Search

Purpose: Although gefitinib prolonged the progression-free survival (PFS) of patients with non–small cell lung cancer (NSCLC), unpredictable resistance limited its clinical efficacy. Novel predictive biomarkers with explicit mechanisms are urgently needed. Experimental Design: A total of 282 patients with NSCLC with gefitinib treatment were randomly assigned in a 7:3 ratio to exploratory (n = 192) and validation (n = 90) cohorts. The candidate polymorphisms were selected with Haploview4.2 in Hapmap and genotyped by a MassARRAY system, and the feature variables were identified through Randomforest Survival analysis. Tanswell and clonogenic assays, base editing and cell-derived tumor xenograft model were performed to uncover the underlying mechanism. Results: We found that the germline missense polymorphism rs3742076 (A>G, S628P), located in transactivation domain of FOXM1, was associated with PFS in exploratory (median PFS: GG vs. GA&AA, 9.20 vs. 13.37 months, P = 0.00039, HR = 2.399) and validation (median PFS: GG vs. GA&AA, 8.13 vs. 13.80 months, P = 0.048, HR = 2.628) cohorts. We elucidated that rs3742076_G conferred resistance to gefitinib by increasing protein stability of FOXM1 and facilitating an aggressive phenotype in vitro and in vivo through activating wnt/β-catenin signaling pathway. Meanwhile, FOXM1 level was highly associated with prognosis in patients with EGFR-mutant NSCLC. Mechanistically, FOXM1 rs3742076_G upregulated wnt/β-catenin activity by directly binding to β-catenin in cytoplasm and promoting transcription of β-catenin in nucleus. Remarkably, inhibition of β-catenin markedly reversed rs3742076_G-induced gefitinib resistance and aggressive phenotypes. Conclusions: These findings characterized rs3742076_G as a gain-of-function polymorphism in mediating gefitinib resistance and tumor aggressiveness, and highlighted the variant as a predictive biomarker in guiding gefitinib treatment.

Purpose:Although gefitinib prolonged the progression-free survival (PFS) of patients with non–small cell lung cancer (NSCLC), unpredictable resistance limited its clinical efficacy. Novel predictive biomarkers with explicit mechanisms are urgently needed.Experimental Design:A total of 282 patients with NSCLC with gefitinib treatment were randomly assigned in a 7:3 ratio to exploratory (n = 192) and validation (n = 90) cohorts. The candidate polymorphisms were selected with Haploview4.2 in Hapmap and genotyped by a MassARRAY system, and the feature variables were identified through Randomforest Survival analysis. Tanswell and clonogenic assays, base editing and cell-derived tumor xenograft model were performed to uncover the underlying mechanism.Results:We found that the germline missense polymorphism rs3742076 (A>G, S628P), located in transactivation domain of FOXM1, was associated with PFS in exploratory (median PFS: GG vs. GA&AA, 9.20 vs. 13.37 months, P = 0.00039, HR = 2.399) and validation (median PFS: GG vs. GA&AA, 8.13 vs. 13.80 months, P = 0.048, HR = 2.628) cohorts. We elucidated that rs3742076_G conferred resistance to gefitinib by increasing protein stability of FOXM1 and facilitating an aggressive phenotype in vitro and in vivo through activating wnt/β-catenin signaling pathway. Meanwhile, FOXM1 level was highly associated with prognosis in patients with EGFR-mutant NSCLC. Mechanistically, FOXM1 rs3742076_G upregulated wnt/β-catenin activity by directly binding to β-catenin in cytoplasm and promoting transcription of β-catenin in nucleus. Remarkably, inhibition of β-catenin markedly reversed rs3742076_G-induced gefitinib resistance and aggressive phenotypes.Conclusions:These findings characterized rs3742076_G as a gain-of-function polymorphism in mediating gefitinib resistance and tumor aggressiveness, and highlighted the variant as a predictive biomarker in guiding gefitinib treatment.

