James R Cerhan

Mayo Clinic - Rochester, Рочестер, Minnesota, United States

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Publications (485)3519.04 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Deficits in health-related quality of life (HRQOL) may be associated with worse patient experiences, outcomes and even survival. While there exists evidence to identify risk factors associated with deficits in HRQOL among patients with individual medical conditions such as cancer, it is less well established in more general populations without attention to specific illnesses. This study used patients with a wide range of medical conditions to identify contributors with the greatest influence on HRQOL deficits. Self-perceived general health and depressive symptoms were assessed using data from 21,736 Mayo Clinic Biobank (MCB) participants. Each domain was dichotomized into categories related to poor health: deficit (poor/fair for general health and ≥3 for PHQ-2 depressive symptoms) or non-deficit. Logistic regression models were used to test the association of commonly collected demographic characteristics and disease burden with each HRQOL domain, adjusting for age and gender. Gradient boosting machine (GBM) models were applied to quantify the relative influence of contributors on each HRQOL domain. The prevalence of participants with a deficit was 9.5 % for perception of general health and 4.6 % for depressive symptoms. For both groups, disease burden had the strongest influence for deficit in HRQOL (63 % for general health and 42 % for depressive symptoms). For depressive symptoms, age was equally influential. The prevalence of a deficit in general health increased slightly with age for males, but remained stable across age for females. Deficit in depressive symptoms was inversely associated with age. For both HRQOL domains, risk of a deficit was associated with higher disease burden, lower levels of education, no alcohol consumption, smoking, and obesity. Subjects with deficits were less likely to report that they were currently working for pay than those without a deficit; this association was stronger among males than females. Comorbid health burden has the strongest influence on deficits in self-perceived general health, while demographic factors show relatively minimal impact. For depressive symptoms, both age and comorbid health burden were equally important, with decreasing deficits in depressive symptoms with increasing age. For interpreting patient-reported metrics and comparison, one must account for comorbid health burden.
    Health and Quality of Life Outcomes 12/2015; 13(1):95. DOI:10.1186/s12955-015-0285-6 · 2.12 Impact Factor
  • James R Cerhan · Susan L Slager
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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.
    Blood 09/2015; DOI:10.1182/blood-2015-04-537498 · 10.45 Impact Factor
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    ABSTRACT: Background: Follicular lymphoma (FL) is the most common indolent non-Hodgkin lymphoma, with median age at diagnosis in the seventh decade. FL in young adults (YA), defined as diagnosis at≤40 years, is uncommon. No standard approaches exist guiding treatment of YA FL, and little is known about their disease characteristics and outcomes. To gain further insight into YA FL, we analyzed the National LymphoCare Study (NLCS) to describe characteristics, initial treatments, and outcomes in this population versus patients aged>40 years. Patients and methods: Using the NLCS database, we stratified FL patients by age: 18-40 (YA), 41-60, 61-70, 71-80, and>80 years. Survival probability was estimated using Kaplan-Meier methodology. We examined associations between age and survival using hazard ratios and 95% confidence intervals (CI) from multivariable Cox models. Results: Of 2652 eligible FL patients in the NLCS, 164 (6%) were YA. Of YA patients, 69% had advanced disease, 80% had low-grade histology, 50% had good-risk disease according to the Follicular Lymphoma International Prognostic Index (FLIPI). Nineteen percent underwent observation, 12% received rituximab monotherapy, 46% received chemo-immunotherapy (in 59% of these: R-CHOP [rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone]). With median follow-up of 8 years, overall survival (OS) at 2, 5, and 8 years was 98% (95% CI 93-99), 94% (95% CI 89-97), and 90% (95% CI 83-94), respectively. Median progression-free survival (PFS) was 7.3 years (95% CI 5.6-not reached). Conclusions: In one of the largest cohorts of YA FL patients treated in the rituximab era, disease characteristics and outcomes were similar to patients aged 41-60 years; with favorable OS and PFS in YA. Longer-term outcomes and YA-specific survivorship concerns should be considered when defining management. These data may not support the need for more aggressive therapies in YA FL.
