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Systems level cancer disease target identification using tumor microenvironment dynamics
Thesis (MSc)--Stellenbosch University, 2018.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2018
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| _version_ | 1867613969653432320 |
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| access_status_str | Open Access |
| author | Makinde, Funmilayo Lydia |
| author2 | Mazandu, Gaston Kuzamunu |
| author_browse | Makinde, Funmilayo Lydia Mazandu, Gaston Kuzamunu |
| author_facet | Mazandu, Gaston Kuzamunu Makinde, Funmilayo Lydia |
| author_sort | Makinde, Funmilayo Lydia |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2018. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/104950 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:44:35.400Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/104950 Systems level cancer disease target identification using tumor microenvironment dynamics Makinde, Funmilayo Lydia Mazandu, Gaston Kuzamunu Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics. Tumor microenvironment (TME) Immunocompetent cells Tumors -- Growth Cytokines Cancer -- Research -- Mathematical models Proteins -- Research -- Mathematical models UCTD Thesis (MSc)--Stellenbosch University, 2018. ENGLISH ABSTRACT : Cancer is the second driving reasons for death around the world. It is an abnormal growth of cells which can migrate and recreate cellular microenvironment and can also be caused by successive alterations that occur in a set of specific genes in the cell. As dangerous and deadly as cancer is, there is still a gap in our understanding of the mechanism of its establishment, its recurrence cycle and its elimination. It was observed that cancer stem cells are regulated by the complex interaction component that makes up the tumor microenvironment (TME), through a network of growth factors and cytokines. Therefore, focusing on and understanding the role of these components and the level of concentration of modulating factors present in the TME can play a significant role in battling cancer. Many research projects have investigated the dynamics of these cytokines in the context of cancer in order to understand the evolution of cancer for improving diagnostic, prognostic and therapeutic strategies. However, these research are mostly restricted to cells that directly perform cytotoxic effect on cancer cells, such as the natural killer (NK) and cytotoxic T lymphocytes (CTLs or CD8 + T) cells. They do not explicitly integrate cytokine-mediated innate-adaptive immunity in the tumor dynamics. Moreover, none of those research has combined cellular-level mathematical models with protein-protein interaction network analysis, to predict essential proteins, biological processes and enriched pathways associated with cancer disease. Hence, in this study, we present a new mathematical model to investigate the dynamics between tumor cell and host immune system component (i.e., innate and adaptive immune cells and cytokines) in the TME . Results from the numerical solution of our model indicate the capacity of a tumor to escape from immunologic surveillance due to low cytotoxic immune cells (i.e. NK cells and CD8 + T cells) at the tumor site while more of these cytotoxic immune cells at the tumor site results in tumor regression. Our model, therefore, supports strengthening the cytotoxic immune cells in the TME which we see as a significant way of contributing to tumor regression. Also, from the analysis of our model, cytokines such as IL-10, IL-23, TNF-α, TGB-β and IFN-γ has been shown to make contributions significantly to the dynamics of tumor development based on the observed dynamics in the level of concentrations in the TME. Finally, switching of an immune (effector) cell from resting to active states, is triggered by some proteins working in a complex protein-protein interaction (PPI) network. We identified proteins which likely regulate immune cells and cytokines contributing to breast cancer disease outcome and infer essential proteins based on Protein-proteins interaction network, as well as significant pathways and enriched biological processes associated with breast cancer. These biological processes and pathways and essential proteins identified can be further assessed to check for their suitability as targets for the breast cancer disease for the improvement of effective therapeutic strategies. AFRIKAANSE OPSOMMING : Kanker is die tweede oorsaak van dood ter wêreld. Dit is ’n abnormale groei van selle wat kan migreer en herskep sellulêre mikro-omgewing en kan ook veroorsaak