Abstract
Introduction:
Although clinical prognostic indicators exist for follicular lymphoma(FL), patient outcomes remain heterogeneous.
Material and Methods:
We evaluated the association between survival and a polygenic risk score(PRS) composed of five previously identified FL susceptibility loci(rs12195582, rs13254990, rs17749561, rs4245081, rs4938573) among women who participated in a case-control study of non-Hodgkin lymphoma in Los Angeles County between 2004-2008. Risk associations were estimated through logistic regression, calculating the odds ratios(OR) and 95% confidence intervals(95% CI). Survival was estimated under a Cox proportional hazards model and hazard ratios(HR) and 95% CI were calculated.
Results:
Among 437 non-Hispanic White controls and 100 non-Hispanic White FL patients, we confirmed a 2.6-fold increased risk of FL associated with the highest PRS tertile (95% CI:1.35-4.86). After accounting for clinical indicators, the PRS was associated with improved overall survival in non-Hispanic women (HR:0.31; 95% CI:0.10-0.96).
Conclusion:
PRS was associated with increased risk of FL, but improved overall survival.
Keywords: Non-Hodgkin lymphoma, epidemiology, genetics, survival
1. Introduction
Follicular lymphoma (FL) is the second most common type of non-Hodgkin lymphoma (NHL)[1]. Prognostic indictors for FL include the International Prognostic Index (FLIPI) and various genetic changes (e.g., TP53 mutation) found in FL which aims to identify high-risk patients with poor survival [2-4]. However, heterogeneity in FL patient outcomes after diagnosis still remains [2, 4-8]. Several large genome wide association studies (GWAS) have now been conducted among Caucasian populations and have identified and validated several susceptibility loci associated with FL risk [9, 10]. Here, we evaluate the potential association between these FL risk loci and outcomes in a population of FL cases diagnosed in Los Angeles County.
2. Material and Methods
2.1. Study Population
Our study population comprised FL cases that consented to participate in a population-based case-control study of women diagnosed with primary B-cell non-Hodgkin lymphoma (NHL) in Los Angeles County. Study methods have previously been published [11]. Briefly, cases were identified through rapid case ascertainment (RCA) by the Los Angeles County cancer registry (Cancer Surveillance Program; CSP) between May 1, 2004 and March 31, 2008. CSP staff review pathology reports on a monthly basis to identify patients eligible for research studies. Additional details of the RCA process are available from the CSP [12]. Eligibility criteria included cases that were English-speaking residents of Los Angeles County between the ages of 20 and 79 years old and not previously diagnosed with Human immunodeficiency virus (HIV), NHL, Hodgkin’s disease, multiple myeloma, or leukemia. Of the 1006 NHL cases enrolled in the parent study, 230 were identified as having FL from the CSP (International Classification of Diseases for Oncology 3rd edition codes 9690, 9691, 9695, 9698). For evaluation of survival outcomes, cases were followed for clinical endpoints through medical record abstractions and data linkages, as described below, through December 31, 2016. As previously described, 1038 controls matched on age, sex, and race/ethnicity were enrolled in the study [11]. The 5-year curve of women in the study (85%) was similar to that of the eligible SEER population in Los Angeles from which the cases were drawn (82%).
2.2. Genetic Analyses
A majority of participants, 93% (n=936) of cases and 93% (n=969) of controls, provided samples using buccal swabs. Of the 230 FL cases, 93% (n=213) provided buccal swabs. DNA was extracted and whole genome amplification was conducted using Repli-G Mini kits (Qiagen, Hilden, Germany). Genotyping was conducted using Fluidigm Dynamic arrays (Fluidigm, San Francisco, California).. Duplicate samples (n=62) were included as genotype quality controls to remove assays of low quality. The final genetic dataset for this analysis included 148 FL cases and 679 controls. FL cases with genetic data were less likely to be smokers and more likely to receive radiotherapy, but otherwise did not differ significantly from cases without genotyping data (Supplemental Table 3).
