Periodic Reporting for period 2 - HaemMetabolome (Deciphering the Metabolism of Haematological Cancers)
Periodo di rendicontazione: 2017-10-01 al 2019-09-30
HaemMetabolome was established to train 10 Early Stage Researchers in the fields of experimental haematological cancer research and metabolism. In the context of a Joint Doctorate scheme, each fellow was supervised and worked at two different sites.
It was the objective of the project to provide research training in metabolic mechanisms associated with cancer-specific metabolic features, the characterisation of key metabolism regulators, studying the effect of drugs on specific metabolic mechanisms and the use of computational approaches to integrate different data-sets to allow predictions of drug interactions, and markers for personalised treatment.
HaemMetabolome focussed on training and research using nuclear magnetic resonance (NMR) and mass spectrometry (MS) in order to understand the mechanism of metabolic regulation in haematological cancers; to link metabolic profiles to the effect of specific metabolic regulators; and to look at the effect of drugs on these cells.
It was the objective of the project to provide research training in metabolic mechanisms associated with cancer-specific metabolic features, the characterisation of key metabolism regulators, studying the effect of drugs on specific metabolic mechanisms and the use of computational approaches to integrate different data-sets to allow predictions of drug interactions, and markers for personalised treatment.
HaemMetabolome focussed on training and research using nuclear magnetic resonance (NMR) and mass spectrometry (MS) in order to understand the mechanism of metabolic regulation in haematological cancers; to link metabolic profiles to the effect of specific metabolic regulators; and to look at the effect of drugs on these cells.
The work of the HaemMetabolome project has focussed on the key scientific and training objectives. Results are described below.
A metabolic interaction between acute myeloid leukaemia (AML) cells and stromal cells has been characterised. Results reveal that AML cells rewire the metabolism of stromal cells to generate metabolites that cancer cells then take up. This involves upregulating several glycolytic genes and altering pyruvate metabolism (ESR1, UoB).
Loss of ATM function induces a change in the metabolism of CLL cells lines and others. The major finding was increased taurine levels in the ATM-defective cells compared to control cells. These increased taurine intracellular levels are likely to be due to more active proteolysis in ATM-null cells (ESR2, UoB).
Systems Biology approaches and computational modelling were used to integrate multi-omics datasets in lymphoma and leukaemia cancers. (ESR3, UoB).
Amino acid depletion studies on AML cells showed a strong dependency on methionine. Depletion of methionine reduces H3K36me3 levels, RNA synthesis and affects the cell cycle. 13C tracing suggests poor recycling of methionine in AML cells. In vivo targeting of AML by dietary methionine depletion is underway (ESR4, UMCG).
Metabolic signatures in AML were identified using multi-omics approaches. FLT3-ITD (internal tandem duplication) mutated and wild type AMLs were shown to have different metabolism even at the sub-clonal level. Various metabolic targets were identified for treatment, though metabolic adaptations were shown to occur in AML upon metabolic intervention (ESR5, UMCG).
The metabolic characterisation of the effect of the overexpression of Transketolase-like 1 (TKTL1) and of the loss of function of TET2 in haematological cancers was analysed by combining Targeted Metabolomics, SIRM using MS and NMR and other biochemical and molecular biology assays (ESR6, UB).
Genome Scale Metabolic Models (GSMMs) have been developed and used to study the metabolic adaptations that emerge in AML and to identify new metabolic vulnerabilities that could be targeted to prevent it (ESR7, UB).
Cell lines resistant to existing AML and CML chemotherapeutics were developed. Metabolic differences between sensitive and resistant cells lines were unveiled. Next, we used drugs that inhibit identified putative targets, thereby overcoming drug resistances (ESR8, UB).
We investigated the metabolic profile associated with FLT3-ITD AML. A key molecular player was identified, and functional validation confirmed it as a targetable vulnerability in AML. A new method for real-time metabolism was developed in collaboration with UoB (ESR9, GUF).
The aim was to decipher the metabolic dependencies of AML under physiological oxygen conditions (1 % O2). We revealed an essential role of glutamine synthetase for cellular growth and showed that AML cells conduct macropinocytosis for nutrient acquisition (ESR10, GUF).
