Organizational Adaptation to Changing Environments

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66531/2/10.1177_000276428502800507.pd

Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification

Background Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored. Methods Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold. Results Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12–3.50, P-value = 4.13 × 10−15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99–2.49, P-value = 5.70 × 10−46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72–0.74). Conclusions Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS

Systematic analyses of regulatory variants in DNase I hypersensitive sites identified two novel lung cancer susceptibility loci

DNase I hypersensitive sites (DHS) are abundant in regulatory elements, such as promoter, enhancer and transcription factor binding sites. Many studies have revealed that disease-associated variants were concentrated in DHS related regions. However, limited studies are available on the roles of DHS-related variants in lung cancer. In the current study, we performed a large-scale case-control study with 20,871 lung cancer cases and 15,971 controls to evaluate the associations between regulatory genetic variants in DHS and lung cancer susceptibility. The eQTL (expression quantitative trait loci) analysis and pathway enrichment analysis were performed to identify the possible target genes and pathways. Additionally, we performed motif-based analysis to explore the lung cancer related motifs using sequence kernel association test (SKAT). Two novel variants, rs186332 in 20q13.3 (C>T, OR = 1.17, 95% CI: 1.10-1.24, P = 8.45×10-7) and rs4839323 in 1p13.2 (T>C, OR = 0.92, 95% CI: 0.89-0.95, P = 1.02×10-6) showed significant association with lung cancer risk. The eQTL analysis suggested that these two SNPs might regulate the expression of MRGBP and SLC16A1 respectively. What's more, the expression of both MRGBP and SLC16A1 were aberrantly elevated in lung tumor tissues. The motif-based analysis identified 10 motifs related to the risk of lung cancer (P < 1.71×10-4). Our findings suggested that variants in DHS might modify lung cancer susceptibility through regulating the expression of surrounding genes. This study provided us a deeper insight into the roles of DHS related genetic variants for lung cancer

Transcriptome‐wide association study reveals candidate causal genes for lung cancer

We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of this study has been to integrate the complete GWAS results with a large‐scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n=1,038) to identify candidate causal genes for lung cancer. We performed transcriptome‐wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma, small cell lung cancer) and smoking subgroups (never‐ and ever‐smokers). We performed replication analysis using lung data from the Genotype‐Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever‐smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (PTWAS=1.09E‐99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (PTWAS=3.72E‐6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3‐adenocarcinoma on 9p13.3 was replicated using GTEx (PTWAS=6.55E‐5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influence lung cancer risk

Changing balances in Dutch higher education

Like many other higher education systems in the Western world, Dutch higher education underwent profound changes during the last decade. In this article we will present an overview of these changes, and try to formulate an analytical framework that might be suited to analyze this process. In order to set the stage, we will begin with an overview of the Dutch higher education system, in which the broad structure is described, and some trends are presented. Next, an overview is given of the retrenchment and restructuring operations with which Dutch higher education was confronted during the last decade. Drawing, mainly, on public administration and political theory, we then attempt to formulate a framework for analysis. In this we focus on the Dutch higher education system as a policy network, and address the relationships that exist between the various key actors in the network: between government and higher education, among higher education institutions themselves, and among the different actors within the institutions, especially administrators and academics. In doing so, we hope to demonstrate that at all these levels some identical basic processes operate which to a large extent determine the outcomes of governmental policies aimed at changing the higher education system. Time and again the modern state stumbles over the academic system (Clark 1983: 137

Mendelian randomization and mediation analysis of leukocyte telomere length and risk of lung and head and neck cancers

