Assay enables fast diagnosis of liposarcomas
Researchers from The University of British Columbia have leveraged the latest advances in RNA technology and machine learning methods to develop a gene panel test that allows for highly accurate diagnosis of the most common types of liposarcoma. Described in The Journal of Molecular Diagnosis, the assay quickly and reliably distinguishes benign lipomas from liposarcomas and can be performed in laboratories at a lower cost than current ‘gold standard’ tests.
“Liposarcomas are a type of malignant cancer that is difficult to diagnose because, even under a microscope, it is hard to differentiate liposarcomas from benign tumours or other types of cancer that need different treatments,” explained Dr Torsten Owen Nielsen, lead investigator on the study. “Many liposarcomas look like their benign and relatively common counterparts, lipomas. Diagnostic delay and uncertainty cause severe stress for patients, and misdiagnosis can have many consequences including delayed or inadequate treatment or unnecessary surgical procedures and long-term postoperative follow-up.”
Among the current recommended diagnostic tests for liposarcomas, immunohistochemistry (IHC) is inaccurate and hard to interpret, and fluorescence in situ hybridisation (FISH) is relatively expensive, as well as labour- and equipment-intensive. Investigators explored whether NanoString technology, ideal for analysing even poor-quality RNA, could allow for more rapid and cost-efficient diagnosis of liposarcomas through gene expression.
The investigators utilised data from The Cancer Genome Atlas, a catalogue of genetic information from over 20,000 cancer samples, to identify the 20 most common genes that are overexpressed in liposarcomas. Probes for these genes were designed by NanoString bioinformatics and run on a set of ‘training samples’ that included lipomas and liposarcomas. Analysis of the NanoString results showed clear separation of lipoma from liposarcoma cases.
A machine learning model was developed to determine the probability that a given sample was positive for liposarcoma and was then applied to 45 retrospective cases to determine boundaries for positive and negative predictions. The test was subsequently applied in a real-world clinical setting. A molecular technologist with no knowledge of the clinical, histologic, IHC or FISH information about the cases was asked to identify each sample as liposarcoma or not liposarcoma from the NanoString test results. The same samples were examined by specialist pathologists using standard testing.
The retrospective and prospective cases probed by the NanoString assay had a 93% success rate and agreed with standard tests 97.8% of the time. Results from the NanoString assay were available in 36 hours, whereas it took 1–2 weeks to get FISH results. NanoString costs amounted to US$270 per case, factoring in reagents, labour and equipment maintenance.
The group previously published a NanoString-based assay for sarcomas bearing diagnostic fusion oncogenes, which is now in clinical use; however, the most common type of liposarcoma carries a different type of mutation (gene amplifications) and so was not covered. The current study therefore expands the types of sarcoma that can be diagnosed accurately with NanoString-based diagnostics and shows how a different category of mutation can be detected, which may help with diagnostics for other types of cancer.
“We applied these new technologies to improve patient care in areas where existing diagnostic methods were inaccurate, slow or costly and saw substantial improvements,” Dr Nielsen said. “There is no patent on this test; anyone can apply the method we describe, and we are happy to help others get set up to run it at their own institution.”
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