Defining an embryonal rhabdomyosarcoma endotype

Abstract

Rhabdomyosarcoma (RMS) is the most common childhood soft-tissue sarcoma. The largest subtype of RMS is embryonal rhabdomyosarcoma (ERMS) and accounts for 53% of all RMS. ERMS typically occurs in the head and neck region, bladder, or reproductive organs and portends a promising prognosis when localized; however, when metastatic the 5-yr overall survival rate is ∼43%. The genomic landscape of ERMS demonstrates a range of putative driver mutations, and thus the recognition of the pathological mechanisms driving tumor maintenance should be critical for identifying effective targeted treatments at the level of the individual patients. Here, we report genomic, phenotypic, and bioinformatic analyses for a case of a 3-yr-old male who presented with bladder ERMS. Additionally, we use an unsupervised agglomerative clustering analysis of RNA and whole-exome sequencing data across ERMS and undifferentiated pleomorphic sarcoma (UPS) tumor samples to determine several major endotypes inferring potential targeted treatments for a spectrum of pediatric ERMS patient cases.

Refractory alveolar rhabdomyosarcoma in an 11-year-old male

“…Given the relative rarity of fusion-negative ARMS cases among the RMS clinical milieu, we investigated genomic similarities between the index case and other clinical RMS samples using a previously published hierarchical clustering dendrogram approach ( Supplemental Fig. 3 ; Ricker et al 2020 ). Similar to previous findings regarding genomic similarities between ERMS and fusion-negative ARMS ( Williamson et al 2010 ), CF-00449 clusters among ERMS samples, as do the majority of other fusion-negative ARMS samples.…”

“…The hierarchical clustering analysis approach was published previously ( Ricker et al 2020 ). Briefly, CF-00449 sequencing data was clustered among a cohort of RMS samples collected from multiple sources.…”

Functional impact of a germline RET mutation in alveolar rhabdomyosarcoma

“…The lack of available RET -mutant aRMS cell models coupled with lack of patient-derived primary cultures at the time of our studies necessitated identifying cellular models genomically and transcriptionally similar to the index case for in vitro experiments. Similarity of cell models was quantified using an aRMS-focused dendrogram endotype computational model ( Ricker et al 2020 ). Multiple aRMS exome and transcriptomes sequencing data sets were obtained from internal cc-TDI CuRe-FAST samples, the Database of Genotypes and Phenotypes (dbGaP), the Gene Expression Omnibus (GEO) database, Foundation Medicine sequencing panels, the Jackson Laboratory PDX sequencing data, and Champions Oncology PDX sequencing data as well as statistical comparison of RET and RET signaling partner expression between ARMS and ERMS cohorts ( Supplemental Tables 1, 2 ).…”

Translational precision medicine: an industry perspective

“…Capturing comprehensive phenotypic data associated with a certain disease can be referred to as phenotyping (or phenomics ), which is the traditional and most common approach to classify diseases irrespective of the biological origins of disease. Utilizing datasets to define disease subtypes at the molecular level can be referred to as endotyping , as exemplified in respiratory medicine [ 16 , 17 ] or oncology [ 18 ]. The National Academy of Sciences of the USA campaigned for a new, molecularly-informed taxonomy to define diseases based on molecular endotypes rather than traditional clinical symptoms [ 19 ].…”