Establishment of a high-fidelity patient-derived xenograft model for cervical cancer enables the evaluation of patient's response to conventional and novel therapies.
Establishment of a high-fidelity patient-derived xenograft model for cervical cancer enables the evaluation of patient's response to conventional and novel therapies.
复制标题
DOI：
10.1186/s12967-023-04444-5
复制
发表时间：
2023-09-09
影响因子：
7.4
通讯作者：
Wang, Hui
中科院分区：
文献类型：
--
作者：
Liu, Liting;Wu, Min;Huang, Anni;Gao, Chun;Yang, Yifan;Liu, Hong;Jiang, Han;Yu, Long;Huang, Yafei;Wang, Hui
复发性或转移性宫颈癌（r/m CC）由于其有限的治疗选择，通常预后不良。由于缺乏能准确反映宫颈癌（CC）生物学和基因组异质性的临床前模型，新治疗策略的开发受到阻碍。因此，我们的目的是建立一个大型的患者来源的异种移植物（PDX）生物样本库CC，评估PDX和原发肿瘤组织的患者之间的生物指标的一致性，并探讨其效用评估患者的反应，传统和新的治疗。将69例新鲜CC肿瘤组织直接植入免疫缺陷小鼠体内建立PDX模型。基于临床病理学特征、蛋白质生物标志物水平和基因组特征，分别通过苏木精和伊红染色、免疫组织化学和全外显子组测序，比较PDX模型与其相应的原发性肿瘤（PT）的一致性。此外，CC患者的临床信息，RNA转录组和原发性肿瘤的免疫表型被整合，以确定可能影响异种移植物植入成功的潜在参数。随后，评价PDX模型反映患者对化疗反应的能力。最后，利用PDX模型和PDX衍生类器官（PDXO）评价来那替尼和过继性细胞治疗（ACT）联合策略对人表皮生长因子受体2（HER 2）突变CC患者的疗效。我们建立了CC的PDX生物样本库，成功率为63.8%（44/69）。已建立的PDX肿瘤的主要特征，包括临床病理学特征、蛋白质生物标志物（包括Ki 67、α-平滑肌肌动蛋白和p16）的表达水平以及基因组学，与其PT高度一致。此外，异种移植物植入可能受到原发肿瘤大小、滤泡辅助性T细胞的存在和原发肿瘤组织中细胞粘附相关基因表达的影响。CC衍生的PDX模型能够重现患者对化疗的反应。在PDX模型中，一种新的治疗策略，ACT和来那替尼联合治疗，显示可有效抑制来自HER 2突变CC患者的PDX肿瘤生长。我们建立了迄今为止最大的PDX生物库，具有CC的高植入率，保留了患者活检样本的组织病理学和遗传学特征，概括了患者对常规治疗的反应，并能够评估CC新治疗方式的疗效。在线版本包含补充材料，可通过10.1186/s12967-023-04444-5获得。
Recurrent or metastatic cervical cancer (r/m CC) often has poor prognosis owing to its limited treatment options. The development of novel therapeutic strategies has been hindered by the lack of preclinical models that accurately reflect the biological and genomic heterogeneity of cervical cancer (CC). Herein, we aimed to establish a large patient-derived xenograft (PDX) biobank for CC, evaluate the consistency of the biologic indicators between PDX and primary tumor tissues of patients, and explore its utility for assessing patient’s response to conventional and novel therapies. Sixty-nine fresh CC tumor tissues were implanted directly into immunodeficient mice to establish PDX models. The concordance of the PDX models with their corresponding primary tumors (PTs) was compared based on the clinical pathological features, protein biomarker levels, and genomic features through hematoxylin & eosin staining, immunohistochemistry, and whole exome sequencing, respectively. Moreover, the clinical information of CC patients, RNA transcriptome and immune phenotyping of primary tumors were integrated to identify the potential parameters that could affect the success of xenograft engraftment. Subsequently, PDX model was evaluated for its capacity to mirror patient’s response to chemotherapy. Finally, PDX model and PDX-derived organoid (PDXO) were utilized to evaluate the therapeutic efficacy of neratinib and adoptive cell therapy (ACT) combination strategy for CC patients with human epidermal growth factor receptor 2 (HER2) mutation. We established a PDX biobank for CC with a success rate of 63.8% (44/69). The primary features of established PDX tumors, including clinicopathological features, the expression levels of protein biomarkers including Ki67, α-smooth muscle actin, and p16, and genomics, were highly consistent with their PTs. Furthermore, xenograft engraftment was likely influenced by the primary tumor size, the presence of follicular helper T cells and the expression of cell adhesion-related genes in primary tumor tissue. The CC derived PDX models were capable of recapitulating the patient’s response to chemotherapy. In a PDX model, a novel therapeutic strategy, the combination of ACT and neratinib, was shown to effectively inhibit the growth of PDX tumors derived from CC patients with HER2-mutation. We established by far the largest PDX biobank with a high engraftment rate for CC that preserves the histopathological and genetic characteristics of patient’s biopsy samples, recapitulates patient’s response to conventional therapy, and is capable of evaluating the efficacy of novel therapeutic modalities for CC. The online version contains supplementary material available at 10.1186/s12967-023-04444-5.
登录
查看更多内容
影响因子：
82.9
作者：
Gao, Hui;Korn, Joshua M.;Sellers, William R.
通讯作者：
Sellers, William R.
影响因子：
64.8
作者：
Hyman DM;Piha-Paul SA;Won H;Rodon J;Saura C;Shapiro GI;Juric D;Quinn DI;Moreno V;Doger B;Mayer IA;Boni V;Calvo E;Loi S;Lockhart AC;Erinjeri JP;Scaltriti M;Ulaner GA;Patel J;Tang J;Beer H;Selcuklu SD;Hanrahan AJ;Bouvier N;Melcer M;Murali R;Schram AM;Smyth LM;Jhaveri K;Li BT;Drilon A;Harding JJ;Iyer G;Taylor BS;Berger MF;Cutler RE Jr;Xu F;Butturini A;Eli LD;Mann G;Farrell C;Lalani AS;Bryce RP;Arteaga CL;Meric-Bernstam F;Baselga J;Solit DB
通讯作者：
Solit DB
影响因子：
16.6
作者：
Domcke, Silvia;Sinha, Rileen;Levine, Douglas A.;Sander, Chris;Schultz, Nikolaus
通讯作者：
Schultz, Nikolaus
影响因子：
16.6
作者：
Hurvitz SA;Caswell-Jin JL;McNamara KL;Zoeller JJ;Bean GR;Dichmann R;Perez A;Patel R;Zehngebot L;Allen H;Bosserman L;DiCarlo B;Kennedy A;Giuliano A;Calfa C;Molthrop D;Mani A;Chen HW;Dering J;Adams B;Kotler E;Press MF;Brugge JS;Curtis C;Slamon DJ
通讯作者：
Slamon DJ
影响因子：
2.7
作者：
Zou, Shuangwei;Ye, Miaomiao;Zhang, Jian-an;Ji, Huihui;Chen, Yijie;Zhu, Xueqiong
通讯作者：
Zhu, Xueqiong