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dc.contributor.advisorAviv Regev and Tyler Jacks.en_US
dc.contributor.authorMarjanovic, Nemanja.en_US
dc.contributor.otherMassachusetts Institute of Technology. Computational and Systems Biology Program.en_US
dc.date.accessioned2021-05-25T18:21:57Z
dc.date.available2021-05-25T18:21:57Z
dc.date.issued2021en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/130830
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February, 2021en_US
dc.descriptionCataloged from the official PDF of thesis. "February 2021."en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractTumor 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.en_US
dc.description.statementofresponsibilityby Nemanja Marjanovic.en_US
dc.format.extent152 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectComputational and Systems Biology Program.en_US
dc.titleApplication of the single cell genomics in deciphering tumor heterogeneity and its role in tumor progression and drug resistanceen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Programen_US
dc.identifier.oclc1252628047en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Computational and Systems Biology Programen_US
dspace.imported2021-05-25T18:21:57Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentCSBen_US


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