RESUMO
Histologic examination neither reliably distinguishes benign lipomas from atypical lipomatous tumor/well-differentiated liposarcoma, nor dedifferentiated liposarcoma from other pleomorphic sarcomas, entities with different prognoses and management. Molecular confirmation of pathognomonic 12q13-15 amplifications leading to MDM2 overexpression is a diagnostic gold standard. Currently the most commonly used assay for this purpose is fluorescence in situ hybridization (FISH), but this is labor intensive. This study assessed whether newer NanoString-based technology could allow for more rapid and cost-efficient diagnosis of liposarcomas on standard formalin-fixed tissues through gene expression. Leveraging large-scale transcriptome data from The Cancer Genome Atlas, 20 genes were identified, most from the 12q13-15 amplicon, that distinguish dedifferentiated liposarcoma from other sarcomas and can be measured within a single NanoString assay. Using 21 cases of histologically ambiguous low-grade adipocytic tumors with available MDM2 amplification status, a machine learning-based analytical pipeline was built that assigns a given sample as negative or positive for liposarcoma based on quantitative gene expression. The effectiveness of the assay was validated on an independent set of 100 sarcoma samples (including 40 incident prospective cases), where histologic examination was considered insufficient for clinical diagnosis. The NanoString assay had a 93% technical success rate, and an accuracy of 97.8% versus an MDM2 amplification FISH gold standard. NanoString had a considerably faster turnaround time and was cheaper than FISH.
