DRUG-CLASS SPECIFIC GENE-BASED MAPK SENSITIVITY SCORES (MSS) PREDICT SENSITIVITY TO MAPK INHIBITORS AND IDENTIFY IMMUNE INFILTRATION AS PUTATIVE TARGET IN PEDIATRIC LOW-GRADE GLIOMAS

Sigaud, Romain; Albert, Thomas K.; Hess, Caroline; Hielscher, Thomas; Winkler, Nadine; Walter, Carolin; Muenter, Daniel; Selt, Florian; Usta, Diren; Ecker, Jonas; Brentrup, Angela; Hasselblatt, Martin; Thomas, Christian; Varghese, Julian; Capper, David; Thomale, Ulrich W.; Driever, Pablo Hernaiz; Simon, Michele; Horn, Svea; Herz, Nina Annika; Koch, Arend; Sahm, Felix; Mann, Stefan Hamel; Andrade, Augusto Faria; Jabado, Nada; Schouten-van Meeteren, Antoinette Y. N.; Hoving, Eelco; Brummer, Tilman; van Tilburg, Cornelis M.; Pfister, Stefan M.; Witt, Olaf; Jones, David T. W.; Kerl, Kornelius; Milde, Till

Abstract

INTRODUCTION Pediatric low-grade gliomas (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 inhibitors (MAPKi) treatment in pLGG. However, the range of response is broad, even within entities sharing the same driving genetic MAPK alteration. A predictive stratification tool is needed to identify patients that will be more likely to benefit from MAPKi therapy. METHODS We generated gene-expression-based MAPKi sensitivity scores (MSS) 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 (ssGSEA) was used to measure and validate our MSSs in the GDSC dataset and an independent PDX dataset (XevaDB). The validated signatures were tested in a pLGG-specific background, using gene expression data from PA cell lines and primary pLGG samples. RESULTS Our MSS could differentiate MAPKi sensitive cells in the GDSC dataset, and significantly correlated with MAPKi response in the XevaDB PDX dataset. The MSS were able to differentiate glioma entities with differing MAPK alterations from non-MAPK altered entities, and showed the highest scores in pLGG. The MSSs were heterogeneous within pLGG entities with a common MAPK alteration, as observed in MAPKi clinical studies. Intriguingly, a strong correlation between our MSS and the predicted immune cell infiltration rate, as determined by the Estimate score, was observed and confirmed in a pLGG scRNA sequencing dataset. CONCLUSION These data demonstrate the relevance of gene-expression signatures to predict response to MAPKi treatment in pLGG, and will be further investigated in a prospective manner in upcoming clinical trials. In addition, our data could suggest a role of immune infiltration in the response to MAPKi in pLGG that warrants further validation.