Pediatric Low-Grade Glioma Classification

Pediatric Low-Grade Gliomas (PLGGs) display heterogeneity regarding morphology, genomic drivers and clinical outcomes.

They constitute the largest, yet clinically and (molecular-) a histologically heterogeneous group of pediatric brain tumors of WHO grade I and II occurring throughout all pediatric age groups and at all central nervous system (CNS) sites. The tumors are characterized by a slow growth rate and may show periods of growth arrest 1).

Pediatric low-grade gliomas were shown to be characterized by an array of distinct molecular aberrations. The cIMPACT-4 consensus proposed pediatric low-grade gliomas of the diffuse type to be characterized by distinct molecular changes rather than distinct histological features.

Fukuoka et al. described a small series of pediatric oligodendroglioma-like tumors with BRAF V600E mutations. Interestingly, they exhibited molecular changes usually associated with adult high-grade gliomas: chromosome instability, chromosome 7 gains, and chromosome 10 loss, but had an indolent natural history 2) 3).

Genetic abnormalities

Mobark et al. profiled a targeted panel of cancer-related genes in 37 Saudi Arabian patients with pLGGs to identify genetic abnormalities that can inform prognostic and therapeutic decision-making. THey detected genetic alterations (GAs) in 97% (36/37) of cases, averaging 2.51 single nucleotide variations (SNVs) and 0.91 gene fusions per patient. The KIAA1549-BRAF fusion was the most common alteration (21/37 patients) followed by AFAP1-NTRK2 (2/37) and TBLXR-PI3KCA (2/37) fusions that were observed at much lower frequencies. The most frequently mutated) genes were NOTCH1-3 (7/37), ATM (4/37), RAD51C (3/37), RNF43 (3/37), SLX4 (3/37) and NF1 (3/37). Interestingly, they identified a GOPCROS1 fusion in an 8-year-old patient whose tumor lacked BRAF alterations and histologically classified as low-grade glioma. The patient underwent gross total resection (GTR). The patient is currently disease-free. To the author’s knowledge, this is the first report of GOPC-ROS1 fusion in PLGG. Taken together, they revealed the genetic characteristics of pLGG patients can enhance diagnostics and therapeutic decisions. In addition, we identified a GOPC-ROS1 fusion that may be a biomarker for pLGG 4).


Pediatric low-grade gliomas (PLGGs) are commonly associated with BRAF gene fusions that aberrantly activate the mitogen-activated protein kinase (MAPK) signaling pathway.

This has led to PLGG clinical trials utilizing RAF– and MAPK pathway-targeted therapeutics. Whole-genome profiling of PLGGs has also identified rare gene fusions involving another RAF isoform, CRAF/RAF1, in PLGGs and cancers occuring in adults. Whereas BRAF fusions primarily dysregulate MAPK signaling, the CRAF fusions QKI-RAF1 and SRGAP3-RAF1 aberrantly activate both the MAPK and phosphoinositide-3 kinase/mammalian target of rapamycin (PI3K/mTOR) signaling pathways. Although ATP-competitive, first-generation RAF inhibitors (vemurafenib/PLX4720, RAFi) cause paradoxical activation of the MAPK pathway in BRAF-fusion tumors, inhibition can be achieved with ‘paradox breaker’ RAFi, such as PLX8394.

Jain et al. report that, unlike BRAF fusions, CRAF fusions are unresponsive to both generations of RAFi, vemurafenib and PLX8394, highlighting a distinct responsiveness of CRAF fusions to clinically relevant RAFi. Whereas PLX8394 decreased BRAF-fusion dimerization, CRAF-fusion dimerization is unaffected primarily because of robust protein-protein interactions mediated by the N-terminal non-kinase fusion partner, such as QKI. The pan-RAF dimer inhibitor, LY3009120, could suppress CRAF-fusion oncogenicity by inhibiting dimer-mediated signaling. In addition, as CRAF fusions activate both the MAPK and PI3K/mTOR signaling pathways, we identify combinatorial inhibition of the MAPK/mTOR pathway as a potential therapeutic strategy for CRAF-fusion-driven tumors. Overall, we define a mechanistic distinction between PLGG-associated BRAF- and CRAF/RAF1 fusions in response to RAFi, highlighting the importance of molecularly classifying PLGG patients for targeted therapy. Furthermore, this study uncovers an important contribution of the non-kinase fusion partner to oncogenesis and potential therapeutic strategies against PLGG-associated CRAF fusions and possibly pan-cancer CRAF fusions 5).

