Abstract Number:
1955
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Chase Sakitis (1), Alex Soupir (2), Mary Townsend (3), Jose Laborde (4), Courtney Johnson (5), Andrew Lawson (6), Joellen Schildkraut (5), Shelley Tworoger (7), Kathryn Terry (8), Lauren Peres (7), Brooke Fridley (1)
Institutions:
(1) Children's Mercy, N/A, (2) Biostatistics and Bioinformatics Shared Resource, Moffitt Cancer Center, Tampa, FL, (3) Division of Oncological Sciences, Knight Cancer Institute Oregon Health and Science University, Portland, OR, (4) Moffitt Cancer Center, N/A, (5) Emory University, Atlanta, GA, (6) Medical University of South Carolina, College of Medicine, N/A, (7) Moffitt Cancer Center, Tampa, FL, (8) Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
Co-Author(s):
Alex Soupir
Biostatistics and Bioinformatics Shared Resource, Moffitt Cancer Center
Mary Townsend
Division of Oncological Sciences, Knight Cancer Institute Oregon Health and Science University
Andrew Lawson
Medical University of South Carolina, College of Medicine
Kathryn Terry
Brigham and Women’s Hospital and Harvard Medical School
First Author:
Presenting Author:
Abstract Text:
Examining the tumor immune microenvironment (TIME) has been revolutionized by advancements in spatial proteomic imaging techniques. These techniques assess multiple markers simultaneously to differentiate different immune cell populations in the TIME. The analysis of these immune profiles has become increasingly significant with the progress of immunotherapy treatments. The over-dispersed nature of the cell count data is accounted for by modeling the count data using a beta-binomial distribution. To account for the correlation between the different cell populations in the TIME (i.e., T cells and Cytotoxic T cells), we developed a Bayesian hierarchical beta-binomial model. The Bayesian model can incorporate different covariance (or relationship) structures between the different immune cell populations to incorporate immune differentiation paths. To illustrate the Bayesian model and different covariance structures that are possible, the model is applied to spatial proteomic data from three large epidemiologic cohorts (N = 486) looking at the TIME of ovarian cancer.
Keywords:
Bayesian|beta-binomial model|covariance structures|hierarchical|spatial protein imaging data|tumor immune microenvironment
Sponsors:
Biometrics Section
Tracks:
Miscellaneous
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