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Blood First Edition Paper, prepublished online September 24, 2015; DOI 10.1182/blood-2015-04-537498
Familial predisposition and genetic risk factors for lymphoma
James R. Cerhan1,3 and Susan L. Slager2,3
1
Division of Epidemiology and 2Division of Biomedical Statistics and Informatics, Department of
Health Sciences Research; 3Genetic Epidemiology and Risk Assessment Program, Mayo Clinic
Comprehensive Cancer Center; Mayo Clinic, Rochester, MN.
Correspondence: James Cerhan, Department of Health Sciences Research, Mayo Clinic, 200
First Street SW, Rochester, MN 55905; e-mail: [email protected].
1
Copyright © 2015 American Society of Hematology
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Abstract
Our understanding of familial predisposition to lymphoma (collectively defined as non-Hodgkin
lymphoma [NHL], Hodgkin lymphoma [HL], and chronic lymphocytic leukemia [CLL]) outside of
rare hereditary syndromes has progressed rapidly during the last decade. First-degree relatives
of NHL, HL and CLL patients have an approximately 1.7-fold, 3.1-fold, and 8.5-fold elevated risk
of developing NHL, HL and CLL, respectively. These familial risks are elevated for multiple
lymphoma subtypes and do not appear to be confounded by non-genetic risk factors,
suggesting at least some shared genetic etiology across the lymphoma subtypes. However, a
family history of a specific subtype is most strongly associated with risk for that subtype,
supporting subtype-specific genetic factors. While candidate gene studies have had limited
success in identifying susceptibility loci, genome-wide association studies (GWAS) have
successfully identified 67 single nucleotide polymorphisms from 41 loci, predominately
associated with specific subtypes. In general, these GWAS-discovered loci are common (minor
allele frequency >5%), have small effect sizes (odds ratios of 0.60-2.0), and are of largely
unknown function. The relatively low incidence of lymphoma, modest familial risk, and the lack
of a screening test and associated intervention all argue against active clinical surveillance for
lymphoma in affected families at this time.
2
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Introduction
Lymphomas, defined as non-Hodgkin (NHL), Hodgkin (HL), and chronic lymphocytic
leukemia/small lymphocytic lymphoma (herein CLL), are the most common hematologic
malignancies in western countries, and combined there are an estimated 95,520 newly
diagnosed cases each year in the United States.1 While there has been a long history of case
reports of familial clustering of lymphomas and leukemias, it has only been relatively recently
that these malignancies were considered to have an important inherited genetic component
outside of very rare hereditary cancer syndromes.2 In 2001, the World Health Organization
(WHO) introduced an updated classification system for lymphomas based on the REAL
classification,3 which became the international gold-standard.4 This classification provided the
first biologically based, integrated framework for consistently defining lymphoma subtypes,
thereby greatly facilitating research on this heterogeneous group of diseases.
Building from prior reviews,5-11 we focus on the strongest data addressing familial
predisposition (including twin, case-control and registry-based studies) and germline
susceptibility loci (including linkage and genetic association studies) for lymphoma, and put
these findings into clinical context. One emerging theme on the etiology of lymphoma is that
there is both commonality and heterogeneity for risk factors by subtype,12 and thus we consider
this issue as well in the context of familial predisposition and genetic risk factors.
Evidence for Familial Predisposition
Twin Studies
If the concordance rate of a phenotype in monozygotic twins (who share all genes) is higher
than the concordance rate for dizygotic twins (who share on average half of their genes), then
there is evidence for a genetic component. In a study of 44,788 pairs of twins from
Scandinavia,13 there was an excess of concordant monozygotic twins compared to dizygotic
twins for leukemia, and the heritability was estimated to be 21% (95%CI 0-0.54); these results
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have been attributed to CLL, as acute lymphoblastic and myeloid leukemia have shown minimal
evidence of familial clustering.14 There were insufficient cases to estimate heritability for NHL or
HL. In a twin study of lymphomas,15 there was a 100-fold higher risk of HL in monozygotic twins
of patients with HL compared background rates (SIR=99; 95%CI 48-182), while there was no
excess risk in dizygotic twins; in contrast, there was a 23-fold higher risk of NHL in monozygotic
twins of patients with NHL and a 14-fold higher risk in dizygotic twins, suggesting a stronger role
for shared environment for NHL.
Familial aggregation (Table 1)
We summarize the strongest results across different study designs that evaluate the extent that
family history of lymphoma is associated with risk of developing lymphoma, including casecontrol, cohort and registry-based studies. We note that none of these study designs can
definitively establish an inherited genetic contribution to risk of lymphoma as these approaches
are unable to distinguish the role of shared genetics from shared environment. Family size itself
may also be associated with lymphoma risk, which can introduce bias in estimating the
association of familial aggregation with lymphoma risk.
Case-Control Studies. In case-control studies, the prevalence of a family history is compared in
case patients to that of controls using an odds ratio (OR) to quantify the magnitude of risk. The
largest study to date is a pooled analysis of 17,471 NHL cases and 23,096 controls from 20
case-control studies in the International Lymphoma Epidemiology Consortium (InterLymph),12
which found an 1.8-fold increased risk of NHL (OR=1.8, 95%CI 1.5-2.1) for those with a firstdegree (blood) relative with NHL; there was also elevated NHL risk for individuals who reported
a first-degree relative with HL (OR=1.7, 95%CI 1.2-2.3) or leukemia (OR=1.5, 95%CI 1.3-1.8),
suggesting susceptibility across these lymphomas.
