Dataset Information

Malignant transformation of uterine leiomyoma to myxoid leiomyosarcoma after morcellation associated with ALK rearrangement and loss of 14q.

ABSTRACT: A 50 year old woman underwent laparoscopic supracervical hysterectomy because of symptomatic fibroids. Histologic examination of samples obtained after morcellation revealed typical uterine leiomyomas in all samples investigated. 28 and 47 months later, respectively, the patient presented with peritoneal spreading of nodules that were surgically removed and histologically classified as leiomyosarcoma. In 3/4 of samples obtained after morcellation copy number/SNP-array hybridization showed complex genomic alterations widely identical to the pattern characterizing the sarcoma. Therefore, we conclude that the leiomyosarcoma had unambiguously developed from one of the leiomyomas as a result of secondary genetic alterations i.e. a rearrangement of ALK and a del(14q). The case is challenging the current risk estimates for spreading of unexpected malignant uterine tumors due to power morcellation and highlights the relevance of certain genetic alterations for rare malignant transformation of uterine benign smooth muscle tumors.

SUBMITTER: Holzmann C

PROVIDER: S-EPMC6021249 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Malignant transformation of uterine leiomyoma to myxoid leiomyosarcoma after morcellation associated with <i>ALK</i> rearrangement and loss of 14q.

Holzmann Carsten C Saager Christian C Mechtersheimer Gunhild G Koczan Dirk D Helmke Burkhard M BM Bullerdiek Jörn J

Oncotarget 20180612 45

A 50 year old woman underwent laparoscopic supracervical hysterectomy because of symptomatic fibroids. Histologic examination of samples obtained after morcellation revealed typical uterine leiomyomas in all samples investigated. 28 and 47 months later, respectively, the patient presented with peritoneal spreading of nodules that were surgically removed and histologically classified as leiomyosarcoma. In 3/4 of samples obtained after morcellation copy number/SNP-array hybridization showed comple ...[more]

PMID: 29963223

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Project description:Objective: The objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. Methods: We manually selected 114 normal uterine tissue and 31 leiomyosarcoma samples from publicly available transcriptome data in UCSC Xena as training/validation sets. We developed pre-processing procedure and gene selection method to sensitively find genes of larger variance in leiomyosarcoma than normal uterine tissues. Through our method, twenty genes were selected to build transcriptome-based classifier. The prediction accuracies of deep feedforward neural network (DNN), support vector machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models were examined. We interpret the biological functionality of selected genes via network-based analysis using Gene-Mania. To validate the performance of trained model, we additionally collected 35 clinical samples of leiomyosarcoma and leiomyoma as a test set (18 + 17 as 1st and 2nd test sets). Results: We discovered genes expressed in a highly variable way in leiomyosarcoma while these genes are expressed in a conserved way in normal uterine samples. These genes were mainly associated with DNA replication, cell cycle, and DNA damage checkpoint. Among evaluated machine learning classifiers, the DNN had the highest accuracy and average AUC value in training data set. As gene selection and model training were made in leiomyosarcoma and uterine normal tissue, proving discriminant of ability between leiomyosarcoma and leiomyoma is necessary. Thus, further validation of trained model was conducted in newly collected clinical samples of leiomyosarcoma and leiomyoma. The DNN classifier performed AUC of 0.917 and 0.914 supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample. Conclusion: The transcriptome-based classifier accurately distinguished uterine leiomyoma from leiomyosarcoma.

Dataset Information

Malignant transformation of uterine leiomyoma to myxoid leiomyosarcoma after morcellation associated with ALK rearrangement and loss of 14q.

Publications

Malignant transformation of uterine leiomyoma to myxoid leiomyosarcoma after morcellation associated with <i>ALK</i> rearrangement and loss of 14q.

Similar Datasets

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