 Despite these advances, response rates to first-line platinum based therapy is approximately 30%, with median survival between 24 to 31 months.27 Those who develop resistance to cisplatin receive pemetrexed, docetaxel, or erlotinib in the second-line setting, but response rates are only 7% to 10%. Improving our ability to manage the disease by optimizing the use of existing drugs and/or developing new agents is essential in this endeavor. To this end, individualizing treatments by identifying patients who will or will not respond to specific agents will potentially increase the overall effectiveness of these drugs and limit the incidence and severity of toxicities that impair the functional status of patients and their ability to tolerate further therapies. In this study, the patterns of cisplatin sensitivity observed in our cohort of 91 NSCLC tumors suggests that not all patients may initially respond to first-line cisplatin-based therapy. As described herein, response rates to first-line platinum based therapy is approximately 30%, with median survival between 24 to 31 months.27 We have made use of in vitro drug sensitivity data in cancer cell lines, coupled with Affymetrix expression data, to develop gene expression signatures reflecting sensitivity to cisplatin and pemetrexed. The capacity of these signatures to predict response in independent sets of cell lines and patient studies begins to define a strategy that addresses the potential to identify cytotoxic agents that best match individual patients with advanced NSCLC and other advanced cancers (ovarian cancer). In addition, it can potentially be applied to patients with early-stage NSCLC to predict who may benefit from adjuvant cisplatin-based therapy. However, as promising as these approaches may seem, these strategies need to be first validated in a prospective clinical trial that would evaluate the performance of a genomic signature-based selection as an initial step in the individualized treatment strategy for patients with advanced NSCLC (Fig 6).
In conclusion, the development of signatures of drug sensitivity provide an opportunity to optimize therapy for patients with NSCLC and perhaps other patients with advanced cancer where cisplatin-based therapy is considered the standard of care.
The author(s) indicated no potential conflicts of interest.
Conception and design: Joseph R. Nevins, Anil Potti Administrative support: David Harpole, Anil Potti Collection and assembly of data: David S. Hsu, Bala S. Balakamaran, Vanja Vlahovic, Kelli S. Walters, Johnathan Lancaster, Anil Potti Data analysis and interpretation: David S. Hsu, Bala S. Balakamaran, Chaitanya R. Acharya, Anil Potti Manuscript writing: David S. Hsu, Bala S. Balakamaran, Chaitanya R. Acharya, Kelli S. Walters, Katherine Garman, Carey Anders, Richard F. Riedel, Holly K. Dressman, Joseph R. Nevins, Phillip G. Febbo, Anil Potti Final approval of manuscript: David S. Hsu, Anil Potti
Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.
1. Bunn PA Jr, Kelly K: New chemotherapeutic agents prolong survival and improve quality of life in non-small cell lung cancer: A review of the literature and future directions. Clin Cancer Res 4: 1087-1100, 1998[Abstract] 2. Kelly K, Crowley J, Bunn PA, et al: Randomized phase III trial of paclitaxel plus carboplatin versus vinorelbine plus cisplatin in the treatment of patients with advanced non-small cell lung cancer: A Southwest Oncology Group trial. J Clin Oncol 19: 3210-3218, 2001 3. Schiller JH, Harrington D, Belani CP, et al: Comparison of four chemotherapy regimens for advanced non-small cell lung cancer. N Engl J Med 346: 92-98, 2002 4. Fossella FV, DeVore R, Kerr RN, et al: Randomized Phase III trial of docetaxel versus vinorelbine or ifosamide in patients with non small cell lung cancer previously treated with platinum containing chemotherapy regiments. J Clin Oncol 18: 2354-2362, 2000 5. Hanna N, Shepherd FA, Fossella FV, et al: Randomized phase III trial of pemetrexed vs docetaxel in patients with NSCLC previously treated with chemotherapy. J Clin Oncol 22: 1589-1597, 2004 6. Shepherd FA, Rodrigues Pereira