References

1)

Gnekow AK, Kandels D, Tilburg CV, Azizi AA, Opocher E, Stokland T, Driever PH, Meeteren AYNSV, Thomale UW, Schuhmann MU, Czech T, Goodden JR, Warmuth-Metz M, Bison B, Avula S, Kortmann RD, Timmermann B, Pietsch T, Witt O. SIOP-E-BTG and GPOH Guidelines for Diagnosis and Treatment of Children and Adolescents with Low Grade Glioma. Klin Padiatr. 2019 May;231(3):107-135. doi: 10.1055/a-0889-8256. Epub 2019 May 20. PubMed PMID: 31108561.
2)

Yang RR, Li KK, Liu APY, Chen H, Chung NY, Chan AKY, Li F, Tat-Ming Chan D, Mao Y, Shi ZF, Ng HK. Low-grade BRAF V600E mutant oligodendroglioma-like tumors of children may show EGFR and MET amplification. Brain Pathol. 2020 Oct 8. doi: 10.1111/bpa.12904. Epub ahead of print. PMID: 33032379.
3)

Fukuoka K, Mamatjan Y, Ryall S, Komosa M, Bennett J, Zapotocky M, Keith J, Myrehaug S, Hazrati LN, Aldape K, Laperriere N, Bouffet E, Tabori U, Hawkins C. BRAF V600E mutant oligodendroglioma-like tumors with chromosomal instability in adolescents and young adults. Brain Pathol. 2020 May;30(3):515-523. doi: 10.1111/bpa.12799. Epub 2019 Nov 10. PMID: 31630459.
4)

Mobark NA, Alharbi M, Alhabeeb L, AlMubarak L, Alaljelaify R, AlSaeed M, Almutairi A, Alqubaishi F, Ahmad M, Al-Banyan A, Alotabi FE, Barakeh D, AlZahrani M, Al-Khalidi H, Ajlan A, Ramkissoon LA, Ramkissoon SH, Abedalthagafi M. Clinical management and genomic profiling of pediatric low-grade gliomas in Saudi Arabia. PLoS One. 2020 Jan 29;15(1):e0228356. doi: 10.1371/journal.pone.0228356. eCollection 2020. PubMed PMID: 31995621.
5)

Jain P, Fierst TM, Han HJ, Smith TE, Vakil A, Storm PJ, Resnick AC, Waanders AJ. CRAF gene fusions in pediatric low-grade gliomas define a distinct drug response based on dimerization profiles. Oncogene. 2017 Aug 14. doi: 10.1038/onc.2017.276. [Epub ahead of print] PubMed PMID: 28806393.

Medulloblastoma classification

Medulloblastoma classification

The diagnosis of medulloblastoma incorporates the histologic and molecular subclassification of clinical medulloblastoma samples into wingless (WNT)-activated, sonic hedgehog (SHH)-activated, group 3 and group 4 subgroups. Accurate medulloblastoma subclassification has important prognostic and treatment implications.

Medulloblastoma, WNT-activated

Medulloblastoma, WNT-activated

Sonic hedgehog medulloblastoma

Sonic hedgehog medulloblastoma.

Medulloblastoma, SHH-activated, and TP53-mutant

Medulloblastoma, SHH-activated, and TP53-mutant.

Medulloblastoma, SHH-activated, and TP53-wildtype

Medulloblastoma, SHH-activated, and TP53-wildtype

Medulloblastoma, non-WNT/non-SSH

Medulloblastoma non-WNT/non-SSH

Group 3 medulloblastoma

Group 3 medulloblastoma

Group 4 medulloblastoma

Group 4 medulloblastoma

Histology

Medulloblastoma histologically defined:

Classic medulloblastoma

Desmoplastic nodular medulloblastoma

Medulloblastoma with extensive nodularity

Medulloblastoma, large cell/anaplastic

Medulloblastoma, NOS.

Localization

see Cerebellar medulloblastomas

see Cerebellopontine angle medulloblastoma

see Multifocal medulloblastoma.

