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Clinical predictors versus epidermal growth factor receptor mutation in gefitinib-treated non-small-cell lung cancer patients
Lung Cancer (2006) 54, 201—207
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Clinical predictors versus epidermal growth factor receptor mutation in gefitinib-treated non-small-cell lung cancer patients Sae-Won Han a, Tae-You Kim a,c,∗, Kyung-Hun Lee a, Pil Gyu Hwang b, Yoon Kyung Jeon b, Do-Youn Oh a,c, Se-Hoon Lee a,c, Dong-Wan Kim a,c, Seock-Ah Im a,c, Doo Hyun Chung b, Dae Seog Heo a,c, Yung-Jue Bang a,c a
Department of Internal Medicine, Seoul National University College of Medicine, 28 Yongon-Dong, Chongno-Gu, Seoul 110-744, Republic of Korea b Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea c Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea Received 24 June 2006; received in revised form 12 July 2006; accepted 12 July 2006
KEYWORDS Non-small-cell lung cancer; Gefitinib; EGFR mutation; Pharmacogenomics
∗
Summary Background: Clinical predictors including Asian, female, adenocarcinoma and never-smoker and epidermal growth factor mutation are associated with gefitinib responsiveness in non-smallcell lung cancer. Direct comparison between clinical predictors and EGFR mutation for their predictive power has not been reported. Patients and methods: For 120 Korean NSCLC patients treated with gefitinib, we have analyzed EGFR mutational status in exons 18, 19 and 21. Patients were grouped according to the number of clinical predictors (female, adenocarcinoma and never-smoker). Response rate (RR), time-toprogression (TTP) and overall survival (OS) were analyzed. Multivariate analysis was performed to investigate which approach yielded better prediction. Results: RRs according to number of clinical predictors were 0: 3.4%, 1: 17.1%, 2: 29.4% and 3: 33.3% (p = 0.002). Patients with gefitinib-sensitive EGFR mutation showed 61.9% RR compared with 12.1% in the remaining patients (p < 0.001). RRs were higher in patients with the EGFR mutations regardless of the number of clinical predictors. In multivariate analysis, gefitinibsensitive EGFR mutation showed higher odds ratio of response (9.6, 95% confidence interval [CI] 3.2—28.7) compared with number of clinical predictors (1.7, 95% CI 1.1—2.7). Hazard ratio (HR) of TTP was also better in gefitinib-sensitive EGFR mutation (0.24, 95% CI 0.12—0.47) than number of clinical predictors (0.83, 95% CI 0.69—0.99). Only gefitinib-sensitive EGFR mutation was associated with improved OS (HR 0.25, 95% CI 0.12—0.52).
Corresponding author. Tel.: +82 2 2072 3943; fax: +82 2 762 9662. E-mail address: [email protected] (T.-Y. Kim).
0169-5002/$ — see front matter © 2006 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.lungcan.2006.07.007
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S.-W. Han et al. Conclusion: EGFR mutation should be analyzed whenever possible for effective prediction of objective benefit from gefitinib in NSCLC patients with one or more clinical predictors. © 2006 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Epidermal growth factor receptor is frequently overexpressed in various malignancies including non-small-cell lung cancer (NSCLC). Its activation has important role in various steps of carcinogenesis and progression [1]. EGFR targeted therapeutic approach by inhibition of EGFR activation with the specific tyrosine kinase inhibitor gefitinib has proven clinically successful especially in NSCLC. Objective responses were more frequently seen in subgroups of patients [2,3]. Therefore, selection of appropriate target population for the treatment may lead to improved treatment outcomes. Clinical predictors of better outcome with gefitinib treatment in NSCLC have been identified in the two randomized phase II trials and the compassionate Expanded Access Program [4—10]. These include East-Asian ethnicity, female sex, adenocarcinoma or bronchioloalveolar carcinoma histology and never-smoking history. Planned subgroup analysis of the phase III gefitinib trial, Iressa Survival Evaluation in Lung Cancer, showed significant survival benefit in never-smokers and Asians [11]. In addition, higher objective responses were seen in females, adenocarcinomas, neversmokers and Asians [11]. In the phase III trial of erlotinib, response rates were also higher in females, adenocarcinomas, never-smokers and Asians although the survival benefit of erlotinib treatment was not limited to these subgroups [12]. Moreover, phase II trial of first-line gefitinib in Korean never-smokers with adenocarcinomas have shown promising results suggesting that patient selection according to clinical parameters may be an effective strategy [13]. Molecular predictors of gefitinib sensitivity include mutations in the tyrosine kinase domain of EGFR and increase in gene copy number of EGFR [14—22]. Presence of EGFR mutation in exons 18, 19 and 21 was significantly associated with higher response rate in gefitinib-treated NSCLC patients [14—19]. Although its prognostic significance is controversial, gefitinib-treated NSCLC patients with EGFR mutations seem to have longer survival compared with the patients without mutations [17—19]. EGFR mutations were predominantly found in Asians, females, adenocarcinomas and never-smokers, which explains the association between the clinical predictors and gefitinib sensitivity [14—19]. Another molecular predictor of gefitinib sensitivity is increase in gene copy number of EGFR [19—22]. As EGFR mutation and high gene copy number are significantly associated with each other [19,20,22], appropriately designed study is needed to uncover which molecular marker has predominant role in determining gefitinib-sensitivity. It is possible that different ethnicities have different mechanisms of EGFR pathway addiction and resulting gefitinib-sensitivity. Identification of resistance mechanism, such as K-ras mutation could also be helpful in selecting most suitable patient for gefitinib treatment [22—24]. Gefitinib shows favorable outcome in aforementioned selected patient population among NSCLC patients. Con-
sidering the dramatic clinical responses, gefitinib could be considered prior to other chemotherapeutics in patients with clinical predictors of gefitinib responsiveness or EGFR mutation. However, no direct comparison between clinical predictors and EGFR mutation for prediction of gefitinib responsiveness has been reported and it is unclear whether patient selection should be based on clinical predictors or EGFR mutation. Therefore, we have analyzed clinical predictors and EGFR mutation in gefitinib-treated Korean NSCLC patients to investigate which leads to better patient selection.
2. Patients and methods 2.1. Patients One hundred and twenty consecutive NSCLC patients treated with gefitinib and assessable for EGFR mutational status in exons 18, 19 and 21 were included in the present analysis. Mutational data of 90 patients had been reported previously elsewhere [17]. All patients had pathologically proven locally advanced or metastatic NSCLC. Baseline patient characteristics are listed in Table 1. Treatment consisted of 250 mg of gefitinib daily continued until disease progression, intolerable toxicity or patient refusal. Patients initiated gefitinib treatment from February 2002 to December Table 1
Baseline patient characteristics
Characteristic
No. of patients (n = 120)
%
Sex Male Female
70 50
58.3 41.7
Age, median years (range) Male Female
60 (30—87) 61 (33—87) 58 (30—77)
Stage 3B 4
10 110
8.3 91.7
ECOG performance status 0—1 2—3
68 52
56.7 43.3
Histology Adenocarcinoma Bronchioloalveolar carcinoma Squamous cell carcinoma Others
71 12 30 7
59.2 10.0 25.0 5.8
Smoking history Never-smoked Smoked
53 67
44.2 55.8
ECOG, Eastern Cooperative Oncology Group.
