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. 2017 Apr 25;13(6):1053–1063. doi: 10.1080/15548627.2017.1308997

The Thr300Ala variant of ATG16L1 is associated with decreased risk of brain metastasis in patients with non-small cell lung cancer

Qian-xia Li a, Xiao Zhou a, Ting-ting Huang a, Yang Tang a, Bo Liu a, Ping Peng a, Li Sun a, Yi-hua Wang b, Xiang-lin Yuan a,✉
PMCID: PMC5486363  PMID: 28441070

ABSTRACT

Non-small cell lung cancer (NSCLC) often metastasizes to the brain, but identifying which patients will develop brain metastases (BM) is difficult. Macroautophagy/autophagy is critical for cancer initiation and progression. We hypothesized that genetic variants of autophagy-related genes may affect brain metastases (BM) in NSCLC patients. We genotyped 16 single nucleotide polymorphisms (SNPs) in 7 autophagy-related (ATG) genes (ATG3, ATG5, ATG7, ATG10, ATG12, ATG16L1, and MAP1LC3/LC3) by using DNA from blood samples of 323 NSCLC patients. Further, we evaluated the potential associations of these genes with subsequent BM development. Lung cancer cell lines stably transfected with ATG16L1: rs2241880 (T300A) were established. Mouse models of brain metastasis were developed using cells transfected with ATG16L1–300T or ATG16L1–300A. ATG10: rs10036653 and ATG16L1: rs2241880 were significantly associated with a decreased risk of BM (respective hazard ratios [HRs]=0.596, 95% confidence interval [CI] 0.398–0.894, P = 0.012; and HR = 0. 655, 95% CI 0.438–0.978, P = 0.039, respectively). ATG12: rs26532 was significantly associated with an increased risk of BM (HR=1.644, 95% CI 1.049–2.576, P = 0.030). Invasion and migration assays indicated that transfection with ATG16L1–300T (vs. 300A) stimulated the migration of A549 cells. An in vivo metastasis assay revealed that transfection with ATG16L1–300T (vs. 300A) significantly increased brain metastasis. Our results indicate that genetic variations in autophagy-related genes can predict BM and that genome analysis would facilitate stratification of patients for BM prevention trials.

KEYWORDS: ATG16L1, autophagy, brain metastasis, non-small cell lung cancer, prevention, SNPs

Introduction

More than 300,000 patients with cancer will be diagnosed with brain metastasis this year,1,2 with the lung being the most common primary site for secondary BM.3,4 Brain metastases (BM) in patients with non-small cell lung cancer (NSCLC) are a devastating problem with profound impact on survival and quality of life (QoL). Survival times after BM diagnosis remain poor at only 1.5–9.5 mo.5,6 Although studies have shown that prophylactic cranial irradiation (PCI) is successful in decreasing the incidence of BM,7–10 preventive treatments of BM are rarely used in clinical practice because of the lack of proven survival advantage and the potential for toxicity. This negative result on survival may be explained by the unintended selection of patients with a low risk of cerebral metastasis. A recent trial revealed that PCI provides significantly lengthened disease-free survival (DFS), but does not have a significant effect on overall survival (OS). In this study, all patients who received PCI were selected on the basis of risk factors of brain metastases; however, these risk factors have not been clarified.11 These findings suggest that PCI may not be suitable for all patients. Therefore, it is necessary to identify the population subset that is at the highest risk of BM and is most likely to benefit from PCI.

Pretreatment factors that predict high rates of BM include histology, extent of disease, and young age. However, previously published studies have reported conflicting results.12–14 Furthermore, these studies did not consider genetic factors. Only one study has reported that the expression levels of 3 genes, CDH2, KIFC1, and BPTF/FALZ, are highly predictive of BM in early and advanced lung cancer.15 The expression levels of genes are affected by several factors; this limits the applicability of the genomic approach for risk prediction. Improvements in predictive accuracy require the identification and inclusion of molecular markers of the risk of BM.

One approach to identifying molecular markers involves studying single nucleotide polymorphisms (SNPs) in signaling pathways that regulate cell proliferation and migration, and assessing the relationship between multiple SNPs and the risk of BM. We previously reported that genetic variations in the phosphoinositide 3-kinase-AKT pathway are associated with an increased risk of BM in patients with NSCLC.16 Additional investigations on candidate genes that are crucial for metastasis may uncover missing links in the heritability of BM. Autophagy is an important adaptive prosurvival mechanism that mediates cancer cell survival during metastasis. In this study, we expand on our previous results by analyzing SNPs in the autophagy pathway.

