Statistical analysis could improve understanding and treatment of different brain tumours
Discovering a brain tumour is a very serious issue but it is not the end of the story. There are many different types of brain tumour with different survival rates and different methods for treatment. However, today many brain tumours are difficult to clearly diagnose, leading to poor prognoses for patients.
Diagnosis today is mainly done by morphological appearance. However, this does not closely correlate with the pathogenesis mechanisms and diagnosis based on morphology may have hampered the discovery of a cure for brain tumours and other types of cancers.
This is something that Xiaolong Fan and colleagues at Beijing Normal University in China are looking to improve. "We try to find ways to classify brain tumours according to the known pathogenesis processes," Fan explained. "This could help with diagnoses and maybe could avoid unnecessary treatment."
The Beijing researchers have a long-term collaboration with researchers at Lund University in Sweden. The two groups have together developed a new way to classify gliomas - the most common brain tumours in adults - into distinct molecular subtypes. As the researchers reported in a recent paper in the journal Proceedings of the National Academy of Sciences of the United States of America, these molecular subtypes show differences in transcriptomic and genomic characteristics, as well as in patient survival rates.
To come up with the classification, the researchers studied two gene co-expression modules around key signalling pathways that are conserved between neural development and the formation of gliomas. In the search for patterns, they use publicly available datasets from three continents, including gene expression data, genomic data and clinical data.
To analyse these datasets and look for links, the researchers turned to a Lund-based scientific software company, Qlucore, founded in 2007 as a spin out of Lund University. The company was formed to develop an interactive software tool to conceptualize the vast amount of high-dimensional data generated by microarray gene expression analysis and it is now used with a range of high-dimensional biological datasets.
"Qlucore has a really interesting approach," said Fan, who has been using the software for four years. Using Qlucore Omics Explorer to carry out Pearson correlation co-efficient analysis, the team was able to identify gene co-expression modules around two receptor tyrosine kinases (RTKs) that govern cell fate specification, cell proliferation, migration in the neural stem cell compartment and glial development in gliomas. The two key RTKs that the team studied are epidermal growth factor receptor (EGFR) and platelet derived growth factor receptor A (PDGFRA).
Based on the expression patterns of these two modules, adult low-grade and high-grade gliomas could be classified into three major subtypes that are distinct in prognosis, genetic abnormalities and correlation to the cell lineages and differentiation stages of glial genesis but independent of glioma morphology.
The three subtypes are EM, PM and EMlowPMlow gliomas. According to the findings presented in the PNAS paper, EM gliomas were associated with higher age at diagnosis, poorer prognosis, and stronger expression of neural stem cell and astrogenesis genes. Both PM and EMlowPMlow gliomas were associated with younger age at diagnosis and better prognosis. In addition, "PM gliomas were enriched in the expression of oligodendrogenesis genes, whereas EMlowPMlow gliomas were enriched in the signatures of mature neurons and oligodendrocytes."
"In this process of this study, we have found that Qlucore has been very helpful in supporting a biologist without sufficient mathematic background to apply bioinformatics approaches in their studies. This has been essential for the implementation of the project," said Fan.
Fan is excited about the potential of this approach to improve classification of gliomas. However, there is plenty of work still to do, he said. The next steps, he said, are to use these classifications to elucidate glioma pathogenesis and to identify new glioma therapeutic targets.
And there are plenty of challenges too. "It is quite easy to bring data together if you have a procedure. We use the software as a mathematical tool," he said. However, he added that the limitation is biology. "There are lots of mathematical choices and statistical possibilities but not all make biological sense."