Ju-Hyun Park

Circulating Inflammation Markers and Prospective Risk for Lung Cancer

BACKGROUNDnDespite growing recognition of an etiologic role for inflammation in lung carcinogenesis, few prospective epidemiologic studies have comprehensively investigated the association of circulating inflammation markers with lung cancer.nnnMETHODSnWe conducted a nested case-control study (n = 526 lung cancer patients and n = 592 control subjects) within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Control subjects were matched to lung cancer case patients on age, sex, follow-up time (median = 2.9 years), randomization year, and smoking (pack-years and time since quitting). Serum levels of 77 inflammation markers were measured using a Luminex bead-based assay. Conditional logistic regression and weighted Cox models were used to estimate odds ratios (ORs) and cumulative risks, respectively.nnnRESULTSnOf 68 evaluable markers, 11 were statistically significantly associated with lung cancer risk (P trend across marker categories < .05), including acute-phase proteins (C-reactive protein [CRP], serum amyloid A [SAA]), proinflammatory cytokines (soluble tumor necrosis factor receptor 2 [sTNFRII]), anti-inflammatory cytokines (interleukin 1 receptor antagonist [IL-1RA]), lymphoid differentiation cytokines (interleukin 7 [IL-7]), growth factors (transforming growth factor alpha [TGF-A]), and chemokines (epithelial neutrophil-activating peptide 78 [ENA 78/CXCL5], monokine induced by gamma interferon [MIG/CXCL9], B cell-attracting chemokine 1 [BCA-1/CXCL13], thymus activation regulated chemokine [TARC/CCL17], macrophage-derived chemokine [MDC/CCL22]). Elevated marker levels were associated with increased lung cancer risk, with odds ratios comparing the highest vs the lowest group ranging from 1.47 (IL-7) to 2.27 (CRP). For IL-1RA, elevated levels were associated with decreased lung cancer risk (OR = 0.71; 95% confidence interval = 0.51 to 1.00). Associations did not differ by smoking, lung cancer histology, or latency. A cross-validated inflammation score using four independent markers (CRP, BCA-1/CXCL13, MDC/CCL22, and IL-1RA) provided good separation in 10-year lung cancer cumulative risks among former smokers (quartile [Q] 1 = 1.1% vs Q4 = 3.1%) and current smokers (Q1 = 2.3% vs Q4 = 7.9%) even after adjustment for smoking.nnnCONCLUSIONSnSome circulating inflammation marker levels are associated with prospective lung cancer risk.

A prospective study of 67 serum immune and inflammation markers and risk of non-Hodgkin lymphoma.

Although severe immune dysregulation is an established risk factor for non-Hodgkin lymphoma (NHL), the importance of subclinical immunologic effects is unclear. We compared baseline serum levels of 67 immune and inflammation markers between 301 patients with NHL diagnosed 5+ years after blood collection and 301 control patients within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. We observed associations with NHL for elevated B-cell-attracting chemokine 1 (BCA-1; fourth quartile vs first: odds ratio [OR], 2.7; 95% confidence interval [CI], 1.7-4.2; Ptrend = 1.0 × 10(-6)), soluble tumor necrosis factor receptor 2 (sTNFR2; OR, 3.4; 95% CI, 2.0-5.8; Ptrend = 1.1 × 10(-6)), and soluble vascular endothelial growth factor receptor 2 (sVEGFR2; OR, 2.3; 95% CI, 1.4-3.9; Ptrend = .0005) that remained significant after Bonferroni correction, simultaneous model adjustment, and restriction to cases diagnosed 8 to 13 years after blood collection. Associations with other markers were observed, although none remained associated with NHL after adjustment for BCA-1, sTNFR2, and sVEGFR2. Our findings suggest that circulating BCA-1, sTNFR2, and sVEGFR2 are associated with NHL risk well in advance of diagnosis. Additional research is needed to replicate these findings and elucidate the underlying biologic mechanisms.

Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner’s-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner’s curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25–50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner’s curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10−5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits and implications for the future

Summary-level statistics from genome-wide association studies are now widely used to estimate heritability and co-heritability of traits using the popular linkage-disequilibrium-score (LD-score) regression method. We develop a likelihood-based approach for analyzing summary-level statistics and external LD information to estimate common variants effect-size distributions, characterized by proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of summary-level results across 32 GWAS reveals that while all traits are highly polygenic, there is wide diversity in the degrees of polygenicity. The effect-size distributions for susceptibility SNPs could be adequately modeled by a single normal distribution for traits related to mental health and ability and by a mixture of two normal distributions for all other traits. Among quantitative traits, we predict the sample sizes needed to identify SNPs which explain 80% of GWAS heritability to be between 300K-500K for some of the early growth traits, between 1-2 million for some anthropometric and cholesterol traits and multiple millions for body mass index and some others. The corresponding predictions for disease traits are between 200K-400K for inflammatory bowel diseases, close to one million for a variety of adult onset chronic diseases and between 1-2 million for psychiatric diseases.

Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits

We developed a likelihood-based approach for analyzing summary-level statistics and external linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases.Analysis of summary statistics from 32 GWAS datasets using a new likelihood-based approach evaluates polygenicity across different traits. Effect-size distributions predict the sample sizes needed to explain the SNP-based heritability of traits.

Two high-risk susceptibility loci at 6p25.3 and 14q32.13 for Waldenström macroglobulinemia

Waldenström macroglobulinemia (WM)/lymphoplasmacytic lymphoma (LPL) is a rare, chronic B-cell lymphoma with high heritability. We conduct a two-stage genome-wide association study of WM/LPL in 530 unrelated cases and 4362 controls of European ancestry and identify two high-risk loci associated with WM/LPL at 6p25.3 (rs116446171, near EXOC2 and IRF4; ORu2009=u200921.14, 95% CI: 14.40–31.03, Pu2009=u20091.36u2009×u200910−54) and 14q32.13 (rs117410836, near TCL1; ORu2009=u20094.90, 95% CI: 3.45–6.96, Pu2009=u20098.75u2009×u200910−19). Both risk alleles are observed at a low frequency among controls (~2–3%) and occur in excess in affected cases within families. In silico data suggest that rs116446171 may have functional importance, and in functional studies, we demonstrate increased reporter transcription and proliferation in cells transduced with the 6p25.3 risk allele. Although further studies are needed to fully elucidate underlying biological mechanisms, together these loci explain 4% of the familial risk and provide insights into genetic susceptibility to this malignancy.Waldenström macroglobulinemia (WM)/lymphoplasmacytic lymphoma (LPL) is a non-Hodgkin-type B cell lymphoma. Here, the authors identify two risk loci for WM/LPL in a two-stage GWAS involving a family-oversampling approach and provide evidence for a functional role of the non-coding SNP rs116446171.

Winners curse correction and variable thresholding improve performance of polygenic risk modeling based on summary-level data from genome-wide association studies

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner’s-curse adjustments for marginal association coefficients that are used to weight the SNPs in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner’s curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner’s curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P=0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P=2χ10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

Abstract 2880: A prospective study of 51 serum immune markers and risk of non-Hodgkin lymphoma.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DCnnBackground: Although severe immune dysregulation is an established risk factor for non-Hodgkin lymphoma (NHL), the etiologic relevance of subclinical immunologic effects is unclear. Few prospective studies have investigated the relationship between circulating immune markers and NHL, and those conducted to date have been limited in sample size, the number of evaluated analytes, or in their ability to examine markers of risk many years preceding disease diagnosis. To better understand this question, we conducted a nested case-control study investigating pre-diagnostic serum levels of 51 immune markers within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.nnMethods: Fifty-one immune markers (including cytokines, chemokines, extracellular matrix proteins, growth factors and soluble products of immune activation) were measured in baseline serum from 301 NHL cases diagnosed 5+ years after blood collection (median 8.0 years, range 5.0-13.9) and 301 individually matched controls using Luminex bead-based assay panels. Odds ratios (ORs) and 95% confidence intervals (CIs) relating marker levels with NHL risk were computed using conditional logistic regression modeling.nnResults: We observed strong associations with NHL for elevated levels of several analytes: a pro-inflammatory marker, soluble tumor necrosis factor receptor 2 (sTNFRII; fourth quartile vs. first: OR 3.4, 95% CI 2.0-5.8; Ptrend = 5.9 x 10−6); a regulator of B-cell traffic, B-cell attracting chemokine 1 (BCA-1; 2.7, 1.7-4.2; Ptrend = 1.1 x 10−5); a regulator of angiogenesis, soluble vascular endothelial growth factor receptor 2 (sVEGFR2; 2.3, CI 1.4-3.9; Ptrend = 0.0005); and the pro-inflammation and apoptotic cytokine TNF-related apoptosis inducing ligand (TRAIL; 1.6, 1.0-2.5; Ptrend = 0.02). These associations remained significant in analyses of cases diagnosed 8-13 years after blood collection and, with the exception of TRAIL, following Bonferroni correction and simultaneous model adjustment.nnConclusions: Our findings suggest that elevated levels of sTNFRII, BCA-1, sVEGFR2, and, possibly, TRAIL may reflect biologic processes contributing to lymphomagenesis. Additional research is needed to replicate these findings, identify cell sources of these markers, and elucidate the underlying biologic mechanisms.nnCitation Format: Mark P. Purdue, Jonathan N. Hofmann, Troy J. Kemp, Anil K. Chaturvedi, Qing Lan, Ju-Hyun Park, Ruth M. Pfeiffer, Allan Hildesheim, Ligia A. Pinto, Nathaniel Rothman. A prospective study of 51 serum immune markers and risk of non-Hodgkin lymphoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2880. doi:10.1158/1538-7445.AM2013-2880