Understanding how cancer mutations hinder the interactions inside proteins
Author
Sáenz Ausejo, CarmenEntity
UAM. Departamento de BioquímicaDate
2018-02Subjects
Breast cancer; Driver mutations; 3D protein claster; Biología y Biomedicina / Biología; InformáticaNote
Trabajo fin de máster en Bioinformática y Biología ComputacionalEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
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
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