Clinical predictors versus epidermal growth factor receptor mutation 2004. Survival status was collected until the end of April 2006. Median duration of follow-up was 27.2 months (range 4.8—52.7 months). Patients were re-evaluated every 4 weeks by chest X-ray or computed tomography, and tumor response was evaluated according to the World Health Organization criteria. All responses were confirmed at least 4 weeks after initial assessment. Formalin-fixed paraffin-embedded tissues obtained prior to any systemic chemotherapy or radiotherapy were retrieved from the archives of the Department of Pathology. The study protocol was reviewed and approved by the institutional review board at the Seoul National University Hospital.
2.2. DNA sequencing Sequencing analysis was performed as previously described [17]. DNA was extracted from five paraffin sections of 10-m thickness containing a representative portion of each tumor block, using the QIAamp DNA Mini kit (Qiagen, Hilden, Germany). One hundred nanograms (ng) of DNA were amplified in a 20 l reaction solution containing 2 l of 10× buffer (Roche, Mannheim, Germany), 1.7—2.5 mM of MgCl2 , 0.3 M of each primer pairs (exon 18, F: 5 -tccaaatgagctggcaagtg, R: 5 -tcccaaacactcagtgaaacaaa; exon 19, F: 5 -atgtggcaccatctcacaattgcc, R: 5 -ccacacagcaaagcagaaactcac; exon 21, F: 5 -gctcagagcctggcatgaa, R: 5 -catcctcccctgcatgtgt), 250 M of deoxynucleoside triphosphate and 2.5 units of DNA polymerase (Roche). Amplifications were performed using a 5 min (min) initial denaturation at 94 ◦ C; followed by 30 cycles of 1 min at 94 ◦ C, 1 min at 55 ◦ C and 1 min at 72 ◦ C; a 10 min final extension at 72 ◦ C. PCR products were then 2% gel-purified with a QIAgen gel extraction kit (Qiagen). DNA templates were processed for the DNA sequencing reaction using the ABI-PRISM BigDye Terminator v3.1 (Applied Biosystems, Foster, CA) with both forward and reverse sequence-specific primers. Twenty nanograms of purified PCR products were used in a 20 l sequencing reaction solution containing 8 l of BigDye Terminator v3.1 and 0.1 M of the same PCR primer. Sequencing reactions were performed using a 2 min initial denaturation at 96 ◦ C, followed by 25 cycles of 10 s (s) at 94 ◦ C, 15 s at 50 ◦ C and 3 min at 60 ◦ C. Sequence data were generated with the ABI PRISM 3100 DNA Analyzer (Applied Biosystems). Sequences were analyzed by Sequencer 3.1.1. software (Applied Biosystems).
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continuous variable. Analysis of TTP and OS was adjusted for performance status (ECOG PS 0—1 versus 2—3). Two-sided p-values of less than 0.05 were considered significant. All analyses were performed using SPSS for Windows, version 12.0 (SPSS Inc., Chicago, IL).
3. Results 3.1. Clinical predictors In accordance of previous reports, response rates were significantly higher in female, adenocarcinoma and neversmokers (Table 2). For more detailed prediction of gefitinib responsiveness, we have classified patients according to the number of favorable clinical predictors (female, adenocarcinoma and never-smoker). Response rate showed a significant trend towards increase as the number of clinical predictors increased (Fig. 1A). Female never-smokers with adenocarcinoma (three clinical predictors) showed a 33.3% response rate, whereas male smokers with nonadenocarcinoma (zero clinical predictors) showed a 3.4% response rate. The univariate odds ratio (OR) of response according to number of clinical predictors was 1.96 (95% CI 1.25—2.92, p = 0.003). Time to progression also showed a trend of increase as number of clinical predictors increased (Fig. 2A). However, Kaplan—Meier plots of overall survival of each patient group showed similar trends except for the zero clinical predictor group which showed the worst survival (Fig. 2B). After adjustment for performance status (ECOG PS 0—1 versus 2—3), number of clinical predictors was significantly associated with better TTP (HR 0.74, 95% CI 0.62—0.89; p = 0.001) and OS (HR 0.78, 95% CI 0.65—0.94; p = 0.01).
3.2. EGFR mutation EGFR mutation was found in 23 patients (19.2%). Deletion in exon 19 was the most predominant type found in 12 patients followed by L858R in six patients, G719A in three patients and A859T and L861Q in one patient each. One patient had an additional mutation: G719A + E709K. Response rates in patients with deletion in exon 19 was 50% (6/12), L858R 83.3% (5/6) and G719A 66.7% (2/3).
Table 2
Clinical predictors of gefitinib responsiveness Total no.
No. of responders (%)
p-Value*
Sex Male Female
70 50
9 (12.9) 16 (32.0)
0.011
Histology Adenocarcinoma Others
83 37
22 (26.5) 3 (8.1)
0.022
Smoking Never-smoker Smoker
53 67
17 (32.1) 8 (11.9)
0.007
2.3. Statistical analysis The statistical analyses of 2 × 2 contingency tables of categorical variables were performed using the Pearson’s 2 -test or the Fisher’s exact test where appropriate. Trends of association between number of clinical variables and response rate or mutational frequency were analyzed using the linearby-linear association test. The median durations of overall survival (OS) and time-to-progression (TTP) were calculated using the Kaplan—Meier method. Comparisons between different groups were made using the log-rank tests. Multivariate analyses were performed using a logistic regression model for response and stepwise Cox regression models for TTP and OS. Number of clinical predictors was entered as a
*
Pearson’s 2 -test.
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Fig. 1 Response rates according to number of clinical predictors (A) and EGFR mutational status (B). EGFR mutation denotes gefitinib-sensitive EGFR mutation. Positive includes deletion in exon 19, L858R and G719A; negative includes wild-type EGFR, A859T and L861Q. Numbers in the parentheses indicate numbers of responders/total patients in each patient group. * Linear-by-linear association test for trend, † Fisher’s exact test.
The two patients with A859T or L861Q did not respond to gefitinib. Collectively, patients with the gefitinib-sensitive mutations (exon 19, L858R and G719A) showed significantly higher response rate compared with the remaining patients (Fig. 1B). TTP was significantly prolonged in patients with gefitinib-sensitive mutations compared with the remaining patients (p < 0.001; median 13.8 months versus 1.9 months, respectively) (Fig. 2C). OS also showed significant prolon-
gation (p < 0.001; median 26.3 months versus 7.4 months, respectively) (Fig. 2D).
