Funded Projects

21CTP.LEUK01 “A phase 2 Trial of Asciminib, Dasatinib, Prednisone, and Blinatumomab for Patients > 65 Years of Age with Newly Diagnosed Philadelphia Chromosome Positive (Ph+) Acute Lymphoblastic Leukemia.”
PI: Megan Othus  •  SWOG-CTP-21CTP.LEUK01
Estimate and evaluate the overall response rate (ORR) of sabatolimab in combination with hypomethylating agent (HMA) in patients with MDS/MPN including CMML or advanced MPN. Estimate and evaluate overall and event-free survival in patients with MDS/MPN overlap syndrome or advanced MPN who receive MBG453 (sabatolimab) + HMA.  Estimate and evaluate the effect of MBG453 (sabatolimab) + HMA on symptom relief (by MPN-SAF), transfusion burden, time in hospital, and immune-related side effects.

CAREER: Advancing the Bioinformatic Infrastructure and Methodology for Single-cell RNA Sequencing
PI: Jingyi Jessica Li • NSF 1846216
Develop databases and bioinformatics methods for analyzing single-cell RNA sequencing data, with focuses on cross-species network analysis and differential gene expression analysis across discrete cell types.

Characterizing Immunogenetics in Type 1 Diabetes
MPI: Lue Ping Zhao, Ake Lernmark, Dan Geraghty • NIH  R01 DK132406
To sequence HLA class I/II and KIR genes, in a prospective birth cohort in which the seroconversion timings were precisely determined through frequent collection of biospecimens from all participants

Collaborative Research: Development of Classification Theory and Methods for Objective asymmetry, Sample Size Limitation, Labeling Ambiguity, and Feature Importance
PI: Jingyi Jessica Li • NSF 2113754
Develop a suite of application-driven, theory-backed methods and algorithms to address pressing data challenges, including sample size limitations, sampling biases, and ambiguous class labels.

Comparative Modeling of Effective Policies for Colorectal Cancer Control
MPI: Carolyn Rutter, Ann Zauber, Karen Kuntz, Amy Knudsen, Iris Landsport-Vogelaar • NIH U01 CA253913
Use microsimulation modeling to evaluate and help prioritize interventions to further reduce the burden of colorectal cancer

Consortium on Translational Research in Early Detection of Liver Cancer: Data Management and Coordinating Center (DMCC)
PI: Ziding Feng • U24 CA230144
Establish the Data Management and Coordinating Center (DMCC) for the Consortium on Translational Research in Early Detection of Liver Cancer.

Developing Methods for Advancing the Early Detection of Pancreatic Ductal Adenocarcinoma Leveraging Electronic Medical Records Data
MPI: Yingqi Zhao,  Suresh Chari • NIH R01 CA289668
Detecting Pancreatic ductal adenocarcinoma (PDAC) at an early stage has been challenging due to its rapid progression. Collaborative efforts using electronic medical records (EMRs) have shown promise in advancing early PDAC detection, but challenges remain in optimally defining at-risk populations for PDAC and identifying biomarkers to effectively enrich PDAC risk. This proposal leverages the EMR platform to address these challenges, aiming to identify high-risk populations and enhance risk stratification using EMR-derived biomarkers.

Development of Innovative Approaches to Risk Stratify and Target Therapy for AML
MPI: Derek Stirewald,  Megan Othus •  SWOG/Hope Foundation for Cancer Research
Leverage our existing data and pipeline for proteomic studies to validate potential prognostic biomarkers and therapeutic targets using a systematic, stepwise approach; examine the molecular function of the verified biomarkers and targets using ex vivo and in vivo models.

Early Detection Initiative for Pancreatic Cancer (EDI)
PI: Ziding Feng • Pancreatic Cancer Action Network (contract) 
Senior Biostatistician for the EDI Study and manuscript development for PANCAN.

