Colleagues: Recently Tenured
JILL BARNHOLTZ-SLOAN PH.D., NCI-DCEG
Senior Investigator, Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute; Associate Director for Informatics and Data Science, NCI Center for Biomedical Informatics and Information Technology
Education: University of Florida, Gainesville, Florida (B.S. in mathematics); University of Texas at Austin, Austin, Texas (M.S. in statistics); University of Texas School of Public Health, Houston (Ph.D. in biostatistics)
Before coming to NIH: Various roles at Case Western Reserve University School of Medicine and University Hospitals of Cleveland (Cleveland) including director, Research Health Analytics and Informatics, University Hospitals Health System; Sally S. Morley Designated Professor in Brain Tumor Research (with tenure), Department of Population and Quantitative Health Sciences; associate director for Translational Informatics at Cleveland Institute for Computational Biology; and associate director for Data Science at Case Comprehensive Cancer Center.
Came to NIH: In May 2021
Outside interests: Walking outside with her dog (a chocolate Labrador retriever named Duke) and her family; listening to music; having friends over for meals
Research interests: Because brain tumors are uncommon and subdivided into many distinct subtypes, with distinct etiologies and outcomes, multisite, multidisciplinary team science approaches have been essential for advancing the field. I am facilitating collaborations in data science and in the study of brain tumors by leveraging my experience in multi-institutional team science and the use of large, complex health care datasets to enhance the data assets already available in the NCI Cancer Research Data Commons and throughout NCI.
My team’s work has contributed to a better understanding of the population burden of disease (Neuro Oncol 2(12 Suppl 2):iv1–iv96, 2020) and identified risk factors and biomarkers (Nat Genet 49:789–794,2017; N Engl J Med 372:2481–2498, 2015; Cell 155:462–477, 2013), and we are working toward uncovering biological mechanisms underlying known sex differences in brain tumors (Sci Transl Med 11:eaao5253, 2019), all of which significantly improve the diagnosis, treatment, and management of brain tumors.
I was a founding member of the Brain Tumor Epidemiology Consortium, which resulted in two highly successful international collaborations, including the first studies to assess genetic risk factors for familial and sporadic brain tumors at the genome-wide level (Nat Genet 49:789–794, 2017; Cancer Res 71:7568–7575, 2011).
Given my dual roles in NCI’s Center for Biomedical Informatics and Information Technology and the Division of Cancer Epidemiology and Genetics (DCEG), I envision bringing data science to all research domains within DCEG, helping to move toward 1) use of cloud resources for computing and data sharing via the NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability Initiative, and 2) use of the Findability, Accessibility, Interoperability, and Reuse principles (FAIR) for research in DCEG. In addition, I am developing and facilitating closer links with all divisions across the NCI and more generally across NIH, especially with the NIH Office for Data Science Strategy, to fully leverage data assets and data analytics for cancer research.
HYOKYOUNG GRACE HONG, PH.D., NCI-DCEG
Senior Investigator, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute
Education: Sungkyunkwan University, Seoul, Korea (B.S. in mathematics education); University of Illinois at Urbana-Champaign, Champaign, Illinois (M.S. in actuarial science; M.S. and Ph.D. in statistics)
Before coming to NIH: Associate professor (tenured), Department of Statistics and Probability, Michigan State University (East Lansing, Michigan)
Came to NIH: In January 2021
Outside interests: Traveling and tasting new cuisines (some favorite travel destinations are Italy, Spain, and China); painting; healthy cooking; reading
Research interests: As a statistical scientist, my goal is to advance scientific knowledge in population-based cancer epidemiology and genetics studies through the development and use of novel statistical methodology. I am developing cutting-edge statistical methods for analyzing complex large-scale datasets, and applying these methods to the fields of public health, medicine, and health-policy research.
Throughout my career, my team and I have led the development of methods in high-dimensional data analysis by proposing a series of novel and innovative ideas. We have made important advances in statistical theory and methodological development in the areas of quantile regression analysis, classification, and time-to-event analysis. Quantile regression has emerged as both an efficient way of linking the whole distribution of an outcome to the covariates of interest and an important alternative to commonly used regression models. In a recent study, we used quantile regression to identify clinical and molecular predictors associated with high-risk lung cancer patients. (Precis Clin Med 2:90–99, 2019).
