**1. Introduction**

Despite the methodological advancements made in cancer detection and treatment administration, colorectal cancer (CRC) remains one of the most common types of gastrointestinal malignancies diagnosed worldwide [1]. Development of this tumour involves genetic, histological and morphological changes which arise within the crypt cells of the colon or rectum. Hyperproliferation of these cells gives rise to benign polyps which protrude the surface of the epithelial cells within the intestinal lumen. Progression of pre-cancerous polyps can take a few years or decades to become malignant polyps, referred to as adenocarcinomas. This phenomenon is associated with different forms of inherited, acquired and epigenetic mutations in different protooncogenes and tumour suppressor genes, which accrue in several mechanisms [2, 3].

To deal with CRC progression and metastasis, different staging and classification systems together with different modes of treatment have been established throughout the years [4, 5]. Despite the advancements made in therapeutic strategies, CRC mortality rate remains high, and development of chemoresistance due to different circumstances remains a major constraint to patients being treated [6–8].

Current research and preclinical treatment development is centred around the traditional tumour biology research models of xenografts and two-dimensional (2D) cell culturing. Unfortunately, cell lines in particularly, do not always present an integrative microenvironment of cells living within a tissue, cannot replicate tumour heterogeneity and at times cannot retain all genetic information. Additionally, for xenografts, genetics and growth environment tend to differ from those of patients, have a lower success rate, are more time consuming and costly [9]. All in all, measures to evaluate the standardisation of CRC therapy are not well established, thus the urge to develop new tumour models and to identify accurate and substantiated predictive markers is required, so that clinicians can appropriately select which chemotherapy to administer.

Throughout the last decade, various research teams have taken the initiative to predict treatment response through different high-throughput methodologies, some of which in the coming years could potentially accompany the current staging and classification systems used. Proteomics, which is the study of proteomes and their functions in cells and tissues, is one of the fields that has stood out the most, due to the promising opportunities it has presented when it comes to understanding treatment response in various tumours, including CRC [10–12]. Additionally, threedimensional (3D) culturing is another high-throughput technique which has made rapid progress in the fields of drug discovery and screening. This form of culturing is an advanced system in which cells from both healthy or tumour tissues are cultured as spheres in a scaffold or non-scaffold-based system. In turn, this approach provides a better representation of an *in vivo* environment when compared to the traditional 2D monolayered cell culturing system [13–15]. This model permits the development of either spheroids (through cell lines using a scaffold or non-scaffold system) or organoids (through tissue samples using a scaffold system). The two models have similar and distinctive purposes, however the preparation, time, and tumour cell sources needed to establish the respective model differs [15]. Patient derived organoids (PDOs), have shown potential in different research fields, including high throughput drug screening analysis and to analyse the efficacy of different treatments [13, 16]. However, their use in predicting treatment response in relation to proteomics is still fairly novel, thus further research is still ongoing.

The purpose of this chapter is to first provide an overview of the current CRC staging and classification systems and their involvement in predicting treatment administration. Then, the chapter will address the involvement and progress of proteomics and PDOs, in predicting therapy response in CRC. Based on this, it will end by discussing the strengths and limitations of these two approaches when linked together, as well as propose potential future perspectives in this field.
