Cancer is a disease of the genome, requiring mutation or epimutation of specific genes to develop. The subsequent progression of cancer and response to therapies is also dictated to some degree by new mutation and clonal selection on that novel variation. However, it is thought that the majority of somatic mutations that occur in cancer are inconsequential passengers, and only a subset of functionally important driver mutations are of importance for cancer biology. This project set out to adapt and apply well-established methods from the field of molecular evolution to measure the selective forces driving the development of cancers. The ultimate objective being an improved understanding of which mutations help or hinder the progression of a cancer. Somatic cancer mutations were identified through the analysis of paired tumour and non-tumour whole-exome sequence data from the same individual. Primary data from 1005 patients was processed and complemented with additional publicly available pre-processed somatic variant calls from 4728 patients. Tumours were classified by tissue of origin and also their spectrum of substitution mutations. An advanced evolutionary analysis framework was established, allowing somatic single nucleotide variant data to be analysed as traditional organismal DNA sequence. Estimates of amino acid changing (non-synonymous) and synonymous mutation rates were derived and maximum likelihood tests of selection applied to identify genes and regions of genes subject to selective pressure during oncogenesis. While the meta-analysis of all patients provided unprecedented power for such a study, more refined analyses based on the stratification of patients gave insights into the pathways of importance for specific tissues of origin. Additionally, stratification of patients by the relative frequencies of different mutation types in a tumour also provided insights into how mutation profile influences the sites, genes and pathways that are perturbed in the development of cancer. Of particular interest here, was to test the hypothesis that both (1.) mutation spectrum and (2.) tissue of origin, set the evolutionary trajectory of a cancer. Building on this I sought to estimate their relative contributions. During this work an unexpected, localised mutation pattern was discovered and subsequent analysis demonstrated some loci to be highly susceptible to small segmental deletions in a subset of cancers. In the absence of a justifiable model of neutral segmental deletion it was not possible to infer whether these major mutations could be considered passengers or drivers of cancer progression. In contrast, an advantage of the evolutionary approach applied to nucleotide substitutions in protein coding sequences is that there is a justified model of neutral evolution (synonymous changes). Using this approach, I have not only been able to detect genes harbouring putative cancer driver mutations, but have also found evidence for genes subject to purifying selection in cancers where potentially disruptive mutations appear to be deleterious to cancer progression. Such genes, if they are non-essential in the adult organism, could provide a novel type of target for anti-cancer therapeutics.

Prostate Cancer (PCa) is the second most diagnosed cancer in men across the world; it is considered the fifth leading cause of cancer related death according to cancer statistics 2012. Being a member of the internal parts in males reproductive system, testing any abnormality with the prostate gland remains both troublesome and inconvenient, and foremost inaccurate. The diagnostic practice starts with prostate-specific antigen (PSA) level testing, which in return is highly indecisive, provoking an over diagnosis and treatment. Genomic alteration and Single Nucleotide Variants (SNV s) are assumed to play a role during PCa progression. On behalf of the RIBOLUTION project, a project with the aim of finding diagnostic biomarkers from RNA sequences, SNV s in RNA sequences were analysed to pinpoint potential candidate genes in PCa. The fact that the cohort provides whole-transcriptome data of pro- static tissue promotes the possibility to obtain comprehensive knowledge of the cancerous changes. The advantage of detecting SNV s in RNA sequences relies in focusing on only those, which could be relevant to the gene’s func- tion. However, methods for detecting and analysing SNV s solely in RNA sequences are currently not yet established. This study aimed to (1) establish fitting and applicable assays to identify, inspect and conclude the potential role of SNV s in RNA sequences, (2) use the obtained knowledge to single out the genes that are potentially relevant for PCa. SNV s in the RIBOLUTION cohort were investigated. Prostate tissue was obtained from 40 PCa patients, and then RNA was sequenced using Next Generation Sequencing. In 16 patients, a pairwise prostatic tissue was