Staff: 

  • Diane Trinh, MSc. 

Trainees: 

  • Dr. Alessia Gagliardi, Post-Doctoral Fellow

    •  KMT2D / MLL2 is a SET domain containing protein that catalyses the methylation of lysine 4 on histone 3 (H3K4me) at enhancer regions, which marks active enhancers. KMT2D is frequently mutated in at least 27 different types of cancer, including the non-Hodgkins lymphomas (NHL) follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL). Many of these mutations are predicted to be inactivating, suggesting selective pressures favour loss of KMT2D function. We have generated knock out cell lines to model KMT2D loss of function and, using large scale cell-based screens, to look for synthetic lethal interactions with other loss-of function alterations across the genome.

  • Vanessa Porter, MSc., PhD Candidate 

    • Genetic and epigenetic mutations have both been implicated to have a driving role in the development of cancer. The epigenetic regulator KMT2D is one of the most mutated genes across all cancers in TCGA, the cancer genome atlas. In particular, KMT2D loss-of-function mutations (LOF) are present in 90% of follicular lymphomas (FL) and 40% of diffuse large B cell lymphoma (DLBCL), indicating it may be an important tumour suppressor in non-Hodgkin lymphomas (NHL). KMT2D is a histone methyltransferase that deposits activating H3K4me1 marks on nucleosomes flanking enhancer regions. Our lab performed ChIP-sequencing analyses within human embryonic kidney cells (HEK293A) and showed that loss of KMT2D results in a decrease of H3K4me1 and H3K27ac marks at KMT2D-dependant active enhancers, resulting in decreased transcription of their target genes. A gene ontology analysis showed that genes affected by KMT2D loss were enriched within the retinoic acid and TGF-b pathways, while their promoter regions were enriched with DNA binding motifs corresponding to TGF-b signalling co-activator complex AP-1. We currently aim to validate these findings in other relevant cell types.

  • Lisa Wei, PhD Candidate 

    • In paediatric AML, ~82% of patients with NUP98/NSD1 fusions also have FLT3/ITD, a known driver of treatment resistance. Patients with both genetic alterations had much lower rate of remission induction (27% vs 69% for FLT3/ITD patients with and without NUP98/NSD1, respectively) (Ostronoff et al. 2017). Although co-occurrence of these two events is associated with the low rate of response of patients to therapy, the mechanisms by which the co-expression of FLT3/ITD and NUP98/NSD1 induces innate treatment resistance is not well understood. We seek to understand such mechanisms, using RNA sequencing performed on 1,055 cases that were profiled as part of the AAML1031 clinical trial (Aplenc et al. 2016). We will infer transcription factor (TF) networks using regulatory network analysis and grouping co-expressed genes sharing common binding motifs of TFs, and attempt to comprehensively deduce the identity and consequences of dysregulated TF networks in treatment resistant paediatric AML. 

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