Supplementary Figure from FOXM1 Variant Contributes to Gefitinib Resistance via Activating Wnt/β-Catenin Signal Pathway in Patients with Non–Small Cell Lung Cancer

Supplementary Table from FOXM1 Variant Contributes to Gefitinib Resistance via Activating Wnt/β-Catenin Signal Pathway in Patients with Non–Small Cell Lung Cancer

Thiopurine dose optimization by thiopurine-S-methyltransferase (TPMT) or nudix hydrolase-15 (NUDT15) significantly reduced early leucopenia in Asia. However, it fails to avoid the late incidence ( 2 months). Although laboratory monitoring of 6-thioguanine nucleotides (6TGN) to optimize thiopurine dose was suggested in White patients the exact association between leucopenia and 6TGN was controversial in Asian patients. In the present study, we aimed to explore whether DNA-thioguanine nucleotides (DNA-TGs) in leukocytes, compared with 6TGN in erythrocytes, can be a better biomarker for late leucopenia. This was a prospective, observational study. Patients with inflammatory bowel disease (IBD) prescribed thiopurine from February 2019 to December 2019 were recruited. Thiopurine dose was optimized by NUDT15 C415T (rs116855232). DNA-TG and 6TGN levels were determined at the time of late leucopenia or 2 months after the stable dose was obtained. A total of 308 patients were included. Thiopurine induced late leucopenia (white blood cells 3.5 × 10

Drug-induced liver injury (DILI) remains a critical clinical issue and has been a treatment challenge today as it was in the past. However, the traditional biomarkers or indicators are insufficient to predict the risks and outcome of patients with DILI due to its poor specificity and sensitivity. Recently, the development of high-throughput technologies, especially omics and multiomics has sparked growing interests in identification of novel clinical DILI biomarkers, many of which also provide a mechanistic insight. Accordingly, in this minireview, we summarize recent advances in novel clinical biomarkers for DILI prediction, diagnosis, and prognosis and highlight the limitations or challenges involved in biomarker discovery or its clinical translation. Although huge work has been done, most reported biomarkers lack comprehensive information and more specific DILI biomarkers are still needed to complement the traditional biomarkers such as alanine aminotransferase (ALT) or aspartate transaminase (AST) in clinical decision-making. SIGNIFICANCE STATEMENT: This current review outlines an overview of novel clinical biomarkers for drug-induced liver injury (DILI) identified in clinical retrospective or prospective clinical analysis. Many of these biomarkers provide a mechanistic insight and are promising to complement the traditional DILI biomarkers. This work also highlights the limitations or challenges involved in biomarker discovery or its clinical translation.

Previous exposure-response analyses for rituximab suggest that higher rituximab concentrations were associated with an improvement in efficacy, however, clinical studies investigating a higher rituximab dose had mixed results. To further explore the exposure-response relationship of rituximab, a prospective observational analysis was performed involving 121 newly diagnosed patients with diffuse large B-cell lymphoma treated with triweekly rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). The trough concentration in the first cycle (C1-trough ) was significantly higher in patients achieving complete response (CR) compared with patients that did not achieve CR (22.00 μg/ml vs. 16.62 μg/ml, p = 0.0016), however, this difference between the two groups disappeared in later cycles. The relationship between rituximab C1-trough and achieving a CR was confirmed by matched-pair logistic regression analysis (odds ratio, 0.79; p = 0.0020). In addition, a higher C1-trough (≥18.40 μg/ml) was associated with longer progression-free survival (p < 0.0001) and overall survival (p = 0.0038). The percentages of patients that did not achieve a CR and had recurrence after CR within 24 months were 35% and 22.50%, respectively, for patients with a C1-trough less than or equal to 18.40 μg/ml, compared with 12.35% and 6.17% for patients with C1-trough greater than 18.40 μg/ml. Disease stage was found to be the most significant influencing factor of C1-trough , with 51.02% of patients at stage IV with an observed C1-trough less than 18.40 μg/ml. For these advanced patients, population pharmacokinetic simulations using an established model suggest that a loading dose of 800 mg/m2 may help to improve clinical outcomes.

