Understanding how cancer mutations hinder the interactions inside proteins

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dc.contributor.advisor González Izarzugaza, José María (tutor)
dc.contributor.advisor Peso Ovalle, Luis del (ponente)
dc.contributor.author Sáenz Ausejo, Carmen
dc.contributor.other UAM. Departamento de Bioquímica es_ES
dc.date.accessioned 2018-05-29T14:41:13Z
dc.date.available 2018-05-29T14:41:13Z
dc.date.issued 2018-02
dc.identifier.uri http://hdl.handle.net/10486/682666
dc.description Trabajo fin de máster en Bioinformática y Biología Computacional es_ES
dc.description.abstract The acquisition of somatic mutations can induce cancer by dysregulating the delicate mechanisms controlling balance between proliferation and apoptosis. Genomic alterations can be classified in driver and passenger mutations. Driver mutations confer selective advantage to tumor development, contrarily to passenger mutations that do not provide growth advantage to tumorigenesis. Most of the driver mutations have unknown functional impact on protein structure and function. Furthermore, not all driver alterations in a cancer gene have the same functional impact. The use of high-throughput sequencing technologies facilitated the discovery of cancer related mutations in case and control studies. The analysis of different tumor types facilitates the identification of recurrent mutations and the functional pathways involved in tumor development. One of the current challenges is to distinguish between drivers and passenger mutations. Mutations occurring with high frequency in tumor samples are considered to be drivers. Therefore, a commonly used method is to consider mutations that occur with higher frequency than a background mutation rate. Tamborero et al., (2013) developed a method to identify cancer related genes by grouping together residues with a significant rate of mutations that are close in the primary sequence of the protein above the background model. The background model was generated considering coding-silent mutations based on the evidences of a nonrandom mutation processes along the genome (Amos, 2010). Recently, Gao et al., (2017) identified genomic mutations affecting residues located in 3-dimensional proximity of protein structures by comparing the mutation frequency against a random background. The first method used gene sequences, considering proteins as single strands, and omitted that distant genomic regions might be close in the 3D space when the protein folds. And the second method assumed a homogeneous mutation probability across the whole genome, which is likely an oversimplification that may introduces a bias in the expected mutation rates (Amos, 2010). Both problems were considered in this study for the development of the algorithm. This method identifies associated with BRCA-mutated breast cancer using coding-silent Understanding how cancer mutations hinder the interactions inside proteins V Summary mutation frequency as a background. Furthermore, the method identified structural and catalytic roles of 3D protein clusters within relevant biological pathways in breast cancer. This method considered that a 3D protein cluster is significant when the residues within it have a higher non-synonymous mutation rate as compared to the background mutation rate. Most of the significant 3D protein clusters were located within PIK3CA gene. Additionally, most of the mutations in the 3D clusters were predominantly found in the kinase and helical domains of the corresponding protein (PI3K). These mutations destabilize the inactive conformation of the proteins or lock the activation loop in an active conformation resulting in constitutive protein activation. Thus, significant 3D protein clusters in PIK3CA contain ideal hot-spot mutants to target with anti-cancer agents (Gabelli, Mandelker, Schmidt-Kittler, Vogelstein, & Amzel, 2010). Nowadays, treatments with PI3K inhibitors are available. However, the oncogenic PI3K pathway activation is achieved in different redundant ways, therefore mono-therapies are not always effective. In conclusion, the results of this Master´s Thesis can help to understand better the interactions of the non-synonymous mutations in the 3D protein space to identify new targets, develop new therapies and consequently maximize the therapeutic benefit es_US
dc.format.extent 71 pag. es_ES
dc.format.mimetype application/pdf es_ES
dc.language.iso eng es_US
dc.subject.other Breast cancer es_US
dc.subject.other Driver mutations es_US
dc.subject.other 3D protein claster es_US
dc.title Understanding how cancer mutations hinder the interactions inside proteins es_US
dc.type masterThesis es_ES
dc.subject.eciencia Biología y Biomedicina / Biología es_ES
dc.subject.eciencia Informática es_ES
dc.rights.cc Reconocimiento – NoComercial – SinObraDerivada es_ES
dc.rights.accessRights openAccess es_ES

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