@article {141, title = {Association between Smoking History and Tumor Mutation Burden in Advanced Non-Small Cell Lung Cancer.}, journal = {Cancer Res}, volume = {81}, year = {2021}, month = {2021 May 01}, pages = {2566-2573}, abstract = {

Lung carcinogenesis is a complex and stepwise process involving accumulation of genetic mutations in signaling and oncogenic pathways via interactions with environmental factors and host susceptibility. Tobacco exposure is the leading cause of lung cancer, but its relationship to clinically relevant mutations and the composite tumor mutation burden (TMB) has not been fully elucidated. In this study, we investigated the dose-response relationship in a retrospective observational study of 931 patients treated for advanced-stage non-small cell lung cancer (NSCLC) between April 2013 and February 2020 at the Dana Farber Cancer Institute and Brigham and Women{\textquoteright}s Hospital. Doubling smoking pack-years was associated with increased and less frequent and mutations, whereas doubling smoking-free months was associated with more frequent . In advanced lung adenocarcinoma, doubling smoking pack-years was associated with an increase in TMB, whereas doubling smoking-free months was associated with a decrease in TMB, after controlling for age, gender, and stage. There is a significant dose-response association of smoking history with genetic alterations in cancer-related pathways and TMB in advanced lung adenocarcinoma. SIGNIFICANCE: This study clarifies the relationship between smoking history and clinically relevant mutations in non-small cell lung cancer, revealing the potential of smoking history as a surrogate for tumor mutation burden.

}, issn = {1538-7445}, doi = {10.1158/0008-5472.CAN-20-3991}, author = {Wang, Xinan and Ricciuti, Biagio and Nguyen, Tom and Li, Xihao and Rabin, Michael S and Awad, Mark M and Lin, Xihong and Johnson, Bruce E and Christiani, David C} } @article {129, title = {Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.}, journal = {Genet Epidemiol}, volume = {45}, year = {2021}, month = {2021 Feb}, pages = {99-114}, abstract = {

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

}, issn = {1098-2272}, doi = {10.1002/gepi.22358}, author = {Sun, Ryan and Xu, Miao and Li, Xihao and Gaynor, Sheila and Zhou, Hufeng and Li, Zilin and Boss{\'e}, Yohan and Lam, Stephen and Tsao, Ming-Sound and Tardon, Adonina and Chen, Chu and Doherty, Jennifer and Goodman, Gary and Bojesen, Stig E and Landi, Maria T and Johansson, Mattias and Field, John K and Bickeb{\"o}ller, Heike and Wichmann, H-Erich and Risch, Angela and Rennert, Gadi and Arnold, Suzanne and Wu, Xifeng and Melander, Olle and Brunnstr{\"o}m, Hans and Le Marchand, Loic and Liu, Geoffrey and Andrew, Angeline and Duell, Eric and Kiemeney, Lambertus A and Shen, Hongbing and Haugen, Aage and Johansson, Mikael and Grankvist, Kjell and Caporaso, Neil and Woll, Penella and Dawn Teare, M and Scelo, Ghislaine and Hong, Yun-Chul and Yuan, Jian-Min and Lazarus, Philip and Schabath, Matthew B and Aldrich, Melinda C and Albanes, Demetrios and Mak, Raymond and Barbie, David and Brennan, Paul and Hung, Rayjean J and Amos, Christopher I and Christiani, David C and Lin, Xihong} } @article {20, title = {Testing for gene-environment interaction under exposure misspecification.}, journal = {Biometrics}, volume = {74}, year = {2018}, month = {2018 Jun}, pages = {653-662}, abstract = {

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties.

}, keywords = {Bias, Confounding Factors, Epidemiologic, Environmental Exposure, Gene-Environment Interaction, Humans, Linear Models, Models, Genetic, Scientific Experimental Error}, issn = {1541-0420}, doi = {10.1111/biom.12813}, author = {Sun, Ryan and Carroll, Raymond J and Christiani, David C and Lin, Xihong} } @article {47, title = {Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case-control genetic association studies.}, journal = {Biometrics}, volume = {73}, year = {2017}, month = {2017 12}, pages = {1210-1220}, abstract = {

We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case-control genetic association studies. To boost analysis power, we assume that the SNP-specific effects are shared across all secondary phenotypes using a scaled mean model. We estimate regression parameters using Inverse Probability Weighted (IPW) estimating equations obtained from the weighted pseudolikelihood, which accounts for case-control sampling to prevent potential ascertainment bias. To test the effect of a SNP set, we propose a weighted variance component pseudo-score test. We also propose a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotypes. We show that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes. We select the tuning parameter using a weighted Bayesian Information-like Criterion (wBIC). We evaluate the finite sample performance of the proposed methods via simulations, and illustrate the methods by the analysis of the multiple secondary smoking behavior outcomes in a lung cancer case-control genetic association study.

}, keywords = {Case-Control Studies, Computer Simulation, Genetic Association Studies, Humans, Likelihood Functions, Lung Neoplasms, Phenotype, Polymorphism, Single Nucleotide, Smoking}, issn = {1541-0420}, doi = {10.1111/biom.12680}, author = {Sofer, Tamar and Schifano, Elizabeth D and Christiani, David C and Lin, Xihong} }