2.3. Covariates
In addition to retrieving 209 patient medical records, cases were linked to two administrative databases: (i) the California Office of Statewide Health Planning and Development (OSHPD) inpatient hospitalization discharge records (n=221), and (ii) the SEER-Medicare database (n=77 of 84 aged 65 or older at diagnosis). Additional information has been published on our validation efforts linking these databases (Zhong C et al, in press). Although records for chemotherapy and stage/number of nodal sites were sufficient (89% complete), we found that insufficient availability in medical record abstraction for lactic acid dehydrogenase (LDH) and hemoglobin (Hgb) laboratory values prohibited us from recreating the full FL-IPI (n=79 with both values). Based on these sources of clinical data, we therefore approximated the FL-IPI, which included age (>60), stage (III-IV), extranodal status, and Eastern Cooperative Oncology Group (ECOG) performance score (3-4). Other clinical variables collected and evaluated included treatment (yes/no) administered, including chemotherapy, surgery, and radiotherapy.
2.4. Survival outcomes
Overall survival (OS) and event-free survival (EFS) at 12 and 24 months were ascertained based on clinical data abstracted from multiple sources. Data on deaths, disease progression, and relapse/refractory disease were abstracted from medical records. Deaths were also obtained from the CSP, which routinely links to state and national death indices. OS was defined as the time from date of initial diagnosis to the date of death or last known follow-up. Event-free survival at 12 months (EFS12) and 24 months (EFS24) was defined as time from the date of initial diagnosis to 12 or 24 months free of disease progression, relapse/refractory disease, or death.
2.5. Statistical Methods
The polygenic risk score (PRS) was created by taking a weighted average of the log odds of previously published, significant SNPs identified in the National Human Genome Research Institute-European Bioinformatics Institute genome wide association study catalog [9]. The PRS consisted of the following SNPs: rs12195582, rs13254990, rs17749561, rs4245081 (tag SNP for reported rs4937362), and rs4938573 [9]. A weighted average of the reported log odds ratios was computed from these five SNPS and tertiles were created based on PRS values in the controls. Risk associations were estimated through logistic regression, calculating the odds ratios (OR) and 95% confidence intervals (95% CI) between PRS with FL risk using an additive model. We stratified our results by race/ethnicity. Multivariate analyses were conducted, adjusting for demographic factors and known FL risk factors, including smoking status, BMI, and family history of hematologic malignancies; which were collected by questionnaire at study enrollment. Associations with FL survival outcomes were estimated under a Cox proportional hazards model by calculating the hazards ratios (HR) and 95% CI for the three FL survival outcomes (OS, EFS12, and EFS24). In the Cox model, the timescale was defined in days from date of diagnosis until the date of the outcome of interest. Multivariate analyses were conducted, adjusting for clinical factors with the alternative FL-IPI. All analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC).
3. Results
3.1. FL risk associations
Genetic allele frequencies for the five SNPs included in the PRS are consistent with those previously reported [9], and are available in Supplemental Table 1. Two individual SNPs were significantly associated with increased FL risk in the overall sample: rs4938573 (per allele OR 1.52; 95% CI 1.13-2.05) and rs12195582 (per allele OR 1.65; 95% CI 1.27-2.14) and for the subset of non-Hispanic whites (rs4938573 OR 1.71; 95% CI 1.19-2.45: rs12195582 OR 1.74; 95% CI 1.26-2.41). In the group of non-Hispanic White women, we observed an overall increased FL risk associated with the third tertile of the FL PRS (OR 2.42, 95% CI 1.47-4.00) compared to the first tertile (Table 1). Sample sizes for other race/ethnic groups were limited but the direction of association appeared consistent in Hispanic women.
Table 1.
