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A novel PTPRZ1-ETV1 fusion in gliomas.

ABSTRACT: The aggressive nature of malignant gliomas and their genetic and clinical heterogeneity present a major challenge in their diagnosis and treatment. Development of targeted therapy brought attention on detecting novel gene fusions, since they represent promising therapeutic targets (eg, TRK inhibitors in NTRK fusion-positive tumors). Using targeted next-generation sequencing, we prospectively analyzed 205 primary brain tumors and detected a novel PTPRZ1-ETV1 fusion transcript in 11 of 191 (5.8%) gliomas, including nine glioblastomas, one anaplastic oligodendroglioma and one pilocytic astrocytoma. PTPRZ1-ETV1 fusion was confirmed by RT-PCR followed by Sanger sequencing, and in-silico analysis predicted a potential driver role. The newly detected fusion consists of the PTPRZ1 promoter in frame with the highly conserved DNA-binding domain of ETV1 transcription factor. The ETV1 and PTPRZ1 genes are known oncogenes, involved in processes of tumor development. ETV1 is a member of the ETS family of transcription factors, already known oncogenic drivers in Ewing sarcoma, prostate cancer and gastrointestinal stromal tumors, but not in gliomas. Its overexpression contributes to tumor growth and more aggressive tumor behavior. PTPRZ1 is already considered to be a tumor growth promoting oncogene in gliomas. In 8%-16% of gliomas, PTPRZ1 is fused to the MET oncogene, resulting in a PTPRZ1-MET fusion, which is associated with poorer prognosis but is also a positive predictive biomarker for treatment with kinase inhibitors. In view of the oncogenic role that the two fusion partners, PTPRZ1 and ETV1, exhibit in other malignancies, PTPRZ1-ETV1 fusion might present a novel potential therapeutic target in gliomas. Although histopathological examination of PTPRZ1-ETV1 fusion-positive gliomas did not reveal any specific or unique pathological features, and the follow-up period was too short to assess prognostic value of the fusion, careful monitoring of patients and their response to therapy might provide additional insights into the prognostic and predictive value of this novel fusion.

SUBMITTER: Matjasic A

PROVIDER: S-EPMC8018154 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Malignant gliomas represent an aggressive class of central nervous system neoplasms. Correlation of interventional outcomes with tumor morphometry data necessitates 3D segmentation of tumors (typically based on magnetic resonance imaging). Expert delineation is the long-held gold standard for tumor segmentation, but is exceptionally resource intensive and subject to intrarater and inter-rater variability. Automated tumor segmentation algorithms have been demonstrated for a variety of imaging modalities and tumor phenotypes, but translation of these methods across clinical study designs is problematic given variation in image acquisition, tumor characteristics, segmentation objectives, and validation criteria. Herein, the authors demonstrate an alternative approach for high-throughput tumor segmentation using Internet-based, collaborative labeling.In a study of 85 human raters and 98 tumor patients, raters were recruited from a general university campus population (i.e., no specific medical knowledge), given minimal training, and provided web-based tools to label MRI images based on 2D cross sections. The labeling goal was characterized as to extract the enhanced tumor cores on T1-weighted MRI and the bright abnormality on T2-weighted MRI. An experienced rater manually constructed the ground truth volumes of a randomly sampled subcohort of 48 tumor subjects (for both T1w and T2w). Raters' taskwise individual observations, as well as the volume wise truth estimates via statistical fusion method, were evaluated over the subjects having the ground truth.Individual raters were able to reliably characterize (with >0.8 dice similarity coefficient, DSC) the gadolinium-enhancing cores and extent of the edematous areas only slightly more than half of the time. Yet, human raters were efficient in terms of providing these highly variable segmentations (less than 20 s per slice). When statistical fusion was used to combine the results of seven raters per slice for all slices in the datasets, the 3D agreement of the fused results with expertly delineated segmentations was on par with the inter-rater reliability observed between experienced raters using traditional 3D tools (approximately 0.85 DSC). The cumulative time spent per tumor patient with the collaborative approach was equivalent to that with an experienced rater, but the collaborative approach could be achieved with less training time, fewer resources, and efficient parallelization.Hence, collaborative labeling is a promising technique with potentially wide applicability to cost-effective manual labeling of medical images.

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A novel PTPRZ1-ETV1 fusion in gliomas.

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