Richard Simon

National Institutes of Health, Maryland, United States

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Publications (218)1314.11 Total impact

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    ABSTRACT: The sensitivity of prostate specific antigen (PSA) increases with lower threshold values but with a corresponding decline in specificity. Magnetic resonance imaging/ultrasound (MR/US) targeted biopsy has been shown to detect prostate cancer (PCa) more efficiently and of higher grade than standard 12-core transrectal ultrasound (TRUS) biopsy, but the optimal population for its use is not well defined. We aimed to evaluate the performance of MR/US targeted versus 12-core biopsy across a PSA continuum.
    The Journal of urology. 08/2014;
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    Kyung In Kim, Richard Simon
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    ABSTRACT: The introduction of next-generation sequencing (NGS) technology has made it possible to detect genomic alterations within tumor cells on a large scale. However, most applications of NGS show the genetic content of mixtures of cells. Recently developed single cell sequencing technology can identify variation within a single cell. Characterization of multiple samples from a tumor using single cell sequencing can potentially provide information on the evolutionary history of that tumor. This may facilitate understanding how key mutations accumulate and evolve in lineages to form a heterogeneous tumor. We provide a computational method to infer an evolutionary mutation tree based on single cell sequencing data. Our approach differs from traditional phylogenetic tree approaches in that our mutation tree directly describes temporal order relationships among mutation sites. Our method also accommodates sequencing errors. Furthermore, we provide a method for estimating the proportion of time from the earliest mutation event of the sample to the most recent common ancestor of the sample of cells. Finally, we discuss current limitations on modeling with single cell sequencing data and possible improvements under those limitations. Inferring the temporal ordering of mutational sites using current single cell sequencing data is a challenge. Our proposed method may help elucidate relationships among key mutations and their role in tumor progression.
    BMC Bioinformatics 01/2014; 15(1):27. · 3.02 Impact Factor
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    ABSTRACT: -To determine the diagnostic yield of analyzing biparametric (T2 and diffusion-weighted) MRI (B-MRI) for prostate cancer detection compared to standard digital rectal exam and PSA-based screening. -Review of patients who were enrolled in a trial to undergo MP-MRI and MR/US fusion-guided prostate biopsy at our institution identified 143 men who underwent MP-MRI in addition to standard DRE and PSA-based PCa screening prior to any prostate biopsy. -Patient demographics, digital rectal exam staging, PSA, PSA density, and B-MRI findings were assessed for association with prostate cancer detection on biopsy. -Men with detected prostate cancer tended to be older, with higher PSA, higher PSA density, and increased number of screen positive lesions (#SPL) on B-MRI. -B-MRI performed well for the detection of prostate cancer with an AUC of 0.80 (compared to 0.66 and 0.74 for PSA and PSA density). -We derived combined PSA and MRI-based formulas for detection of prostate cancer with optimized thresholds. (1) for PSA and B-MRI: PSA + 6 x (#SPL) > 14 and (2) for PSA density and B-MRI: 14 x (PSA density) + (#SPL) >4.25. -Area under the curve for equations 1 and 2 were 0.83 and 0.87 and overall accuracy of prostate cancer detection was 79% in both models. -Number of lesions positive on B-MRI outperforms PSA alone in detection of prostate cancer. -Furthermore, this imaging criteria coupled as an adjunct with PSA and PSA density, provides even more accuracy in detecting clinically-significant prostate cancer.
    BJU International 01/2014; · 3.05 Impact Factor
  • Kyung In Kim, Richard Simon
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    ABSTRACT: Accurate class probability estimation is important for medical decision making but is challenging, particularly when the number of candidate features exceeds the number of cases. Special methods have been developed for nonprobabilistic classification, but relatively little attention has been given to class probability estimation with numerous candidate variables. In this paper, we investigate overfitting in the development of regularized class probability estimators. We investigate the relation between overfitting and accurate class probability estimation in terms of mean square error. Using simulation studies based on real datasets, we found that some degree of overfitting can be desirable for reducing mean square error. We also introduce a mean square error decomposition for class probability estimation that helps clarify the relationship between overfitting and prediction accuracy.
