Consecuencias

As described in the previous section, biomarkers can originate from various omic levels, such as genomics, transcriptomics and proteomics. The described complexity of sample material highlighted the need for more thorough analysis; however, some limitations in the analysis are still present, especially in the field of proteomics. To overcome such challenges and to achieve a more complete picture of interactions, not only across one type of omic, but also across biological levels, novel approaches, such as the integration of multi-omics platforms, offer an alternative for the discovery of new biomarkers.

1.4.4.1 Strategies for the discovery of novel cancer biomarkers

1.4.4.1.1 Genomic profiling

The human genome is made of a genetic sequence, which represents the instructions to a functional biological system. The genetic code is comprised of building blocks, the so- called nucleotides, which code for single, coding or non-coding, genes. The completed sequencing of the human genome in 2001 (Venter, Adams et al. 2001) by the Human Genome Project, has reformed the world of science. The human genome enabled a comprehensive search for abnormal sequences, mutations, within the genome, generating a greater understanding of the genetic landscape in diseases, such as cancer. A widely- known example of disease-associated genetic mutations lay within the BRCA1 (Miki, Swensen et al. 1994) and BRCA2 (Wooster, Bignell et al. 1995) genes. Mutations within these genes can increase the risk for the development of ovarian and breast cancer. Patients with a known family history of BCa are categorised into high-risk groups and are nowadays regularly screened for potential changes in the sequence of these two genes (Wagner, Ball et al. 2018).

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1.4.4.1.2 Transcriptomic profiling

The Transcriptome describes the complete collection of actively transcribed genes within a cell at a set point of time. The transcriptome is comprised of coding and non-coding RNA molecules, of which the first can be translated into proteins. After many years, in which the main attention was focussed on coding genes, in recent years, more and more attention was given to transcripts that do not result in a protein (non-coding RNA) (Khurana, Fu et al. 2016, Shabalina, Spiridonov 2004, Wagner, Ball et al. 2018). These non-coding RNA include microRNAs, and small nucleolar RNAs (Mattick, Makunin 2006). Commonly used techniques for transcriptome profiling of samples include gene expression microarrays and RNA-sequencing.

Microarrays are based on cDNA molecules, spotted on a chip, to which complimentary sequences within a sample of interest can bind (Baldi, Hatfield 2011, Schulze, Downward 2001). Microarrays are commonly used due to their affordability and robustness; however, the approach is limited to a priori knowledge of genes. For this reason, RNA-sequencing shows a great advantage over microarrays, based on this independence from sequence knowledge (Wang, Z., Gerstein et al. 2009). Furthermore, RNA-sequencing offers a large range of magnitude in the detection and quantification of RNA molecules. RNA- sequencing platforms can not only analyse coding-RNA, but can also be used to focus on non-coding RNA or a closer look can be taken at active translated genes through the screening of ribosome-bound transcripts (Ingolia, Brar et al. 2012). The understanding of the complete complexity of tumour cells and associated interactions can be achieved through recent advantages that enable the analysis of single cell transcriptomes (Ramskold, Luo et al. 2012). The generated transcriptomic profiles of sample material can provide, to a certain degree, information in the potentially present proteome, however the correlation can be influenced by factors such as half-life time of transcripts and protein, as well as post-translational modifications, which can lead to variations between the transcriptome and proteome (Maier, Guell et al. 2009, Kulasingam, V., Diamandis 2008a).

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1.4.4.1.3 Proteomic profiling

The proteome is defined by the entirety of proteins present within a cell at the point of sampling. Currently the human proteome compendium consists of approximately 30 000 proteins, which are represented by around 17 000 genes (Human Proteome Map, 2018). This increased number of proteins compared to the number of genes can be explained through alternative splicing event resulting within one gene that can result in the translation of different protein isoforms (Black 2000). Compared to the generation of transcriptomic profiles, the study of the proteome presents difficulties and limitations resulting in lower numbers of routinely quantified proteins.

A continuous challenge is the complexity and large dynamic range of proteins in lysates, especially material derived from clinical specimens (See section 1.4.3.1). As previously mentioned in section 1.4.4.1.2, also in proteomic studies, it is possible to focus the analysis on a subsection of particular interest, such as proteins associated with distinct compartments (e.g. membrane, cytoplasmic, nuclear). A big improvement in the analysis of proteomes was achieved through the development of data-independent acquisition approach in tandem mass spectrometry (Gillet, Navarro et al. 2012), which enabled the routine quantification of more than 3000 proteins present in one sample. Applied to a high-throughput approach, such numbers of protein quantifications could be achieved within 60 to 120 min per sample. Furthermore, current developments in technology have resulted in a higher mass accuracy, higher detection capability and shorter cycling times (Sciex, 2018a), which further helped to increase the quality of results and the throughput of sample material (Gillet, Navarro et al. 2012, Domon, Aebersold 2006).

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1.5 Aims and Objectives of the Study

The underlying questions of this study was whether the use of parallel generated multi- omic profiles of two cell-line derived metastasis models will enable and facilitate the discovery of novel disease-associated biomarkers. In addition to this, the study should also investigate the potentially improved correlation of gene and protein expression data through parallel sample collection and omics profiling and furthermore, if the use of proteomic profiling will contribute to a better understanding of underlying changes. Based on these questions, the study was separated into three separate miles stones represented by the chapter 3, 4 and 5.

Milestone 1 (Chapter 3): Development of two in vitro models of EMT and their characterisation using the analysis of EMT markers on a gene and protein expression level. The successful development of both models will present the basis for the generation of matching multi-omic profiles in the following chapter 4.

Milestone 2 (Chapter 4): The previously development models of EMT are used for the generation of matching transcriptomic and proteomic profiles of both cell line models and the validation of their desired phenotype using pathway analyses. Furthermore, the generated profiles will be used for the integration of matching genes and proteins and the analysis of their expression correlation. The successful validation and additional characterisation of underlying changes within the transcriptomic and proteomic profiles of both models will support the further use of these profiles as part of an integrative biomarker discovery approach, which is described in chapter 5.

Milestone 3 (Chapter 5): The omic profiles generated in chapter 4 will we integrated for the identification of a core marker set, followed by the characterisation and validation of a selection of markers in a broader context through the screening of cell lines and clinical specimens. Furthermore, in silico analyses are to be used for the identification of an association of clinical parameters with the expression of the selected markers.

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