Clinica Chimica Acta 430 (2014) 63–70
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Clinical significance of pretreatment plasma biomarkers in advanced non-small cell lung cancer patients Shuai Wang, Xiaohong Han, Xingsheng Hu, Xiaoyuan Wang, Lingdi Zhao, Le Tang, Yun Feng, Di Wu, Yan Sun, Yuankai Shi ⁎ Department of Medical Oncology, Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College; Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
a r t i c l e
i n f o
Article history: Received 16 August 2013 Received in revised form 19 December 2013 Accepted 19 December 2013 Available online 27 December 2013 Keywords: EGFR Lung cancer Biomarker Plasma
a b s t r a c t Background: The use of biomarkers for selecting non-small cell lung cancer (NSCLC) patients for treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is essential. The aim of this study was to explore whether biomarkers detected in plasma were predictive for response to EGFR-TKIs and survival time of NSCLC patients. Methods: Tumor tissues and paired blood were collected from 134 advanced NSCLC patients treated with EGFRTKIs. EGFR mutations in both types of specimens, and expression of transforming growth factor-alpha and beta one (TGF-α and TGF-β1) were assessed in NSCLC patients. Concentrations of circulating free DNA were detected in plasma from both NSCLC patients and healthy subjects. The clinical significance of EGFR mutations, levels of cytokines, and circulating free DNA was assessed in advanced NSCLC patients. Results: EGFR mutations were detected in 68 tumor samples and 17 plasma samples of 134 NSCLC patients. The concentrations of circulating free DNA were higher in NSCLC patients than in healthy subjects. Patients with high TGF-β1 level showed shorter overall survival and worse response to EGFR-TKIs than patients with low TGF-β1 level. Conclusions: Plasma levels of TGF-β1 may be a marker for predicting response to EGFR-TKIs and survival time in NSCLC patients. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Lung cancer is the leading cause of cancer related death in the world, and is associated with a high rate of morbidity [1]. Histologically, approximately 85% of patients with lung cancer are non-small cell lung cancer (NSCLC) [2]. Most NSCLC patients are diagnosed at an advanced stage of their disease, are ineligible for curative surgery and are offered only palliative treatment. Palliative treatment includes chemo- and radiotherapy and more recently, targeting therapy, such as epidermal growth factor receptor-tyrosine kinase inhibitors (EGFRTKIs) gefitinib, erlotinib, and icotinib [3], and the use of EGFR-TKIs has improved the survival of patients with NSCLC [4]. Identifying those patients who will benefit from treatment with EGFR-TKIs is essential to avoid unnecessary toxicities from other treatments, and to ensure that each patient receives the optimal treatment. The use of individualized targeted therapies requires detectable and reliable biomarkers; while
⁎ Corresponding author at: Department of Medical Oncology, Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College; Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China. Tel.: +86 10 87788293; Fax: +86 10 87778740. E-mail addresses: [email protected], [email protected] (Y. Shi). 0009-8981/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.cca.2013.12.026
several biomarkers have been measured in numerous studies, only a few have been applied clinically. Plasma samples from NSCLC patients contain high levels of DNA, are readily available, and can be obtained by noninvasive procedures. Samples of tumor tissues from advanced NSCLC patients are usually inadequate for molecular testing. Therefore, certain biomarkers detected in plasma may have important implications for selecting therapy. Several groups have detected EGFR mutations in plasma or serum samples and showed some correlation between mutation status in plasma and tumor tissues [5,6]. Thus, plasma could possibly be used as a substitute for tumor tissue when detecting cancer-specific molecular markers to predict response and prognosis. Transforming growth factor-alpha (TGF-α) is an EGFR-specific ligand, which directly binds to and activates EGFR. TGF-α is overexpressed in various tumors, particularly in ovarian, colon, and lung cancers [7], and may have prognostic significance. TGF-beta 1(TGF-β1) is also a cytokine and is considered to play a role in tumor transformation, progression, and regression [8]. Therefore, this study was conducted to measure EGFR mutation, cytokines, and circulating free DNA in plasma, evaluate the relationship between these biomarkers and the clinical characteristics of NSCLC patients, and explore their roles on predicting response to EGFR-TKIs
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and survival time of NSCLC patients. We hope to identify molecular biomarkers in plasma associated with response to EGFR-TKIs and survival of NSCLC patients.
2. Patients and methods 2.1. Study design In this study a cohort of 134 patients was collected from icotinib versus gefitinib in previously treated advanced NSCLC (ICOGEN) trial from February 2009 to November 2009. All patients had failed platinum based chemotherapy previously, then were treated daily with EGFRTKIs (gefitinib 250 mg/d or icotinib 375 mg/d) until disease progression. The study patients included 69 men and 65 women, with a median age of 56 y (range, 28–75 y); 108 patients with lung adenocarcinoma, and 26 patients with non-adenocarcinoma (including squamous carcinoma, adenosquamous carcinoma, and large-cell carcinoma). Sixty-two patients were former or current smokers and 72 patients were never smokers. A total of 115 patients had stage IV disease and 19 patients had stage IIIB disease. Clinical characteristics such as the Eastern Cooperative Oncology Group (ECOG) score and performance status (PS) were collected from all NSCLC patients. Short-term objective tumor responses were evaluated according to the Response Evaluation Criteria in the Solid Tumors guidelines. A cohort of 32 healthy individuals was also included in this study as control subjects, and peripheral blood samples were collected from these individuals. This study was approved by the Institutional Review Board of Cancer Institute/Hospital, Chinese Academy of Medical Sciences. Written informed consent was obtained from all subjects included in the study. 2.2. Analysis of EGFR mutations and circulating free DNA Formalin-fixed, paraffin-embedded (FFPE) tumor tissues and paired peripheral blood samples were collected from the 134 NSCLC patients enrolled in the study. Peripheral blood was also collected from 24 healthy individuals and used as control samples. Areas of tumor tissue were marked, scraped, and digested overnight with proteinase. Two milliliter aliquot peripheral blood samples were collected in sterile EDTA-coated vials, centrifuged at 1000 ×g for 10 min within 30 min following collection, and 400 μl plasma was stored at −20 °C for DNA extraction. DNA was extracted using the QIAamp DNA Blood Kit (Qiagen) according to the manufacturer's protocol. Extracted DNA was dissolved in 50 μl deionized water, detected by measuring optical absorbance at 260 nm using a NanoDrop2000 spectrophotometer (Thermo Scientific), and stored at − 20 °C until use. EGFR mutations in tumor tissues and paired plasma samples from NSCLC patients were detected with the EGFR RGQ PCR Kit (Qiagen), using the ARMS/ Scorpion assay; PCR results were analyzed according to the manufacturer's instructions. 2.3. Analysis of TGF-α and TGF-β1 in plasma Peripheral blood was collected in sterile EDTA-coated vials, centrifuged at 1000 ×g for 10 min, and the plasma was removed, aliquoted, and stored at −80 °C. Plasma levels of TGF-α were measured with an enzyme-linked immunosorbent assay (ELISA), using the Human TGF-α Quantikine ELISA Kit (R&D Systems), according to the manufacturer's instructions. A standard curve was prepared for each 96-well plate using human recombinant TGF-α diluted in assay diluent (provided by the ELISA-kit) as a reference. The minimum limit of the TGF-α detection assay was 3.0 pg/ml, and the coefficient of variation was b5.0%. Levels of TGF-β1 were quantified using the Human TGF-β1 Quantikine ELISA Kit (R&D Systems). The minimum limit of the assay was 7.0 pg/ml, and the coefficient of variation was b5.0%.