3.3. Clinical predictors versus EGFR mutation In multivariate analysis, gefitinib-sensitive EGFR mutation and number of clinical predictors were both significantly associated with objective response and TTP (Table 3). How-
Fig. 2 Kaplan—Meier plots of time to progression (A and C) and overall survival (B and D) according to number of clinical predictors and EGFR mutational status. EGFR mutation denotes gefitinib-sensitive EGFR mutation. Positive includes deletion in exon 19, L858R and G719A; negative includes wild-type EGFR, A859T and L861Q. * Log-rank test.
Clinical predictors versus epidermal growth factor receptor mutation Table 3
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Multivariate analyses of response and survival Objective response
Gefitinib-sensitive EGFR mutationb Number of clinical predictors
Time-to-progression a
Overall survival p-Value
Hazard ratioa (95% CI)
p-Value
0.24 (0.12—0.47)
<0.001
0.25 (0.12—0.52)
<0.001
0.83 (0.69—0.99)
0.039
Odds ratio (95% CI)
p-Value
Hazard ratio (95% CI)
9.6 (3.2—28.7)
<0.001
1.7 (1.1—2.7)
0.027
N/S
N/S, not significant. a Adjusted for performance status (ECOG PS 0—1 vs. 2—3). b vs. wild-type and other EGFR mutations (A859T and L861Q).
ever, only gefitinib-sensitive EGFR mutation was significantly associated with OS. This shows that analysis of EGFR mutational status is more helpful in prediction of objective benefit from gefitinib than the clinical predictors. The frequency of gefitinib-sensitive EGFR mutation was 3.4% (1/29) in zero clinical predictor group, 17.1% (6/35) in one, 17.6% (3/17) in two and 28.2% (11/39) in three (p = 0.011). In every clinical predictor group, patients with the EGFR mutations showed higher response rate suggesting that identification of EGFR mutational status may be helpful in prediction of gefitinib responsiveness regardless of the number of clinical predictors (Fig. 3). Moreover, there was no significant association between the number of clinical predictors and response in patients with gefitinib-sensitiveEGFR mutation. In contrast, RR increased as number of clinical predictor increased: 0% (0/27) in zero clinical predictor, 10.3% (3/29) in one, 15.4% (2/13) in two and 25.0% (7/28) in three (p = 0.005) in patients with wild-type EGFR. However, these differences did not result in significant difference in terms of TTP or OS (data not shown). Patients with gefitinib-sensitive EGFR mutation showed significantly higher response rate in every individual patient group with good response: female [64.3% (9/14) versus 19.4%
Fig. 3 Response rates according to EGFR mutational status in clinical predictor groups. (+) Denotes gefitinib-sensitive EGFR mutation cases, (−) denotes wild-type or other EGFR mutation cases. Numbers in the parentheses indicate numbers of responders/total patients in each patient group. * p-Values by Fisher’s exact test.
(7/36), p = 0.0005], adenocarcinoma [55.6% (10/18) versus 18.5% (12/65), p = 0.005] and never-smoker [61.5% (8/13) versus 22.5% (9/40), p = 0.016]. In a multivariate analysis where the three clinical predictors were included separately as covariates together with gefitinib-sensitive mutation, the EGFR mutation (OR 10.7, 95% CI 3.6—32.2; p < 0.001) and never-smoker (OR 3.0, 95% CI 1.1—8.5, p = 0.038) were independent predictors of response. Among female never-smokers with adenocarcinoma (three clinical predictors), the group of patients who have the best treatment outcome as determined by clinical factors, EGFR mutational status could additionally improve prediction of objective benefit. Response rates in patients with gefitinib-sensitive EGFR mutation was 54.5% (6/11) compared with 25.0% (7/28) in patients without such mutation (p = 0.13) (Fig. 3). Median TTP was 13.8 months in gefitinibsensitive mutations and 1.9 months in the remaining patients (p = 0.0029 by log-rank) (Fig. 4A). OS was also significantly longer in patients with the mutations (p = 0.023; median 31.3 months versus 7.4 months, respectively) (Fig. 4B).
4. Discussion Previous studies have shown that objective response to gefitinib is more frequently seen in Asians, females, adenocarcinomas and never-smokers [4—12]. In the present study, response rate showed significant increase as number of clinical predictors increased. Moreover, adjusted HR showed significant improvement of TTP and OS according to increase in number of clinical predictors. Our results demonstrate that number of clinical predictors can be used as a useful predictor of objective benefit from gefitinib when adequate tissue for mutational analysis is not available. The present study shows the superiority of EGFR mutational status compared to clinical predictors in prediction of gefitinib responsiveness. Although the response rate and survival showed improvement according to increase in the number of clinical predictors, response rate, TTP and OS were all better in patients with the gefitinib-sensitive EGFR mutations than the three clinical predictor group patients (female never-smokers with adenocarcinoma). Moreover, in the multivariate analysis, gefitinib-sensitive EGFR mutation showed a higher OR of 9.6 in objective response and a significant HR of 0.25 in terms of OS whereas number of clinical predictors showed an OR of 1.7 and no significant association with OS (Table 3). Presence of gefitinib-sensitive
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Fig. 4 Kaplan—Meier plots of time-to-progression (A) and overall survival (B) according to EGFR mutational status in female neversmokers with adenocarcinoma. EGFR mutation denotes gefitinib-sensitive EGFR mutation. Positive includes deletion in exon 19 and L858R; negative includes wild-type EGFR. * Log-rank test.
EGFR mutation conferred higher response rate in every clinical predictor group (Fig. 3). Moreover, analysis in the most clinically favorable group (female never-smokers with adenocarcinoma) shows that EGFR mutational status could further improve response and survival prediction. Therefore, it would be reasonable to perform EGFR mutation analysis for more precise prediction of better outcome in patients with adequate tissue for the analysis. However, considering the low mutational frequency and response rate and poor survival in patients with zero clinical predictors (male smokers with non-adenocarcinoma), other treatment options should be recommended rather than testing for EGFR mutation or giving gefitinib in these patients. In patients with EGFR mutation, no difference in response was found according to the number of clinical predictors, which suggests that presence of EGFR mutation is the major determinant of gefitinib sensitivity in patients with EGFR mutation. In contrast, RR showed an increase as number of clinical predictors increased among the patients with wildtype EGFR. This increase may be due to gefitinib-sensitizing mechanism other than EGFR mutation, such as increased gene copy number potentially associated with clinical predictors. However, association between increased gene copy number and clinical predictors is controversial [20,22,25]. Another possible explanation for the association in the nonmutants is the potential limitation, false negativity, of the mutational analysis in the present study which used archival paraffin-embedded tissue most of which were obtained at initial diagnosis. The present study was performed by retrospective analysis of consecutive patients treated at our institutions. These results need to be validated in future prospective trials. In addition, as all the study patients were Koreans, we could not analyze the effect of ethnicity. Whether these results could be applied to other ethnicities should also be investigated. In conclusion, EGFR mutation should be analyzed in patients with one or more clinical predictors whenever possible for effective prediction of objective benefit from gefitinib in NSCLC patients. In case when adequate tissue for EGFR mutational analysis cannot be obtained, response prediction can be made with the number of clinical predictors.
