Every year, around 6,000 people in the UK are diagnosed with multiple myeloma, a blood cancer driven by scrambled DNA that differs from patient to patient. The problem is that standard genome sequencing struggles to read the most tightly packed regions of DNA, called compact heterochromatin. In a pilot study of 23 myeloma genomes, the researchers found that over 60% of cases had DNA breakages in these previously unreadable areas. Until the 2022 human genome map revealed their sequence, these regions were effectively invisible. The team has now developed a new computational method to analyse them, and their early data suggest these breakages are linked to worse patient outcomes and may activate genes that help tumours grow. If this project succeeds, it will map the precise architecture of these hidden breakages across a large set of myeloma genomes, from early-stage disease through to treatment resistance. The immediate impact is fundamental: a deeper understanding of how compact DNA regions contribute to cancer evolution. The computational tools developed here will also be applicable to other cancers, opening up a new layer of the genome for study. There is no immediate clinical application, but identifying the genes involved could eventually point to new drug targets or biomarkers for predicting disease progression.
View original technical description
Multiple myeloma is the second most common type of blood cancer, affecting approximately 6,000 new cases in the UK every year. This malignancy originates from plasma cells, a type of immune cell, which undergo genetic changes that trigger uncontrolled cell division, leading to significant health complications. Our genetic code is organised into strings of DNA within every cell, where the active units, known as genes, serve as the fundamental instructions guiding cellular function and behaviour. Each gene occupies a designated position within one of these strings, ensuring coordinated biological processes. However, in the case of myeloma cells, this precise arrangement can become disrupted; like a string breaking and tangling, causing genes to get lost, duplicated, or rearranged. The exact breaks and affected genes differ from one patient to another. Such variability in genetic alterations underlies the wide range of symptoms and treatment responses observed in myeloma, presenting a major challenge in understanding and treating this cancer. In an initial study of 23 myeloma genomes, we discovered something unexpected. Over 60% of these cases had DNA breakages in areas that are usually tightly packed and protected from such damage. These areas, known as compact heterochromatin, had been a mystery until the latest human genome map in 2022 revealed their DNA sequence, enabling them to be studied for the first time. Yet, extracting meaningful information about these areas through genome sequencing presents significant analytical challenges, leaving them largely unexplored in any cancer. To address these challenges, we have developed a new method that enables us to uncover the intricate details and implications of these breakages. Our findings indicate that these breakages are not random but are associated with worse outcomes for patients, affecting crucial genes that can aid tumour growth. Our project aims to dive deeper into these genetic abnormalities and study their significance in myeloma. We suspect that disturbances in these compact DNA regions could lead to a cascading effect, making the myeloma cells more robust and resistant to treatment. By analysing a large set of myeloma genomes and employing modern genetic techniques across various molecular levels, we aim to pinpoint the architecture and mechanisms of these changes. We will identify their precise locations and the genes involved, exploring their influence on myeloma development from its asymptomatic stage through to advanced disease progression and treatment resistance. The insights from this study promise to revolutionize our understanding of multiple myeloma, identifying key factors that could predict the disease's biology and uncover new ways to target the cancer cells. Furthermore, the computational methods we develop will have broad applications in cancer research, offering new avenues to study genetic abnormalities across different cancers.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know