2.4. Statistical analysis Statistical analyses were performed using SPSS 13.0 software. The χ2 test was used to compare clinical characteristics and response to EGFRTKIs in patients with different EGFR status and expression levels of circulating free DNA, TGF-α, and TGF-β1. The Student's t-test was used to compare the expression levels of circulating free DNA, TGF-α, and TGF-β1 between NSCLC patients and healthy individuals. The time from the date of lung cancer diagnosis to disease progression was used to compare progression-free survival (PFS) time between different groups. The time from the date of lung cancer diagnosis to patient death was used to compare overall survival (OS) time between different groups. Survival curves were estimated by the Kaplan–Meier method with a log-rank test. All factors were analyzed by multivariate assay in the Cox regression model to explore the effect of different factors on survival time of NSCLC patients. In all tests, P value below 0.05 was considered to be statistically significant. 3. Results 3.1. Patient clinical characteristics Pathologically confirmed FFPE tissue specimens and paired peripheral blood samples were obtained from 134 advanced NSCLC patients in this study. In multivariate analysis, patients with younger age, adenocarcinoma, or who were never smokers had a long PFS (age ≥ 60 vs. b60 y: PFS hazard ratio (HR): 1.49; 95% CI, 1.02–2.18; P b 0.05; nonadenocarcinoma vs. adenocarcinoma: PFS HR: 1.67; 95% CI, 1.04–2.67; P b 0.05; smoker vs. never smoker: PFS HR: 1.54; 95% CI, 1.07–2.21; P b 0.05, Table 1). Patients with Han nationality, adenocarcinoma, low PS, or who were never smokers had a long OS time (other nationality vs. Han nationality: OS HR: 2.65; 95% CI, 1.08–6.55; P b 0.05; nonadenocarcinoma vs. adenocarcinoma: OS HR: 2.00; 95% CI, 1.22–3.28; P b 0.05; smoker vs. never smoker: OS HR: 2.14; 95% CI, 1.41–3.25; P b 0.05; PS 2 vs. PS 0–1: OS HR: 2.38; 95% CI, 1.22–4.65; P b 0.05, Table 1). 3.2. EGFR mutation in both tumor tissue and plasma samples EGFR mutations were found in the tumor tissue samples from 68 patients (50.7%). Forty-one tumors had an Exon 19 deletion, 20 tumors had a L858R mutation, 2 tumors had a L861Q mutation, 2 tumors had an Exon 20 insertion, 1 tumor had a T790M mutation, and 2 tumors had both G719X and S768I mutations. Also, EGFR mutations were detected in the plasma samples from 17 NSCLC patients (12.7%). Eleven plasma samples had an Exon 19 deletion, 4 samples had a L858R mutation, 2 samples had a L861Q mutation and these two test results were consistent with results from the paired tumor tissues of two NSCLC patients. Considering EGFR mutation detected in tumor tissues as a reference, sensitivity and specificity for EGFR mutation detected in plasma were 22.06% (15/68) and 96.97% (64/66), positive and negative predictive value were 88.24% (15/17) and 54.70% (64/117). The concordance rate of the two test results was 0.188 (P b 0.001, Table 2), indicating that EGFR mutations detected in the plasma and tumor tissues were inconsistent. The association between EGFR somatic mutation and patient characteristics was summarized in Table 3. The presence of an EGFR somatic mutation in tumor tissue was associated with female and good response to EGFR-TKIs (P b 0.05). Patients with an EGFR somatic mutation had significantly longer PFS and OS times (Fig. 1A&B, Table 5) compared to patients with a wild type EGFR (mutation vs. wild EGFR: PFS 290.2 vs. 117.9d, P b 0.001; OS 605.7 vs. 337.9d, P b 0.001). In multivariate analyses, we found that the deletion in exon 19 and L858R mutation in exon 21 were predictive factors: patients with these two types of mutations had longer PFS and OS when compared to patients with a wild type EGFR in tumor tissues (deletion in exon 19 vs. wild EGFR: PFS HR: 0.40; 95% CI, 0.26–0.60; P b 0.001; OS HR: 0.34; 95% CI, 0.21–0.54;
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Table 1 Multivariate Cox proportional-hazard regression model predicting progression-free survival and overall survival. Progression-free survival
Overall survival
Parameter
Hazard ratio
95% CI
P
Hazard ratio
95% CI
P
Sex Male Female
1.00 1.60
0.88–2.91
NS
1.00 1.16
0.53–2.50
NS
Nationality Han Other
1.00 1.00
0.41–2.46
NS
1.00 2.65
1.08–6.55
0.03
Age, y b60 ≥60
1.00 1.49
1.02–2.18
0.04
1.00 1.17
0.77–1.78
NS
ECOG score 0 1, 2
1.00 1.19
0.76–1.88
NS
1.00 1.06
0.70–1.60
NS
Stage III IV
1.00 1.32
0.79–2.23
NS
1.00 1.28
0.68–2.40
NS
Histology Adenocarcinoma Non-adenocarcinoma
1.00 1.67
1.04–2.67
0.03
1.00 2.00
1.22–3.28
0.01
Smoking history Never Ever
1.00 1.54
1.07–2.21
0.02
1.00 2.14
1.41–3.25
0.00
Performance status 0–1 2
1.00 0.76
0.37–1.54
NS
1.00 2.38
1.22–4.65
0.01
EGFR mutation in tumor tissues Wild-type Deletion L858R G719X&S768I Insertion L861Q T790M
1.00 0.40 0.39 0.47 0.52 4.19 0.47
0.26–0.60 0.23–0.68 0.11–1.95 0.12–2.21 0.98–17.91 0.06–3.49
0.00 0.00 NS NS NS NS
1.00 0.34 0.31 2.59 0.15 3.97 1.00
0.21–0.54 0.16–0.61 0.60–11.19 0.02–1.13 0.83–18.91 0.13–7.64
0.00 0.00 NS NS 0.08 NS
EGFR mutation in plasma Wild-type Deletion L858R L861Q
1.00 1.68 0.97 4.19
0.83–3.41 0.35–2.72 0.98–17.91
NS NS NS
1.00 1.60 0.89 3.97
0.74–3.49 0.31–2.56 0.83–18.91
NS NS NS
TGF-α level Low High
1.00 1.16
0.75–1.81
NS
1.00 1.01
0.63–1.61
NS
TGF-β1 level Low High
1.00 1.26
0.86–1.85
NS
1.00 1.74
1.15–2.64
0.01
Circulating free DNA Low High
1.00 0.79
0.53–1.16
NS
1.00 0.94
0.61–1.47
NS
P b 0.001; L858R mutation in exon 21 vs. wild EGFR: PFS HR: 0.39; 95% CI, 0.23–0.68; P b 0.001; OS HR: 0.31; 95% CI, 0.16–0.61; P b 0.001), but there was no difference between the other types of EGFR mutations and wild EGFR in tumor tissues on survival time. However, there was no significant association between EGFR mutations in plasma and pathological Table 2 Correlation between EGFR mutation in tumor tissues and plasma samples in NSCLC patients (kappa value: 0.188). EGFR mutation status
Plasma
Mutation Wild-type Total
Tumor tissue Mutation
Wild-type
Total
15 53 68
2 4 66
17 117 134
characteristics or response to EGFR-TKIs (Table 3), and patients with different types of EGFR mutation status in plasma had no difference on PFS or OS (Table 5). 3.3. Circulating free DNA in plasma Circulating free DNA was extracted from the plasma samples of 134 NSCLC patients and 24 healthy individuals, analyzed by a colorimetric assay, and was measurable in all samples. Expression levels of circulating free DNA in NSCLC patients ranged from 1.9 to 31.8 ng/μl, with a mean value of 5.82 ng/μl, and in healthy individuals, the values ranged from 2.3 to 9.6 ng/μl, with a mean value of 4.37 ng/μl. A nonparametric Kolmogorov–Smirnov test confirmed that both populations satisfied conditions for a normal distribution (P b 0.001). The mean value of circulating free DNA expression was used as a cut-off point to
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Table 3 NSCLC patient characteristics according to EGFR mutations in tumor tissues and plasma. EGFR mutation in tumor tissue
EGFR mutation in plasma
Wild-type (n = 66)
Wild-type (n=117)
Mutation (n = 68)
Mutation (n = 17)
Characteristics
Total
No.
%
No.
%
P
No.
%
No.
