Past Research Funding Awards

One Ignition Award went to Dr. Sanjay Srivatsan, an assistant professor in the Basic Sciences Division. Sanjay’s funded project will focus on developing a new platform investigating the single cell whole genome and transcriptome of pediatric patients with acute leukemia. With this detailed data set, Dr. Srivatsan intends on utilizing a novel statistical framework called "genomic velocity," which looks at mutations shared between cells to estimate the rate of change of each given clone in a sample. Collectively, this will create a longitudinal clinical profile for leukemia patients that is more comprehensive and will inform the clinical decision-making process.

A second Ignition Award is going to Dr. Savannah Partridge, a professor in the Department of Radiology at UW Medicine, and co-investigator Dr. Anum Kazerouni. Dr. Partridge’s project aims to utilize quantitative imaging and a novel radiomics approach to better characterize tumor microenvironment heterogeneity in HER2+ breast cancer to predict treatment response and long-term outcomes. With this two-pronged approach, Dr. Partridge intends to identify intrinsic imaging phenotypes that are predictive of a treatment outcome and give providers a better set of tools to assess disease recurrence and develop personalized treatment strategies.

Dr. Elizabeth Swisher, professor in the Division of Gynecologic Oncology at the University of Washington School of Medicine, received an Ignition Award with collaborator Dr. Eric Konnick in the Department of Laboratory Medicine and Pathology for their project titled "Launching a multiplex BRCA 1/RAD51C ddPCR methylation assay as a clinical HRD test." PARP inhibitors are standard treatment for ovarian cancers with a BRCA mutation and Dr. Swisher’s new assay is designed to identify additional cancer characteristics for patients that might benefit from this treatment. The goal is to have this assay ready for clinical testing, and to obtain a Washington state license for medical diagnostics, so that the genomic makeup of patient tumors can be accurately determined to receive appropriate life-changing therapy.

A Technology Dissemination Award went to Dr. Liangcai Gu, associate professor in the Department of Biochemistry at University of Washington, for his project titled "Pixel-seqV2: Cost-effective and scalable 0.6um-resolution spatial transcriptomics." Spatial transcriptomics is a new sequencing method that enables the 3D mapping of gene expression in the tumor microenvironment. By utilizing an assay with sub-micron resolution, Liangcai plans to investigate tumor resistance mechanisms, discover new biomarkers and inform novel therapeutic strategies. With unparalleled single cell resolution and a new gel fabrication method to increase resolution, this methodology can reduce costs by approximately 50-fold, enabling broader use and application when investigating lung adenocarcinoma or prostate cancer tissues. In year one, the Gu Lab will collaborate to disseminate the technology with the Alice Berger, Sanjay Srivatsan and Michael Haffner labs at Fred Hutch. Liangcai aims to expand the dissemination to additional local and national labs in year two. 

A second Technology Dissemination Award went to Dr. Steven Henikoff, professor in the Basic Sciences Division, for his project titled "Affordable Precision Oncology based on FFPE-CUTAC" with co-investigator Dr. Ronald Paranal. For precision oncology to scale to meet the demands of personalized therapies, processing costs for FFPE tissues must come down so that sequencing at greater depths is more affordable. Dr. Henikoff’s proposal aims to extend and automate their FFPE-CUTAC method for mapping the active DNA regulome by combining it with their AutoCUT&Tag protocol. With these two methods, the goal is to drive down sequencing costs and make precision oncology more affordable by making the cost of a sample <$100 at scale. Dissemination of the technology will occur through collaborations with Fred Hutch faculty and through the Cancer Consortium Genomics & Bioinformatics Shared Resource.