Risk (OR: odds ratio and 95% CI: 95% confidence interval) for follicular lymphoma among non-Hispanic and Hispanic White women in the Los Angeles County non-Hodgkin Lymphoma case control study with genotyping data(RAF: risk allele frequency)
| Controls (n=679) | Overall (n=148) | non-Hispanic White (n=100) | Hispanic White (n=30) | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | RAF | n | RAF | OR | 95% CI | n | RAF | OR | 95% CI | n | RAF | OR | 95% CI | |||
| Risk Association of Individual Alleles | rs4938573 | TT | 452 | 79 | 1.0 (ref) | 50 | 1.0 (ref) | 17 | 1.0 (ref) | |||||||
| CT | 200 | 59 | 1.69 | (1.16-2.46) | 42 | 1.76 | (1.12-2.79) | 13 | 2.69 | (1.15-6.28) | ||||||
| CC | 25 | 0.18 | 8 | 1.83 | (0.80-4.20) | 7 | 2.74 | (1.07-7.07) | 0 | - | ||||||
| per allele | 0.18 | 1.52 | (1.13-2.05) | 0.19 | 1.71 | (1.19-2.45) | 0.15 | 1.55 | (0.77-3.11) | |||||||
| rs4245081* | CC | 166 | 39 | 1.0 (ref) | 21 | 1.0 (ref) | 12 | 1.0 (ref) | ||||||||
| CT | 347 | 71 | 0.87 | (0.57-1.34) | 50 | 1.03 | (0.59-1.80) | 12 | 0.42 | (0.17-1.04) | ||||||
| TT | 142 | 0.48 | 28 | 0.84 | (0.49-1.43) | 24 | 0.97 | (0.51-1.85) | 4 | 0.45 | (0.13-1.60) | |||||
| per allele | 0.48 | 0.91 | (0.70-1.19) | 0.48 | 0.98 | (0.72-1.36) | 0.47 | 0.59 | (0.31-1.13) | |||||||
| rs17749561 | AA | 2 | 0 | 0 | 0 | |||||||||||
| AG | 86 | 14 | 1.0 (ref) | 13 | 1.0 (ref) | 1 | 1.0 (ref) | |||||||||
| GG | 590 | 0.92 | 133 | 1.39 | (0.76-2.51) | 86 | 1.23 | (0.65-2.32) | 29 | 4.22 | (0.56-33.3) | |||||
| per allele | 0.93 | 1.43 | (0.80-2.57) | 0.92 | 1.29 | (0.69-2.39) | 0.94 | 4.22 | (0.56-33.3) | |||||||
| rs13254990 | CC | 346 | 75 | 1.0 (ref) | 44 | 1.0 (ref) | 17 | 1.0 (ref) | ||||||||
| CT | 265 | 51 | 0.89 | (0.60-1.31) | 39 | 0.9 | (0.56-1.45) | 9 | 1.1 | (0.45-2.72) | ||||||
| TT | 54 | 0.32 | 20 | 1.71 | (0.97-3.02) | 15 | 1.6 | (0.82-3.14) | 4 | 3.44 | (0.83-14.2) | |||||
| per allele | 0.28 | 1.15 | (0.88-1.51) | 0.32 | 1.15 | (0.83-1.59) | 0.20 | 1.53 | (0.82-2.86) | |||||||
| rs12195582 | CC | 221 | 34 | 1.0 (ref) | 20 | 1.0 (ref) | 7 | 1.0 (ref) | ||||||||
| CT | 342 | 65 | 1.24 | (0.79-1.93) | 43 | 1.19 | (0.67-2.11) | 15 | 1.52 | (0.57-4.08) | ||||||
| TT | 109 | 0.44 | 45 | 2.68 | (1.63-4.43) | 34 | 2.86 | (1.54-5.30) | 8 | 2.03 | (0.65-6.34) | |||||
| per allele | 0.42 | 1.65 | (1.27-2.14) | 0.44 | 1.74 | (1.26-2.41) | 0.43 | 1.42 | (0.81-2.51) | |||||||
| Polygenic risk score | Tertile 1 | 198 | 25 | 1.0 (ref) | 14 | 1.0 (ref) | 6 | 1.0 (ref) | ||||||||
| Tertile 2 | 226 | 42 | 1.47 | (0.87-2.50) | 25 | 1.3 | (0.65-2.61) | 12 | 2.35 | (0.80-6.94) | ||||||
| Tertile 3 | 209 | 64 | 2.42 | (1.47-4.00) | 50 | 2.56 | (1.35-4.86) | 10 | 2.15 | (0.70-6.56) | ||||||
| p-trend | 0.001 | p-trend | 0.004 | p-trend | 0.26 | |||||||||||
tagSNP for previous reported rs4937362, Skibola et al 2014
3.2. FL survival associations
The median follow-up time among FL cases was 8.5 years (interquartile range: 7.1-10.1 years). During follow-up, 50 (22%) FL cases died, 198 (86%) achieved EFS12, and 186 (81%) achieved EFS24. The most common treatment was chemotherapy (n=124), followed by surgery (n=63) and radiotherapy (n=32). Most (102/111 with regimen information, Supplemental Table 3) of the cases that received chemotherapy received a rituximab based regimen. Surgical treatment was significantly associated with worse overall survival (HR 2.06, 95% CI 1.20-3.55). In non-Hispanic White women, the approximated FL-IPI was significantly associated with OS (HR 3.55, 95% CI 1.00-12.58, Table 2), but not significantly with EFS12 (HR 3.34, 95% CI 0.37-30.1) or EFS24 (HR 3.06, 95% CI 0.63-14.8, Supplemental Table 2). The PRS was also associated with improved overall survival in multivariate analyses for non-Hispanic White women, after adjustment for the alternative FL-IPI (HR 0.31, 95% CI 0.10-0.96). Evaluation of FL survival endpoints by individual SNPs suggested a strong contribution from rs12195582 (HR 0.62, 95% CI 0.37-1.04).