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Further evaluation of NHL subtypes in Table 1 reveals that risk of CLL was only slightly
stronger for a family history of leukemia (OR=2.4) than for NHL (OR=1.9). In contrast, risk of
diffuse large B-cell (DLBCL) and follicular (FL) lymphoma were more strongly associated with a
family history of NHL (ORs 1.8-2.0) than for leukemia (ORs=1.0-1.2), while marginal zone
(MZL), mantle cell (MCL) and peripheral T-cell (PTCL) lymphomas showed similar risks for
either type of family history (ORs=1.7-2.0). A family history of HL was associated with
increased risk of DLBCL (OR=2.1) and MZL (OR=2.7), but was not significantly associated with
risk of CLL, FL, lymphoplasmacytic lymphoma/Waldenstroms Macroglobulinemia (LPL/WM),
MCL or PTCL, although ORs were above 1.0 for all subtypes except PTCL. In a comprehensive
analysis of all subtypes simultaneously, there was no statistically significant heterogeneity
across risk of most common NHL subtypes for either a family history of NHL (PHomogeneity=0.52)
or HL (PHomogeneity=0.47). In contrast, there was strong evidence for heterogeneity for a family
history of leukemia (PHomogeneity=3.9x10-5), with family history of leukemia most strongly
associated with risk of CLL, LPL/WM, MCL and PTCL. Of note, the associations for family
history of NHL with risk of NHL12 or specific NHL subtypes (e.g., DLBCL,16 FL,17 CLL,18 MZL,19
LPL/WM20 and PTCL21) remained unchanged after adjusting for extensive subtype-specific risk
factors, suggesting that the association of family history may be predominately driven by shared
genetics over shared environment.
While InterLymph did not report pooled results for risk of HL, a large case-control study
conducted in Scandinavia22 reported elevated risk of HL with a family history of HL (OR=3.3),
NHL (OR=3.3) and CLL (OR=6.3). Some smaller studies have reported larger ORs for risk of
HL with a family history of HL. 23,24
These data provide strong evidence for familial predisposition to lymphoma. However, the
case-control study design is susceptible to several types of bias, particularly selection and
reporting bias. The former bias can occur when there are systematic differences in how cases
and controls are enrolled, most commonly due to exclusion of more aggressive cases (who die
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before they can be enrolled into a study) and how controls are selected (i.e., controls who are
not representative of the underlying population that generated the cases due to selection factors
or participation rates). The main concern with reporting bias is that cases and controls can
differentially report a family history. In a study from Scandinavia that compared self-report to
cancer registry data,25 specificity of reporting a hematologic malignancy was very high for both
cases (98%) and controls (99%), while sensitivity was much lower at 60% for cases and only
38% for controls. This led to inflated odds ratios (up to 30%) based on self-reported family
history data.
Cohort studies. Prospective cohort studies overcome many of the limitations of case-control
studies, but there are few cohorts that have had detailed data on lymphoma in family members
or a sufficient number of lymphoma outcomes to assess risk of specific NHL subtypes. In a
national cohort study of 3.5 million people in Sweden born 1973-2008, family history of HL in a
parent or sibling was associated with an 7.2 and 8.8-fold higher risk of childhood/young adult
HL, respectively,26 while another study reported a 6-fold higher risk for siblings.27 In a cohort
study of over 120,000 female California teachers,28 a history of lymphoma in a first-degree
relative was associated with a 1.7-fold higher risk of B-cell NHL (RR=1.74, 95%CI 1.16-2.60)
based on 478 cases; data on risk for NHL subtypes were not available. The latter finding was
highly consistent with pooled case-control data (Table 1) and suggests a lack of major biases at
least for the overall NHL association.
Registry-based studies. Another major approach to evaluate familial aggregation is to link
population-based family registry data with cancer registry data to determine the excess risk of
cancer in people with a family history of cancer. Advantages of this approach include
population-based assessment, which minimizes selection bias and enhances generalizability,
and validation of cancer diagnoses through the use of cancer registries, which eliminates
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reporting bias. Based on data from the Utah Population Database and the Utah Cancer
Registry,29 the risk of NHL was increased 1.7-fold in first-degree relatives of a proband with NHL
(familial RR=1.68, 95%CI 1.04-2.48) and the risk of lymphocytic leukemia was >5-fold in firstdegree relatives of a proband with lymphocytic leukemia (familial RR=5.69, 95%CI 2.58-10.0).
In contrast, the risk of HL was only elevated 1.3-fold in first-degree relatives of a HL proband
(familial RR=1.27, 95%CI 0.12-3.65), although power for this estimate was low (only 2 exposed
cases). Using updated data and a different analytic approach that estimates the Genealogical
Index of Familiality,14 excess relatedness was observed for NHL, HL and CLL. For CLL and
NHL, but not HL, the excess relatedness was observed for both distant and overall relatedness.
Distant relatedness is due to distant relatives and may be interpreted as providing evidence that
familial clustering is more likely due to shared genetic versus shared environmental contribution,
as the latter would be lower in distant relationships.
