Eukaryotic Translation Elongation Factor 1 Alpha (EEF1A) exists as two forms with different tissue specificities and encoded by separate loci: eEF1A1 on 6q13 and eEF1A2 on 20q13.3. eEF1A1 is ubiquitously expressed whereas eEF1A2 expression is normally limited to the heart, brain and skeletal muscles. eEF1A proteins are GTP-binding proteins that recruit an amino-acylated tRNA to the ribosome during the elongation phase of protein translation. eEF1A2 mRNA and protein are highly expressed in 50–60% of primary human breast tumors and metastases but not in normal breast epithelium. Since it is also overexpressed in 30% of primary human ovarian tumors, transforms rodent fibroblasts and increases their tumorigenicity in nude mice, eEF1A2 is considered to be a potential human oncogene. The mechanism of eEF1A2 expression is yet to be determined. Studies showed no gene mutation and no correlation between locus amplification or methylation and gene expression. Phosphate Tyrosine Kinase-6 (PTK6) is also located on 20q13.3. It is 48kb upstream of EEF1A2. PTK6 is a non-receptor tyrosine-kinase that is normally expressed in epithelial linings, prostate, skin and oral epithelium but it is not detected in the normal human mammary epithelium. PTK6 has been found to be expressed in many breast cancer cell lines and in approximately 60% of primary human breast tumors but it has not been detected in normal human breast tissue nor in fibroadenomas. Like other tyrosine kinases, PTK6 phosphorylates and activates downstream substrates that would be predicted to lead to increased transcriptional activity and therefore mediates proliferation of breast cancer cells. PTK6 is considered a prognostic marker of metastasis-free survival in breast cancer independent of the classical markers of tumor size, lymph node involvement and HER2 status. The aim of this project was to characterize for the first time the genomic region containing the two mentioned breast cancer oncogenes and understand their various roleswhether they act in tandem or independently in breast tumorigenesis. Immunohistochemistry was performed on tissue microarrays from 300 breast cancer patients to detect the expression levels of eEF1A2 and PTK6. Tumors that showed a high co-expression were analyzed for the genes’ copy number. An increased copy number of PTK6 was detected but not of eEF1A2 nor of adjacent genes on the 20q13.3 amplicon. To understand the effect of EEF1A2 expression on other genes, microarray analysis was performed on NIH-3T3 cells stably transfected with EEF1A2. Many upregulated genes were associated with different types of cancers. This was further confirmed by real-time PCR. However, when the NIH-3T3 cells were transiently transfected with EEF1A2, the genes that were upregulated in the microarray study showed no change in expression. In conclusion, EEF1A2 and PTK6 act independently and each acts through a different mechanism in breast tumorigenesis.

Breast cancer type 1 susceptibility protein (BRCA1) has been established as an important tumour suppressor protein and its loss of function is associated with hereditary breast and ovarian cancer. An emerging body of work suggests that BRCA1 is involved in sporadic cases of breast and ovarian cancers and may also have a role in other cancers, indicating a more global role in tumourigenesis. BRCA1-mutated cancers can be early-onset and are characterised by being highly aggressive with a propensity to metastasize, thus from a clinical perspective there is a requirement to understand the molecular mechanisms in order to be able to tailor treatments and develop therapeutics. BRCA1 has numerous cellular functions, many ascribed to its role in maintenance of genome integrity, transcription and checkpoint control. More recently, a number of extra-nuclear roles have been established. An interesting novel function is the role of the E3 ubiquitin ligase activity on cell motility. Abrogation of the ubiquitin ligase activity of BRCA1 results in cells exhibiting a hypermotile, invasive phenotype which may help to account for the metastatic nature of BRCA1-mutated tumours. Our aim was to further elucidate BRCA1’s role in cell motility, starting with the identification of relevant candidate ubiquitin ligase substrates. To date, there has yet to be a systematic approach to identify BRCA1’s ubiquitin ligase substrates. Thus we undertook an unbiased proteomic approach to identify extra-nuclear candidates by comparing the profiles of ubiquitinated proteins in breast cancer epithelial cells expressing either functional BRCA1 or ubiquitin ligase-dead BRCA1. We identified 55 candidates which were differentially enriched between the two cell lines and through pathway analysis we determined a significant proportion were cytoskeletal and translation related proteins. Using an ubiquitin-remnant profiling approach, we also identified the site(s) of ubiquitination for many of the candidates. To assess the role of these candidates in cell motility initially we adopted an in silico approach. We used existing time-lapse movies from the online database (www.mitocheck.org) which systematically siRNA knocked down every single gene in the human genome. We developed a series of algorithms which track cell motility from these movies and used these to analyse 192,000 movies containing 3.5 billion cell steps. We have produced a complete database containing motility information after siRNA knockdown of every gene in the human genome, which has been annotated with gene ontologies, KEGG families, Gene Descriptions, SwissProt, Ensembl IDs and siRNA information. In addition to providing motility data of our candidates, we also carried out gene set enrichment analysis on the whole dataset to uncover structural or functional families that may be involved in up-regulating motility when knocked down by siRNA. This is the first report of a genome-wide motility database. Based on overlaps between the results from these two large-scale unbiased proteomic and in silico datasets, we selected 4 candidates, namely, ezrin, moesin, fermitin-2 and delta-catenin. Through monolayer wound healing, cell spreading and single cell motility assays, we determined that ezrin was a particularly relevant and informative candidate. The hypermotile phenotype observed in cells expressing ubiquitin ligase dead BRCA1 was rescued through siRNA knockdown of ezrin and thus we suggest that BRCA1 may regulate cell motility through effects on ezrin. This thesis has investigated candidate BRCA1’s role in cell motility, identified candidate substrates for the E3 ubiquitin ligase activity, established a genome-wide motility database and proposed a possible pathway through which BRCA1 may mediate cell motility and by extension metastasis.