Training objectives were addressed by activities on a local and network level. All fellows received training in different technologies; this includes secondment to their partner University within the joint-doctorate scheme. Training undertaken by the fellows includes cell biology and analytical techniques, along with training in computational analysis of data sets. Workshops for the project have been held in Barcelona, Groningen, and Frankfurt. At the workshops, fellows were able to present their projects to the rest of the network, and discuss their results with the other fellows, and the academics at the different Beneficiaries.
A metabolic interaction between acute myeloid leukaemia (AML) cells and stromal cells has been characterised. Results reveal that AML cells rewire the metabolism of stromal cells to generate metabolites that cancer cells then take up. This involves upregulating several glycolytic genes and altering pyruvate metabolism (ESR1, UoB).
Loss of ATM function induces a change in the metabolism of CLL cells lines and others. The major finding was increased taurine levels in the ATM-defective cells compared to control cells. These increased taurine intracellular levels are likely to be due to more active proteolysis in ATM-null cells (ESR2, UoB).
Systems Biology approaches and computational modelling were used to integrate multi-omics datasets in lymphoma and leukaemia cancers. (ESR3, UoB).
Amino acid depletion studies on AML cells showed a strong dependency on methionine. Depletion of methionine reduces H3K36me3 levels, RNA synthesis and affects the cell cycle. 13C tracing suggests poor recycling of methionine in AML cells. In vivo targeting of AML by dietary methionine depletion is underway (ESR4, UMCG).
Metabolic signatures in AML were identified using multi-omics approaches. FLT3-ITD (internal tandem duplication) mutated and wild type AMLs were shown to have different metabolism even at the sub-clonal level. Various metabolic targets were identified for treatment, though metabolic adaptations were shown to occur in AML upon metabolic intervention (ESR5, UMCG).
The metabolic characterisation of the effect of the overexpression of Transketolase-like 1 (TKTL1) and of the loss of function of TET2 in haematological cancers was analysed by combining Targeted Metabolomics, SIRM using MS and NMR and other biochemical and molecular biology assays (ESR6, UB).
Genome Scale Metabolic Models (GSMMs) have been developed and used to study the metabolic adaptations that emerge in AML and to identify new metabolic vulnerabilities that could be targeted to prevent it (ESR7, UB).
Cell lines resistant to existing AML and CML chemotherapeutics were developed. Metabolic differences between sensitive and resistant cells lines were unveiled. Next, we used drugs that inhibit identified putative targets, thereby overcoming drug resistances (ESR8, UB).
We investigated the metabolic profile associated with FLT3-ITD AML. A key molecular player was identified, and functional validation confirmed it as a targetable vulnerability in AML. A new method for real-time metabolism was developed in collaboration with UoB (ESR9, GUF).
The aim was to decipher the metabolic dependencies of AML under physiological oxygen conditions (1 % O2). We revealed an essential role of glutamine synthetase for cellular growth and showed that AML cells conduct macropinocytosis for nutrient acquisition (ESR10, GUF).
Training objectives were addressed by activities on a local and network level. All fellows received training in different technologies; this includes secondment to their partner University within the joint-doctorate scheme. Training undertaken by the fellows includes cell biology and analytical techniques, along with training in computational analysis of data sets. Workshops for the project have been held in Barcelona, Groningen, and Frankfurt. At the workshops, fellows were able to present their projects to the rest of the network, and discuss their results with the other fellows, and the academics at the different Beneficiaries.
The collaborative nature of HaemMetabolome will drive forward research by generating a better understanding of various haematological cancers by bringing together different research fields. Cutting edge advances are detailed below:
• Identification of how metabolite secretion by stromal cells benefits AML cells will provide a full picture on this interaction and will hopefully facilitate the development of new therapies (ESR1, UoB).
• One-third of patients with CLL present genetic aberrations of ATM. For these patients, it is essential to find new therapeutic targets that act independently of the DDR. Here we focused on studying the metabolism of CLL cells defective for ATM in order to find metabolic targets that offer new therapeutic perspectives and help ATM-null patients with poor survival (ESR2, UoB).
