Paul is the recipient of the Penny and Phil Knight professorship in Cancer Research Innovation in the Department of Molecular and Medical Genetics within the OHSU School of Medicine. Paul is a co-director of CEDAR, and co-leader of the Quantitative Oncology Program in the Knight Cancer Institute. He uses genetics to help determine who is at risk for cancer, how to computationally analyze genomic data to identify early changes in cancers, and how to accurately screen different populations for the disease. He also works to inform the public about ways that genetics shape cancer risk. Dr. Spellman received his Ph.D. in genetics from Stanford University.
Kami is a Bioinformatics Research Associate primarily focused on customization and implementation of algorithms for analysis of multidimensional "omics" data. With a particular interest in transcriptomics, she works to identify tumor-specific gene targets for immunologic and/or pharmacologic therapies. She also assists with development and containerization of computational pipelines designed for high-throughput DNA or RNA sequence analysis of a variety of features, such as mutation timing, copy number variants, structural aberrations, cluster analysis, and alternative splicing events.
Carol is interested in understanding the heterogeneity of breast cancer tumors at the single cell level through the use of Flow Cytometry, Cytof (Mass Cytometry), and single cell RNA sequencing. These approaches have the power to show different populations of cancer cells within a tumor. Carol also manages the regulatory compliance for the group in regards to human subjects research projects.
Ece Eksi, Ph.D., is a postdoctoral scholar at CEDAR. She works on understanding the molecular and cellular heterogeneity in localized prostate cancer. She is specifically interested in revealing the systemic changes that occur in the gene regulatory networks of epithelial and stromal cells in prostate tumors. Dr. Eksi is merging single-cell sequencing with multiplex imaging to study the bidirectional interactions between cancer and neuronal, as well as neuronal and immune cells. In parallel, she develops liquid biopsy tools to stratify prostate cancer patients with localized disease into further risk groups. She received her Ph.D. in Molecular, Cell and Developmental biology from the University of Illinois at Chicago.
Trainees and volunteers
Michael is a Ph.D. candidate in Molecular and Medical Genetics and an F99 pre-doctoral fellow of the National Cancer Institute. He is currently studying the role of long non-coding RNA's in the regulation of DNA replication timing and maintenance of chromosome stability.
Burcu is an MD/PhD student at OHSU, currently pursuing the Ph.D. portion of her training. Her interest is in implementing statistical methods to find quantitative insights in clinical and high throughput medical data, which provides evidence-based strategies to improve patient care. Burcu has been working on the normalization of a pipeline of T-Cell Receptor sequencing, of which downstream applications would have a strong impact on immunotherapy. The other topics Burcu is interested in are genomics, epigenetics, clonal evolution, cancer heterogeneity, and liquid tumors, especially AML. Burcu holds a BSc in Statistics from Middle East Technical University and an M.A. degree in Statistics from Columbia University. Upon graduating, she worked at the United Nations, New York, and at Nielsen as a statistical modeling analyst, as well as at Cornell Medical College and Columbia University Hospital as a biostatistical researcher. She was also teaching statistics at the New Jersey Institute of Technology before joining OHSU.
Prior to becoming a graduate student, Chris worked under Dr. Spellman for six years as a research associate and lab manager. This work was focused on longitudinally monitoring circulating tumor DNA in metastatic breast cancer patients on multiple clinical trials overseen by Drs. Joe Gray and Gordon Mills. Chris developed patient-specific ctDNA monitoring assays using UMI-based error-correction and custom software to detect tumor-derived mutations at one allele in 100k in cell free DNA. Chris is currently focused on using iterative computational algorithms to model cancer evolution and disease heterogeneity using longitudinal ctDNA data. He uses these techniques to generate a comprehensive picture of tumor evolution over the course of treatment that can identify both therapy-resistant and therapy-sensitive subclones across an entire, multi-tumor cell population. Such modeling can also elucidate details of intratumoral heterogeneity, particularly in cases where solid tissue sampling isn’t possible or is limited to a single biopsy.
Kate is a Ph.D. candidate in the biomedical engineering department. Her primary research focus is on targeted sequencing of low-microbial biomass samples and establishing robust methods to analyze samples with high amounts of DNA contamination. Kate applies these techniques towards studying bacterial translocation and the composition of the urinary microbiome of patients with prostate cancer.