COPD- dependent effects of genetic variation in key inflammation pathway genes on lung cancer risk

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155975/1/ijc32780.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155975/2/ijc32780-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155975/3/ijc32780_am.pd

High-throughput allele-specific expression across 250 environmental conditions

Gene-by-environment (GxE) interactions determine common disease risk factors and biomedically relevant complex traits. However, quantifying how the environment modulates genetic effects on human quantitative phenotypes presents unique challenges. Environmental covariates are complex and difficult to measure and control at the organismal level, as found in GWAS and epidemiological studies. An alternative approach focuses on the cellular environment using in vitro treatments as a proxy for the organismal environment. These cellular environments simplify the organism-level environmental exposures to provide a tractable influence on subcellular phenotypes, such as gene expression. Expression quantitative trait loci (eQTL) mapping studies identified GxE interactions in response to drug treatment and pathogen exposure. However, eQTL mapping approaches are infeasible for large-scale analysis of multiple cellular environments. Recently, allele-specific expression (ASE) analysis emerged as a powerful tool to identify GxE interactions in gene expression patterns by exploiting naturally occurring environmental exposures. Here we characterized genetic effects on the transcriptional response to 50 treatments in five cell types. We discovered 1455 genes with ASE (FDR \u3c 10%) and 215 genes with GxE interactions. We demonstrated a major role for GxE interactions in complex traits. Genes with a transcriptional response to environmental perturbations showed sevenfold higher odds of being found in GWAS. Additionally, 105 genes that indicated GxE interactions (49%) were identified by GWAS as associated with complex traits. Examples include GIPR–caffeine interaction and obesity and include LAMP3–selenium interaction and Parkinson disease. Our results demonstrate that comprehensive catalogs of GxE interactions are indispensable to thoroughly annotate genes and bridge epidemiological and genome-wide association studies

Immune/inflammatory polymorphisms predict lung cancer survival

Lung cancer is the leading cause of cancer related mortality in the United States, with a median five year survival of ~16%. Recent advancements in the treatment of lung cancer focus on modulating the immune system to improve patient outcomes with markedly reduced toxicity profiles. This study aims to investigate single nucleotide polymorphisms (SNPs) within functional units of the immune system genome to identify genetic markers indicative of overall survival in a cohort of metropolitan Detroit lung cancer patients. This cohort presents a unique population of lung cancer patients for analysis, consisting of 40% African Americans, whom are known to have worse outcomes than their Caucasian counterparts. To assess SNPs within functional units of the immune genome, a hierarchical immune system gene and pathway list was constructed, incorporating genes and pathway identifiers from the Reactome database as well as previous genetic and inflammatory specific cancer studies to generate a well curated gene and pathway dataset. Genomic locations for the genes, together with proximal regulatory regions, were obtained from the UCSC genome browser for cross-referencing with the Illumina MEGA SNP array to generate SNPs for inclusion. Preliminary analysis of 848 NSCLC patients in association with 29,126 immune specific SNPs identified six SNPs as significantly associated with survival (p-value \u3c10-4) in this lung cancer cohort, implicating the following six genes, PXN, GFRA3, HLA-DQA1, KSR2, BTBD1, and ATF2; although further gene/pathway analysis is necessary to elucidate how these associations interact in the context of genes and pathways of the immune system. PXN over expression has been previously implicated in both high risk lung epithelial dysplasia as well as in the development of lung adenocarcinoma in prior clinical studies. Additionally, GFRA3 has been identified as an activator of the RET kinase and increased GFRA3 activity in context of the Artemin pathway is a known promoter of NSCLC progression. As well, select polymorphisms in the HLA-DQA1 gene are known clinical modifiers of lung squamous cell carcinoma risk. Finally, ATF2 is a broadly active transcription factor and ATF2 upregulation has been associated with tumorigenesis and metastasis in both cell models and patient tumor samples in multiple cancer studies. The previous association of these genes in lung cancer is promising and further immune system gene/pathway analysis is warranted

Environmental perturbations lead to extensive directional shifts in RNA processing.