Acknowledgement This study was supported in part by a grant from the Korean Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (03-PJ10-PG13-GD01-0002).
References [1] Rowinsky EK. The erbB family: targets for therapeutic development against cancer and therapeutic strategies using monoclonal antibodies and tyrosine kinase inhibitors. Annu Rev Med 2004;55:433—57. [2] Herbst RS, Fukuoka M, Baselga J. Gefitinib—–a novel targeted approach to treating cancer. Nat Rev Cancer 2004;4:956—65. [3] Giaccone G. Epidermal growth factor receptor inhibitors in the treatment of non-small-cell lung cancer. J Clin Oncol 2005;23:3235—42. [4] Fukuoka M, Yano S, Giaccone G, Tamura T, Nakagawa K, Douillard JY, et al. Multi-institutional randomized phase II trial of gefitinib for previously treated patients with advanced non-small-cell lung cancer (The IDEAL 1 Trial). J Clin Oncol 2003;21:2237—46. [5] Kris MG, Natale RB, Herbst RS, Lynch Jr TJ, Prager D, Belani CP, et al. Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non-small cell lung cancer: a randomized trial. JAMA 2003;290:2149—58. [6] Miller VA, Kris MG, Shah N, Patel J, Azzoli C, Gomez J, et al. Bronchioloalveolar pathologic subtype and smoking history predict sensitivity to gefitinib in advanced non-small-cell lung cancer. J Clin Oncol 2004;22:1103—9. [7] Han SW, Hwang PG, Chung DH, Kim DW, Im SA, Kim YT, et al. Epidermal growth factor receptor (EGFR) downstream molecules as response predictive markers for gefitinib (Iressa, ZD1839) in chemotherapy-resistant non-small cell lung cancer. Int J Cancer 2005;113:109—15. [8] Haringhuizen A, van Tinteren H, Vaessen HF, Baas P, van Zandwijk N. Gefitinib as a last treatment option for non-small-cell lung cancer: durable disease control in a subset of patients. Ann Oncol 2004;15:786—92. [9] Janne PA, Gurubhagavatula S, Yeap BY, Lucca J, Ostler P, Skarin AT, et al. Outcomes of patients with advanced non-small cell lung cancer treated with gefitinib (ZD1839, ‘‘Iressa’’) on an expanded access study. Lung Cancer 2004;44:221—30.
Clinical predictors versus epidermal growth factor receptor mutation [10] Park J, Park BB, Kim JY, Lee SH, Lee SI, Kim HY, et al. Gefitinib (ZD1839) monotherapy as a salvage regimen for previously treated advanced non-small cell lung cancer. Clin Cancer Res 2004;10:4383—8. [11] Thatcher N, Chang A, Parikh P, Rodrigues Pereira J, Ciuleanu T, von Pawel J, et al. Gefitinib plus best supportive care in previously treated patients with refractory advanced non-small-cell lung cancer: results from a randomised, placebo-controlled, multicentre study (Iressa Survival Evaluation in Lung Cancer). Lancet 2005;366:1527—37. [12] Shepherd FA, Rodrigues Pereira J, Ciuleanu T, Tan EH, Hirsh V, Thongprasert S, et al. Erlotinib in previously treated non-smallcell lung cancer. N Engl J Med 2005;353:123—32. [13] Lee DH, Han JY, Lee HG, Lee JJ, Lee EK, Kim HY, et al. Gefitinib as a first-line therapy of advanced or metastatic adenocarcinoma of the lung in never-smokers. Clin Cancer Res 2005;11:3032—7. [14] Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-smallcell lung cancer to gefitinib. N Engl J Med 2004;350:2129—39. [15] Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 2004;304:1497—500. [16] Pao W, Miller V, Zakowski M, Doherty J, Politi K, Sarkaria I, et al. EGF receptor gene mutations are common in lung cancers from ‘‘never smokers’’ and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci USA 2004;101:13306—11. [17] Han SW, Kim TY, Hwang PG, Jeong S, Kim J, Choi IS, et al. Predictive and prognostic impact of epidermal growth factor receptor mutation in non-small-cell lung cancer patients treated with gefitinib. J Clin Oncol 2005;23:2493—501. [18] Mitsudomi T, Kosaka T, Endoh H, Horio Y, Hida T, Mori S, et al. Mutations of the epidermal growth factor receptor gene predict prolonged survival after gefitinib treatment in patients
[19]
[20]
[21]
[22]
[23]
[24]
[25]
207
with non-small-cell lung cancer with postoperative recurrence. J Clin Oncol 2005;23:2513—20. Takano T, Ohe Y, Sakamoto H, Tsuta K, Matsuno Y, Tateishi U, et al. Epidermal growth factor receptor gene mutations and increased copy numbers predict gefitinib sensitivity in patients with recurrent non-small-cell lung cancer. J Clin Oncol 2005;23:6829—37. Cappuzzo F, Hirsch FR, Rossi E, Bartolini S, Ceresoli GL, Bemis L, et al. Epidermal growth factor receptor gene and protein and gefitinib sensitivity in non-small-cell lung cancer. J Natl Cancer Inst 2005;97:643—55. Hirsch FR, Varella-Garcia M, McCoy J, West H, Xavier AC, Gumerlock P, et al. Increased epidermal growth factor receptor gene copy number detected by fluorescence in situ hybridization associates with increased sensitivity to gefitinib in patients with bronchioloalveolar carcinoma subtypes: a Southwest Oncology Group Study. J Clin Oncol 2005;23:6838—45. Han SW, Kim TY, Jeon YK, Hwang PG, Im SA, Lee KH, et al. Optimization of patient selection for gefitinib in non-small cell lung cancer by combined analysis of epidermal growth factor receptor mutation, K-ras mutation, and Akt phosphorylation. Clin Cancer Res 2006;12:2538—44. Pao W, Wang TY, Riely GJ, Miller VA, Pan Q, Ladanyi M, et al. KRAS mutations and primary resistance of lung adenocarcinomas to gefitinib or erlotinib. PLoS Med 2005;2:e17. Eberhard DA, Johnson BE, Amler LC, Goddard AD, Heldens SL, Herbst RS, et al. Mutations in the epidermal growth factor receptor and in KRAS are predictive and prognostic indicators in patients with non-small-cell lung cancer treated with chemotherapy alone and in combination with erlotinib. J Clin Oncol 2005;23:5900—9. Hirsch FR, Varella-Garcia M, Bunn Jr PA, Di Maria MV, Veve R, Bremmes RM, et al. Epidermal growth factor receptor in non-small-cell lung carcinomas: correlation between gene copy number and protein expression and impact on prognosis. J Clin Oncol 2003;21:3798—807.
