Unraveling the interactions between environmental factors and genetic polymorphisms in non-Hodgkin lymphoma risk

Figures & data

Table 1. Exposures and genes found associated with non-Hodgkin lymphoma and potential interactions.

Risk factors [24,31]		Subtype-specificity	Genes [48,50–52]	Subtype-specificity	Potential interactions
Established	>↑ Age ↑ Male gender ↑ Family history ↑ Inherited immunodeficiencies ↑ Autoimmune conditions • Sjogren’s syndrome • Celiac disease • Systemic lupus erythematosus • Severe arthritis ↑ Infectious diseases • HTLV1 • Helicobacter pylori • EBV • HHV-8 • Hepatitis C • HIV/AIDS ↑ Benzene	B-cell lymphomas DLBCL, MZ T-cell lymphomas B-cell lymphomas ATL MALT BL PEL CLL	Confirmed SNPs rs6457327 (6p21.33, near PSORS1) ↑ TNF G-308A ↑ IL10 T-3575A rs17483466 (2q13) rs13397985 (2q37.1) rs872071 (6p25.3, IRF4) rs735665 (11q24.1) rs7176508 (15q23) rs11083846 (19q13.32, PRKD2)	Follicular DLBCL DLBCL CLL CLL CLL CLL CLL CLL	rs6457327 (PSORS1) – autoimmune conditions Immune genes (including TNF, IL-10 and HLA) – infections, autoimmune conditions, occupational exposures, sunlight, obesity Oxidative stress Nutrient intake – one-carbon metabolism Detoxification genes – occupational exposures DNA repair – sunlight
Hypothesized	↑ Obesity ↓ Atopy (asthma and allergies) ↑ Mild infections (infectious mononucleosis, early birth order and transfusions) ↓ Alcohol ↓ Sunlight ↑ Serum dioxin ↑ Smoking ↑ Hair dyes ↑ Occupational exposures (serum polychlorinated biphenyl level, occupational pesticides and solvent exposures) Diet: ↑ nitrates, nitrosamines, meat, protein, ↓ vegetables, fruits, fish and green tea	DLBCL B-cell lymphomas Follicular Follicular, CLL/SLL	Candidate pathways/regions: • Proinflammatory genes • Th1/Th2/Treg genes • Innate immunity • Oxidative stress • One-carbon metabolism • DNA repair • Energy regulation • Sex hormone production • Detoxification/bioactivation • HLA region		Gender – sex hormone Obesity – energy regulation

EBV: Epstein–Barr virus; HHV:Human herpesvirus; HTLV1: Human T-cell lymphotrophic virus type 1; TCE: Tetrachloroethylene and trichloroethylene.

Table 2. Published literature on gene–environment interaction.

Gene family	Exposure	Summary of main results	Ref.
Immune genes	Organochlorine	Relationship between organochlorine exposure and NHL risk may be modified by immune genes (IFNG, IL-4, IL-16, IL-8 and IL-10)	[54]
	Autoimmune conditions	Increased DLBCL risk among persons with autoimmune conditions with variant TNF G-308A or IL-10 T-3575A allele	[53]
	Sun sensitivity	No joint effect between IRF4 and sun sensitivity in mediating NHL risk	[57]
		No joint effect between immune genes (IL-4, IL-10, IL-12A, IL-12B and TNF) and sun sensitivity	[60]
	Obesity	Increased DLBCL risk among obese persons with variant TNF G-308A or IL-10 T-3575A allele	[53]
	NSAIDs	IL-1 haplotype associated with increased B-NHL risk among nonregular NSAID users	[66]
	Birth order	Increased DLBCL risk among last born persons with variant TNF G-308A or IL-10 T-3575A allele	[53]
Energy homeostasis	BMI	Associations between leptin gene variants and follicular lymphoma independent of BMI	[67]
		Genetic interactions between LEP-G2548A and LEPR Q223R polymorphisms evidence for increased NHL risk	[68]
Oxidative stress	Vegetable/fruit intake, antioxidant intake	Two single nucleotide polymorphisms in NOS1 modified the association between total vegetable and fruit intake and risk of NHL	[69]
		NHL risk modified among those with: higher total vegetable intake and GSTM3 polymorphism; GSTP1 and higher cruciferous vegetable intake; OGG1 and higher daily zinc intake; XRCC3 and higher green leafy vegetable intake	[38]
Detoxification GST, CYP450	Tobacco	Increased DLBCL risk in nonsmokers was associated with variant allele for GSTP1 and CYP1A1; decreased risk observed for those with variant CYP1B1 allele	[70]
NAT1, NAT2	Tobacco	Compared with nonsmokers, NHL risk estimates for current cigarette smoking were increased only among NAT2 intermediate/rapid-acetylators	[58]
		Compared with nonsmokers, increased risk of NHL for current smokers without the NAT1*10 genotype but not among smokers with the NAT1*10 genotype	[70]
One-carbon metabolism	Folate, methyl donor nutrients, co-factors for C1-metabolism	Protective effect of vitamin B6 on NHL risk limited to FPGS, MTHFS. Protective association of methionine limited to FTHFD, MTHFR and MTRR	[59]
Vitamin D receptor	Sun	Decreased NHL risk with time in the midday sun dependent upon vitamin D receptor gene	[60]