Enhancing Rigor and Reliability of Single-cell Data Science
PI: Jingyi Jessica Li • Chan-Zuckerberg Foundation
Develop a realistic data-driven simulator with ground truths for method benchmarking and robust false-discovery control approach to improve the reliability of single-cell data discoveries.

Generating Research Opportunities Within Statistics at Fred Hutch (GROWS@FredHutch)
MPI: Megan Othus, Ruth Etzioni • NIH R25 CA272187
GROWS@FredHutch (Generating Research Opportunities Within Statistics at Fred Hutch) is a summer mentored research program for undergraduates, and will include an engaging and supportive mentored research experience along with career development, community building, and social/emotional support activities to encourage students to (1) gain skills and confidence in cancer statistics and data science and (2) feel a sense of belonging and rightful place in the statistics and cancer research communities.   

Genetics, Epigenetics, and Risk Prediction for Esophageal Adenocarcinoma
MPI: Charles Kooperberg (contact), Matthew Buas • NIH R01 CA266386
Study underlying susceptibility to and develop prediction models for EAC.

Health and Financial Costs of Unequal Care: Colorectal Cancer as a Case Study
PI: Carolyn Rutter • NIH R01 MD017599
Summarize current knowledge about racial/ethnic disparities in CRC screening, treatment, and outcomes, and will use this information to estimate the extent to which disparities in care explain disparities in CRC outcomes, and to evaluate policies to reduce disparities.

Improving the Design and Analysis of Randomized Screening Trials in a New Era of Cancer Early Detection
MPI: Yingqi Zhao Y (contact), Yingye Zheng Y • PCORI ME-2023C3-35543 
To improve the design and analysis of RSTs to accelerate the evaluation of cancer-specific mortality benefits and associated harms of new biomarker tests.

Integrative Genomics into Genetic Association Studies of Blood Pressure and Stroke in African Americans
MPI: Li Hsu (contact), Charles Kooperberg, Alex Reiner • NIH R01 HL152439
Identify variants predicting genomic features in study samples and integrate this functional information into genetic association analysis of blood pressure and stroke.

Joint analysis of H&E images and PhenoCycler spatial proteomic data
MPI: Wei Sun (contact), Li Hsu • NIH  P50 CA285275
This project aims to address the disparity in colorectal cancer (CRC) mortality across racial and ethnic groups by investigating tumor microenvironment.

Modeling and Analytics for Cancer Diagnostics: Traversing the Data-evidence Divide
PI: Ruth Etzioni • NIH R35 CA274442
This research program aims to carefully combine all the available data to learn which new tests we should be recommending to the public.

Modeling Precision Interventions for Prostate Cancer Control
MPI: Ruth Etzioni (contact), Nora Pashayan, Dimitris Rizopoulos, Alexander Tsodikov • NCI NIH U01 CA253915
Advance the evidence necessary to make informed decisions and individualized screening and treatment for prostate cancer while reducing racial disparities in care.

Polygenic Risk Scores for Diverse Populations — Bridging Research and Clinical Care
MPI: Charles Kooperberg (contact), Christopher Gignoux, Kari North • NIH R01 HL151152
Leverage PAGE study data with extension to a network of biobanks with linked electronic health records (EHR), thereby capturing cardiovascular disease (CVD) and its risk factors to demonstrate the utility of PRS for CVD across multiple populations and ensure applicability across broad populations.

Precompetitive Collaboration on Liquid Biopsy for Early Cancer Assessment: Data Management and Coordinating Unit
PI: Yingye Zheng (contact), Wei Sun • NIH  U24 CA288185 
To support the liquid biopsy consortium for consortium coordination, data management, protocol development, as well as innovative and state-of-the-art statistical and computational analysis.