I have also contributed to statistical methodology for classification problems with high-dimensional covariates. For example, many classification problems emerge from analyses of gene expression and imaging data to identify individuals with disease or at high risk of developing disease. To circumvent these issues, I proposed a novel high-dimensional classification method that could predict clinical diagnosis of autism spectrum disorder using imaging predictors, integrating known different sources of anatomical information, correlation among imaging predictors, and spatial information (Biometrika 104:785–800, 2017).
DIMITRIOS KAPOGIANNIS, M.D., NIA
Senior Investigator, Human Neuroscience Section, National Institute on Aging
Education: National University of Athens Medical School, Athens, Greece (M.D.)
Training: Preliminary internal medicine internship at the Evanston Hospital/Northwestern University (Evanston, Illinois); neurology residency at the Massachusetts General Hospital/Brigham and Women’s Hospital/Harvard Medical School (Boston); clinical fellowship in behavioral neurology at the National Institute of Neurological Disorders and Stroke.
Came to NIH: In 2006 for training; staff clinician in NIA (2009–2014) then investigator and chief of NIA’s Human Neuroscience Unit (2014–2021); is also an adjunct associate professor in neurology at Johns Hopkins School of Medicine (Baltimore)
Outside interests: Is passionate about history, archaeology, and philosophy; enjoys traveling and swimming
Research interests: My lab and I are identifying biomarkers for neurodegenerative diseases, particularly Alzheimer disease (AD) and related dementias (ADRD), as well as neurologic and psychiatric diseases that may affect the aging brain. Our ultimate goal is to arrive at precision-medicine treatments for AD and ADRD by characterizing individual patients for multiple pathogenic processes simultaneously and using biomarkers to predict their response to experimental treatments. The bulk of our work has focused on the analysis of extracellular vesicles (EVs) in plasma.
A limitation of many AD biomarkers measured in the soluble phase of blood is their tenuous link to brain pathology, because they are often produced by multiple tissues and their brain-derived fraction has to cross multiple barriers before reaching the blood.
To address this limitation, my lab and I have taken a new approach to biomarker discovery in AD: harvesting EVs enriched for neuronal and astrocytic origin from blood. These EVs are akin to a brain “liquid biopsy” and can be used to interrogate pathogenic processes that were previously inaccessible in vivo. We have identified EV biomarkers that reflect many pathogenic processes involved in AD and other neurodegenerative diseases. (JAMA Neurol 76:1340–1351, 2019; Annals Neurol 83:544–552, 2018) We have also pioneered the use of these EVs to demonstrate target engagement and biomarker responses in clinical trials for various neurological and psychiatric disorders (JAMA Neurol 76:420–429, 2019).
We are currently in the process of conducting large-scale studies using longitudinal study cohorts to further validate EV biomarkers, demonstrate their ability to predict AD or ADRD diagnosis at the preclinical stage, and assess whether they can identify disease subgroups with different biologies and clinical trajectories.
We are also busy clinically. We are conducting early-phase interventional studies targeting brain metabolism to ameliorate pathogenic processes leading to AD or ADRD. We conducted a pilot double-blind placebo-controlled randomized clinical trial of the antidiabetic agent exendin-4 in early AD. In addition, we are conducting controlled randomized clinical trials in middle-aged individuals at risk for cognitive impairment—testing a 5:2 calorie restriction diet (eating regularly for five days and very little for two)and an oral ketone ester—and will be looking at changes in EV and magnetic resonance spectroscopy biomarkers as well as cognitive outcomes.
PETER W. SCHUCK, PH.D., NIBIB
Senior Investigator and Chief, Laboratory of Dynamics of Macromolecular Assembly, National Institute of Biomedical Imaging and Bioengineering
Education: Goethe University, Frankfurt am Main, Germany (B.S. in physics; Ph.D. in biophysics)
Training: Postdoctoral fellowship, Laboratory of Biochemical Pharmacology, National Institute of Diabetes and Digestive and Kidney Diseases
Came to NIH: In 1994 for training; became a staff scientist in 1999; chief, Protein Biophysics Resource, Division of Bioengineering and Physical Science, Office of Research Services (2003–2007); and chief, Dynamics of Macromolecular Assembly Section, Laboratory of Cellular Imaging and Macromolecular Biophysics, NIBIB (2007–2021); appointed an Earl Stadtman Tenure-Track Investigator in 2014
Outside interests: Enjoying jazz music—listening and playing bass guitar; playing carom billiards; cooking; bicycling
Research interests: It’s exciting to determine how macromolecules are assembled and interact with each other and to apply that knowledge to understanding the inner workings of a cell. My group and I are developing biophysical methods to study protein interactions and the assembly of multiprotein complexes.
