Author ORCID Identifier
https://orcid.org/0000-0002-6790-3998
Date of Award
Winter 2-10-2026
Document Type
Thesis (Ph.D.)
Department or Program
Molecular and Systems Biology
First Advisor
Aaron McKenna
Second Advisor
Steve Leach
Third Advisor
Pamela Rosato
Abstract
Cancer is a highly complex disease that often evolves in response to therapy. Unfortunately, this means relapse is common and typically results in more aggressive tumors that are challenging to treat. A better understanding of cellular dynamics within tumors that enable treatment resistance would greatly improve our capabilities to truly cure cancer. Lineage tracing, a variety of methods used to record cell relationships, is a promising technique to explore clonal dynamics within tumors. A range of lineage tracing tools exist and have been applied to multiple cancer models, yielding insights into treatment resistance mechanisms, clonal competition, and pathways to metastasis. In this work, we apply two distinct lineage tracing systems to different cancer models to explore how insights from lineage tracing can inform treatment. First, an in vitro model of glioblastoma (GBM) is engineered with a dynamic base editing lineage tracing system to follow inheritance dynamics of extrachromosomal DNA (ecDNA) through treatment. This reveals both how ecDNA contributes to therapeutic resistance and how different treatment modalities influence ecDNA. Exploring ecDNA’s contribution to GBM treatment resistance is clinically relevant as these tumors almost inevitably recur and ecDNA is found in most tumors at diagnosis. Second, an in vivo lineage tracing system is combined with a transgenic spontaneous model of breast cancer to characterize malignant and immune cell responses to treatment. We utilize the mouse for actively recording cells 1 (MARC1) to label the lineage of all cells in a mouse model of aggressive luminal b breast cancer. We apply this system to explore how radiation may improve the efficacy of immunotherapy in estrogen receptor (ER) positive breast cancer. This is an important clinical question as immunotherapy outcomes vary and approaches to increase its efficacy will benefit patients. These two distinct projects are united by their application of lineage tracing to explore cancer and highlight the potential of lineage tracing to understand treatment resistance and treatment responses across cancer types.
Recommended Citation
Marshall, Abigail Catherine, "The Forest Through the Trees: Applying Lineage Tracing to Unravel Cancer Models" (2026). Dartmouth College Ph.D Dissertations. 455.
https://digitalcommons.dartmouth.edu/dissertations/455
Included in
Cancer Biology Commons, Genetics and Genomics Commons, Laboratory and Basic Science Research Commons, Other Chemicals and Drugs Commons
