Publicação
Assess the effect of angiogenesis inhibition in intra-tumor heterogeneity
| Resumo: | The genetic diversity of the populations that arise within a tumor following cancer pro- gression is known as intra-tumor heterogeneity. Angiogenesis is the formation of new blood vessels from the existing vascular network. It is involved in different physiolog- ical processes, including wound healing. Tumors induce the excessive production of pro-angiogenic factors that promote the proliferation and variety of cancer cells. The inhibitors of tumor angiogenesis were initially designed to destroy the tumor blood ves- sels, causing the death of cancer cells. However, they have been associated with selecting therapy-resistant cells, thus leading to treatment failure. This thesis aims to study the effect of angiogenesis inhibition in intra-tumor heterogeneity based on an experiment in which a breast cancer cell line, designated as MDA-MB-231, was injected into four mice. Two of them were administered with sunitinib, an anti-angiogenic drug. In partic- ular, this thesis investigates whether the two groups of mice, control and treatment, are distinct. Two variables were analyzed to distinguish between the mice: the intra-tumor heterogeneity and the mutational profiles of the genes. Three different heterogeneity esti- mation methods were chosen: the tumor heterogeneity index, PyClone-VI, and Canopy. These worked with the sequencing data of the mice tumor biopsies, specifically with the somatic mutations and copy number alterations. Dimensionality reduction techniques were applied to extract information from several genes. These relied not only on the mice samples but also on the tumor data of patients stored in The Cancer Genome Atlas, which allowed access to more examples. None of the methods could identify a clear difference between the two groups of mice. Their intra-tumor heterogeneity values were similar, and the mutational profiles of their genes appeared to follow the same pattern. Consid- ering these results, we can assume that destroying the tumor blood vessels of the mice from the treatment group did not drive the diversification of cancer cells. Nonetheless, further research should be conducted to confirm this conclusion. For example, test the latest heterogeneity estimation methods and explore the capabilities of neural networks. |
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| Autores principais: | Pais, David Miguel Pratas |
| Assunto: | intra-tumor heterogeneity angiogenesis anti-angiogenic therapy clonal reconstruction machine learning |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The genetic diversity of the populations that arise within a tumor following cancer pro- gression is known as intra-tumor heterogeneity. Angiogenesis is the formation of new blood vessels from the existing vascular network. It is involved in different physiolog- ical processes, including wound healing. Tumors induce the excessive production of pro-angiogenic factors that promote the proliferation and variety of cancer cells. The inhibitors of tumor angiogenesis were initially designed to destroy the tumor blood ves- sels, causing the death of cancer cells. However, they have been associated with selecting therapy-resistant cells, thus leading to treatment failure. This thesis aims to study the effect of angiogenesis inhibition in intra-tumor heterogeneity based on an experiment in which a breast cancer cell line, designated as MDA-MB-231, was injected into four mice. Two of them were administered with sunitinib, an anti-angiogenic drug. In partic- ular, this thesis investigates whether the two groups of mice, control and treatment, are distinct. Two variables were analyzed to distinguish between the mice: the intra-tumor heterogeneity and the mutational profiles of the genes. Three different heterogeneity esti- mation methods were chosen: the tumor heterogeneity index, PyClone-VI, and Canopy. These worked with the sequencing data of the mice tumor biopsies, specifically with the somatic mutations and copy number alterations. Dimensionality reduction techniques were applied to extract information from several genes. These relied not only on the mice samples but also on the tumor data of patients stored in The Cancer Genome Atlas, which allowed access to more examples. None of the methods could identify a clear difference between the two groups of mice. Their intra-tumor heterogeneity values were similar, and the mutational profiles of their genes appeared to follow the same pattern. Consid- ering these results, we can assume that destroying the tumor blood vessels of the mice from the treatment group did not drive the diversification of cancer cells. Nonetheless, further research should be conducted to confirm this conclusion. For example, test the latest heterogeneity estimation methods and explore the capabilities of neural networks. |
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