Search

Abstract PIK3CA-related overgrowth spectrum (PROS) disorders are caused by somatic mosaic variants that result in constitutive activation of the phosphatidylinositol-3-kinase/AKT/mTOR pathway. Promising responses to molecularly targeted therapy have been reported, although identification of an appropriate agent can be hampered by the mosaic nature and corresponding low variant allele frequency of the causal variant. Moreover, our understanding of the molecular consequences of these variants—for example how they affect gene expression profiles—remains limited. Here we describe in vitro expansion of a human capillary malformation followed by molecular characterization using exome sequencing, single cell gene expression, and targeted long-read single cell RNA-sequencing in a patient with clinical features consistent with Megalencephaly-Capillary Malformation Syndrome (MCAP, a PROS condition). These approaches identified a targetable PIK3CA variant with expression restricted to PAX3+ fibroblast and undifferentiated keratinocyte populations. This study highlights the innovative combination of next-generation single cell sequencing methods to better understand unique transcriptomic profiles and cell types associated with MCAP, revealing molecular intricacies of this genetic syndrome.

IntroductionIn the setting of pediatric and adolescent young adult cancer, increased access to genomic profiling has enhanced the detection of genetic variation associated with cancer predisposition, including germline syndromic conditions. Noonan syndrome (NS) is associated with the germline RAS pathway activating alterations and increased risk of cancer. Herein, we describe our comprehensive molecular profiling approach, the association of NS with glioma and glioneuronal tumors, and the clinical and histopathologic characteristics associated with the disease.MethodsWithin an institutional pediatric cancer cohort (n = 314), molecular profiling comprised of paired somatic disease–germline comparator exome analysis, RNA sequencing, and tumor classification by DNA methylation analysis was performed.ResultsThrough the implementation of paired analysis, this study identified 4 of 314 (1.3%) individuals who harbored a germline PTPN11 variant associated with NS, of which 3 individuals were diagnosed with a glioma or glioneuronal tumor. Furthermore, we extend this study through collaboration with a peer institution to identify two additional individuals with NS and a glioma or glioneuronal tumor. Notably, in three of five (60%) individuals, paired genomic profiling led to a previously unrecognized diagnosis of Noonan syndrome despite an average age of cancer diagnosis of 16.8 years. The study of the disease-involved tissue identified signaling pathway dysregulation through somatic alteration of genes involved in cellular proliferation, survival, and differentiation.DiscussionComparative pathologic findings are presented to enable an in-depth examination of disease characteristics. This comprehensive analysis highlights the association of gliomas and glioneuronal tumors with RASopathies and the potential therapeutic challenges and importantly demonstrates the utility of genomic profiling for the identification of germline cancer predisposition.

Abstract Background Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. Results Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information. Conclusions The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.