%
P
Sex Male Female
69 65
40 26
57.97 40.00
29 39
42.03 60.00
0.04
62 55
89.86 84.62
7 10
10.14 15.38
NS
Nationality Han Other
128 6
65 1
50.78 16.67
63 5
49.22 83.33
NS
112 5
87.50 83.33
16 1
12.50 16.67
NS
Age, y b60 ≥60
90 44
42 24
46.67 54.55
48 20
53.33 45.45
NS
78 39
86.67 88.64
12 5
13.33 11.36
NS
ECOG score 0 1 2
29 88 17
15 41 10
51.72 46.59 58.82
14 47 7
48.28 53.41 41.18
NS
26 78 13
89.66 88.64 76.47
3 10 4
10.34 11.36 23.53
NS
Tumor stage III IV
19 115
12 54
63.16 46.96
7 61
36.84 53.04
NS
17 100
89.47 86.96
2 15
10.53 13.04
NS
Histology Adenocarcinoma Non-adenocarcinoma
108 26
49 17
45.37 65.38
59 9
54.63 34.62
NS
93 24
86.11 92.31
15 2
13.89 7.69
NS
Smoking history Ever Never
62 72
35 31
56.45 43.06
27 41
43.55 56.94
NS
56 61
90.32 84.72
6 11
9.68 15.28
NS
Performance status 0–1 2
122 12
59 7
48.36 58.33
63 5
51.64 41.67
NS
108 9
88.52 75.00
14 3
11.48 25.00
NS
Response to EGFR-TKI Partial response/stable disease Progressive disease/deceased
102 32
40 26
39.22 81.25
62 6
60.78 18.75
0.00
88 29
86.27 90.92
14 3
13.73 9.38
NS
separate samples into high or low expression level group. The mean plasma DNA concentration in 134 NSCLC patients was higher than that observed in 24 healthy control subjects (5.82 ± 0.34 vs. 4.37 ±0.31 ng/ μl, P b 0.05; Fig. 2). When comparing the plasma DNA concentrations in NSCLC patients with pathological characteristics and the response to EGFR-TKIs, we found that there was no significant difference between them (Table 4). There was also no significant difference of PFS or OS between NSCLC patients with high and low levels of circulating free DNA (Table 5). 3.4. TGF-α and TGF-β1 in plasma Pretreatment plasma samples were obtained from 134 NSCLC patients and 32 healthy individuals, and used to measure the expression levels of TGF-α and TGF-β1. A non-parametric Kolmogorov–Smirnov test confirmed that both populations satisfied requirements for a normal distribution (P b 0.001). The mean value of cytokine expression was used as a cut-off point to separate all samples into high or low expression group. There was no significant difference between NSCLC patients and healthy individuals regarding expression levels of TGF-α and TGF-β1. A high TGF-β1 level was associated with a better ECOG performance status, non-adenocarcinoma, and a worse response to EGFR-TKIs (P b 0.05, Table 4). The baseline level of TGF-β1 was also a predictive factor: patients with a high baseline level of TGF-β1 had significantly worse OS when compared to patients with low baseline level of TGF-β1 (Fig. 1C&D, Table 5) (high vs. low TGF-β1: OS 378.7 vs. 539.5 d, P b 0.05), but there was no difference for PFS (high vs. low TGF-β1: PFS 183.3 vs. 219.6 d, P = 0.27). These results were verified in multivariate analyses (high vs. low TGF-β1: PFS HR: 1.26; 95% CI, 0.86–1.85; P = 0.23; OS HR: 1.74; 95% CI, 1.15–2.64; P b 0.05, Table 1). However,
there was no significant difference between expression levels of TGF-α and pathological characteristics or response to EGFR-TKIs in NSCLC patients (Table 4), and NSCLC patients with different levels of TGF-α had no difference for PFS or OS (Table 5). 4. Discussion In this study, we detected EGFR somatic mutations in the tumor tissues from 68 of 134 advanced NSCLC patients, and in the plasma samples from 17 patients. Then we found that the concentrations of circulating free DNA were higher in NSCLC patients than in healthy subjects. Patients with high levels of TGF-β1 showed shorter OS and worse response to EGFR-TKIs treatment than patients with low TGF-β1 levels, and patients with different expression levels of TGF-α showed no significant difference in either PFS or OS. Many patients with stage IV NSCLC are always diagnosed with small biopsies, which often yield insufficient DNA for molecular biomarker testing. Detecting EGFR mutations in plasma samples would be a significant advantage for patients whose tumor tissue is not sufficient or available for testing. In our study, we detected 17 EGFR mutations in the plasma samples from 134 NSCLC patients, giving a total mutation rate of 12.7%. Analysis showed that 64.7% (11/17) of all these EGFR mutations were Exon 19 deletion, 23.5% (4/17) were EGFR L858R mutation, and 2 cases had EGFR L861Q mutation. The sensitivity of detecting EGFR mutation in plasma was low, which may result in no association between EGFR mutation in plasma and OS, PFS or response to EGFR-TKIs. The concordance rate of the results from tumor tissues and plasma samples was 59%, with a kappa value of 0.188; these results showed that plasma could not replace tumor tissue for EGFR testing by using the ARMS/Scorpion assay, and more advances in detection methods are required. The EGFR
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Fig. 1. Association between EGFR mutation status in tumor tissues and (A) progression-free survival (PFS) and (B) overall survival (OS) for NSCLC patients. Association between plasma baseline TGF-β1 levels and (C) PFS and (D) OS for NSCLC patients.
mutation rates reported by Zhao X and Kimura H were 17.11% (19/111) and 16.67% (7/42), respectively, which were lower than those in tumor tissues [5,9]. Several studies have detected EGFR mutations in both tumor tissue and plasma, and the results showed that concordance rates for EGFR mutation status between the two types of samples varied from 58% to 97% [10–15]. Although detection of EGFR mutations in plasma is possible in some patients, more data is required to evaluate clinical applicability. The limitations of plasma analysis may be related to low overall levels of tumor DNA in plasma, a variable ratio of tumor to wild type circulating free DNA or low sensitivity of detection methods. Wild
Fig. 2. Distribution of plasma circulating free DNA concentrations in NSCLC patients and healthy controls as shown by box-plots. Mean plasma circulating free DNA level in NSCLC patients was higher than that in healthy controls (P b 0.05).
type DNA accounts for the largest quantity of circulating free DNA, and may result in false negative analysis of plasma/serum samples [16]. Marie Brevet found that EGFR mutations were more often detectable in plasma samples from patients who received treatment prior to blood collection rather than in samples from patients who received treatment after the blood collection [17]. These could be explained by an increased amount of circulating free DNA due to tumor cell death in response to treatment. In our study, blood from all NSCLC patients was collected before EGFR-TKIs treatment, and the amount of circulating free DNA might be low. Numerous reports have validated the tumor-promoting role of TGFβ1 through its effects on tumor cell invasion and alterations. Previous studies have observed increased TGF-β1 levels after radiotherapy in lung cancer patients [18,19]. Kumar et al. found that increased levels of TGF-β1 were observed in NSCLC patients compared with healthy individuals, and TGF-β1 levels did not correlate with survival time or response to chemotherapy [20]. In the former studies, data measuring the correlation between TGF-β1 levels and overall survival in NSCLC patients are lacking. In our study, we found that plasma TGF-β1 levels could not be distinguished between patients with lung cancer and healthy individuals; but there was a significant difference between TGF-β1 levels in the plasma of responders and non-responders to EGFR-TKIs treatment; and pretreatment plasma TGF-β1 levels were associated with OS in NSCLC patients treated with EGFR-TKIs, but not with progression. Our study suggests that quantifying TGF-β1 levels may help in predicting EGFR-TKIs therapy outcomes and survival time. More studies will be needed with larger numbers of patients to certify the exact potential for using plasma TGF-β1 to predict survival time and therapeutic outcome in lung cancer. We also found that plasma TGF-β1 levels correlated with ECOG performance status and pathology. TGF-α is one of EGFR ligands, which could rapidly disassociate from EGFR in the environment of lysosomes and once again become
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Table 4 NSCLC patient characteristics according to plasma TGF-α, TGF-β1 and circulating free DNA levels. TGF-α
TGF-β1
b4.67 pg/mL (n = 87)
N4.67 pg/mL (n = 47)
Circulating free DNA
b359.97 pg/mL (n = 102)
N359.97 pg/mL (n = 32)
b5.82 ng/uL (n = 80)
N5.82 ng/uL (n = 54)
Total No.
No.
%
No.
%
P
No.
%
No.
%
P
No.
%
No.
%
P
Sex Male Female
69 65
54 48
78.26 73.85
15 17
21.74 26.15
NS
40 40
57.97 61.54
29 25
42.03 38.46
NS
46 41
66.67 63.08
23 24
33.33 36.92
NS
Nationality Han Other
128 6
97 5
75.78 83.33
31 1
24.22 16.67
NS
74 6
57.81 100
54 0
42.19 0
NS
83 4
64.84 66.67
45 2
35.16 33.33
NS
Age, y b60 ≥60
90 44
66 36
73.33 81.82
24 8
26.67 18.18
NS
51 29
56.67 65.91
39 15
43.33 34.09
NS
57 30
63.33 68.18
33 14
36.67 31.82
NS
ECOG score 0 1 2
29 88 17
26 63 13
89.66 71.59 76.47
3 25 4
10.34 28.41 23.53
NS
15 59 10
51.72 67.05 58.82
14 29 7
48.28 32.95 41.18
0.031
21 53 10
72.41 60.23 58.82
8 35 7
27.59 39.77 41.18
NS
Tumor stage III IV
19 115
14 88
73.68 76.52
5 27
26.32 23.48
NS
13 67
68.42 58.26
6 48
31.58 41.74
NS
11 76
57.89 66.09
8 39
42.11 33.91
NS
Histology Adenocarcinoma Non-adenocarcinoma
108 26
83 19
76.85 73.08
25 7
23.15 26.92
NS
69 11
63.89 42.31
39 15
36.11 57.69
0.04
71 16
65.74 61.54
37 10
34.26 38.46
NS
Smoking history Ever Never
62 72
50 52
80.65 72.22
12 20
19.35 27.78
NS
34 46
54.84 63.89
28 26
45.16 36.11
NS
41 46
66.13 63.89
21 26
33.87 36.11
NS
Performance status 0–1 2
122 12
92 10
75.41 83.33
30 2
24.59 16.67
NS
76 4
62.3 33.33
46 8
37.7 66.67
NS
78 9
63.93 75.00
44 3
36.07 25.00
NS
Response to EGFR-TKI Partial response/stable disease Progressive disease/deceased
