The reprogramming of cellular metabolism is an established hallmark of cancer, which enables cancer cells to survive, proliferate, and metastasize even under harsh environmental conditions. These cancer-associated metabolic changes can affect several pathways one of which is mitochondrial metabolism. The suppression of mitochondrial metabolism has been associated with poor clinical outcomes and mitochondrial dysfunction has been associated with some hereditary and sporadic forms of cancer that arise from mutations in mitochondrial genes. Understanding the mechanisms responsible for cellular transformation and subsequent tumour formation in these hereditary, metabolically-impaired tumours could link dysregulated mitochondrial function and tumour formation. Hereditary mutations and subsequent loss of the mitochondrial TCA cycle enzyme fumarate hydratase (FH) leads to Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC), an aggressive form of renal cancer associated with poor clinical outcome. The loss of FH triggers the accumulation of fumarate, which induces a multi-layer cellular reprogramming that contributes to tumorigenesis. Yet, it is unclear how FH loss influences the whole gene expression landscape and if the gene expression is regulated on the level of DNA-methylation, transcription or translation. In this thesis, I generated the first FH-deficient human renal epithelial cell lines using CRISPR/Cas9-based genome editing, and applied proteomics, metabolomics, and transcriptomics approaches to investigate how the loss of FH alters these cellular layers. First, I confirmed that this model faithfully recapitulates the biochemical and phenotypic markers of FH-deficiency as previously reported. Next, I developed a novel multi-omics tool, SiRCle (Signature Regulatory Clusters) to disentangle this interconnected network of signalling cascades. Using SiRCle, I extracted clusters of increased/decreased gene expression that are regulated at the level of DNA methylation, transcription, and/or translation, and identified which clusters drive which phenotype of FH loss. By mapping the transcription factors that drive the genes of each cluster, I identified unique drivers that could be responsible for the cellular rewiring after FH loss. It is now clear that the tumour microenvironment affects the phenotype of cancer cells, and hence that metabolic rewiring becomes essential for tumour cells to strive even under harsh environmental conditions. Yet, its effect on FH-deficient cells’ behaviour is currently unknown. In this part of the thesis, I used a Tumour Roll for Analysis of Cellular Environment and Response (TRACER), a 3D scaffold that develops oxygen and nutrient gradients similar to those observed in tumours. Using TRACER, I show that the main metabolic signature of FH loss, which is driven by the high levels of fumarate, is not influenced by the nutrient and oxygen gradients generated in this 3D model. Consequently, FH loss is a stronger driver of the metabolic signature than environmental cues. Moreover, by applying linear modelling to the metabolic profile of the cells over the different layers, I identify specific layer-dependent metabolic signatures in FH-deficient cells that are not observed in 2D culture. These results imply that in vivo FH loss could undergo previously unacknowledged compensatory metabolic changes, which underlines the important role of the microenvironment in dictating the phenotype of cancer cells.