Functional Genomics of Leukaemia
Survival rates now approach 90% for children diagnosed with acute lymphoblastic leukaemia (ALL). However, current treatment often results in severe chronic health conditions, and outcomes for relapsed ALL cases remain poor. Considerable effort has been made to understand the molecular aberrations that underpin disease manifestation and progression, with the ultimate aim of developing novel therapeutic approaches tailored to an individual’s mutational signature.
Although critical oncogenic mutations have now been identified through large-scale Next Generation Sequencing efforts, many of these mutations have only been studied in isolation, and targeting such mutations using a single therapeutic agent often fails to yield true clinical benefit. Hence, there is a critical need to move away from the current paradigm of ‘one variant and one therapy'. To maximise clinical impact, we need to better understand the functional relationship between multiple co-occurring mutations, to enable the design of intelligent combination approaches for the treatment of ALL.
Our group aims to expand the understanding of how ectopic signalling and somatic mutations drive the development of ALL. For example, in T-cell ALL (T-ALL), these include activating mutations within JAK1, JAK3 and IL7R that lead to ligand independent activation of STAT5; inactivating mutations of PTEN that lead to PI3K/AKT signalling pathway activation; and a high frequency of NOTCH1 mutations. In addition, T-ALL is also characterised by the mutually exclusive expression of transcription factors including TAL1, TLX1, TLX3, HOXA9/10, LMO2 and NKX2-1, often the consequence of chromosomal rearrangements.
As part of our ongoing efforts, we use both cell line and in vivo mouse models together with CRISPR-Cas9, ChIP-seq, ATAC-seq, nanopore-sequencing and RNA-seq base technologies to model how these different mutations cooperate with one another and drive disease. It is hoped that this in turn will help us design more targeted therapies for patients.
Dr Charley De Bock
This project will focus on the mechanisms by which oncogenic tyrosine kinase mutations cooperate with ectopically expressed transcription factors. This program builds on previous work showing activated STAT5 cooperates with HOXA9 in driving T-ALL, and will expand to acute myeloid leukaemia (AML), where activated STAT5 and HOXA9 also occurs in a significant proportion of patients.
The project will utilise a unique set of in vivo and in vitro HOXA9/STAT5 dependent leukaemia models to:
(i) characterise higher order transcriptional complexes recruited to DNA,
(ii) use CRISPR dropout screening to identify essential genetic dependencies, and
(iii) test novel drug combinations within relevant PDX models.
Dr Charley De Bock
Transcriptional deregulation is a hallmark of cancer and driven not only by the ectopic expression of transcription factors but also by the deregulation of co-factors involved in transcriptome splicing. We have shown using proteomics that mutant JAK3 signalling regulates a number of RNA-binding proteins involved in RNA splicing.
This project will:
(i) characterise the alternatively spliced transcriptome downstream of mutant JAK3 signalling using long read sequencing in combination with short read sequencing in clinically relevant T-ALL samples,
(ii) use targeted proteomics and real-time quantitative PCR to confirm the presence and expression of novel isoforms, and
(iii) assess the in vivo leukaemogenic potential of novel alternative transcript usage within our established mutant JAK3(M511I) T-ALL mouse model.
Dr Charley De Bock
Disease progression and relapse for the majority of cancers, including leukaemia, is driven by intratumoral heterogeneity that results from branching tumour evolution. Indeed, relapsed acute lymphoblastic leukaemia (ALL) is often derived from an ancestral and therapeutically resistant leukaemic clone. Therefore, understanding leukaemia evolution will help to define the order and constraints of early mutational events that drive disease, and simultaneously identify genetic dependencies that can be therapeutically targeted.