102 32
77 25
75.49 78.13
25 7
24.51 21.87
NS
67 13
65.69 40.62
35 19
34.31 59.38
0.01
66 21
64.71 65.63
36 11
35.29 34.37
NS
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Characteristics
S. Wang et al. / Clinica Chimica Acta 430 (2014) 63–70 Table 5 Association between biomarkers and progression-free survival and overall survival for NSCLC patients. Parameter
Progression-free survival/Days
Overall survival/Days
Average
P
Average
Range
P
EGFR mutation in tumor tissues Wild-type 118 75–160 Mutation 290 228–352
0.00
338 606
273–403 534–678
0.00
EGFR mutation in plasma Wild-type 211 Mutation 169
166–257 94–245
NS
483 420
424–541 282–558
NS
TGF-α level Low High
215 176
166–265 111–242
NS
474 474
411–538 372–575
NS
TGF-β1 level Low High
220 183
169–270 118–249
NS
539 379
471–608 298–460
0.00
Circulating free DNA Low 181 High 250
135–227 173–326
NS
456 507
391–521 412–601
NS
Range
biologically available to re-stimulate cells; hence, TGF-α may have rather consistent soluble concentrations [21]. In the BR.21 study conducted by the NCIC clinical trials group, TGF-α levels in plasma were analyzed retrospectively, and results showed that TGF-α levels did not have prognostic significance on survival time, but high levels of TGF-α in NSCLC patients predicted a lack of benefit from treatment with erlotinib. Two Japanese groups found that high levels of TGF-α were associated with progressive disease and a worse overall survival in NSCLC patients treated with gefitinib [22,23]. However, we did not find any utility of TGF-α for predicting survival or therapeutic efficacy from EGFR-TKIs treatment. Also, plasma TGF-α levels could not be distinguished between patients with lung cancer and healthy individuals. To date, several groups have evaluated plasma DNA levels in NSCLC patients. Sozzi et al. first reported that levels of plasma DNA from 84 NSCLC patients were higher than that in 43 healthy control individuals, as measured by a simple colorimetric assay [24]. No correlation between plasma DNA level and stage or histology was observed. Both Paci et al. and Ludovini et al. verified that higher concentrations of circulating free DNA were found in patients with lung cancer than in healthy individuals [25,26]. Neither of these investigators found any correlation between plasma DNA levels and the clinical characteristics of NSCLC patients. Similarly, we showed significantly higher mean pretreatment plasma DNA concentrations in NSCLC patients as compared to healthy controls. Plasma DNA levels were not significantly related to the clinical characteristics of NSCLC patients. Our study confirmed the predictive value of circulating free DNA for distinguishing NSCLC patients from healthy individuals, and circulating free DNA can be detected by a simple colorimetric assay. In summary, the concordance rate between the EGFR mutation status in tumor tissues and plasma samples was 59%, TGF-β1 levels in plasma could be predictive for both response to EGFR-TKIs and OS for NSCLC patients treated with EGFR-TKIs, and circulating free DNA is a biomarker for distinguishing NSCLC patients from healthy individuals. Acknowledgments The samples of tumor tissue and blood were collected form ICOGEN trial. The authors thank the patients and investigators for their participation in this study. This study was supported in part by grants from the Research Special Fund for Public Welfare Industry of Health (200902002-1), Chinese National Major Project for New Drug Innovation (2008ZX09312, 2008ZX09101, 2012ZX09101103, 2012ZX09303012), Chinese National High Technology Research and Development Program
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of China (863 Program) (2006AA02Z4A3, 2011AA02A110), Beijing Municipal Science and Technology Commission Major Project for New Drug Innovation (Z121107005112005, Z121102009212055), Chinese Central Health Authority Special Fund (B2009B124), Major Research Program of Hospital, Chinese Academy of Medical Sciences, Hospital of Chinese Academy of Medical Sciences & Peking Union Medical College (LC2012A18), Key Special Program for Science and Technology of Zhejiang Province (2007C13003), and Zhejiang Beta Pharma Inc.. The authors thank these groups for their support. Hospitals and investigators attended in ICOGEN trial include: Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China (Y Sun MD, Y Shi MD); Sun Yat-Sen University Cancer Center, Guangzhou, China (L Zhang MD); 307 Hospital of the Academy of Military Medical Sciences, Beijing, China (X Liu MD); Shanghai Pulmonary Hospital, Tongji University, Shanghai, China (C Zhou MD); Peking Union Medical Hospital, Beijing, China (L Zhang MD); Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China (S Zhang MD); Daping Hospital, Third Military Medical University, Chongqing, China (D Wang MD); Changhai Hospital, Second Military Medical University, Shanghai, China (Q Li MD); Nanjing Bayi Hospital of People's Liberation Army, Nanjing, China (S Qin MD); Second Xiangya Hospital of Central South University, Changsha, China (C Hu MD); Zhejiang Cancer Hospital, Hangzhou, China (Y Zhang MD); Hunan Cancer Hospital, Changsha, China (J Chen MD); Jilin Cancer Hospital, Changchun, China (Y Cheng MD); Jiangsu Cancer Hospital, Nanjing, China (J Feng MD); Tangdu Hospital (H Zhang MD) and Xijing Hospital (W Liu MD), Fourth Military Medical University, Xian, China; Nanjing Military General Hospital, Nanjing, China (Y Song MD); Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (Y Wu MD); First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China (N Xu MD, J Zhou MD); Nanfang Hospital, Southern Medical University, Guangzhou, China (R Luo MD); Affiliated Zhongshan Hospital of Fudan University, Shanghai, China (C Bai MD); Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China (Y Jin MD); Beijing Chao-Yang Hospital, Beijing, China (H Dai MD); General Hospital of People's Liberation Army, Beijing, China (S Jiao MD); Beijing Cancer Hospital, Beijing, China (J Wang MD); Peking University Third Hospital, Beijing, China (L Liang MD); Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China (W Zhang MD); Hangzhou Tigermed Consulting, Hangzhou, China (Z Wei PhD); and Zhejiang Beta Pharma, Hangzhou, China (F Tan MD, Y Wang PhD, L Ding MD). References [1] Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011;61:69–90. [2] Ramalingam SS, Owonikoko TK, Khuri FR. Lung cancer: new biological insights and recent therapeutic advances. CA Cancer J Clin 2011;61:91–112. [3] Shi Y, Zhang L, Liu X, et al. Icotinib versus gefitinib in previously treated advanced non-small-cell lung cancer (ICOGEN): a randomised, double-blind phase 3 noninferiority trial. Lancet Oncol 2013;14:953–61. [4] Mok TS, Wu YL, Thongprasert S, et al. Gefitinib or carboplatin–paclitaxel in pulmonary adenocarcinoma. N Engl J Med 2009;361:947–57. [5] Mack PC, Holland WS, Burich RA, et al. EGFR mutations detected in plasma are associated with patient outcomes in erlotinib plus docetaxel-treated non-small cell lung cancer. J Thorac Oncol 2009;4:1466–72. [6] Kimura H, Suminoe M, Kasahara K, et al. Evaluation of epidermal growth factor receptor mutation status in serum DNA as a predictor of response to gefitinib (IRESSA). Br J Cancer 2007;97:778–84. [7] Normanno N, Bianco C, Strizzi L, et al. The ErbB receptors and their ligands in cancer: an overview. Curr Drug Targets 2005;6:243–57. [8] Iyer S, Wang ZG, Akhtari M, et al. Targeting TGF-β signaling for cancer therapy. Cancer Biol Ther 2005;4:261–6. [9] Zhao X, Han RB, Zhao J, et al. Comparison of epidermal growth factor receptor mutation statuses in tissue and plasma in stage I–IV non-small cell lung cancer patients. Respiration 2013;85:119–25. [10] Kimura H, Kasahara K, Shibata K, et al. EGFR mutation of tumor and serum in gefitinib-treated patients with chemotherapy-naive non-small cell lung cancer. J Thorac Oncol 2006;1:260–7.
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[11] Yung TK, Chan KC, Mok TS, et al. Single-molecule detection of epidermal growth factor receptor mutations in plasma by microfluidics digital PCR in nonsmall cell lung cancer patients. Clin Cancer Res 2009;15:2076–84. [12] Bai H, Mao L, Wang HS, et al. Epidermal growth factor receptor mutations in plasma DNA samples predict tumor response in Chinese patients with stages IIIB to IV non small-cell lung cancer. J Clin Oncol 2009;27:2653–9. [13] Goto K, Ichinose Y, Ohe Y, et al. Epidermal growth factor receptor mutation status in circulating free DNA in serum: from IPASS, a phase III study of gefitinib orcarboplatin/ paclitaxel in non-small cell lung cancer. J Thorac Oncol 2012;7:115–21. [14] Kuang Y, Rogers A, Yeap BY, et al. Noninvasive detection of EGFR T790M in gefitinib or erlotinib resistant non-small cell lung cancer. Clin Cancer Res 2009;15:2630–6. [15] Brevet M, Johnson ML, Azzoli CG, et al. Detection of EGFR mutations in plasma DNA from lung cancer patients by mass spectrometry genotyping is predictive of tumor EGFR status and response to EGFR inhibitors. Lung Cancer 2011;73:96–102. [16] Jahr S, Hentze H, Englisch S, et al. DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res 2001;61:1659–65. [17] Brevet M, Johnson ML, Azzoli CG, et al. Detection of EGFR mutations in plasma DNA from lung cancer patients by mass spectrometry genotyping is predictive of tumor EGFR status and response to EGFR inhibitors. Lung Cancer 2011;73:96–102. [18] Zhao L, Sheldon K, Chen M, et al. The predictive role of plasma TGF-beta1 during radiation therapy for radiation-induced lung toxicity deserves further study in patients with non-small cell lung cancer. Lung Cancer 2008 Feb;59:232–9.
