Epidermal growth factor receptor somatic mutation analysis in 354 Chinese patients with non-small cell lung cancer.

Lung cancer is one of the most common types of cancer worldwide, with the highest mortality rate of all types of cancer. In the present study, epidermal growth factor receptor (EGFR) mutations of 354 primary patients with non-small cell lung cancer (NSCLC) of Chinese ethnicity were detected following formalin-fixed and paraffin-embedded specimen DNA extraction, polymerase chain reaction amplification, and sanger sequencing. The total rate of occurrence of EGFR somatic mutation in these 354 patients was 48.02%. Of these detected EGFR mutations, 27.40% were located in exon 19 and 25.99% in exon 21. The most frequent mutation in exon 19 was E746-A750del (8.47%), and in exon 21, L858R (10.17%). EGFR mutation rates were significantly associated with sex [female vs. male: 60.13 vs. 38.81%; adjusted odds ratio (OR), 1.93, 95% confidence interval (CI), 1.07-3.51, P=0.029], age (<60 vs. ≥60; 58.62 vs. 40.67%; adjusted OR, 1.87; 95% CI, 1.20-2.92; P=0.006) and histology [adenocarcinoma (ADC) vs. non-ADC; 52.76 vs. 26.56%; adjusted OR, 2.35; 95% CI, 1.28-4.50; P=0.007]. The frequency of E746_A750del, Q787Q and L858R mutations were significantly different in ADC patients compared with squamous cell carcinoma patients (P<0.001). Furthermore, a novel EGFR mutation, M793K, was detected in 7 NSCLC patients with possible gefitinib resistance. The present study analyzed the EGFR exon 18-21 mutation occurrence profile for Chinese patients with NSCLC and identified significant associations between different EGFR mutations with demographic and histological factors. These results may offer clinical benefits and potential novel treatments.

Introduction

Lung cancer is the most frequent type of primary cancer for men in China, and it has the highest mortality rate for any type of cancer worldwide (1). Despite the lower incidence of lung cancer than breast cancer for women, its mortality rate is also the highest for women (2). There are three major types of lung cancer, including non-small cell lung cancer (NSCLC), small cell lung cancer and carcinoid lung cancer. NSCLC is the most common type of lung cancer, accounting for 85% of all lung cancer cases. NSCLC can be further divided into three major histological subtypes: Adenocarcinoma (ADC), squamous cell carcinoma (SCC) and large cell carcinoma (LCC) (3,4). Patients with lung cancer do not always present distinct symptoms, and are commonly diagnosed at an advanced stage or after the primary cancer has metastasized. This causes a poor prognosis and high mortality rate for patients with lung cancer (4). The prevention and treatment of lung cancer urgently requires improvement through further understanding the molecular origins and development of the disease.

An increased exposure to smoking is associated with an increased risk of developing NSCLC (5,6). However, only 10–24% of smokers develop NSCLC, indicating that other environmental and genetic factors also contribute to NSCLC development (7,8). Mutations in the epidermal growth factor receptor (EGFR) gene are common in NSCLC patients, with mutation rates differing in males, females, smokers and non-smokers (9,10). EGFR mutations affect the EGF-EGFR-RAS-RAF signaling pathway, and are usually driver mutations for NSCLC development (11). EGFR is, therefore, one of the most important targets in NSCLC treatment. Small molecule tyrosine kinase inhibitors (TKIs) that target EGFR, including gefitinib and erlotinib, have significantly improved the overall survival rate of patients with EGFR-activating mutations. The efficacy of TKI drugs differs depending on the region of the EGFR kinase domain in which the mutation is located (12,13). Among the NSCLC patients with EGFR mutations, the overall response rate for treatment with gefitinib is ~75%, with a progression-free survival time of 9–13 months (14). Despite their low prevalence, new targetable EGFR mutations may improve the treatment and elongate the overall survival rate of patients with NSCLC.

In this study, EGFR mutations were detected in 354 patients with NSCLC of Chinese ethnicity by sequencing exons 18–21 from tumor samples. Further analysis was performed to determine the association between EGFR mutations and other variables, including age, gender, smoking status, and histology groups. A novel EGFR mutation, M793K, was detected in 7 patients with potential resistance to gefitinib.