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journal homepage: www.elsevier.com/locate/lungcan
Clinical predictors versus epidermal growth factor receptor mutation in gefitinib-treated non-small-cell lung cancer patients Sae-Won Han a, Tae-You Kim a,c,∗, Kyung-Hun Lee a, Pil Gyu Hwang b, Yoon Kyung Jeon b, Do-Youn Oh a,c, Se-Hoon Lee a,c, Dong-Wan Kim a,c, Seock-Ah Im a,c, Doo Hyun Chung b, Dae Seog Heo a,c, Yung-Jue Bang a,c a
Department of Internal Medicine, Seoul National University College of Medicine, 28 Yongon-Dong, Chongno-Gu, Seoul 110-744, Republic of Korea b Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea c Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea Received 24 June 2006; received in revised form 12 July 2006; accepted 12 July 2006
KEYWORDS Non-small-cell lung cancer; Gefitinib; EGFR mutation; Pharmacogenomics
∗
Summary Background: Clinical predictors including Asian, female, adenocarcinoma and never-smoker and epidermal growth factor mutation are associated with gefitinib responsiveness in non-smallcell lung cancer. Direct comparison between clinical predictors and EGFR mutation for their predictive power has not been reported. Patients and methods: For 120 Korean NSCLC patients treated with gefitinib, we have analyzed EGFR mutational status in exons 18, 19 and 21. Patients were grouped according to the number of clinical predictors (female, adenocarcinoma and never-smoker). Response rate (RR), time-toprogression (TTP) and overall survival (OS) were analyzed. Multivariate analysis was performed to investigate which approach yielded better prediction. Results: RRs according to number of clinical predictors were 0: 3.4%, 1: 17.1%, 2: 29.4% and 3: 33.3% (p = 0.002). Patients with gefitinib-sensitive EGFR mutation showed 61.9% RR compared with 12.1% in the remaining patients (p < 0.001). RRs were higher in patients with the EGFR mutations regardless of the number of clinical predictors. In multivariate analysis, gefitinibsensitive EGFR mutation showed higher odds ratio of response (9.6, 95% confidence interval [CI] 3.2—28.7) compared with number of clinical predictors (1.7, 95% CI 1.1—2.7). Hazard ratio (HR) of TTP was also better in gefitinib-sensitive EGFR mutation (0.24, 95% CI 0.12—0.47) than number of clinical predictors (0.83, 95% CI 0.69—0.99). Only gefitinib-sensitive EGFR mutation was associated with improved OS (HR 0.25, 95% CI 0.12—0.52).
Corresponding author. Tel.: +82 2 2072 3943; fax: +82 2 762 9662. E-mail address: [email protected] (T.-Y. Kim).
0169-5002/$ — see front matter © 2006 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.lungcan.2006.07.007
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S.-W. Han et al. Conclusion: EGFR mutation should be analyzed whenever possible for effective prediction of objective benefit from gefitinib in NSCLC patients with one or more clinical predictors. © 2006 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Epidermal growth factor receptor is frequently overexpressed in various malignancies including non-small-cell lung cancer (NSCLC). Its activation has important role in various steps of carcinogenesis and progression [1]. EGFR targeted therapeutic approach by inhibition of EGFR activation with the specific tyrosine kinase inhibitor gefitinib has proven clinically successful especially in NSCLC. Objective responses were more frequently seen in subgroups of patients [2,3]. Therefore, selection of appropriate target population for the treatment may lead to improved treatment outcomes. Clinical predictors of better outcome with gefitinib treatment in NSCLC have been identified in the two randomized phase II trials and the compassionate Expanded Access Program [4—10]. These include East-Asian ethnicity, female sex, adenocarcinoma or bronchioloalveolar carcinoma histology and never-smoking history. Planned subgroup analysis of the phase III gefitinib trial, Iressa Survival Evaluation in Lung Cancer, showed significant survival benefit in never-smokers and Asians [11]. In addition, higher objective responses were seen in females, adenocarcinomas, neversmokers and Asians [11]. In the phase III trial of erlotinib, response rates were also higher in females, adenocarcinomas, never-smokers and Asians although the survival benefit of erlotinib treatment was not limited to these subgroups [12]. Moreover, phase II trial of first-line gefitinib in Korean never-smokers with adenocarcinomas have shown promising results suggesting that patient selection according to clinical parameters may be an effective strategy [13]. Molecular predictors of gefitinib sensitivity include mutations in the tyrosine kinase domain of EGFR and increase in gene copy number of EGFR [14—22]. Presence of EGFR mutation in exons 18, 19 and 21 was significantly associated with higher response rate in gefitinib-treated NSCLC patients [14—19]. Although its prognostic significance is controversial, gefitinib-treated NSCLC patients with EGFR mutations seem to have longer survival compared with the patients without mutations [17—19]. EGFR mutations were predominantly found in Asians, females, adenocarcinomas and never-smokers, which explains the association between the clinical predictors and gefitinib sensitivity [14—19]. Another molecular predictor of gefitinib sensitivity is increase in gene copy number of EGFR [19—22]. As EGFR mutation and high gene copy number are significantly associated with each other [19,20,22], appropriately designed study is needed to uncover which molecular marker has predominant role in determining gefitinib-sensitivity. It is possible that different ethnicities have different mechanisms of EGFR pathway addiction and resulting gefitinib-sensitivity. Identification of resistance mechanism, such as K-ras mutation could also be helpful in selecting most suitable patient for gefitinib treatment [22—24]. Gefitinib shows favorable outcome in aforementioned selected patient population among NSCLC patients. Con-
sidering the dramatic clinical responses, gefitinib could be considered prior to other chemotherapeutics in patients with clinical predictors of gefitinib responsiveness or EGFR mutation. However, no direct comparison between clinical predictors and EGFR mutation for prediction of gefitinib responsiveness has been reported and it is unclear whether patient selection should be based on clinical predictors or EGFR mutation. Therefore, we have analyzed clinical predictors and EGFR mutation in gefitinib-treated Korean NSCLC patients to investigate which leads to better patient selection.
2. Patients and methods 2.1. Patients One hundred and twenty consecutive NSCLC patients treated with gefitinib and assessable for EGFR mutational status in exons 18, 19 and 21 were included in the present analysis. Mutational data of 90 patients had been reported previously elsewhere [17]. All patients had pathologically proven locally advanced or metastatic NSCLC. Baseline patient characteristics are listed in Table 1. Treatment consisted of 250 mg of gefitinib daily continued until disease progression, intolerable toxicity or patient refusal. Patients initiated gefitinib treatment from February 2002 to December Table 1
Baseline patient characteristics
Characteristic
No. of patients (n = 120)
%
Sex Male Female
70 50
58.3 41.7
Age, median years (range) Male Female
60 (30—87) 61 (33—87) 58 (30—77)
Stage 3B 4
10 110
8.3 91.7
ECOG performance status 0—1 2—3
68 52
56.7 43.3
Histology Adenocarcinoma Bronchioloalveolar carcinoma Squamous cell carcinoma Others
71 12 30 7
59.2 10.0 25.0 5.8
Smoking history Never-smoked Smoked
53 67
44.2 55.8
ECOG, Eastern Cooperative Oncology Group.
