Computational Biology Thesis Defense
Speaker
HAOYUN LEI
Ph.D. Candidate
Joint CMU-PITT Ph.D. Program in Computational Biology
Computational Biology Department
Carnegie Mellon University
When
-
Where
Virtual Presentation - ET
Description
Cancer is an often lethal terminal disease that usually results from an accumulation of mutations that progress in an evolutionary manner, which leads to heterogeneity between tumor cells even within one patient. Characterizing such intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is usually hampered by the limits of available data resources. Bulk DNA sequencing is the most common technology to assess ITH but involves the analysis of a mixture of many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing (SCS) is a promising alternative, but its limitations--e.g., high noise, difficulty scaling to large populations, technical artifacts, and large data sets –– have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. Multiplex interphase fluorescence in situ hybridization (miFISH) can profile tumor evolution in single cells at small numbers of probes without normalization artifacts that make ploidy a challenge for purely sequence-based studies, but limits one to just a few copy number markers per cell.
Our work develops methods to improve the resolution of Copy Number Aberration (CNA) driven evolution in cancers via a strategy of multi-omic data integration, which takes advantage of each aforementioned data type. We first developed a mixed integer linear programming (MILP) model that incorporates a minimum evolution phylogenetic tree cost to study deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with a specific focus on deconvolving genomic copy number data. Then we advanced this model to combine both bulk and single-cell sequencing with miFISH to exploit the additional complementary advantages of FISH technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. Finally, we extend the idea of genomic deconvolution and provide a more general framework to study a wider concept of tumor heterogeneity using reference single-cell RNA-seq to interpret sample collections for which only bulk RNA-seq is available for some samples, e.g., clonally resolving archived primary tissues using single-cell RNA-seq from metastases.
Thesis Committee:
Russell Schwartz (Chair, CMU)
Veronica Hinman (CMU)
Miller Lee (PITT)
Huaiying Zhang (CMU)
Zoom Participation. See announcement.