The most comprehensive data available on familial aggregation by lymphoma subtypes has
been published using registry data from Sweden and Denmark (summarized in Table 1). This
approach compares the cancer experience in first-degree relatives of lymphoma patients with
the cancer experience in relatives of matched population controls. First-degree relatives of HL
patients had a 3.1-fold increase in risk of HL (95%CI 1.8-5.3), while risk of HL was not
associated with a family history of NHL (RR=1.3, 95%CI 0.9-1.8) but was associated with a
family history of CLL (RR=2.1, 95%CI 1.2-3.8).30 In other registry-based studies, risk of HL in
first degree relatives of HL patients has ranged from 1.2 to 5.8.31-33 Across studies, risk of HL is
stronger for HL in siblings than in parents.26,30-32
First-degree relatives of cases with NHL had a 1.7-fold higher risk of developing NHL
(95%CI 1.4-2.2), while risk of NHL was weaker and not statistically significant for first-degree
relatives with HL (RR=1.4, 95%CI 1.0-2.0) or CLL (RR=1.3, 95%CI 0.9-1.9).34 First-degree
relatives of CLL patients had an 8.5-fold increased risk of CLL (RR=8.5, 95%CI 6.1-12), while
risk of CLL was also increased with a first-degree relative with NHL (RR=1.9, 95%CI 1.5-2.3) or
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HL (RR=1.5, 95%CI 1.0-2.3).35 It is notable that most of the risk estimates from the populationbased registry studies in Table 1 were very similar or only modestly weaker than the estimates
from the pooled case-control studies, again suggesting that there was only modest bias in
estimates from case-control studies. The most prominent exception is for a family history of
CLL, which showed a much stronger association in the registry studies compared to that of
case-control studies. This may be in part due to the confusion of patients reporting a CLL as a
leukemia or lymphoma.
The registry studies have also been able to evaluate risk for more detailed lymphoma
subtypes. One striking finding is the clustering of risk by NHL subtype. For example, firstdegree relatives of DLBCL cases had a 9.8-fold increased risk of DLBCL,36 first-degree relatives
of FL had a 4-fold increased risk of FL,36 and first-degree relatives of LPL/WM had a 20-fold
increased risk of LPL/WM.37 In contrast, risk of a different subtype was much weaker, and
notably relatives of DLBCL patients were not at increased risk of FL and relatives of FL patients
were not at increased risk of DLBCL.36 There are very limited data on PTCL, and registry data
suggests no increased risk among first-degree relatives with HL, CLL, DLBCL, or FL.36
Summary. Multiple lines of data suggest that a family history of lymphoma is associated with an
increased risk of lymphoma, familial risk is elevated for multiple lymphoma subtypes, and
familial risk does not seem to be confounded by non-genetic risk factors, although there are
likely unidentified risk factors and clustering of known (and unknown) risk factors within families
is difficult to exclude. This suggests at least some shared genetic etiology across the lymphoma
subtypes. However, because a family history of a specific lymphoma subtype is also most
strongly associated with risk for that specific lymphoma, genetic factors are also likely to be
unique to a subtype.
Genetic Risk Factors
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We now review studies that show not only clear evidence of a genetic contribution to lymphoma
risk, but also provide chromosomal locations that are associated with risk.
Linkage Studies
Linkage studies use multi-case families or sib pairs to screen the genome in an unbiased
manner to identify chromosomal regions that show excessive sharing of inherited alleles among
affected individuals. These regions can then be interrogated for causal variants using a variety
of approaches, most commonly fine-mapping using dense genotyping or sequencing. The
expectation is to identify highly penetrant variants of modest to large effect size, although these
variants are generally rare or very rare in the general population. Linkage studies in HL have
identified both HLA class I (for EBV+) and class II (for EBV-) risk and protective alleles and
haplotypes.10,11 Beyond HLA, linkage studies in CLL,38 HL,39 and WM40 have not definitively
identified genes with large effects, and there are no published studies in FL, DLBCL or other
NHL subtypes. For CLL, significant linkage was identified at 2q21.2, which contains the
chemokine receptor (CXCR4) gene and for which rare coding mutations have been identified.41
The lack of strong findings for these linkage studies may be due to small sample sizes, but also
raises the hypothesis that multiple, low to moderate risk variants that are common in the
population, defined as minor allele frequency (MAF) >5%, may be more relevant in lymphoma
etiology than single, highly penetrant variants that are very rare, which is referred to as the
common-disease, common-variant hypothesis.42
Genetic Association Studies
With the advent of high-throughput and relatively inexpensive genotyping technologies, casecontrol studies (also commonly called association studies in the genetics literature) of sequence
variation in germline DNA have become a predominant study design in genetic epidemiology.43
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This design is a very efficient strategy to identify low penetrance alleles relative to linkage
studies, which are underpowered for this task.44 The most common type of genetic variation in
the human genome is the single nucleotide polymorphism (SNP), which is a single base pair
change in the DNA sequence. In this setting, SNP allele or genotype frequencies in cases
(patients) are compared to that in unrelated controls (who do not have the phenotype of interest)
using an OR. When the genetic model (e.g., dominant vs. recessive) is not known a priori, the
OR is typically modeled as “per risk allele” (i.e., ordinal test of 0, 1 or 2 risk alleles). While other
genetic variation is of interest, including rare variants (<5% frequency), insertion/deletions, block
substitutions, inversions, translocations and copy number alterations, these have not been
studied as extensively.45 Two major types of association studies are candidate gene and
genome-wide association studies (GWAS).
