Dataset Information

Genetic Modifiers of Progression-Free Survival in Never-Smoking Lung Adenocarcinoma Patients Treated with First-Line Tyrosine Kinase Inhibitors.

ABSTRACT:

Rationale

Patients with non-small cell lung cancer (NSCLC) with mutated epidermal growth factor receptor (EGFR) are relatively sensitive to EGFR-tyrosine kinase inhibitor (TKI) treatment and have longer progression-free survival (PFS) when treated with EGFR-TKI compared with platinum-based chemotherapy. However, many patients with advanced NSCLC who have mutated EGFR do not respond to first-line EGFR-TKI treatment and still have shorter PFS.

Objectives

The aim of this study was to identify genetic variants associated with PFS among patients with lung adenocarcinoma who were treated with first-line EGFR-TKIs.

Methods

A genome-wide association study on PFS was performed in never-smoking women diagnosed with lung adenocarcinoma and who were treated with first-line EGFR-TKIs (n = 128). Significant single-nucleotide polymorphisms (SNPs) were selected for follow-up association analysis (n = 198) and for replication assay in another independent cohort (n = 153).

Measurements and main results

We identified SNPs at 4q12 associated with PFS at genome-wide significance (P < 10^-8) and with an estimated hazard ratio of more than 4. This association was also replicated in a larger but similar cohort and in an independent NSCLC cohort. Follow-up functional analyses showed that these SNPs were associated with the expression of EGFR, which encodes the TKI target, and with a nearby gene neuromedin-U, which encodes a G protein-coupled receptor ligand known to be involved in the progression of NSCLC. Considering these as possible prognostic biomarkers for the treatment of patients with late-stage lung cancer, we found that these SNPs were not associated with EGFR mutation status or with polymorphism of the Bcl2-interacting mediator of cell death gene.

Conclusions

Genetic variants in 4q12 merit further investigation to assess their potential as pharmacogenomic predictors for and to understand the biology underlying its influence on PFS in patients treated with TKI therapy.

SUBMITTER: Chang IS

PROVIDER: S-EPMC5363976 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Genetic Modifiers of Progression-Free Survival in Never-Smoking Lung Adenocarcinoma Patients Treated with First-Line Tyrosine Kinase Inhibitors.

Chang I-Shou IS Jiang Shih Sheng SS Yang James Chih-Hsin JC Su Wu-Chou WC Chien Li-Hsin LH Hsiao Chin-Fu CF Lee Jih-Hsiang JH Chen Chih-Yi CY Chen Chung-Hsing CH Chang Gee-Chen GC Wang Zhaoming Z Lo Fang-Yi FY Chen Kuan-Yu KY Wang Wen-Chang WC Chen Yuh-Min YM Huang Ming-Shyan MS Tsai Ying-Huang YH Su Yu-Chun YC Hsieh Wan-Shan WS Shih Wen-Chi WC Shieh Shwn-Huey SH Yang Tsung-Ying TY Lan Qing Q Rothman Nathaniel N Chen Chien-Jen CJ Chanock Stephen J SJ Yang Pan-Chyr PC Hsiung Chao A CA

American journal of respiratory and critical care medicine 20170301 5

<h4>Rationale</h4>Patients with non-small cell lung cancer (NSCLC) with mutated epidermal growth factor receptor (EGFR) are relatively sensitive to EGFR-tyrosine kinase inhibitor (TKI) treatment and have longer progression-free survival (PFS) when treated with EGFR-TKI compared with platinum-based chemotherapy. However, many patients with advanced NSCLC who have mutated EGFR do not respond to first-line EGFR-TKI treatment and still have shorter PFS.<h4>Objectives</h4>The aim of this study was to ...[more]

PMID: 27669169

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Project description:We aim to determine the feasibility of a novel radiomic biomarker that can integrate with other established clinical prognostic factors to predict progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) undergoing first-line immunotherapy. Our study includes 107 patients with stage 4 NSCLC treated with pembrolizumab-based therapy (monotherapy: 30%, combination chemotherapy: 70%). The ITK-SNAP software was used for 3D tumor volume segmentation from pre-therapy CT scans. Radiomic features (n = 102) were extracted using the CaPTk software. Impact of heterogeneity introduced by image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) was mitigated by resampling the images to the minimum voxel spacing parameters and harmonization by a nested ComBat technique. This technique was initialized with radiomic features, clinical factors of age, sex, race, PD-L1 expression, ECOG status, body mass index (BMI), smoking status, recurrence event and months of progression-free survival, and image acquisition parameters as batch variables. Two phenotypes were identified using unsupervised hierarchical clustering of harmonized features. Prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in Cox regression models of PFS and Kaplan Meier (KM) curve-fitting. Cox model based on clinical factors had a c-statistic of 0.57, which increased to 0.63 upon addition of phenotypes derived from harmonized features. There were statistically significant differences in survival outcomes stratified by clinical covariates, as measured by the log-rank test (p = 0.034), which improved upon addition of phenotypes (p = 0.00022). We found that mitigation of heterogeneity by image resampling and nested ComBat harmonization improves prognostic value of phenotypes, resulting in better prediction of PFS when added to other prognostic variables.

Dataset Information

Genetic Modifiers of Progression-Free Survival in Never-Smoking Lung Adenocarcinoma Patients Treated with First-Line Tyrosine Kinase Inhibitors.

Rationale

Objectives

Methods

Measurements and main results

Conclusions

Publications

Genetic Modifiers of Progression-Free Survival in Never-Smoking Lung Adenocarcinoma Patients Treated with First-Line Tyrosine Kinase Inhibitors.

Similar Datasets

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