Patients and methods

Patients

A total of 354 patients with NSCLC at the 307th Hospital of the Chinese People's Liberation Army (Beijing, China) and the General Hospital of the Chinese People's Liberation Army (Beijing, China) were enrolled in this study between November 2012 and April 2016. Informed consent was obtained from all individual participants included in the study, which was approved by the Ethics Committee of the Affiliated Hospital of Academy of Military Medical Sciences and the Ethics Committee of the General Hospital of Chinese People's Liberation Army. Formalin-fixed and paraffin-embedded (FFPE) tumor samples were prepared from primary surgical or biopsy specimens from patient lung tissue. All FFPE tissue specimens were identified by pathologists as primary NSCLC.

Detection of EGFR mutations

Tumor genomic DNA from each FFPE sample was extracted using the ALLPrep DNA/RNA FFPE kit (Qiagen, Inc., Valencia, CA, USA) according to the manufacturer's instructions. The DNA samples were examined for purity and concentration, and were diluted to a working concentration of 10 ng/µl. The detection of EGFR mutations was performed using Sanger sequencing with the ABI 3130 genetic analyzer (Applied Biosystems; Thermo Fisher Scientific, Inc., Waltham, MA, USA). The primers used are listed in Table I and were generated according to the manufacturer's protocol (Tianyi Huiyuan LLC, Beijing, China).

Table I.

Primers for the detection of EGFR mutations in patients with non-small cell lung cancer.

Primer name	Primer sequence, 5′-3′
EGFR(E18)-F	GAAGCTCCCAACCAAGCTCT
EGFR(E18)-R	CTCCCCACCAGACCATGAGA
EGFR(E19)-F	TGCCAGTTAACGTCTTCCTTC
EGFR(E19)-R	CCCACACAGCAAAGCAGAAA
EGFR(E20)-F	CCAGGAAGCCTACGTGATGG
EGFR(E20)-R	GACATAGTCCAGGAGGCAGC
EGFR(E21)-F	GTGAAAACACCGCAGCATGT
EGFR(E21)-R	GCCACCTCCTTACTTTGCCT

EGFR, epidermal growth factor receptor; E, exon; F, forward; R, reverse.

Statistical analysis

χ2 tests for univariate analysis were performed to investigate the association between EGFR mutation frequency and clinicopathological features. Associations between EGFR mutation status and sex, age, smoking history and clinicopathological characteristics were further evaluated by multivariate logistic regression analysis. The adjusted odds ratio (OR) and 95% confidence interval (CI) were then identified. Associations between sex, age, smoking history and pathology with specific mutations were analyzed using an exact binomial test. A two-sided P<0.05 was considered to indicate a statistically significant difference. All statistical analysis was performed using R (version 3.2.3; http://www.r-project.org/).

3D model protein building and EGFR-gefitinib affinity estimation

The EGFR kinase domain with M793K mutation was constructed using structure 2JIT from the Protein Data Bank (originally the EGFR kinase domain including a T790M mutation; 3.1 Å, complete from 696–986) as a template using the SWISS-model server (https://swissmodel.expasy.org/). The structure for wild type EGFR domain was from 1M14 from the Protein Data Bank (2.6 Å, complete from 672–960). SWISS-DOCK (http://www.swissdock.ch/) was used to test the binding energy of gefitinib (from ZINC; 19632614) to the 3 structures using the CHARMM energy field method.

Results

Demographic profile of NSCLC patients

The EGFR mutation status was analyzed in 354 NSCLC patients. Of these patients, 43.22% were female and 56.78% were male. The patient age ranged from 32–92 years, with a median age of 62. A total of 59.04% of the patients were ≥60 years old and 40.96% <60 years old. Of the 354 patients, 50.85% had never smoked. Pathological analysis revealed that 81.92% of the samples were from ADC, 16.95% from SCC, and 1.13% from LCC (Table II).

Table II.

Characteristics of 354 patients with non-small cell lung cancer.