[19] Zhao L, Ji W, Zhang L, et al. Changes of circulating transforming growth factor-beta1 level during radiation therapy are correlated with the prognosis of locally advanced non-small cell lung cancer. J Thorac Oncol 2010;5:521–5. [20] Kumar S, Guleria R, Mohan A, et al. Utility of plasma tumour necrosis factor-alpha and transforming growth factor-beta1 as predictors of survival and treatment outcome in advanced non-small cell lung carcinoma. Biomarkers 2010;15:446–53. [21] Addison CL, Ding K, Zhao H, et al. Plasma transforming growth factor alpha and amphiregulin protein levels in NCIC Clinical Trials Group BR.21. J Clin Oncol 2010;28:5247–56. [22] Ishikawa N, Daigo Y, Takano A, et al. Increases of amphiregulin and transforming growth factor-alpha in serum as predictors of poor response to gefitinib among patients with advanced non-small cell lung cancers. Cancer Res 2005;65:9176–84. [23] Masago K, Fujita S, Hatachi Y, et al. Clinical significance of pretreatment serum amphiregulin and transforming growth factor-alpha, and an epidermal growth factor receptor somatic mutation in patients with advanced non-squamous, non-small cell lung cancer. Cancer Sci 2008;99:2295–301. [24] Sozzi G, Conte D, Mariani L, et al. Analysis of circulating tumor DNA in plasma at diagnosis and during follow-up of lung cancer patients. Cancer Res 2001;61:4675–8. [25] Paci M, Maramotti S, Bellesia E, et al. Circulating plasma DNA as diagnostic biomarker in non-small cell lung cancer. Lung Cancer 2009;64:92–7. [26] Ludovini V, Pistola L, Gregorc V, et al. Plasma DNA, microsatellite alterations, and p53 tumor mutations are associated with disease-free survival in radically resected nonsmall cell lung cancer patients: a study of the Perugia multidisciplinary team for thoraciconcology. J Thorac Oncol 2008;3:365–73.
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Clinica Chimica Acta journal homepage: www.elsevier.com/locate/clinchim
Clinical significance of pretreatment plasma biomarkers in advanced non-small cell lung cancer patients Shuai Wang, Xiaohong Han, Xingsheng Hu, Xiaoyuan Wang, Lingdi Zhao, Le Tang, Yun Feng, Di Wu, Yan Sun, Yuankai Shi ⁎ Department of Medical Oncology, Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College; Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
a r t i c l e
i n f o
Article history: Received 16 August 2013 Received in revised form 19 December 2013 Accepted 19 December 2013 Available online 27 December 2013 Keywords: EGFR Lung cancer Biomarker Plasma
a b s t r a c t Background: The use of biomarkers for selecting non-small cell lung cancer (NSCLC) patients for treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is essential. The aim of this study was to explore whether biomarkers detected in plasma were predictive for response to EGFR-TKIs and survival time of NSCLC patients. Methods: Tumor tissues and paired blood were collected from 134 advanced NSCLC patients treated with EGFRTKIs. EGFR mutations in both types of specimens, and expression of transforming growth factor-alpha and beta one (TGF-α and TGF-β1) were assessed in NSCLC patients. Concentrations of circulating free DNA were detected in plasma from both NSCLC patients and healthy subjects. The clinical significance of EGFR mutations, levels of cytokines, and circulating free DNA was assessed in advanced NSCLC patients. Results: EGFR mutations were detected in 68 tumor samples and 17 plasma samples of 134 NSCLC patients. The concentrations of circulating free DNA were higher in NSCLC patients than in healthy subjects. Patients with high TGF-β1 level showed shorter overall survival and worse response to EGFR-TKIs than patients with low TGF-β1 level. Conclusions: Plasma levels of TGF-β1 may be a marker for predicting response to EGFR-TKIs and survival time in NSCLC patients. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Lung cancer is the leading cause of cancer related death in the world, and is associated with a high rate of morbidity [1]. Histologically, approximately 85% of patients with lung cancer are non-small cell lung cancer (NSCLC) [2]. Most NSCLC patients are diagnosed at an advanced stage of their disease, are ineligible for curative surgery and are offered only palliative treatment. Palliative treatment includes chemo- and radiotherapy and more recently, targeting therapy, such as epidermal growth factor receptor-tyrosine kinase inhibitors (EGFRTKIs) gefitinib, erlotinib, and icotinib [3], and the use of EGFR-TKIs has improved the survival of patients with NSCLC [4]. Identifying those patients who will benefit from treatment with EGFR-TKIs is essential to avoid unnecessary toxicities from other treatments, and to ensure that each patient receives the optimal treatment. The use of individualized targeted therapies requires detectable and reliable biomarkers; while
⁎ Corresponding author at: Department of Medical Oncology, Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College; Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China. Tel.: +86 10 87788293; Fax: +86 10 87778740. E-mail addresses: [email protected], [email protected] (Y. Shi). 0009-8981/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.cca.2013.12.026
several biomarkers have been measured in numerous studies, only a few have been applied clinically. Plasma samples from NSCLC patients contain high levels of DNA, are readily available, and can be obtained by noninvasive procedures. Samples of tumor tissues from advanced NSCLC patients are usually inadequate for molecular testing. Therefore, certain biomarkers detected in plasma may have important implications for selecting therapy. Several groups have detected EGFR mutations in plasma or serum samples and showed some correlation between mutation status in plasma and tumor tissues [5,6]. Thus, plasma could possibly be used as a substitute for tumor tissue when detecting cancer-specific molecular markers to predict response and prognosis. Transforming growth factor-alpha (TGF-α) is an EGFR-specific ligand, which directly binds to and activates EGFR. TGF-α is overexpressed in various tumors, particularly in ovarian, colon, and lung cancers [7], and may have prognostic significance. TGF-beta 1(TGF-β1) is also a cytokine and is considered to play a role in tumor transformation, progression, and regression [8]. Therefore, this study was conducted to measure EGFR mutation, cytokines, and circulating free DNA in plasma, evaluate the relationship between these biomarkers and the clinical characteristics of NSCLC patients, and explore their roles on predicting response to EGFR-TKIs
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and survival time of NSCLC patients. We hope to identify molecular biomarkers in plasma associated with response to EGFR-TKIs and survival of NSCLC patients.
2. Patients and methods 2.1. Study design In this study a cohort of 134 patients was collected from icotinib versus gefitinib in previously treated advanced NSCLC (ICOGEN) trial from February 2009 to November 2009. All patients had failed platinum based chemotherapy previously, then were treated daily with EGFRTKIs (gefitinib 250 mg/d or icotinib 375 mg/d) until disease progression. The study patients included 69 men and 65 women, with a median age of 56 y (range, 28–75 y); 108 patients with lung adenocarcinoma, and 26 patients with non-adenocarcinoma (including squamous carcinoma, adenosquamous carcinoma, and large-cell carcinoma). Sixty-two patients were former or current smokers and 72 patients were never smokers. A total of 115 patients had stage IV disease and 19 patients had stage IIIB disease. Clinical characteristics such as the Eastern Cooperative Oncology Group (ECOG) score and performance status (PS) were collected from all NSCLC patients. Short-term objective tumor responses were evaluated according to the Response Evaluation Criteria in the Solid Tumors guidelines. A cohort of 32 healthy individuals was also included in this study as control subjects, and peripheral blood samples were collected from these individuals. This study was approved by the Institutional Review Board of Cancer Institute/Hospital, Chinese Academy of Medical Sciences. Written informed consent was obtained from all subjects included in the study. 2.2. Analysis of EGFR mutations and circulating free DNA Formalin-fixed, paraffin-embedded (FFPE) tumor tissues and paired peripheral blood samples were collected from the 134 NSCLC patients enrolled in the study. Peripheral blood was also collected from 24 healthy individuals and used as control samples. Areas of tumor tissue were marked, scraped, and digested overnight with proteinase. Two milliliter aliquot peripheral blood samples were collected in sterile EDTA-coated vials, centrifuged at 1000 ×g for 10 min within 30 min following collection, and 400 μl plasma was stored at −20 °C for DNA extraction. DNA was extracted using the QIAamp DNA Blood Kit (Qiagen) according to the manufacturer's protocol. Extracted DNA was dissolved in 50 μl deionized water, detected by measuring optical absorbance at 260 nm using a NanoDrop2000 spectrophotometer (Thermo Scientific), and stored at − 20 °C until use. EGFR mutations in tumor tissues and paired plasma samples from NSCLC patients were detected with the EGFR RGQ PCR Kit (Qiagen), using the ARMS/ Scorpion assay; PCR results were analyzed according to the manufacturer's instructions. 2.3. Analysis of TGF-α and TGF-β1 in plasma Peripheral blood was collected in sterile EDTA-coated vials, centrifuged at 1000 ×g for 10 min, and the plasma was removed, aliquoted, and stored at −80 °C. Plasma levels of TGF-α were measured with an enzyme-linked immunosorbent assay (ELISA), using the Human TGF-α Quantikine ELISA Kit (R&D Systems), according to the manufacturer's instructions. A standard curve was prepared for each 96-well plate using human recombinant TGF-α diluted in assay diluent (provided by the ELISA-kit) as a reference. The minimum limit of the TGF-α detection assay was 3.0 pg/ml, and the coefficient of variation was b5.0%. Levels of TGF-β1 were quantified using the Human TGF-β1 Quantikine ELISA Kit (R&D Systems). The minimum limit of the assay was 7.0 pg/ml, and the coefficient of variation was b5.0%.
