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for the Myeloma Stem Cell Network
Human peripheral blood B-cell
Homing of CD41CD561 T lympho-
Circulating regulatory T cells in
Quality assurance for polychro-
Prevalence of monoclonal
Inherited genetic variation and overall survival following
Todd M. Gibson,1,2* Sophia S. Wang,3James R. Cerhan,4Matthew J. Maurer,4Patricia Hartge,1
Thomas M. Habermann,4Scott Davis,5,6Wendy Cozen,7,8Charles F. Lynch,9Richard K. Severson,10,11
Nathaniel Rothman,1Stephen J. Chanock,1,12and Lindsay M. Morton1
Follicular lymphoma (FL) has variable progression and survival, and
improved identification of patients at high risk for progression would
aid in identifying patients most likely to benefit from alternative ther-
apy. In a sample of 244 FL cases identified during a population-based
case-control study of non-Hodgkin lymphoma (NHL), we examined
6,679 tag SNPs in 488 gene regions for associations with overall FL
survival. Over a median follow-up of 89 months with 65 deaths in this
preliminary study, we identified 5 gene regions (BMP7, GALNT12,
DUSP2, GADD45B, and ADAM17) that were associated with overall sur-
vival from FL. Results did not meet the criteria for statistical signifi-
cance after adjustment for multiple hypothesis testing. These results,
which support a role for host factors in determining the variable pro-
gression of FL, serve as an initial examination that can inform future
studies of genetic variation and FL survival. However, they require
replication in independent populations, as well as assessment in
Follicular lymphoma (FL) is one of the most common subtypes of NHL,
with over 24,000 new cases diagnosed each year in the United States
[1,2]. Prior to the rituximab era, FL had a median survival of ?10 years,
but progression varied and survival ranged from <1 year to >20 years
after diagnosis . Deaths from FL follow resistance to treatments or
transformation to a more aggressive lymphoma type, usually diffuse large
B-cell lymphoma (DLBCL) . The Follicular Lymphoma International Prog-
nostic Index (FLIPI) separates FL patients into three risk groups based on
age, stage of disease, concentrations of b2-microglobulin and hemoglobin,
nodal area involvement, serum lactate dehydrogenase levels, and bone
marrow involvement [5,6]. Treatment strategies for FL have included
‘‘watchful waiting,’’ chemotherapy,
radioimmunoconjugates, and stem cell transplantation . Although new
treatment strategies have changed the survival of patients with FL, patients
continue to die from disease or complications of survival from disease .
Improved identification of patients at high risk for progression would aid in
developing targeted approaches for those that might benefit from alterna-
tive therapeutic interventions.
The heterogeneous survival times among FL patients reflects genetic
properties of the tumors, but the immune microenvironment likely plays a
significant role as well [8,9]. We hypothesized that germline genetic variation
could also influence FL survival due to differences in repair or clearance of
genetic damage, immune response, inflammatory response, or other proc-
esses . We previously reported associations between overall survival of
FL and common genetic variants in cytokine genes (IL8, IL2, IL12B, and
IL1RN) and one-carbon metabolism genes (MTHFR, FTHFD, and GGH) in
studies of ?100 candidate single nucleotide polymorphisms (SNPs) from 88
genes [10,11]. Because numerous pathways and processes have been
implicated in histologic transformation and overall prognosis of FL [8,12], we
conducted a broad, exploratory study of the association of 488 candidate
genes with overall survival. Genes were selected for their potential to influ-
ence lymphoma survival based on a literature review [8,13–15], and we
comprehensively tagged these genes/gene regions with 6,679 SNPs.
TABLE I. Summary of Gene-Level and SNP-Level Association Tests for
Genes Associated With Overall Survivalain Follicular Lymphoma Patients at
P < 0.01
Tag SNP with
aDichotomous survival: yes/no.
bDetermined using principal components analysis.
cAdjusted for multiple hypothesis testing using the false discovery rate.
dCox proportional hazards models.