Escitalopram, one of the Selective Serotonin Reuptake Inhibitors (SSRIs), has been widely used in the patients with major depression. In this study, a simple, sensitive and rapid method was established and validated for simultaneous quantification of Escitalopram (S-CT), desmethyl escitalopram (S-DCT), didemethyl escitalopram (S-DDCT) and escitalopram N-Oxide (S-NOCT) in human plasma by ultra-high performance liquid chromatography-tandem with mass spectrometry (UHPLC-MS/MS). Analytes were extracted from plasma by utilizing protein precipitation and then separated on a Hypersil GOLD C18 column (50 mm × 2.1 mm, 1.9 µm). The mobile phase was water: acetonitrile (70:30, v/v) with 0.25% formic acid at a flow-rate of 0.3 mL/min, within a 5 min run time. The mass analysis used positive electro-spray ionization (ESI) in selection reaction monitoring (SRM). The calibration ranges of the analytes were: S-CT: 2.0-200.0 ng/mL, S-DCT: 1.0-100.0 ng/mL, S-DDCT: 0.5-50.0 ng/mL, S-NOCT: 0.2-20.0 ng/mL. The method has been fully validated for selectivity, linearity, accuracy, precision, matrix effect, recovery, stability and carry over and all the results met the admissible limits according to the the US Food and Drug Administration guidelines. Mean plasma concentration (ng/mL) of S-CT, S-DCT, S-DDCT and S-NOCT in 93 depressed patients were 51.10 ± 45.73, 10.32 ± 15.25, 1.53 ± 1.79 and 0.87 ± 0.94, respectively. it is the first time that a UHPLC-MS/MS method for simultaneous quantification of S-CT and its 3 metabolites in human plasma was established and validated.

Background Trough levels of the post-induction serum infliximab (IFX) are associated with short-term and long-term responses of Crohn’s disease patients to IFX, but the inter-individual differences are large. We aimed to elucidate whether single gene polymorphisms (SNPs) within FCGR3A, ATG16L1, C1orf106, OSM, OSMR, NF-κB1, IL1RN, and IL10 partially account for these differences and employed a multivariate regression model to predict patients’ post-induction IFX levels. Methods The retrospective study included 189 Crohn’s disease patients undergoing IFX therapy. Post-induction IFX levels were measured and 41 tag SNPs within eight genes were genotyped. Associations between SNPs and IFX levels were analysed. Then, a multivariate logistic-regression model was developed to predict whether the patients’ IFX levels achieved the threshold of therapy (3 μg/mL). Results Six SNPs (rs7587051, rs143063741, rs442905, rs59457695, rs3213448, and rs3021094) were significantly associated with the post-induction IFX trough level (P = 0.015, P Conclusions Polymorphisms in C1orf106, IL1RN, and IL10 play an important role in the variability of IFX post-induction levels, as indicated in this multivariate prediction model of IFX levels with fair performance.

Gefitinib, the first approved oral epidermal growth factor receptor (EGFR) inhibitor, has been demonstrated effective in cancers with EGFR active mutations. In this study, we established and validated a method for determining gefitinib and its main metabolites, M605211, M387783, M537194 and M523595 in patients with non-small cell lung cancer (NSCLC) by liquid chromatography–tandem mass spectrometry (LC–MS/MS) method. The mobile phase was water: acetonitrile (35:65, v/v) with 0.1% formic acid at a flow‐rate of 0.35 mL/min, within a 3 min run time. Gefitinib and its main metabolites were separated on a X-Terra RP18 column (50 × 2.1 mm, 3.5 μm) at 40 ℃ and subjected to mass analysis using positive electro-spray ionization (ESI). The calibration ranges of gefitinib and M523595 were 0.5–1000 ng/mL, and other compounds were 0.05–100 ng/mL with the correlation coefficients (r2) ≥ 0.99. Accuracies ranged from 92.60%–107.58 and the inter- and intra-assay precision were less than 15% for all analytes in quality control samples. There was no significant matrix effect. The ranges of extraction recoveries were 86–105% for all analytes and IS. Thirty plasmas were obtained from Sun Yat-sen university cancer center. The mean plasma concentration of (± SD) of gefitinib M537194, M523595, M387783 and M605211 were 247.18 (± 140.39) ng/mL, 7.78 (± 6.74) ng/mL, 101.09 (± 93.44) ng/mL, 1.6 (± 0.9) ng/mL and 11.63 (± 4.98) ng/mL, respectively. The validated LC/MS/MS method was effectively used in the determination of gefitinib and its four metabolites in NSCLC patients.

