MLPA in Challenging Glioblastoma Cases
A brief research report published in Pathology & Oncology Research by researchers from the Czech Republic highlighted the inherent challenges of characterising glioblastoma by comparing multiple widely used methodologies, including MLPA. Glioblastoma is the most common malignant brain tumour and is characterized by poor prognosis and short overall survival.
The authors found that MLPA remains a cost-effective and efficient tool for detecting CNAs and mutations in glioblastoma. Its targeted approach enables quick analysis of abundant aberrations, and was able to cover all relevant CNAs, albeit with the combined use of multiple probemixes.
Although broader methods like whole genome sequencing (WGS) or comparative genomic hybridization array and single-nucleotide polymorphism (aCGH/SNP) assays provide a more comprehensive analysis in a single assay and perform better in the detection of certain aberrations such as aneuploidies, they are also more expensive, time-intensive, and require expertise.
The authors concluded that MLPA continues to play a key role in an integrated glioblastoma characterisation strategy, offering a practical balance of affordability and utility when combined with complementary techniques.
Probemixes used to analyse glioblastoma in this study:
P370 BRAF-IDH1-IDH2
P105 Glioma. This probemix just received a major update and now includes probes targeting two common TERT promoter point mutations.
P088 Oligodendroglioma 1p-19q
ME012 MGMT-IDH-TERT