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Application of the single cell genomics in deciphering tumor heterogeneity and its role in tumor progression and drug resistance

Author(s)
Marjanovic, Nemanja.
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Massachusetts Institute of Technology. Computational and Systems Biology Program.
Advisor
Aviv Regev and Tyler Jacks.
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Tumor progression, from the single mutated cell to the advanced stages of cancer, represents an evolutionary process. During tumor progression, cancer cells acquire new genetic mutations, becoming more heterogeneous, leading to tumor progression and resistance to therapy. However, clear genetic drivers of progression, metastasis, and therapeutic resistance are identified in only a subset of tumors, pointing to non-genetic contributors to cancer progression. Also, somatic evolution in cancer is occurring at the level of the single cell. Therefore, the application of the single cell genomic method is crucial for deciphering phenotypic heterogeneity. Here, we profiled single cell transcriptomes from genetically engineered mouse lung tumors at seven stages spanning tumor progression from atypical adenomatous hyperplasia to lung adenocarcinoma. The diversity of transcriptional states spanned by tumor cells increased over time and was reproducible across tumors and mice, but was not explained by genomic copy number variation. Cancer cells progressively adopted alternate lineage identities, computationally predicted to be mediated through a common transitional, high-plasticity cell state (HPCS). HPCS cells prospectively isolated from mouse tumors had robust potential for phenotypic switching and tumor formation and were more chemoresistant in mice. Our study reveals transitions that connect cell states across tumor evolution and motivates therapeutic targeting of the HPCS.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February, 2021
 
Cataloged from the official PDF of thesis. "February 2021."
 
Includes bibliographical references.
 
Date issued
2021
URI
https://hdl.handle.net/1721.1/130830
Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Publisher
Massachusetts Institute of Technology
Keywords
Computational and Systems Biology Program.

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