Breaking the code of neuroblastoma

Next-generation data analysis software reveals how the methylation profile of certain gene sets can reveal key information on tumour growth and treatment. Importantly it also allows the actual researchers involved to study the data without relying on statisticians or computer experts

DNA methylation is a crucial part of normal development and cellular differentiation in higher organisms. As such, DNA methylation plays a significant role in the epi genetic regulation of chromatin structure, which in the last decade has been recognised to be important in the regulation of gene expression, development and genetic imprinting. Changes in the methylation pattern and level have also been shown to contribute to cancer and various developmental diseases.

For example, hypermethylation at the promoter CpG islands of a tumour suppressor gene, which in turn leads to its silencing, is frequently associated with tumourgenesis. A large scale measurement of DNA methylation patterns from a wide selection of genes may therefore enable scientists to understand the relationships between epigenetic changes and the genesis of different diseases better, and also provide a clearer understanding of the role that epigenetics play in tissue specific differentiation.

Dr Helena Carén has been using sophisticated data analysis software to analyse methylation data in order to identify patterns that could help to classify tumours into different categories of seriousness, and also to predict how the tumour is likely to develop. She has been using the Qlucore Omics Explorer to research tumour growth and treatment whilst at The Sahlgrenska Academy at the University of Gothenburg in Sweden.

In particular, Dr Carén is interested in neuroblastoma, a tumour of the nerve cells that affects small children, most of whom are diagnosed before they reach their fifth birthday. It is the third most common form of cancer in children, after leukaemia and brain tumours. It appears during the development phase of the sympathetic nervous system.

Neuroblastoma displays a high degree of heterogeneity, including a milder or a benign tumour, lethal tumour progression despite intensive therapy, and the unusual ability to regress spontaneously – the latter occurring particularly in infants. This makes classification of tumours extremely important to achieve the best possible survival rates, and yet still avoid the side effects from treatment as much as possible.

As such, the ultimate goal of the study is to identify a set of genes whose methylation profile can accurately determine how aggressive a tumour is, as well as the most effective method of treatment. In the longer term, these studies will also help to identify the specific genes that have contributed to formation of the tumour itself.

Gene regulation through DNA methylation is involved in many processes and is essential for normal development. Carén and her team used the Illumina Infinium 27K arrays to analyse a large number of CpG sites in tumours from different subgroups of neuroblastoma tumours. They then analysed the DNA methylation data together with clinical information of the patients using the Qlucore software.

"By using very powerful data analysis software, we have been able to find patterns in the data very easily, and to identify gene sets that are differentially methylated in neuroblastoma tumours associated with different clinical features such as survival, tumour stage and prognostic chromosomal aberrations." She adds: “The analysis pinpoints interesting genes that need further investigation, and also suggests gene signatures that potentially can be used for a more accurate classification of the tumours in the future, which is something that needs to be studied more comprehensively."

Until now, most of the software that has been designed to study areas like array-based DNA methylation has focused on the ability to handle increasingly vast amounts of data, which means that the role of the scientist/researcher has been largely set aside. As a result, a lot of data analysis has been passed on to bioinformaticians and biostatisticians.

A new generation of data analysis software is helping to redress the balance, however, and is already playing a key role in unveiling important new discoveries, since it allows the actual researchers involved to study the data and to look for patterns and structures, without having to be statisticians or computer experts.

At the same time, the overall performance of data analysis software has been optimised significantly over the past three years. With key actions and plots now displayed within a fraction of a second, researchers can increasingly perform the research they want and find the results they need instantly.

New breakthroughs in technology are now making it possible for scientists to analyse proteomic, genomic and microarray data with a combination of statistical methods and visualisation techniques such as Heat maps and Principal Component Analysis (PCA).

In addition, the latest data analysis software can generate PCA-plots between various sample data interactively and in real time, directly on the computer screen, and work with all annotations and other links in a fully integrated way, all at the same time. This approach has helped to open up new ways of working with data analysis and, as a consequence, has helped the biologists to be more actively involved in the analysis process.

As a result, scientists studying DNA methylation analysis and other genomic data can now analyse all of this important information in real-time, by themselves, directly on their computer screen, since the software can provide instant user feedback on all actions, as well as an intuitive user interface that can present all data in 3D.

"The use of 3D graphics, in particular, has been very helpful, since it is easier to spot important patterns when you can view your results as a 3D image, and even rotate the image, if needed, directly on the computer screen," Carén says. "Plus, not only is it very easy for biologists to identify patterns in the data set very quickly by themselves, it is also easy to produce impressive charts and figures, which is very useful when presenting important findings for publication."

Although studies like Dr Carén's are proving invaluable to the study of human biology, the amount of data that is produced by this kind of research is enormous. As a result, it is impossible to derive any real biological meaning from these findings unless sophisticated analysis methods are used to help interpret the data effectively.

Fortunately, the latest technological advances in this area are making it much easier for scientists to compare the vast quantity of data generated by epigenetic studies, to test different hypotheses, and to explore alternative scenarios within seconds. As a result, the latest generation of data analysis software is helping scientists to regain control of this analysis, and to realise the true potential of research in this area.

“One of the key benefits of using such user-friendly data analysis software is that it has allowed me to manipulate all of my data myself, which means that it wasn't necessary to consult bioinformatics specialists every time I wanted to consider a new theory," Dr Carén adds. "It is often very difficult to find meaningful patterns in very large datasets, but having access to such powerful data analysis software has made it much easier for me to understand the relevance of the data produced during my methylation analysis."