SNPs in PI3K/PTEN/mTOR and brain metastases in NSCLCclincancerres.aacrjournals.org/content/clincanres/early/2013/09/24/... · Pathway and Increased Risk of Brain Metastasis in Patients

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Associations between Single Nucleotide Polymorphisms in the PI3K/PTEN/AKT/mTOR

Pathway and Increased Risk of Brain Metastasis in Patients with Non–Small-Cell Lung

Cancer

Qianxia Li,1 Ju Yang,1 Qianqian Yu,1 Huanlei Wu,1 Bo Liu,1 Huihua Xiong,1 Guangyuan Hu,1

Jing Zhao,1 Xianglin Yuan,1* and Zhongxing Liao2

1Department of Oncology, Tongji Hospital, Huazhong University of Science and Technology,

Wuhan, Hubei Province, China; and 2Department of Radiation Oncology, The University of

Texas MD Anderson Cancer Center, Houston, Texas, USA

Running title: SNPs in PI3K/PTEN/mTOR and brain metastases in NSCLC

Keywords: NSCLC; SNP; PI3K; signaling pathways; cerebral metastasis; predictive

biomarker

Corresponding author: Xianglin Yuan, Department of Oncology, Tongji Hospital,

Huazhong University of Science and Technology, Wuhan, Hubei Province, China; e-mail

[email protected]

Disclosure of Potential Conflicts of Interest: The authors have no conflicts of interest to

disclose.

on May 2, 2018. © 2013 American Association for Cancer Research.clincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 27, 2013; DOI: 10.1158/1078-0432.CCR-13-1093

http://clincancerres.aacrjournals.org/

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Translational Relevance

Brain metastasis is a common complication of non-small cell lung cancer associated with

severe morbidity and mortality. This complication may be prevented or delayed by

prophylactic therapy. However, there are no clear biomarkers that can predict which patients

are at high risk of developing brain metastasis. In this study, we identified genetic predictors

within a key signaling pathway, PI3K/PTEN/AKT/mTOR, that identified high-risk non-small

cell lung cancer patients. The ability to predict which patients are at higher risk of developing

brain metastasis would help in selecting patients for future prospective studies to establish

appropriate cancer management strategies to reduce or prevent the occurrence of brain

metastasis, thus improving the clinical outcome for such patients.




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Abstract

Purpose: Non-small cell lung cancer (NSCLC) metastasizes fairly often to the brain, but

identifying which patients will develop brain metastases (BM) is problematic. The

phosphatidylinositol-3 kinase (PI3K)–AKT–mammalian target of rapamycin (mTOR)

signaling pathway is important in the control of cell growth, tumorigenesis, and cell invasion.

We hypothesized that genotype variants in this pathway could predict BM in patients with

NSCLC.

Methods: We genotyped 16 single nucleotide polymorphisms (SNPs) in 5 core genes

(PIK3CA, PTEN, AKT1, AKT2, and FRAP1) by using DNA from blood samples of 317

patients with NSCLC and evaluated potential associations with the subsequent development

of BM, the cumulative incidence of which was estimated with Kaplan-Meier analysis.

Multivariate Cox regression analysis was used to analyze correlations between genotype

variants and the occurrence of BM.

Results: In analysis of individual SNPs, the GT/GG genotype of AKT1: rs2498804, CT/TT

genotype of AKT1: rs2494732 and AG/AA genotype of PIK3CA: rs2699887 were associated

with higher risk of BM at 24 months’ follow-up (respective hazard ratios [HRs] 1.860, 95%

confidence interval [CI] 1.199-2.885, P=0.006; HR 1.902, 95% CI 1.259-2.875, P = 0.002;

and HR 1.933, 95% CI 1.168-3.200, P=0.010). We further found that these SNPs had a

cumulative effect on BM risk, with that risk being highest for patients carrying both of these

unfavorable genotypes (P=0.003).