For follicular lymphoma (FL) with grade 1/2, the complete response (CR) rate of the first-line R-CHOP treatment was significantly low. In this study, we assessed the rationality of the administration of rituximab for FL patients with grade 1/2 based on concentration–response relationship analyses. Thus, we conducted a prospective pharmacokinetic (PK) study in 68 FL patients with grades 1–3 treated with R-CHOP at 21-day intervals. Plasma rituximab concentrations were quantified using ELISA and the population PK modeling was established with Phoenix ® NLME TM . The first cycle trough concentration ( C 1-trough ) of rituximab was a significant independent risk factor for achieving CR in matched-pair logistic regression analysis, rather than the concentrations in later cycles; the recommendatory minimum optimal C 1-trough was 13.60 μg/mL. Patients with grade 1/2 had significantly lower C 1-trough compared with grade 3 (12.21 μg/mL vs. 23.45 μg/mL, P

Background: Gefitinib-induced rash is a sign of favorable outcomes for patients with non-small cell lung cancer (NSCLC). However, whether patients with more severe rash, the more survival benefits obtained from gefitinib is still unknown, and as for the severe rash a risk factor prediction model is needed. Method: A total of 346 patients were enrolled in this study. Gefitinib/metabolites and 9 gene polymorphisms, were determined by pharmacometabolomic and pharmacogenomics methods in an exploratory cohort. External cohort patients were enrolled to validate this model. Findings: The survival for patients with rash were significantly higher than patients without rash (p=0.0002, p= 0.0089)，but no difference was found between grade 1/2 or grade 3/4. Only the concentration of gefitinib, but not its metabolites, was associated with severe rash, and the cutoff value of gefitinib was 204.6 ng/mL conducted by ROC curve (AUC=0.685). A predictive model for severe rash was established: gefitinib concentration (OR = 11.523, 95%CI=2.898-64.016, p = 0.0016), SLC22A8 rs4149179(CT vs CC, OR = 3.156, 95%CI = 0.958-11.164, p = 0.0629), SLC22A1 rs4709400(CG vs CC, OR = 10.267, 95%CI = 2.067-72.465, p = 0.0087; GG vs CC, OR=5.103, 95%CI=1.032-33.938, p=0.061). This model was confirmed in validation cohort with an excellent predictive ability (AUC = 0.749, 95%CI = 0.710-0.951). Interpretation: Our finding demonstrated that patients developed any grade rash predicted improved survival, the gefitinib concentration and polymorphisms of SLC22A8 and SLC22A1 were recommended to manage severe rash. However, our model would require further validation in a biomarker-led trial. Trial Registration: This study was registered at ClinicalTrials.gov (NCT01994057). Funding Statement: This study was funded by the National Natural Science Foundation of China (Grant Nos. 81730103, 81320108027, 81573507, 81473283, 81173131 and 81973398), The National Key Research and Development Program (Grant Nos. 2017YFC0909303 and 2016YFC0905001), Guangdong Provincial Key Laboratory of Construction Foundation (Grant No. 2017B030314030), Science and Technology Program of Guangzhou（201607020031）, National Engineering and Technology Research Center for New drug Druggability Evaluation (Seed Program of Guangdong Province (No. 2017B090903004) and the 111 project (Grant: B16047). Declaration of Interests: The authors declare no conflict of interest. Ethics Approval Statement: The study was approved by Human Ethics of Sun Yat-sen University Cancer Center (B2013-038-1) and conducted in accordance with the principles of the Declaration of Helsinki and the Good Clinical Practice Guidelines of the International Conference on Harmonization.