taken, one a confirmed tumor tissue and the second a tumor-free tissue. As a control, samples from 8 men with benign prostatic hyperplasia were likewise sequenced. Different computational pipelines were established and successfully fulfilled the aim. The CVR Module (Calling Variants in RNA-Seq) is a computer- based pipeline intended to identify SNV s and discriminate between false positive and true positive calls. Validating the SNV s reported by the accomplished Module has shown high sensitivity (> 80% validated SNV s). Much as novel SNV s that had ∼ 101% higher median calling quality in comparison to SNV s found in dbSNP, the Single Nucleotide Polymorphism Database. In agreement with current knowledge, novel SNV s was observed in tumor samples with slight but significant increase vs. tfree tissue (P < 0.05, testing on proportion). On top of that, positive correlation between non-silent effect and novel SNV in tumor samples was also observed (P < 0.05, r = 0.33, Pearson’s correlation). Moreover, more than 40% of the candidate genes were found in COSMIC, the Catalog Of Somatic Mutations In Cancer; some of them are confirmed somatic mutation (cancer associated). About 11% were also reported in studies to be disease associated or observed in other diseases, mostly heredity related. Potential PCa associated genes were identified via combination of three different systematic methods: mutational clustering, mutational functional bias, and covariates of the mutated genes. The first method (mutational clustering), however, did not reveal any significant insight. The top candidate genes were then selected in accordance with the latter methods. The list of top candidate genes includes > 50% genes with direct association with PCa; > 80% genes previously reported in other cancer types, while ∼ 35% that are in- volved in PCa associated complexes. Besides well known and validated PCa biomarker (alpha-methylacyl-CoA racemase (AMACR)), we identify for the first time, from mutational prospective, 22% of the genes to be potentially associated with PCa. Among those, one of the most promising candidate genes is NWD1 (NACHT and WD repeat domain containing 1). This gene was mentioned in a previous study to be a potential player in PCa prognosis. We add to this our novel observation, NWD1 was found significantly mutated in the entire tumor samples. These significant findings were proven to be tumor-specific when they were compared to the available control and tumor-free (P < 0.05, non-parametric ranking). We conclude that analyzing SNV s from RNA is as useful and informative as DNA-based ones, and accomplish further benefits that could be gained once the suggested methods are adapted.

Proteins are among the most versatile organic macromolecules essential for living systems and present in almost all biological processes. Cancer is associated with mutations that either enhance or disrupt the conformation of proteins. These mutations have been shown to accumulate in specific regions of a proteins three dimensional structure. In this thesis, the aim is to find connections that secondary structure elements make and explore them using a self-organizing map (SOM). The detection of these connections is done by first mapping the three-dimensional structure onto a novice type of distance matrix that also incorporates chemical information, and then deploying a density-based clustering algorithm. The connections found are mapped onto the SOM and later analyzed in order to see if highly mutated connections are more common among certain SOM-nodes. This was tested with an ANOVA that indicated that there are indeed mutational asymmetries among the nodes. By further analyzing the map it could also be stated that certain nodes were to a large extent activated by connections from genes associated with cancer. / Proteiner tillhör några av de mest mångsidiga organiska makromolekylerna, och är direkt nödvändiga för alla levande system och biologiska processer. Cancer orsakas av mutationer som antingen förstärker eller stör strukturen hos proteinet. Dessa mutationer tenderar att att samlas i specifika områden av proteinets tredimensionella struktur. I den här rapporten är målet att hitta kopplingar som sekundärstrukturselement skapar, och utforska dem med hjälp av en självorganiserande karta. Dessa kopplingar finnes genom att först skapa en tvådimensionell representation av proteinets tredimensionella struktur, och sedan använda en densitetsbaserad klustringsalgoritm. De funna kopplingarna mappas till de olika neuronerna i kartan och analyseras sedan för att se om kopplingar med hög mutationsnivå är mer vanliga hos vissa neuron. För att undersöka detta användes ett ANOVA-test som visade att så var fallet. Genom att ytterligare studera kartan upptäcktes fynd som indikerade att vissa neuron i högre utsträckning var aktiverade av kopplingar som härstammar från gener vi vet är associerade med cancer.