Candidate Gene Studies
The choice of a candidate gene has been mainly driven by a priori biologic knowledge of
lymphoma and diseases associated with lymphoma (e.g., infectious or autoimmune), or results
identified in other cancers. Candidate gene studies have included pathways related to immune
function, cell cycle/proliferation, apoptosis, DNA repair, and carcinogen metabolism. Early
studies tended to evaluate a small number of genes (i.e., <5) and were generally restricted to 1
or 2 SNPs within a gene. These SNPs often had some evidence for their functionality based on
laboratory data or anticipated changes in protein coding or gene activity (e.g., changes in
promotor function). As genotyping technologies increased in throughput and decreased in cost,
more SNPs within genes and more genes (often grouped into pathways) were assessed. Also,
the International Haplotype Map (“HapMap”)46 and later the 1000 Genomes47 projects, which
catalogue human genetic variation, became available as a reference and allowed “tagging” of
genes and gene regions to take advantage of linkage disequilibrium (LD) to efficiently cover all
of the common genetic variation for more comprehensive genotyping studies.43
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While many studies of candidate genes have been published (reviewed in7-9,11,48), most
findings have failed to replicate likely due to study design, bias from population stratification
(i.e., confounding by race or ethnicity), small sample size (low power), uncontrolled multiple
testing (leading to false positive associations), and unrealistic expectations in our ability to
choose variants and genes.49 The most robust findings have been for an LTA-TNF haplotype
with DLBCL (P=2.93x10-8);50,51 a SNP (rs3789068) in the proapoptotic BCL2L11 gene and risk
for B-cell NHL (OR=1.21, P=2.21x10-11);52 SNPs in CASP8/CASP10 and risk of CLL;53 a SNP
(rs3132453) in PRRC2A in HLA class III and risk of B-cell NHL (OR=0.68, P=1.07x10-9);52 and
certain HLA alleles in class I (including HLA-A*01 and *02) with EBV+ HL and class II (including
HLA-DRB1) with EBV- HL.11
GWAS (Table 2)
In contrast to candidate gene/pathway studies, GWA studies use dense microarrays with a large
number of SNPs (commonly 250,000 to 750,00 or more) spread across all chromosomes to
identify genetic markers associated with case-control status.54 While SNPs on these platforms
have generally focused on common variants (MAF≥5%), more recent arrays are enriching for
rarer variants (MAF<5%). GWAS is considered agnostic (“hypothesis-free”) as all loci are
considered equally. Given the large number of statistical tests involved, a stringent level of
evidence (currently P<5 x 10-8) and replication across multiple independent studies are required
to declare an association as “genome-wide significant.” An advantage of having a large number
of typed SNPs is that any underlying difference in population structure between cases and
controls can be identified and controlled to ensure that confounding by race/ethnicity does not
bias the results.
CLL. The estimated contribution of all common variation to the heritability of CLL is 46-59%.55,56
The first GWAS in a lymphoid malignancy was conducted for CLL57 and to date, GWAS
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analyses55,58-62 have identified 32 SNPs from 28 loci for CLL, which accounts for approximately
19% of familial risk of CLL.61 Many of the established SNPs are near or in genes plausibly
linked to CLL, including genes involved in apoptosis (including FAS, PMAIP1, BAK1, BCL2,
BCL2L11, BMF, CASP8/CASP10), telomere function (POT1, TERT, TERC), transcription
factors important in B-cell differentiation (IRF8, LEF1, PRKD3, SP140), and B-cell receptor
activation (IRF3, HLA-DQA1). Notably, there has been little evidence of interaction among
these SNPs, suggesting independent effects. None of the SNPs have individually shown a
strong relationship with age at diagnosis, although cases diagnosed at a younger age tended to
carry a greater number of risk alleles,61 supporting the hypothesis that early onset CLL is
enriched for genetic susceptibility.
In an East Asian population, GWAS-discovered SNPs for CLL near IRF4 (rs872071), SP140
(rs13397985), and ACOXL (rs17483466) were associated with CLL risk (nominal P<0.05), with
a suggestive association with GRAMD1B (rs735665).63 The minor allele frequencies of these
SNPs were much lower than in populations of European descent, supporting the hypothesis that
the lower prevalence of CLL genetic risk factors might explain part of the lower incidence of CLL
in East Asian populations.
FL. Three early GWA studies based on small discovery sets (<400 cases) identified loci at
6p21.3364 and 6p21.3265,66 in the major histocompatibility complex (MHC) associated with FL.
In a meta-analysis of those studies plus a new GWAS of over 2100 cases, the HLA region
showed overwhelming association with FL, with 8,104 SNPs achieving genome-wide
significance. A top SNP from this region, rs12195582, reached P=5.35x10-100 after additional
validation.67 HLA alleles and amino acids (AA) were imputed and the top signal mapped to four
linked DRβ1 multiallelic AA at positions at 11, 13, 28 and 30, suggesting an important role for
DRβ1 peptide presentation in FL.67 Additional independent signals were also identified in HLA
class II (rs17203612) and class I (rs3130437, near HLA-C); after accounting for all of these
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signals, no other previously identified SNPs from the MHC achieved genome-wide significance.
Outside of the HLA region, 5 novel loci have been identified including 11q23.3 (near CXCR5),
11q24.3 (near ETS1), 3q28 (in LPP), 18q21.33 (near BCL2), and 8q24 (near PVT1).67 These
genes are linked to B-cell biology making them plausible in the etiology of FL.