Conclusions: Confirmation of our findings, the first to indicate that genetic variations in

PI3K–AKT–mTOR can predict BM, in prospective studies would facilitate stratification of




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patients for BM prevention trials.

Introduction

Every year, about 150,000 patients with cancer in the United States develop brain metastasis

(BM) (1), with the lung being the most common primary site for secondary BM (2, 3).

Survival times after the diagnosis of BM remain poor at only 1.5–9.5 months (4, 5).

Therefore, prevention of BM is an important consideration for improving survival among

patients with cancer. However, to date preventive strategies have met with mixed results, in

part because of differences in tumor biology across different types of cancer. For example,

radiation therapy has a well-defined role in preventing BM in patients with leukemia or small

cell lung cancer, for whom it can improve local control and survival; the role of preventive

radiation, if any, in locally advanced non-small cell lung cancer (NSCLC) is unclear. In most

trials conducted to date, the use of prophylactic cranial irradiation (PCI) has reduced the

cumulative incidence of BM compared with that in control groups, but thus far use of PCI has

not affected overall survival (6-9). Findings from a recent trial by the Radiation Therapy

Oncology Group (RTOG 0214) that PCI decreased the rate of BM but did not improve overall

or disease-free survival for patients with stage III disease imply that not all such patients

would benefit from PCI and thus it should not be recommended routinely for patients with

stage III NSCLC (10). The ability to predict which patients are at higher risk of developing

BM would help in selecting patients for future prospective studies to establish appropriate

cancer management strategies to reduce or prevent the occurrence of BM, thus improving the

clinical outcome for such patients.




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Currently, no way has been found to predict which patients with non-metastatic

NSCLC will develop BM. Although correlations have been found between genetic variations

and survival in patients with NSCLC (11-13), no study to date has specifically addressed

genetic variations in the context of BM. Currently, only clinicopathologic variables, such as

tumor histology, extent of disease, and patient age, are used to predict the risk of BM, and

studies have produced conflicting findings, with some showing significant correlations and

others not (14-16). In one study, the expression levels of three genes, CDH2 (coding for

N-cadherin), KIFC1, and FALZ, were found to be highly predictive of BM in patients with

early or advanced lung cancer (17). Unfortunately gene expression levels are influenced by

many factors and many of the measurement techniques used are not precise, which seriously

limits the usefulness of this approach for risk prediction. Improvements in predictive

accuracy still require the identification and inclusion of molecular markers of BM risk.

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 risk of BM. We previously

reported that genetic variations in the transforming growth factor (TGF) -β-Smad-dependent

pathway are associated with increased risk of BM in patients with NSCLC (18). Because

other pathways that are TGF-β-Smad-independent also participate in the development of

metastasis, we chose here to expand upon our previous results by analyzing SNPs in other

signaling pathways.

The PI3K/AKT pathway consists of phosphatidylinositol-3-kinase (PI3K),

phosphatase and tensin homolog (PTEN), v-akt murine thymoma viral oncogene homolog




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(AKT), and mammalian target of rapamycin (mTOR) and participates in the balance of cell

survival and apoptosis (19, 20). The epithelial-to-mesenchymal transition (EMT) is

associated with cellular acquisition of motility and invasive properties that promote the

formation of metastasis (21). In several cell culture models, EMT is induced by TGF-β or by

peptide growth factors via receptor tyrosine kinase signaling (22, 23). In both cases, PI3K is a

critical mediator of EMT. This pathway is often activated in many cancers, and uncontrolled

PI3K–AKT–mTOR signaling has been linked with poor clinical outcome in patients with

lung, bladder, esophageal, or cervical cancer (11, 24-26). Moreover, PI3K–AKT–mTOR has

been implicated in the regulation of tumor cell invasion in bladder cancer (27), and the PI3K

pathway has been shown to be activated in BMs from breast cancer (28). To our knowledge,

no studies have addressed how genetic variations in PI3K–AKT–mTOR influence the risk of

BM in patients with NSCLC. To fill this gap, we sought here to identify potential associations

between genetic variations in 5 genes in this pathway—AKT1, AKT2, PIK3CA (catalytic

subunit of PI3K), PTEN, and FRAP1 (encoding for mTOR)—with the occurrence of BM in

patients with NSCLC.