DLBCL: Diffuse large B-cell lymphoma; FPGS: Folylpolygutamate synthetase; FTHFD: 10-formyl-THF dehydrogenase; MTHFS: Methenyltetrahydrofolate synthetase; MTHFR: Methylenetetrahydrofolate reductase; MTRR: Methionine synthase reductase; NAT: N-acetyl transferase; NHL: Non-Hodgkin lymphoma.

Table 3. Potential avenues for NHL research in gene–environment interactions and their respective challenges.

Study design	Recommended for	Specific challenges
Case–control study	Pooled data analyses of rare exposures, rare subtypes (i.e., InterLymph consortium)	Definition of exposure – Exposure assessment at one point in time – For some time-sensitive exposures, further confirmation in cohorts may be required – May not be adequate for some biological markers that are affected by disease or treatment
Nested case–control studies within cohort studies	Prevalent suspected risk factors that are affected by disease onset, treatment or recall bias (i.e., alcohol, diet and biomarkers [vitamin D, IgE])	Lymphoma classification
Exposure-specific cohorts Occupationally exposed cohorts (e.g., benzene, pesticides, chronic antigen-stimulating agents and UV-exposure)	Better definition of exposure biomarkers, internal and effective dose markers; (i.e., chromosomal aberrations (t14;18)(q32;q21), DNA or protein adducts, END, TRECs)	Repeated blood sampling Lymphoma classification
Infectious disease cohorts with repeat blood sampling (e.g., Helicobacter pylori, hepatitis C virus, HIV, EBV)	Identification of biologically effective dose; (i.e., CagA level, free plasma EBV DNA and EBV latency types) Identification of early markers for (pre)carcinogenesis/lymphogenesis (i.e., inflammatory index and type II mixed cryoglobulinemia)	Repeated blood sampling Lymphoma classification
Allergy/atopy cohorts	Definition of temporal sequence between allergy/atopy and NHL	Lymphoma classification
Autoimmune condition cohorts (e.g., Sjogren’s syndrome, celiac disease and systemic lupus erythematosus)	Identification of early markers/intermediate steps for lymphomagenesis Assessment of treatment effect on NHL risk; (i.e., TNF-blocker and corticosteroids)	Sample size
Immunodeficiency patient cohorts	Joint effect between common exposures and high-risk genetic background (i.e., diet, smoking and solvent exposure)	Heterogeneity in phenotypes Sample size
Solid-organ transplantation cohorts	Joint effects between intense immunosuppression and common genetic risk factors Identification of viral reactivation markers	Heterogeneity in phenotypes Sample size
Benign B-cell lymphoproliferative condition cohorts Monoclonal B-cell lymphocytosis, IgM monoclonal gammopathy of undetermined significance, t(14;18)(q32;q21) cohort	Identification of early markers/intermediate steps for lymphomagenesis (i.e., 13q14 deletions and mutated IGHV genes)	Few studies available Repeated blood sampling

EBV: Epstein–Barr virus; END: Endoreduplication; NHL: Non-Hodgkin lymphoma; TREC: T-cell receptor excision DNA circle.

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