    Annals of Oncology 09/2015; DOI:10.1093/annonc/mdv375 · 7.04 Impact Factor
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    ABSTRACT: Hospital risk stratification models using electronic health records (EHRs) often use age and comorbid health burden. Our primary aim was to determine if quality of life or health behaviors captured in an EHR-linked biobank can predict future risk of hospitalization. Participants in the Mayo Clinic Biobank completed self-administered questionnaires at enrollment that included quality of life and health behaviors. Participants enrolled as of December 31, 2010 were followed for one year to ascertain hospitalization. Data on comorbidities and hospitalization were derived from the Mayo Clinic EHR. Hazard ratios (HR) and 95% confidence interval (CI) were used, adjusted for age and sex. We used gradient boosting machines models to integrate multiple factors. Different models were compared using C-statistic. Of the 8,927 eligible Mayo Clinic Biobank participants, 834 (9.3%) were hospitalized. Self-perceived health status and alcohol use had the strongest associations with risk of hospitalization. Compared to participants with excellent self-perceived health, those reporting poor/fair health had higher risk of hospitalization (HR =3.66, 95% CI 2.74-4.88). Alcohol use was inversely associated with hospitalization (HR =0.57 95% CI 0.45-0.72). The gradient boosting machines model estimated self-perceived health as the most influential factor (relative influence =16%). The predictive ability of the model based on comorbidities was slightly higher than the one based on the self-perceived health (C-statistic =0.67 vs 0.65). This study demonstrates that self-perceived health may be an important piece of information to add to the EHR. It may be another method to determine hospitalization risk.
    International Journal of General Medicine 08/2015; 8:247-54. DOI:10.2147/IJGM.S85473
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    ABSTRACT: Lack of remission or early relapse remains a major clinical issue in diffuse large B-cell lymphoma (DLBCL), with 30% of patients failing standard of care. Although clinical factors and molecular signatures can partially predict DLBCL outcome, additional information is needed to identify high-risk patients, particularly biologic factors that might ultimately be amenable to intervention. Using whole-exome sequencing data from 51 newly diagnosed and immunochemotherapy-treated DLBCL patients, we evaluated the association of somatic genomic alterations with patient outcome, defined as failure to achieve event-free survival at 24 months after diagnosis (EFS24). We identified 16 genes with mutations, 374 with copy number gains and 151 with copy number losses that were associated with failure to achieve EFS24 (P<0.05). Except for FOXO1 and CIITA, known driver mutations did not correlate with EFS24. Gene losses were localized to 6q21-6q24.2, and gains to 3q13.12-3q29, 11q23.1-11q23.3 and 19q13.12-19q13.43. Globally, the number of gains was highly associated with poor outcome (P=7.4 × 10(-12)) and when combined with FOXO1 mutations identified 77% of cases that failed to achieve EFS24. One gene (SLC22A16) at 6q21, a doxorubicin transporter, was lost in 54% of EFS24 failures and our findings suggest it functions as a doxorubicin transporter in DLBCL cells.
    Blood Cancer Journal 08/2015; 5(8):e346. DOI:10.1038/bcj.2015.69 · 3.47 Impact Factor
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    ABSTRACT: It is unknown whether individuals with monoclonal B-cell lymphocytosis (MBL) are at risk for adverse outcomes associated with chronic lymphocytic leukemia (CLL), such as the risk of non-hematologic cancer. We identified all locally-residing individuals diagnosed with high count MBL at Mayo Clinic between 1999 and 2009 and compared their rates of non-hematologic cancer to that of patients with CLL and two control cohorts: general medicine patients and patients who underwent clinical evaluation with flow cytometry but who had no hematologic malignancy. After excluding individuals with prior cancers, there were 107 high count MBL cases, 132 CLL cases, 589 clinic controls, and 482 flow cytometry controls. With 4.6 years median follow-up, 14 (13%) individuals with high count MBL, 21 (4%) clinic controls (comparison MBL P<0.0001), 18 (4%) flow controls (comparison MBL P=0.0001), and 16 (12%) CLL patients (comparison MBL P=0.82) developed non-hematologic cancer. On multivariable Cox regression analysis, individuals with high count MBL had higher risk of non-hematologic cancer than flow controls (HR=2.36; P=0.04) and borderline higher risk than clinic controls (HR=2.00; P=0.07). Patients with high count MBL appear to be at increased risk for non-hematologic cancer, further reinforcing that high count MBL has a distinct clinical phenotype despite low risk of progression to CLL.Leukemia accepted article preview online, 27 August 2015. doi:10.1038/leu.2015.235.