Patients and Methods

Study population and data collection

All patients in this retrospective analysis had histologically confirmed primary or metastatic

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, sex, or disease stage were

applied, but all patients must have had blood samples available for analysis. The Karnofsky




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performance score (KPS) of all patients was at least 70, and all had a life expectancy of at

least 6 months. Epidemiologic 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.

Computed tomography (CT) or magnetic resonance imaging (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 months for the first 2-3 years after completion of treatment and every 6

months thereafter. Repeat brain CT or MRI scans were obtained only in the event of clinical

indications, such as neurological symptoms, as 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, 46 were excluded, 11 without sufficient DNA for genotyping, 11 with

incomplete data on disease staging, and 24 who had died or been lost to follow-up without

information on BM, leaving 317 patients with complete information for the current analysis.

Disease had been 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 smoker, as described previously (18). The diagnosis of BM

was based on CT scans or MRI scans obtained as noted above. The time to BM was 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




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with follow-up intervals longer than 24 months 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 the QuickGene

DNA whole blood kit S (Fuji Film) according to the manufacturer’s protocol, and stored at

–80°C until use. We selected tagging SNPs from 5-kb flanking and within the gene regions of

5 genes: AKT1, AKT2, PIK3CA, PTEN and FRAP1 (mTOR) by using the tagger algorithm

(29). Fourteen tagging SNPs were identified with a cut-off value of r2 = 0.8 and a minor allele

frequency greater than 0.1 in the Chinese population based on data from Centre d’Etude du

Polymorphisme Human samples genotyped by the HapMap Project (www.hapmap.org) (30).

Other SNPs previously reported as being associated with survival in NSCLC, or metastasis in

general, were also included, such as rs2494732 and rs3803300 (12). A total of 16 SNPs,

including haplotype-tagging SNPs and potential functional SNPs, were selected for

genotyping (Table 1). Among them, 13 SNPs were genotyped by using matrix-assisted laser

desorption/ionization-time of flight mass spectrophotometry to detect allele-specific primer

extension products with the MassARRAY platform (Sequenom, Inc.). Assay data were

analyzed using Sequenom TYPER software (version 4.0). The individual call rate threshold

was at least 95%. Three SNPs (rs2494732, rs892119, and rs8100018) were genotyped by

TaqMan assay (25).




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Statistical analysis

Statistical analyses were done with SPSS software (version 16.0). A Cox proportional hazards

model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) to

evaluate the influence of genotypes on BM risk. The model was adjusted for sex, age, disease

stage, tumor histology, Karnofsky performance status, smoking status, and receipt of

chemotherapy or radiotherapy. Kaplan–Meier curves were plotted to assess the cumulative

BM probability. Log-rank tests were used to compare the difference between groups. All P

values were two-sided, and P values <0.05 were considered statistically significant.

Results

Patient characteristics

Characteristics of the 317 patients (216 men and 101 women) are shown in Table 2. At a

median follow-up interval of 24 months (range, 0-135 months), BM had developed in 99

patients. The sites of metastases included brain only (n=31), bone, lung, adrenals, liver, and

other unspecified sites (n= 142), or both (n= 68). Of the 68 patients who had metastases in

both brain and other sites, 8 had BM the first site of recurrence, 43 had first recurrence at

other sites, and 17 had simultaneous occurrence in more than one site. The median age of all

patients was 58 years (range 26–82 years); 55% had stage I-IIIA disease; 68% had

adenocarcinoma, and 51% had smoked tobacco (71.7% of men and 5.9% of women). The

median time from NSCLC diagnosis to detection of BM was 9 months. Possible associations

between patient- and tumor-related characteristics and BM tested by univariate and

multivariate analyses (Table 2) revealed that disease stage was associated with BM, with




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patients having stage IIIB or stage IV disease at higher risk of BM (P<0.001). Neither tumor

histology nor smoking status was associated with BM in this population.