Characteristic	Patients, n (%)
Sex, n (%)
Male	201 (56.78)
Female	153 (43.22)
Age, years
≥60, n (%)	209 (59.04)
<60, n (%)	145 (40.96)
Median (range)	62 (32–92)
Smoking status, n (%)
Smokers	174 (49.15)
Non-smokers	180 (50.85)
Histology type, n (%)
Adenocarcinoma	290 (81.92)
Squamous cell carcinoma	60 (16.95)
Large cell carcinoma	4 (1.13)

EGFR mutation distributions

EGFR mutations were identified in 48.02% (170) patients, with a single mutation identified in the majority of these patients (121 out of 170, 71.18%). Patients with more than one EGFR mutation were relatively uncommon: 13.53% (23/170) exhibited double mutations, 6.74% (11/170) triple mutations, 2.94% (5/170) quadruple mutations, 1.76% (2/170) quintuple mutations, 1.76% (3/170) sextuple mutations, 1.18% (2/170) septuple mutations and 1.18% (3/170) nonuple mutations. Of the 170 patients with EGFR mutations, 97 patients had point mutations in exon 19 and 92 in exon 21. The remaining mutations were located in exon 20 (58 patients) and exon 18 (38 patients). The most common mutation in exon 19 was E746-A750del (30 patients), and the most common in exon 21 was L858R (36 patients). In exon 20, Q787Q and M793K were detected in 11 and 7 patients, respectively (Table III).

Table III.

Summary of epidermal growth factor receptor exon 18–21 mutations in 354 non-small cell lung cancer tissue samples.

Exon	Mutation	Frequency (%)
18	V689M	0.28
18	P691S	0.28
18	P694L	0.28
18	Q701L	0.56
18	Q701R	0.28
18	Q701X	0.28
18	L703P	0.28
18	R705G	0.56
18	L707S	0.56
18	K708E	0.56
18	K708R	0.28
18	E709_710T>D	0.28
18	E709K	0.28
18	E711A	0.28
18	E711V	0.28
18	F712L	0.28
18	F712Q	0.28
18	K713H	0.56
18	I715V	0.56
18	V717A	0.28
18	G719A	0.28
18	G719D	0.28
18	G719V	0.28
18	S720P	0.56
18	S720T	0.28
18	G721S	0.28
18	F723C	0.28
18	F723L	0.56
18	K728M	0.28
18	K728T	0.28
19	L730I	1.13
19	I732V	0.28
19	E734A	0.28
19	E734R	0.85
19	G735D	0.28
19	E736V	0.28
19	K737R	0.28
19	V738L	0.28
19	I740F	0.28
19	P741R	0.28
19	P741S	0.28
19	V742A	0.56
19	I744V	0.28
19	K745R	0.28
19	E746_A750del	8.47
19	E746_E749del	0.28
19	E746_S752>A	0.28
19	E746_S752>D	0.56
19	E746_T751>A	1.69
19	E746_T751del	0.28
19	E746K	0.56
19	L747_750A>S	0.28
19	L747_A750del	0.85
19	L747_E749del	0.56
19	L747_P753>S	1.98
19	L747_T751del	1.13
19	R748G	0.28
19	E749G	0.28
19	S752_I759del	0.85
19	P753T	0.28
19	K754R	0.28
19	A755V	0.28
19	K757E	0.28
19	K757T	0.28
19	E758K	0.28
19	L760P	0.85
19	D761H	0.28
19	D761Q	0.56
20	A763T	0.28
20	V765A	0.28
20	A767S	0.28
20	A767V	0.28
20	S768_V769insGGQ	0.28
20	S768I	0.28
20	S768R	0.28
20	D770_N771insG	0.28
20	D770_N771insTP	0.28
20	N771>KT	0.28
20	C775Y	0.28
20	R776A	0.28
20	R776H	0.28
20	L777C	0.28
20	L777P	0.28
20	L778Q	0.28
20	G779D	0.28
20	I780S	0.28
20	T783A	0.28
20	S784P	0.28
20	V786A	0.28
20	Q787Q	3.11
20	L788Q	0.28
20	I789F	0.28
20	T790M	1.13
20	Q791L	0.28
20	M793K	1.98
20	M793L	0.28
20	P794L	0.28
20	F795L	0.56
20	C797S	0.28
20	L799Q	1.41
20	V802A	0.28
20	L814M	0.28
21	M825T	0.28
21	N826H	0.28
21	N826S	0.28
21	E829D	0.28
21	V834A	0.56
21	V834M	0.28
21	R836H	0.28
21	A840T	0.28
21	V845A	0.28
21	K846I	1.13
21	Q849L	0.28
21	Q849R	0.28
21	H850Y	0.28
21	I853T	0.28
21	T854A	0.28
21	D855A	0.28
21	F856L	1.13
21	G857E	0.28
21	G857W	0.56
21	L858H	0.28
21	L858P	0.28
21	L858R	10.17
21	K860R	0.28
21	K860Tfs	0.28
21	L861Q	0.56
21	L862Q	0.28
21	G863C	0.28
21	G863S	0.28
21	G863V	0.28
21	E865G	0.28
21	E865K	0.56
21	E866G	0.28
21	E866R	0.56
21	E866X	0.28
21	K867D	0.28
21	K867I	0.28
21	K867N	0.85
21	E868D	0.28
21	E868V	0.28
21	H870Q	0.28
21	H870R	0.28
21	A871T	0.28
21	A871V	0.28
21	G873E	0.28

