Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster Discussion session - Non-metastatic NSCLC and other thoracic malignancies

3833 - Gene expression signature of DNA damage response to predict the prognosis of early stage lung adenocarcinoma

Date

21 Oct 2018

Session

Poster Discussion session - Non-metastatic NSCLC and other thoracic malignancies

Topics

Translational Research

Tumour Site

Presenters

Zhijie Wang

Citation

Annals of Oncology (2018) 29 (suppl_8): viii483-viii487. 10.1093/annonc/mdy290

Authors

Z. Wang1, C. Xu2, J. Zhao3, X. Zhao3, S. Cai3, Y. Song4, J. Wang5

Author affiliations

  • 1 Department Of Medical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 100021 - Beijing/CN
  • 2 Department Of Pathology, Fujian Cancer Hospital, 350014 - Fuzhou/CN
  • 3 The Medical Department, Shanghai 3D Medicines.Inc, 201114 - Shanghai/CN
  • 4 Department Of Respiratory Medicine, Jinling Hospital, 210002 - Nanjing/CN
  • 5 Department Of Medical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing/CN

Resources

Login to access the resources on OncologyPRO.

If you do not have an ESMO account, please create one for free.

Abstract 3833

Background

For early-stage lung cancer, a clinically reliable prognostic biomarker is still in unmet needs. DNA damage response (DDR) system is necessary for genomic stability, whose alterations may affect prognosis via modulating immune response. We aimed to develop and validate a DDR expression signature to optimize prognostic stratification in stage I-II lung adenocarcinoma (LUAD).

Methods

A 254-patient training cohort including 4 Gene Expression Omnibus (GEO) data sets was used to develop prognostic algorithm. A 628-patient cohort from another 3 GEO data sets and a 387-patient cohort from The Cancer Genome Atlas (TCGA) data set were defined as validation cohort 1 and 2 respectively. Only resected LUAD of stage I-II with mRNA and survival data were included. Furthermore, we analyzed the associations of DDR genes signature and tumor mutation burden (TMB), copy number variations (CNVs) and tumor infiltrating lymphocytes (TILs).

Results

An 8-gene signature score was developed and stratified patients into high- and low-risk groups, including FNACA, NUDT1, CHEK1, RAD51, RAD51B, RAD54B, FAN1 and MBD4. In validation cohort 1, low-risk group showed longer disease-free survival (DFS, 102 vs 41.4 months, P = 0.008, Hazard ratio (HR) = 1.51(1.11-2.06)) and overall survival (OS, 128.8 vs 105.4 months, P = 0.003, HR = 1.53(1.15-2.02)) compared with high-risk group. For low-risk stage II patients, no significant difference was observed between patients with and without adjuvant therapy. For high-risk stage II patients, adjuvant therapy tended to improve DFS (31.0 vs 20.4months) and OS (95.0 vs 61.4months) compared with observation, but P values were not significant with limited sample size. In validation cohort 2, patients had similar gene expression pattern to that of training cohort and high-risk group showed worse survival outcomes. Analyses of TCGA cohort revealed that high-risk group had remarkably higher TMB and CNVs, and lower TILs (all P < 0.001).

Conclusions

The 8-gene DDR signature is a promising biomarker to optimize prognostic staging and personalize adjuvant therapy of early-stage LUAD. An independent multi-center study is underway to further validate the predictive value and clinical feasibility of this model.

Clinical trial identification

Legal entity responsible for the study

Jie Wang.

Funding

This work was supported by the National Natural Sciences Foundation Key Program (81630071, 81330062), National Key R&D Program of China (2016YFC0902300), National High Technology Research and Development Program 863 (SS2015AA020403), CAMS Innovation Fund for Medical Sciences (CIFMS 2016-I2M-3-008), China National Natural Sciences Foundation (81472206), Beijing Novel Program Grants (Z141107001814051).

Editorial Acknowledgement

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.