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Integrated diagnosis based on transcriptome analysis in suspected pediatric sarcomas.

ABSTRACT: Pediatric solid tumors are a heterogeneous group of neoplasms with over 100 subtypes. Clinical and histopathological diagnosis remains challenging due to the overlapping morphological and immunohistochemical findings and the presence of atypical cases. To evaluate the potential utility of including RNA-sequencing (RNA-seq) in the diagnostic process, we performed RNA-seq in 47 patients with suspected pediatric sarcomas. Histopathologists specialized in pediatric cancer re-evaluated pathological specimens to reach a consensus diagnosis; 42 patients were diagnosed with known subtypes of solid tumors whereas 5 patients were diagnosed with undifferentiated sarcoma. RNA-seq analysis confirmed and refined consensus diagnoses and further identified diagnostic genetic variants in four of the five patients with undifferentiated sarcoma. Genetic lesions were detected in 23 patients, including the novel SMARCA4-THOP1 fusion gene and 22 conventional or recently reported genetic events. Unsupervised clustering analysis of the RNA-seq data identified a distinct cluster defined by the overexpression of rhabdomyosarcoma-associated genes including MYOG and CHRNG. These findings suggest that RNA-seq-based genetic analysis may aid in the diagnosis of suspected pediatric sarcomas, which would be useful for the development of stratified treatment strategies.

SUBMITTER: Ichikawa D

PROVIDER: S-EPMC8206218 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Pediatric sarcomas provide a unique diagnostic challenge. There is considerable morphologic overlap between entities, increasing the importance of molecular studies in the diagnosis, treatment, and identification of therapeutic targets. We developed and validated a genome-wide DNA methylation based classifier to differentiate between osteosarcoma, Ewing's sarcoma, and synovial sarcoma. DNA methylation status of 482,421 CpG sites in 10 Ewing's sarcoma, 11 synovial sarcoma, and 15 osteosarcoma samples were determined using the Illumina Infinium HumanMethylation450 array. We developed a random forest classifier trained from the 400 most differentially methylated CpG sites within the training set of 36 sarcoma samples. This classifier was validated on data drawn from The Cancer Genome Atlas (TCGA) synovial sarcoma, TARGET Osteosarcoma, and a recently published series of Ewing's sarcoma. Methylation profiling revealed three distinct patterns, each enriched with a single sarcoma subtype. Within the validation cohorts, all samples from TCGA were accurately classified as synovial sarcoma (10/10, sensitivity and specificity 100%), all but one sample from TARGET-OS were classified as osteosarcoma (85/86, sensitivity 98%, specificity 100%) and 14/15 Ewing's sarcoma samples classified correctly (sensitivity 93%, specificity 100%). The single misclassified osteosarcoma sample demonstrated high EWSR1 and ETV1 expression on RNA-seq although no fusion was found on manual curation of the transcript sequence. Two additional clinical samples, that were difficult to classify by morphology and molecular methods, were classified as osteosarcoma when previously suspected to be a synovial sarcoma and Ewing's sarcoma on initial diagnosis, respectively. Osteosarcoma, synovial sarcoma, and Ewing's sarcoma have distinct epigenetic profiles. Our validated methylation-based classifier can be used to provide diagnostic assistance when histological and standard techniques are inconclusive.

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Integrated diagnosis based on transcriptome analysis in suspected pediatric sarcomas.

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