Associations between EGFR mutation occurrence and clinicopathological features

Patients were divided into two groups (with and without EGFR mutations) for clinicopathological feature association analysis (Fig. 1). Multivariate logistic regression analysis revealed that EGFR mutations were more frequently detected in females than in males (female vs. male; 60.13 vs. 38.81%; adjusted OR, 1.93; 95% CI, 1.07–3.51; P=0.029). Patients ≥60 were more likely to have EGFR mutations than patients <60 years old (<60 vs. ≥60; 58.62 vs. 40.67%; adjusted OR, 1.87; 95% CI, 1.20–2.92; P=0.006). ADC patients had a higher chance of exhibiting EGFR mutations than non-ADC patients (ADC vs. non-ADC; 52.76 vs. 26.56%; adjusted OR, 2.35; 95% CI, 1.28–4.50; P=0.007). There was no significant difference between smokers and non-smokers in the EGFR mutation rate (smokers vs. non-smokers; 55.56 vs. 40.23%; adjusted OR, 1.02; 95% CI, 0.56–1.82; P=0.952). However, if only patients <60 are considered, EGFR mutation rate in non-smokers was significantly higher than in smokers (non-smokers vs. smokers; 66.27 vs. 48.39%; adjusted OR, 2.10; 95% CI, 1.07–4.11; P=0.046). Similar results were identified between non-smokers and smokers with ADC (non-smokers with ADC vs. smokers with ADC; 58.64 vs. 45.31%; adjusted OR, 1.71; 95% CI, 1.07–2.73; P=0.032; Table IV).

Figure 1.

Analysis of the statistical associations between specific mutations and clinicopathological features. Association of mutations with (A) sex, (B) smoking status, (C) cancer type and (D) age.

Table IV.

Association of EGFR mutations with the clinicopathological features of patients with non-small cell lung cancer.