724American Journal of Hematology
The median age at diagnosis of the 244 FL cases was 56 years, and
42% were age 60 or older. Patients were predominantly white (91%) with
similar numbers of men and women (49% female). Participants who
reported Hispanic ethnicity (n 5 10) were categorized based on self-
reported race; eight were included in the ‘‘white’’ category and two in the
‘‘other’’ category. The majority of FL cases were diagnosed with advanced
stage disease (56%), 14% reported B symptoms, and the most common
treatments were chemotherapy-based regimens (63%), observation (25%),
and radiation only (11%). Over a median follow-up of 89 months (range,
9–112 months), 65 (27%) patients died of any cause, with 75% of these
having lymphoma coded as the underlying cause of death. As described
previously, the observed survival for patients in our study was similar to
that observed in the SEER program . The risk score constructed to
capture effects of the above clinical and demographic factors was signifi-
cantly associated with overall survival (HR 5 3.10, 95% CI 5 2.08–6.60)
in a Cox model.
Among the 488 gene regions examined, we identified five independent
gene regions that were associated (P < 0.01) with FL overall survival (Table
I) prior to adjustment for multiple hypothesis testing. The two regions with
the smallest gene-based P values were BMP7 and GALNT12 (P 5 0.002),
but neither gene region was significantly associated with survival after FDR
adjustment (FDR; P 5 0.41). Other gene regions of interest included
DUSP2, GADD45B, and ADAM17, but again associations were not signifi-
cant after FDR adjustment. In analysis of individual SNPs (Table II), four
SNPs were associated with survival at a Ptrend< 0.001. A SNP in the BMP7
gene (rs6025446, Ptrend 5 0.00003; FDR P 5 0.22) had the lowest
observed P value for association and was significantly associated with
improved overall survival after adjustment for clinical and demographic fac-
tors (HRAG5 0.41, 95% CI 5 0.21–0.69; HRGG5 0.17, 95% CI 5 0.05–
0.54). As with the gene-based tests, none of the SNP associations remained
significant after adjustment for multiple hypothesis testing. Other SNPs with
suggestive associations that may be of interest for follow-up studies are
shown in Table II, and genotype frequencies and associations with overall
survival for all 6,679 SNPs examined are available in Supporting Information
Table SI. The SNP associations reported in Table II were similar when the
same models were used with outcomes restricted to death from follicular
lymphoma (Supporting Information Table SII).
In a sample of 244 FL cases identified during a population-based case-
control study of NHL, we identified five gene regions (BMP7, GALNT12,
DUSP2, GADD45B, and ADAM17) that were associated with overall survival
from FL after accounting for clinical and demographic factors. In addition to
SNPs located within the five associated gene regions, a SNP in the IRF2
gene was also associated with FL overall survival. In this exploratory investi-
gation of a large number of genetic variants (>450 genes) and survival after
FL, results did not meet the criteria for significance after adjustment for mul-
tiple hypothesis testing, thus replication in other populations is critical.
Nevertheless, our results suggest a small number of gene regions that may
be of particular interest in future studies of survival after FL.
The gene regions identified in our study can all be linked to regulation of
growth factors and cell signaling pathways, and expression levels have been
associated with FL survival or control of B-cell activity [8,13]. The bone mor-
phogenetic protein encoded by BMP7 is known to induce apoptosis, inhibit
proliferation, and inhibit metastasis of a number of cancer cell types through
antagonism of TGF-b pathways . ADAM17 gene products have protei-
nase activity and are responsible for membrane-shedding of TGF-b and
other growth-regulators . GADD45B has been identified as a prosurvival
factor for tumors that mediates activation of the mitogen-activated protein
kinase (MAPK) p38/JNK pathway . DUSP2 encodes a dual-specificity
phosphatase that can inhibit MAPK activity via dephosphorylation and is
involvedin regulationof immune
GALNT12 mutations are associated with aberrant glycosylation, which is a
feature of many cancers and can affect cell growth and differentiation
[20,21]. The associations observed in this study may suggest that inherited
differences in growth-regulatory pathways in immune cells can impact the
phenotype and progression of FL, but FDR-adjusted P values were not sig-
nificant and replication in further studies is required. The variants identified
are tag SNPs, and none of the identified SNPs are located within coding
exons. The most significant SNP in DUSP2 is located in the 30UTR, and
while rs4806857 tags variation in GADD45B, it is located almost 10-kb 30of
the coding region. If additional studies find associations between these
genes and survival after FL, then further work to evaluate functional conse-
quences of specific variants may be warranted.
