James R Cerhan

Mayo Clinic - Rochester, Рочестер, Minnesota, United States

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Publications (495)3579.98 Total impact

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    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
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    ABSTRACT: Purpose: To investigate the risk of non-Hodgkin lymphoma (NHL) associated with residential carpet dust measurements of polycyclic aromatic hydrocarbons (PAHs). Methods: We evaluated the relationship between residential carpet dust PAH concentrations (benz(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(k)fluoranthene, chrysene, dibenz(a,h)anthracene, and indeno(1,2,3-c,d)pyrene, and their sum) and risk of NHL (676 cases, 511 controls) in the National Cancer Institute Surveillance Epidemiology and End Results multicenter case-control study. As a secondary aim, we investigated determinants of dust PAH concentrations. We computed odds ratios (OR) and 95 % confidence interval (CI) for associations between NHL and concentrations of individual and summed PAHs using unconditional logistic regression, adjusting for age, gender, and study center. Determinants of natural log-transformed PAHs were investigated using multivariate least-squares regression. Results: We observed some elevated risks for NHL overall and B cell lymphoma subtypes in association with quartiles or tertiles of PAH concentrations, but without a monotonic trend, and there was no association comparing the highest quartile or tertile to the lowest. In contrast, risk of T cell lymphoma was significantly increased among participants with the highest tertile of summed PAHs (OR = 3.04; 95 % CI, 1.09-8.47) and benzo(k)fluoranthene (OR = 3.20; 95 % CI, 1.13-9.11) compared with the lowest tertile. Predictors of PAH dust concentrations in homes included ambient air PAH concentrations and the proportion of developed land within 2 km of a residence. Older age, more years of education, and white race were also predictive of higher levels in homes. Conclusion: Our results suggest a potential link between PAH exposure and risk of T cell lymphoma and demonstrate the importance of analyzing risk by NHL histologic type.
    Cancer Causes and Control 11/2015; DOI:10.1007/s10552-015-0660-y · 2.74 Impact Factor