Chemotherapy is one of the common treatments in cancer therapy. The effectiveness of chemotherapy is limited by several factors one of which is the emergence of multidrug resistance (MDR). MDR is caused by the activity of diverse ATP binding cassette (ABC) transporters that pump drugs out of the cells. There are several drugs which have been used in treatment of cancer. One of them is doxorubicin that intercalates and inhibits DNA replication. However, doxorubicin has been found to cause development of MDR in tumors. It has been reported that there is a correlation between multidrug resistance and invasiveness of cancer cells. Vimentin is a type III intermediate filament protein that is expressed frequently in epithelial carcinomas correlating with invasiveness and also poor prognosis of cancer. There are several studies that have shown the connection between expression level of vimentin and invasiveness. In this study, MCF-7 cell line (MCF-7/S), which is a model cell line for human mammary carcinoma, and doxorubicin resistant MCF-7 cell line (MCF-7/Dox) were used. The resistant cell line was previously obtained by stepwise selection in our laboratory. The main purpose of this study was to investigate changes of metastatic behaviour in MCF-7/Dox cell line, after transient silencing of vimentin gene by siRNA. In conclusion, down-regulation of vimentin gene expression in MCF-7/Dox cell lines was expected to change the characteristics in migration and invasiveness shown by migration and invasion assays.

Breast cancer is one of the most common cancers among women in the world, and the average error rate among radiologists during diagnosis is 30%. Computer-aided medical diagnosis aims to assist doctors by giving them a second opinion, thus decreasing the error rate. Convolutional neural networks (CNNs) have shown to be good for visual detection and recognition tasks, and have been explored in combination with transfer learning. However, the performance of a deep learning model does not only rely on the model itself, but on the nature of the dataset as well In breast cancer diagnosis, the area surrounding a mass provides useful context for diagnosis. In this study, we explore providing different amounts of context to the CNN model ResNet50, to see how it affects the model’s performance. We test masses with no additional context, twice the amount of original context and four times the amount of original context, using 10-fold cross-validation with ROC AUC and average precision (AP ) as our metrics. The results suggest that providing additional context does improve the model’s performance. However, giving two and four times the amount of context seems to give similar performance. / Bröstcancer är en av de vanligaste cancersjukdomar bland kvinnor i världen, och den genomsnittliga felfrekvensen under diagnoser är 30%. Datorstödd medicinsk diagnos syftar till att hjälpa läkare genom att ge dem en andra åsikt, vilket minskar felfrekvensen. Konvolutionella neurala nätverk (CNNs) har visat sig vara bra för visuell detektering och igenkännande, och har utforskats i samband med det s.k. “transfer learning”. Prestationen av en djup inlärningsmodell är däremot inte enbart beroende på modellen utan också på datasetets natur. I bröstcancerdiagnos ger området runt en bröstmassa användbar kontext för diagnos. I den här studien testar vi att ge olika mängder kontext till CNNmodellen ResNet50, för att se hur det påverkar modellens prestanda. Vi testar bröstmassor utan ytterligare kontext, dubbelt så mycket som den originala mängden kontext och fyra gånger så mycket som den orginala mängden kontext, med hjälp av “10-fold cross-validation” med ROC AUC och “average precision” (AP ) som våra mätvärden. Resultaten visar att mer kontext förbättrar modellens prestanda. Däremot verkar att ge två och fyra gånger så mycket kontext resultera i liknande prestanda.