Using somatic evolution to unravel cell fate dynamics to gain tissue level insights
<p>Cancer initiation and progression is an evolutionary process where somatic alterations convey fitness advantages to cells that exist in a complex environment acting to shape cell populations. Homeostasis, being the normal state of a tissue prior to disease, prioritizes and protects long liv...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2022
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| _version_ | 1797109733099831296 |
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| author | Schenck, R |
| author2 | Anderson, A |
| author_facet | Anderson, A Schenck, R |
| author_sort | Schenck, R |
| collection | OXFORD |
| description | <p>Cancer initiation and progression is an evolutionary process where somatic alterations convey fitness advantages to cells that exist in a complex environment acting to shape cell populations. Homeostasis, being the normal state of a tissue prior to disease, prioritizes and protects long lived stem cells that are capable of incurring these somatic alterations and contributes to the early evolutionary dynamics from which cancer initiation occurs. Research over the past two decades has made substantial inroads into our understanding of somatic mutations through characterization of healthy tissue. In parallel, agent based mechanistic models have emerged as an important tool for understanding somatic mutation and stem cell dynamics in both cancer and homeostatic tissues. In this thesis we combine first principles mathematical modeling of homeostasis, complete with high-resolution genotypic information, with healthy patient sequence data in various tissue types to answer critical biological questions about somatic evolution, stem cell dynamics, and cancer initiation.</p>
<p>We first develop a three-dimensional mechanistic model of the homeostatic epidermis and methodology necessary to incorporate base pair resolution mutation data. We use this model to examine neutrally expanding subclonal populations and compare simulations to healthy patient data. We then introduce functionally heterogenous oncogenic drivers in our <i>in-silico</i> model to examine non-neutral dynamics in the epidermis. Next, we develop a novel lineage tracing method using fluctuating methylation sites through analysis of patient data combined with a spatial model of a homeostatic intestinal crypt to determine stem cell dynamics in glandular tissue. Our spatial model reveals the necessary dynamics of fluctuating methylation sites to be used as fluctuating methylation clocks that allows us to determine stem cell behavior over short timescales <i>in vivo</i>.</p>
<p>In the last chapters of this thesis, we focus on using our novel fluctuating methylation clock and somatic evolution as an important biomarker to understand clonal hematopoiesis. As the hematopoietic stem cells become homogenized through disease, a distinctive distribution emerges within patient data that can be exploited to diagnose clonal hematopoiesis without the need to understand somatic drivers. Combined, the work in this thesis reveals the power of somatic evolution on various timescales as a marker that can provide tissue level insights. We conclude by outlining a future direction where combining fluctuating methylation markers with DNA alteration could be a powerful tool in diagnostics and prognosis within the clinic.</p> |
| first_indexed | 2024-03-07T07:45:37Z |
| format | Thesis |
| id | oxford-uuid:8570ed87-eb63-4c78-afdf-8df197fdf35f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:45:37Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | oxford-uuid:8570ed87-eb63-4c78-afdf-8df197fdf35f2023-06-07T07:10:55ZUsing somatic evolution to unravel cell fate dynamics to gain tissue level insightsThesishttp://purl.org/coar/resource_type/c_db06uuid:8570ed87-eb63-4c78-afdf-8df197fdf35fMathematical modelsEvolutionComputational biologyCarcinogenesisEnglishHyrax Deposit2022Schenck, RAnderson, ALeedham, SHall, BColes, M<p>Cancer initiation and progression is an evolutionary process where somatic alterations convey fitness advantages to cells that exist in a complex environment acting to shape cell populations. Homeostasis, being the normal state of a tissue prior to disease, prioritizes and protects long lived stem cells that are capable of incurring these somatic alterations and contributes to the early evolutionary dynamics from which cancer initiation occurs. Research over the past two decades has made substantial inroads into our understanding of somatic mutations through characterization of healthy tissue. In parallel, agent based mechanistic models have emerged as an important tool for understanding somatic mutation and stem cell dynamics in both cancer and homeostatic tissues. In this thesis we combine first principles mathematical modeling of homeostasis, complete with high-resolution genotypic information, with healthy patient sequence data in various tissue types to answer critical biological questions about somatic evolution, stem cell dynamics, and cancer initiation.</p> <p>We first develop a three-dimensional mechanistic model of the homeostatic epidermis and methodology necessary to incorporate base pair resolution mutation data. We use this model to examine neutrally expanding subclonal populations and compare simulations to healthy patient data. We then introduce functionally heterogenous oncogenic drivers in our <i>in-silico</i> model to examine non-neutral dynamics in the epidermis. Next, we develop a novel lineage tracing method using fluctuating methylation sites through analysis of patient data combined with a spatial model of a homeostatic intestinal crypt to determine stem cell dynamics in glandular tissue. Our spatial model reveals the necessary dynamics of fluctuating methylation sites to be used as fluctuating methylation clocks that allows us to determine stem cell behavior over short timescales <i>in vivo</i>.</p> <p>In the last chapters of this thesis, we focus on using our novel fluctuating methylation clock and somatic evolution as an important biomarker to understand clonal hematopoiesis. As the hematopoietic stem cells become homogenized through disease, a distinctive distribution emerges within patient data that can be exploited to diagnose clonal hematopoiesis without the need to understand somatic drivers. Combined, the work in this thesis reveals the power of somatic evolution on various timescales as a marker that can provide tissue level insights. We conclude by outlining a future direction where combining fluctuating methylation markers with DNA alteration could be a powerful tool in diagnostics and prognosis within the clinic.</p> |
| spellingShingle | Mathematical models Evolution Computational biology Carcinogenesis Schenck, R Using somatic evolution to unravel cell fate dynamics to gain tissue level insights |
| title | Using somatic evolution to unravel cell fate dynamics to gain tissue level insights |
| title_full | Using somatic evolution to unravel cell fate dynamics to gain tissue level insights |
| title_fullStr | Using somatic evolution to unravel cell fate dynamics to gain tissue level insights |
| title_full_unstemmed | Using somatic evolution to unravel cell fate dynamics to gain tissue level insights |
| title_short | Using somatic evolution to unravel cell fate dynamics to gain tissue level insights |
| title_sort | using somatic evolution to unravel cell fate dynamics to gain tissue level insights |
| topic | Mathematical models Evolution Computational biology Carcinogenesis |
| work_keys_str_mv | AT schenckr usingsomaticevolutiontounravelcellfatedynamicstogaintissuelevelinsights |