  • Cancer Epidemiology Biomarkers & Prevention 11/2015; DOI:10.1158/1055-9965.EPI-15-0613 · 4.13 Impact Factor
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    ABSTRACT: Purpose: Pre-clinical studies suggest that single nucleotide polymorphisms (SNPs) in the Fcγ receptor (FCGR) genes influence response to rituximab, but the clinical relevance of this is uncertain. Experimental design: We prospectively obtained specimens for genotyping in the RESORT study, where 408 previously untreated, low tumor burden follicular lymphoma (FL) patients were treated with single agent rituximab. Patients received rituximab in 4 weekly doses and responders were randomized to rituximab re-treatment (RR) upon progression versus maintenance rituximab (MR). SNP genotyping was performed in 321 consenting patients. Results: Response rates to initial therapy and response duration were correlated with the FCGR3A SNP at position 158 (rs396991) and the FCGR2A SNP at position 131 (rs1801274). The response rate to initial rituximab was 71%. No FCGR genotypes or grouping of genotypes were predictive of initial response. 289 patients were randomized to RR (n = 143) or to MR (n = 146). With a median follow up of 5.5 years, the 3-yr response duration in the RR arm and the MR arm was 50% and 78%, respectively. Genotyping was available in 235 of 289 randomized patients. In patients receiving RR (n = 115) or MR (n =120), response duration was not associated with any FCGR genotypes or genotype combinations. Conclusions Based on this analysis of treatment-naïve, low tumor burden FL, we conclude that the FCGR3A and FCGR2A SNPs do not confer differential responsiveness to rituximab.
    Clinical Cancer Research 10/2015; DOI:10.1158/1078-0432.CCR-15-1848 · 8.72 Impact Factor
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    ABSTRACT: We recently defined event-free survival at 24 months (EFS24) as a clinically relevant outcome for patients with DLBCL. Patients who fail EFS24 have very poor overall survival, while those who achieve EFS24 have a subsequent overall survival equivalent to that of the age- and sex-matched general population. Here, we develop and validate a clinical risk calculator (IPI24) for EFS24. Model building was performed on a discovery dataset of 1348 patients with DLBCL and treated with anthracycline-based immunochemotherapy. A multivariable model containing age, Ann Arbor stage, normalized serum LDH, ALC, ECOG performance status, bulky disease and sex was identified. The model was then applied to an independent validation dataset of 1177 DLBCL patients. The IPI24 score estimates the probability of failing to achieve the EFS24 endpoint for an individual patient. The IPI24 model showed superior discriminatory ability (c-statistic=0.671) in the validation dataset compared to the IPI (c-statistic=0.649) or the NCCN-IPI (c-statistic=0.657). After recalibration of the model on the combined dataset, the median predicted probability of failing to achieve EFS24 was 36% (range, 12% to 88%), and the IPI24 showed an EFS24 gradient in all IPI groups. The IPI24 also identified a significant percentage of patients with high risk disease, with over 20% of patients having a 50% or higher risk of failing to achieve EFS24. The IPI24 provides an individual patient level probability of achieving the clinically relevant EFS24 endpoint. It can be utilized via electronic apps. This article is protected by copyright. All rights reserved.
    American Journal of Hematology 10/2015; DOI:10.1002/ajh.24223 · 3.80 Impact Factor
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    ABSTRACT: Background: T-cell malignancies are heterogeneous in their clinical presentation, pathology, and have a poor prognosis. New biomarkers are needed to predict prognosis and to provide insights into signal pathways used by these cells. The goal of this study was to evaluate pre-treatment serum cytokines in patients with newly diagnosed T-cell neoplasms and correlate with clinical outcome. Patients and methods: We evaluated 30 cytokines in pre-treatment serum from 68 untreated patients and 14 normal controls. Significantly elevated cytokines were correlated with patterns of abnormalities and relationship to event-free survival (EFS) and overall survival (OS). Results: Our data demonstrated significantly elevated levels (vs controls) of 7 cytokines - EGF, IL-6, IL-12, IP-10, sIL-2Rα, MIG, and IL-1RA in all T-cell neoplasms (p<0.05). In the angioimmunoblastic subset, all 7 cytokines except IP-10 and in the peripheral T-cell lymphoma-not otherwise specified subset, only IP-10, sIL-2Rα, MIG, and IL-8 were statistically elevated compared to control. Of these elevated cytokines, all but EGF were predictive of an inferior EFS; IL-1RA, sIL-2Rα and MIG predicted an inferior OS. In a multivariate analysis, sIL-2Rα (Hazard Ratio (HR)=3.95; 95% confidence interval (CI) 1.61-8.38) and IL-1RA (HR=3.28; 95% CI 1.47-7.29) levels remained independent predictors of inferior EFS. T-cell lymphoma (TCL) cell lines secreted high levels of sIL-2Rα and expressed the IL-2Rα surface receptor. Conclusions: This report describes the cytokines relevant to prognosis in patients with untreated TCL and provides the rationale to include serum IL-1RA and sIL-2Rα as biomarkers in future trials. Inhibition of these cytokines may also be of therapeutic benefit.
    Annals of Oncology 10/2015; DOI:10.1093/annonc/mdv486 · 7.04 Impact Factor
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    ABSTRACT: Purpose: We performed a multistage genome-wide association study to identify inherited genetic variants that predict outcome in diffuse large B-cell lymphoma patients treated with immunochemotherapy. Methods: We conducted a meta-analysis of two genome-wide association study data sets, one from the LNH2003B trial (N = 540), a prospective clinical trial from the Lymphoma Study Association, and the other from the Molecular Epidemiology Resource study (N = 312), a prospective observational study from the University of Iowa-Mayo Clinic Lymphoma Specialized Program of Research Excellence. Top single nucleotide polymorphisms were then genotyped in independent cohorts of patients from the Specialized Program of Research Excellence (N = 391) and the Groupe Ouest-Est des Leucémies Aiguës et Maladies du Sang (GOELAMS) -075 randomized trial (N = 294). We calculated the hazard ratios (HRs) and 95% CIs for event-free survival (EFS) and overall survival (OS) using a log-additive genetic model with adjustment for age, sex, and age-adjusted International Prognostic Index. Results: In a meta-analysis of the four studies, the top loci for EFS were marked by rs7712513 at 5q23.2 (near SNX2 and SNCAIP; HR, 1.39; 95% CI, 1.23 to 1.57; P = 2.08 × 10(-7)), and rs7765004 at 6q21 (near MARCKS and HDAC2; HR, 1.38; 95% CI, 1.22 to 1.57; P = 7.09 × 10(-7)), although they did not reach conventional genome-wide significance (P = 5 × 10(-8)). Both rs7712513 (HR, 1.49; 95% CI, 1.29 to 1.72; P = 3.53 × 10(-8)) and rs7765004 (HR, 1.47; 95% CI, 1.27 to 1.71; P = 5.36 × 10(-7)) were also associated with OS. In exploratory analyses, a two-single nucleotide polymorphism risk score was highly predictive of EFS (P = 1.78 × 10(-12)) and was independent of treatment, IPI, and cell-of-origin classification. Conclusion: Our study provides encouraging evidence for associations between loci at 5q23.2 and 6q21 with EFS and OS in patients with diffuse large B-cell lymphoma treated with immunochemotherapy, suggesting novel biology and the potential contribution of host genetics to the prognosis of this aggressive malignancy.
    Journal of Clinical Oncology 10/2015; DOI:10.1200/JCO.2014.60.2573 · 18.43 Impact Factor
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    ABSTRACT: Background: Socioeconomic status (SES) is an important predictor for outcomes of chronic diseases. However, it is often unavailable in clinical data. We sought to determine whether an individual housing-based SES index termed HOUSES can influence the likelihood of multiple chronic conditions (MCC) and hospitalisation in a community population. Methods: Participants were residents of Olmsted County, Minnesota, aged >18 years, who were enrolled in Mayo Clinic Biobank on 31 December 2010, with follow-up until 31 December 2011. Primary outcome was all-cause hospitalisation over 1 calendar-year. Secondary outcome was MCC determined through a Minnesota Medical Tiering score. A logistic regression model was used to assess the association of HOUSES with the Minnesota tiering score. With adjustment for age, sex and MCC, the association of HOUSES with hospitalisation risk was tested using the Cox proportional hazards model. Results: Eligible patients totalled 6402 persons (median age, 57 years; 25th-75th quartiles, 45-68 years). The lowest quartile of HOUSES was associated with a higher Minnesota tiering score after adjustment for age and sex (OR (95% CI) 2.4 (2.0 to 3.1)) when compared with the highest HOUSES quartile. Patients in the lowest HOUSES quartile had higher risk of all-cause hospitalisation (age, sex, MCC-adjusted HR (95% CI) 1.53 (1.18 to 1.98)) compared with those in the highest quartile. Conclusions: Low SES, as assessed by HOUSES, was associated with increased risk of hospitalisation and greater MCC health burden. HOUSES may be a clinically useful surrogate for SES to assess risk stratification for patient care and clinical research.
    Journal of epidemiology and community health 10/2015; DOI:10.1136/jech-2015-205925 · 3.50 Impact Factor
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    ABSTRACT: BACKGROUND: Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites. METHODS: Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers. RESULTS: GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl (2), on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (rho = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (rho = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (rho = 0.51, SE =0.18), and bladder and lung (rho = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures. CONCLUSION: Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.
    JNCI Journal of the National Cancer Institute 10/2015; 107(12). DOI:10.1093/jnci/djv279 · 12.58 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

