President's Showcase
Ziya "Tina" Tian she/her
Supervising Professor: Dr. Xian Mallory
Tina is a senior pursuing dual degrees in Biological Science and Computer Science. She began her research journey by studying protein structures and interactions in the Mycobacterium tuberculosis divisome, but her interest in bioinformatics eventually led her to the Mallory group, where she develops computational tools to infer cancer phylogeny. Currently, her work integrates bulk and single-cell sequencing data to better understand cancer evolution. Upon graduation, she plans to pursue a PhD in computational biology, aiming to leverage her combined knowledge to tackle complex biological questions.
Abstract
Cancer is driven by somatic mutations that cause abnormal cell growth and replication. As the disease develops, new cancer cells inherit mutations from their parent cells and may gain new mutations that confer different characteristics, thus branching into distinct clusters of clones. This intra-tumor heterogeneity (ITH) necessitates an evolutionary approach to designing cancer treatment. Single-cell DNA sequencing (scDNA-seq) has greatly facilitated the computational reconstruction of cancer phylogeny; however, it can detect either single-nucleotide variations (SNVs) or copy number aberrations (CNAs) but not both simultaneously. Though it is possible to computationally reconcile SNVs and CNAs on a single phylogenetic tree using separate scDNA-seq data, performing scDNA-seq on two sets of cells is costly, especially for SNV detection. This project aims to develop a computational tool that can infer a phylogenetic tree using SNVs detected from traditional “bulk” sequencing and CNAs detected from scDNA-seq.
First, a phylogenetic tree is generated using single-cell CNA data, then SNVs are placed on the tree by comparing their variant allele frequencies (VAFs) with the sizes of CNA clones. Additionally, the SNV signals from clustered CNA cells will guide the placement of the SNVs. Cases where a SNV resides inside a CNA, the sampling bias between the SNV cells and the CNA cells, and the SNV read count fluctuation are also addressed. The results of this computational tool will reveal insights into the interplay between SNVs and CNAs in tumor growth and inform the development of cancer treatments.
Presentation Materials