We are developing quantitative hydrodynamic methods using analytical ultracentrifugation in conjunction with mathematical modeling of reaction, diffusion, and sedimentation processes. Although analytical ultracentrifugation is a classical biophysical discipline, it has undergone a renaissance in the last decade due to new computational capabilities that allow us to fully exploit mass-based separation in solution to obtain macromolecular size distributions and measure interactions. There are increasing applications in structural biology and immunology for the study of protein interactions and multiprotein complexes, and in the biotechnology industry for the characterization of protein pharmaceuticals and nanoparticles for drug delivery. In a recent study, we used a suite of biophysical methods including analytical ultracentrifugation to gain insights into the molecular mechanisms of SARS-CoV-2, the virus responsible for COVID-19 (iScience 24:102523, 2021).
Recently, we embarked on the extension of analytical ultracentrifugation to concentrated macromolecular solutions to better mimic conditions in cytosol, serum, and pharmaceutical formulations. In crowded solutions, ultraweak attractive and even repulsive interactions can play key roles in controlling dynamic assemblies that would fall apart in more dilute solutions. Developing new experimental and computational strategies, we have achieved unprecedented resolution of transient macromolecular complexes at protein concentrations close to those in serum.
In the other extreme, using a fluorescence detector in combination with newly developed computational tools and exploiting properties of photo-switchable fluorescent molecules, we have achieved unprecedented sensitivity in characterizing architectural principles and driving forces of protein assemblies in the low picomolar concentration range. These methods have great potential to provide information on macromolecular organization complementary to structural and microscopy methods.
In addition, we are involved in several collaborative applications in various fields including immunological protein complexes, viral proteins, membrane receptor complexes, and eye lens crystallins.
MEREDITH SHIELS, PH.D., NCI-DCEG
Senior Investigator, Infections and Immunoepidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute
Education: The Pennsylvania State University, Schreyer Honors College, State College, Pennsylvania (B.S. in biobehavioral health); Johns Hopkins Bloomberg School of Public Health, Baltimore (M.H.S. and Ph.D. in cancer epidemiology)
Training: Postdoctoral fellow and research fellow, Infections and Immunoepidemiology Branch, NCI-DCEG
Came to NIH: In 2009 for training; became a tenure-track investigator in 2016
Outside interests: Spending time with her three daughters, ages 3, 6, and 9
Research interests: My research program uses a combination of innovative contemporary approaches—using descriptive analyses, population-based databases, and real-world data—to confront high-impact public health questions. My work focuses on 1) quantifying cancer risk and burden in people with HIV; 2) estimating the impact of risk factors on changing cancer rates; and 3) using careful investigation of population-based surveillance data to provide insights into emerging public health crises, including the opioid epidemic and the COVID-19 pandemic.
People with HIV have a higher risk of certain types of cancer. I am the co-principal investigator of the HIV/AIDS Cancer Match Study, an observational study of nearly one million people with HIV across the United States. We have projected future cancer rates and burden among U.S. adults with HIV, showing a decline in the number of cases of Kaposi sarcoma and non-Hodgkin lymphoma and an increase in the number of lung and prostate cancers (Ann Int Med 168:866–873 , 2018).
My work uses linked databases and statistical modeling to disaggregate cancer-risk rates based on etiology, which is important for targeted prevention efforts. For example, we estimated the role of the impact of the increasing prevalence of overweight and obesity on rising rates of papillary thyroid cancers (J Nat Cancer Inst 112:810–817, 2020).
I have applied my expertise to interrogating large, population-based data to address specific public health questions related to the underlying drivers of premature mortality rates (deaths among 25- to 64-year-olds) in the United States. Some of this work has focused on the evolving drug-overdose epidemic, which is a main contributor to rising premature mortality rates in some groups. In 2020, I expanded my research program to include descriptive analyses of COVID-19. This includes recent work that estimated excess deaths in the United States during the pandemic (Ann Intern Med 174:437–443, 2021).
This page was last updated on Monday, January 31, 2022