Effects of single SNPs on risk of BM

We assessed potential associations of each of the 16 individual SNP with BM risk by using a

multivariate Cox model. We found that three SNPs, AKT1: rs2498804, AKT1: rs2494732 and

PIK3CA: rs2699887, were significantly associated with BM risk. BM rates were higher for

patients with the GT/GG genotype of AKT1: rs2498804 (P = 0.012, Figure 1A), the CT/TT

genotype of AKT1: rs2494732 (P = 0.004, Figure 1B) or the AG/AA genotype of PIK3CA:

rs2699887 (P = 0.030, Figure 1C). None of the other 13 SNPs analyzed were associated with

risk of BM. In general, BM developed more often in patients with the GT/GG genotype of

AKT1: rs2498804 (37%), the CT/TT genotype of AKT1: rs2494732 (39%) or the AG/AA

genotype of PIK3CA: rs2699887 (44%) than in patients with the TT (23%), CC (24%) or GG

genotypes (29%) (Table 3). Multivariate Cox proportional hazard analyses showed the

GT/GG genotype of AKT1: rs2498804, the CT/TT genotype of AKT1: rs2494732 and AG/AA

genotype of PIK3CA: rs2699887 to be associated with significantly higher risk of BM (HR

1.860, 95% CI 1.199–2.885, P=0.006; HR 1.902, 95% CI 1.259-2.875, P =0.002; and HR

1.933, 95% CI 1.168–3.200, P=0.010, respectively), after adjustment for sex, patient age,

disease stage, tumor histology, KPS, smoking status, and receipt of chemotherapy or

radiotherapy. When we repeated this analysis excluding the patients who had BM at diagnosis,

this association was significant only for patients with the GT/GG genotype of AKT1:

rs2498804 (P=0.019) and CT/TT genotype of AKT1: rs2494732 (P=0.003, Table 3). Similar




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analyses of the other 13 SNPs showed no associations between any other genotype and

incidence of BM (Supplementary Tables 1 and 2). None of the three genotypes tested was

associated with metastasis at sites other than brain (data not shown).

Combined effect of SNPs on risk of BM

For these analyses, we defined the GT/GG genotype of AKT1: rs2498804, CT/TT of AKT1:

rs2494732, and AG/AA of PIK3CA: rs2699887, genotypes associated with increased risk of

BM, as “unfavorable” genotypes. Among them, AKT1: rs2498804 and rs2494732 were in

strong linkage disequilibrium (D�= 0.95) (Figure 2), and thus we only included AKT1:

rs2498804 in the subsequent joint analysis. When we grouped patients according to number

of unfavorable genotypes (i.e., 0, 1, or 2), the risk of BM increased with increasing number of

unfavorable genotypes: BM developed in 41% of those with both unfavorable genotypes, in

37% of those with either unfavorable genotype, and 20% of those with neither unfavorable

genotype. This increase in risk of BM from having both unfavorable genotypes was

confirmed in Kaplan–Meier analyses (P=0.006, Fig. 1D). Multivariate Cox proportional

hazard analyses showed that the HR for individuals with 1 unfavorable genotype was 2.219

(95% CI 1.367–3.604, P=0.001), and the HR for those with both unfavorable genotypes was

2.724 (95% CI 1.391–5.334, P=0.003) (Table 4). This association between genotypes and risk

of BM remained significant after patients who had BM at diagnosis were excluded (Table 4).

Discussion

The PI3K/PTEN/AKT/mTOR pathway is important in balancing cell growth and death. This




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pathway has been shown to be aberrantly activated in several cancer types, including NSCLC

(20, 31, 32). In this study, we determined whether genetic variations in the genes for PI3K,

PTEN, AKT1, AKT2, and mTOR were associated with BM risk. We found that SNPs in AKT1:

rs2498804, AKT1: rs2494732 or PIK3CA: rs2699887 were associated with BM. To the best of

our knowledge, this is the first evidence showing this association in patients with lung cancer.

