Comprehensive Molecular Profiling of Desmoplastic Small Round Cell Tumor

Abstract

Desmoplastic small round cell tumor (DSRCT) is characterized by the EWSR1-WT1 t(11;22) (p13:q12) translocation. Few additional putative drivers have been identified, and research has suffered from a lack of model systems. Next-generation sequencing (NGS) data from 68 matched tumor-normal samples, whole-genome sequencing data from 10 samples, transcriptomic and affymetrix array data, and a bank of DSRCT patient-derived xenograft (PDX) are presented. EWSR1-WT1 fusions were noted to be simple, balanced events. Recurrent mutations were uncommon, but were noted in TERT (3%), ARID1A (6%), HRAS (5%), and TP53 (3%), and recurrent loss of heterozygosity (LOH) at 11p, 11q, and 16q was identified in 18%, 22%, and 34% of samples, respectively. Comparison of tumor-normal matched versus unmatched analysis suggests overcalling of somatic mutations in prior publications of DSRCT NGS data. Alterations in fibroblast growth factor receptor 4 (FGFR4) were identified in 5 of 68 (7%) of tumor samples, whereas differential overexpression of FGFR4 was confirmed orthogonally using 2 platforms. PDX models harbored the pathognomic EWSR1-WT1 fusion and were highly representative of corresponding tumors. Our analyses confirm DSRCT as a genomically quiet cancer defined by the balanced translocation, t(11;22)(p13:q12), characterized by a paucity of secondary mutations but a significant number of copy number alterations. Against this genomically quiet background, recurrent activating alterations of FGFR4 stood out, and suggest that this receptor tyrosine kinase, also noted to be highly expressed in DSRCT, should be further investigated. Future studies of DSRCT biology and preclinical therapeutic strategies should benefit from the PDX models characterized in this study. IMPLICATIONS: These data describe the general quiescence of the desmoplastic small round cell tumor (DSRCT) genome, present the first available bank of DSRCT model systems, and nominate FGFR4 as a key receptor tyrosine kinase in DSRCT, based on high expression, recurrent amplification, and recurrent activating mutations.

Figure 1

A. Oncoprint of commonly occurring somatic alterations in 68 DSRCT samples; brackets delineate single patients for whom multiple samples were analyzed B. Comparison of tumor mutation burden when analyzing 68 DSRCT patient samples from 53 patients with matched normal DNA analysis obtained from blood (green) versus unmatched, pooled normal control analysis (red)

Figure 2. Whole Genome Sequencing

A. Circos plot summarizing whole genome sequencing analysis of a representative DSRCT sample with the outermost track showing the intermutation distance for substitutions each plotted according to the type of nucleotide change; the middle track shows the genomic positions of the small insertion (green) and deletion (burgundy) along the genome; the pathognomic EWSR1-WT1 rearrangement is plotted as an arc inside the circos B. Mutation signature analysis using deconstructSigs C. Summary of the insertion and deletion data D. Summary of rearrangement data E. Sashimi plot representing the balanced and simple EWSR1-WT1 fusion in a representative DSRCT sample

Figure 3.. FGFR4 in DSRCT

A. Copy number plots of 2 different DSRCT samples exemplifying FGFR4 amplification B. Transcriptome analysis of FGFR4 expression in 8 DSRCT samples when compared with a cohort of 154 samples representing solid tumor malignancies in children and adolescents C. Microarray expression data of FGFR4 expression from 137 tumor samples across 5 different fusion-associated sarcoma subtypes including alveolar rhabdomyosarcoma (ARMS), alveolar soft part sarcoma (ASPS), desmoplastic small round cell tumor (DSRCT), Ewing sarcoma (ES), and synovial sarcoma (SS) normalized using the mas5 function from the R package affy in the Affymetrix Microarray Suite (MAS) Version 5 software

Figure 4.

Oncoprint comparing commonly occurring somatic mutations in 24 patient derived xenograft (PDX) models from 10 patients; relevant somatic mutations in PDX models (top group) and the patient samples (bottom group) are depicted with each column representing a single patient but possibly more than one PDX