Autophagy is a lysosomal degradation process that regulates turnover of damaged proteins and organelles and promotes cell survival during nutrient deprivation or micro-environmental stress.17 Cancer cells face diverse environmental and cellular stresses during metastatic progression.18 To cope with this, tumor cells induce adaptive pathways such as autophagy.19,20 A previous study has reported that autophagy inhibition suppresses pulmonary metastasis of hepatocellular carcinoma in mice.21 Another study revealed that upregulated autophagy further enhances epithelial-to-mesenchymal transition (EMT) and migration ability in pancreatic cell lines.22 EMT is a reversible phenotypic change in which cells lose intercellular adhesion and epithelial polarization, and gain motility and invasiveness.23 In cancer, EMT plays a key role in the induction of cancer cell invasion and metastasis.24

During the formation of mammalian autophagosomes, 2 ubiquitin-like protein conjugation systems, ATG12 conjugation and LC3 modification, are required, and autophagy-related genes (ATG3, ATG5, ATG7, ATG10, ATG12, ATG16L1, and LC3) are involved in this process. Increased ATG10 expression is observed in colorectal cancer associated with lymphovascular invasion and lymph node metastasis.25 Recently, Desai et al. revealed that high ATG7 expression level is associated with poor patient survival in breast cancer.26 Similar important roles of ATGs have also been demonstrated in the development of other cancers.27,28 Together, these findings indicate that autophagy plays an important role in carcinogenesis. To our knowledge, no study has focused on the association between polymorphisms in the ATG genes and the risk of BM in patients with NSCLC. Therefore, we sought to identify potential associations between genetic variations in 7 genes in this pathway—ATG3, ATG5, ATG7, ATG10, ATG12, ATG16L1, and LC3—with the occurrence of BM in patients with NSCLC to identify potential candidates for intervention to reduce brain relapses.

Results

Patient characteristics

The characteristics of the 323 patients (221 men and 102 women) included in the study are listed in Table 1. At a median follow-up interval of 25 mo (range, 0–135 mo), BM had developed in 101 patients. The following sites of metastases were noted: brain only (n = 31); bone, lungs, adrenals, liver, and other unspecified sites (n = 148), or both (n = 70). Of the 70 patients who had metastases in both the brain and other sites, 10 had BM as the first site of recurrence, 45 had first recurrence at other sites, and 15 had simultaneous recurrence in more than one site. The median age of all patients was 57 y (range, 26–82 years); 54% had stage I-IIIA disease; 69% had adenocarcinoma, and 51% had smoked tobacco (72.4% of men and 5.9% of women). The median time from NSCLC diagnosis to the detection of BM was 9 mo. Univariate and multivariate analyses (Table 1) of patient- and tumor-related characteristics and BM revealed that disease stage was associated with BM, with patients having stage IIIB or stage IV disease at a higher risk of BM (P < 0.001). Neither tumor histology nor smoking status was associated with BM in this population.

Table 1.

Patient- and disease-related characteristics and their association with brain metastasis.

      Univariate analysis
 Multivariate analysis*
Characteristic No. of patients (%) No. of events (%) HR (95% CI) P Value HR (95% CI) P Value
Sex                
 Female 102 (32) 30 (29) 1.000     1.000    
 Male 221 (68) 71 (32) 1.105 0.721–1.693 0.646 0.972 0.571–1.655 0.918
Age, years                
 ≥ 60 years 132(41) 37 (28) 1.000     1.000    
 < 60 years 191(59) 64 (34) 1.253 0.836–1.879 0.275 1.056 0.696–1.603 0.796
Median (range) 58 (26–82)              
Disease stage at diagnosis                
 I, II, IIIA 177 (55) 38 (22) 1.000     1.000    
 IIIB, IV 146 (45) 63 (43) 2.520 1.683–3.772 <0.001 2.517 1.657–3.823 <0.001
Tumor histology                
 Squamous cell 80 (25) 19 (24) 1.000     1.000    
 Adenocarcinoma 223 (69) 78 (35) 1.567 0.949–2.588 0.079 1.419 0.827–2.435 0.204
 NSCLC, NOS 20 (6) 4 (20) 0.816 0.278–2.400 0.712 0.764 0.256–2.280 0.630
KPS Score                
 >80 39 (12) 11 (28) 1.000     1.000    
 80 210 (65) 63 (30) 1.073 0.565–2.035 0.830 0.731 0.373–1.433 0.362
 <80 74 (23) 27 (37) 1.371 0.680–2.763 0.378 1.059 0.516–2.173 0.876
Tobacco Smoking Status                
 Current 131 (40) 45 (34) 1.000     1.000    
 Former 35 (11) 13 (37) 1.123 0.606–2.082 0.713 0.931 0.495–1.751 0.825
 Never 157 (49) 43 (27) 0.781 0.514–1.186 0.246 0.677 0.401–1.145 0.146
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*

Multivariate analyses were adjusted for all of the factors listed in this table.

