Abstract
Next-generation sequencing platforms are routinely used for molecular assignment due to their high impact for risk stratification and prognosis in medulloblastomas. Yet, low and middle-income countries still lack an accurate cost-effective platform to perform this allocation. TaqMan Low Density array (TLDA) assay was performed using a set of 20 genes in 92 medulloblastoma samples. The same methodology was assessed in silico using microarray data for 763 medulloblastoma samples from the GSE85217 study, which performed MB classification by a robust integrative method (Transcriptional, Methylation and cytogenetic profile). Furthermore, we validated in 11 MBs samples our proposed method by Methylation Array 450 K to assess methylation profile along with 390 MB samples (GSE109381) and copy number variations. TLDA with only 20 genes accurately assigned MB samples into WNT, SHH, Group 3 and Group 4 using Pearson distance with the average-linkage algorithm and showed concordance with molecular assignment provided by Methylation Array 450 k. Similarly, we tested this simplified set of gene signatures in 763 MB samples and we were able to recapitulate molecular assignment with an accuracy of 99.1% (SHH), 94.29% (WNT), 92.36% (Group 3) and 95.40% (Group 4), against 97.31, 97.14, 88.89 and 97.24% (respectively) with the Ward.D2 algorithm. t-SNE analysis revealed a high level of concordance (k = 4) with minor overlapping features between Group 3 and Group 4. Finally, we condensed the number of genes to 6 without significantly losing accuracy in classifying samples into SHH, WNT and non-SHH/non-WNT subgroups. Additionally, we found a relatively high frequency of WNT subgroup in our cohort, which requires further epidemiological studies. TLDA is a rapid, simple and cost-effective assay for classifying MB in low/middle income countries. A simplified method using six genes and restricting the final stratification into SHH, WNT and non-SHH/non-WNT appears to be a very interesting approach for rapid clinical decision-making.
Highlights
Medulloblastoma (MB) is the most common malignant brain tumor of children and adolescents, representing 20% of all pediatric brain tumors and is considered to be a complex disease from a genetic perspective [31]
We present an elucidative algorithm for MB subgroup assignment, validating our approach and comparing our findings to data from 763 MB samples molecularly assigned through a robust integrative methodology (GSE85217), as well as confirming our subgroup findings by Methylation array in a sample subset
Thirty-nine patients died because of the disease (DOD), 37 patients showed no evidence of disease (NED) at their last follow-up, and 4 patients (4.81%) died due to other unrelated events
Summary
Medulloblastoma (MB) is the most common malignant brain tumor of children and adolescents, representing 20% of all pediatric brain tumors and is considered to be a complex disease from a genetic perspective [31]. Current consensus divides MB into four main molecular subgroups: SHH, WNT, Group 3 and Group 4. These subgroups have distinct transcriptional profiles, copy-number aberrations, somatic mutations and clinical outcomes [2, 18, 22,23,24]. The molecular subgroups of MB have been incorporated into risk stratification along with conventional biomarkers and preclinical models to evaluate novel targeted inhibitors and to substantiate further clinical trials [21, 23, 31]. Low and even middle-income countries cannot afford to routinely use these next-generation sequencing (NSG) platforms for MB molecular subgrouping. High costs related to these new technologies (e.g Illumina Methylation array 450 k and NanoString nCounter®) preclude their routine clinical application in most low-income Nations
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