With validation, this test could be used to predict which patients are at risk for BM, and

therefore could be useful for helping to select candidates for trials designed to evaluate

preventive interventions.

Two of the polymorphisms associated with BM risk were in AKT1, which consists of

14 exons and is about 26 kb in size. AKT is a central node in cell signaling that regulates

several processes, including cell survival, proliferation, and protein synthesis (33). AKT has

been reported to be constitutively activated in NSCLC, enhancing cell survival by blocking

induction of apoptosis (31). AKT has been implicated in the regulation of angiogenesis and

metastasis, both important processes in cancer development and progression (34, 35). SNPs

and their haplotypes of AKT1 were recently reported to be associated with AKT1 protein

expression level and with apoptotic capacity (36). The protein kinase Akt can induce the

EMT and promote enhanced motility in cancer cells in vitro and cellular invasion in vivo (35).

Expression of pAKT has also been observed in BM from breast cancer (28). Several studies

have linked activated AKT expression to prognosis for patients with lung, bladder, or

esophageal cancer (11, 24, 25). Although the mechanism by which the AKT1 SNPs affect

survival remains to be elucidated, it may reflect the fact that these SNPs affect the capacity to

eliminate micrometastatic tumor cells via AKT1-mediated apoptosis, thus contributing to




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survival (37). Collectively, these observations indicate that our finding of an association of

SNPs with BM in patients with NSCLC may be biologically plausible.

We also found PIK3CA: rs2699887 polymorphisms to be associated with BM risk.

Patients carrying at least one variant allele in PIK3CA: rs2699887 had nearly twice the risk of

BM as those without those variants. PIK3CA is the catalytic domain for PI3K, a known

oncogene, and is responsible for initiating signaling through this pathway, activating cell

survival signals (38). Overexpression of PIK3CA was demonstrated in primary lung

carcinomas and their metastases (39). Genomic amplification of PIK3CA in NSCLC is also

common, occurring in 70% of squamous cell carcinomas and 19% of adenocarcinomas (40).

Increased PI3K activity would result in increased cell survival signals, causing increased

metastases. Multiple mechanisms of PI3K activation may be responsible for the high levels of

PI3K pathway activation, including PIK3CA mutations (28). Therefore, the genetic variation

tagged by the PIK3CA: rs2699887 SNP would likely cause an increase in PI3K signaling.

Further studies are warranted to validate the association between this polymorphism and BM

risk. However, this pathway is complex, and our results suggest that several other genes

warrant investigation as well.

Indeed, complexities of cellular signaling pathways often means that a single SNP

may produce a modest or undetectable effect, whereas the amplified effects of combined

SNPs in the same pathway may enhance predictive power. When we combined two SNPs in

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




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PI3K/PTEN/AKT/mTOR pathway have a cumulative influence and may further enhance

predictive power. However, because the two SNPs identified in this study were tagging SNPs,

we are unable to identify the causative SNP and mechanism responsible. Future studies are

needed to validate these SNPs in independent patient populations and to perform fine

mapping in the vicinity of these gene regions to identify potential causal variants.

Prophylactic radiotherapy has a clearly defined role in the treatment of patients with

high-risk acute lymphocytic leukemia. In SCLC, PCI has significantly improved overall

survival rates in patients with either limited-stage disease (from 15% to 20% at 3 years) or

extensive-stage disease (from 13% to 27% at 1 year) in patients who respond to first-line

treatment. Thus, PCI should be considered part of treatment for 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 also quite common in NSCLC, with the incidence ranging from 13% to 54% (1). 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 done to date has ever shown a survival benefit

(7, 8, 14). As noted previously, the recent RTOG 0214 trial of PCI in NSCLC showed that

PCI reduced BM rates at 1 year (18% vs. 7.7%, P=0.004), but did not affect overall survival

(10). According to Bovi and White (1), it is unclear whether this lack of survival benefit

results from a failure to identify the cohort best suited for preventive therapy, and further

implies 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 to date

can identify which patients are at high risk of developing BM, PCI has been given




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unselectively to all patients, which may result in unnecessary toxicity with little potential

benefit for some patients. 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 clinicopathologic data, could become the basis for selecting patient

subgroups at high risk of BM to receive PCI.