	EGFR status, n		Univariate		Multivariate
Variable	Mutant	Wild type	Crude OR (95% CI)	P-value	Adjusted OR (95% CI)	P-value
Sex
Male	78	123	Ref.		Ref.
Female	92	61	2.378 (1.547–3.657)	<0.001	1.929 (1.072–3.506)	0.029
Age, years
≥60	85	124	Ref.		Ref.
<60	85	60	2.067 (1.344–3.179)	0.001	1.869 (1.200–2.923)	0.006
Smoking status
Smoker	70	104	Ref.		Ref.
Non-smoker	100	80	1.857 (1.218–2.833)	0.005	1.018 (0.564–1.815)	0.952
Pathology
Non-ADC	17	47	Ref.		Ref.
ADC	153	137	3.088 (1.693–5.630)	<0.001	2.352 (1.275–4.497)	0.007
Smokers
Male	63	94	Ref.
Female	7	10	1.044 (0.378–2.888)	1
Non-smokers
Male	15	29	Ref.
Female	85	51	3.222 (1.579–6.577)	0.002
ADC
Male	65	82	Ref.
Female	88	55	2.019 (1.264–3.225)	0.005
SCC
Male	12	38	Ref.
Female	4	6	2.111 (0.509–8.751)	0.514
Non-smokers with ADC
Male	14	20	Ref.
Female	81	47	2.462 (1.138–5.327)	0.033
Male
Smokers	63	94	Ref.
Non-smokers	15	29	0.772 (0.383–1.555)	0.582
Female
Smokers	7	10	Ref.
Non-smokers	85	51	2.381 (0.853–6.645)	0.153
≥60
Smokers	40	72	Ref.
Non-smokers	45	52	1.558 (0.894–2.715)	0.154
<60
Smokers	30	32	Ref.
Non-smokers	55	28	2.096 (1.067–4.114)	0.046
ADC
Smokers	58	70	Ref.
Non-smokers	95	67	1.711 (1.072–2.732)	0.032
SCC
Smokers	11	31	Ref.
Non-smokers	5	13	1.084 (0.314–3.745)	1
Males with ADC
Smokers	51	62	Ref.
Non-smokers	14	20	0.851 (0.391–1.851)	0.833
Females with ADC
Smokers	7	8	Ref.
Non-smokers	81	47	1.970 (0.671–5.778)	0.331
≥60 male
Smokers	36	63	Ref.
Non-smokers	7	22	0.557 (0.217–1.431)	0.316
<60 female
Smokers	3	1	Ref.
Non-smokers	47	21	0.746 (0.073–7.598)	1
≥60 ADC
Smokers	33	45	Ref.
Non-smokers	43	43	1.364 (0.736–2.527)	0.407
<60 ADC
Smokers	25	25	Ref.
Non-smokers	52	24	2.167 (1.038–4.522)	0.059

EGFR, epidermal growth factor receptor; OR, odds ratio; CI, confidence interval; Ref., reference value; ADC, adenocarcinoma; SCC, squamous cell carcinoma.

Association between specific mutations and clinicopathological features

The association between specific mutations and clinicopathological features was analyzed using an exact binomial test. Between patients with ADC and SCC, the frequencies of E746_A750del, Q787Q and L858R mutations were significantly different (P<0.001). Furthermore, the L858R mutation was significantly more frequent in smokers than in non-smokers. No significant associations were identified between specific mutations and other clinicopathological features (Fig. 1).

Analysis of the M793K mutation

The EGFR mutation M793K was detected in 7 out of 354 patients with NSCLC, including five smokers. No KRAS mutations or EGFR drug-resistance mutations, including T790M or C797S, were detected in these 7 patients. However, the follow-up information for these patients demonstrated that they responded poorly to treatment with gefitinib. Similar to the T790M mutation, M793K also occurs in the inhibitor-binding cleft between the N-lobe and C-lobe of the EGFR kinase domain, indicating that it is likely to be a novel drug-resistance mutation.

Gefitinib was always identified in the cleft between the N-lobe and C-lobe in the 3D models of wild type EGFR, EGFR with T790M and EGFR with M793K (Fig. 2). A total of 256 binding poses for wild type EGFR, 256 for EGFR with T790M and 252 for EGFR with M793K were determined. The full fitness scores between gefitinib and EGFR ranged from −2196.87 to −2160.79 kcal/mol for wild type EGFR, from −1791.71 to −1756.65 kcal/mol for M793K-mutated EGFR, and from −1805.53 to-1759.77 kcal/mol for T790M-mutated EGFR (Table V). The reduced affinity between gefitinib and EGFR with M793K may be reflective of the gefitinib drug resistance of patients with M793K.

Figure 2.

3D models of (A) the wild type EGFR kinase domain, (B) the EGFR kinase domain with a T790M mutation and (C) the EGFR kinase domain with an M793K mutation. EGFR, epidermal growth factor receptor.

Table V.

Full fitness score between gefitinib and EGFR wild type and EGFR T790M and M793K mutants, as predicted by Swiss-Dock.

EGFR	Full fitness score (kcal/mol)
Wild type	(−2,196.87; −2,160.79)
T790M	(−1,805.53; −1,759.77)
M793K	(−1,791.71; −1,756.65)

EGFR, epidermal growth factor receptor.