e21046 Background: Gefitinib-induced diarrhea can be severe, if not properly handled, and can result in severe dose reductions and treatment interruptions or discontinuation. However, the mechanism of gefitinib-induced diarrhea remains unclear. STAT6 promotes disruption of epithelial tight junction integrity, intestinal inflammation and allergic disorders in intestine. Therefore, we explored the correlation between variations in STAT6 and gefitinib-induced diarrhea in advanced non-small cell lung cancer (NSCLC) patients. Methods: A total of 160 advanced NSCLC patients carrying EGFR mutations were enrolled. 8 tag single nucleotide polymorphisms (SNP) in STAT6, including rs10876963, rs167769, rs3122929, rs3024975, rs841718, rs324013, rs324015 and rs703817, were selected by Heploview 4.2 and sequenced by Agena MassARRAY System. The associations between tag SNPs of STAT6 and diarrhea were analyzed by Chi square test and multivariate logistic regression. This study was approved by the ethical committee of Sun Yat-Sen University Cancer Center. Results: 71 patients (44.4%) suffered at least grade 1 gefitinib-induced diarrhea. We found rs167769 was associated with gefitinib- induced diarrhea in NSCLC patients. STAT6 rs167769 TT carriers were all suffering from gefitinib-induced diarrhea (OR = 2.4*1010, p = 0.999). Patients with STAT6 rs167769 TT have higher risk of developing severe (grade ≥ 2) diarrhea than those with STAT6 rs167769 CC/CT. Conclusions: STAT6 rs167769 is a predictive biomarker for gefitinib-induced diarrhea and the analysis of polymorphisms of STAT6 rs167769 might be beneficial to optimize gefitinib treatment. Further studies are needed to clarify the underlying mechanisms. Clinical trial information: NCT01994057.

e21041 Background: Gefitinib is first-line therapy for patients with EGFR-activating mutation in non-small cell lung cancer (NSCLC). Meanwhile, ̃30% patients were progression within 12 months even harboring EGFR-activating mutation. Furthermore, integrative scoring models for prediction of gefitinib outcomes are still lacking. Variations in genes of DNA repair and cell cycle pathway may have potential impacts on proliferation, migration and invasion of tumor cells. Therefore, we investigated a series of single nucleotide polymorphisms (SNP) in DNA repair and cell cycle genes based on an efficient algorithm machine learning algorithm for establishing an integrative scoring system for survival prediction following gefitinib therapy in NSCLC patients. Methods: A total of 282 patients with activating EGFR mutations were enrolled and divided into train cohort and test cohort, randomly. 125 SNPs in 47 candidate genes were selected by Heploview 4.2 and sequenced by Agena MassARRAY System. Predictive features were selected by randomForestSRC, an efficient algorithm machine learning algorithm to analyze survival data, for PFS following gefitinib therapy. The top candidate variables were presented into COX regression models with hazard ratio as weight for each predictor (HR = 1-1.49 = 1; HR = 1.5-2.49 = 2; HR = 2.5-3.49 = 3), providing p < 0.1. This study was approved by the ethical committee of Sun Yat-Sen University Cancer Center. Results: The progression risk predictive model was established by random forest survival and validated by COX regression analysis, included XRCC1 rs3213263(CT vs CC, RR = 1.997, 95%CI = 1.314-3.036, p = 0.001), FOXO3 rs75544369(GA vs GG, RR = 1.942, 95%CI = 1.139-3.309, p = 0.012), FOXM1 rs2302257(CC vs GG, RR = 2.889, 95%CI = 1.477-5.691, p = 0.002) and ERCC1 rs10408989(TT vs GT, RR = 1.735, 95%CI = 1.152-2.613, p = 0.008; GG vs GT, RR = 3.788, 95%CI = 1.625-8.832, p = 0.002). The progression risk score ranged from 0-10. 49 patients (25.52%) and 20 patients (22.22%) were scored 0, 18(9.38%) and 10(11.11%) patients were scored more than 5 in train cohort and test cohort, respectively. According to the risk score of patients, we divided the patients into four groups, the median PFS were 26.23(95%CI:14.23-35.37), 16.80(95%CI:12.87-23.60), 11.13(95%CI:8.87-16.70) and 6.25 (95% CI:4.43-11.67) months for 0, 2, 3&4 and ≥5 group (p < 0.0001) in the train cohort. Meanwhile, the median PFS were 18.80(95%CI:11.80-64.53),13.60(95%CI:9.17-27.33), 13.47(95%CI:9.83-18.40), 13.47(95%CI:9.83-18.40) and 7.40(95%CI:3.33- NA) months for 0, 2, 3&4 and ≥5 group in validation cohort (p = 0.0017). Conclusions: The risk score system is a simple tool for risk stratification in patients undergoing gefitinib. Widespread application of the score system will require further independent validation.

