Application of the single cell genomics in deciphering tumor heterogeneity and its role in tumor progression and drug resistance
Name
1252628047-MIT.pdf
Size
13.56 MB
Format
Adobe PDF
Checksum (MD5)
e4ebb8f1b0d1233ee594d9d00bbfbd38
Author(s)
Marjanovic, Nemanja.
Advisor(s)
Aviv Regev and Tyler Jacks.
Date Issued
2021
Publisher
Massachusetts Institute of Technology
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.
Subjects
Computational and Systems Biology Program.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
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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.
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