In our study, the incidence of BM was 31% (99 of 317 patients), which is slightly

higher than in some studies. Clinicopathologic 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 (58 years) was lower than are typical for patients with NSCLC. These differences

may explain the relative high incidence of BM in our population, and thus we adjusted for

these variables in our multivariate analyses. We further assessed whether the three genotypes

were associated with risk of metastasis at other sites, and we found no such association.

These results suggest that metastases in brain and elsewhere may arise through different

mechanisms.

In conclusion, this is the first study to evaluate associations between genetic

variations in the PI3K–AKT–mTOR pathway and BM risk. We found that 3 SNPs (AKT1:

rs2498804, AKT1: rs2494732, and PIK3CA: rs2699887) had both individual and cumulative

effects with regard to BM risk. Because these results are based on an analysis of relatively

small numbers of patients, we could not rule out the possibility of false-positive findings. A

further potential shortcoming is that we obtained posttreatment CT or MRI scans only if




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clinical evaluation revealed suggestive findings, such as neurological symptoms. As is true in

other studies of 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

clinicopathologic variables to identify patients at high risk of BM who could benefit from

PCI.

Acknowledgments

This study was funded by three grants from the National Natural Science Foundation of

China (grants 81071832, 81272492, and 81101691).




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FIGURE LEGENDS

Fig. 1. Kaplan-Meier estimates of the cumulative probability of brain metastasis among

patients with non-small cell lung cancer according to the following genotypes: (A) AKT1

rs2498804; (B) AKT1 rs2494732; (C) PIK3CA rs2699887; and (D) combined. The GT/GG

genotype at rs2498804, the CT/TT genotype at rs2494732 and the AG/AA genotype at

rs2699887 were associated with higher cumulative probability of brain metastasis than the

other genotypes.

Fig. 2. Linkage disequilibrium map of genotyped AKT1 SNPs.




Table 1. Genes and single nucleotide polymorphisms selected for analysis

Genes and Single Nucleotide Polymorphisms Allelic Change

AKT1

rs3803300 A > G

rs3803304 G > C

rs2494732 C > T

rs2494738 A > G

rs2498804 T > G

rs1130214 G > T

AKT2 rs892119 G > A

rs8100018 G > C

FRAP1 rs11121704 T > C

rs2295080 T > G

PIK3CA rs7651265 A > G

rs7621329 C> T

rs6443624 C > A

rs2699887 G > A

PTEN

rs2299939 C > A

rs12569998 T > G




Table 2. Patient- and disease-related characteristics and their association with brain metastasis

Characteristic No. of Patients

(%) HR

Univariate Analysis (95% CI)

P Value HR Multivariate Analysis

(95% CI) P Value

Sex Female 101 (32) 1.000 1.000 Male 216 (68) 1.083 0.706-1.663 0.714 0.960 0.563-1.638 0.881

Age, years ≥ 60 years 132(42) 1.000 1.000 < 60 years 185(58) 1.257 0.836-1.888 0.272 1.090 0.716-1.658 0.688 Median (range) 58 (26-82)

Disease stage at diagnosis I, II, IIIA 173 (55) 1.000 1.000 IIIB, IV 144 (45) 2.541 1.689-3.822 <0.01 2.471 1.614-3.784 <0.01

Tumor histology Squamous cell 81 (26) 1.000 1.000 Adenocarcinoma 216 (68) 1.608 0.972-2.658 0.064 1.424 0.817-2.484 0.212 NSCLC, NOS 20 (6) 0.826 0.281-2.428 0.728 0.752 0.250-2.258 0.611

