LGG-26. Predicting MAPK inhibitor sensitivity in pediatric low-grade gliomas with novel gene expression-derived signatures

Abstract Pediatric low-grade glioma (pLGG), the most common brain tumors in children, are driven by alterations in the MAPK pathway. Several clinical trials have shown the potential for MAPK inhibitor (MAPKi) treatment in pLGG. However, the range of response to MAPKi is heterogeneous, even between t...

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Published in:	Neuro-oncology (Charlottesville, Va.) Vol. 24; no. Supplement_1; pp. i93 - i94
Main Authors:	Sigaud, Romain, Heß, Caroline, Hielscher, Thomas, Winkler, Nadine, Selt, Florian, Kocher, Daniela, Nonnenbroich, Leo, Usta, Diren, Sommerkamp, Alex, Büdenbender, Isabel, Capper, David, Thomale, Ulrich W, Driever, Pablo Hernáiz, le Simon, Michè, Koch, Arend, Jabado, Nada, Andrade, Augusto F, van Meeteren, Netteke Schouten, Hoving, Eelco, van Tilburg, Cornelis M, Pfister, Stefan M, Witt, Olaf, Jones, David T W, Milde, Till
Format:	Journal Article
Language:	English
Published:	US Oxford University Press 03-06-2022
Subjects:	Low Grade Glioma
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Summary:	Abstract Pediatric low-grade glioma (pLGG), the most common brain tumors in children, are driven by alterations in the MAPK pathway. Several clinical trials have shown the potential for MAPK inhibitor (MAPKi) treatment in pLGG. However, the range of response to MAPKi is heterogeneous, even between tumors sharing the same driving MAPK alteration. A predictive stratification tool is needed to identify tumors that will be sensitive to MAPK inhibition. We generated sensitivity gene signatures for each MAPKi class (BRAFi, MEKi, ERKi), based on MAPK-related genes differentially regulated between MAPKi sensitive and non-sensitive cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Single sample Gene Set Enrichment Analysis was used to measure and validate the MAPKi predictive sensitivity scores in the GDSC dataset and an independent patient-derived xenograft (PDX) dataset (XevaDB). The validated signatures were tested in a pLGG-specific background, using gene expression data from pLGG cell lines and primary pLGG samples. Our MAPKi sensitivity signatures discriminated MAPKi sensitive and non-sensitive cells in the GDSC dataset, and significantly correlated with MAPKi response in the PDX dataset. The sensitivity scores discerned gliomas with varying MAPK alterations from those without MAPK alterations, and showed higher scores in pLGG compared to high-grade gliomas and normal brain tissue. MAPKi-predicted sensitivity was heterogeneous within pLGG groups with a common MAPK alteration, as observed in MAPKi clinical trials. Intriguingly, we observed a strong positive correlation between our MAPKi sensitivity signature scores and the predicted immune cell infiltration rate as determined by the ESTIMATE score. These data demonstrate the potential relevance of gene-expression signatures to predict response to MAPKi treatment in pLGG patients, worth of further investigation in a prospective manner in upcoming clinical trials. In addition, our data could support a role of immune cell infiltration in the response to MAPKi in pLGG, warranting further validation.
ISSN:	1522-8517 1523-5866
DOI:	10.1093/neuonc/noac079.340