Effects of single SNPs on the risk of BM

We assessed the potential association of each of the 16 individual SNPs with BM risk by using a multivariate Cox model. We found that 3 SNPs, ATG10: rs10036653, ATG16L1: rs2241880, and ATG12: rs26532 were associated with BM risk. BM rates were lower for patients with the AT/TT genotype of ATG10: rs10036653 (P = 0.063, Fig. 1A) and the AG/GG genotype of ATG16L1: rs2241880 (P = 0.014, Fig. 1B). BM rates were higher for patients with the AC/CC genotype of ATG12: rs26532 (P = 0.015, Fig. 1C). In general, BM developed less often in patients with the AT/TT genotype of ATG10: rs10036653 (39%), the AG/GG genotype of ATG16L1: rs2241880 (39%), or the AA genotype of ATG12: rs26532 (37%) than in patients with the AA (29%), AA (26%), or AC/CC genotypes (22%; Table 2). Multivariate Cox proportional hazard analyses showed that the AT/TT genotype of ATG10: rs10036653 and the AG/GG genotype of ATG16L1: rs2241880 were associated with a significantly lower risk of BM (hazard ratio [HR] 0.596, 95% confidence interval [CI] 0.398–0.894, P = 0.012; and HR 0.655, 95% CI 0.438–0.978, P = 0.039, respectively), and that the AA genotype of ATG12: rs26532 was associated with a significantly higher risk of BM (HR 1.644, 95% CI 1.049–2.576, P = 0.030), after adjustment for gender, patient age, disease stage, tumor histology, Karnofsky performance status (KPS), and smoking status. However, the ATG10 genotype was significant only in the multivariate analysis. Similar analyses of the other 13 SNPs showed no associations between any other genotype and the incidence of BM (Table S1). None of the 3 genotypes tested was associated with metastasis at sites other than the brain (data not shown).

Figure 1.

Figure 1.

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Kaplan-Meier estimates of the cumulative probability of brain metastasis among patients with non-small cell lung cancer according to the following genotypes: (A) ATG10: rs10036653; (B) ATG16L1: rs2241880; (C) ATG12: rs26532; and (D) combined. The AA genotype at rs10036653, the AA genotype at rs2241880, and the AC/CC genotype at rs26532 were associated with higher cumulative probability of brain metastasis than the other genotypes.

Table 2.

Associations between genotypes and brain metastases.

      Univariate analysis
 Multivariate analysis*
Characteristic No. of patients No. of events (%) HR (95% CI) P Value HR (95% CI) P Value
ATG10: rs10036653                
AA 116 45 (39) 1.000     1.000    
AT + TT 192 55 (29) 0.692 0.467–1.026 0.067 0.596 0.398–0.894 0.012
ATG16L1: rs2241880                
AA 131 51 (39) 1.000     1.000    
AG + GG 186 49 (26) 0.617 0.416–0.913 0.016 0.655 0.438–0.978 0.039
ATG12: rs26532                
AA 117 26 (22) 1.000     1.000    
AC + CC 200 73 (37) 1.718 1.098–2.689 0.018 1.644 1.049–2.576 0.030
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*

NOTE. Multivariate analyses in this table were adjusted for sex, patient age, tumor histology, disease stage, Karnofsky performance status, and smoking status.

Abbreviations: HR, hazard ratio; CI, confidence interval; BM, brain metastases.

Combined effect of SNPs on the risk of BM

To analyze the combined effect of SNPs on the risk of BM, we defined the AA genotype of ATG16L1: rs2241880 and the AC/CC genotypes of ATG12: rs26532, which were associated with an increased risk of BM, as “unfavorable” genotypes. When we grouped the patients according to the number of unfavorable genotypes (i.e., 0, 1, or 2), the risk of BM increased as the number of unfavorable genotypes increased; BM developed in 45% of patients with both unfavorable genotypes, in 31% of those with either unfavorable genotype, and in 17% of those with no unfavorable genotype. This increase in the risk of developing BM from having both unfavorable genotypes was confirmed by Kaplan-Meier analyses (P = 0.002, Fig. 1D). Multivariate Cox proportional hazard analyses showed that the HR for individuals with 1 unfavorable genotype was 1.942 (95% CI 1.010–3.735, P = 0.047), and the HR for those with both unfavorable genotypes was 3.051 (95% CI 1.543–6.033, P = 0.001; Table 3).

Table 3.

Associations between genotypes and brain metastases.

      Univariate analysis
 Multivariate analysis*
Characteristic No. of patients No. of events (%) HR (95% CI) P Value HR (95% CI) P Value
0 65 11 (17) 1.000     1.000    
1 170 53 (31) 1.992 1.041–3.814 0.038 1.942 1.010–3.735 0.047
2 78 35(45) 3.077 1.562–6.061 0.001 3.051 1.543–6.033 0.001
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*

NOTE. Multivariate analyses in this table were adjusted for sex, patient age, tumor histology, disease stage, Karnofsky performance status, and smoking status.