SeattleStatSummer for Biomedical Data Science Research Training
MPI: Megan Othus (contact), Jennifer Bobb, Jeanne Chowning  •  NIH R25 LM014210
The driving objective of SeattleStatSummer (SeattleStatSummer for Biomedical Data Science Research Training) is to enrich the field of Biostatistics by engaging undergraduates and inspiring them to explore the discipline as a career path. We will develop and implement a new summer mentored research program for undergraduates. The program will offer: (1) an engaging and supportive mentored research experience that increases their awareness of and interest in pursuing careers in statistics and data science research and (2) career development, community building, and social/emotional support activities.

Startup Effort On CTP Preferred Partnership Trials
PI:  Michael LeBlanc  •  SWOG CTP
Leadership efforts for study goals and design. 

Statistical Methods for Analyzing Objectively Measured Physical Activity Data
PI: Chongzhi Di • NIH R01 HL130483
This project aims to develop new analytic tools for understanding how accumulation patterns of physical activ- ity, sedentary behavior and sleep in the 24-hour activity cycle are associated with health outcomes, such as cardiovascular diseases.

Statistical Methods for Elucidating Regulatory Mechanisms and Functional Impacts of Transcriptome Variation at Population and Single-Cell Scales
PI: Jingyi Jessica Li • NIH R35GM140888
Develop powerful and interpretable statistical methods for elucidating regulatory mechanisms and functional impacts of transcriptome variation at population and single-cell scales.

Statistics and Data Management Center (SDMC) for the NCI Cancer Screening Research Network (CSRN)
MPI: Charles Kooperberg, Ziding Feng, Katherine Guthrie • NIH UG1 CA287013
The SDMC of the CSRN will offer a rigorous system for CSRN trial design, management, and analysis so that the information generated by CSRN trials forms a sound basis for national cancer screening policy.

Statistical Methods for Enhanced Mapping of Microbiome Relationships
PI: Michael Wu • NIH R01 GM151301 
To develop statistical and computational approaches to enhance the mapping of critical relationships of microbiome profiling studies. The knowledge gained from these methods offer the potential to improve etiology and also drive the development of new therapies, particularly as the microbe is modifiable.

Statistical Methods for Inferring Gene-Phenotype Associations Using Omic Data from Gene Knockout and Human Phenotype Studies
MPI: Wei Sun (Contact), Li Hsu, Ali Shojaie • NIH U01 HG013177
To develop statistical methods to combine MorPhiC resources and omic data in human phenotype studies to infer gene functions in diverse settings and identify potential causal genes of human phenotypes. These methods will pave the way for utilizing MorPhiC resources to study the impact of gene loss on complex phenotypes.

Statistical Methods for Large-Scale Microbiome Studies of Cardiovascular Disease Risk
PI: Michael Wu • NIH R01 HL155417
Within the context of three of the largest and richest microbiome profiling studies, this proposal seeks to develop statistical and computational approaches for enabling a more robust and sophisticated understanding of how microbes are related to cardiovascular disease risk factors.

Statistical Methods for Network-based Integrative Analysis of Microbiome Data
PI: Jing Ma • NIH R01 GM145772
Develop novel statistical methods for analysis of microbiome and other -omics data types.

Statistical Methods for Precision Prevention
PI: Li Hsu • NIH R01CA297681
The objective of this application is to develop statistical and computational approaches to harness state-of-the-art genomic data and translate these findings into data-driven prevention through risk-based intervention strategies.

Statistical Methods for RNA-seq Data Analysis
PI: Wei Sun  •  NIH R01 GM105785
Our methods and data analysis results will help translate the rich information in single cell RNA-seq or spatial transcriptomics data into knowledge of biology and potentially actionable conclusions to improve human health.

SWOG Statistics and Data Management Center (SDMC)
PI:  Michael LeBlanc • NIH U10CA180819
The SDMC provides statistical leadership and data management expertise in the design, implementation, monitoring, analysis and interpretation of clinical trials and translational medicine studies.

The Early Detection Research Network: Data Management and Coordination Center
MPI: Yingye Zheng (contact), Ruth Etzioni, Ziding Feng  • NIH U24 CA086368
Provide comprehensive data management and coordination for biomarker discovery and validation.