    Leukemia: official journal of the Leukemia Society of America, Leukemia Research Fund, U.K 08/2015; DOI:10.1038/leu.2015.235 · 10.43 Impact Factor
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    ABSTRACT: Whole exome sequencing (WES) is increasingly being used for diagnosis without adequate information on predictive characteristics of reportable variants typically found on any given individual and correlation with clinical phenotype. In this study, we performed WES on 89 deceased individuals (mean age at death 74 years, range 28-93) from the Mayo Clinic Biobank. Significant clinical diagnoses were abstracted from electronic medical record via chart review. Variants [Single Nucleotide Variant (SNV) and insertion/deletion] were filtered based on quality (accuracy >99%, read-depth >20, alternate-allele read-depth >5, minor-allele-frequency <0.1) and available HGMD/OMIM phenotype information. Variants were defined as Tier-1 (nonsense, splice or frame-shifting) and Tier-2 (missense, predicted-damaging) and evaluated in 56 ACMG-reportable genes, 57 cancer-predisposition genes, along with examining overall genotype-phenotype correlations. Following variant filtering, 7046 total variants were identified (~79/person, 644 Tier-1, 6402 Tier-2), 161 among 56 ACMG-reportable genes (~1.8/person, 13 Tier-1, 148 Tier-2), and 115 among 57 cancer-predisposition genes (~1.3/person, 3 Tier-1, 112 Tier-2). The number of variants across 57 cancer-predisposition genes did not differentiate individuals with/without invasive cancer history (P > 0.19). Evaluating genotype-phenotype correlations across the exome, 202(3%) of 7046 filtered variants had some evidence for phenotypic correlation in medical records, while 3710(53%) variants had no phenotypic correlation. The phenotype associated with the remaining 44% could not be assessed from a typical medical record review. These data highlight significant continued challenges in the ability to extract medically meaningful predictive results from WES.
    Frontiers in Genetics 08/2015; 6:244. DOI:10.3389/fgene.2015.00244
  • Cancer Research 08/2015; 75(15 Supplement):LB-191-LB-191. DOI:10.1158/1538-7445.AM2015-LB-191 · 9.33 Impact Factor
  • Cancer Research 08/2015; 75(15 Supplement):2764-2764. DOI:10.1158/1538-7445.AM2015-2764 · 9.33 Impact Factor
  • Cancer Research 08/2015; 75(15 Supplement):5267-5267. DOI:10.1158/1538-7445.AM2015-5267 · 9.33 Impact Factor
  • Cancer Research 08/2015; 75(15 Supplement):877-877. DOI:10.1158/1538-7445.AM2015-877 · 9.33 Impact Factor
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    ABSTRACT: Tumor budding in colorectal carcinoma has been associated with poor outcome in multiple studies, but the absence of an established histologic cutoff for "high" tumor budding, heterogeneity in study populations, and varying methods for assessing tumor budding have hindered widespread incorporation of this parameter in clinical reports. We used an established scoring system in a population-based cohort to determine a histologic cutoff for "high" tumor budding and confirm its prognostic significance. We retrieved hematoxylin and eosin-stained sections from 553 incident colorectal carcinoma cases. Each case was previously characterized for select molecular alterations and survival data. Interobserver agreement was assessed between 2 gastrointestinal pathologists and a group of 4 general surgical pathologists. High budding (≥10 tumor buds in a ×20 objective field) was present in 32% of cases, low budding in 46%, and no budding in 22%. High tumor budding was associated with advanced pathologic stage (P<0.001), microsatellite stability (P=0.005), KRAS mutation (P=0.010), and on multivariate analysis with a >2 times risk of cancer-specific death (hazard ratio=2.57 [1.27, 5.19]). After multivariate adjustment, by penalized smoothing splines, we found increasing tumor bud counts from 5 upward to be associated with an increasingly shortened cancer-specific survival. By this method, a tumor bud count of 10 corresponded to approximately 2.5 times risk of cancer-specific death. The interobserver agreement was good with weighted κ of 0.70 for 2 gastrointestinal pathologists over 121 random cases and 0.72 between all 6 pathologists for 20 random cases. Using an established method to assess budding on routine histologic stains, we have shown that a cutoff of 10 for high tumor budding is independently associated with a significantly worse prognosis. The reproducibility data provide support for the routine widespread implementation of tumor budding in clinical reports.
