Establishment and characterization of uterine sarcoma and carcinosarcoma patient-derived xenograft models

癌肉瘤 医学 细胞角蛋白 肉瘤 波形蛋白 免疫组织化学 病理 肉瘤样癌 癌症研究 平滑肌肉瘤
作者
Tine Cuppens,Jeroen Depreeuw,Daniela Annibali,Debby Thomas,Els Hermans,Ellen Gommé,Xuan Bich Trinh,David Debruyne,Philippe Moerman,Diether Lambrechts,Frédéric Amant
出处
期刊:Gynecologic Oncology [Elsevier BV]
卷期号:146 (3): 538-545 被引量:16
标识
DOI:10.1016/j.ygyno.2017.06.005
摘要

Uterine sarcomas (US) and carcinosarcomas (CS) are rare, aggressive cancers. The lack of reliable preclinical models hampers the search for new treatment strategies and predictive biomarkers. To this end, we established and characterized US and CS patient-derived xenograft (PDX) models.Tumor fragments of US and CS were subcutaneously implanted into immunocompromised mice. Engrafted xenograft and original tumors were compared by means of histology, immunohistochemistry, whole-genome low-coverage sequencing for copy number variations, and RNA sequencing.Of 13 implanted leiomyosarcomas (LMS), 10 engrafted (engraftment rate of 77%). Also 2 out of 7 CS (29%) and one high-grade US (not otherwise specified) models were successfully established. LMS xenografts showed high histological similarity to their corresponding human tumors. Expression of desmin and/or H-caldesmon was detected in 8/10 LMS PDX models. We noticed that in CS models, characterized by the concomitant presence of a mesenchymal and an epithelial component, the relative distribution of the components is varying over the generations, as confirmed by changes in vimentin and cytokeratin expression. The similarity in copy number profiles between original and xenograft tumors ranged from 57.7% to 98.2% for LMS models and from 47.4 to 65.8% for CS models. Expression pattern stability was assessed by clustering RNA expression levels of original and xenograft tumors. Six xenografts clustered together with their original tumor, while 3 (all LMS) clustered apart.We present here a panel of clinically annotated uterine sarcoma and carcinosarcoma PDX models, which will be a useful tool for preclinical testing of new therapies.
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