• Genome Scale Metabolic Modeling integration revealed the FDFT1 metabolic gene as a metabolic vulnerability in CLL (ESR3, UoB).
• A complete screen of the dependency of AML for all amino acids was provided opening the potential to target AML simply by amino acid dietary restrictions or with inhibitors. Furthermore, this study increases our understanding of how identified metabolic targets have an impact on therapy effectiveness between individual patients (ESR4 & ESR5, UMCG).
• Elucidation of the role of TKTL1 and TET2 in metabolic reprogramming of haematological cancers, revealing vulnerabilities to be exploited for new therapies in AML and CML patients that possess this profile (ESR6, UB).
• In AML, the role of TKTL1 in metabolism, cell cycle regulation and signalling events has been unveiled. New therapeutic targets to address resistance to Cytarabine and Doxorubicin have been identified. Moreover, the combination of GSMMs with machine learning has provided valuable insights into biomarkers for AML prognosis (ESR7 & ESR8, UB).
• In FLT3-ITD AML, a key molecular player was identified, and functional validation confirmed it as a targetable vulnerability in this type of AML (ESR9, GUF).
• A deepened understanding of both the role of glutamine synthetase in cellular growth and of macropinocytotic protein uptake to feed metabolic pathways. These are potential targets for novel therapeutics in the treatment of AML (ESR10, GUF).
Potential impact
Impact in HaemMetabolome arises from a new understanding of several metabolic pathways in haematological cancer cells. This will lead to more effective treatments, diagnosis and better prognosis. HaemMetabolome members have identified key metabolic regulators linked to the effect of inhibitors and drugs. New cutting-edge NMR and MS methods were developed for tracing metabolic mechanisms in cancer cells and new computational tools were developed to link genetic profiles and metabolism. Overall the tools developed in this context will enable others to study the metabolism of cancer cells.
• Identification of how metabolite secretion by stromal cells benefits AML cells will provide a full picture on this interaction and will hopefully facilitate the development of new therapies (ESR1, UoB).
• One-third of patients with CLL present genetic aberrations of ATM. For these patients, it is essential to find new therapeutic targets that act independently of the DDR. Here we focused on studying the metabolism of CLL cells defective for ATM in order to find metabolic targets that offer new therapeutic perspectives and help ATM-null patients with poor survival (ESR2, UoB).
• Genome Scale Metabolic Modeling integration revealed the FDFT1 metabolic gene as a metabolic vulnerability in CLL (ESR3, UoB).
• A complete screen of the dependency of AML for all amino acids was provided opening the potential to target AML simply by amino acid dietary restrictions or with inhibitors. Furthermore, this study increases our understanding of how identified metabolic targets have an impact on therapy effectiveness between individual patients (ESR4 & ESR5, UMCG).
• Elucidation of the role of TKTL1 and TET2 in metabolic reprogramming of haematological cancers, revealing vulnerabilities to be exploited for new therapies in AML and CML patients that possess this profile (ESR6, UB).
• In AML, the role of TKTL1 in metabolism, cell cycle regulation and signalling events has been unveiled. New therapeutic targets to address resistance to Cytarabine and Doxorubicin have been identified. Moreover, the combination of GSMMs with machine learning has provided valuable insights into biomarkers for AML prognosis (ESR7 & ESR8, UB).
• In FLT3-ITD AML, a key molecular player was identified, and functional validation confirmed it as a targetable vulnerability in this type of AML (ESR9, GUF).
• A deepened understanding of both the role of glutamine synthetase in cellular growth and of macropinocytotic protein uptake to feed metabolic pathways. These are potential targets for novel therapeutics in the treatment of AML (ESR10, GUF).
Potential impact
Impact in HaemMetabolome arises from a new understanding of several metabolic pathways in haematological cancer cells. This will lead to more effective treatments, diagnosis and better prognosis. HaemMetabolome members have identified key metabolic regulators linked to the effect of inhibitors and drugs. New cutting-edge NMR and MS methods were developed for tracing metabolic mechanisms in cancer cells and new computational tools were developed to link genetic profiles and metabolism. Overall the tools developed in this context will enable others to study the metabolism of cancer cells.