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Histological markers, sickle-shaped blood vessels, myxoid area, and infiltrating growth pattern help stratify the prognosis of patients with myxofibrosarcoma/undifferentiated sarcoma.

ABSTRACT: Myxofibrosarcoma (MFS) and undifferentiated sarcoma (US) have been considered as tumors of the same lineage based on genetic/epigenetic profiling. Although MFS shows a notably better prognosis than US, there are no clear criteria for distinguishing between them. Here, we examined 85 patients with MFS/US and found that tumors with infiltrative growth patterns tended to have more myxoid areas and higher local recurrence rates but fewer distant metastases and better overall survival. Morphologically characteristic sickle-shaped blood vessels, which tended to have fewer αSMA-positive cells, were also observed in these tumors, compared with normal vessels. Based on the incidence of these sickle-shaped blood vessels, we subdivided conventionally diagnosed US into two groups. This stratification was significantly correlated with metastasis and prognosis. RNA sequencing of 24 tumors (9 MFS and 15 US tumors) demonstrated that the proteasome, NF-kB, and VEGF pathways were differentially regulated among these tumors. Expression levels of KDR and NFATC4, which encode a transcription factor responsible for the neuritin-insulin receptor angiogenic signaling, were elevated in the sickle-shaped blood vessel-rich US tumors. These findings indicate that further analyses may help elucidate the malignant potential of MFS/US tumors as well as the development of therapeutic strategies for such tumors.

SUBMITTER: Washimi K

PROVIDER: S-EPMC10130155 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Histological markers, sickle-shaped blood vessels, myxoid area, and infiltrating growth pattern help stratify the prognosis of patients with myxofibrosarcoma/undifferentiated sarcoma.

Washimi Kota K Kasajima Rika R Shimizu Eigo E Sato Shinya S Okubo Yoichiro Y Yoshioka Emi E Narimatsu Hiroto H Hiruma Toru T Katayama Kotoe K Yamaguchi Rui R Yamaguchi Kiyoshi K Furukawa Yoichi Y Miyano Satoru S Imoto Seiya S Yokose Tomoyuki T Miyagi Yohei Y

Scientific reports 20230425 1

Myxofibrosarcoma (MFS) and undifferentiated sarcoma (US) have been considered as tumors of the same lineage based on genetic/epigenetic profiling. Although MFS shows a notably better prognosis than US, there are no clear criteria for distinguishing between them. Here, we examined 85 patients with MFS/US and found that tumors with infiltrative growth patterns tended to have more myxoid areas and higher local recurrence rates but fewer distant metastases and better overall survival. Morphologicall ...[more]

PMID: 37185612

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Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Dataset Information

Histological markers, sickle-shaped blood vessels, myxoid area, and infiltrating growth pattern help stratify the prognosis of patients with myxofibrosarcoma/undifferentiated sarcoma.

Publications

Histological markers, sickle-shaped blood vessels, myxoid area, and infiltrating growth pattern help stratify the prognosis of patients with myxofibrosarcoma/undifferentiated sarcoma.

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