Subgrouping

Immunohistochemistry (IHC)-based and nanoString-based subgrouping methodologies have been independently described as options for medulloblastoma subgrouping, however, they have not previously been directly compared. D’Arcy described the experience with nanoString-based subgrouping in a clinical setting and compare this with our IHC-based results. Study materials included FFPE tissue from 160 medulloblastomas. Clinical data and tumor histology were reviewed. Immunohistochemical-based subgrouping using β-catenin, filamin A and p53 antibodies and nanoString-based gene expression profiling was performed. The sensitivity and specificity of IHC-based subgrouping of WNT and SHH-activated medulloblastomas was 91.5% and 99.54%, respectively. Filamin A immunopositivity highly correlated with SHH/WNT-activated subgroups (sensitivity 100%, specificity 92.7%, p < 0.001). Nuclear β-catenin immunopositivity had a sensitivity of 76.2% and specificity of 99.23% for the detection of WNT-activated tumors. Approximately 23.8% of WNT cases would have been missed using an IHC-based subgrouping method alone. nanoString could confidently predict medulloblastoma subgroup in 93% of cases and could distinguish group 3/4 subgroups in 96.3% of cases. nanoString-based subgrouping allows for a more prognostically useful classification of clinical medulloblastoma samples 1).


Molecular subgrouping was performed by immunohistochemistry (IHC) for beta catenin, GAB1 and YAP1; FISH for MYC amplification, and sequencing for CTNNB1, and by NanoString Assay on the same set of MBs. A subset of cases was subjected to 850k DNA methylation array.

IHC + FISH classified MBs into 15.8% WNT, 16.8% SHH, and 67.4% non-WNT/non-SHH subgroups; with MYC amplification identified in 20.3% cases of non-WNT/non-SHH. NanoString successfully classified 91.6% MBs into 25.3% WNT, 17.2% SHH, 23% Group 3 and 34.5% Group 4. However, NanoString assay failure was seen in eight cases, all of which were > 8-years-old formalin-fixed paraffin-embedded tissue blocks. Concordant subgroup assignment was noted in 88.5% cases, while subgroup switching was seen in 11.5% cases. Both methods showed prognostic correlation. Methylation profiling performed on discordant cases revealed 1 out of 4 extra WNT identified by NanoString to be WNT, others aligned with IHC subgroups; extra SHH by NanoString turned out to be SHH by methylation.

Both IHC supplemented by FISH and NanoString are robust methods for molecular subgrouping, albeit with few disadvantages. IHC cannot differentiate between Groups 3 and 4, while NanoString cannot classify older-archived tumors, and is not available at most centres. Thus, both the methods complement each other and can be used in concert for high confidence allotment of molecular subgroups in clinical practice 2).


The maturation of medulloblastoma into a ganglion cell-rich lesion is very rare, with few well-characterized previous reports. Given the rare nature of this entity, it would be of great value to understand the process of posttreatment maturation and the genetic and treatment factors which contribute to this phenomenon 3).

References

1)

D’Arcy CE, Nobre LF, Arnaldo A, Ramaswamy V, Taylor MD, Naz-Hazrati L, Hawkins CE. Immunohistochemical and nanoString-Based Subgrouping of Clinical Medulloblastoma Samples. J Neuropathol Exp Neurol. 2020 Jan 30. pii: nlaa005. doi: 10.1093/jnen/nlaa005. [Epub ahead of print] PubMed PMID: 32053195.
2)

Kaur K, Jha P, Pathak P, Suri V, Sharma MC, Garg A, Suri A, Sarkar C. Approach to molecular subgrouping of medulloblastomas: Comparison of NanoString nCounter assay versus combination of immunohistochemistry and fluorescence in-situ hybridization in resource constrained centres. J Neurooncol. 2019 May 18. doi: 10.1007/s11060-019-03187-y. [Epub ahead of print] PubMed PMID: 31104222.
3)

Mullarkey MP, Nehme G, Mohiuddin S, et al. Posttreatment Maturation of Medulloblastoma into Gangliocytoma: Report of 2 Cases [published online ahead of print, 2020 Sep 3]. Pediatr Neurosurg. 2020;1-10. doi:10.1159/000509520

Hydrocephalus Classification

Hydrocephalus Classification

There is no international consensus on the classification of hydrocephalus, and there are various systems based on the age of onset, cerebrospinal fluid dynamics and anatomical area of accumulation, the levels of cerebrospinal fluid pressure and the presence of symptoms.

