DIAGNÓSTICO Y EVALUACIÓN:

1.9.1 CONCEPT

We have thus far established that tumours such as HGSOC, PDAC, and complex karyotypic sarcomas all are defined as being genomically unstable and thus carry a very poor prognosis, making them very attractive tumour types to study together. They frequently harbor p53 mutations and their genomes are constantly mutated and rearranged to facilitate the tumour formation and evasion of death. The constant genetic changes also inherently highlights the problem with current cancer therapies; as the cell continues to evolve, it may no longer be dependent on a particular function that the therapeutic agent targets resulting in loss of effectiveness. Previous data have already shown common markers of poor prognosis shared between the three tumour types; for example, it has been shown that Trop-2, a marker of aggressive phenotypic tumours, are overexpressed in EOC, PDAC and carcinosarcomas and thus serve as a marker of poor prognosis in these tumours (Fong et al, 2008, Bignotti et al, 2010, Raji et al, 2011). We therefore aim to exploit the commonalities these tumours appear to share to see if we can further identify targetable markers that have prognostic significance, in particular in relation to response to DNA damaging chemotherapy.

Recent technological advances have revolutionized the way we subgroup tumours and our general understanding of cancer genetics. Transcriptional profiling, copy number variation (CNVs) and next generation sequencing (NGS) have revised the classification of many tumour types into more relevant subgroups, providing useful prognostic information. CNVs are an important source of variation in the genome that can be detected routinely by single nucleotide polymorphism (SNP) arrays and array comparative hybridization (aCGH) (Pang et al, 2010). SNP arrays offer great robustness, high resolution and are able to detect copy number variations such as submicroscopic deletions and amplifications (Nowak et al, 2009). The large genomic instability and structural variation that characterizes cancer cells are features that would make CNV interesting to study with respect to cancer initiation and progression. Ultimately, a detailed characterization of the genetic defects present in most cancers is necessary to be able to understand it better at the molecular level. This requires computational analysis that can firstly identify addition or deletion events and then identify whether these events are causal or functionally

neutral (Taylor et al, 2008). This project aims to look at existing datasets, which is why SNP array data will be analysed instead of next generation sequencing analysis as there are many publicly available SNP array datasets with corresponding clinical data.

It is known that each cancer is characterized by several somatic mutations, of which only a subset contributes to tumour progression. Even though genome-wide sequencing studies have identified hundreds of mutations within a tumour, it is still difficult to ascertain which of these mutations contribute to the pathogenesis. It is therefore of great interest to identify these 'driver' mutations that characterize each cancer. Some of these mutations may be useful in determining prognosis or treatment choice whilst other driver mutations may in fact be druggable and could potentially further advance treatment options (Dancey et al, 2012). Some mutations may also be predictive of drug response in one form of cancer (i.e. BRAF V600E and vemurafenib in melanoma; Chapman et al, 2011) and there may be a likelihood that other tumours from different origins harboring the same mutations may confer the same sensitivity/resistance to the drug. This however needs to be explicitly tested as previous studies have shown that this may not always be the case. For example, Herceptin is sensitive in HER2+ breast and gastric cancers, however this response is not observed in HER2+ ovarian or endometrial cancers (Bookman et al, 2003; Fleming et al, 2010). If a functional consequence of an identified mutation is similar across several tumour types, then the therapeutic implications are huge.

The incredibly complex genomic rearrangements observed in cancers such as pancreatic adenocarcinomas (PDAC), epithelial ovarian cancers (EOC) and a subtype of sarcomas makes it very difficult to understand their molecular biology but also highlights possible targets that could be identified and further validated. Previous studies have identified and highlighted CNVs that may predispose one to a particular cancer. For example, a recent paper by Huang et al (2012) was one of the first studies to suggest an association between germline CNV and pancreatic cancer risk; a common 10,379bp deletion at 6q13 was found at a higher frequency in patients with sporadic pancreatic cancer compared to the controls. Interestingly, further studies showed possible involvement in long-range regulation of the tumour suppression gene, CDKN2B. Similarly, studies in ovarian cancer have shown that amplification of CCNE1 (cyclin E) is strongly associated with treatment-resistance in ovarian carcinomas. Furthermore,

the identification of this relationship has suggested potential for therapeutic exploitation, where patients carrying this CCNE1 amplification could potentially benefit from cyclin-related targeted treatments (Etemadmoghadam et al, 2009). Work carried out in Dr. Stronach’s lab has highlighted AKT signalling to be a key driver in cancer cell survival following chemotherapy. Further studies have also identified DNA- PKcs to be the activating kinase and thus suggests a link between DNA repair pathways and cancer cell survival (Stronach et al, 2011). Further studies looking at the gene loci of DNA-PKcs identified frequent chromosomal amplifications, which was significantly associated with poor progression free survival. These examples highlight the potential benefits of carrying out such analysis to further identify markers of poor prognosis and validate them as potential therapeutic targets.

1.9.2 AIMS

The broad aims of the project were:

1. To analyze publically available datasets for HGSOC, PDAC and a subset of type II sarcomas with complex karyotypes for copy number amplifications in genes relating to DDR/repair and apoptosis and correlate these results to the response to chemotherapeutic agents. Highlighted candidates would then be functionally validated and their therapeutic potential would be assessed.

2. To focus on a key component in DNA repair, DNA-PKcs, which has been implicated in contributing to poor prognosis in these tumour types through DNA repair and through the PI3K signalling (via AKT) and to further characterize the functional mechanisms behind its role in poor prognosis and signalling dynamic changes that occur with its inhibition using proteomic analyses

3. To further address the second aim by investigating nuclear specific changes in response to chemotherapeutic agents to attempt to identify biomarkers of chemo-resistance and examine how this nuclear/cytoplasmic shuttling dynamic changes with DNA-PKcs inhibition in combination with chemotherapeutic agents.

CHAPTER 2: MATERIALS AND METHODS

2.1 GENOMIC ANALYSIS TO IDENTIFY COMMON COPY NUMBER