자료유형  

 학위논문

Control Number  

0017161804

International Standard Book Number  

9798382807485

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Muscarella, Antonio Dominic.

Publication, Distribution, etc. (Imprint  

[S.l.] : Princeton University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

83 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

General Note  

Advisor: Singh, Mona.

Dissertation Note  

Thesis (Ph.D.)--Princeton University, 2024.

Summary, Etc.  

요약The rapid proliferation of large-scale cancer sequencing data and the subsequent development of computational methods to analyze it has afforded us a unique opportunity to interrogate the fundamental processes disrupted in cancer. However, there remain many unresolved questions in cancer biology, including: the complex interplay between changes in the cancer genome and transcriptome with downstream changes in the metabolome, which has shown to play a crucial role in a variety of cancer contexts; and the impact of variants of unknown significance which arise in clinical tumor sequencing panels. Both of these outstanding questions hold a particular importance in the development of targeted therapeutics and precision cancer medicine. In this dissertation, I introduce novel computational approaches to advance our understanding of cancer biology on these fronts. First, I train a machine learning method to predict metabolite levels in cancer from gene expression data. I employ a sophisticated cross-validation procedure to maximize power and minimize bias, apply state-of-the-art feature importance analysis to uncover underlying gene-metabolite relationships behind model predictions, and demonstrate that the methodology is broadly applicable to both cancer cell line and patient-derived primary tumor data. Next, I develop a library of aggregated features to characterize cancer somatic mutations with the aim of identifying driver events. I employ this library in the development of an interactive visualization tool to aid in the interrogation of variants of unknown significance in individual patient sequencing data. I then demonstrate the utility of this tool in distinguishing mutations with known cancer effects in a large cohort of targeted tumor sequencing patients. Together, these two approaches provide deeper insights into cancer biology and will enable the development of more effective therapies to treat cancer in the clinic.

Subject Added Entry-Topical Term  

Bioinformatics.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Biology.

Subject Added Entry-Topical Term  

Oncology.

Index Term-Uncontrolled  

Cancer biology

Index Term-Uncontrolled  

Cancer genomics

Index Term-Uncontrolled  

Cancer metabolism

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Metabolomics

Index Term-Uncontrolled  

Transcriptomics

Added Entry-Corporate Name  

Princeton University Quantitative Computational Biology

Host Item Entry  

Dissertations Abstracts International. 85-12B.

Electronic Location and Access  

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Control Number  

joongbu:656828