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.
