Molecular fingerprinting reflects different histotypes and brain region in low grade gliomas
- Equal contributors
1 Neurosurgery Unit, Istituto Giannina Gaslini, via G. Gaslini 5, 16147, Genoa, Italy
2 DISI - Department of Computer Science, Università degli Studi di Genova, Via Dodecaneso, 35-16146, Genoa, Italy
3 Pathology Unit, Istituto Giannina Gaslini, via G. Gaslini 5, 16147, Genoa, Italy
4 Molecular Medicine Unit, Istituto Giannina Gaslini, via G. Gaslini 5, 16147, Genoa, Italy
5 Neuroradiology Unit, Istituto Giannina Gaslini, via G. Gaslini 5, 16147, Genoa, Italy
6 Siemens AG, Corporate Technology, Freyeslebenstr. 1, 91058, Erlangen, Germany
7 SCR - Siemens Corporate Research, Princeton, NJ, USA
8 Neuro-oncology Unit, Istituto Giannina Gaslini, via G. Gaslini 5, 16147, Genoa, Italy
BMC Cancer 2013, 13:387 doi:10.1186/1471-2407-13-387Published: 15 August 2013
Paediatric low-grade gliomas (LGGs) encompass a heterogeneous set of tumours of different histologies, site of lesion, age and gender distribution, growth potential, morphological features, tendency to progression and clinical course. Among LGGs, Pilocytic astrocytomas (PAs) are the most common central nervous system (CNS) tumours in children. They are typically well-circumscribed, classified as grade I by the World Health Organization (WHO), but recurrence or progressive disease occurs in about 10-20% of cases. Despite radiological and neuropathological features deemed as classic are acknowledged, PA may present a bewildering variety of microscopic features. Indeed, tumours containing both neoplastic ganglion and astrocytic cells occur at a lower frequency.
Gene expression profiling on 40 primary LGGs including PAs and mixed glial-neuronal tumours comprising gangliogliomas (GG) and desmoplastic infantile gangliogliomas (DIG) using Affymetrix array platform was performed. A biologically validated machine learning workflow for the identification of microarray-based gene signatures was devised. The method is based on a sparsity inducing regularization algorithm l1l2 that selects relevant variables and takes into account their correlation. The most significant genetic signatures emerging from gene-chip analysis were confirmed and validated by qPCR.
We identified an expression signature composed by a biologically validated list of 15 genes, able to distinguish infratentorial from supratentorial LGGs. In addition, a specific molecular fingerprinting distinguishes the supratentorial PAs from those originating in the posterior fossa. Lastly, within supratentorial tumours, we also identified a gene expression pattern composed by neurogenesis, cell motility and cell growth genes which dichotomize mixed glial-neuronal tumours versus PAs. Our results reinforce previous observations about aberrant activation of the mitogen-activated protein kinase (MAPK) pathway in LGGs, but still point to an active involvement of TGF-beta signaling pathway in the PA development and pick out some hitherto unreported genes worthy of further investigation for the mixed glial-neuronal tumours.
The identification of a brain region-specific gene signature suggests that LGGs, with similar pathological features but located at different sites, may be distinguishable on the basis of cancer genetics. Molecular fingerprinting seems to be able to better sub-classify such morphologically heterogeneous tumours and it is remarkable that mixed glial-neuronal tumours are strikingly separated from PAs.