HL. Classical HL (cHL) makes up approximately 95% of HL, and cHL compromises several
subtypes: in young children and older adults, mixed cellularity HL (typically EBV+)
predominates, while in adolescents and young adults, nodular sclerosing HL (typically EBV-)
predominates.68 Five GWAS analyses have been published in HL69-73, and the strongest
findings have been for SNPs mapping to HLA class II69-71 in close proximity to HLA-DRA and
HLA-DRB1, regions previously linked to HL by HLA-typing studies.74,75 The 6p21.32 locus
marked by rs6903608 (near HLA-DRA), was associated with cHL overall and more specifically
to EBV-negative cHL,69,70 early onset70,76 and young adult nodular sclerosing HL71 (largely EBV). Additional GWAS signals at 6p21 have been identified in HLA class I,69 with statistically
independent associations for rs2248462 (near MICB) for all cHL (irrespective of EBV status);
and rs2734986 (3’ untranslated region of HLA-G and near HLA-A) and rs6904029 (near HCG9)
for EBV+ cHL. These results confirm earlier studies linking HLA-A*01 and *02 to EBV+ cHL77-79
and support a role for class I but not class II genes in EBV+ HL. Using SNPs to impute classic
HLA alleles, two independent signals in the HLA class II region (rs6903608 and rs2281389)
were linked to early onset HL, but no specific classical HLA alleles from this region were
significant after conditioning on these two SNPs.76 The class II SNP rs6903608 was estimated
to account for ~6% of the familial risk in HL.76
Outside of the MHC region, GWAS-discovered loci for HL include 2p16.170 (near REL),
10p1470 (near GATA3), 8q24.2170 (telomeric to PVT1 and near MYC), 5q3169 (a
nonsynonymous SNP in IL13), 3p24.173 (5’ to EOMES), 6q23.373 (intergenic to HBS1L and
MYB) and 19p13.372 (in intron 2 of TCF3), with only the 2p16.1 and 5q31 loci showing stronger
associations with EBV (negative) status. Genes from these non-HLA regions are involved in
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hematopoiesis and immunoregulation, making them plausible susceptibility loci for cHL. HLA
and non-HLA-linked loci appear to be independent, and non-HLA loci were estimated to account
for ~7% of the familial risk in HL.73
DLBCL. In a GWAS conducted in an East Asian population, a locus at 3q27 (near BCL6 and
LPP) was identified,80 although this could not be replicated in independent studies of East
Asian81 or European ancestry.82 In a large GWAS of European ancestry,82 novel loci identified
included 6p25.3 (EXOC2), 6p21.33 (HLA-B), 2p23.3 (NCOA1), and 8q24.21 (near PVT1 and
MYC); the strongest finding after imputing HLA alleles and amino acids (AA) was with HLAB*08:01, although this could not be statistically distinguished from the HLA-B SNP due to high
LD. The latter study also estimated that common SNPs, including but not limited to the GWASdiscovered loci, explained approximately 16% of the variance in DLBCL risk overall.
Three of the five 5 GWAS-discovered SNPs for DLBCL in Europeans were significantly
associated with DLBCL in an East Asian population,81 including EXOC2 (OR=2.04, P=3.9x1010
), PVT1 (OR=1.34, P=2.1x10-6), and HLA-B (OR=3.05, P=0.009). Overall, MAFs were similar
or only modestly lower in the East Asian population for all SNPs except for one of the 8q24
SNPs, which was much rarer.
MZL. The only GWAS of this subtype83 identified two distinct loci at 6p21.32 (intragenic to
BTNL2, in HLA class II) and 6p21.33 (HLA-B, in HLA class I); these two loci were in low LD and
were statistically independent of each other. There was no strong heterogeneity in these results
when stratified on mucosa associated lymphoid tissue (MALT) versus non-MALT (splenic MZL
and nodal MZL) subtypes, although this was based on a modest sample size. These loci are
also associated with autoimmune diseases and immune response, suggesting shared biologic
underpinnings with MZL.
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Lymphoma. Only one GWAS has been conducted based on all lymphomas (including HL,
multiple myeloma and T-cell cases) as the outcome in both the discovery and validation
stages.84 A SNP at 11q12.1 (near LPXN) was identified, and the associations were consistent
across the common subtypes. However, this locus has not been replicated in larger GWAS
studies based on specific subtypes.
Summary
To date, GWAS have successfully identified 67 SNPs from 41 genetic loci, mainly associated
with specific subtypes (Figure 1), with only two regions—the HLA region and 8q24—associated
with multiple lymphoma subtypes; few candidate gene loci have been replicated by GWAS. As
shown in Figure 2, the established loci are common (minor allele frequency >5%) and have
small effect sizes, supporting a polygenic model for susceptibility. In contrast to GWAS,
candidate gene studies in lymphoma have had only minimal success, similar to other cancers.85
Linkage studies have also not been successful in identifying rare alleles causing Mendelian
disease, and the evaluation of low-frequency variants with intermediate effects is still in early
research phases for lymphoma, but will be challenged by sample size issues.86 The GWASidentified SNPs that have been identified are largely of unknown function. However, a leading
hypothesis related for the mechanistic role of these common SNPs is their effect on gene
expression (e.g., through effects on promotors or enhancers), but this effect is difficult to identify
given an expected modest impact of these SNPs on gene expression and the fact that this
impact could occur at any time before diagnosis.61
Practice Implications
Given the estimated lifetime risk of NHL is 1 in 48 (2.1%) in the United States1 and an RR of 1.7
for the risk of NHL in a first-degree relative, then the absolute lifetime risk of NHL is 3.6% in firstdegree relatives of an NHL patient. The absolute risk is even lower for specific lymphoma
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subtypes, which are less common. While the absolute lifetime risk of NHL is not trivial, the
relatively low incidence of lymphoma, the modest familial risk, and the lack of a screening test
and associated intervention all argue against active clinical surveillance of family members of
lymphoma patients at this time. One hope is that genetic risk scores, alone or in combination
with other risk factors, might improve prediction ability.87 While there are currently no validated
risk scores for lymphoma, this advance is anticipated as more loci are characterized.
