Development of TGF-beta antagonists for cancer therapy

基于有希望的临床前结果，几种不同的转化生长因子-β途径拮抗剂正在进行治疗晚期癌症的早期临床试验。然而，考虑到转化生长因子-β的复杂生物学，成功开发用于癌症治疗的转化生长因子-β拮抗剂将取决于对这些药物如何发挥作用的清楚了解，以及如何选择将从这种治疗中受益的患者的相关问题。使用12个转移性乳腺癌小鼠同基因同种异体移植模型，以转移负荷为主要终点，我们揭示了对转化生长因子-β拮抗的异质性反应。在某些模型中，阻断转化生长因子-β途径可抑制转移，而在另一些模型中则不影响或刺激转移。我们正在继续应用有针对性的和基于发现的方法来解决治疗反应异质性的分子和生物学机制，并产生有用的预测性生物标记物。出乎意料的是，在原发肿瘤中，转化生长因子-β的表达水平和转化生长因子-β途径的激活程度都不能预测对抗转化生长因子-β治疗的反应。然而，模型中的肿瘤对转化生长因子-β拮抗剂表现出良好的反应，其特征是转录证据表明转化生长因子-β途径在未经治疗的状态下被激活。因此，在体内，转化生长因子-β信号的输出可能比任何输入参数，如配体水平和信号转导元件的状态，更敏感地反映转化生长因子-β途径的激活状态。我们正在生成可能预测抗转化生长因子-β治疗反应的基因表达特征，并将在其他临床前模型中测试这些特征。我们还确定了对转化生长因子-β的治疗反应的候选分子决定因素，并在肿瘤模型中通过敲除和过度表达的方法来测试这些决定因素。我们还在继续研究如何提高转化生长因子-β拮抗剂的治疗效果。相关的临床数据和文献证据表明，在许多癌症中，转化生长因子-β1主要与不良预后有关，而转化生长因子-β3实际上可能与转化生长因子-β1相反，并与良好的预后相关。我们优化了一种准确定量表达转化生长因子-β蛋白亚型的方法，并表明乳腺是少数几个表达显著水平的转化生长因子-β3的成年器官之一。我们发现，与正常乳腺相比，转化生长因子-β1持续上调，转化生长因子-β3持续下调。我们的转化生长因子-β亚型选择性抗体的初步数据表明，就治疗效果而言，不使用转化生长因子-β3没有优势，但这种方法可能会产生积极的代谢后果。我们的综合基因组方法也在建议可以与转化生长因子-β拮抗剂联合使用的药物来提高治疗效果，我们正在积极寻求临床前模型中的顶级领先地位。

Based on promising preclinical results, several different TGF-beta pathway antagonists are in early phase clinical trials for the treatment of advanced cancer. 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. Using a panel of 12 mouse syngeneic allograft models of metastatic breast cancer, with metastatic burden as the primary endpoint, we uncovered heterogeneous responses to TGF-beta antagonism. TGF-beta pathway blockade inhibited metastasis in some models, while having no effect on or stimulating metastasis in other models. We are continuing to apply targeted and discovery-based approaches to address molecular and biological mechanisms underlying the heterogeneity of therapeutic response and to generate useful predictive biomarkers. Unexpectedly, neither TGF-beta expression levels nor extent of TGF-beta pathway activation in the primary tumor predict response to anti-TGF-beta therapy. However, tumors from models showing a desirable response to TGF-beta antagonism are characterized by transcriptomic evidence of TGF-beta pathway activation in the untreated state. Thus in vivo, the output of TGF-beta signaling may be a more sensitive indicator of the activation status of the TGF-beta pathway than any of the input parameters such as ligand levels and status of signal transduction components. We are generating gene expression signatures that might predict response to anti-TGF-beta therapy and will test these in additional preclinical models. We have also identified candidate molecular determinants of the therapeutic response to TGF-beta and are testing these by knockdown and overexpression approaches in the tumor models. We are also continuing to address ways of improving the therapeutic efficacy of TGF-beta antagonism. Correlative clinical data and literature evidence suggest that whereas TGF-beta1 is primarily associated with poor outcome in many cancers, TGF-beta3 may actually oppose TGF-beta1 and be associated with good outcome. We have optimized a method for accurate quantitation of TGF-beta protein isoform expression and have shown that the mammary gland is one of the few adult organs to express significant levels of TGF-beta3. We find TGF-beta1 to be consistently upregulated and TGF-beta3 to be downregulated when compared with the normal mammary gland. Our preliminary data with TGF-beta isoform-selective antibodies suggests that there is no advantage to sparing TGF-beta3 in terms of therapeutic efficacy, but that there may be positive metabolic consequences of this approach. Our integrated genomic approaches are also suggesting drugs that could be combined with TGF-beta antagonists to improve therapeutic efficacy, and we are actively pursuing the top leads in our preclinical models.