Abbreviations: HR, hazard ratio; CI, confidence interval; BM, brain metastases.

ATG16L1–300T increases cell migration and invasion

We also tested if the ATG16L1: rs2241880 (T300A) variant genotype influenced the metastatic potential of lung cancer cells in vitro. First, 2 A549 cell lines stably transfected with ATG16L1 (300T or 300A) were established via lentivirus-mediated transfection (Fig. 2). The transfection efficiency for the A549 lung cancer cell lines was approximately 99%. Cell migration and cell invasiveness were assessed by using Transwell® assays. Upon transfection with the ATG16L1–300T construct, the cell line demonstrated increased motility relative to the 300A transfectants (Fig. 3). These results suggest that the ATG16L1–300T genotype increased the metastatic potential of this lung cancer cell line.

Figure 2.

Figure 2.

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Transfection of the A549 lung cancer cell line with lentivirus for ATG16L1 rs2241880 300T or 300A. (A) pLV(Exp)-Neo-EF1A>ATG16L1>IRES/EGFP and pLV(Exp)-Neo-EF1A>ATG16L1(mutation)>IRES/EGFP system. (B) Fluorescence labeling indicated that the transfection efficiency was 99% for both cell lines. (C) Western blot analysis confirmed that transfection with either 300T or 300A led to overexpression of ATG16L1. A549-Mock (transfectants that received empty lentiviral vectors) served as controls. GAPDH (glyceraldehyde-3-phosphate dehydrogenase) was used as a loading control.

Figure 3.

Figure 3.

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Effect of transfection with rs2241880 T300A on the migration and invasion of A549 cells. (A) In the Transwell® migration assays, the 300T transfectants showed greater migration than the 300A transfectants. (B) In the Transwell® invasion assays, the 300T transfectants showed greater invasiveness than the A549-Mock cells. *P < 0.05, **P < 0.01. Results are presented as means from 3 independent experiments; error bars represent standard deviation.

ATG16L1–300T increases BM in nude mice

To explore whether autophagy plays a role in brain metastasis, we examined the effect of the ATG16L1: rs2241880 (T300A) variant genotypes on A549 metastasis in a nude mouse model of brain metastasis. The model was established with A549–300T/A549–300A cells, and a control cell line (A549-Mock) was established with a control virus. Two mice died one wk after intracardiac injection with the A549–300T/A549–300A cells, and these 2 mice were excluded from the study. In the A549-Mock group, 1mouse died at the 7th wk after intracardiac injection. However, histological examination confirmed that brain metastasis had not occurred in this mouse, and therefore, we included it in the final analysis. Consequently, there were 8 mice in the A549-Mock group and 7 mice in the A549–300T/A549–300 group at the time of the final analysis in the 7th wk. Small-animal imaging analysis of the nude mouse model using green fluorescent protein (GFP)-luciferase-expressing A549–300T, A549–300A, and A549-Mock cells corroborated the results of the histopathological analysis (Fig. 4). In summary, the percentage of BM in the A549–300T group (42.9%) was higher than that in the A549–300A group (14.2%) and the A549-Mock group (12.5%).

Figure 4.

Figure 4.

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In vivo analysis of the effect of transfection with rs2241880 T300A on metastasis. (A) Metastasis was detected by measuring the bioluminescence with an IVIS 200 Xenogen system. (B) Percentage of brain metastasis: 42.9% in the A549–300T group, 14.2% in the A549–300A group, and 12.5% in the A549-Mock group, respectively. (C) Three-dimensional imaging of brain metastases. (D) Metastasis was detected by histology; brain metastasis (100 ×) and brain metastasis (200 ×).

Discussion

In this study, we investigated whether genetic variations in autophagy-related genes, ATG3, ATG5, ATG7, ATG10, ATG12, ATG16L1, and LC3, are associated with BM risk. We found that SNPs in ATG10: rs10036653, ATG16L1: rs2241880, or ATG12: rs26532 were associated with BM.

