A simplified approach using Taqman low-density array for medulloblastoma subgrouping

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, 22‐24].

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]. The updated World Health Organization (WHO) classification of the central nervous system (CNS) acknowledges some of these molecular features as risk-stratification factors for MB [17]. Still, 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.

As an initial attempt to equate this subject, Kunder and colleagues [15] described a miRNA-based real-time PCR assay platform that performed subgroup assignment using a reduced set of 21 probes. However, analyses of Group 3 and Group 4 MB subgroups were not precisely discriminative when this approach was used and no algorithm accuracy was validated for their method. Similarly, Kaur and colleagues [12] published a simplified approach based on immunohistochemistry (IHC) and real time PCR (qPCR) methods for MB subgroup allocation [12]. However, overlapping IHC staining was observed between subgroups. More recently, complete datasets from cohort studies have become publicly available, allowing the validation for new molecular classification and comparing novel stratification proposals for gold standard NGS data. Accordingly, the validation of new algorithms seems to be critical considering their increasing genomic and molecular importance for therapeutic decisions [4, 7, 10, 16].

Here, we describe a low-cost and straightforward method for molecular allocation of MB patients. We hypothesized that a combination of qPCR with precise algorithms would be a useful, simple and potent tool for molecular assignment of MB tumors. We have optimized the number of genes to molecularly classify patients into four and three groups of interest for clinical management. We also 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 (transcriptional, methylation and cytogenetic profiles) (GSE85217), as well as confirming our subgroup findings by Methylation array in a sample subset.

In the present study, differential expression analysis of 20 genes from the CodeSet described by Northcott and colleagues [19] by TDLA approach permitted us to molecularly assign a cohort of 92 MB patients to the four major MB subgroups. Additionally, we validated the same gene set in a cohort of 763 MB patients from the GSE85217 reference study, which applied the integrative-clustering method to molecularly classify MB samples. The WNT and SHH subgroups were robustly identified since they formed a solid and concise cluster generated by the Average-linkage or Ward.D2 algorithms and confirmed by t-SNE analysis. In agreement, similar patterns were detected using GSE85217 data analysis. We demonstrated that assessment of the transcription profile is not sufficient to completely discriminate all Group 3 MB from Group 4 MB since a minority of these patients share transcription and common molecular features [10, 12, 15, 18].

Next, in order to exam the concordance of our TDLA approach with NGS subgrouping for MB we validated molecular assignment of 11 MBs samples by Methylation Array 450 K. We found a high frequency of monosomy in chromosome 6 within WNT (5 out of 6) subgroup corroborating with previous studies [2, 8, 13, 28]. In one SHH MB samples evaluated by Methylation array we identified GLI2 amplification. For Group 3, one MB specimen bears isochromosome 17q, a reliable marker for this subgroup [28] (Fig. 2B). Only one sample for group 4 was identified, and it also clustered to group 4 by TLDA method accordingly. Full concordance between eleven MB samples by NGS and TDLA was observed. Despite only a small set of samples was assessed, the results from NGS data support our molecular assignment provided by TLDA [2, 8, 13, 28].

In the present study, we found 27% of WNT MBs (Additional file 6: Figure S3a-S3d and Additional file 7: Figure S4). Although this is a high frequency when compared to studies performed in North American and European continents [19], Kunder and colleagues [15] reported 24% of WNT MBs in an Indian cohort. Moreover, pediatric neoplasms subtypes vary in frequency depending on the genetic population background (i.e: high frequency of Promyelocitic Leukemia in Latin America) [20, 25]. Interestingly, we found 2 cases of desmoplastic and 1 LCA in WNT MBs. Besides it is unlikely to find desmosplastic histological variants in WNT MBs, our data are supported by other studies [27]. In summary, these epidemiological facts highlight the urge for a reliable, feasible and low-cost method to perform molecular assignment of MBs in low and middle-income countries.

The average-linkage and Ward.D2 algorithms were assessed regarding their clustering features and subgroup assignment. In the GSE85217 study conducted on 763 MB patients, average-linkage provided better accuracy for SHH and Group 3 assignment compared to the Ward.D2 method. However, Ward.D2 was able to accurately classify WNT and Group 4 tumors. Interestingly, the pick of an accurate clustering algorithm may be subgroup specific. However, it is very important to understand the limitations of transcriptional data and information that can be extracted from a single feature such as gene expression [2, 27, 33].