This study examines associations with FL survival for more genetic var-
iants than any reported to date, and our tag SNP approach provides a more
comprehensive assessment of genetic variation in these genes and sur-
rounding regions. The genes included in the study were selected a priori
based on existing literature and knowledge of lymphoma mechanisms. Rig-
orous quality control procedures ensured high quality genotype data, and
our statistical analyses accounted for linkage among SNPs and the large
number of genes/SNPs examined. The population-based ascertainment and
rapid-reporting mechanisms employed to identify new FL cases provided a
study population likely to be generalizable, as suggested by similarities to
the mortality observed in SEER .
An important limitation of our study is the lack of detailed treatment data
and limited information on prognostic factors, although our clinical risk score
predicted at a similar level to FLIPI . Rituximab was not yet widely used
as an initial FL therapy at the time of the case-control study, so treatment of
patients in our study was different from current practice . Despite the
use of rapid reporting mechanisms, some of the most aggressive cases of
FL were not included in our study. An analysis of FL etiology in the same
case-control study did not find associations for the SNPs reported here ,
which may suggest involvement of different factors in FL etiology and prog-
nosis, although replication is needed.
The gene variants reported here have not previously been linked to FL,
and we have made numerous comparisons. These results therefore serve
as an important initial examination that can inform future studies, but require
replication in independent populations, as well as assessment in rituximab-
treated patients. Further investigation is warranted, as identification of inher-
ited genetic variants that affect FL prognosis could yield new insights into
clinical outcomes and treatment options.
and inflammatory responses .
Materials and Methods
Patients included 244 FL cases identified in a population-based case-con-
trol study of NHL in the United States, details of which have been reported
previously [10,23]. Briefly, 20- to 74-year-old patients with histologically con-
firmed NHL and without known HIV infection were identified from four sur-
veillance, epidemiology, and end results (SEER) cancer registries (Detroit,
MI; Seattle/Puget Sound, Washington; Los Angeles, CA; Iowa) between July
1998 and June 2000. Each of the recruitment centers obtained local IRB
approval for the study, the overall study was approved by the National Insti-
tutes of Health, and all participants provided written informed consent.
Patients with FL were identified using the International Classification of Dis-
eases—Oncology (ICD-O), 3rd edition  codes: 9690–9693, 9695–9698.
Patients provided data on age, sex, race, Hispanic ethnicity, and education
TABLE II. Individual SNPs Most Strongly Associated With Overall Survivalain Follicular Lymphoma Patients (Ptrend< 0.001)
Tag SNP with smallest Ptrend
Heterozygous for minor alleleHomozygous for minor allele
dbSNP IDMinor allele/MAFNHR (95% CI)b
NHR (95% CI)b
aDichotomous survival: yes/no.
bAdjusted for prognostic risk score; HR based on Cox proportional hazards models with ‘‘homozygous for major allele’’ as the reference group.
cAdjusted for multiple hypothesis testing using the false discovery rate.
American Journal of Hematology 725
level via interviews conducted for the case-control study. Linkage to routine
follow-up from SEER registry databases at each study site in the spring of
2008 provided information on histology, stage, presence of B-symptoms,
date of last follow-up, vital status, and first course of therapy (use of single
or multiagent chemotherapy, radiation, other therapies exclusive of chemo-
therapy and/or radiation, and no therapy or observation); information on indi-
vidual agents or doses was not available.
DNA was extracted from blood clots, buffy coats, or buccal cell sam-
ples as described previously , and genotype frequencies were equiva-
lent for individuals who provided blood or buccal cell samples . Geno-
typing of 7,943 tag SNPs from 488 gene regions putatively involved in
lymphoma survival was conducted at the NCI Core Genotyping Facility as
previously described . Tag SNPs were selected within the region
spanning 20-kb 50of the transcription start site to 10-kb 30of the end of
the last exon and were grouped using r2> 0.80 to define a gene region.