Highlights • A total of 346 patients were enrolled in this study. • Severe rash (grade 3&4) did not gain more bonification compare to grade 1&2 rash. • Gefitinib and its four metabolites were detected in patients’ plasma. • A specific and sensitive predictive model were established based on pharmacometabolomic profiling and pharmacogenomics approach., Background Rash is a well-known predictor of survival for patients with gefitinib therapy with non-small cell lung cancer (NSCLC). However, whether patients with more severe rash obtain the more survival benefits from gefitinib is still unknown, and predicted model for severe rash is needed. Methods The relationship between gefitinib-induced rash and progression free survival (PFS) was primarily explored in the retrospective cohort. The association between rash and gefitinib/metabolites concentration and genetic polymorphisms were determined by pharmacometabolomic and pharmacogenomics methods in the exploratory cohort and validated in an external cohort. Results The survival for patients with rash was significantly higher than that of patients without rash (p = 0.0002, p = 0.0089), but no difference was found between grade 1/2 or grade 3/4. Only the concentration of gefitinib, but not its metabolites, was found to be associated with severe rash, and the cutoff value of gefitinib was 204.6 ng/mL conducted by ROC curve analysis (AUC=0.685). A predictive model for severe rash was established: gefitinib concentration (OR = 11.523, 95% CI = 2.898-64.016, p = 0.0016), SLC22A8 rs4149179(CT vs CC, OR = 3.156, 95% CI = 0.958–11.164, p = 0.0629), SLC22A1 rs4709400(CG vs CC, OR = 10.267, 95% CI = 2.067–72.465, p = 0.0087; GG vs CC, OR = 5.103, 95% CI = 1.032–33.938, p = 0.061). This model was confirmed in the validation cohort with an excellent predictive ability (AUC = 0.749, 95% CI = 0.710–0.951). Conclusions Our finding demonstrated that the incidence, not the severity, of gefitinib-induced rash predicted improved survival, the gefitinib concentration and polymorphisms of SLC22A8 and SLC22A1 were recommended to manage severe rash.

