Genetically Determined Height and Risk of Non-hodgkin Lymphoma

Although the evidence is not consistent, epidemiologic studies have suggested that taller adult height may be associated with an increased risk of some non-Hodgkin lymphoma (NHL) subtypes. Height is largely determined by genetic factors, but how these genetic factors may contribute to NHL risk is unknown. We investigated the relationship between genetic determinants of height and NHL risk using data from eight genome-wide association studies (GWAS) comprising 10,629 NHL cases, including 3,857 diffuse large B-cell lymphoma (DLBCL), 2,847 follicular lymphoma (FL), 3,100 chronic lymphocytic leukemia (CLL), and 825 marginal zone lymphoma (MZL) cases, and 9,505 controls of European ancestry. We evaluated genetically predicted height by constructing polygenic risk scores using 833 height-associated SNPs. We used logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) for association between genetically determined height and the risk of four NHL subtypes in each GWAS and then used fixed-effect meta-analysis to combine subtype results across studies. We found suggestive evidence between taller genetically determined height and increased CLL risk (OR = 1.08, 95% CI = 1.00–1.17, p = 0.049), which was slightly stronger among women (OR = 1.15, 95% CI: 1.01–1.31, p = 0.036). No significant associations were observed with DLBCL, FL, or MZL. Our findings suggest that there may be some shared genetic factors between CLL and height, but other endogenous or environmental factors may underlie reported epidemiologic height associations with other subtypes

Genetic overlap between autoimmune diseases and non-Hodgkin lymphoma subtypes

Epidemiologic studies show an increased risk of non-Hodgkin lymphoma (NHL) in patients with autoimmune disease (AD), due to a combination of shared environmental factors and/or genetic factors, or a causative cascade: chronic inflammation/antigen-stimulation in one disease leads to another. Here we assess shared genetic risk in genome-wide-association-studies (GWAS). Secondary analysis of GWAS of NHL subtypes (chronic lymphocytic leukemia, diffuse large B-cell lymphoma, follicular lymphoma, and marginal zone lymphoma) and ADs (rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis). Shared genetic risk was assessed by (a) description of regional genetic of overlap, (b) polygenic risk score (PRS), (c)"diseasome", (d)meta-analysis. Descriptive analysis revealed few shared genetic factors between each AD and each NHL subtype. The PRS of ADs were not increased in NHL patients (nor vice versa). In the diseasome, NHLs shared more genetic etiology with ADs than solid cancers (p = .0041). A meta-analysis (combing AD with NHL) implicated genes of apoptosis and telomere length. This GWAS-based analysis four NHL subtypes and three ADs revealed few weakly-associated shared loci, explaining little total risk. This suggests common genetic variation, as assessed by GWAS in these sample sizes, may not be the primary explanation for the link between these ADs and NHLs

Genome-wide homozygosity and risk of four non-Hodgkin lymphoma subtypes

AIM: Recessive genetic variation is thought to play a role in non-Hodgkin lymphoma (NHL) etiology. Runs of homozygosity (ROH), defined based on long, continuous segments of homozygous SNPs, can be used to estimate both measured and unmeasured recessive genetic variation. We sought to examine genome-wide homozygosity and NHL risk. METHODS: We used data from eight genome-wide association studies of four common NHL subtypes: 3061 chronic lymphocytic leukemia (CLL), 3814 diffuse large B-cell lymphoma (DLBCL), 2784 follicular lymphoma (FL), and 808 marginal zone lymphoma (MZL) cases, as well as 9374 controls. We examined the effect of homozygous variation on risk by: (1) estimating the fraction of the autosome containing runs of homozygosity (FROH); (2) calculating an inbreeding coefficient derived from the correlation among uniting gametes (F3); and (3) examining specific autosomal regions containing ROH. For each, we calculated beta coefficients and standard errors using logistic regression and combined estimates across studies using random-effects meta-analysis. RESULTS: We discovered positive associations between FROH and CLL (β = 21.1, SE = 4.41, P = 1.6 × 10(-6)) and FL (β = 11.4, SE = 5.82, P = 0.02) but not DLBCL (P = 1.0) or MZL (P = 0.91). For F3, we observed an association with CLL (β = 27.5, SE = 6.51, P = 2.4 × 10(-5)). We did not find evidence of associations with specific ROH, suggesting that the associations observed with FROH and F3 for CLL and FL risk were not driven by a single region of homozygosity. CONCLUSION: Our findings support the role of recessive genetic variation in the etiology of CLL and FL; additional research is needed to identify the specific loci associated with NHL risk

HLA class I and II diversity contributes to the etiologic heterogeneity of non-Hodgkin lymphoma subtypes