Genotyping was successfully completed for 6,830 tag SNPs. Of these,
SNPs with a low completion rate (<90% of samples) or with a concord-
ance <95% in QC samples were also excluded, resulting in a final ana-
lytic study population of 244 FL cases with data for 6,679 SNPs. Of the
1,321 cases in the case-control study, 1,286 had valid registry data at
the time of linkage, of which 328 were FL. Of the 328 FL cases, 50
were excluded because they did not provide a blood or buccal sample
and 34 were excluded based on genotyping QC. This left a total of 244
FL cases for analysis.
We examined the association between individual SNPs and overall sur-
vival in FL patients by time-to-event analysis using multivariable Cox pro-
portional hazards regression models to estimate hazard ratios (HRs) and
95% confidence intervals (95% CI). Models were adjusted for a prognos-
tic risk score generated by the linear combination of the following clinical
and demographic variables multiplied by their Cox regression coefficients:
stage (local, regional, distant, missing), presence of B-symptoms (yes, no,
missing), type of initial therapy (chemotherapy 1 radiation, chemotherapy
1 other therapy, radiation only, no therapy), age (<60 years, 601 years,
as categorized in FLIPI), sex, race (white, all other), study center (Detroit,
Iowa, Los Angeles, Seattle), and years of education (<12, 12–15, 161
years) . We examined associations at the gene level using principal
components analysis as described previously . Briefly, linear combina-
tions of ordinally scaled SNPs were used to identify principal components
that explained ?90% of the SNP variance within the gene region. The
principal components were then included in multivariable Cox proportional
hazards regression models . To address the potential for Type I error
due to multiple comparisons, we calculated the false discovery rate (FDR)
for individual SNPs and gene-level tests. The FDR is defined as the
expected proportion of falsely rejected hypotheses among all rejected
hypotheses . A secondary analysis restricted to lymphoma-specific
death was conducted for genes and SNPs associated with overall
1Division of Cancer Epidemiology and Genetics, Department of Health and Human
Services, National Cancer Institute, National Institutes of Health,
Rockville, Maryland;2Department of Health and Human Services, Cancer
Prevention Fellowship Program, Center for Cancer Training, National Cancer
Institute, National Institutes of Health, Rockville, Maryland;3Division of Cancer
Etiology, Department of Population Sciences, Beckman Research Institute and the
City of Hope, Duarte, California;4Division of Epidemiology, Mayo Clinic College of
Medicine, Rochester, Minnesota;5Program in Epidemiology, Fred Hutchinson
Cancer Research Center, Seattle, Washington;6Department of Epidemiology,
University of Washington, Seattle, Washington;7Department of Preventive
Medicine, Keck School of Medicine, University of Southern California, Los
Angeles, California;8Department of Pathology, Keck School of Medicine,
University of Southern California, Los Angeles, California;9Department of
Epidemiology, University of Iowa, Iowa City, Iowa;10Department of Family
Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan;
11Karmanos Cancer Institute, Detroit, Michigan;12Core Genotyping Facility,
Division of Cancer Epidemiology and Genetics, Department of Health and Human
Services, National Cancer Institute, National Institutes of Health,
Contract grant sponsor: Intramural Program of the National Cancer Institute,
National Institutes of Health; Contract grant number: R01-CA96704; Contract grant
sponsor: Public Health Service Contracts; Contract grant numbers: N01-PC-
67010, N01-PC-67008, N01-PC-67009, N01-PC-65064, N02-PC-71105.
Additional Supporting Information may be found in the online version of this article.
*Correspondence to: Todd M. Gibson, Division of Cancer Epidemiology and
Genetics, Department of Health and Human Services, National Cancer Institute,
National Institutes of Health, 6120 Executive Blvd, Rockville, MD
Conflict of interest: Nothing to report.
Published online 31 March 2012 in Wiley Online Library
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