2.4. Statistical analysis Statistical analyses were performed using SPSS 13.0 software. The χ2 test was used to compare clinical characteristics and response to EGFRTKIs in patients with different EGFR status and expression levels of circulating free DNA, TGF-α, and TGF-β1. The Student's t-test was used to compare the expression levels of circulating free DNA, TGF-α, and TGF-β1 between NSCLC patients and healthy individuals. The time from the date of lung cancer diagnosis to disease progression was used to compare progression-free survival (PFS) time between different groups. The time from the date of lung cancer diagnosis to patient death was used to compare overall survival (OS) time between different groups. Survival curves were estimated by the Kaplan–Meier method with a log-rank test. All factors were analyzed by multivariate assay in the Cox regression model to explore the effect of different factors on survival time of NSCLC patients. In all tests, P value below 0.05 was considered to be statistically significant. 3. Results 3.1. Patient clinical characteristics Pathologically confirmed FFPE tissue specimens and paired peripheral blood samples were obtained from 134 advanced NSCLC patients in this study. In multivariate analysis, patients with younger age, adenocarcinoma, or who were never smokers had a long PFS (age ≥ 60 vs. b60 y: PFS hazard ratio (HR): 1.49; 95% CI, 1.02–2.18; P b 0.05; nonadenocarcinoma vs. adenocarcinoma: PFS HR: 1.67; 95% CI, 1.04–2.67; P b 0.05; smoker vs. never smoker: PFS HR: 1.54; 95% CI, 1.07–2.21; P b 0.05, Table 1). Patients with Han nationality, adenocarcinoma, low PS, or who were never smokers had a long OS time (other nationality vs. Han nationality: OS HR: 2.65; 95% CI, 1.08–6.55; P b 0.05; nonadenocarcinoma vs. adenocarcinoma: OS HR: 2.00; 95% CI, 1.22–3.28; P b 0.05; smoker vs. never smoker: OS HR: 2.14; 95% CI, 1.41–3.25; P b 0.05; PS 2 vs. PS 0–1: OS HR: 2.38; 95% CI, 1.22–4.65; P b 0.05, Table 1). 3.2. EGFR mutation in both tumor tissue and plasma samples EGFR mutations were found in the tumor tissue samples from 68 patients (50.7%). Forty-one tumors had an Exon 19 deletion, 20 tumors had a L858R mutation, 2 tumors had a L861Q mutation, 2 tumors had an Exon 20 insertion, 1 tumor had a T790M mutation, and 2 tumors had both G719X and S768I mutations. Also, EGFR mutations were detected in the plasma samples from 17 NSCLC patients (12.7%). Eleven plasma samples had an Exon 19 deletion, 4 samples had a L858R mutation, 2 samples had a L861Q mutation and these two test results were consistent with results from the paired tumor tissues of two NSCLC patients. Considering EGFR mutation detected in tumor tissues as a reference, sensitivity and specificity for EGFR mutation detected in plasma were 22.06% (15/68) and 96.97% (64/66), positive and negative predictive value were 88.24% (15/17) and 54.70% (64/117). The concordance rate of the two test results was 0.188 (P b 0.001, Table 2), indicating that EGFR mutations detected in the plasma and tumor tissues were inconsistent. The association between EGFR somatic mutation and patient characteristics was summarized in Table 3. The presence of an EGFR somatic mutation in tumor tissue was associated with female and good response to EGFR-TKIs (P b 0.05). Patients with an EGFR somatic mutation had significantly longer PFS and OS times (Fig. 1A&B, Table 5) compared to patients with a wild type EGFR (mutation vs. wild EGFR: PFS 290.2 vs. 117.9d, P b 0.001; OS 605.7 vs. 337.9d, P b 0.001). In multivariate analyses, we found that the deletion in exon 19 and L858R mutation in exon 21 were predictive factors: patients with these two types of mutations had longer PFS and OS when compared to patients with a wild type EGFR in tumor tissues (deletion in exon 19 vs. wild EGFR: PFS HR: 0.40; 95% CI, 0.26–0.60; P b 0.001; OS HR: 0.34; 95% CI, 0.21–0.54;
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Table 1 Multivariate Cox proportional-hazard regression model predicting progression-free survival and overall survival. Progression-free survival
Overall survival
Parameter
Hazard ratio
95% CI
P
Hazard ratio
95% CI
P
Sex Male Female
1.00 1.60
0.88–2.91
NS
1.00 1.16
0.53–2.50
NS
Nationality Han Other
1.00 1.00
0.41–2.46
NS
1.00 2.65
1.08–6.55
0.03
Age, y b60 ≥60
1.00 1.49
1.02–2.18
0.04
1.00 1.17
0.77–1.78
NS
ECOG score 0 1, 2
1.00 1.19
0.76–1.88
NS
1.00 1.06
0.70–1.60
NS
Stage III IV
1.00 1.32
0.79–2.23
NS
1.00 1.28
0.68–2.40
NS
Histology Adenocarcinoma Non-adenocarcinoma
1.00 1.67
1.04–2.67
0.03
1.00 2.00
1.22–3.28
0.01
Smoking history Never Ever
1.00 1.54
1.07–2.21
0.02
1.00 2.14
1.41–3.25
0.00
Performance status 0–1 2
1.00 0.76
0.37–1.54
NS
1.00 2.38
1.22–4.65
0.01
EGFR mutation in tumor tissues Wild-type Deletion L858R G719X&S768I Insertion L861Q T790M
1.00 0.40 0.39 0.47 0.52 4.19 0.47
0.26–0.60 0.23–0.68 0.11–1.95 0.12–2.21 0.98–17.91 0.06–3.49
0.00 0.00 NS NS NS NS
1.00 0.34 0.31 2.59 0.15 3.97 1.00
0.21–0.54 0.16–0.61 0.60–11.19 0.02–1.13 0.83–18.91 0.13–7.64
0.00 0.00 NS NS 0.08 NS
EGFR mutation in plasma Wild-type Deletion L858R L861Q
1.00 1.68 0.97 4.19
0.83–3.41 0.35–2.72 0.98–17.91
NS NS NS
1.00 1.60 0.89 3.97
0.74–3.49 0.31–2.56 0.83–18.91
NS NS NS
TGF-α level Low High
1.00 1.16
0.75–1.81
NS
1.00 1.01
0.63–1.61
NS
TGF-β1 level Low High
1.00 1.26
0.86–1.85
NS
1.00 1.74
1.15–2.64
0.01
Circulating free DNA Low High
1.00 0.79
0.53–1.16
NS
1.00 0.94
0.61–1.47
NS
P b 0.001; L858R mutation in exon 21 vs. wild EGFR: PFS HR: 0.39; 95% CI, 0.23–0.68; P b 0.001; OS HR: 0.31; 95% CI, 0.16–0.61; P b 0.001), but there was no difference between the other types of EGFR mutations and wild EGFR in tumor tissues on survival time. However, there was no significant association between EGFR mutations in plasma and pathological Table 2 Correlation between EGFR mutation in tumor tissues and plasma samples in NSCLC patients (kappa value: 0.188). EGFR mutation status
Plasma
Mutation Wild-type Total
Tumor tissue Mutation
Wild-type
Total
15 53 68
2 4 66
17 117 134
characteristics or response to EGFR-TKIs (Table 3), and patients with different types of EGFR mutation status in plasma had no difference on PFS or OS (Table 5). 3.3. Circulating free DNA in plasma Circulating free DNA was extracted from the plasma samples of 134 NSCLC patients and 24 healthy individuals, analyzed by a colorimetric assay, and was measurable in all samples. Expression levels of circulating free DNA in NSCLC patients ranged from 1.9 to 31.8 ng/μl, with a mean value of 5.82 ng/μl, and in healthy individuals, the values ranged from 2.3 to 9.6 ng/μl, with a mean value of 4.37 ng/μl. A nonparametric Kolmogorov–Smirnov test confirmed that both populations satisfied conditions for a normal distribution (P b 0.001). The mean value of circulating free DNA expression was used as a cut-off point to
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Table 3 NSCLC patient characteristics according to EGFR mutations in tumor tissues and plasma. EGFR mutation in tumor tissue
EGFR mutation in plasma
Wild-type (n = 66)
Wild-type (n=117)
Mutation (n = 68)
Mutation (n = 17)
Characteristics
Total
No.
%
No.
%
P
No.
%
No.