    Biometrical Journal 12/2013; · 1.15 Impact Factor
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    Ahrim Youn, Richard Simon
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    ABSTRACT: Recent developments in high-throughput genomic technologies make it possible to have a comprehensive view of genomic alterations in tumors on a whole genome scale. Only a small number of somatic alterations detected in tumor genomes are driver alterations which drive tumorigenesis. Most of the somatic alterations are passengers that are neutral to tumor cell selection. Although most research efforts are focused on analyzing driver alterations, the passenger alterations also provide valuable information about the history of tumor development. In this paper, we develop a method for estimating the age of the tumor lineage and the timing of the driver alterations based on the number of passenger alterations. This method also identifies mutator genes which increase genomic instability when they are altered and provides estimates of the increased rate of alterations caused by each mutator gene. We applied this method to copy number data and DNA sequencing data for ovarian and lung tumors. We identified well known mutators such as TP53, PRKDC, BRCA1/2 as well as new mutator candidates PPP2R2A and the chromosomal region 22q13.33. We found that most mutator genes alter early during tumorigenesis and were able to estimate the age of individual tumor lineage in cell generations. This is the first computational method to identify mutator genes and to take into account the increase of the alteration rate by mutator genes, providing more accurate estimates of the tumor age and the timing of driver alterations.
    BMC Bioinformatics 12/2013; 14(1):363. · 3.02 Impact Factor
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    ABSTRACT: The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to 'omics'-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests for guiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy.
    Nature 10/2013; 502(7471):317-20. · 38.60 Impact Factor
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    ABSTRACT: Background:Adoptive therapy with tumour-infiltrating lymphocytes (TILs) induces durable complete responses (CR) in ∼20% of patients with metastatic melanoma. The recruitment of T cells through CXCR3/CCR5 chemokine ligands is critical for immune-mediated rejection. We postulated that polymorphisms and/or expression of CXCR3/CCR5 in TILs and the expression of their ligands in tumour influence the migration of TILs to tumours and tumour regression.Methods:Tumour-infiltrating lymphocytes from 142 metastatic melanoma patients enrolled in adoptive therapy trials were genotyped for CXCR3 rs2280964 and CCR5-Δ32 deletion, which encodes a protein not expressed on the cell surface. Expression of CXCR3/CCR5 in TILs and CXCR3/CCR5 and ligand genes in 113 available parental tumours was also assessed. Tumour-infiltrating lymphocyte data were validated by flow cytometry (N=50).Results:The full gene expression/polymorphism model, which includes CXCR3 and CCR5 expression data, CCR5-Δ32 polymorphism data and their interaction, was significantly associated with both CR and overall response (OR; P=0.0009, and P=0.007, respectively). More in detail, the predicted underexpression of both CXCR3 and CCR5 according to gene expression and polymorphism data (protein prediction model, PPM) was associated with response to therapy (odds ratio=6.16 and 2.32, for CR and OR, respectively). Flow cytometric analysis confirmed the PPM. Coordinate upregulation of CXCL9, CXCL10, CXCL11, and CCL5 in pretreatment tumour biopsies was associated with OR.Conclusion:Coordinate overexpression of CXCL9, CXCL10, CXCL11, and CCL5 in pretreatment tumours was associated with responsiveness to treatment. Conversely, CCR5-Δ32 polymorphism and CXCR3/CCR5 underexpression influence downregulation of the corresponding receptors in TILs and were associated with likelihood and degree of response.British Journal of Cancer advance online publication, 15 October 2013; doi:10.1038/bjc.2013.557 www.bjcancer.comPublished online 15 October 2013.
    British Journal of Cancer 10/2013; · 5.08 Impact Factor
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    ABSTRACT: The NCI-60 cell lines are the most frequently studied human tumor cell lines in cancer research. This panel has generated the most extensive cancer pharmacology database worldwide. In addition, these cell lines have been intensely investigated, providing a unique platform for hypothesis-driven research focused on enhancing our understanding of tumor biology. Here, we report a comprehensive analysis of coding variants in the NCI-60 panel of cell lines identified by whole exome sequencing, providing a list of possible cancer specific variants for the community. Furthermore, we identify pharmacogenomic correlations between specific variants in genes such as TP53, BRAF, ERBBs, and ATAD5 and anticancer agents such as nutlin, vemurafenib, erlotinib, and bleomycin showing one of many ways the data could be used to validate and generate novel hypotheses for further investigation. As new cancer genes are identified through large-scale sequencing studies, the data presented here for the NCI-60 will be an invaluable resource for identifying cell lines with mutations in such genes for hypothesis-driven research. To enhance the utility of the data for the greater research community, the genomic variants are freely available in different formats and from multiple sources including the CellMiner and Ingenuity websites. Cancer Res; 73(14); 4372-82. ©2013 AACR.