One of the polymorphisms associated with BM risk was in ATG16L1. This gene has been mapped to chromosome 2q37.1,29 and the SNP in the ATG16L1 c.898A>G (rs2241880) gene results in the substitution of threonine with alanine (T300A/Thr300Ala), thereby changing the polarity of the protein. This SNP has been shown to affect the autophagy process,30 and the G allele has been identified as a risk allele in Crohn disease.31,32 One possible explanation for the observed association between the ATG16L1 genotype and BM is that this effect is mediated through modulation of the pro-inflammatory cytokine interleukin IL1B. The T300A polymorphism significantly increases CASP3/caspase 3- and CASP7-mediated cleavage of ATG16L1, resulting in lower levels of full-length ATG16LlT300A protein.33 Loss of the autophagy protein ATG16L1 enhances endotoxin-induced IL1B production.34 Besides its functional role in immune responses, IL1B also affects the cell growth and differentiation of various cell types.35 Similar to our findings, the presence of the ATG16L1 G allele has been associated with a protective effect against epithelial thyroid carcinoma.28 In a recent study, ATG16L1 (T300A) was found to be associated with reduced metastasis in colorectal cancer patients.36 Collectively, these observations indicate that our finding of an association between SNPs and BM in patients with NSCLC may be biologically plausible.

We also found that the ATG10: rs10036653 and ATG12: rs26532 polymorphisms are associated with BM risk. ATG10, which is an autophagic E2 enzyme, interacts with ATG7 to receive a ubiquitin-like molecule, ATG12. Additionally, ATG10 and ATG12 are involved in the ATG12–ATG5 conjugation reaction.37 The chromosomal region of ATG10 (5q24) is frequently lost in multiple cancers.38,39 In a recent study, SNPs in ATG10 were found to be associated with the risk of developing breast cancer.40 Increased ATG10 expression in colorectal cancer is associated with lymphovascular invasion and lymph node metastasis,25 suggesting that ATG10 is an oncogene. However, univariate analysis has indicated that the ATG10 genotype is not a significant prediction factor. Together, these data suggest that the ATG10 variant may be an independent predictor of the risk of developing BM, but interference by other tumor characteristics cannot be excluded and needs to be studied in a multifactorial model in future studies. Additionally, the biology of AGT10 in the development of lung cancer needs to be investigated further.

The complex nature of cellular signaling pathways often means that a single SNP may produce only a modest or undetectable effect, whereas the amplified effects of combined SNPs in the same pathway may enhance the predictive power of genome analysis. When we combined 2 SNPs in 2 different genes, both showing significant association with BM, we found substantial increases in the risk of BM for patients with 2 unfavorable genotypes compared with those with no unfavorable genotypes. These results suggest that multiple genetic variants within the autophagy pathway have a cumulative influence and may further enhance the predictive power of SNP analysis.

The putative function for each of the selected variants was predicted by the SNPinfo program. We also used VEP (http://asia.ensembl.org/info/docs/tools/vep/index.html) to predict the functions and obtained similar results. Because the 3 SNPs identified in this study were tag SNPs, there may be other variants in LD with the genotyped candidates as potentially functional. Future studies are necessary to validate these SNPs in independent patient populations. Additionally, fine mapping in the vicinity of these gene regions need to be performed to identify potential causal variants.

We also further tested the rs2241880 variant in the A549 cell line. We found that the effect of this variant was only observed in the migration assay. In the invasion assay, differences were detected only between A549–300T and A549-Mock. The T300A polymorphism only increased the migration ability of the cells. It is possible that this effect of increasing the migration ability but not the invasion ability plays a role in the development of BM.

Prophylactic radiotherapy has a clearly defined role in the treatment of patients with high-risk acute lymphocytic leukemia. In SCLC, PCI has significantly improved the overall survival rate in patients with either limited-stage disease (from 15% to 20% at 3 y) or extensive-stage disease (from 13% to 27% at 1 y) in patients who respond to first-line treatment. Thus, PCI should be considered for the treatment of all patients with extensive SCLC that responds to therapy and for patients with limited-stage SCLC that responds to therapy. Even though the risk of brain failure in NSCLC is not as high as that in SCLC, BM are quite common in NSCLC, with the incidence ranging from 13% to 54%.2 Thus, the use of PCI is also being considered for NSCLC. PCI has consistently reduced or delayed the appearance of BM, but none of the studies conducted to date has shown survival benefit.8–10,12 According to Bovi and White,2 it is unclear whether this lack of survival benefit results from a failure to identify the cohort best suited for preventive therapy; further, they imply that not all patients with NSCLC should receive PCI. Moreover, the use of PCI to prevent metastases can have both positive and negative effects.41 Because no test can identify which patients are at a high risk of developing BM, PCI has been administered unselectively to all patients, which may result in unnecessary toxicity with little potential benefit for some patients. Therefore, a validated nomogram should be developed to predict the likelihood of BM in patients diagnosed with NSCLC. If the findings from the current study are validated prospectively, in a study with adequate statistical power, these results, in combination with clinicopathological data, could become the basis for selecting patient subgroups at a high risk of developing BM to receive PCI.