Clinical predictors versus epidermal growth factor receptor mutation 2004. Survival status was collected until the end of April 2006. Median duration of follow-up was 27.2 months (range 4.8—52.7 months). Patients were re-evaluated every 4 weeks by chest X-ray or computed tomography, and tumor response was evaluated according to the World Health Organization criteria. All responses were confirmed at least 4 weeks after initial assessment. Formalin-fixed paraffin-embedded tissues obtained prior to any systemic chemotherapy or radiotherapy were retrieved from the archives of the Department of Pathology. The study protocol was reviewed and approved by the institutional review board at the Seoul National University Hospital.
2.2. DNA sequencing Sequencing analysis was performed as previously described [17]. DNA was extracted from five paraffin sections of 10-m thickness containing a representative portion of each tumor block, using the QIAamp DNA Mini kit (Qiagen, Hilden, Germany). One hundred nanograms (ng) of DNA were amplified in a 20 l reaction solution containing 2 l of 10× buffer (Roche, Mannheim, Germany), 1.7—2.5 mM of MgCl2 , 0.3 M of each primer pairs (exon 18, F: 5 -tccaaatgagctggcaagtg, R: 5 -tcccaaacactcagtgaaacaaa; exon 19, F: 5 -atgtggcaccatctcacaattgcc, R: 5 -ccacacagcaaagcagaaactcac; exon 21, F: 5 -gctcagagcctggcatgaa, R: 5 -catcctcccctgcatgtgt), 250 M of deoxynucleoside triphosphate and 2.5 units of DNA polymerase (Roche). Amplifications were performed using a 5 min (min) initial denaturation at 94 ◦ C; followed by 30 cycles of 1 min at 94 ◦ C, 1 min at 55 ◦ C and 1 min at 72 ◦ C; a 10 min final extension at 72 ◦ C. PCR products were then 2% gel-purified with a QIAgen gel extraction kit (Qiagen). DNA templates were processed for the DNA sequencing reaction using the ABI-PRISM BigDye Terminator v3.1 (Applied Biosystems, Foster, CA) with both forward and reverse sequence-specific primers. Twenty nanograms of purified PCR products were used in a 20 l sequencing reaction solution containing 8 l of BigDye Terminator v3.1 and 0.1 M of the same PCR primer. Sequencing reactions were performed using a 2 min initial denaturation at 96 ◦ C, followed by 25 cycles of 10 s (s) at 94 ◦ C, 15 s at 50 ◦ C and 3 min at 60 ◦ C. Sequence data were generated with the ABI PRISM 3100 DNA Analyzer (Applied Biosystems). Sequences were analyzed by Sequencer 3.1.1. software (Applied Biosystems).
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continuous variable. Analysis of TTP and OS was adjusted for performance status (ECOG PS 0—1 versus 2—3). Two-sided p-values of less than 0.05 were considered significant. All analyses were performed using SPSS for Windows, version 12.0 (SPSS Inc., Chicago, IL).
3. Results 3.1. Clinical predictors In accordance of previous reports, response rates were significantly higher in female, adenocarcinoma and neversmokers (Table 2). For more detailed prediction of gefitinib responsiveness, we have classified patients according to the number of favorable clinical predictors (female, adenocarcinoma and never-smoker). Response rate showed a significant trend towards increase as the number of clinical predictors increased (Fig. 1A). Female never-smokers with adenocarcinoma (three clinical predictors) showed a 33.3% response rate, whereas male smokers with nonadenocarcinoma (zero clinical predictors) showed a 3.4% response rate. The univariate odds ratio (OR) of response according to number of clinical predictors was 1.96 (95% CI 1.25—2.92, p = 0.003). Time to progression also showed a trend of increase as number of clinical predictors increased (Fig. 2A). However, Kaplan—Meier plots of overall survival of each patient group showed similar trends except for the zero clinical predictor group which showed the worst survival (Fig. 2B). After adjustment for performance status (ECOG PS 0—1 versus 2—3), number of clinical predictors was significantly associated with better TTP (HR 0.74, 95% CI 0.62—0.89; p = 0.001) and OS (HR 0.78, 95% CI 0.65—0.94; p = 0.01).
3.2. EGFR mutation EGFR mutation was found in 23 patients (19.2%). Deletion in exon 19 was the most predominant type found in 12 patients followed by L858R in six patients, G719A in three patients and A859T and L861Q in one patient each. One patient had an additional mutation: G719A + E709K. Response rates in patients with deletion in exon 19 was 50% (6/12), L858R 83.3% (5/6) and G719A 66.7% (2/3).
Table 2
Clinical predictors of gefitinib responsiveness Total no.
No. of responders (%)
p-Value*
Sex Male Female
70 50
9 (12.9) 16 (32.0)
0.011
Histology Adenocarcinoma Others
83 37
22 (26.5) 3 (8.1)
0.022
Smoking Never-smoker Smoker
53 67
17 (32.1) 8 (11.9)
0.007
2.3. Statistical analysis The statistical analyses of 2 × 2 contingency tables of categorical variables were performed using the Pearson’s 2 -test or the Fisher’s exact test where appropriate. Trends of association between number of clinical variables and response rate or mutational frequency were analyzed using the linearby-linear association test. The median durations of overall survival (OS) and time-to-progression (TTP) were calculated using the Kaplan—Meier method. Comparisons between different groups were made using the log-rank tests. Multivariate analyses were performed using a logistic regression model for response and stepwise Cox regression models for TTP and OS. Number of clinical predictors was entered as a
*
Pearson’s 2 -test.
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Fig. 1 Response rates according to number of clinical predictors (A) and EGFR mutational status (B). EGFR mutation denotes gefitinib-sensitive EGFR mutation. Positive includes deletion in exon 19, L858R and G719A; negative includes wild-type EGFR, A859T and L861Q. Numbers in the parentheses indicate numbers of responders/total patients in each patient group. * Linear-by-linear association test for trend, † Fisher’s exact test.
The two patients with A859T or L861Q did not respond to gefitinib. Collectively, patients with the gefitinib-sensitive mutations (exon 19, L858R and G719A) showed significantly higher response rate compared with the remaining patients (Fig. 1B). TTP was significantly prolonged in patients with gefitinib-sensitive mutations compared with the remaining patients (p < 0.001; median 13.8 months versus 1.9 months, respectively) (Fig. 2C). OS also showed significant prolon-
gation (p < 0.001; median 26.3 months versus 7.4 months, respectively) (Fig. 2D).