Table 2.
Overall survival estimates (HR: hazard ratio and 95% CI: 95% confidence interval) for follicular lymphoma among non-Hispanic and Hispanic White women in the Los Angeles County non-Hodgkin Lymphoma case control study
| Overall (n=230) | non-Hispanic White (n=157) | Hispanic White (n=43) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Univariate | n | HR | 95% CI | n | HR | 95% CI | n | HR | 95% CI | |
| Association of Individual Alleles | rs4938573 | 1.38 | (0.81-2.34) | 1.42 | (0.79-2.53) | 0.79 | (0.13-4.78) | |||
| rs4245081* | 0.98 | (0.60-1.59) | 1.03 | (0.59-1.82) | 0.44 | (0.10-1.94) | ||||
| rs17749561 | 0.57 | (0.22-1.48) | 0.59 | (0.22-1.54) | NA | |||||
| rs13254990 | 0.74 | (0.45-1.20) | 0.83 | (0.49-1.40) | 0.27 | (0.40-1.91) | ||||
| rs12195582 | 0.68 | (0.43-1.07) | 0.62 | (0.37-1.04) | 1.16 | (0.34-4.04) | ||||
| Polygenic risk score (HR, 95% CI) | Tertile 1 | 25 | ref | 14 | ref | 6 | ref | |||
| Tertile 2 | 42 | 0.59 | (0.26-1.36) | 25 | 0.56 | (0.21-1.49) | 12 | 1.4 | (0.13-15.8) | |
| Tertile 3 | 64 | 0.37 | (0.16-0.86) | 50 | 0.29 | (0.11-0.74) | 10 | 1.58 | (0.14-17.5) | |
| p-trend | 0.07 | p-trend | 0.04 | p-trend | 0.93 | |||||
| International Prognostic Index (approximated) | Low | 28 | ref | 17 | ref | 8 | ref | |||
| Med | 132 | 1.32 | (0.46-3.78) | 92 | 0.95 | (0.27-3.27) | 24 | 1.84 | (0.22-15.36) | |
| High | 26 | 4.00 | (1.32-12.16) | 22 | 3.55 | (1.00-12.58) | 2 | 2.81 | (0.17-45.72) | |
| Treatment | ||||||||||
| Surgery | No | 167 | ref | 112 | ref | 35 | ref | |||
| Yes | 63 | 2.06 | (1.20-3.55) | 45 | 2.14 | (1.13-4.06) | 8 | 4.16 | (1.04-16.70) | |
| Chemotherapy | No | 106 | ref | 71 | ref | 21 | ref | |||
| Yes | 124 | 1.01 | (0.59-1.73) | 86 | 1 | (0.53-1.90) | 22 | 0.54 | (0.13-2.25) | |
| Radiotherapy | No | 198 | ref | 135 | ref | 35 | ref | |||
| Yes | 32 | 1.28 | (0.63-2.62) | 22 | 1.21 | (0.51-2.89) | 8 | 2.54 | (0.60-10.60) | |
| Multivariate** | ||||||||||
| Polygenic risk score (HR, 95% CI) | Tertile 1 | 20 | ref | 13 | ref | 4 | ref | |||
| Tertile 2 | 35 | 0.65 | (0.24-1.74) | 22 | 0.76 | (0.23-2.57) | 9 | NA | ||
| Tertile 3 | 54 | 0.32 | (0.13-0.82) | 42 | 0.31 | (0.10-0.96) | 9 | NA | ||
| p-trend | 0.06 | p-trend | 0.11 | p-trend | NA | |||||
| *tagSNP for previous reported rs4937362, Skibola et al 2014 | ||||||||||
adjusted for IPI and treatment
Approximated IPI consists of : age (>60), stage (III-IV), extranodal status, and Eastern Cooperative Oncology Group (ECOG) performance score (3-4)
In our limited sample size of Hispanic White women, we also observed an increased FL risk for surgical treatment (HR 4.16, 95% CI 1.04-16.7), but no association between PRS and OS (HR 1.58, 95% CI 0.14-17.5).