In our study, the incidence of BM was 31% (101 of 323 patients), which is slightly higher than in some studies. Clinicopathological variables that may portend high risk of BM include adenocarcinomatous histology, high-volume disease, and young age.10 Most of the patients in our study had adenocarcinoma histology, 45% had advanced disease, and the median age (57 y) was lower than that typical for patients with NSCLC. These differences may explain the relatively high incidence of BM in our population; therefore, we adjusted for these variables in our multivariate analyses. We further assessed whether the 3 genotypes were associated with metastasis risk at other sites; no such association was detected. These results suggest that metastases in the brain and elsewhere may arise through different mechanisms.

In our study, we only selected rs2241880 for our initial downstream functional analysis, because the SNP in rs2241880 results in the substitution of threonine with alanine, thereby changing the autophagy process. With regard to the other 2 SNPs, ATG10: rs10036653 is near the 5′ end and ATG12: rs26532 is in the intron. Therefore, we selected rs2241880 for our initial downstream functional analysis. The downstream functions of the other 2 SNPs need to be analyzed in future studies.

In conclusion, to our knowledge, this study is the first to evaluate the associations between genetic variations in the autophagy pathway and BM risk. We found that 3 SNPs (ATG16L1: rs2241880, ATG10: rs10036653, and ATG12: rs26532) were associated with BM risk. Because these results are based on the analysis of a relatively small number of patients, we could not rule out the possibility of false-positive findings. A further potential shortcoming is that we obtained post-treatment computed tomography (CT) or magnetic resonance imaging (MRI) scans only if clinical evaluation revealed suggestive findings such as neurological symptoms. As is true in other studies analyzing the risk factors for BM, this could limit the accuracy of a putative molecular marker of BM risk. Independent external patient cohorts are needed to validate our findings. If validated, these SNPs may prove to be valuable biomarkers for use in combination with clinicopathological variables to identify patients at high risk of BM who could benefit from PCI.

Materials and methods

Study population and data collection

All patients in this retrospective analysis had histologically confirmed NSCLC that had been treated at either the Tongji Hospital Cancer Center or the Hubei Provincial Tumor Hospital in 2008–2011. No restrictions on age, gender, or disease stage were applied, but all patients were required to have blood samples available for analysis. The KPS of all patients was at least 70, and all had a life expectancy of at least 6 mo. Epidemiological data were collected with a structured questionnaire and included information on demographics, smoking history, alcohol consumption, medical history, family history of cancer, and occupational exposures to potential carcinogens. Clinical and follow-up data on treatment regimens, disease stage, pretreatment performance status, and vital status at the time of analysis were obtained from the patients’ medical records. CT or MRI scans had been obtained from each patient before treatment as part of the disease staging process. All the patients were asked to return to the hospital for examination (which included CT scans of the chest and abdomen) every 2–3 mo for the first 2–3 y after completion of treatment and every 6 mo thereafter. Repeat brain CT or MRI scans were obtained only in the event of clinical indications such as neurologic symptoms, as per the standard of care. BM and survival information was collected from each patient's follow-up records. Of the 363 patients eligible for this study, 40 were excluded, 16 because of insufficient DNA for genotyping, 11 because of incomplete data on disease staging, and 13 who had died or been lost to follow-up without information on BM, leaving 323 patients with complete information for the current analysis. Disease was staged according to the tumor/nodes/metastasis system in the sixth (2002) edition of the American Joint Committee on Cancer staging manual. Smoking status was coded as current, former, or never smoked, as described previously.42 The diagnosis of BM was based on CT scans or MRI scans obtained as noted above. The time to BM was defined as the interval from the date of NSCLC diagnosis to the date of BM diagnosis. The follow-up time was the interval from NSCLC diagnosis to BM, death, or to the last hospital visit. Patients with follow-up intervals longer than 24 mo and those without BM were censored at the date of the last contact. The study was approved by the Ethics Committee of Tongji Medical College. Written informed consent was obtained from all patients before interview.

Polymorphism selection and genotyping

Genomic DNA was isolated from peripheral blood lymphocytes by using a QuickGene DNA whole blood kit S (Fujifilm, DB-S) according to the manufacturer's protocol, and stored at −80°C until use. Based on the public HapMap SNP database and the HaploView 4.2 software, common SNPs (MAF ≥ 0.05) in 6 core genes of autophagy (ATG3, ATG5, ATG7, ATG10, ATG12, and LC3) were screened in gene regions (including the 10-kb upstream region of each gene) in the Chinese Han population. After prediction with the SNPinfo Web Server (http:// snpinfo.niehs.nih.gov/), a total of 27 potentially functional SNPs were selected. Linkage disequilibrium (LD) analysis with an r2 threshold of 0.80 was further applied to filter these functional SNPs. As a result, 16 loci were finally selected for genotyping. However, rs2705507 was excluded because of design failure. Other SNPs previously reported as being associated with survival or metastasis in general, were also included, such as ATG16L1: rs2241880. A total of 16 SNPs were selected for genotyping (Table 4).