3.3. Clinical predictors versus EGFR mutation In multivariate analysis, gefitinib-sensitive EGFR mutation and number of clinical predictors were both significantly associated with objective response and TTP (Table 3). How-
Fig. 2 Kaplan—Meier plots of time to progression (A and C) and overall survival (B and D) according to number of clinical predictors and EGFR mutational status. EGFR mutation denotes gefitinib-sensitive EGFR mutation. Positive includes deletion in exon 19, L858R and G719A; negative includes wild-type EGFR, A859T and L861Q. * Log-rank test.
Clinical predictors versus epidermal growth factor receptor mutation Table 3
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Multivariate analyses of response and survival Objective response
Gefitinib-sensitive EGFR mutationb Number of clinical predictors
Time-to-progression a
Overall survival p-Value
Hazard ratioa (95% CI)
p-Value
0.24 (0.12—0.47)
<0.001
0.25 (0.12—0.52)
<0.001
0.83 (0.69—0.99)
0.039
Odds ratio (95% CI)
p-Value
Hazard ratio (95% CI)
9.6 (3.2—28.7)
<0.001
1.7 (1.1—2.7)
0.027
N/S
N/S, not significant. a Adjusted for performance status (ECOG PS 0—1 vs. 2—3). b vs. wild-type and other EGFR mutations (A859T and L861Q).
ever, only gefitinib-sensitive EGFR mutation was significantly associated with OS. This shows that analysis of EGFR mutational status is more helpful in prediction of objective benefit from gefitinib than the clinical predictors. The frequency of gefitinib-sensitive EGFR mutation was 3.4% (1/29) in zero clinical predictor group, 17.1% (6/35) in one, 17.6% (3/17) in two and 28.2% (11/39) in three (p = 0.011). In every clinical predictor group, patients with the EGFR mutations showed higher response rate suggesting that identification of EGFR mutational status may be helpful in prediction of gefitinib responsiveness regardless of the number of clinical predictors (Fig. 3). Moreover, there was no significant association between the number of clinical predictors and response in patients with gefitinib-sensitiveEGFR mutation. In contrast, RR increased as number of clinical predictor increased: 0% (0/27) in zero clinical predictor, 10.3% (3/29) in one, 15.4% (2/13) in two and 25.0% (7/28) in three (p = 0.005) in patients with wild-type EGFR. However, these differences did not result in significant difference in terms of TTP or OS (data not shown). Patients with gefitinib-sensitive EGFR mutation showed significantly higher response rate in every individual patient group with good response: female [64.3% (9/14) versus 19.4%
Fig. 3 Response rates according to EGFR mutational status in clinical predictor groups. (+) Denotes gefitinib-sensitive EGFR mutation cases, (−) denotes wild-type or other EGFR mutation cases. Numbers in the parentheses indicate numbers of responders/total patients in each patient group. * p-Values by Fisher’s exact test.
(7/36), p = 0.0005], adenocarcinoma [55.6% (10/18) versus 18.5% (12/65), p = 0.005] and never-smoker [61.5% (8/13) versus 22.5% (9/40), p = 0.016]. In a multivariate analysis where the three clinical predictors were included separately as covariates together with gefitinib-sensitive mutation, the EGFR mutation (OR 10.7, 95% CI 3.6—32.2; p < 0.001) and never-smoker (OR 3.0, 95% CI 1.1—8.5, p = 0.038) were independent predictors of response. Among female never-smokers with adenocarcinoma (three clinical predictors), the group of patients who have the best treatment outcome as determined by clinical factors, EGFR mutational status could additionally improve prediction of objective benefit. Response rates in patients with gefitinib-sensitive EGFR mutation was 54.5% (6/11) compared with 25.0% (7/28) in patients without such mutation (p = 0.13) (Fig. 3). Median TTP was 13.8 months in gefitinibsensitive mutations and 1.9 months in the remaining patients (p = 0.0029 by log-rank) (Fig. 4A). OS was also significantly longer in patients with the mutations (p = 0.023; median 31.3 months versus 7.4 months, respectively) (Fig. 4B).
4. Discussion Previous studies have shown that objective response to gefitinib is more frequently seen in Asians, females, adenocarcinomas and never-smokers [4—12]. In the present study, response rate showed significant increase as number of clinical predictors increased. Moreover, adjusted HR showed significant improvement of TTP and OS according to increase in number of clinical predictors. Our results demonstrate that number of clinical predictors can be used as a useful predictor of objective benefit from gefitinib when adequate tissue for mutational analysis is not available. The present study shows the superiority of EGFR mutational status compared to clinical predictors in prediction of gefitinib responsiveness. Although the response rate and survival showed improvement according to increase in the number of clinical predictors, response rate, TTP and OS were all better in patients with the gefitinib-sensitive EGFR mutations than the three clinical predictor group patients (female never-smokers with adenocarcinoma). Moreover, in the multivariate analysis, gefitinib-sensitive EGFR mutation showed a higher OR of 9.6 in objective response and a significant HR of 0.25 in terms of OS whereas number of clinical predictors showed an OR of 1.7 and no significant association with OS (Table 3). Presence of gefitinib-sensitive
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Fig. 4 Kaplan—Meier plots of time-to-progression (A) and overall survival (B) according to EGFR mutational status in female neversmokers with adenocarcinoma. EGFR mutation denotes gefitinib-sensitive EGFR mutation. Positive includes deletion in exon 19 and L858R; negative includes wild-type EGFR. * Log-rank test.
EGFR mutation conferred higher response rate in every clinical predictor group (Fig. 3). Moreover, analysis in the most clinically favorable group (female never-smokers with adenocarcinoma) shows that EGFR mutational status could further improve response and survival prediction. Therefore, it would be reasonable to perform EGFR mutation analysis for more precise prediction of better outcome in patients with adequate tissue for the analysis. However, considering the low mutational frequency and response rate and poor survival in patients with zero clinical predictors (male smokers with non-adenocarcinoma), other treatment options should be recommended rather than testing for EGFR mutation or giving gefitinib in these patients. In patients with EGFR mutation, no difference in response was found according to the number of clinical predictors, which suggests that presence of EGFR mutation is the major determinant of gefitinib sensitivity in patients with EGFR mutation. In contrast, RR showed an increase as number of clinical predictors increased among the patients with wildtype EGFR. This increase may be due to gefitinib-sensitizing mechanism other than EGFR mutation, such as increased gene copy number potentially associated with clinical predictors. However, association between increased gene copy number and clinical predictors is controversial [20,22,25]. Another possible explanation for the association in the nonmutants is the potential limitation, false negativity, of the mutational analysis in the present study which used archival paraffin-embedded tissue most of which were obtained at initial diagnosis. The present study was performed by retrospective analysis of consecutive patients treated at our institutions. These results need to be validated in future prospective trials. In addition, as all the study patients were Koreans, we could not analyze the effect of ethnicity. Whether these results could be applied to other ethnicities should also be investigated. In conclusion, EGFR mutation should be analyzed in patients with one or more clinical predictors whenever possible for effective prediction of objective benefit from gefitinib in NSCLC patients. In case when adequate tissue for EGFR mutational analysis cannot be obtained, response prediction can be made with the number of clinical predictors.
