Dr. Hyrien's research focuses include
- Statistical methods for bioassays (e.g., flow cytometry, sequencing)
- Machine learning (e.g., nonparametric clustering and classification)
- Dynamic modeling (e.g., B-cell repertoire)
A series of four presentations on vaccines and immunotherapy. Researchers are learning how to empower a patient’s own immune system to do what it does naturally — fight disease. They continue discovering new ways to give the immune cell army the upper hand against cancer and learning how immune cells respond to disease and how to safely enhance immune responses to better control, cure and potentially prevent cancers and other serious diseases.
Organized by Raphael Gottardo.
Dr. Hyrien's research focuses include
Dr. De Rosa has worked with the HIV Vaccine Trials Network (HVTN) for the past eight years, and leads the flow cytometry laboratory. In this role, he oversees the operation and maintenance of the flow cytometer analyzers and sorters, directly supervises HVTN Research & Development technicians in the development and optimization of new flow cytometry-based assays, and consults with the Endpoints Laboratory manager concerning the performance and analysis of the validated flow cytometric assays conducted on clinical trial samples. Following graduation from medical school and some brief clinical training, he has pursued a full-time career in basic and applied research. He trained as a postdoctoral fellow in the Herzenberg Laboratory at Stanford, the laboratory that first developed flow cytometry technology 40 years ago. He then continued his training with Mario Roederer, an internationally-recognized flow expert, at the NIH Vaccine Research Center (VRC). While at the VRC, he implemented intracellular cytokine staining assays for evaluation of vaccine-induced T cells. He later implemented and validated similar assays within the HVTN. He is internationally recognized for this work in implementing and validating functional assays using cutting-edge technology for high throughput analysis of clinical trial samples.
Dr. Gottardo's research interests include
Dr. Paulson is a senior hematology/oncology fellow in the Chapuis Lab of Fred Hutch's Clinical Research Division. Her research is focused on the development of combination T cell immunotherapies for Merkel cell carcinoma. Dr. Paulson's research includes the application of cutting-edge technologies to answer relevant biological and clinical questions and the development of effective prognostic tools,
Mike LeBlanc, Cathy Tangen, Yingqi Zhao, and Megan Othus will give brief overviews of some of their work within SWOG. Mike will give some background on the NCI cooperative group structure, Cathy will talk about statistical challenges in large prevention trials, Yingqi Zhao will talk about challenges with with developing personalized trial designs in the cooperative group settings, and Megan Othus will talk about using SWOG historical data to gain insight on endpoints in future clinical trials. Light refreshments will be served.
Dr. LeBlanc's research interests include the design and analysis of clinical trials, methods for exploratory analysis of survival data and adaptive non-parametric regression. Most of his collaborative research focuses on the design, analysis and conduct of therapeutic clinical trials. As Head of the SWOG Statistical Center, he investigates design and analysis methods for targeting patient subgroups appropriate for Phase II and Phase III clinical trials. He also studies adaptive regression methods and their application to data arising from clinical trials, developing extensions or alternatives to tree-based methods to yield simple prognostic decision rules. He recently developed an algorithm called Extreme Regression for constructing either high- or low-risk outcome groups. He is currently working on methods that allow specification of genetic structure into the high dimensional regression problem.
Dr. Othus' collaborative research focuses on the design, conduct and analysis of cancer studies, specifically the Southwest Oncology Group’s phase 2 and phase 3 melanoma and leukemia clinical trials. Othus is also interested in developing statistical methods for correlated survival data. Correlated survival data arise in many types of biomedical research. For example, data from population-based studies can be geographically correlated or data from multi-center clinical trials can be correlated within an institution.
Dr. Tangen's research interests include the design and analysis of clinical trials and non-parametric covariance adjustment, particularly for survival data. She is the statistician for Phase II, Phase III and ancillary studies for genitourinary cancers (e.g., prostate, bladder, renal, testes) in the Southwest Oncology Group. In addition to therapeutic trials, she is also the primary statistical investigator for the Prostate Cancer Prevention Trial (PCPT). The PCPT is a large (18,800 men), randomized, double-blind trial whose primary objective is to test the difference in biopsy-proven period prevalence of carcinoma of the prostate between a group of participants treated with finasteride and a group treated with placebo for seven years. The primary results, which were reported in June 2003, showed a 25% reduction in the period-prevalence of prostate cancer among those on finasteride relative to placebo. However, there was also a small but statistically significant increase in the rate of high grade cancer on the finasteride arm. A subsequent paper related to the prevalence of prostate cancer in men with normal PSA levels was published in the summer of 2004. A number of other manuscripts addressing the high grade issue and other topics are planned or currently underway.
Yingqi Zhao received her PhD in biostatistics from the University of North Carolina, Chapel Hill in 2012. She is currently an Assistant Member at the Fred Hutchinson Cancer Research Center. Her research focus includes methodologies for personalized medicine, dynamic treatment regimes, observational studies and machine learning. Specific applications of these work include cancer treatment and prevention, health care delivery for complex type II diabetes patients and childhood obesity surveillance. Her work in personalized medicine is particularly notable for these applications, which has been the basis for much subsequent work on developing biomarker-based treatment rules. She is actively engaged with collaborations with the SWOG clinical oncology cooperative group.