DNA patterns provide key insights in the diagnosis of childhood leukemia microbiologystudy

The treatment of leukemia among children could be made more precise with fewer side effects with the help of more accurate methods of diagnosis. A new study led by Umeå University, Sweden, reveals that analyzing methylation patterns in the child’s leukemic cells DNA can enhance risk assessment, helping to ensure that only the children who need the most intensive treatment receive it. The work is published in the journal Blood.

“The health care system has become much better at saving lives in childhood leukemia, but the downside of intensive treatments is severe side effects. Our results show that DNA methylation analysis may be a viable way to individualize treatment according to the type of leukemia, with the hope of a better quality of life,” says Professor Sofie Degerman at Umeå University, who has led the study with participating researchers from several countries.

The subtype of leukemia that the researchers in Umeå have studied is T-cell acute lymphoblastic leukemia, T-ALL. The treatment mainly consists of chemotherapy, administered over two years, and in some cases supplemented with a bone marrow transplant. Treatment is usually effective; today, almost 9 out of 10 children diagnosed with T-ALL survive.

The problem is that the treatment causes side effects in the form of nausea, fatigue, susceptibility to infection and hair loss. In the long term, there is also a risk of heart problems, cognitive impairment, fertility problems and suffering from other cancers.

The risk of relapse varies within the T-ALL patient group, but it is difficult to determine the risk of relapse with today’s methods of diagnosis. This means that many children receive more demanding treatment for safety’s sake and thus risk more side effects than they might need.

One conclusion of the Umeå researchers’ study is that analysis of DNA methylation patterns at diagnosis could contribute to a refined risk stratification to determine which children need which treatment. The method also identifies those patients who respond poorly to current treatments, and these patients may be in need of new treatment strategies.

“The method is already used in several hospitals in the diagnosis of brain cancer, and our study shows that it could also be implemented for leukemia,” says Fernanda S. Hackenhaar, Department of Medical Biosciences at Umeå University, who has contributed to the study’s bioinformatic analysis.

Methylation is an epigenetic process in which chemical groups are added to the DNA sequence, influencing which genes should be expressed and which genes should be silenced. This process occurs in every genome.

The methylation pattern varies between different cell types and contributes to cells unique characteristics and properties. If the methylation pattern is disrupted, it can contribute to tumor development. In a way, the DNA methylation pattern acts as a kind of molecular fingerprint, that can provide detailed insights about the properties of the cancer cells.

“We are continuing our research to investigate the possibility of including DNA methylation analysis in clinical diagnostics for patients with T-ALL,” says Sofie Degerman.

The current study includes diagnostic leukemia samples from 348 children with T-cell acute lymphoblastic leukemia (T-ALL) treated between 2008 and 2020 in the Nordic countries and in the Netherlands. The researchers have used advanced array technology and bioinformatics to analyze the methylation patterns of approximately 850,000 DNA methylation positions in the genome.

In the study, the researchers also investigated the gene expression and genetic changes of the leukemia cells to better understand how leukemias with different methylation patterns can be treated. This work will continue in future studies with the aim of further improving treatment strategies.

The research team has also established an epigenetic platform in Umeå in collaboration between Umeå University and Region Västerbotten. This platform facilitates DNA methylation array analysis of both research samples and diagnostic samples, strengthening the possibilities for advanced epigenetic research and clinical diagnostics.