J, Cieleanu T, et al: Erlotinib in previously treated non small cell lung cancer. N Engl J Med 353: 123-132, 2005 7. Golub TR, Slonim DK, Mesirov J, et al: Molecular classification of cancer: Class discovery and class prediction by gene expression profiling. Science 286: 531-537, 1999 8. Golub TR: Genome-wide views of cancer. N Engl J Med 344: 601-602, 2001 9. Bild A, Yao G, Chang JT, et al: Oncogenic pathways signatures in human cancers as guide to targeted therapies. Nature 439: 353-357, 2006[CrossRef][Medline] 10. Perou CM, Sorlie T, Eisen MB, et al: Molecular portraits of human breast tumors. Nature 406: 747-752, 2000[CrossRef][Medline] 11. Rosenwald A, Wright G, Chan WC, et al: The use of molecular profiling to predict survival after chemotherapy for diffuse large B cell lymphoma. N Engl J Med 346: 1937-1947, 2002 12. Shipp MA, Ross KN, Tamayo P, et al: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8: 68-74, 2002[CrossRef][Medline] 13. van de Vijver MJ, He YD, van't Veer LJ, et al: A gene expression signature as a prediction of survival in breast cancer. N Engl J Med 347: 1999-2009, 2002 14. Ramaswamy S, Ross KN, Lander ES, et al: A molecular signature of metastasis in primary solid tumor. Nat Genet 33: 49-54, 2003[CrossRef][Medline] 15. Potti A, Dressman HK, Bild A, et al: Genomic signatures to guide the use of chemotherapeutics. Nat Med 12: 1294-1300, 2006[CrossRef][Medline] 16. Pittman J, Huang E, Nevins J, et al: Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes. Biostatistics 5: 587-601, 2004[Abstract] 17. Györffy B, Surowiak P, Kiesslich O, et al: Gene expression profiling of 30 cancer cell lines predicts resistance towards 11 anticancer drugs at clinically achieved concentrations. Int J Cancer 118: 1699-1712, 2006[CrossRef][Medline] 18. Therasse P, Arbuck SG, Eisenhauer EA, et al: New guidelines to evaluate the response to treatment in solid tumors: European Organization for Research and Treatment of Cancer, National Cancer Institute of the US, National Cancer Institute of Canada. J Natl Cancer Inst 92: 205-216, 2000 19. Rustin GJ, Timmers P, Nelstrop A, et al: Comparison of CA-125 and standard definitions of progression of ovarian cancer in the intergroup trial of cisplatin and paclitaxel versus cisplatin and cyclophosphamide. J Clin Oncol 24: 45-51, 2006 20. Mosmann T: Rapid Colorimetric Assay for Cellular Growth and Survival: Application to proliferation and cytotoxic assay. J Immunol Meth 65: 55-63, 1983[CrossRef][Medline] 21. Berridge MV, Tan AS: Characterization of the cellular reduction of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT): Subcellular localization, substrate dependence, and involvement if mitochondrial electron transport in MTT reduction. Arch Biochem Biophys 303: 474-482, 1993[CrossRef][Medline] 22. Johnson S, Perez R, Godwin A, et al: Role of platinum-DNA adduct formation and removal in cisplatin resistance in human ovarian cancer cell lines. Biochem Pharmacol 47: 689-697, 1994[CrossRef][Medline] 23. Yen L, Woo A, Christopoulopoulos G, et al: Enhanced host cell reactivation capacity and expression of DNA repair genes in human breast cancer cells resistant to bi-functional alkylating agents. Mutat Res 337: 179-189, 1995[CrossRef][Medline] 24. Paz-Ares, Bezares S, Tabernero J, et al: Review of a promising new agent in pemetrexed disodium. Cancer 97: 2056-2063, 2003[CrossRef][Medline] 25. Potti A, Mukherjee S, Petersen R, et al: A genomic strategy to refine prognosis in early-stage non–small-cell lung cancer. N Engl J Med 355: 570-580, 2006 26. Shepherd FA: Treatment of advanced non-small cell lung cancer. Semin Oncol 21: 7-18, 1994 (suppl)[Medline] 27. Breathnach OS, Freidlin B, Conley B, et al: Twenty-two years of phase III trials for patients with advanced non-small-cell lung cancer: Sobering results. J Clin Oncol 19: 1734-1742, 2001 Submitted January 31, 2007; accepted May 14, 2007.
Related Editorial
This article has been cited by other articles:
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
|||||||||||
|
|||||||||||
|
|
Copyright © 2007 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
|
TOP
ABSTRACT
INTRODUCTION