A growing number of loci within the human leukocyte antigen (HLA) region have been implicated in non-Hodgkin lymphoma (NHL) etiology. Here, we test a complementary hypothesis of "heterozygote advantage" regarding the role of HLA and NHL, whereby HLA diversity is beneficial and homozygous HLA loci are associated with increased disease risk. HLA alleles at class I and II loci were imputed from genome-wide association studies (GWAS) using SNP2HLA for: 3,617 diffuse large B-cell lymphomas (DLBCL), 2,686 follicular lymphomas (FL), 2,878 chronic lymphocytic leukemia/small lymphocytic lymphomas (CLL/SLL), 741 marginal zone lymphomas (MZL), and 8,753 controls of European descent. Both DLBCL and MZL risk were elevated with homozygosity at class I HLA-B and -C loci (OR DLBCL=1.31, 95% CI=1.06-1.60; OR MZL=1.45, 95% CI=1.12-1.89) and class II HLA-DRB1 locus (OR DLBCL=2.10, 95% CI=1.24-3.55; OR MZL= 2.10, 95% CI=0.99-4.45). Increased FL risk was observed with the overall increase in number of homozygous HLA class II loci (p-trend<0.0001, FDR=0.0005). These results support a role for HLA zygosity in NHL etiology and suggests that distinct immune pathways may underly the etiology of the different NHL subtypes

The Use of Optimal Treatment for DLBCL Is Improving in All Age Groups and Is a Key Factor in Overall Survival, but Non-Clinical Factors Influence Treatment

Introduction: Diffuse large B cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma for which a cure is usually the therapeutic goal of optimal treatment. Using a large population-based cohort we sought to examine the factors associated with optimal DLBCL treatment and survival. Methods: DLBCL cases were identified through the population-based Victorian Cancer Registry, capturing new diagnoses for two time periods: 2008&ndash;2009 and 2012&ndash;2013. Treatment was pre-emptively classified as &lsquo;optimal&rsquo; or &lsquo;suboptimal&rsquo;, according to compliance with current treatment guidelines. Univariable and multivariable logistic regression models were fitted to determine factors associated with treatment and survival. Results: Altogether, 1442 DLBCL cases were included. Based on multivariable analysis, delivery of optimal treatment was less likely for those aged &ge;80 years (p &lt; 0.001), women (p = 0.012), those with medical comorbidity (p &lt; 0.001), those treated in a non-metropolitan hospital (p = 0.02) and those who were ex-smokers (p = 0.02). Delivery of optimal treatment increased between 2008&ndash;2009 and the 2012&ndash;2013 (from 60% to 79%, p &lt; 0.001). Delivery of optimal treatment was independently associated with a lower risk of death (hazard ratio (HR) = 0.60 (95% confidence interval (CI) 0.45&ndash;0.81), p = 0.001). Conclusion: Delivery of optimal treatment for DLBCL is associated with hospital location and category, highlighting possible demographic variation in treatment patterns. Together with an increase in the proportion of patients receiving optimal treatment in the more recent time period, this suggests that treatment decisions in DLBCL may be subject to non-clinical influences, which may have implications when evaluating equity of treatment access. The positive association with survival emphasizes the importance of delivering optimal treatment in DLBCL

DNA methylation marks in peripheral blood and the risk of developing mature B cell neoplasms

© 2018 Dr. Nicole Wong DooDysregulation of DNA methylation is a feature of mature B cell neoplasms (MBCN) but it is not known whether methylation changes can be detected in blood-derived DNA prior to MBCN diagnosis. In this prospective cohort study, peripheral blood was collected from healthy participants at recruitment (1990-1994). Participants who were subsequently diagnosed with MBCN (chronic lymphocytic lymphoma, B cell non-Hodgkin lymphoma and myeloma) up to 2012 were matched to the same number of controls based on age, sex, ethnicity, and type of blood sample (Guthrie cards, mononuclear cells, buffy coats). DNA methylation was measured using the Infinium®HumanMethylation450 BeadChip. Peripheral blood DNA was collected from 438 matched case-control pairs, a median of 10.6 years prior to diagnosis with MBCN. A series of analytical approaches was used in order to evaluate whether there was a distinct methylation profile associated with MBCN. First, global methylation analysis was performed, identifying increased methylation in CpG island and promoter-associated CpGs and widespread hypomethylation. Second, conditional logistic regression was used to identify differentially methylated CpG sites (DMPs) and kernel smoothing was used to identify differentially methylated regions (DMRs). Third, differential methylation variability, considered to be a distinctive feature in cancer, was assessed. In total, 1,338 DMPs were identified, of which 90 had gain of methylation in CpG sites associated with homeobox genes and 1,248 had loss of methylation in CpG sites associated with MAPK signaling pathway genes and genes involved in chemokine signaling pathways. There were 9,857 DMRs, with a cluster of 151 DMRs located in a 3.8kb region on 6p21.3, corresponding to the major histocompatibility locus. Differential methylation variability analysis identified 144 novel CpG sites distinctively located outside CpG islands. Conclusion: Distinctive changes in peripheral blood DNA methylation can be detected many years prior to diagnosis with MBCN, suggesting that changes in DNA methylation are an early epigenetic event. This contributes to our understanding of the timing of methylation changes in the development of MBCN

