Development of TGF-beta antagonists for cancer therapy

开发用于癌症治疗的 TGF-β 拮抗剂

• 批准号: 8349219
• 负责人: Lalage Wakefield
• 金额: $ 87.28万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8349219
• 关键词: Advanced Malignant Neoplasm; Adverse effects; Antibodies; Biological Markers; Biology; Clinical; Clinical Treatment; Clinical Trials; Combined Modality Therapy; Complex; Cooperative Research and Development Agreement; Data; Development; Disease; Epithelial; Genes; Genetically Engineered Mouse; Goals; Growth; Growth Factor; Histology; Homeostasis; Human; Immune response; Immunocompetent; Immunologic Surveillance; Malignant Neoplasms; Mammary Neoplasms; Mammary gland; Mediating; Modeling; Monoclonal Antibodies; Mouse Strains; Mus; National Cancer Institute; Neoplasm Metastasis; Patient Selection; Patients; Phase; Play; Pre-Clinical Model; Proteins; Role; Signal Pathway; Staging; Testing; Therapeutic; Therapeutic Effect; Transforming Growth Factor beta; Transforming Growth Factor-Beta Overexpression; Transgenic Organisms; Transplantation; Tumor Promoters; Tumor Suppression; Tumor Suppressor Proteins; Work; angiogenesis; base; cancer therapy; carcinogenesis; cell motility; in vivo; insight; malignant breast neoplasm; mouse model; neoplastic cell; novel strategies; outcome forecast; pre-clinical; programs; protein expression; response; treatment strategy; tumor; tumor progression; tumorigenesis

Despite the dual role for TGF-beta as both tumor suppressor and tumor promoter in carcinogenesis, preclinical data from our lab and others has previously suggested that strategies to antagonize TGF-beta may selectively reduce the undesirable tumor promoting effects of this growth factor while sparing the desirable effects on tumor suppression and normal homeostasis. Based on these promising preclinical results, an anti-TGF-beta antibody is in early phase clinical trials for the treatment of advanced cancer (NCI-06-C-0200). However, given the complex biology of TGF-beta, the successful development of TGF-beta antagonists for cancer therapy will depend on a clear understanding of how these agents work, and the related question of how to select patients who will benefit from this type of treatment. Our major focus in FY11 has been to establish a panel of transplantable syngeneic mouse models of metastatic breast cancer, assess the therapeutic effects of anti-TGF-beta antibodies in the different models and then use the panel to develop predictive biomarkers of response to anti-TGF-beta therapy. We have assembled a panel of 11 metastatic mammary models derived from spontaneous or genetically engineered mouse mammary tumors that can be transplanted into immunocompetent mouse hosts. A range of tumor histologies and mouse strain background are represented. We have successfully assessed the effect of a pan-TGF-beta neutralizing mouse monoclonal antibody on metastasis (1D11) in 9 of the models, and we find that while 1D11 inhibits metastasis in some models, it has no effect or can even stimulate metastasis in other models. Using this panel, we have assessed the predictive power of a number of plausible candidate biomarkers. We have found that therapeutic response to TGF-beta antagonism is not successfully predicted by (a) TGF-beta expression levels in the tumor cells, (b) loss of growth inhibitory response of the tumor cells to TGF-beta, (c) Six-1 expression levels, (d) Claudin-low status, (e) activation of non-canonical TGF-beta signaling pathways. We are now proceeding with discovery-based approaches to the identification of predictive biomarkers, including testing gene signatures of TGF-beta-mediated tumor suppression that we identified in the related project ZIA BC 005785. We have also examined mechanisms underlying the undesirable stimulation of metastasis by 1D11 in some models, and we have shown that unlike the therapeutic effect, this stimulatory effect is not dependent on the presence of an adaptive immune response.

尽管TGF-β在癌变中既是肿瘤抑制剂又是肿瘤启动子的双重作用，但我们实验室和其他人的临床前数据以前提出，与TGF-β拮抗TGF-β的策略可能有选择地减少这种生长对这种生长的不良肿瘤促进对肿瘤抑制和正常施加的影响。基于这些有希望的临床前结果，抗TGF-β抗体是在治疗晚期癌症的早期临床试验中（NCI-06-C-0200）。但是，鉴于TGF-β的复杂生物学，TGF-beta拮抗剂用于癌症治疗的成功发展将取决于对这些药物的工作原理的清晰了解，以及如何选择将从这种类型治疗中受益的患者的相关问题。 我们在FY11上的主要重点是建立一组可移植的合成小鼠转移性乳腺癌模型，评估在不同模型中抗TGF-beta抗体的治疗作用，然后使用面板来开发对抗TGF-beta治疗的反应的预测生物标志物。我们组装了一个由自发或遗传工程的小鼠乳腺肿瘤衍生而来的11个转移性乳腺模型，这些模型可以移植到免疫能力的小鼠宿主中。 表示一系列肿瘤组织学和小鼠应变背景。我们已经成功地评估了pan-tgf-beta中和小鼠单克隆抗体对9个模型中转移（1d11）的影响，并且我们发现，尽管1d11在某些模型中抑制了转移，但它在其他模型中没有效果，甚至可以刺激其他模型的转移。使用此面板，我们评估了许多合理的候选生物标志物的预测能力。我们发现，（a）肿瘤细胞中TGF-β表达水平（a）肿瘤细胞对TGF-β的生长抑制反应丧失，（c）六1-1表达水平，（d）Claudin-low状态，（E）非canonical tgffffffffffffffbeta pation tage pation，（e）非cannonical cymenta tgfFf-beta tagfffffffffffffffffffffffffffffffffffffffffffffffffffff beta t gffffffffffffffffffffffffff beta，则发现（a）肿瘤细胞生长抑制反应丧失的治疗反应并没有成功预测。 现在，我们正在采用基于发现的方法来识别预测性生物标志物，包括测试TGF-beta介导的肿瘤抑制的基因特征，我们在相关的项目Zia BC 005785中确定，我们还检查了某些模型中不良刺激的效果，我们还检查了不可能的效果，我们已经检查了这种效应，我们已经依赖于1D11的效应，并且我们已经探讨了我们的效应，并且我们已经探讨了我们的效应，并且我们已经探讨了我们的效应，并且我们已经探讨了我们的效应，并且我们已经研究了我们的效果。关于自适应免疫反应的存在。