    Cancer Research 07/2013; 73(14):4372-82. · 8.65 Impact Factor
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    ABSTRACT: Active surveillance (AS) is an attempt to avoid overtreatment of clinically insignificant prostate cancer (PCa); however, patient selection remains controversial. Multiparametric prostate magnetic resonance imaging (MP-MRI) may help better select AS candidates. We reviewed a cohort of men who underwent MP-MRI with MRI/Ultrasound fusion-guided prostate biopsy and selected potential AS patients at entry using Johns Hopkins criteria. MP-MRI findings were assessed, including number of lesions, dominant lesion diameter, total lesion volume, prostate volume, and lesion density (calculated as total lesion volume/prostate volume). Lesions were assigned a suspicion score for cancer by MRI. AS criteria were reapplied based on the confirmatory biopsy, and accuracy of MP-MRI in predicting AS candidacy was assessed. Logistic regression modeling and chi-square statistics were used to assess associations between MP-MRI interpretation and biopsy results. Eighty-five patients qualified for AS with a mean age of 60.2 years and mean prostate-specific antigen level of 4.8 ng/mL. Of these, 25 patients (29%) were reclassified as not meeting AS criteria based on confirmatory biopsy. Number of lesions, lesion density, and highest MRI lesion suspicion were significantly associated with confirmatory biopsy AS reclassification. These MRI-based factors were combined to create a nomogram that generates a probability for confirmed AS candidacy. As clinicians counsel patients with PCa, MP-MRI may contribute to the decision-making process when considering AS. Three MRI-based factors (number of lesions, lesion suspicion, and lesion density) were associated with confirmatory biopsy outcome and reclassification. A nomogram using these factors has promising predictive accuracy for which future validation is necessary. Cancer 2013. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.
    Cancer 07/2013; · 5.20 Impact Factor
  • Jyothi Subramanian, Richard Simon
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    ABSTRACT: The growing recognition that human diseases are molecularly heterogeneous has stimulated interest in the development of prognostic and predictive classifiers for patient selection and stratification. In the process of classifier development, it has been repeatedly emphasized that in situations where the number of candidate predictor variables is much larger than the number of observations, the apparent (training set, resubstitution) accuracy of the classifiers can be highly optimistically biased and hence, classification accuracy should be reported based on evaluation of the classifier on a separate test set or using complete cross-validation. Such evaluation methods have however not been the norm in the case of low dimensional, p < n data that arise, for example, in clinical trials when a classifier is developed on a combination of clinico-pathological variables and a small number of genetic biomarkers selected from an understanding of the biology of the disease. We undertook simulation studies to investigate the existence and extent of the problem of overfitting with low dimensional data. The results indicate that overfitting can be a serious problem even for low dimensional data, especially if the relationship of outcome to the set of predictor variables is not strong. We hence encourage the adoption of either a separate test set or complete cross-validation to evaluate classifier accuracy, even when the number of candidate predictor variables is substantially smaller than the number of cases.
    Contemporary clinical trials 06/2013; · 1.51 Impact Factor
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    ABSTRACT: PURPOSEAlveolar soft part sarcoma (ASPS) is a rare, highly vascular tumor, for which no effective standard systemic treatment exists for patients with unresectable disease. Cediranib is a potent, oral small-molecule inhibitor of all three vascular endothelial growth factor receptors (VEGFRs). PATIENTS AND METHODS We conducted a phase II trial of once-daily cediranib (30 mg) given in 28-day cycles for patients with metastatic, unresectable ASPS to determine the objective response rate (ORR). We also compared gene expression profiles in pre- and post-treatment tumor biopsies and evaluated the effect of cediranib on tumor proliferation and angiogenesis using positron emission tomography and dynamic contrast-enhanced magnetic resonance imaging.ResultsOf 46 patients enrolled, 43 were evaluable for response at the time of analysis. The ORR was 35%, with 15 of 43 patients achieving a partial response. Twenty-six patients (60%) had stable disease as the best response, with a disease control rate (partial response + stable disease) at 24 weeks of 84%. Microarray analysis with validation by quantitative real-time polymerase chain reaction on paired tumor biopsies from eight patients demonstrated downregulation of genes related to vasculogenesis. CONCLUSION In this largest prospective trial to date of systemic therapy for metastatic ASPS, we observed that cediranib has substantial single-agent activity, producing an ORR of 35% and a disease control rate of 84% at 24 weeks. On the basis of these results, an open-label, multicenter, randomized phase II registration trial is currently being conducted for patients with metastatic ASPS comparing cediranib with another VEGFR inhibitor, sunitinib.