4. Discussion
In this evaluation of a FL PRS, we confirmed its association with FL risk among a non-Hispanic Caucasian population. Sample sizes were limited yielding insufficient statistical power among Hispanic and other race/ethnicities, but the direction of risk was consistent in Hispanics. We show for the first time that this FL PRS may also be associated with favorable FL outcomes, specifically OS, even after adjustment for key clinical and demographic factors. Specific SNPs appeared to have more prominent roles in this association, including rs12195582 which is in the HLA-DR region. This is potentially consistent with previous reports that have suggested a role for HLA-DRB1*13 in FL prognosis [13]. Foo and colleagues further reported evidence of specific amino acid and peptide binding for HLA-DRB1*13, though in relation to follicular lymphoma risk. [14]. As HLA-DRB1*13 has also been implicated in response to chemotherapy for breast cancer [15], further queries regarding the binding site and chemotherapy in relation to follicular lymphoma may be warranted. Future investigations in larger populations and other race/ethnic groups are also needed to replicate and fully understand these observations.
Our study strengths included the abstraction of key clinical data using three different sources, including medical records and linkages to inpatient hospitalization database and the SEER-Medicare database. Study limitations include the original study population which included only women and the subset of who agreed to genetic research and for whose samples provided high quality data. Our study was limited to a handful of a priori SNPs of high allele frequency, which may not entirely encompass the genetic complexity of FL [16]. Due to incomplete clinical laboratory data, we also were not able to re-create the FL-IPI in full; however, our approximated FL-IPI was associated with the prognostic outcomes. Finally, we acknowledge the possibility that our results for improved survival among “risk alleles” may reflect survival bias whereby patients with better prognosis typically enroll into population-based studies. However, comparison of our study FL patients to that of the broader SEER registry did not yield significant differences in survival or characteristics, and comparison of our study of FL patients who had genetic data compared to those without did not yield significant differences in their survival or clinical attributes.
In summary, we observed increased risk of FL with higher FL PRS, but favorable OS among FL cases with higher FL PRS. Further studies are warranted to replicate our results and to further our understanding of the biological mechanisms underlying the potential interactions between these loci for FL risk and survival.
Supplementary Material
Highlights.
Polygenic risk score (PRS) created from identified follicular lymphoma (FL) risk loci
Confirmed risk association between PRS and FL risk among non-Hispanic White women
Higher PRS was associated with better overall survival among FL patients
Acknoledgements
The collection of cancer incidence data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention’s (CDC) National Program of Cancer Registries, under cooperative agreement 5NU58DP003862-04/DP003862; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute. The ideas and opinions expressed herein are those of the author(s) and endorsement by the State of California, Department of Public Health, the National Cancer Institute, and the CDC or their Contractors and Subcontractors is not intended nor should be inferred.
Funding Sources
This work was supported by the National Cancer Institute under grants, R01 CA108634, R01 CA166219, P01 CA017054, and P30 CA033572.