Discussion

EGFR mutations were detected in 170 out of the 354 patients with NSCLC of Chinese ethnicity by Sanger sequencing of EGFR exons 18–21. Associations between EGFR mutation occurrence and patient clinicopathological factors were further analyzed. A new EGFR mutation, M793K, was detected and predicted to be a TKI resistance mutation.

Of the 354 NSCLC patients, 43.22% were female and 56.78% were male, 50.85% patients were non-smokers and 49.15% were smokers. Pathological slides were collected and diagnosed for all patients, with 81.92% samples identified as ADCs, 16.95% as SCCs and 1.13% as LCCs. It is generally accepted that the median age of patients with NSCLC worldwide is 71 (15). The lower median age in this study (62 years), supports the indication that the median age of patients with NSCLC in Asia has lowered (9,16,17).

Less than 30% patients had multiple EGFR mutations in the present study. The most common mutations were L858R (36 patients) and E746-A750del (30 patients), which is consistent with previous reports (9,10). Multivariate logistic regression analysis revealed that EGFR mutations happened more frequently in females (P=0.029), older patients (≥60 years old; P=0.006) and ADC (P=0.007), which consolidates the results of previous reports (18–21). There was no significant difference in the EGFR mutation rate between smokers and non-smokers (P=0.952). However, in patients <60 years old, univariate analysis revealed that non-smokers are much more likely to have EGFR mutations than smokers (P=0.046). This was also true for patients with ADC, whereas non-smokers exhibited a higher EGFR mutation rate than smokers (P=0.032).

A significant association was observed between histology type and specific EGFR mutation rates as the frequencies of E746_A750del, Q787Q and L858R mutations were significantly different between patients with ADC and patients with SCC (P<0.001). Smokers were also more likely to have the L858R mutation than non-smokers.

The EGFR gene encodes a receptor protein that dimerizes upon ligand binding, activating tyrosine kinase activity and receptor phosphorylation. The kinase activity of EGFR can be increased by mutations to EGFR, inducing the hyperactivation of downstream pro-survival signaling pathways (22). Initial studies on the TKIs gefitinib (Iressa) and erlotinib (Tarceva) demonstrated biological and clinical significance in a subset of lung cancers (23). Further investigation demonstrated that patients with advanced NSCLC and EGFR-activating mutations (particularly exon 19 deletions, L858R in exon 21, and G719X in exon 18) demonstrated the highest response rates to these TKIs (24). However, over 6–12 months of treatment, the majority of tumor cells gained resistance to EGFR-TKIs through a secondary mutation. Previous studies reported that T790M occurs in 50% of EGFR-mutated patients with TKI resistance (25,26) and it is considered a TKI acquired resistance mutation (27–29). In this study, a new mutation, M793K, was detected in 7 out of 354 NSCLC patients. These 7 patients gained drug resistance following their treatment with gefitinib, and no other previously identified drug resistance mutations were detected in these patients.

The docking analysis of gefitinib and EGFR kinase domain with/without T790M or M793K mutations demonstrated that gefitinib was always identified in the cleft between the N-lobe and C-lobe of the EGFR kinase domain, which is also the location of M793K. TKIs form direct H-bonds with M793 and T790 (30–33). Computational simulation and prediction methodologies have predicted that M793 forms a high proportion of these H-bonds with inhibitors (34) and M793K was previously predicted to be associated with drug resistance (resistance score, 0.057) (35). The full fitness score between gefitinib and the EGFR M793K structure was higher than that between gefitinib and the wild type EGFR structure, and between gefitinib and the EGFR T790M structure. These findings indicate that M793K reduces the binding affinity between gefitinib and EGFR and may induce the development of the resistance to TKI treatment.

In coclusion, the present study presents a complete picture of exon 18–21 EGFR mutations based on 354 Chinese patients with NSCLC, and investigates the association between EGFR mutations with sex, age, smoking history, and histology. The EGFR M793K mutation was identified for the first time in NSCLC patients and may have been associated with resistance to TKI treatment. This finding laid the basis for the further investigation of the association between M793K mutation and TKI treatment clinical outcomes.