Future Directions
Characterization of genetic susceptibility in lymphoma is rapidly evolving. It is expected that
additional common variants will be discovered for the different lymphoma subtypes,88 and
perhaps pan-lymphoma loci will also be identified. As new lymphoma entities and precursor
lesions are defined, evaluation of heritability and genetic susceptibility should be addressed.
Additional work needs to occur in other racial and ethnic groups, particularly with contrasting
lymphoma incidence rates. It is not yet clear if rare and low-frequency variants will play a major
role in lymphoma susceptibility. This will be challenging to address due to phenotype
heterogeneity and the need for large sample sizes for these relatively rare entities, and both
family and association study designs along with bioinformatics and laboratory-based studies will
all need to be integrated to achieve progress.86 Other genetic mechanisms (e.g., copy number
variation), epigenetics and gene-environment interactions are additional frontiers.85 Finally,
integrating somatic and germline genomics should provide additional insights into lymphoma
etiology and pathogenesis,89 and hopefully provide novel insights into how to prevent and treat
this malignancy.
16
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Acknowledgements
This work was supported by the National Cancer Institute grants R01 CA92153, U01
CA118444, and P50 CA97274. We thank Dr. Thomas Habermann for his critical review of the
manuscript, Mr. Curtis Olswald for technical assistance, and Ms. Sondra Buehler for editorial
assistance.
Authorship: J.R.C. and S.L.S. did the background research and wrote the manuscript.
Conflict of interest: The authors declare no competing financial interests.
17
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27
Table 1. Risk* of lymphoma subtypes by family history of selected cancers in first degree relatives
Family history of lymphoma subtype in first degree relative
Outcome‡
Study Design
References
HL
CLL/Leukemia
DLBCL
FL
LPL/WM
12
1.8 (1.5-2.1)
1.7 (1.4-2.2)
1.7 (1.2-2.3)
1.4 (1.0-2.0)
1.5 (1.3-1.8)
1.3 (0.9-1.9)
---
---
---
12
1.9 (1.4-2.6)
1.9 (1.5-2.3)
1.3 (0.6-2.6)
1.5 (1.0-2.3)
2.4 (1.9-3.1)
8.5 (6.1-12)
-1.0 (0.4-2.5)
-1.6 (0.9-2.8)
-4.0 (2.0-8.2)
NHL
Case-control
Registry
Morton et al. (2014)
Goldin et al. (2005)34
CLL
Case-control
Registry
Morton et al. (2014)
Goldin et al. (2009)35
DLBCL
Case-control
Registry
Morton et al. (2014)
Goldin et al. (2009)36
12
1.8 (1.5-2.3)
2.1 (1.4-3.2)
2.4 (P<0.05)
1.2 (0.9-1.5)
-9.8 (3.1-31)
-no increase†
---
FL
Case-control
Registry
Morton et al. (2014)12
Goldin et al. (2009)36
2.0 (1.6-2.5)
1.5 (0.9-2.4)
1.4 (NS)
1.0 (0.7-1.3)
1.8 (1.0-3.3)
-no increase†
-4.0 (1.6-9.5)
---
LPL/WM
Case-control
Registry
Morton et al. (2014)
Kristinsson et al. (2008)37
12
1.2 (0.5-2.8)
3.0 (2.0-4.4)
2.2 (0.5-9.4)
0.8 (0.3-2.2)
2.2 (1.2-4.0)
3.4 (1.7-6.6)
---
---
-20 (4.1-98)
MZL
Case-control
Morton et al. (2014)12
1.7 (1.1-2.5)
2.7 (1.4-5.5)
1.7 (1.2-2.4)
--
--
--
MCL
Case-control
Morton et al. (2014)12
2.0 (1.1-3.3)
1.5 (0.5-5.0)
2.0 (1.2-3.2)
--
--
--
PTCL
Case-control
Registry
Morton et al. (2014)12
Goldin et al. (2009)36
1.7 (0.9-3.1)
0.9 (0.1-4.4)
no increase†
1.8 (1.1-3.1)
no increase†
-no increase†
-no increase†
---
HL
Case-control
Registry
Chang et al. (2005)22
Goldin et al. (2004, 2009)30,36
3.3 (1.3-8.0)
1.3 (0.9-1.8)
3.3 (0.5-22)
3.1 (1.8-5.3)
6.3 (1.3-30)
2.1 (1.2-3.8)
-2.0 (1.1-4.0)
-1.4 (P>0.05)
---
*Odds ratios (for case-control design) or relative risk (for registry design) and 95% confidence intervals
Subtype abbreviations: NHL (non-Hodgkin lymphoma); CLL (chronic lymphocytic leukemia/small lymphocytic lymphoma); DLBCL (diffuse large B-cell lymphoma); FL
(follicular lymphoma); LPL/WM (lymphoplasmacytic lymphoma/Waldenstrom Macroglobulinemia); MZL (marginal zone lymphoma); MCL (mantle cell lymphoma);
PTCL (peripheral T-cell lymphoma); HL (Hodgkin lymphoma).
‡
†
Estimate not reported
28
From www.bloodjournal.org by guest on June 16, 2017. For personal use only.