However, no definitive classification exists comprehensively to cover the variety of these aspects.

Wu et al. proposed a classification Based on Ventricular Pressure 1).


Walter Edward Dandy first described the basic mechanism and classification of hydrocephalus as:

Obstructive hydrocephalus or Non Obstructive hydrocephalus.

Despite advances in understanding of the underlying process, current classification systems still rely upon Dandy’s classification scheme 2).


The aim of a study was to evaluate the diagnostic utility of three-dimensional sampling perfection with application optimized contrast using different flip angle evolution (3D SPACE) sequence and Susceptibility Weighted Imaging (SWI) in hydrocephalus and to propose a refined definition and classification of hydrocephalus with relevance to the selection of treatment option.

A prospective study of 109 patients with hydrocephalus was performed with magnetic resonance imaging (MRI) brain using standardized institutional sequences along with additional sequences 3D SPACE and SWI. The images were independently read by two senior neuroradiologists and the etiopathogenesis of hydrocephalus was arrived by consensus.

With conventional sequences, 46 out of 109 patients of hydrocephalus were diagnosed as obstructive of which 21 patients showed direct signs of obstruction and 25 showed indirect signs. In the remaining 63 patients of communicating hydrocephalus, cause could not be found out in 41 patients. Whereas with 3D SPACE sequence, 88 patients were diagnosed as obstructive hydrocephalus in which all of them showed direct signs of obstruction and 21 patients were diagnosed as communicating hydrocephalus. By including SWI, we found out hemorrhage causing intraventricular obstruction in three patients and hemorrhage at various sites in 24 other patients. With these findings, we have classified the hydrocephalus into communicating and noncommunicating, with latter divided into intraventricular and extraventricular obstruction, which is very well pertaining to the selection of surgical option.

Chellathurai et al., strongly suggest to include 3D SPACE and SWI sequences in the set of routine MRI sequences, as they are powerful diagnostic tools and offer complementary information regarding the precise evaluation of the etiopathogenesis of hydrocephalus and have an effective impact in selecting the mode of management 3).

Terms used

Acquired hydrocephalus

Adult hydrocephalus

Arrested hydrocephalus or Compensated hydrocephalus

Chronic hydrocephalus

Communicating hydrocephalus or Non obstructive hydrocephalus

Congenital hydrocephalus

External hydrocephalus

Focal hydrocephalus

Hydrocephalus Ex Vacuo

Idiopathic normal pressure hydrocephalus

Infantile hydrocephalus or Pediatric hydrocephalus.

Internal hydrocephalus

Non obstructive hydrocephalus or Communicating hydrocephalus

Normal pressure hydrocephalus for Idiopathic normal pressure hydrocephalus or Secondary normal pressure hydrocephalus.

Obstructive hydrocephalus.

Pediatric hydrocephalus or Infantile hydrocephalus

Secondary normal pressure hydrocephalus.

Unilateral hydrocephalus.


With the rare exception of hydrocephalus associated with overproduction of CSF in patients with choroid plexus papillomas (CPPs), all hydrocephalus is basically obstructive hydrocephalus. That the rare CPP causes hydrocephalus is not debated, but why it does so is the subject of some discussion. CPPs are known to lead to increases in the rate of CSF production and are known to cause hydrocephalus.

Normal CSF absorptive mechanisms can clear the amount of spinal fluid produced in the ventricular system at extremely high rates without producing ventriculomegaly. If CSF production and ICP increase substantially, ventricular size increases 4). When CSF flow is obstructed in the context of increased CSF production, there is a great tendency for ventriculomegaly or hydrocephalus to develop. CPPs, in themselves, can create the only pure form of communicating hydrocephalus. However, that these tumors tend to be large and to restrict CSF flow through the foramen of Monro or aqueduct of Sylvius, is more likely to account for the severity of hydrocephalus in this context 5).