%
P
Sex Male Female
69 65
40 26
57.97 40.00
29 39
42.03 60.00
0.04
62 55
89.86 84.62
7 10
10.14 15.38
NS
Nationality Han Other
128 6
65 1
50.78 16.67
63 5
49.22 83.33
NS
112 5
87.50 83.33
16 1
12.50 16.67
NS
Age, y b60 ≥60
90 44
42 24
46.67 54.55
48 20
53.33 45.45
NS
78 39
86.67 88.64
12 5
13.33 11.36
NS
ECOG score 0 1 2
29 88 17
15 41 10
51.72 46.59 58.82
14 47 7
48.28 53.41 41.18
NS
26 78 13
89.66 88.64 76.47
3 10 4
10.34 11.36 23.53
NS
Tumor stage III IV
19 115
12 54
63.16 46.96
7 61
36.84 53.04
NS
17 100
89.47 86.96
2 15
10.53 13.04
NS
Histology Adenocarcinoma Non-adenocarcinoma
108 26
49 17
45.37 65.38
59 9
54.63 34.62
NS
93 24
86.11 92.31
15 2
13.89 7.69
NS
Smoking history Ever Never
62 72
35 31
56.45 43.06
27 41
43.55 56.94
NS
56 61
90.32 84.72
6 11
9.68 15.28
NS
Performance status 0–1 2
122 12
59 7
48.36 58.33
63 5
51.64 41.67
NS
108 9
88.52 75.00
14 3
11.48 25.00
NS
Response to EGFR-TKI Partial response/stable disease Progressive disease/deceased
102 32
40 26
39.22 81.25
62 6
60.78 18.75
0.00
88 29
86.27 90.92
14 3
13.73 9.38
NS
separate samples into high or low expression level group. The mean plasma DNA concentration in 134 NSCLC patients was higher than that observed in 24 healthy control subjects (5.82 ± 0.34 vs. 4.37 ±0.31 ng/ μl, P b 0.05; Fig. 2). When comparing the plasma DNA concentrations in NSCLC patients with pathological characteristics and the response to EGFR-TKIs, we found that there was no significant difference between them (Table 4). There was also no significant difference of PFS or OS between NSCLC patients with high and low levels of circulating free DNA (Table 5). 3.4. TGF-α and TGF-β1 in plasma Pretreatment plasma samples were obtained from 134 NSCLC patients and 32 healthy individuals, and used to measure the expression levels of TGF-α and TGF-β1. A non-parametric Kolmogorov–Smirnov test confirmed that both populations satisfied requirements for a normal distribution (P b 0.001). The mean value of cytokine expression was used as a cut-off point to separate all samples into high or low expression group. There was no significant difference between NSCLC patients and healthy individuals regarding expression levels of TGF-α and TGF-β1. A high TGF-β1 level was associated with a better ECOG performance status, non-adenocarcinoma, and a worse response to EGFR-TKIs (P b 0.05, Table 4). The baseline level of TGF-β1 was also a predictive factor: patients with a high baseline level of TGF-β1 had significantly worse OS when compared to patients with low baseline level of TGF-β1 (Fig. 1C&D, Table 5) (high vs. low TGF-β1: OS 378.7 vs. 539.5 d, P b 0.05), but there was no difference for PFS (high vs. low TGF-β1: PFS 183.3 vs. 219.6 d, P = 0.27). These results were verified in multivariate analyses (high vs. low TGF-β1: PFS HR: 1.26; 95% CI, 0.86–1.85; P = 0.23; OS HR: 1.74; 95% CI, 1.15–2.64; P b 0.05, Table 1). However,
there was no significant difference between expression levels of TGF-α and pathological characteristics or response to EGFR-TKIs in NSCLC patients (Table 4), and NSCLC patients with different levels of TGF-α had no difference for PFS or OS (Table 5). 4. Discussion In this study, we detected EGFR somatic mutations in the tumor tissues from 68 of 134 advanced NSCLC patients, and in the plasma samples from 17 patients. Then we found that the concentrations of circulating free DNA were higher in NSCLC patients than in healthy subjects. Patients with high levels of TGF-β1 showed shorter OS and worse response to EGFR-TKIs treatment than patients with low TGF-β1 levels, and patients with different expression levels of TGF-α showed no significant difference in either PFS or OS. Many patients with stage IV NSCLC are always diagnosed with small biopsies, which often yield insufficient DNA for molecular biomarker testing. Detecting EGFR mutations in plasma samples would be a significant advantage for patients whose tumor tissue is not sufficient or available for testing. In our study, we detected 17 EGFR mutations in the plasma samples from 134 NSCLC patients, giving a total mutation rate of 12.7%. Analysis showed that 64.7% (11/17) of all these EGFR mutations were Exon 19 deletion, 23.5% (4/17) were EGFR L858R mutation, and 2 cases had EGFR L861Q mutation. The sensitivity of detecting EGFR mutation in plasma was low, which may result in no association between EGFR mutation in plasma and OS, PFS or response to EGFR-TKIs. The concordance rate of the results from tumor tissues and plasma samples was 59%, with a kappa value of 0.188; these results showed that plasma could not replace tumor tissue for EGFR testing by using the ARMS/Scorpion assay, and more advances in detection methods are required. The EGFR
S. Wang et al. / Clinica Chimica Acta 430 (2014) 63–70
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Fig. 1. Association between EGFR mutation status in tumor tissues and (A) progression-free survival (PFS) and (B) overall survival (OS) for NSCLC patients. Association between plasma baseline TGF-β1 levels and (C) PFS and (D) OS for NSCLC patients.
mutation rates reported by Zhao X and Kimura H were 17.11% (19/111) and 16.67% (7/42), respectively, which were lower than those in tumor tissues [5,9]. Several studies have detected EGFR mutations in both tumor tissue and plasma, and the results showed that concordance rates for EGFR mutation status between the two types of samples varied from 58% to 97% [10–15]. Although detection of EGFR mutations in plasma is possible in some patients, more data is required to evaluate clinical applicability. The limitations of plasma analysis may be related to low overall levels of tumor DNA in plasma, a variable ratio of tumor to wild type circulating free DNA or low sensitivity of detection methods. Wild
Fig. 2. Distribution of plasma circulating free DNA concentrations in NSCLC patients and healthy controls as shown by box-plots. Mean plasma circulating free DNA level in NSCLC patients was higher than that in healthy controls (P b 0.05).
type DNA accounts for the largest quantity of circulating free DNA, and may result in false negative analysis of plasma/serum samples [16]. Marie Brevet found that EGFR mutations were more often detectable in plasma samples from patients who received treatment prior to blood collection rather than in samples from patients who received treatment after the blood collection [17]. These could be explained by an increased amount of circulating free DNA due to tumor cell death in response to treatment. In our study, blood from all NSCLC patients was collected before EGFR-TKIs treatment, and the amount of circulating free DNA might be low. Numerous reports have validated the tumor-promoting role of TGFβ1 through its effects on tumor cell invasion and alterations. Previous studies have observed increased TGF-β1 levels after radiotherapy in lung cancer patients [18,19]. Kumar et al. found that increased levels of TGF-β1 were observed in NSCLC patients compared with healthy individuals, and TGF-β1 levels did not correlate with survival time or response to chemotherapy [20]. In the former studies, data measuring the correlation between TGF-β1 levels and overall survival in NSCLC patients are lacking. In our study, we found that plasma TGF-β1 levels could not be distinguished between patients with lung cancer and healthy individuals; but there was a significant difference between TGF-β1 levels in the plasma of responders and non-responders to EGFR-TKIs treatment; and pretreatment plasma TGF-β1 levels were associated with OS in NSCLC patients treated with EGFR-TKIs, but not with progression. Our study suggests that quantifying TGF-β1 levels may help in predicting EGFR-TKIs therapy outcomes and survival time. More studies will be needed with larger numbers of patients to certify the exact potential for using plasma TGF-β1 to predict survival time and therapeutic outcome in lung cancer. We also found that plasma TGF-β1 levels correlated with ECOG performance status and pathology. TGF-α is one of EGFR ligands, which could rapidly disassociate from EGFR in the environment of lysosomes and once again become
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Table 4 NSCLC patient characteristics according to plasma TGF-α, TGF-β1 and circulating free DNA levels. TGF-α
TGF-β1
b4.67 pg/mL (n = 87)
N4.67 pg/mL (n = 47)
Circulating free DNA
b359.97 pg/mL (n = 102)
N359.97 pg/mL (n = 32)
b5.82 ng/uL (n = 80)
N5.82 ng/uL (n = 54)
Total No.
No.
%
No.
%
P
No.
%
No.
%
P
No.
%
No.
