Oncogenesis in PLAG1‐rearranged tumors often results from PLAG1 transcription factor overexpression driven by promoter‐swapping between constitutively expressed fusion partners. PLAG1‐rearranged tumors demonstrate diverse morphologies. This study adds to this morphologic heterogeneity by introducing two tumors with PLAG1 rearrangements that display distinct histologic features. The first arose in the inguinal region of a 3‐year‐old, appeared well‐circumscribed with a multinodular pattern, and harbored two fusions: ZFHX4‐PLAG1 and CHCHD7‐PLAG1. The second arose in the pelvic cavity of a 15‐year‐old girl, was extensively infiltrative and vascularized with an adipocytic component, and demonstrated a COL3A1‐PLAG1 fusion. Both showed low‐grade cytomorphology, scarce mitoses, no necrosis, and expression of CD34 and desmin. The ZFHX4‐/CHCHD7‐PLAG1‐rearranged tumor showed no evidence of recurrence after 5 months. By contrast, the COL3A1‐PLAG1‐rearranged tumor quickly recurred following primary excision with positive margins; subsequent re‐excision with adjuvant chemotherapy resulted in no evidence of recurrence after 2 years. While both tumors show overlap with benign and malignant fibroblastic and fibrovascular neoplasms, they also display divergent features. These cases highlight the importance of appropriate characterization in soft tissue tumors with unusual clinical and histologic characteristics.

Gastroblastomas are rare tumors with a biphasic epithelioid/spindle cell morphology that typically present in early adulthood and have recurrent MALAT1‐GLI1 fusions. We describe an adolescent patient with Wiskott‐Aldrich syndrome who presented with a large submucosal gastric tumor with biphasic morphology. Despite histologic features consistent with gastroblastoma, a MALAT1‐GLI1 fusion was not found in this patient's tumor; instead, comprehensive molecular profiling identified a novel EWSR1‐CTBP1 fusion and no other significant genetic alterations. The tumor also overexpressed NOTCH and FGFR by RNA profiling. The novel fusion and expression profile suggest a role for epithelial‐mesenchymal transition in this tumor, with potential implications for the pathogenesis of biphasic gastric tumors such as gastroblastoma.

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.

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 detection 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 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 ensemble fusion-calling 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: Our ensemble fusion detection 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.

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