When hydrocephalus is severe, especially in the very young, it may not be possible to define the point of CSF obstruction without introducing tracers into the CSF pathways. In patients treated early in life whose ventricles have become smaller with treatment, it is possible to determine the first site of obstruction to CSF flow on MRI or CT.

Patients with complex congenital anomalies such as hydrocephalus related to a Chiari II malformation and spina bifida often have multiple sites of obstruction 6) 7). It may not be possible to predict a second or downstream point of obstruction. In these patients, only one point may be obstructed or all of these sites may be restricted.

Based on a analyses from a mathematical modeling, of the work on the circuitry of CSF flow, and these potential sites of obstruction, Rekate et al., proposed a classification

It is generally assumed that endoscopic third ventriculostomy (ETV) is only effective for treating obstructive hydrocephalus, and many assume that obstructive hydrocephalus is synonymous with aqueductal stenosis. The growing number of reports on the efficacy of ETV for treating “communicating hydrocephalus” has generated considerable consternation 8).


The “Multi-categorical Hydrocephalus Classification” (Mc HC), was invented and developed to cover the entire aspects of hydrocephalus with all considerable classification items and categories.

Ten categories include “Mc HC” category I: onset (age, phase), II: cause, III: underlying lesion, IV: symptomatology, V: pathophysiology 1-CSF circulation, VI: pathophysiology 2-ICP dynamics, VII: chronology, VII: post-shunt, VIII: post-endoscopic third ventriculostomy, and X: others. From a 100-year search of publication related to the classification of hydrocephalus, 14 representative publications were reviewed and divided into the 10 categories.

The Baumkuchen classification graph made from the round o’clock classification demonstrated the historical tendency of deviation to the categories in pathophysiology, either CSF or ICP dynamics.

In the preliminary clinical application, it was concluded that “Mc HC” is extremely effective in expressing the individual state with various categories in the past and present condition or among the compatible cases of hydrocephalus along with the possible chronological change in the future 9).

References

1)

Wu X, Zang D, Wu X, Sun Y, Yu J, Hu J. Diagnosis and Management for Secondary Low- or Negative-Pressure Hydrocephalus and a New Hydrocephalus Classification Based on Ventricular Pressure. World Neurosurg. 2019 Jan 4. pii: S1878-8750(18)32946-2. doi: 10.1016/j.wneu.2018.12.123. [Epub ahead of print] PubMed PMID: 30611954.
2)

Dandy WE, Blackfan KD. Internal hydrocephalus: an experimental, clinical and pathological study. Am J Dis Child. 1914;8(6):406-482.
3)

Chellathurai A, Subbiah K, Abdul Ajis BN, Balasubramaniam S, Gnanasigamani S. Role of 3D SPACE sequence and susceptibility weighted imaging in the evaluation of hydrocephalus and treatment-oriented refined classification of hydrocephalus. Indian J Radiol Imaging. 2018 Oct-Dec;28(4):385-394. doi: 10.4103/ijri.IJRI_161_18. PubMed PMID: 30662197; PubMed Central PMCID: PMC6319109.
4) , 5)

Rekate HL, Erwood S, Brodkey JA, Chizeck HJ, Spear T, Ko W, Montague F. Etiology of ventriculomegaly in choroid plexus papilloma. Pediatr Neurosci. 1985;12:196–201.
6)

Rekate HL. Neurosurgical management of the newborn with spinal bifida. In: Rekate HL, editor. Comprehensive Management of Spina Bifida. Boca Raton, FL, CRC Press; 1991. pp. 1–20.
7)

Rekate HL. Neurosurgical mangement of the child with spinal bifida. In: Rekate HL, editor. Comprehensive Management of Spina Bifida. Boca Raton, FL, CRC Press; 1991. pp. 93–112.
8)

Rekate HL. Selecting patients for endoscopic third ventriculostomy. Neurosurg Clin N Am. 2004;15:39–49. doi: 10.1016/S1042-3680(03)00074-3.
9)

Oi S. Classification of hydrocephalus: critical analysis of classification categories and advantages of “Multi-categorical Hydrocephalus Classification” (Mc HC). Childs Nerv Syst. 2011 Oct;27(10):1523-33. doi: 10.1007/s00381-011-1542-6. Epub 2011 Sep 17. Review. PubMed PMID: 21928018.
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