%
P
Sex Male Female
69 65
54 48
78.26 73.85
15 17
21.74 26.15
NS
40 40
57.97 61.54
29 25
42.03 38.46
NS
46 41
66.67 63.08
23 24
33.33 36.92
NS
Nationality Han Other
128 6
97 5
75.78 83.33
31 1
24.22 16.67
NS
74 6
57.81 100
54 0
42.19 0
NS
83 4
64.84 66.67
45 2
35.16 33.33
NS
Age, y b60 ≥60
90 44
66 36
73.33 81.82
24 8
26.67 18.18
NS
51 29
56.67 65.91
39 15
43.33 34.09
NS
57 30
63.33 68.18
33 14
36.67 31.82
NS
ECOG score 0 1 2
29 88 17
26 63 13
89.66 71.59 76.47
3 25 4
10.34 28.41 23.53
NS
15 59 10
51.72 67.05 58.82
14 29 7
48.28 32.95 41.18
0.031
21 53 10
72.41 60.23 58.82
8 35 7
27.59 39.77 41.18
NS
Tumor stage III IV
19 115
14 88
73.68 76.52
5 27
26.32 23.48
NS
13 67
68.42 58.26
6 48
31.58 41.74
NS
11 76
57.89 66.09
8 39
42.11 33.91
NS
Histology Adenocarcinoma Non-adenocarcinoma
108 26
83 19
76.85 73.08
25 7
23.15 26.92
NS
69 11
63.89 42.31
39 15
36.11 57.69
0.04
71 16
65.74 61.54
37 10
34.26 38.46
NS
Smoking history Ever Never
62 72
50 52
80.65 72.22
12 20
19.35 27.78
NS
34 46
54.84 63.89
28 26
45.16 36.11
NS
41 46
66.13 63.89
21 26
33.87 36.11
NS
Performance status 0–1 2
122 12
92 10
75.41 83.33
30 2
24.59 16.67
NS
76 4
62.3 33.33
46 8
37.7 66.67
NS
78 9
63.93 75.00
44 3
36.07 25.00
NS
Response to EGFR-TKI Partial response/stable disease Progressive disease/deceased
102 32
77 25
75.49 78.13
25 7
24.51 21.87
NS
67 13
65.69 40.62
35 19
34.31 59.38
0.01
66 21
64.71 65.63
36 11
35.29 34.37
NS
S. Wang et al. / Clinica Chimica Acta 430 (2014) 63–70
Characteristics
S. Wang et al. / Clinica Chimica Acta 430 (2014) 63–70 Table 5 Association between biomarkers and progression-free survival and overall survival for NSCLC patients. Parameter
Progression-free survival/Days
Overall survival/Days
Average
P
Average
Range
P
EGFR mutation in tumor tissues Wild-type 118 75–160 Mutation 290 228–352
0.00
338 606
273–403 534–678
0.00
EGFR mutation in plasma Wild-type 211 Mutation 169
166–257 94–245
NS
483 420
424–541 282–558
NS
TGF-α level Low High
215 176
166–265 111–242
NS
474 474
411–538 372–575
NS
TGF-β1 level Low High
220 183
169–270 118–249
NS
539 379
471–608 298–460
0.00
Circulating free DNA Low 181 High 250
135–227 173–326
NS
456 507
391–521 412–601
NS
Range
biologically available to re-stimulate cells; hence, TGF-α may have rather consistent soluble concentrations [21]. In the BR.21 study conducted by the NCIC clinical trials group, TGF-α levels in plasma were analyzed retrospectively, and results showed that TGF-α levels did not have prognostic significance on survival time, but high levels of TGF-α in NSCLC patients predicted a lack of benefit from treatment with erlotinib. Two Japanese groups found that high levels of TGF-α were associated with progressive disease and a worse overall survival in NSCLC patients treated with gefitinib [22,23]. However, we did not find any utility of TGF-α for predicting survival or therapeutic efficacy from EGFR-TKIs treatment. Also, plasma TGF-α levels could not be distinguished between patients with lung cancer and healthy individuals. To date, several groups have evaluated plasma DNA levels in NSCLC patients. Sozzi et al. first reported that levels of plasma DNA from 84 NSCLC patients were higher than that in 43 healthy control individuals, as measured by a simple colorimetric assay [24]. No correlation between plasma DNA level and stage or histology was observed. Both Paci et al. and Ludovini et al. verified that higher concentrations of circulating free DNA were found in patients with lung cancer than in healthy individuals [25,26]. Neither of these investigators found any correlation between plasma DNA levels and the clinical characteristics of NSCLC patients. Similarly, we showed significantly higher mean pretreatment plasma DNA concentrations in NSCLC patients as compared to healthy controls. Plasma DNA levels were not significantly related to the clinical characteristics of NSCLC patients. Our study confirmed the predictive value of circulating free DNA for distinguishing NSCLC patients from healthy individuals, and circulating free DNA can be detected by a simple colorimetric assay. In summary, the concordance rate between the EGFR mutation status in tumor tissues and plasma samples was 59%, TGF-β1 levels in plasma could be predictive for both response to EGFR-TKIs and OS for NSCLC patients treated with EGFR-TKIs, and circulating free DNA is a biomarker for distinguishing NSCLC patients from healthy individuals. Acknowledgments The samples of tumor tissue and blood were collected form ICOGEN trial. The authors thank the patients and investigators for their participation in this study. This study was supported in part by grants from the Research Special Fund for Public Welfare Industry of Health (200902002-1), Chinese National Major Project for New Drug Innovation (2008ZX09312, 2008ZX09101, 2012ZX09101103, 2012ZX09303012), Chinese National High Technology Research and Development Program
69
of China (863 Program) (2006AA02Z4A3, 2011AA02A110), Beijing Municipal Science and Technology Commission Major Project for New Drug Innovation (Z121107005112005, Z121102009212055), Chinese Central Health Authority Special Fund (B2009B124), Major Research Program of Hospital, Chinese Academy of Medical Sciences, Hospital of Chinese Academy of Medical Sciences & Peking Union Medical College (LC2012A18), Key Special Program for Science and Technology of Zhejiang Province (2007C13003), and Zhejiang Beta Pharma Inc.. The authors thank these groups for their support. Hospitals and investigators attended in ICOGEN trial include: Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China (Y Sun MD, Y Shi MD); Sun Yat-Sen University Cancer Center, Guangzhou, China (L Zhang MD); 307 Hospital of the Academy of Military Medical Sciences, Beijing, China (X Liu MD); Shanghai Pulmonary Hospital, Tongji University, Shanghai, China (C Zhou MD); Peking Union Medical Hospital, Beijing, China (L Zhang MD); Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China (S Zhang MD); Daping Hospital, Third Military Medical University, Chongqing, China (D Wang MD); Changhai Hospital, Second Military Medical University, Shanghai, China (Q Li MD); Nanjing Bayi Hospital of People's Liberation Army, Nanjing, China (S Qin MD); Second Xiangya Hospital of Central South University, Changsha, China (C Hu MD); Zhejiang Cancer Hospital, Hangzhou, China (Y Zhang MD); Hunan Cancer Hospital, Changsha, China (J Chen MD); Jilin Cancer Hospital, Changchun, China (Y Cheng MD); Jiangsu Cancer Hospital, Nanjing, China (J Feng MD); Tangdu Hospital (H Zhang MD) and Xijing Hospital (W Liu MD), Fourth Military Medical University, Xian, China; Nanjing Military General Hospital, Nanjing, China (Y Song MD); Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (Y Wu MD); First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China (N Xu MD, J Zhou MD); Nanfang Hospital, Southern Medical University, Guangzhou, China (R Luo MD); Affiliated Zhongshan Hospital of Fudan University, Shanghai, China (C Bai MD); Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China (Y Jin MD); Beijing Chao-Yang Hospital, Beijing, China (H Dai MD); General Hospital of People's Liberation Army, Beijing, China (S Jiao MD); Beijing Cancer Hospital, Beijing, China (J Wang MD); Peking University Third Hospital, Beijing, China (L Liang MD); Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China (W Zhang MD); Hangzhou Tigermed Consulting, Hangzhou, China (Z Wei PhD); and Zhejiang Beta Pharma, Hangzhou, China (F Tan MD, Y Wang PhD, L Ding MD). References [1] Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011;61:69–90. [2] Ramalingam SS, Owonikoko TK, Khuri FR. Lung cancer: new biological insights and recent therapeutic advances. CA Cancer J Clin 2011;61:91–112. [3] Shi Y, Zhang L, Liu X, et al. Icotinib versus gefitinib in previously treated advanced non-small-cell lung cancer (ICOGEN): a randomised, double-blind phase 3 noninferiority trial. Lancet Oncol 2013;14:953–61. [4] Mok TS, Wu YL, Thongprasert S, et al. Gefitinib or carboplatin–paclitaxel in pulmonary adenocarcinoma. N Engl J Med 2009;361:947–57. [5] Mack PC, Holland WS, Burich RA, et al. EGFR mutations detected in plasma are associated with patient outcomes in erlotinib plus docetaxel-treated non-small cell lung cancer. J Thorac Oncol 2009;4:1466–72. [6] Kimura H, Suminoe M, Kasahara K, et al. Evaluation of epidermal growth factor receptor mutation status in serum DNA as a predictor of response to gefitinib (IRESSA). Br J Cancer 2007;97:778–84. [7] Normanno N, Bianco C, Strizzi L, et al. The ErbB receptors and their ligands in cancer: an overview. Curr Drug Targets 2005;6:243–57. [8] Iyer S, Wang ZG, Akhtari M, et al. Targeting TGF-β signaling for cancer therapy. Cancer Biol Ther 2005;4:261–6. [9] Zhao X, Han RB, Zhao J, et al. Comparison of epidermal growth factor receptor mutation statuses in tissue and plasma in stage I–IV non-small cell lung cancer patients. Respiration 2013;85:119–25. [10] Kimura H, Kasahara K, Shibata K, et al. EGFR mutation of tumor and serum in gefitinib-treated patients with chemotherapy-naive non-small cell lung cancer. J Thorac Oncol 2006;1:260–7.
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