Browsing by Subject "cancer"

Cancer is a leading cause of death worldwide. Unlike its name would suggest, cancer is not a single disease. It is a group of diseases that arises from the expansion of a somatic cell clone. This expansion is thought to be a result of mutations that confer a selective advantage to the cell clone. These mutations that are advantageous to cells that result in their proliferation and escape of normal cell constraints are called driver mutations. The genes that contain driver mutations are known as driver genes. Studying these mutations and genes is important for understanding how cancer forms and evolves. Various methods have been developed that can discover these mutations and genes. This thesis focuses on a method called Deep Mutation Modelling, a deep learning based approach to predicting the probability of mutations. Deep Mutation Modelling’s output probabilities offer the possibility of creating sample and cancer type specific probability scores for mutations that reflect the pathogenicity of the mutations. Most methods in the past have made scores that are the same for all cancer types. Deep Mutation Modelling offers the opportunity to make a more personalised score. The main objectives of this thesis were to examine the Deep Mutation Modelling output as it was unknown what kind of features it has, see how the output compares against other scoring methods and how the probabilities work in mutation hotspots. Lastly, could the probabilities be used in a common driver gene discovery method. Overall, the goal was to see if Deep Mutation Modelling works and if it is competitive with other known methods. The findings indicate that Deep Mutation Modelling works in predicting driver mutations, but that it does not have sufficient power to do this reliably and requires further improvements.

Cancer is a worldwide health problem; in 2018 9.6 million people died of cancer, meaning that about 1 in 6 deaths was caused by it. The challenge with cancer drug therapy has been the development of cancer drugs that are effective against cancer but are not harmful to the healthy cells. One of the solutions to this has been antibody-drug conjugates (ADCs), where a cytotoxic drug is bound to an antibody. The antibody binds to specific antigen present on the surface of the cancer cell, thus working as a vessel to carry the drug specifically to the cancer cells. Monomethyl auristatin E (MMAE) and monomethyl auristatin F (MMAF) are mitosis preventing cancer drugs. The auristatins are pentapeptides that were developed from dolastatin 10. MMAE consist of monomethyl valine (MeVal), valine (Val), dolaisoleiune (Dil), dolaproine (Dap) and norephedrine (PPA). MMAF has otherwise similar structure, but norephedrine is replaced by phenylalanine (Phe). They prevent cell division and cancer cell proliferation by binding to microtubules and are thus able to kill any kind of cell. By attaching the auristatin to an antibody that targets cancer cells, they can effectively be used in the treatment of cancer. MMAE and MMAF exist as two conformers in solution, namely as cis- and trans-conformers. The trans-conformer resembles the biologically active conformer. It was recently noted that in solution 50-60 % of the MMAE and MMAF-molecules exist in the biologically inactive cis-conformer. The molecule changes from one conformer to the other by the rotation of an amide bond. However, this takes several hours in body temperature. As the amount of the cis-conformer is significant, the efficacy of the drug is decreased, and the possibility of side effects is increased. It is possible that the molecule leaves the cancer cell in its inactive form, migrates to healthy cells and tissue, and transforms to the active form there, damaging the healthy cell. The goal of this study was to modify the structure of the auristatins so that the cis/trans-equilibrium would change to favor the biologically active trans-conformer. The modifications were done virtually, and the relative energies were computed using high-level quantum chemical methods, at density functional theory (DFT), 2nd order perturbation theory (MP2) and coupled cluster levels. Intramolecular interactions were analyzed computationally, employing symmetry-adapted perturbation theory and the non-covalent interactions analysis. The results suggest that simple halogenation of the benzene ring para-position is able to significantly shift the cis/trans-equilibrium to favor the trans-conformer. This is due to changes in intramolecular interactions that favor the trans-conformer after halogenation. For example, the NCI analysis shows that the halogen atom invokes stabilizing intramolecular interactions with the Dil amino acid; there is no such interaction between the para-position hydrogen and Dil in the original molecules. We also performed docking studies that show that the halogenated molecules can bind to microtubules, thus confirming that the modified structures have potential to be developed into new, more efficient and safe cancer drugs. The most promising drug candidates are Cl-MMAF, F-MMAF, and F-MMAE where 94, 90, and 79 % of the molecule is predicted to exist in the biologically active trans-conformer, respectively.