Indeed, as reported by Cavalli and colleagues [2], the gold standard method for subgroup assignment is the assessment of the molecular features of the patient (transcription profile, methylation profile, cytogenetic profile) along with clinical information. However, in low-income countries, most molecular techniques are onerous for application to daily clinical practice. Using expression analysis of a gene set, algorithm assessment and bioinformatic analysis, we sought to identify the minimal number of genes needed to molecularly classify MB as WNT, SHH and non-SHH /non-WNT. In our study, by using a set of six differentially expressed genes we were able to distinguish SHH and WNT from non-WNT/non-SHH without s significant loss of accuracy. Both the Average-linkage and Ward.D2 algorithms conserved 100% accuracy for assignment to the WNT subgroup, with a decline to 85.18% for the SHH subgroup. As shown in the t-SNE map, there was a minor overlapping of samples of the non-SHH/non-WNT cluster with those of the SHH cluster. Additionally, we found high concordance between our data set and GSE85217, with 100% accuracy for the WNT subgroup and 86% accuracy for SHH. These results shed new light on a potential method for low-income countries based on a simple and feasible technique such as qPCR along with six probe/primer pairs plus reference genes with implementation of an approach recently described by Gómes and colleagues [3]. Their method fully discriminates between Group 3 and Group 4 based on the methylation status of 5 CpG’s, which is feasible for the real-time PCR platform through High Resolution Melting technology, and shall improve the molecular assignment [26].

Northcott et al. described a molecular classification method for MB that relies on the NanoString nCounter System. Besides the high accuracy of the method (~ 98%), the average cost is estimated at 60.00 USD per sample and the method takes 3–4 days to perform bioinformatic analysis [19]. The same method was reproduced by Leal and colleagues [16]; however, due to the high cost of the equipment (287,817.60 USD – average price in South America; 2018), it is challenging for most low-income countries to apply this method to clinical routine. Kaur et al. proposed a minimal panel comprising a combination of IHC antibodies and FISH probes to classify MB, with an estimate cost around 150.00 to 250.00 USD (average) per sample [12]. Although feasible, their approach does not seem to be as cost-effective as other methods and IHC analysis remains challenging due to different antibody batches and inter-observer consistency [19]. More recently, the minimal methylation classifier (MIMIC) was described as a highly efficient methodology that might be superior to Illumina 450 K and Methylation EPIC array for MB molecular assignment regarding feasibility for clinical routines; however, the average cost per sample with this approach is around 200.00 USD [26] and requires the acquisition of a MALDI-TOF mass-spectrometer (approximately 150,000.00 USD), along with a conventional PCR device. Our method using TLDA has an estimated cost of 70.00 USD per sample (including reagents, primers and laboratory implements). The equipment necessary to run TLDA costs about 92,600.00 USD and complete data analysis is ready within one working day. Moreover, when we condensed the number of studied genes to six (a set of TaqMan probes for: SFRP1, HHIP, EYA1, WIFI1, EMX2 and DKK2 along with reference genes HPRT and Gus-ß), the cost of molecular assignment to the WNT, SHH and N-SHH/N-WNT MB subgroups dropped to 26.82 USD per sample. Also, the real time-PCR (30,000.00 USD) platform is relatively inexpensive and commonly available in most hospitals due to its ample use for other routine laboratory applications. Finally, another advantage of the qPCR method is that it does not require batched minimal number of samples per run, being readily available to run single tumor samples upon arrival at the laboratory.

In conclusion, we have developed a simplified approach and validated TDLA method in random samples by Methylation Array 450 K. In addition, our findings were challenged at a large cohort study GSE85217 through accurate algorithms for molecular assignment of MB. The proposed assay is cost-effective and discriminates most of SHH, WNT, non-SHH/non-WNT tumors. The TLDA method for MB subgroup stratification might be an affordable tool to be used to drive therapies in low-income countries. Moreover, it may also be an important approach for prompt classification and decision-making algorithms in MB, before NGS data analysis become available.