Millions of somatic mutations in human cancers have been identified by sequenc- ing. Identifying and distinguishing cancer driver genes amongst the millions of candi- date mutations remains a major challenge. Accurate identification of driver genes and mutations is essential for the progress of cancer research and personalizing treatment based on accurate stratification of patients. Because of inter-tumor genetic hetero- geneity, numerous driver mutations within a gene can be found at low frequencies. This makes them difficult to differentiate from other non-driver mutations. Inspired by these challenges, we devised a novel way of identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, function, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In ad- dition to identifying known driver genes, we identify several novel candidate driver genes. We provide an external evaluation of the predicted genes using a dataset of 26 nasopharyngeal cancer samples that underwent whole exome sequencing. We find that the predicted driver genes have a significantly higher rate of mutation than non-driver genes, both in publicly available data and in the nasopharyngeal cancer samples we use for validation. Additionally, we characterize sub-networks of genes that are jointly involved in specific tumors.

Mutational analyses of cancer genomes have identified novel candidate cancer genes with hitherto unknown function in cancer. To enable phenotyping of mutations in such genes, we have developed a scalable technology for gene knock-in and knock-out in human somatic cells based on recombination-mediated construct generation and a computational tool to design gene targeting constructs. Using this technology, we have generated somatic cell knock-outs of the putative cancer genes ZBED6 and DIP2C in human colorectal cancer cells. In ZBED6-/- cells complete loss of functional ZBED6 was validated and loss of ZBED6 induced the expression of IGF2. Whole transcriptome and ChIP-seq analyses revealed relative enrichment of ZBED6 binding sites at upregulated genes as compared to downregulated genes. The functional annotation of differentially expressed genes revealed enrichment of genes related to cell cycle and cell proliferation and the transcriptional modulator ZBED6 affected the cell growth and cell cycle of human colorectal cancer cells. In DIP2C-/-cells, transcriptome sequencing revealed 780 differentially expressed genes as compared to their parental cells including the tumour suppressor gene CDKN2A. The DIP2C regulated genes belonged to several cancer related processes such as angiogenesis, cell structure and motility. The DIP2C-/-cells were enlarged and grew slower than their parental cells. To be able to directly compare the phenotypes of mutant KRAS and BRAF in colorectal cancers, we have introduced a KRASG13D allele in RKO BRAFV600E/-/-/ cells. The expression of the mutant KRAS allele was confirmed and anchorage independent growth was restored in KRASG13D cells. The differentially expressed genes both in BRAF and KRAS mutant cells included ERBB, TGFB and histone modification pathways. Together, the isogenic model systems presented here can provide insights to known and novel cancer pathways and can be used for drug discovery.

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京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22036号 / 医博第4521号 / 新制||医||1038(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 滝田 順子, 教授 松田 道行, 教授 山田 亮 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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.