    Journal of Clinical Oncology 04/2013; · 18.04 Impact Factor
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    Dataset: nat med99
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    ABSTRACT: High-throughput ?omics? technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.
    BMC Medicine 01/2013; 11(1):220. · 6.68 Impact Factor
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    ABSTRACT: BACKGROUND: Interferon regulatory factor (IRF)-5 is a transcription factor involved in type I interferon signaling whose germ line variants have been associated with autoimmune pathogenesis. Since relationships have been observed between development of autoimmunity and responsiveness of melanoma to several types of immunotherapy, we tested whether polymorphisms of IRF5 are associated with responsiveness of melanoma to adoptive therapy with tumor infiltrating lymphocytes (TILs). METHODS: 140 TILs were genotyped for four single nucleotide polymorphisms (rs10954213, rs11770589, rs6953165, rs2004640) and one insertion-deletion in the IRF5 gene by sequencing. Gene-expression profile of the TILs, 112 parental melanoma metastases (MM) and 9 cell lines derived from some metastases were assessed by Affymetrix Human Gene ST 1.0 array. RESULTS: Lack of A allele in rs10954213 (G > A) was associated with non-response (p < 0.005). Other polymorphisms in strong linkage disequilibrium with rs10954213 demonstrated similar trends. Genes differentially expressed in vitro between cell lines carrying or not the A allele could be applied to the transcriptional profile of 112 melanoma metastases to predict their responsiveness to therapy, suggesting that IRF5 genotype may influence immune responsiveness by affecting the intrinsic biology of melanoma. CONCLUSIONS: This study is the first to analyze associations between melanoma immune responsiveness and IRF5 polymorphism. The results support a common genetic basis which may underline the development of autoimmunity and melanoma immune responsiveness.
    Journal of Translational Medicine 08/2012; 10(1):170. · 3.46 Impact Factor
  • Richard M Simon
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    ABSTRACT: The standard paradigm for the design of phase III clinical trials is not suitable for evaluation of molecularly targeted treatments in biologically heterogeneous groups of patients. Here, we comment on alternative clinical trial designs and propose a prospective discovery/evaluation framework for using tumor genomics in the design of phase III trials.
    Clinical Cancer Research 06/2012; 18(15):4001-3. · 7.84 Impact Factor
  • Ahrim Youn, Richard Simon
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    ABSTRACT: Tumors are thought to develop and evolve through a sequence of genetic and epigenetic somatic alterations to progenitor cells. Early stages of human tumorigenesis are hidden from view. Here, we develop a method for inferring some aspects of the order of mutational events during tumorigenesis based on genome sequencing data for a set of tumors. This method does not assume that the sequence of driver alterations is the same for each tumor, but enables the degree of similarity or difference in the sequence to be evaluated. To evaluate the new method, we applied it to colon cancer tumor sequencing data and the results are consistent with the multi-step tumorigenesis model previously developed based on comparing stages of cancer. We then applied the new method to DNA sequencing data for a set of lung cancers. The model may be a useful tool for better understanding the process of tumorigenesis. The software is available at: http://linus.nci.nih.gov/Data/YounA/OrderMutation.zip.
    Bioinformatics 04/2012; 28(12):1555-61. · 5.47 Impact Factor
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    ABSTRACT: Developments in biotechnology and genomics are providing a biological basis for the heterogeneity of clinical course and response to treatment that have long been apparent to clinicians. The ability to molecularly characterize human diseases presents new opportunities to develop more effective treatments and new challenges for the design and analysis of clinical trials. In oncology, treatment of broad populations with regimens that benefit a minority of patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine. We review prospective designs for the development of new therapeutics and predictive biomarkers to inform their use. We cover designs for a wide range of settings. At one extreme is the development of a new drug with a single candidate biomarker and strong biological evidence that marker negative patients are unlikely to benefit from the new drug. At the other extreme are Phase III clinical trials involving both genome‐wide discovery of a predictive classifier and internal validation of that classifier. We have outlined a prediction‐based approach to the analysis of randomized clinical trials that both preserves the Type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from the new regimen. Copyright © 2012 John Wiley & Sons, Ltd.