    The American journal of surgical pathology 07/2015; 39(10). DOI:10.1097/PAS.0000000000000504 · 5.15 Impact Factor
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    ABSTRACT: Twenty percent of patients with follicular lymphoma (FL) experience progression of disease (POD) within 2 years of initial chemoimmunotherapy. We analyzed data from the National LymphoCare Study to identify whether prognostic FL factors are associated with early POD and whether patients with early POD are at high risk for death. In total, 588 patients with stage 2 to 4 FL received first-line rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Two groups were defined: patients with early POD 2 years or less after diagnosis and those without POD within 2 years, the reference group. An independent validation set, 147 patients with FL who received first-line R-CHOP, was analyzed for reproducibility. Of 588 patients, 19% (n = 110) had early POD, 71% (n = 420) were in the reference group, 8% (n = 46) were lost to follow-up, and 2% (n = 12) died without POD less than 2 years after diagnosis. Five-year overall survival was lower in the early-POD group than in the reference group (50% v 90%). This trend was maintained after we adjusted for FL International Prognostic Index (hazard ratio, 6.44; 95% CI, 4.33 to 9.58). Results were similar for the validation set (FL International Prognostic Index-adjusted hazard ratio, 19.8). In patients with FL who received first-line R-CHOP, POD within 2 years after diagnosis was associated with poor outcomes and should be further validated as a standard end point of chemoimmunotherapy trials of untreated FL. This high-risk FL population warrants further study in directed prospective clinical trials. © 2015 by American Society of Clinical Oncology.
    Journal of Clinical Oncology 06/2015; 33(23). DOI:10.1200/JCO.2014.59.7534 · 18.43 Impact Factor
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    ABSTRACT: The World Health Organization classification of non-Hodgkin lymphoma (NHL) was introduced in 2001. However, its incorporation into clinical practice is not well-described. We studied the distribution of NHL subtypes in adults diagnosed from 1998-2011, evaluated time trends, geo-demographic correlates, and changes in 5-year overall survival (OS). We obtained data prospectively collected by the National Cancer Data Base, which covers 70% of US cancer cases. There were 596,476 patients diagnosed with NHL. The major subtypes were diffuse large B-cell (32.5%), chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL; 18.6%), follicular (17.1%), marginal zone (8.3%), mantle cell (4.1%), peripheral T-cell not-otherwise-specified (1.7%), Burkitt (1.6%), hairy cell (1.1%), lymphoplasmacytic (1.1%), and NHL not-otherwise-specified (10.8%). Over the study period, the proportion of NHL not-otherwise-specified declined by half, while marginal zone lymphoma doubled. The distribution of major and rare NHL subtypes varied according to demographics but less so geographically or by type of treatment facility. We noted several novel findings among Hispanics (lower proportion of CLL/SLL, but higher Burkitt lymphoma and nasal NK/T-cell lymphoma), Asians (higher enteropathy-associated T-cell and angioimmunoblastic T-cell lymphomas), Blacks (higher hepatosplenic T-cell lymphoma), and Native Americans (similar proportions of CLL/SLL and nasal NK/T-cell lymphoma as Asians). With the exception of peripheral T-cell not-otherwise-specified and hairy cell leukemia, 5-year OS has improved for all the major NHL subtypes. This article is protected by copyright. All rights reserved. © 2015 Wiley Periodicals, Inc.
    American Journal of Hematology 06/2015; 90(9). DOI:10.1002/ajh.24086 · 3.80 Impact Factor
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    ABSTRACT: Non-Hodgkin lymphoma (NHL) is an enigmatic disease with few known risk factors. Spatio-temporal epidemiologic analyses have the potential to reveal patterns that may give clues to new risk factors worthy of investigation. We sought to investigate clusters of NHL through space and time based on life course residential histories. We used residential histories from a population-based NHL case-control study of 1300 cases and 1044 controls with recruitment centers in Iowa, Detroit, Seattle, and Los Angeles, and diagnosed in 1998-2000. Novel methods for cluster detection allowing for residential mobility, called Q-statistics, were used to quantify nearest neighbor relationships through space and time over the life course to identify cancer clusters. Analyses were performed on all cases together and on two subgroups of NHL: Diffuse large B-cell lymphoma and follicular lymphoma. These more homogenous subgroups of cases might have a more common etiology that could potentially be detected in cluster analysis. Based on simulation studies designed to help account for multiple testing across space and through time, we required at least four significant cases nearby one another to declare a region a potential cluster, along with confirmatory analyses using spatial-only scanning windows (SaTScan). Evidence of a small cluster in southeastern Oakland County, MI was suggested using residences 10-18 years prior to diagnosis, and confirmed by SaTScan in a time-slice analysis 20 years prior to diagnosis, when all cases were included in the analysis. Consistent evidence of clusters was not seen in the two histologic subgroups. Suggestive evidence of a small space-time cluster in southeastern Oakland County, MI was detected in this NHL case-control study in the USA.
