2026-05-24T10:04:40Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1579662024-12-20T18:25:12Zoai_pmh:alloai_pmh:repo_items157966
https://hdl.handle.net/2286/R.I.55668
http://rightsstatements.org/vocab/InC/1.0/
2019
xii, 130 pages : color illustrations
Doctoral Dissertation
Academic theses
Text
eng
Ahmadinejad, Navid
Liu, Li
Maley, Carlo
Dinu, Valentin
Arizona State University
Partial requirement for: Ph.D., Arizona State University, 2019
Includes bibliographical references
Field of study: Bioinformatics
Understanding intratumor heterogeneity and their driver genes is critical to<br/><br/>designing personalized treatments and improving clinical outcomes of cancers. Such<br/><br/>investigations require accurate delineation of the subclonal composition of a tumor, which<br/><br/>to date can only be reliably inferred from deep-sequencing data (>300x depth). The<br/><br/>resulting algorithm from the work presented here, incorporates an adaptive error model<br/><br/>into statistical decomposition of mixed populations, which corrects the mean-variance<br/><br/>dependency of sequencing data at the subclonal level and enables accurate subclonal<br/><br/>discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer<br/><br/>simulations and real-world data, this new method, named model-based adaptive grouping<br/><br/>of subclones (MAGOS), consistently outperforms existing methods on minimum<br/><br/>sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports<br/><br/>subclone analysis using single nucleotide variants and copy number variants from one or<br/><br/>more samples of an individual tumor. GUST algorithm, on the other hand is a novel method<br/><br/>in detecting the cancer type specific driver genes. Combination of MAGOS and GUST<br/><br/>results can provide insights into cancer progression. Applications of MAGOS and GUST<br/><br/>to whole-exome sequencing data of 33 different cancer types’ samples discovered a<br/><br/>significant association between subclonal diversity and their drivers and patient overall<br/><br/>survival.
Bioinformatics
Cancer Evolution
Model Based Hierarchical Clustering
Tumor Heterogeneity
Tumors--Genetic aspects--Data processing.
Tumors
Cancer--Genetic aspects--Data processing.
Cancer
Discovering subclones and their driver genes in tumors sequenced at standard depths