Environmental perturbations have large effects on both organismal and cellular traits, including gene expression, but the extent to which the environment affects RNA processing remains largely uncharacterized. Recent studies have identified a large number of genetic variants associated with variation in RNA processing that also have an important role in complex traits; yet we do not know in which contexts the different underlying isoforms are used. Here, we comprehensively characterized changes in RNA processing events across 89 environments in five human cell types and identified 15,300 event shifts (FDR = 15%) comprised of eight event types in over 4,000 genes. Many of these changes occur consistently in the same direction across conditions, indicative of global regulation by trans factors. Accordingly, we demonstrate that environmental modulation of splicing factor binding predicts shifts in intron retention, and that binding of transcription factors predicts shifts in alternative first exon (AFE) usage in response to specific treatments. We validated the mechanism hypothesized for AFE in two independent datasets. Using ATAC-seq, we found altered binding of 64 factors in response to selenium at sites of AFE shift, including ELF2 and other factors in the ETS family. We also performed AFE QTL mapping in 373 individuals and found an enrichment for SNPs predicted to disrupt binding of the ELF2 factor. Together, these results demonstrate that RNA processing is dramatically changed in response to environmental perturbations through specific mechanisms regulated by trans factors

Prognostic modeling of the immune-centric transcriptome reveals interleukin signaling candidates contributing to differential patient outcomes

Immunotherapy is a promising advancement in the treatment of non-small-cell lung carcinoma (NSCLC), although much of how lung tumors interact with the immune system in the natural course of disease remains unknown. We investigated the impact of the expression of immune-centric genes and pathways in tumors on patient survival to reveal novel candidates for immunotherapeutic research. Tumor transcriptomes and detailed clinical characteristics were obtained from patients with NSCLC who were participants of either the Inflammation, Health and Lung Epidemiology (INHALE) (discovery, N = 280) or The Cancer Genome Atlas (TCGA) Lung (replication, N = 1026) studies. Expressions of 2253 genes derived from 48 major immune pathways were assessed for association with patient prognosis using a multivariable Cox model and pathway effects were assessed with an in-house implementation of the Gene Set Enrichment Analysis (GSEA) algorithm. Prognosis-guided gene and pathway analysis of immune-centric expression in tumors revealed significant survival enrichments across both cohorts. The \u27Interleukin Signaling\u27 pathway, containing 430 genes, was found to be statistically and significantly enriched with prognostic signal in both the INHALE (P = 0.008) and TCGA (P = 0.039) datasets. Subsequent leading-edge analysis identified a subset of genes (N = 23) shared between both cohorts, driving the pathway enrichment. Cumulative expression of this leading-edge gene signature was a strong predictor of patient survival [discovery: hazard ratio (HR) = 1.59, P = 3.0 × 10-8; replication: HR = 1.29, P = 7.4 × 10-7]. These data demonstrate the impact of immune-centric expression on patient outcomes in NSCLC. Furthermore, prognostic gene effects were localized to discrete immune pathways, of which Interleukin Signaling had the greatest impact on overall survival and the subset of genes driving these effects have promise for future therapeutic intervention

COPD-dependent effects of genetic variation in key inflammation pathway genes on lung cancer risk

Genome-wide association studies (GWAS) have identified several loci contributing to lung cancer and COPD risk independently; however, inflammation-related pathways likely harbor additional lung cancer risk-associated variants in biologically relevant immune genes that differ dependent on COPD. We selected single nucleotide polymorphisms (SNPs) proximal to 2,069 genes within 48 immune pathways. We modeled the contribution of these variants to lung cancer risk in a discovery sample of 1,932 lung cancer cases and controls stratified by COPD status and validation sample of 953 cases and controls also stratified by COPD. There were 43 validated SNPs in those with COPD and 60 SNPs in those without COPD associated with lung cancer risk. Furthermore, 29 of 43 and 28 of 60 SNPs demonstrated a statistically significant interaction with COPD in the pooled sample. These variants demonstrated tissue-dependent effects on proximal gene expression, enhanced network connectivity and resided together in specific immune pathways. These results reveal that key inflammatory related genes and pathways, not found in prior GWAS, impact lung cancer risk in a COPD-dependent manner. Genetic variation identified in our study supplements prior lung cancer GWAS and serves as a foundation to further interrogate risk relationships in smoking and COPD populations

Effect of trans-factor binding on RNA processing shifts.

<p>A) and D) show models of hypothesized mechanism of splicing or transcription factor influence on RNA processing and exon usage. B) An example of a correlation between the changes in gene expression of an RNA processing factor (LARP7) and the percent of RIs that shift towards the intron retention across all environments for which gene expression could be assessed. E) An example of a correlation between the changes in gene expression of a transcription factor (HSF1) and the percent of AFEs that shift towards the upstream AFE across all environments for which gene expression could be assessed. The correlation for B) and E) was tested using Spearman’s rho and the p-value shown is Benjamini-Hochberg corrected while the trendline depicts the best-fit line. C) Graph indicating the predictability (AUC as a proxy) of SE or RI shifts in a certain environment given predicted splicing factor binding sites (RNAcompete). F) Graph indicating the predictability (AUC as a proxy) of AFE shifts in a certain environment given transcription factor footprints [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006995#pgen.1006995.ref048" target="_blank">48</a>].</p