    Environmental Health 06/2015; 14(1):48. DOI:10.1186/s12940-015-0034-7 · 3.37 Impact Factor
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    ABSTRACT: Excess adiposity has been associated with lymphomagenesis, possibly mediated by increased cytokine production causing a chronic inflammatory state. The relationship between obesity, cytokine polymorphisms and selected mature B-cell neoplasms is reported. Data on 4979 cases and 4752 controls from nine American/European studies from the InterLymph consortium (1988-2008) were pooled. For diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL) and chronic lymphocytic leukaemia/small lymphocytic lymphoma (CLL/SLL), joint associations of body mass index (from self-reported height and weight) and 12 polymorphisms in cytokines IL1A (rs1800587), IL1B (rs16944, rs1143627), IL1RN (rs454078), IL2 (rs2069762), IL6 (rs1800795, rs1800797), IL10 (rs1800890, rs1800896), TNF (rs1800629), LTA (rs909253), and CARD15 (rs2066847) were investigated using unconditional logistic regression. BMI-polymorphism interaction effects were estimated using the relative excess risk due to interaction (RERI). Obesity (BMI≥30kg m-2) was associated with DLBCL risk (OR=1.33, 95%CI 1.02-1.73), as was TNF-308GA+AA (OR=1.24, 95%CI 1.07-1.44). Together, being obese and TNF-308GA+AA increased DLBCL risk almost two-fold relative to those of normal weight and TNF-308GG (OR=1.93 95%CI 1.27-2.94), with a RERI of 0.41 (95%CI -0.05,0.84, P(interaction)=0.13). For FL and CLL/SLL, no associations with obesity or TNF-308GA+AA, either singly or jointly, were observed. No evidence of interactions between obesity and the other polymorphisms were detected. Our results suggest that cytokine polymorphisms do not generally interact with BMI to increase lymphoma risk but obesity and TNF-308GA+AA may interact to increase DLBCL risk. Studies using better measures of adiposity are needed to further investigate the interactions between obesity and TNF-308G>A in the pathogenesis of lymphoma. Copyright © 2015, American Association for Cancer Research.
    Cancer Epidemiology Biomarkers & Prevention 05/2015; DOI:10.1158/1055-9965.EPI-14-1355 · 4.13 Impact Factor
  • Cancer Research 05/2015; 75(9 Supplement):P6-07-03-P6-07-03. DOI:10.1158/1538-7445.SABCS14-P6-07-03 · 9.33 Impact Factor
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    ABSTRACT: Data from the National LymphoCare Study (a prospective, multicentre registry that enrolled follicular lymphoma (FL) patients from 2004 to 2007) were used to determine disease characteristics, treatment patterns, outcomes and prognosis for elderly FL (eFL) patients. Of 2650 FL patients, 209 (8%) were aged >80 years; these eFL patients more commonly had grade 3 disease, less frequently received chemoimmunotherapy and anthracyclines, and had lower response rates when compared to younger patients. With a median follow-up of 6.9 years, 5-year overall survival (OS) for eFL patients was 59%; 38% of deaths were lymphoma-related. No treatment produced superior OS among eFL patients. In multivariate Cox models, anaemia, B-symptoms and male sex predicted worse OS (P < 0·01); a prognostic index of these factors (0, 1 or ≥2 present) predicted OS [hazard ratio (95% CI): ≥2 vs. 0, 4·72 (2·38-9·33); 1 vs. 0, 2·63 (1·39-4·98)], with a higher concordance index (0·63) versus the Follicular Lymphoma International Prognostic Index (0·55). The index was validated in an independent cohort. In the largest prospective US-based eFL cohort, no optimal therapy was identified and nearly 40% of deaths were lymphoma-related, representing baseline outcomes in the modern era. © 2015 John Wiley & Sons Ltd.