    Statistics in Medicine 01/2012; 31(25). · 2.04 Impact Factor
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    Richard Simon
    EMBO Molecular Medicine 08/2011; 3(8):429-35. · 7.80 Impact Factor
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    Richard Simon, Noah Robin Simon
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    ABSTRACT: We demonstrate that clinical trials using response adaptive randomized treatment assignment rules are subject to substantial bias if there are time trends in unknown prognostic factors and standard methods of analysis are used. We develop a general class of randomization tests based on generating the null distribution of a general test statistic by repeating the adaptive randomized treatment assignment rule holding fixed the sequence of outcome values and covariate vectors actually observed in the trial. We develop broad conditions on the adaptive randomization method and the stochastic mechanism by which outcomes and covariate vectors are sampled that ensure that the type I error is controlled at the level of the randomization test. These conditions ensure that the use of the randomization test protects the type I error against time trends that are independent of the treatment assignments. Under some conditions in which the prognosis of future patients is determined by knowledge of the current randomization weights, the type I error is not strictly protected. We show that response-adaptive randomization can result in substantial reduction in statistical power when the type I error is preserved. Our results also ensure that type I error is controlled at the level of the randomization test for adaptive stratification designs used for balancing covariates.
    Statistics [?] Probability Letters 07/2011; 81(7):767-772. · 0.53 Impact Factor
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    Jyothi Subramanian, Richard Simon
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    ABSTRACT: Resampling techniques are often used to provide an initial assessment of accuracy for prognostic prediction models developed using high-dimensional genomic data with binary outcomes. Risk prediction is most important, however, in medical applications and frequently the outcome measure is a right-censored time-to-event variable such as survival. Although several methods have been developed for survival risk prediction with high-dimensional genomic data, there has been little evaluation of the use of resampling techniques for the assessment of such models. Using real and simulated datasets, we compared several resampling techniques for their ability to estimate the accuracy of risk prediction models. Our study showed that accuracy estimates for popular resampling methods, such as sample splitting and leave-one-out cross validation (Loo CV), have a higher mean square error than for other methods. Moreover, the large variability of the split-sample and Loo CV may make the point estimates of accuracy obtained using these methods unreliable and hence should be interpreted carefully. A k-fold cross-validation with k = 5 or 10 was seen to provide a good balance between bias and variability for a wide range of data settings and should be more widely adopted in practice.
    Statistics in Medicine 03/2011; 30(6):642-53. · 2.04 Impact Factor

Publication Stats

13k Citations
1,314.11 Total Impact Points

Institutions

  • 1990–2014
    • National Institutes of Health
      • • Branch of Biometric Research
      • • Branch of Urologic Oncology
      • • Section on Cellular Neurobiology
      • • Branch of Neuroimmunology and Virology
      • • Bioinformatics and Molecular Analysis Section (BIMAS)
      Maryland, United States
  • 1991–2012
    • National Cancer Institute (USA)
      • • Biometrics Research Branch
      • • Cancer Therapy Evaluation Program
      • • Division of Cancer Treatment and Diagnosis
      Maryland, United States
  • 2011
    • University of Georgia
      • Department of Epidemiology and Biostatistics
      Athens, GA, United States
  • 2009
    • Georgetown University
      • Department of Biochemistry and Molecular and Cellular Biology
      Washington, D. C., DC, United States
  • 2008
    • Concordia University Montreal
      Montréal, Quebec, Canada
  • 2007
    • Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori
      Meldola, Emilia-Romagna, Italy
  • 2006
    • University of North Carolina at Chapel Hill
      • Department of Neurology
      Chapel Hill, NC, United States
  • 2004
    • National Institute of Child Health and Human Development
      Maryland, United States
    • Cancer Research Institute
      New York City, New York, United States
  • 2001–2003
    • NCI-Frederick
      Maryland, United States
    • Torrey Pines Institute for Molecular Studies
      Port St. Lucie, Florida, United States
    • Biomedical Research Institute, Rockville
      Maryland, United States
  • 2002
    • Ludwig Institute for Cancer Research
      La Jolla, California, United States
  • 1997–1999
    • University of Haifa
      • Department of Statistics
      Haifa, Haifa District, Israel
  • 1996
    • Medical Research Council (UK)
      Londinium, England, United Kingdom
  • 1993
    • Mario Negri Institute for Pharmacological Research
      Milano, Lombardy, Italy