Acknowledgement This study was supported in part by a grant from the Korean Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (03-PJ10-PG13-GD01-0002).
References [1] Rowinsky EK. The erbB family: targets for therapeutic development against cancer and therapeutic strategies using monoclonal antibodies and tyrosine kinase inhibitors. Annu Rev Med 2004;55:433—57. [2] Herbst RS, Fukuoka M, Baselga J. Gefitinib—–a novel targeted approach to treating cancer. Nat Rev Cancer 2004;4:956—65. [3] Giaccone G. Epidermal growth factor receptor inhibitors in the treatment of non-small-cell lung cancer. J Clin Oncol 2005;23:3235—42. [4] Fukuoka M, Yano S, Giaccone G, Tamura T, Nakagawa K, Douillard JY, et al. Multi-institutional randomized phase II trial of gefitinib for previously treated patients with advanced non-small-cell lung cancer (The IDEAL 1 Trial). J Clin Oncol 2003;21:2237—46. [5] Kris MG, Natale RB, Herbst RS, Lynch Jr TJ, Prager D, Belani CP, et al. Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non-small cell lung cancer: a randomized trial. JAMA 2003;290:2149—58. [6] Miller VA, Kris MG, Shah N, Patel J, Azzoli C, Gomez J, et al. Bronchioloalveolar pathologic subtype and smoking history predict sensitivity to gefitinib in advanced non-small-cell lung cancer. J Clin Oncol 2004;22:1103—9. [7] Han SW, Hwang PG, Chung DH, Kim DW, Im SA, Kim YT, et al. Epidermal growth factor receptor (EGFR) downstream molecules as response predictive markers for gefitinib (Iressa, ZD1839) in chemotherapy-resistant non-small cell lung cancer. Int J Cancer 2005;113:109—15. [8] Haringhuizen A, van Tinteren H, Vaessen HF, Baas P, van Zandwijk N. Gefitinib as a last treatment option for non-small-cell lung cancer: durable disease control in a subset of patients. Ann Oncol 2004;15:786—92. [9] Janne PA, Gurubhagavatula S, Yeap BY, Lucca J, Ostler P, Skarin AT, et al. Outcomes of patients with advanced non-small cell lung cancer treated with gefitinib (ZD1839, ‘‘Iressa’’) on an expanded access study. Lung Cancer 2004;44:221—30.
Clinical predictors versus epidermal growth factor receptor mutation [10] Park J, Park BB, Kim JY, Lee SH, Lee SI, Kim HY, et al. Gefitinib (ZD1839) monotherapy as a salvage regimen for previously treated advanced non-small cell lung cancer. Clin Cancer Res 2004;10:4383—8. [11] Thatcher N, Chang A, Parikh P, Rodrigues Pereira J, Ciuleanu T, von Pawel J, et al. Gefitinib plus best supportive care in previously treated patients with refractory advanced non-small-cell lung cancer: results from a randomised, placebo-controlled, multicentre study (Iressa Survival Evaluation in Lung Cancer). Lancet 2005;366:1527—37. [12] Shepherd FA, Rodrigues Pereira J, Ciuleanu T, Tan EH, Hirsh V, Thongprasert S, et al. Erlotinib in previously treated non-smallcell lung cancer. N Engl J Med 2005;353:123—32. [13] Lee DH, Han JY, Lee HG, Lee JJ, Lee EK, Kim HY, et al. Gefitinib as a first-line therapy of advanced or metastatic adenocarcinoma of the lung in never-smokers. Clin Cancer Res 2005;11:3032—7. [14] Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-smallcell lung cancer to gefitinib. N Engl J Med 2004;350:2129—39. [15] Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 2004;304:1497—500. [16] Pao W, Miller V, Zakowski M, Doherty J, Politi K, Sarkaria I, et al. EGF receptor gene mutations are common in lung cancers from ‘‘never smokers’’ and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci USA 2004;101:13306—11. [17] Han SW, Kim TY, Hwang PG, Jeong S, Kim J, Choi IS, et al. Predictive and prognostic impact of epidermal growth factor receptor mutation in non-small-cell lung cancer patients treated with gefitinib. J Clin Oncol 2005;23:2493—501. [18] Mitsudomi T, Kosaka T, Endoh H, Horio Y, Hida T, Mori S, et al. Mutations of the epidermal growth factor receptor gene predict prolonged survival after gefitinib treatment in patients
[19]
[20]
[21]
[22]
[23]
[24]
[25]
207
with non-small-cell lung cancer with postoperative recurrence. J Clin Oncol 2005;23:2513—20. Takano T, Ohe Y, Sakamoto H, Tsuta K, Matsuno Y, Tateishi U, et al. Epidermal growth factor receptor gene mutations and increased copy numbers predict gefitinib sensitivity in patients with recurrent non-small-cell lung cancer. J Clin Oncol 2005;23:6829—37. Cappuzzo F, Hirsch FR, Rossi E, Bartolini S, Ceresoli GL, Bemis L, et al. Epidermal growth factor receptor gene and protein and gefitinib sensitivity in non-small-cell lung cancer. J Natl Cancer Inst 2005;97:643—55. Hirsch FR, Varella-Garcia M, McCoy J, West H, Xavier AC, Gumerlock P, et al. Increased epidermal growth factor receptor gene copy number detected by fluorescence in situ hybridization associates with increased sensitivity to gefitinib in patients with bronchioloalveolar carcinoma subtypes: a Southwest Oncology Group Study. J Clin Oncol 2005;23:6838—45. Han SW, Kim TY, Jeon YK, Hwang PG, Im SA, Lee KH, et al. Optimization of patient selection for gefitinib in non-small cell lung cancer by combined analysis of epidermal growth factor receptor mutation, K-ras mutation, and Akt phosphorylation. Clin Cancer Res 2006;12:2538—44. Pao W, Wang TY, Riely GJ, Miller VA, Pan Q, Ladanyi M, et al. KRAS mutations and primary resistance of lung adenocarcinomas to gefitinib or erlotinib. PLoS Med 2005;2:e17. Eberhard DA, Johnson BE, Amler LC, Goddard AD, Heldens SL, Herbst RS, et al. Mutations in the epidermal growth factor receptor and in KRAS are predictive and prognostic indicators in patients with non-small-cell lung cancer treated with chemotherapy alone and in combination with erlotinib. J Clin Oncol 2005;23:5900—9. Hirsch FR, Varella-Garcia M, Bunn Jr PA, Di Maria MV, Veve R, Bremmes RM, et al. Epidermal growth factor receptor in non-small-cell lung carcinomas: correlation between gene copy number and protein expression and impact on prognosis. J Clin Oncol 2003;21:3798—807.