Publication Stats

17k Citations
3,579.98 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
    • Fred Hutchinson Cancer Research Center
      • Division of Public Health Sciences
      Seattle, Washington, United States
    • 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
  • 2011
    • Rochester College
      Rochester, New York, United States
  • 2010
    • Mayo Foundation for Medical Education and Research
      Rochester, Michigan, United States
    • University of California, Berkeley
      • School of Public Health
      Berkeley, California, United States
  • 1996-2010
    • University of Minnesota Duluth
      • • Department of Family Medicine and Community Health
      • • Department of Mechanical and Industrial Engineering
      Duluth, Minnesota, United States
    • University of Florence
      Florens, Tuscany, Italy
  • 1998-2009
    • University of Iowa
      Iowa City, Iowa, United States
    • University of Southern California
      • Department of Preventive Medicine
      Los Ángeles, California, 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 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
    • Columbia University
      • Department of Health and Behavior Studies
      New York, New York, United States
    • Vanderbilt University
      • Center for Health Services Research
      Нашвилл, Michigan, United States
    • National Institute of Environmental Health Sciences
      • Epidemiology Branch
      Durham, NC, United States
  • 1999
    • Utah State University
      • Department of Nutrition, Dietetics and Food Sciences
      لوگان، اوهایو, Ohio, United States