Innovative Technologies in Pediatric Neuro-Oncology

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Abstract

At the present time pediatric neuro-oncology develops rapidly mostly due to the deep understanding of etiology and pathogenesis of the brain tumors in children, widespread introduction of molecular genetic technologies into diagnostic workflow and emergence of targeted therapeutic agents directing to the neoplastic cells. Many tumor entities undistinguishable at the level of histopathology were classified by the molecular techniques and now present as unique disorders. Clinical heterogeneity unraveled by molecular classification is a basis for modern risk stratification approaches. Variety of new tumor entities were discovered only because of implementation of advanced molecular diagnostics, which led to identification of the recurrent genetic aberration in neuroepithelial tumors with BCOR and PATZ1 genes alteration, intracranial mesenchymal tumors with FET-CREB rearrangements. The discovery of the targetable molecular drivers in gliomas allows the introduction of targeted therapies to the pediatric neuro-oncology with high results unreachable by other methods. In the current article we describe the experience of D. Rogachev National Medical Research Center in molecular diagnostics of pediatric brain tumors and targeted therapy in patients with different types of gliomas.

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About the authors

Galina A. Novichkova

D. Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology

Author for correspondence.
Email: gnovichkova@yandex.ru
ORCID iD: 0000-0002-2322-5734
SPIN-code: 7890-1419

д.м.н., профессор

Россия, Moscow

Ludmila I. Papusha

D. Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology

Email: ludmila.mur@mail.ru
ORCID iD: 0000-0001-7750-5216

MD, PhD

Россия, Moscow

Alexander E. Druy

D. Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology

Email: Dr-Drui@yandex.ru
ORCID iD: 0000-0003-1308-8622
SPIN-code: 9072-9427

MD, PhD

Россия, Moscow

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Supplementary files

Supplementary Files
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1. JATS XML
2. Fig.1. A - uncontrolled hierarchical clustering of tumor samples based on targeted gene expression profiling allows us to identify medulloblastomas of the WNT (purple cluster), SHH (red cluster), group 3 (yellow cluster), group 4 (green cluster) and HG-NET BCOR (gray cluster) groups ); B - event-free survival of patients with medulloblastoma of various molecular groups (WNT - blue curve, SHH - red curve, group 3 - yellow curve, group 4 - green curve). Research method: NanoString expression profiling, n = 195

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3. Fig.2. Neuroepithelial tumor with PATZ1 gene rearrangement. A — hematoxylin-eosin staining (×100): a compact arrangement of ovoid tumor cells with the formation of perivascular pseudorosettes is noted; B — diffuse expression of GFAP (×200), punctate expression of EMA (×100, inset); B, transmission electron microscopy revealed a loose arrangement of cells, abundant stroma rich in fibrillar proteins, and the absence of intercellular contacts (×3000); D — schematic representation of the chimeric transcript MN1::PATZ1 identified using high-throughput RNA sequencing. ENST00000302326.5 and ENST00000266269.10 were used as reference transcripts for the MN1 and PATZ1 genes, respectively

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4. Fig.3. Uncontrolled hierarchical clustering of choroid plexus carcinoma samples from children based on targeted gene expression profiling allows us to identify two groups Ped_CPC1 (red cluster) and Ped_CPC2 (blue cluster). Black rectangles correspond to cases with the presence of mutations in the TP53 gene (solid circle fill - germinal mutation, circle outline - somatic mutation, × - mutation status unknown). Research method: NanoString expression profiling, n = 20

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5. Fig.4. Impact of TP53 gene status, karyotype and expression group on overall and event-free survival of pediatric patients with choroid plexus carcinomas of the brain

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6. Fig.5. Best tumor response during therapy with BRAF inhibitors and BRAF/MEK inhibitors in patients with gliomas with the presence of the BRAF V600E mutation. Response assessment based on magnetic resonance imaging of the brain, n = 24

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7. Fig.6. Best tumor response during MEK inhibitor monotherapy in patients with low-grade gliomas with the presence of the chimeric KIAA1549::BRAF transcript. Response assessment based on magnetic resonance imaging of the brain, n = 41

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8. Fig.7. A procedure for identifying diagnostic markers of midline brain tumors in cerebrospinal fluid, allowing to formulate an integral diagnosis within 7 hours

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