e21712 Background: Gefitinib induced rash is the most common adverse reaction and severe rash of gefitinib often leads to discontinuation or termination of treatment. The concentrations of drug and its metabolites may affect drug induced toxicities, however, the association of gefitinib/metabolites with gefitinib-induced rash are poorly investigated. Therefore, we explored the association between concentrations of gefitinib and its four metabolites with gefitinib-induced rash in non-small cell lung cancer (NSCLC) patients. Methods: A total of 180 advanced NSCLC patients carrying EGFR sensitive mutations receiving gefitinib were enrolled. The concentrations of gefitinib, and its four metabolites including M537194, M387783, M523595 and M605211 were determined by liquid chromatography–tandem mass spectrometry (LC–MS/MS). The associations between concentration of gefitinib/its metabolites and gefitinib-induced rash were analyzed by Mann-Whiney U test. Operating characteristic curves(ROC) were used to determine gefitinib/metabolites cutoff values for gefitinib-induced rash. Results: M605211 was first detected in plasma in NSCLC patients. The concentrations of gefitinib and M605211, M537194 were found to be correlated with the incidence of gefitinib-induced rash ( P= 0.0002, 0.027 and 0.0097, respectively), moreover, the concentration of gefitinib was correlated with severe rash (Grade 0,1 vs. 2+, P= 0.017). Multivariate Logistic regression analysis showed that only gefitinib concentration was independent risk factor for gefitinib-induced rash (grade 0 vs grade1+, OR = 1.006, 95%CI (1.002-1.009), P = 0.00078; grade0,1 vs grade2+, OR = 1.003, 95%CI (1.001-1.005), P = 0.015, respectively). The cutoff values of gefitinib were 160.2 ng/ml (grade 0 vs grade1+, sensitivity = 78.7%, specificity = 47.7%, area under the curve (AUC) = 0.686, P = 0.0002, 95% CI (0.592-0.779)) and 201.7ng/ml (grade0,1 vs grade2+, sensitivity = 69.3%, specificity = 47.6%, AUC = 0.605, P = 0.0168, 95%CI (0.521-0.689)). Conclusions: This research demonstrated that the concentration of gefitinib and its metabolites were associated with gefitinib induced rash in NSCLC patients. Therapeutic drug monitoring of gefitinib concentration may have potential improvement for optimization of treatment with gefitinib. Clinical trial information: NCT01994057.

9578 Background: Drug-induced liver injury (DILI) is one of the most safety concern in drug development and clinical therapy. Severe hepatotoxicity of gefitinib often leads to acute/chronic liver injury, drug discontinuation and further treatment failure, however, the mechanism of gefitinib-induced hepatotoxicity remains unclear. AKT1/FOXO3 regulates expression of genes involved in multiple biological/pathological processes in liver cells, including apoptosis, oxidative stress, and cell-cycle transition, as well as expression of autophagy-related (Atg) genes. Therefore, we investigated the correlation between single nucleotide polymorphisms (SNP) in AKT1/ FOXO3 and gefitinib-induced hepatotoxicity in patients with advanced non-small cell lung cancer (NSCLC). Methods: A total of 172 advanced NSCLC patients with activating EGFR mutations were enrolled and administered with gefitinib 250mg daily. 22 tag SNPs in AKT1/FOXO3 were selected by Heploview 4.2 and sequenced by Agena MassARRAY System. The associations between polymorphisms of AKT1/FOXO3 and gefitinib-induced hepatotoxicity were analyzed by Chi square test. This study was approved by the ethical committee of Sun Yat-Sen University Cancer Center. Results: FOXO3 rs4946935 and FOXO3 rs75544369 were found to be associated with gefitinib-induced hepatotoxicity in NSCLC patients. FOXO3 rs4946935 AA carriers have higher risk of developing gefitinib-induced hepatotoxicity than those with FOXO3 rs4946935 AG/GG. ( P = 0.018, OR = 12.414, 95%CI (1.53-100.711)). Patients with FOXO3 rs75544369 GA have higher risk of developing hepatotoxicity with P of 0.0002 (OR = 5.241, CI%(1.85-14.851)), or developing severe hepatotoxicity with P of 0.033 (OR = 2.963, 95%CI (1.090-8.059)), than those with FOXO3 rs75544369 GG. Conclusions: FOXO3 rs4946935 and FOXO3 rs75544369 are predictive biomarkers for gefitinib-induced hepatotoxicity in NSCLC patients. The mechanism underlying the association between FOXO3 polymorphisms and gefitinib-induced hepatotoxicity are worth investigating in further studies. Clinical trial information: NCT01994057 .