SEMINAR – September 27, 2024

Abstract

A personalized cancer-specific integrated network estimation (PRECISE), a general framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures. We develop a Bayesian regression model for protein-protein interactions that integrates with known pathway annotations and protein-protein interactions. Using the pan-cancer functional proteomic data on 32 cancer types from The Cancer Genome Atlas, we demonstrate the utility of PRECISE in inferring commonalities and differences in network biology across tumor lineages and in using patient-specific pathway-based signatures for robust tumor stratification and prediction in a pan cancer study.

The microbiome plays a critical role in human health and disease, and there is a strong scientific interest in linking specific features of the microbiome to clinical outcomes. There are key aspects of microbiome data, however, that limit the applicability of standard variable selection methods. In particular, the observed data are compositional, as the counts within each sample have a fixed-sum constraint. In addition, microbiome features, typically quantified as operational taxonomic units, often reflect microorganisms that are similar in function, and may therefore have a similar influence on the response variable. To address the challenges posed by these aspects of the data structure, a variable selection technique is proposed with the following novel features: a generalized transformation and z-prior to handle the compositional constraint, and an Ising prior that encourages the joint selection of microbiome features that are closely related in terms of their genetic sequence similarity. The proposed method outperforms existing penalized approaches for microbiome variable selection in both simulation and the analysis of real data exploring the relationship of the gut microbiome to body mass index.

About the Speaker

Dr Kim-Anh Do is a Professor and former Chair of the Department of Biostatistics at The University of Texas MD Anderson Cancer Center. She was a recipient of the MD Anderson Faculty Scholar Award in 2003, was awarded the President’s Faculty Excellence Award in 2014, the Texas 4000 Distinguished Professorship in 2014, and the Electa C. Taylor Chair in Cancer Research in 2017. She is a Fellow of the American Statistical Association. the American Association for the Advancement of Science (AAAS), the Royal Statistical Society and an Elected Member of the International Statistical Institute. This year, she is the Janet Norwood Awardee as an outstanding woman in the statistical sciences.

Dr Do has served as a primary statistician, core director, co-investigator on several National Institutes of Health (NIH) funded grants and clinical trials in prostate cancer, epidemiology, leukemia, and upper aerodigestive cancer, Breast Cancer SPORE, and the Brain SPORE at MD Anderson. She has more than 260 impactful publications in statistical methodology, computing, biomedical, and in other applied specialist journals. Her most recent interest is in the development of clustering and analytic methods for multi-omic expressions including microbiome data. She has developed bioinformatics/biostatistics software programs and written three books on analytical methods for gene expression and proteomics. She has had long-term and successful collaborations with scientists in numerous cancer types and provide comprehensive biostatistics and bioinformatics expertise, in particular, novel clinical trial designs, including Bayesian designs, to ensure statistical integrity and to optimize data analysis. She has more than 35 years of experience in supervising graduate students and postdoctoral fellows in statistics, biostatistics, bioinformatics, the School of Public Health, and engineering. She has participated in several teaching activities, including graduate student recruitment in the joint biostatistics program (MD Anderson, Rice University, and the Graduate School of Biomedical Sciences (GSBS) at The University of Texas), curriculum review and redesign with the GSBS program. To date, She has supervised more than 60 postdoctoral fellows and graduate students.

Event Organizer

Jonathan L. Auerbach
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University

Ben Seiyon Lee
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University