NHL
From www.bloodjournal.org by guest on June 16, 2017. For personal use only.
Table 2. GWAS-discovered loci for lymphoma
Subtype
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
CLL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
DLBCL
DLBCL
DLBCL
DLBCL
DLBCL
DLBCL
MZL
MZL
HL
HL
HL (EBV-)
HL
HL
HL (EBV+)
HL (EBV+)
HL
HL
HL (NS)
HL (NS)
Locus
2p22.2
2q13
2q13
2q33.1
2q37.1
2q37.3
3q26.2
4q25
4q26
5p15.33
6p21.31
6p21.32
6p21.32
6p25.3
6q25.2
7q31.33
8q22.3
8q24.21
9p21.3
10q23.31
11p15.5
11q24.1
12q24.13
15q15.1
15q21.3
15q23
16q24.1
16q24.1
18q21.32
18q21.33
18q21.33
19q13.32
3q28
6p21.32
6p21.32
6p21.32
6p21.32
6p21.33
6p21.33
8q24.21
11q23.3
11q24.3
18q21.33
2p23.3
3q27
6p21.33
6p25.3
8q24.21
8q24.21
6p21.32
6p21.33
2p16.1
3p24.1
5q31
6p21
6p21
6p21
6p21
6p21.32
6p21.32
6p21.32
6p21.32
SNP
rs3770745
rs13401811
rs17483466
rs3769825
rs13397985
rs757978
rs10936599
rs898518
rs6858698
rs10069690
rs210142
rs9273363
rs674313
rs872071
rs2236256
rs17246404
rs2511714
rs2456449
rs1679013
rs4406737
rs7944004
rs735665
rs10735079
rs8024033
rs7169431
rs7176508
rs305061
rs2292982
rs4368253
rs4987855
rs4987852
rs11083846
rs6444305
rs10484561
rs2647012
rs17203612
rs3130437
rs6457327
rs13254990
rs4938573
rs4937362
rs17749561
rs79480871
rs6773854
rs2523607
rs116446171
rs13255292
rs4733601
rs9461741
rs2922994
rs1432295
rs3806624
rs20541
rs2248462
rs2395185
rs2734986
rs6904029
rs2281389
rs6903608
rs2858870
rs204999
Nearest Gene
QPCT, PRKD3
ACOXL, BCL2L11
ACOXL, BCL2L11
CASP10/CASP8
SP140, SP110
FARP2
MYNN
LEF1
CAMK2D
TERT
BAK1
HLA-DQB1
HLA-DRB5
IRF4
IPCEF1
POT1
ODF1
CASC19
CDKN2B-AS1
ACTA2, FAS
C11orf21
GRAMD1B
OAS3
BMF
RFX7
RPLP1
IRF8
IRF8
PMAIP1
BCL2
BCL2
PRKD2
LPP
MHC class II
HLA-DQB1
HLA-DRβ1 Glu-28
HLA-DRA
HLA-C
C6orf15 et al. (STG)
PVT1
CXCR5
ETS1
BCL2
NCOA1
BCL6/LPP
HLA-B
EXOC2
PVT1
PVT1
BTNL2
HLA-B
REL
EOMES
IL13
MICB
HLA-DRA
HLA-A
HCG9
HLA-DPB1
HLA-DRA
HLA-DRB1
PRRT1
RAF†
(controls)
0.22
0.81
0.20
0.45
0.19
0.11
0.75
0.59
0.16
0.25
0.70
0.27
0.26
0.54
0.44
0.71
0.41
0.36
0.52
0.57
0.49
0.21
0.36
0.51
0.08
0.37
0.66
0.34
0.69
0.91
0.06
0.22
0.27
0.13
0.44
0.30
0.49
0.62
0.38
0.32
0.20
0.46
0.91
0.076
0.22
0.12
0.019
0.32
0.48
0.018
0.11
0.40
0.45
0.18
0.22
0.33
0.18
0.30
0.83
0.27
0.13
0.27
29
‡
OR
1.24
1.41
1.39
1.19
1.41
1.39
1.26
1.20
1.31
1.20
1.40
1.24
1.69
1.54
1.23
1.22
1.16
1.26
1.19
1.27
1.20
1.45
1.18
1.22
1.36
1.37
1.22
0.65
1.19
1.47
1.41
1.35
1.21
1.95
0.64
1.86
1.44
1.23
0.59
1.18
1.34
1.19
1.34
1.34
1.47
1.32
2.20
1.22
1.18
2.66
1.64
1.22
1.26
1.53
0.61
0.56
2.45
0.46
1.73
1.70
0.40
0.60
‡
P-value
−8
1.68 x 10
−18
2.08 x 10
–10
2.36 x 10
−9
2.50 x 10
–10
5.40 x 10
−9
2.11 x 10
-9
1.74 x 10
−10
4.24 x 10
-9
3.07 x 10
-10
1.12 x 10
-16
9.47 x 10
-10
2.24 x 10
-9
6.92 x 10
–20
1.91 x 10
-10
1.50 x 10
-8
3.40 x 10
-9
2.90 x 10
−10
7.84 x 10
−8
1.27 x 10
−14
1.22 x 10
2.15 x 10−10
3.78 x 10–12
2.34 x 10−8
2.71 x 10−10
4.74 x 10−7¶
4.54 x 10–12
3.60 x 10−7¶
6.48 x 10-9
2.51 x 10−8
2.66 x 10−12
7.76 x 10−11
3.96 x 10–9
1.10 x 10-10
1.12 x 10-29
2.00 x 10-21
7.89 x 10-69
4.59 x 10-16
8.23 x 10-9
4.70 x 10-11
1.06 x 10-8
5.79 x 10-20
6.76 x 10-11
8.28 x 10-10
4.23 x 10-8
1.14 x 10-11
2.40 x 10-10
2.33 x 10-21
9.98 x 10-13
3.63 x 10-11
3.95 x 10-15
2.43 x 10-9
1.91 x 10−8
1.14 x 10-12
5.40 × 10–9
1.30 x 10-13
8.30 x 10-25
1.20 x 10-15
5.50 x 10-10
6.31 x 10-13
2.84 × 10−50
5.61 x 10-9
2.34 x 10-8
Reference
55
Berndt et al. (2013)
55
Berndt et al. (2013)
57
Di Bernardo et al. (2008)
55
Berndt et al. (2013)
57
Di Bernardo et al. (2008)
58
Crowther-Swanepoel et al. (2010)
61
Speedy et al. (2014)
55
Berndt et al. (2013)
61