Abbreviations
- NHL
non-Hodgkin lymphoma
- FL
follicular lymphoma
- FLIPI
follicular lymphoma International Prognostic Index
Footnotes
Declaration of Interests
The authors declare that they have no conflict of interests.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
References
- [1].Morton LM, Wang SS, Devesa SS, Hartge P, Weisenburger DD, Linet MS, Lymphoma incidence patterns by WHO subtype in the United States, 1992-2001, Blood 107(1) (2006) 265–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].O'Shea D, O'Riain C, Taylor C, Waters R, Carlotti E, MacDougall F, Gribben J, Rosenwald A, Ott G, Rimsza LM, Smeland EB, Johnson N, Campo E, Greiner TC, Chan WC, Gascoyne RD, Wright G, Staudt LM, Lister TA, Fitzgibbon J, The presence of TP53 mutation at diagnosis of follicular lymphoma identifies a high-risk group of patients with shortened time to disease progression and poorer overall survival, Blood 112(8) (2008) 3126–3129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Qu X, Li H, Braziel RM, Passerini V, Rimsza LM, Hsi ED, Leonard JP, Smith SM, Kridel R, Press O, Weigert O, LeBlanc M, Friedberg JW, Fang M, Genomic alterations important for the prognosis in patients with follicular lymphoma treated in SWOG study S0016, Blood 133(1) (2019) 81–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Pastore A, Jurinovic V, Kridel R, Hoster E, Staiger AM, Szczepanowski M, Pott C, Kopp N, Murakami M, Horn H, Leich E, Moccia AA, Mottok A, Sunkavalli A, Van Hummelen P, Ducar M, Ennishi D, Shulha HP, Hother C, Connors JM, Sehn LH, Dreyling M, Neuberg D, Möller P, Feller AC, Hansmann ML, Stein H, Rosenwald A, Ott G, Klapper W, Unterhalt M, Hiddemann W, Gascoyne RD, Weinstock DM, Weigert O, Integration of gene mutations in risk prognostication for patients receiving first-line immunochemotherapy for follicular lymphoma: a retrospective analysis of a prospective clinical trial and validation in a population-based registry, The Lancet Oncology 16(9) (2015) 1111–1122. [DOI] [PubMed] [Google Scholar]
- [5].Wight JC, Chong G, Grigg AP, Hawkes EA, Prognostication of diffuse large B-cell lymphoma in the molecular era: moving beyond the IPI, Blood Reviews 32(5) (2018) 400–415. [DOI] [PubMed] [Google Scholar]
- [6].Solal-Celigny P, Roy P, Colombat P, White J, Armitage JO, Arranz-Saez R, Au WY, Bellei M, Brice P, Caballero D, Coiffier B, Conde Garcia E, Doyen C, Federico M, Fisher RI, Garcia-Conde JF, Guglielmi C, Hagenbeek A, Haioun C, LeBlanc M, Lister AT, Lopez-Guillermo A, Mc Laughlin P, Milpied N, Morel P, Mounier N, Proctor SJ, Rohatiner A, Smith P, Soubeyran P, Tilly H, Vitolo U, Zinzani P-L, Zucca E, Montserrat E, Follicular lymphoma international prognostic index, Blood 104(5) (2004) 1258–65. [DOI] [PubMed] [Google Scholar]
- [7].Pezzella F, Jones M, Ralfkiaer E, Ersbøll J, Gatter KC, Mason DY, Evaluation of bcl-2 protein expression and 14; 18 translocation as prognostic markers in follicular lymphoma, British Journal Of Cancer 65 (1992)87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Huet S, Szafer-Glusman E, Tesson B, Xerri L, Fairbrother WJ, Mukhyala K, Bolen C, Punnoose E, Tonon L, Chassagne-Clément C, Feugier P, Viari A, Jardin F, Salles G, Sujobert P, BCL2 mutations do not confer adverse prognosis in follicular lymphoma patients treated with rituximab, American Journal of Hematology 92(6) (2017) 515–519. [DOI] [PubMed] [Google Scholar]
