Skip to main content

Table 6 T-cell acute lymphoblastic leukaemia. Status: solved

From: Gene editing in the context of an increasingly complex genome

In T-cell acute lymphoblastic leukaemia (T-ALL), 25% of cases exhibit high expression of the TAL1 oncogene, which is due to a large deletion occurring at 1q33 that brings the coding sequences of TAL1 (a transcription factor) in proximity to the promoter of STIL, a ubiquitously-expressed gene. This results in the ubiquitous/over- expression of TAL1 and drives cancer. In many cases of T-ALL, however, overexpression of TAL1 is observed without the large deletion – in these cases, H3K27ac binding (a marker of an enhancer region) is also found upstream of TAL1. Despite this information, the exact mechanism of disease had remained elusive for many years in these cases. Mansour and colleagues [252] observed these cases and found small heterozygous insertion variants of varying lengths in the same region as the previously found H3K27ac marks. The insertion variants, they found, were introducing new binding sites for the MYB transcription factor family, resulting in the over-expression of TAL1 and the driving of cancer.
Conclusion: The Mansour study shows how data from DNA, RNA, and DNA-binding interactions can be used in combination to clearly define a disease mechanism. In this example, observing the intergenic upstream insertion variants (DNA), the heightened expression of TAL1 (RNA), or the acetylation marks (DNA-binding interactions) alone would not explain the mechanism of disease. The Mansour study, however, although difficult and summing up years of work and studies, was made relatively easier by the fact that only a single gene was involved: TAL1. Thus, technically, no expert analytics or bioinformatics input was required. However, for complex diseases like most other cancers, cardiovascular diseases, etc., describing disease mechanisms is made extremely difficult by the fact that there can be any number of variants —be they SNPs, insertions, deletions, translocations, or copy number variants— involved in augmenting risk of the disease, with none on their own contributing a large amount to the disease phenotype. Thus, for complex diseases, there is much room for computational methods to be introduced in order to assist in clearly defining diseases mechanisms, but it involves a greater appreciation away from solely the genome.