Speedy et al. (2014)
61
Speedy et al. (2014)
60
Slager et al. (2012)
55
Berndt et al. (2013)
59
Slager et al. (2011)
57
Di Bernardo et al. (2008)
61
Speedy et al. (2014)
61
Speedy et al. (2014)
61
Speedy et al. (2014)
58
Crowther-Swanepoel (2010)
55
Berndt et al. (2013)
55
Berndt et al. (2013)
Berndt et al. (2013)55
Di Bernardo et al. (2008)57
Sava et al. (2014)62
Berndt et al. (2013)55
Crowther-Swanepoel et al. (2010)58
Di Bernardo et al. (2008)57
Crowther-Swanepoel et al. (2010)58
Slager et al. (2011)59
Berndt et al. (2013)55
Berndt et al. (2013)55
Berndt et al. (2013)55
Di Bernardo et al. (2008)57
Skibola et al. (2014)67
Conde et al. (2010)65
Smedby et al. (2011)66
Skibola et al. (2014)67
Skibola et al. (2014)67
Skibola et al. (2014)67
Skibola et al. (2009)64
Skibola et al. (2014)67
Skibola et al. (2014)67
Skibola et al. (2014)67
Skibola et al. (2014)67
Cerhan et al. (2014)82
Tan et al. (2013)80
Cerhan et al. (2014)82
Cerhan et al. (2014)82
Cerhan et al. (2014)82
Cerhan et al. (2014)82
Vijai et al. (2015)83
Vijai et al. (2015)83
Enciso-Mora et al. (2010)70
Frampton et al. (2013)73
Urayama et al. (2012)69
Urayama et al. (2012)69
Urayama et al. (2012)69
Urayama et al. (2012)69
Urayama et al. 2012)69
Moutsianas et al. (2011)76
Enciso-Mora et al. (2010)70
Cozen et al. (2012)71
Cozen et al. (2012)71
From www.bloodjournal.org by guest on June 16, 2017. For personal use only.
HL
6q23.3
HL
8q24.21
HL
10p14
HL
19p13.3
LYM
11q12.1
*Subtype abbreviations:
rs7745098
rs2019960
rs501764
rs1860661
rs12289961
HBS1L, MYB
PVT1
GATA3
TCF3
LPXN
0.48
0.23
0.19
0.41
0.28
1.21
1.33
1.25
0.81
1.29
-9
3.42 x 10
1.26 x 10−13
−8
7.05 x 10
-10
3.50 x 10
3.89 x 10-8
73
Frampton et al. (2013)
Enciso-Mora et al. (2010)70
70
Enciso-Mora et al. (2010)
72
Cozen et al. (2014)
Vijai et al. (2013)84
CLL (chronic lymphocytic leukemia/small lymphocytic lymphoma); FL (follicular lymphoma); DLBCL (diffuse
large B-cell lymphoma); MZL (marginal zone lymphoma); HL (Hodgkin lymphoma), NS (nodular sclerosis); LYM (lymphoma).
†
Risk allele frequency among controls
Odds Ratio (per allele)
Considered genome-wide significant at the time of initial publication
‡
¶
30
From www.bloodjournal.org by guest on June 16, 2017. For personal use only.
Figure 1. GWAS-discovered loci for lymphoma subtypes mapped to chromosomal location.
Except for 6p21 and 8q24, there is minimal little overlap of loci for lymphoma subtype-specific
susceptibility loci.
31
From www.bloodjournal.org by guest on June 16, 2017. For personal use only.
nds
Figure 2. Lymphoma susceptibility loci by effect size and allele frequency. The blue diamond
represent established lymphoma susceptibility loci plotted by allele frequency (x-axis) versus effect
ct
size (y-axis). For lymphoma, most of the loci are common variants of low to modest effect size
ave
(mainly discovered by genome-wide association studies), although a few low-frequency variants hav
been identified. No rare alleles of low frequency (generally identified through linkage studies and
sequencing) have been definitively linked to lymphoma. Very rare variants of low effect size are
difficult to identify using current genetic approaches, while there are very few examples of common
n
variants of high effect size for common diseases (and none in lymphoma).
32
From www.bloodjournal.org by guest on June 16, 2017. For personal use only.
Prepublished online September 24, 2015;
doi:10.1182/blood-2015-04-537498
Familial predisposition and genetic risk factors for lymphoma
James R. Cerhan and Susan L. Slager
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