Background: Evidence from observational studies of telomere length (TL) has been conflicting regarding its direction of association with cancer risk. We investigated the causal relevance of TL for lung and head and neck cancers using Mendelian Randomization (MR) and mediation analyses. Methods: We developed a novel genetic instrument for TL in chromosome 5p15.33, using variants identified through deep-sequencing, that were genotyped in 2051 cancer-free subjects. Next, we conducted an MR analysis of lung (16 396 cases, 13 013 controls) and head and neck cancer (4415 cases, 5013 controls) using eight genetic instruments for TL. Lastly, the 5p15.33 instrument and distinct 5p15.33 lung cancer risk loci were evaluated using two-sample mediation analysis, to quantify their direct and indirect, telomere-mediated, effects. Results: The multi-allelic 5p15.33 instrument explained 1.49-2.00% of TL variation in our data (p = 2.6 × 10-9). The MR analysis estimated that a 1000 base-pair increase in TL increases risk of lung cancer [odds ratio (OR) = 1.41, 95% confidence interval (CI): 1.20-1.65] and lung adenocarcinoma (OR = 1.92, 95% CI: 1.51-2.22), but not squamous lung carcinoma (OR = 1.04, 95% CI: 0.83-1.29) or head and neck cancers (OR = 0.90, 95% CI: 0.70-1.05). Mediation analysis of the 5p15.33 instrument indicated an absence of direct effects on lung cancer risk (OR = 1.00, 95% CI: 0.95-1.04). Analysis of distinct 5p15.33 susceptibility variants estimated that TL mediates up to 40% of the observed associations with lung cancer risk. Conclusions: Our findings support a causal role for long telomeres in lung cancer aetiology, particularly for adenocarcinoma, and demonstrate that telomere maintenance partially mediates the lung cancer susceptibility conferred by 5p15.33 loci

Genome-wide interaction study of smoking behavior and non-small cell lung cancer risk in Caucasian population.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Both environmental and genetic risk factors contribute to lung carcinogenesis. We conducted a genome-wide interaction analysis between SNPs and smoking status (never vs ever smokers) in a European-descent population. We adopted a two-step analysis strategy in the discovery stage: we first conducted a case-only interaction analysis to assess the relationship between SNPs and smoking behavior using 13,336 NSCLC cases. Candidate SNPs with p-value less than 0.001 were further analyzed using a standard case-control interaction analysis including 13970 controls. The significant SNPs with p-value less than 3.5x10-5 (correcting for multiple tests) from the case-control analysis in the discovery stage were further validated using an independent replication dataset comprising 5377 controls and 3054 NSCLC cases. We further stratified the analysis by histological subtypes. Two novel SNPs, rs6441286 and rs17723637, were identified for overall lung cancer risk. The interaction odds ratio and meta-analysis p-value for these two SNPs were 1.24 with 6.96x10-7 and 1.37 with 3.49x10-7, respectively. Additionally, interaction of smoking with rs4751674 was identified in squamous cell lung carcinoma with an odds ratio of 0.58 and p-value of 8.12x10-7. This study is by far the largest genome-wide SNP-smoking interaction analysis reported for lung cancer. The three identified novel SNPs provide potential candidate biomarkers for lung cancer risk screening and intervention. The results from our study reinforce that gene-smoking interactions play important roles in the etiology of lung cancer and account for part of the missing heritability of this disease

Genetic interaction analysis among oncogenesis-related genes revealed novel genes and networks in lung cancer development

The development of cancer is driven by the accumulation of many oncogenesis-related genetic alterationsand tumorigenesis is triggered by complex networks of involved genes rather than independent actions. To explore the epistasis existing among oncogenesis-related genes in lung cancer development, we conducted pairwise genetic interaction analyses among 35,031 SNPs from 2027 oncogenesis-related genes. The genotypes from three independent genome-wide association studies including a total of 24,037 lung cancer patients and 20,401 healthy controls with Caucasian ancestry were analyzed in the study. Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in RGL1:RAD51B (OR=0.44, p value=3.27x10-11 in overall lung cancer and OR=0.41, p value=9.71x10-11 in non-small cell lung cancer), SYNE1:RNF43 (OR=0.73, p value=1.01x10-12 in adenocarcinoma) and FHIT:TSPAN8 (OR=1.82, p value=7.62x10-11 in squamous cell carcinoma) in our analysis. None of these genes have been identified from previous main effect association studies in lung cancer. Further eQTL gene expression analysis in lung tissues provided information supporting the functional role of the identified epistasis in lung tumorigenesis. Gene set enrichment analysis revealed potential pathways and gene networks underlying molecular mechanisms in overall lung cancer as well as histology subtypes development. Our results provide evidence that genetic interactions between oncogenesis-related genes play an important role in lung tumorigenesis and epistasis analysis, combined with functional annotation, provides a valuable tool for uncovering functional novel susceptibility genes that contribute to lung cancer development by interacting with other modifier genes