高通量药物筛选确定了治疗低度浆液性卵巢癌的新型疗法。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-19 DOI:10.1038/s41597-024-03869-x
Kathleen I Pishas, Karla J Cowley, Marta Llaurado-Fernandez, Hannah Kim, Jennii Luu, Robert Vary, Nikola A Bowden, Ian G Campbell, Mark S Carey, Kaylene J Simpson, Dane Cheasley
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引用次数: 0

摘要

低级别浆液性癌(LGSOC)是一种罕见的上皮性卵巢癌,与更常见的输卵管卵巢高级别浆液性卵巢癌相比,具有独特的分子特征。指导上皮性卵巢癌治疗的关键性临床试验缺乏足够的 LGSOC 病例来进行有意义的亚组分析,因此总体研究结果不能外推至 LGSOC 等较罕见的化疗耐药亚型。此外,由于治疗方案有限,需要更有效的疗法来治疗复发疾病。为了解决这个问题,我们在 12 个患者来源的 LGSOC 细胞系和一个正常卵巢细胞系中进行了最大规模的定量高通量药物筛选工作(n = 3436 个化合物),以确定尚未探索的治疗途径。我们的数据集结合使用了高通量机器人技术、高内容成像技术和新型数据分析管道,确定了 60 种高置信度和 19 种中等置信度的药物,这些药物在最低化合物评估剂量(0.1 µM)下可诱导癌细胞特异性细胞毒性。我们还发现了一系列已知(mTOR/PI3K/AKT)和新型(表皮生长因子受体和 MDM2-p53)药物类别,LGSOC 细胞系对这些药物有明显的敏感性。
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High-throughput drug screening identifies novel therapeutics for Low Grade Serous Ovarian Carcinoma.

Low grade serous carcinoma (LGSOC) is a rare epithelial ovarian cancer with unique molecular characteristics compared to the more common tubo-ovarian high-grade serous ovarian carcinoma. Pivotal clinical trials guiding the management of epithelial ovarian cancer lack sufficient cases of LGSOC for meaningful subgroup analysis, hence overall findings cannot be extrapolated to rarer chemo-resistant subtypes such as LGSOC. Furthermore, there is a need for more effective therapies for the treatment of relapsed disease, as treatment options are limited. To address this, we conducted the largest quantitative high-throughput drug screening effort (n = 3436 compounds) in 12 patient-derived LGSOC cell lines and one normal ovary cell line to identify unexplored therapeutic avenues. Using a combination of high-throughput robotics, high-content imaging and novel data analysis pipelines, our data set identified 60 high and 19 moderate confidence hits which induced cancer cell specific cytotoxicity at the lowest compound dose assessed (0.1 µM). We also revealed a series of known (mTOR/PI3K/AKT) and novel (EGFR and MDM2-p53) drug classes in which LGSOC cell lines showed demonstrable susceptibility to.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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