Survival outcomes for lung cancer, the leading cause of cancer-related mortality in the United States, remain poor. Improving lung cancer survival requires a multi-pronged approach, including smoking cessation and better elucidation of gene-environment interactions in risk, identification of new promising drug targets, as well as identification of potential prognostic and predictive markers that optimize treatment for patients. Our molecular epidemiology group has investigated the role of candidate germline polymorphisms and survival in lung cancer since the 2002 initiation of this study, and we have made significant contributions to understanding genetic and other markers of NSCLC survival. In this competing renewal, we will employ high- density genome-wide genotyping and epidemiologic approaches to identify better prognostic and predictive genetic markers of NSCLC survival. The ultimate goal of identifying such markers is to find ways to select the best treatment course for each individual patient. While the candidate approach has the strength of being based in biologic hypotheses, there are limitations to such an approach. A new approach is to use genome wide scans that capitalize on advances in high-throughput technology and in completion of projects such as the HapMap project, to allow for assessment of the human genome. In this renewal, we will take advantage of the large collection of clinical, epidemiologic and over 3,000 biospecimens and clinical data from the parent project begun in 1992. For gene discovery, we will use the new Illumina 610 Quad platform for a genome-wide approach to genotyping of SNPs. Once we have identified high priority SNPs, we will seek to replicate these findings in our larger case-cohort, as well as in separate external validation sets from 4 collaborating centers. Although our primary endpoint will be overall survival (OS), we will also assess disease-free survival (DFS) and progression- free survival (PFS), where appropriate. In Phase 1, we will use the new Illumina 610 Quad (610,000 SNP's and 60,000 Copy Number Variants) among 1000 lung cancer cases in the parent study, to identify the most promising SNPs that show evidence of association with lung cancer survival. In Phase 2, the top 3,000 SNPs will be selected from this discovery phase for further validation/replication in the remainder of patients. Then, in Phase III, we will assess the top 100 SNP's (50 each for early and late stages) in 3 external case cohorts with a minimum of 5 year follow-up information: MD Anderson, U. of Toronto, Mayo Clinic. To extend our findings to another case-cohort of different ethnicity, and maximize the capture of relevant SNP's, we will perform Phase 2 (2 Golden Gate 1536 SNP arrays) and Phase 3 (top 100 SNP's;50 for early and 50 for late stage) in a Chinese case cohort in Nanjing, China. Finally, we will conduct exploratory functional assays to assess variants effects on gene expression in the final set of replicated candidates. This will be the largest and most complete genetic analysis of NSCLC to date, and will move the field significantly towards to goal of more effective, personalized therapy for NSCLC patients.

Public Health Relevance

Survival outcomes for lung cancer, the leading cause of cancer-related mortality in the United States, remain poor, and genetic determinants of survival are not well-defined. The proposed molecular epidemiologic study will employ genome- wide scanning approaches, with multiple replicates, to identify better prognostic and predictive genetic markers of NSCLC survival. The ultimate goal of identifying such markers is to find ways to select the best treatment course for each individual

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA092824-10
Application #
8288660
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Divi, Rao L
Project Start
2001-07-01
Project End
2014-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
10
Fiscal Year
2012
Total Cost
$559,058
Indirect Cost
$167,329
Name
Harvard University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Xu, Yinghui; Liu, Hongliang; Liu, Shun et al. (2018) Genetic variant of IRAK2 in the toll-like receptor signaling pathway and survival of non-small cell lung cancer. Int J Cancer 143:2400-2408
Ji, Xuemei; Bossé, Yohan; Landi, Maria Teresa et al. (2018) Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk. Nat Commun 9:3221
Duan, Weiwei; Zhang, Ruyang; Zhao, Yang et al. (2018) Bayesian variable selection for parametric survival model with applications to cancer omics data. Hum Genomics 12:49
Guo, Yichen; Zhang, Ruyang; Shen, Sipeng et al. (2018) DNA Methylation of LRRC3B: A Biomarker for Survival of Early-Stage Non-Small Cell Lung Cancer Patients. Cancer Epidemiol Biomarkers Prev 27:1527-1535
Wang, Zhaoxi; Wei, Yongyue; Zhang, Ruyang et al. (2018) Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma. EBioMedicine 32:93-101
Ferreiro-Iglesias, Aida; Lesseur, Corina; McKay, James et al. (2018) Fine mapping of MHC region in lung cancer highlights independent susceptibility loci by ethnicity. Nat Commun 9:3927
Qian, Danwen; Liu, Hongliang; Wang, Xiaomeng et al. (2018) Potentially functional genetic variants in the complement-related immunity gene-set are associated with non-small cell lung cancer survival. Int J Cancer :
Shen, Sipeng; Zhang, Ruyang; Guo, Yichen et al. (2018) A multi-omic study reveals BTG2 as a reliable prognostic marker for early-stage non-small cell lung cancer. Mol Oncol 12:913-924
Sofer, Tamar; Schifano, Elizabeth D; Christiani, David C et al. (2017) Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case-control genetic association studies. Biometrics 73:1210-1220
Kinsey, C Matthew; San José Estépar, Raul; van der Velden, Jos et al. (2017) Lower Pectoralis Muscle Area Is Associated with a Worse Overall Survival in Non-Small Cell Lung Cancer. Cancer Epidemiol Biomarkers Prev 26:38-43

Showing the most recent 10 out of 108 publications