Analysis of Locus-specific LINE-1 and Alu Element DNA Methylation Reveals Novel Early Epigenetic Changes in Chronic Lymphocytic Leukaemia

BACKGROUND: Retrotransposons, such as LINE-1 (L1) and Alu elements, comprise more than 25% of the human genome. Their ability to retrotranspose throughout the genome is normally suppressed by epigenetic mechanisms. However, this repression is frequently lost in solid tumours through internal and external stimuli, and consequently somatic retrotransposition can be an initiating event in carcinogenesis. The epigenome in chronic lymphocytic leukaemia (CLL) is shaped by the maturation stage of the cell of origin, and its evolution during disease progression is correlated with the acquisition of genetic abnormalities associated with poor patient prognosis. Early work has demonstrated that L1 and Alu hypomethylation are associated with the acquisition of 17p deletions in CLL, but to date there has been no comprehensive or locus-specific analysis of retrotransposon DNA methylation. AIMS: To develop an approach to enable locus-specific analysis of L1 and Alu subfamily DNA methylation using the Illumina Infinium 450K microarray platform (H450K) and apply this to study aberrant methylation of L1 and Alu elements in CLL. METHODS: H450K probes mapping to retrotransposons were identified using RepeatMasker. The probeset was applied to a publicly-available dataset from a study of 138 CLL patients and 13 healthy individuals available from the International Cancer Genome Consortium. Leading hits were further analysed in Gene Expression Omnibus (GEO) datasets from 1,169 healthy individuals, 764 acute lymphoblastic leukaemia (ALL) patients, 174 acute myeloid leukaemia (AML) patients, and 31 diffuse large B-cell and Burkitt’s lymphoma patients, and also prospective samples from 82 future CLL cases (<18 years from diagnosis) and 82 age-matched controls within the Melbourne Collaborative Cohort Study. RESULTS: We identified 9,549 probes mapping to 117 L1 subfamilies, and 12,806 mapping to 37 Alu subfamilies. In normal B-cells from healthy individuals, DNA methylation at these sites was routinely high (mean β: 0.75), with greater variation observed in older subfamilies (L1M and AluJ) in comparison to the youngest (L1H/L1PA and AluY), especially at CpGs within 200 bases of TSS. We identified 10,782 CpG sites within L1 and Alusequences that were differentially methylated between CLL patients and healthy individuals (Pfdr90% of CLL patients but never in healthy individuals. Hypomethylation of Alu elements was associated with evolutionary age, with older subfamilies (AluJ) displaying greater changes than younger ones (AluY). Hypomethylation of 17 leading hits was highly confined to CLL, never observed in healthy individuals and infrequently in ALL, AML and lymphoma. In prospective samples, methylation at each of the 17 loci, located across the genome, was highly correlated within individual patients. In contrast to diagnosed CLL patients, hypomethylation at the loci was observed in only 9 future CLL cases (11%). Notably, however, this was more commonly observed in samples taken <7 years before diagnosis (7 of 24, 29%) than in those taken more than 7 years before diagnosis (2 of 58, 3%). CONCLUSIONS: We have identified locus-specific hypomethylation events of L1 and Alu elements that are highly frequent and specific to CLL, and which are present prior to diagnosis for some patients. Further work is required to establish how these epigenetic changes correspond to modulation of global DNA methylation patterns in leukaemogenesis

Analysis of retrotransposon subfamily DNA methylation reveals novel early epigenetic changes in chronic lymphocytic leukaemia

Retrotransposons such as LINE-1 and Alu comprise >25% of the human genome. While global hypomethylation of these elements has been widely reported in solid tumours, their epigenetic dysregulation is yet to be characterised in chronic lymphocytic leukaemia, and there has been scant consideration of their evolutionary history that mediates sensitivity to hypomethylation. Here, we developed an approach for locus- and evolutionary subfamily-specific analysis of retrotransposons using the Illumina Infinium Human Methylation 450K microarray platform, which we applied to publicly-available datasets from chronic lymphocytic leukaemia and other haematological malignancies. We identified 9,797 microarray probes mapping to 117 LINE-1 subfamilies and 13,130 mapping to 37 Alu subfamilies. Of these, 10,782 were differentially methylated (PFDR90% patients) but not observed in healthy individuals or other leukaemias, and was detectable in blood samples taken prior to chronic lymphocytic leukaemia diagnosis in 9 of 82 individuals from the Melbourne Collaborative Cohort Study. Our results demonstrate differential methylation of retrotransposons in chronic lymphocytic leukaemia by their evolutionary heritage that modulates expression of proximal genes