- [9].Skibola Christine F., Berndt Sonja I., Vijai J, Conde L, Wang Z, Yeager M, de Bakker Paul I.W., Birmann Brenda M., Vajdic Claire M., Foo J-N, Bracci Paige M., Vermeulen Roel C.H., Slager Susan L., de Sanjose S, Wang Sophia S., Linet Martha S., Salles G, Lan Q, Severi G, Hjalgrim H, Lightfoot T, Melbye M, Gu J, Ghesquières H, Link Brian K., Morton Lindsay M., Holly Elizabeth A., Smith A, Tinker Lesley F., Teras Lauren R., Kricker A, Becker N, Purdue Mark P., Spinelli John J., Zhang Y, Giles Graham G., Vineis P, Monnereau A, Bertrand Kimberly A., Albanes D, Zeleniuch-Jacquotte A, Gabbas A, Chung Charles C., Burdett L, Hutchinson A, Lawrence C, Montalvan R, Liang L, Huang J, Ma B, Liu J, Adami H-O, Glimelius B, Ye Y, Nowakowski Grzegorz S., Dogan A, Thompson Carrie A., Habermann Thomas M., Novak Anne J., Liebow M, Witzig Thomas E., Weiner George J., Schenk M, Hartge P, De Roos Anneclaire J., Cozen W, Zhi D, Akers Nicholas K., Riby J, Smith Martyn T., Lacher M, Villano Danylo J., Maria A, Roman E, Kane E, Jackson Rebecca D., North Kari E., Diver WR, Turner J, Armstrong Bruce K., Benavente Y, Boffetta P, Brennan P, Foretova L, Maynadie M, Staines A, McKay J, Brooks-Wilson Angela R., Zheng T, Holford Theodore R., Chamosa S, Kaaks R, Kelly Rachel S., Ohlsson B, Travis Ruth C., Weiderpass E, Clavel J, Giovannucci E, Kraft P, Virtamo J, Mazza P, Cocco P, Ennas Maria G., Chiu Brian C.H., Fraumeni Joseph F. Jr, Nieters A, Offit K, Wu X, Cerhan James R., Smedby Karin E., Chanock Stephen J., Rothman N, Genome-wide Association Study Identifies Five Susceptibility Loci for Follicular Lymphoma outside the HLA Region, The American Journal of Human Genetics 95(4) (2014) 462–471. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Skibola CF, Conde L, Foo JN, Riby J, Humphreys K, Sille FC, Darabi H, Sanchez S, Hjalgrim H, Liu J, Bracci PM, Smedby KE, A meta-analysis of genome-wide association studies of follicular lymphoma, BMC Genomics 13 (2012) 516. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Wang SS, Luo J, Cozen W, Lu Y, Halley-Sullivan J, Voutsinas J, Zhong C, Song J, Lacey JV Jr., Weisenburger D, Bernstein L, Sun sensitivity, indoor tanning and B-cell non-Hodgkin lymphoma risk among Caucasian women in Los Angeles County, Br J Haematol 177(1) (2017) 153–156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].L.A.C.C.S. Program, Procedure Guide, 2009.
- [13].Lu Y, Abdou AM, Cerhan JR, Morton LM, Severson RK, Davis S, Cozen W, Rothman N, Bernstein L, Chanock S, Hartge P, Wang SS, Human leukocyte antigen class I and II alleles and overall survival in diffuse large B-cell lymphoma and follicular lymphoma, ScientificWorldJournal 11 (2011) 2062–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Foo Jia N., Smedby Karin E., Akers Nicholas K., Berglund M, Irwan Ishak D., Jia X, Li Y, Conde L, Darabi H, Bracci Paige M., Melbye M, Adami H-O, Glimelius B, Khor Chiea C., Hjalgrim H, Padyukov L, Humphreys K, Enblad G, Skibola Christine F., de Bakker Paul I.W., Liu J, Coding Variants at Hexa-allelic Amino Acid 13 of HLA-DRB1 Explain Independent SNP Associations with Follicular Lymphoma Risk, The American Journal of Human Genetics 93(1) (2013) 167–172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Saraiva DP, Jacinto A, Borralho P, Braga S, Cabral MG, HLA-DR in Cytotoxic T Lymphocytes Predicts Breast Cancer Patients' Response to Neoadjuvant Chemotherapy, Frontiers in Immunology 9(2605) (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Devan J, Janikova A, Mraz M, New concepts in follicular lymphoma biology: From BCL2 to epigenetic regulators and non-coding RNAs, Seminars in Oncology 45(5) (2018) 291–302. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.