Understanding the metabolic signatures of haematological cancers through an integrative multi-Omics approach

Abstract

Haematological cancers are heterogenous diseases caused by a series of events which drive cells to uncontrolled proliferation and tumour progression. Nowadays, our understanding is that one hallmark of cancer cells is to reprogram their normal cellular metabolism to sustain their anabolic requirements for continuous cell growth and proliferation. Despite the remarkable progress in cancer metabolism, the exact mechanisms behind cancer metabolic reprogramming are not yet fully understood. The work presented in this thesis aims to provide novel biological insights into the metabolic reprogramming of haematological cancers and highlight potential metabolic vulnerabilities for therapeutic targeting approached to be investigated in future studies. A multi-Omics data integration approach was selected to achieve such ambitious aims. Herein, recent computational methodologies were applied to integrate and analyse transcriptomic with metabolomic profiles derived from cancer patients, as well as cell lines, mostly from mature B-cell neoplasms. Mature B-cell neoplasms, such as Chronic Lymphocytic Leukaemia (CLL) and Non-Hodgkin Lymphomas (NHL), rise from the clonal expansion of mature B-cells and they are responsible for most newly diagnosed cases of haematological cancers worldwide. The second chapter of this thesis presents an investigation into the transcriptome profile of CLL patients characterised by a distinct clinical response. Deregulated metabolic genes and pathways were identified between rare CLL cases that have undergone spontaneous regression versus CLL cases with poor clinical outcome. CLL cells from cases with poor outcome presented a differential reliance on oxidative phosphorylation and mitochondrial respiration compared to spontaneous regressed CLL cells. Going beyond traditional gene expression analysis, we performed an integration of transcriptomics profiles with Genome Scale Metabolic Models to identify metabolic genes as potential vulnerabilities in CLL. Our findings emphasise the important role of metabolic reprogramming in CLL and suggest the possibility of targeting metabolism for future studies and therapeutic approaches. The third chapter of this thesis describes a study exploring cancer metabolism in aggressive NHL associated with germinal centre development, focusing on endemic Burkitt Lymphoma (BL) and the germinal-centre–like subtype Diffuse Large B-cell Lymphomas (DLBCL). Analysis of the transcriptome of primary tumours revealed that BL cases possessed a distinct gene expression profile compared to DLBCL cases. This BL profile is suggestive of altered function of metabolism with elevated expression in serine metabolic genes, the c-Myc and mTORC1 pathways. On the opposite, DLBCL cases appeared to be dependent on extracellular signals from cytokines (INFγ response) or inflammation, possibly to trigger activation of intracellular signalling pathways that impact metabolism. Furthermore, integrative analysis at the pathway level between transcriptomic and metabolomic datasets from cell lines, indicated a dependency of BL cells on non-essential amino acid metabolism and particularly on the alanine, aspartate and glutamine metabolic pathways. These results not only highlighted key metabolic regulators in NHL, but most importantly, demonstrated the necessity of understanding and monitoring metabolic properties in these lymphomas. Finally, chapter four describes work undertaken to explore the transcriptomic and metabolic diversity of cancer cell lines. Machine learning approaches were applied to integrate and analyse Omics datasets retrieved from the Cancer Cell Line Encyclopaedia (CCLE) database. Unsupervised analysis highlighted the distinct transcriptomic and metabolomic profile of haematopoietic cell lines compared to other tumours. Taking a supervised approach enabled us to associate gene expression changes in cytoskeleton and cell adhesion molecules with aberrant metabolites levels, such as xanthine and creatinine. Together, these observations provide proof of concept for the highly dynamic variations between transcriptome and metabolome in different cancers. In summary, this work portrays the power of multi-Omics data integration to unveil key elements in metabolic reprogramming of haematological cancers and raises numerous questions and new hypotheses for future metabolic studies.

Οι αιματολογικοί καρκίνοι είναι ετερογενείς ασθένειες που προκαλούνται από μια σειρά γεγονότων, οδηγώντας τα κύτταρα σε ανεξέλεγκτο πολλαπλασιασμό. Σήμερα, κατανοούμε ότι ένα από τα χαρακτηριστικά των καρκινικών κυττάρων είναι ο επανα-προγραμματισμός του φυσιολογικού κυτταρικού μεταβολισμού τους, ώστε να διατηρήσουν τις αναβολικές ανάγκες για συνεχή ανάπτυξη και πολλαπλασιασμό. Ωστόσο, παρά την αξιοσημείωτη πρόοδο στην κατανόηση του μεταβολισμού των καρκινικών κυττάρων, οι ακριβείς μηχανισμοί πίσω από τον μεταβολικό επανα-προγραμματισμό τους δεν είναι ακόμη πλήρως κατανοητοί. Η εργασία που παρουσιάζεται σε αυτή τη διατριβή στοχεύει να παράσχει νέες βιολογικές γνώσεις σχετικά με τον μεταβολικό επανα-προγραμματισμό των αιματολογικών καρκίνων και να αναδείξει πιθανούς μεταβολικούς στόχους για μελλοντικές θεραπευτικές παρεμβάσεις. Για το σκοπό αυτό επιλέχθηκε μια προσέγγιση ενοποίησης δεδομένων πολλαπλών Ομικών τεχνολογιών. Εφαρμόστηκαν πρόσφατες υπολογιστικές μεθοδολογίες για την ενοποίηση και ανάλυση μεταγραφικών και μεταβολικών δεδομένων που προέρχονται από δείγματα ασθενών με αιματολογικούς καρκίνους, καθώς και αυτά κυτταρικών σειρών.