Innovative Applications of Hidden Markov Models in Cancer Data Science

• Activity Number: 176 - New England Statistical Society Invited Papers on Novel Developments and Future Directions of Statistics in Data Science
• Type: Invited
• Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
• Sponsor: New England Statistical Society
• Author(s): Paul S. Albert*
• Companies: National Cancer Institute
• Keywords: Hidden Markov Model ; Correlated Data ; Natural History Modeling ; Disease Surveillance ; Stochastic Models

Abstract

During the past 30 years, Hidden Markov modeling (HMM) has had a big impact in the analysis of biomedical data, with a number of important application areas in genomics, natural history modeling, environmental monitoring, and the analysis of longitudinal data. In cancer genomics, for example, the use of HMM has played an important role in uncovering both susceptibility (germline) and tumor progression (somatic) of cancer. In this talk, I will present a series of novel applications of HMMs in cancer epidemiology and genetics. I will describe the use of HMM to identify multiple subclones in next-generation sequences of tumor samples (Choo-Wosoba et al., Biostatistics 2021). I will also discuss the application of HMMs for characterizing the natural history of natural history of human papillomavirus and cervical precancer (Aron et al., Statistics in Medicine, 2021). Last, I will discuss the application of HMM for cancer surveillance. All three examples required interesting adaptations of standard HMM estimation that will be highlighted. Time permitting, I will talk about research opportunities using HMM in biomedical data science.