KPS Score >80 38 (12) 1.000 1.000 80 208 (66) 1.145 0.587-2.233 0.692 0.782 0.388-1.577 0.492 <80 71 (22) 1.551 0.751-3.204 0.236 1.194 0.567-2.516 0.641

Tobacco Smoking Status Current 127 (40) 1.000 1.000 Former 34 (11) 1.173 0.631-2.182 0.613 0.945 0.500-1.786 0.862 Never 156 (49) 0.806 0.528-1.230 0.317 0.697 0.409-1.189 0.185

Surgery* Yes 186 (59) 1.000 1.000 No 131 (41) 3.486 2.305-5.273 <0.01 3.383 2.227-5.140 <0.01

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Table 2.continued

Characteristic No. of Patients

(%) HR

Univariate Analysis (95% CI)

P Value HR Multivariate Analysis

(95% CI) P Value

Chemotherapy Yes 294(93) 1.000 1.000 No 23(7) 0.951 0.441-2.051 0.898 0.991 0.451-2.182 0.983

Radiotherapy Yes 97(31) 1.000 1.000 No 220(69) 1.286 0.822-2.012 0.271 1.122 0.705-1.783 0.628

Multivariate analyses were adjusted for all of the factors listed in this table; however, either disease stage at diagnosis or receipt of surgery was included, not both. Abbreviations: HR, hazard ratio; CI, confidence interval; NSCLC NOS, non-small cell lung cancer, not otherwise specified; KPS, Karnofsky performance status.

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Table 3. Associations between genotypes and brain metastases

Polymorphisms and Genotypes

No. of Patients

(All)

No. of Events

(%) HR 95% CI

P Value

No. of Patients

without BM at Diagnosis

No. of Events

(%) HR 95% CI

P Value

AKT1: rs2498804

TT 125 29 (23) 1.000 122 26 (21) 1.000

GT+GG 192 70 (37) 1.860 1.199-2.885 0.006 181 59 (33) 1.749 1.096-2.792 0.019

AKT1: rs2494732

CC 156 37(24) 1.000 151 32(21) 1.000 CT+TT 159 62(39) 1.902 1.259-2.875 0.002 150 53(35) 1.947 1.246-3.042 0.003

PIK3CA: rs2699887

GG 272 79 (29) 1.000 263 70 (27) 1.000

AG+AA 45 20 (44) 1.933 1.168-3.200 0.010 40 15 (37) 1.642 0.923-2.920 0.091

NOTE. Multivariate analyses in this table were adjusted for sex, patient age, disease stage, tumor histology, Karnofsky Performance Status, smoking status, and receipt of chemotherapy or radiotherapy.

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

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Table 4. Associations between genotypes and brain metastases (combined)

No. of Unfavorable Genotypes*

No. of Patients

(All)

No. of Events

(%) HR 95% CI

P Value

No. of Patients

without BM at Diagnosis

No. of Events

(%) HR 95% CI

P Value

0 114 23 (20) 1.000 113 22 (19) 1.000

1 169 62 (37) 2.219 1.367–3.604 0.001 159 52 (33) 1.919 1.158–3.179 0.011

2 34 14 (41) 2.724 1.391–5.334 0.003 31 11 (36) 2.288 1.099–4.761 0.027

NOTE. Multivariate analyses in this table were adjusted for sex, patient age, disease stage, tumor histology, Karnofsky Performance Status,

smoking status, and receipt of chemotherapy or radiotherapy.

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

Genotypes identified as being unfavorable in terms of risk of BM were AKT1: rs2498804 GT/GG and PIK3CA: rs2699887 AG/AA.

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Published OnlineFirst September 27, 2013.Clin Cancer Res Qianxia Li, Ju Yang, Qianqian Yu, et al. Metastasis in Patients with Non-Small-Cell Lung CancerPI3K/PTEN/AKT/mTOR Pathway and Increased Risk of Brain Associations between Single Nucleotide Polymorphisms in the

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