Table 4.

Genes and single nucleotide polymorphisms selected for analysis.

Gene (number of SNPs) (number of SNPs) SNP Allelic change SNP position TFBS Splicing (ESE or ESS) nsSNP Polyphen Impact
ATG3 (1) rs7652377 C > A intron Y — — — modifier
ATG5 (3) rs510432 G > A near 5′ Y — — — modifier
  rs688810 T > C intergenic Y — — — modifier
  rs3804338 C > T intron Y — — — modifier
ATG7 (3) rs8154 T > C synon — Y — — modifier
  rs1375206 C > G intron Y — — — modifier
  rs1470612 G > A intron Y — — — modifier
ATG10 (5) rs1864183 A> G missense — Y Y possibly damaging moderate
  rs1864182 T > G missense — Y Y benign moderate
  rs10514231 T > C intron Y — — — modifier
  rs10036653 A > T near 5′ Y — — — modifier
  rs3734114 T > C missense — Y Y benign moderate
ATG12 (3) rs26532 A > C intron Y — — — modifier
  rs26534 G > A near 5′ Y — — — modifier
  rs26538 C > T intron Y — — — modifier
ATG16L1 (1) rs2241880 T > C missense — Y Y benign moderate
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The SNPs were genotyped as described previously.42 Sixteen of the SNPs were genotyped by using MALDI-TOF mass spectrophotometry to detect allele-specific primer extension products with the MassARRAY platform (Sequenom, Inc.). Assay data were analyzed using the Sequenom TYPER software (version 4.0). The individual call rate threshold was at least 95%. To assess reproducibility, 5% of the DNA samples were blindly and randomly analyzed in duplicates, and the results revealed a reproducibility of 99%.

Cell lines and animals

The A549 cell line, originating from human lung adenocarcinoma, was purchased directly from ATCC (CCL−185) before the described assays. Female BALB/c nu/nu mice (6 wk old, Institute of Laboratory Animal Science) were bred in specific pathogen-free conditions. Studies were conducted in compliance with the Chinese guidelines for the care and use of laboratory animals and were approved by the Institutional Animal Care and Use Committee of Tongji Medical College.

Vector constructions

First, we synthesized the ATG16L1 genetic template. To generate the entry vectors, the EEF1A1/EF1α promoter, EGFP, or ATG16L1 was cloned into the genetic template, by using the Gateway® BP recombination reaction following the manufacturer's (Invitrogen) instructions. To generate the entry vectors encoding ATG16L1 (mutation), the cDNAs were first amplified by polymerase chain reaction with the generated template. The resulting vectors, which we named pUp-EF1, pTail-IRES/eGFP, pDown-ATG16L1, or pDown-ATG16L1 (mutation), were then recombined into the pDestpuro vector generated following the protocol for the LR recombination reaction using the Gateway® LR plus clonase enzyme mix (Invitrogen, 12538–120) to construct expression lentiviral vectors, designated as pLV(Exp)-Neo-EF1A > ATG16L1 >IRES/EGFP and pLV(Exp)-Neo-EF1A >ATG16L1(mutation) > IRES/EGFP.

The primers were as follows:

  • pD-ATG16L1(mutation)-PF1(59.8) 5′-GGGGACAAGTTTGTACAAAAAAGCAGGCTGCCACCATGTCGTCGGGCCTCCG-3′

  • pD-ATG16L1(mutation)-PR1(63.2) 5′-GTAGCTGGTACCCTCACTTCTTTACCAGAACCAGGATGAGCATCCACATTGTCCTGGGGGAC-3′

  • pD- ATG16L1(mutation)-PF2(63.6) 5′-GTCTCTTCCTTCCCAGTCCCCCAGGACAATGTGGATGCTCATCCTGGTTCTGGTAAAGAAG-3′

  • pD-ATG16L1(mutation)-PR2(59.5) 5′-GGGGACCACTTTGTACAAGAAAGCTGGGTTCAGTACTGTGCCCACAGC-3′

In vitro assessment of ATG16L1 300T or ATG16L1 300A stable transfectants

A549 cell lines stably expressing ATG16L1T300A were generated by lentivirus-mediated overexpression. The ATG16L1–300T or ATG16L1–300A cells were transfected with an ATG16L1T300A-overexpressing lentivirus at a multiplicity of infection of 100 and then selected in media containing 2 μg/mL puromycin (Invivogen, ant-pr-1). The transfection efficiency was measured in terms of cellular expression of GFP by fluorescence microscopy (Leica DMI4000B). A549 cells stably transfected with a plasmid encoding ATG16L1 or ATG16L1T300A were termed as A549–300T and A549–300A. Transfectants receiving empty lentiviral vectors served as controls (A549-Mock).