word deur opeenvolgende veranderinge wat voorkom in ’n stel spesifieke gene in die sel. So gevaarlik en dodelik soos kanker vir ons samelewing is, is daar steeds ’n gaping in ons begrip van die meganisme van sy vestiging, sy herhalingsiklus en die uitskakeling daarvan. Dit is opgemerk uit navorsing dat kanker stamselle gereguleer word deur die komplekse interaksie komponent wat die tumor mikro-omgewing (TME), deur middel van ’n netwerk van groeifaktore en sitokiene. Daarom fokus op en verstaan die rol van hierdie komponente en die vlak van konsentrasie van moduleringfaktore wat in die TME teenwoordig is, kan ’n belangrike rol speel in die stryd teen kanker. Baie navorsingsprojekte het die dinamika van hierdie sitokiene ondersoek in die konteks van kanker om die evolusie van kanker te verstaan vir die verbetering van diagnostiese, prognostiese en terapeutiese strategieë. Hierdie navorsing word egter meestal beperk tot selle wat direk sitotoksiese effek op kankerselle, soos die natuurlike moordenaar (NK) en sitotoksiese T limfosiete (CTLs of CD8 + T) selle. Hulle integreer eksplisiet sitokien-gemedieerde aangebore-adaptiewe immuniteit in die tumor dinamika. Daarbenewens het geen van hierdie navorsing op wiskundige modelle op sellulêre vlak met proteïen interaksie netwerk analise ondersoek om noodsaaklike proteïene, biologiese prosesse en verrykde paaie wat met kanker geassosieer word te voorspel nie. Daarom bied ons in hierdie studie ’n nuwe wiskundige model wat saamgestel is uit gewone differensiaalvergelykings om die dinamika tussen tumorselle en gasheer immuunstelsel komponent (dws aangebore en adaptiewe immuunselle en sitokiene) in die TME. Resultate van die numeriese oplossing van ons model dui aan die kapasiteit van ’n tumor om te ontsnap uit immunologiese toesig is as gevolg van lae sitotoksiese immuun selle (dws NK-selle en CD8 + T selle) by die tumor terwyl meer van hierdie sitotoksiese immuun selle op die tumor site in tumor lei regressie. Ons model ondersteun dus die versterking van die sitotoksiese immuun selle in die TME wat dien as ’n duidelike manier om by te dra tot tumorregressie. Ook, uit die analise van ons model, sitokiene soos IL-10, IL-23, TNF-α, TGB-β en IFN-γ is getoon dat dit ’n bydrae lewer tot die dinamika van die tumor ontwikkeling gebaseer op die waargenome dinamika in die vlak van konsentrasies in die tumor mikro-omgewing. Laastens, die omskakeling van ’n immuun (effektor) sel van rus na aktiewe toestande word gereguleer deur sommige proteïenwerk in ’n komplekse proteïen-proteïen interaksie (PPI) netwerk. Ons het proteïene geïdentifiseer wat waarskynlik immuun selle en sitokiene reguleer wat bydra tot die uitkoms van borskanker siektes en essensiële proteïene gebaseer op proteïen-proteïen interaksie netwerk, asook belangrike paaie en verrykde biologiese prosesse wat met borskanker geassosieer word, aflei. Hierdie biologiese prosesse en paaie en noodsaaklik proteïene geïdentifiseer kan verder geassesseer word om na te gaan of hulle geskik is vir teikens vir die borskanker siekte vir die verbetering van effektiewe terapeutiese strategieë. 2018-11-26T17:27:57Z 2018-12-07T06:51:56Z 2018-11-26T17:27:57Z 2018-12-07T06:51:56Z 2018-12 Thesis http://hdl.handle.net/10019.1/104950 en_ZA Stellenbosch University xii, 82 pages : illustrations (chiefly colour) application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Tumor microenvironment (TME) Immunocompetent cells Tumors -- Growth Cytokines Cancer -- Research -- Mathematical models Proteins -- Research -- Mathematical models UCTD Makinde, Funmilayo Lydia Systems level cancer disease target identification using tumor microenvironment dynamics |
| title | Systems level cancer disease target identification using tumor microenvironment dynamics |
| title_full | Systems level cancer disease target identification using tumor microenvironment dynamics |
| title_fullStr | Systems level cancer disease target identification using tumor microenvironment dynamics |
| title_full_unstemmed | Systems level cancer disease target identification using tumor microenvironment dynamics |
| title_short | Systems level cancer disease target identification using tumor microenvironment dynamics |
| title_sort | systems level cancer disease target identification using tumor microenvironment dynamics |
| topic | Tumor microenvironment (TME) Immunocompetent cells Tumors -- Growth Cytokines Cancer -- Research -- Mathematical models Proteins -- Research -- Mathematical models UCTD |
| url | http://hdl.handle.net/10019.1/104950 |
| work_keys_str_mv | AT makindefunmilayolydia systemslevelcancerdiseasetargetidentificationusingtumormicroenvironmentdynamics |