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用于评估肿瘤特异性治疗的临床相关炎性乳腺癌患者来源异种移植物衍生的离体模型。

Clinically relevant inflammatory breast cancer patient-derived xenograft-derived ex vivo model for evaluation of tumor-specific therapies.

机构信息

Department of Breast Medical Oncology, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.

Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas, MD, Anderson Cancer Center, Houston, Texas, United States of America.

出版信息

PLoS One. 2018 May 16;13(5):e0195932. doi: 10.1371/journal.pone.0195932. eCollection 2018.

Abstract

Inflammatory breast cancer (IBC) is a rare and aggressive presentation of invasive breast cancer with a 62% to 68% 5-year survival rate. It is the most lethal form of breast cancer, and early recognition and treatment is important for patient survival. Like non-inflammatory breast cancer, IBC comprises multiple subtypes, with the triple-negative subtype being overrepresented. Although the current multimodality treatment regime of anthracycline- and taxane-based neoadjuvant therapy, surgery, and radiotherapy has improved the outcome of patients with triple-negative IBC, overall survival continues to be worse than in patients with non-inflammatory locally advanced breast cancer. Translation of new therapies into the clinics to successfully treat IBC has been poor, in part because of the lack of in vitro preclinical models that can accurately predict the response of the original tumor to therapy. We report the generation of a preclinical IBC patient-derived xenograft (PDX)-derived ex vivo (PDXEx) model and show that it closely replicates the tissue architecture of the original PDX tumor harvested from mice. The gene expression profile of our IBC PDXEx model had a high degree of correlation to that of the original tumor. This suggests that the process of generating the PDXEx model did not significantly alter the molecular signature of the original tumor. We demonstrate a high degree of similarity in drug response profile between a PDX mouse model and our PDXEx model generated from the same original PDX tumor tissue and treated with the same panel of drugs, indicating that our PDXEx model had high predictive value in identifying effective tumor-specific therapies. Finally, we used our PDXEx model as a platform for a robotic-based high-throughput drug screen of a 386-drug anti-cancer compound library. The top candidates identified from this drug screen all demonstrated greater therapeutic efficacy than the standard-of-care drugs used in the clinic to treat triple-negative IBC, doxorubicin and paclitaxel. Our PDXEx model is simple, and we are confident that it can be incorporated into a PDX mouse system for use as a first-pass screening platform. This will permit the identification of effective tumor-specific therapies with high predictive value in a resource-, time-, and cost-efficient manner.

摘要

炎性乳腺癌 (IBC) 是一种罕见且侵袭性的乳腺癌表现形式,其 5 年生存率为 62%至 68%。它是乳腺癌中最致命的形式,早期识别和治疗对患者的生存至关重要。与非炎性乳腺癌一样,IBC 包含多种亚型,其中三阴性亚型占比过高。尽管目前采用蒽环类和紫杉类新辅助治疗、手术和放疗的多模式治疗方案改善了三阴性 IBC 患者的预后,但总体生存率仍不如非炎性局部晚期乳腺癌患者。将新疗法转化为临床成功治疗 IBC 的效果不佳,部分原因是缺乏能够准确预测原始肿瘤对治疗反应的体外临床前模型。我们报告了一种临床前 IBC 患者来源异种移植 (PDX) 衍生的离体 (PDXEx) 模型的生成,并表明它非常接近从小鼠中采集的原始 PDX 肿瘤的组织结构。我们的 IBC PDXEx 模型的基因表达谱与原始肿瘤高度相关。这表明生成 PDXEx 模型的过程并未显著改变原始肿瘤的分子特征。我们证明了来自同一原始 PDX 肿瘤组织的 PDX 小鼠模型和我们生成的 PDXEx 模型在药物反应谱上具有高度相似性,并使用相同的药物组合进行治疗,表明我们的 PDXEx 模型在识别有效肿瘤特异性疗法方面具有很高的预测价值。最后,我们使用我们的 PDXEx 模型作为平台,对来自同一原始 PDX 肿瘤组织的 PDX 小鼠模型和我们生成的 PDXEx 模型进行了 386 种抗癌化合物库的机器人高通量药物筛选。从该药物筛选中鉴定出的顶级候选药物的疗效均优于临床上用于治疗三阴性 IBC 的标准治疗药物阿霉素和紫杉醇。我们的 PDXEx 模型简单,我们有信心将其纳入 PDX 小鼠系统,用作初步筛选平台。这将以资源高效、时间高效和成本高效的方式识别具有高预测价值的有效肿瘤特异性疗法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86a0/5955489/c4f86c3af453/pone.0195932.g001.jpg

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