Western blot analysis

Cells were lysed in radioimmunoprecipitation assay (RIPA) buffer (Beyotime, P0013B) supplemented with protease inhibitor (Beyotime, ST506). Protein concentrations of the supernatant were determined using the BCA protein assay kit (Beyotime, P0012S). Total protein (50 µg) was separated by SDS-PAGE and then transferred onto a polyvinylidene fluoride membrane (Millipore, IPVH00010). After blocking with 5% bovine serum albumin (BSA; Sigma-Aldrich, A9647–100G), the membranes were probed with the appropriate antibodies: monoclonal rabbit anti-human ATG16L1 antibody (Cell Signaling Technology, 8089s) diluted at 1:1,000 and monoclonal mouse anti-human GAPDH antibody (Beyotime, AF0006) diluted at 1:1,000. Horseradish peroxidase-conjugated goat anti-rabbit IgG (Beyotime, A0208) and horseradish peroxidase-conjugated goat anti-mouse IgG (Beyotime, A0216) diluted at 1:5,000 was used as the secondary antibody. Proteins were detected with an enhanced SuperSignal West Pico chemiluminescence kit (Pierce, 32109). GAPDH served as the internal standard. The expression levels of GAPDH and ATG16L1 were quantified by using ImageJ.

In vitro cell migration and invasion assays

Transwell® migration assay

Cells (1 × 105) were suspended in 200 μL of Dulbecco's modified Eagle's medium with 1% BSA and seeded on the top chamber of the Transwell® (Corning, 3422). Medium (900 μL) was added to the bottom chamber. The cells were allowed to migrate for 12 h, and then stained with 0.1% crystal violet and counted under a microscope.

Transwell® invasion assay

The Transwell® invasion assays (in vitro matrigel invasion assays) were performed as described previously.43 A549 cells (3 × 105) were suspended in 200 μL of Dulbecco's modified Eagle's medium with 1% bovine serum albumin, and were added to the upper compartments of a 24-well Transwell® chamber containing polycarbonate filters with 8-mm pores and coated with 60 mL of Matrigel (Sigma Aldrich, E1270; 1:9 dilution). Dulbecco's modified Eagle's medium (900 µL) with 10% BSA was added to the lower chambers, and the chambers were incubated for 24 h. Then, cells in the upper compartment were removed with a cotton swab, rinsed with PBS (HyClone, SH30256.01B), and fixed in 100% methanol. Cells that had invaded through the Matrigel to the lower surface were stained with 4,6-diamidino-2-phenylindole (Sigma-Aldrich, D9542) and quantified by counting the number of fluorescent cells in 5 random microscopy fields per filter at 200 × magnification.

Metastasis assay via intracardiac inoculation

The nude mouse model of brain metastasis via intracardiac inoculation was established as described previously.44 Briefly, NSCLC cell lines were engineered to stably express a triple modality vector encoding a GFP-luciferase fusion. Between 104 and 105 A549–300T, A549–300A, or A549-Mock cells were resuspended in 0.1 mL PBS (HyClone, SH30256.01B) and were injected into the right ventricle of nude mice (n = 8 per group). The animals were killed 7 wk later. Metastasis was detected by bioluminescence with an IVIS 200 Xenogen system and by histology. Incidence of brain metastasis was quantified on the basis of the presence of luminescent signal in the brain at 1, 3, 5, and 7 wk after intracardiac inoculation.

Statistical analysis

Statistical analyses were performed with the SPSS software (version 16.0). A Cox proportional hazards model was used to calculate the HRs and 95% CIs to evaluate the influence of genotypes on BM risk. The model was adjusted for gender, age, disease stage, tumor histology, KPS, and smoking status. Kaplan-Meier curves were plotted to assess the cumulative BM probability. Log-rank tests were used to compare the differences between groups. All P values were 2-sided, and P values <0.05 were considered statistically significant.

The in vitro data were expressed as means ± SD from 3 independent experiments (each of which had been performed in triplicate) and were compared with Student t tests. P values of 0.05 were considered to indicate statistically significant differences.

Supplementary Material

1308997_Supplemental_Material.doc

Abbreviations

BM

brain metastases

CDH2

cadherin 2

CI

confidence interval

DFS

disease-free survival

EMT

epithelial-to-mesenchymal transition

HR

hazard ratios

KPS

Karnofsky performance status

NSCLC

non-small cell lung cancer

PCI

prophylactic cranial irradiation

QoL

quality of life

SNPs

single nucleotide polymorphisms

Disclosure of potential conflicts of interest

No potential conflicts of interest were disclosed.

Acknowledgments

We would like to thank Editage [www.editage.cn] for English language editing.

Funding

This study was funded by 3 grants from the National Natural Science Foundation of China (grants 81472921, 81502521, and 81372664).

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