    British Journal of Haematology 04/2015; 170(1). DOI:10.1111/bjh.13399 · 4.71 Impact Factor
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    ABSTRACT: Peripheral T-cell lymphomas (PTCLs) are generally aggressive non-Hodgkin lymphomas with poor overall survival rates following standard therapy. One-third of PTCLs express interferon regulatory factor-4 (IRF4), a tightly-regulated transcription factor involved in lymphocyte growth and differentiation. IRF4 drives tumor growth in several lymphoid malignancies and has been proposed as a candidate therapeutic target. Since direct IRF4 inhibitors are not clinically available, we sought to characterize the mechanism by which IRF4 expression is regulated in PTCLs. We demonstrated that IRF4 is constitutively expressed in PTCL cells and drives Myc expression and proliferation. Using an inhibitor screen, we identified NF-κB as a candidate regulator of IRF4 expression and cell proliferation. We then demonstrated that the NF-κB subunits, p52 and RelB, were transcriptional activators of IRF4. Further analysis showed that activation of CD30 promotes p52 and RelB activity and subsequent IRF4 expression. Finally, we showed that IRF4 transcriptionally regulates CD30 expression. Taken together, these data demonstrate a novel positive feedback loop involving CD30, NF-κB, and IRF4; further evidence for this mechanism was demonstrated in human PTCL tissue samples. Accordingly, NF-κB inhibitors may represent a clinical means to disrupt this feedback loop in IRF4-positive PTCLs. Copyright © 2015 American Society of Hematology.
    Blood 04/2015; 125(20). DOI:10.1182/blood-2014-05-578575 · 10.45 Impact Factor
  • Journal of Clinical Oncology 03/2015; 33(14). DOI:10.1200/JCO.2014.60.5535 · 18.43 Impact Factor

Publication Stats

16k Citations
3,519.04 Total Impact Points


  • 2000–2015
    • Mayo Clinic - Rochester
      • • Department of Health Science Research
      • • Department of Internal Medicine
      Рочестер, Minnesota, United States
  • 2014
    • University of Minnesota Rochester
      Рочестер, Minnesota, United States
  • 2006–2014
    • National Institutes of Health
      • • Branch of Radiation Epidemiology
      • • Division of Cancer Epidemiology and Genetics
      베서스다, Maryland, United States
    • Samuel Lunenfeld Research Institute
      Toronto, Ontario, Canada
    • University of New Mexico
      • Department of Pathology
      Albuquerque, New Mexico, United States
  • 2004–2014
    • National Cancer Institute (USA)
      • • Radiation Epidemiology
      • • Division of Cancer Epidemiology and Genetics
      베서스다, Maryland, United States
    • University of Minnesota Twin Cities
      • Department of Pediatrics
      Minneapolis, Minnesota, United States
    • Moffitt Cancer Center
      • Department of Cancer Epidemiology
      Tampa, Florida, United States
  • 2004–2013
    • Mayo Foundation for Medical Education and Research
      • • Department of Medicine
      • • Department of Health Sciences Research
      Rochester, MI, United States
  • 2011
    • Rochester College
      Rochester, New York, United States
  • 2010
    • University of California, Berkeley
      • School of Public Health
      Berkeley, California, United States
  • 1996–2010
    • University of Minnesota Duluth
      • Department of Mechanical and Industrial Engineering
      Duluth, Minnesota, United States
    • University of Florence
      Florens, Tuscany, Italy
  • 1996–2009
    • University of Iowa
      Iowa City, Iowa, United States
  • 2008
    • University of Cambridge
      • Department of Oncology
      Cambridge, England, United Kingdom
  • 2005–2007
    • University of Washington Seattle
      Seattle, Washington, United States
    • University of Toronto
      Toronto, Ontario, Canada
    • University of South Florida
      Tampa, Florida, United States
    • University of California, San Diego
      San Diego, California, United States
  • 2003
    • University of Nebraska Medical Center
      Omaha, Nebraska, United States
  • 2002–2003
    • University of Alabama at Birmingham
      • • Department of Medicine
      • • Division of Clinical Immunology and Rheumatology
      Birmingham, Alabama, United States
  • 2001
    • National Institute of Environmental Health Sciences
      • Epidemiology Branch
      Durham, NC, United States
    • Vanderbilt University
      • Center for Health Services Research
      Нашвилл, Michigan, United States
    • Columbia University
      • Department of Health and Behavior Studies
      New York, New York, United States
  • 1999
    • Utah State University
      • Department of Nutrition, Dietetics and Food Sciences
      لوگان، اوهایو, Ohio, United States
  • 1998
    • University of Southern California
      • Department of Preventive Medicine
      Los Ángeles, California, United States