Identifying Effective Immune Checkpoint Therapy Strategies in Triple Negative Breast Cancer

免疫检查点疗法已经在人类癌症如黑色素瘤中产生了显著的临床反应， 结直肠癌和非小细胞肺癌。有效使用这些治疗的关键是患者选择 根据分子指标准确预测反应。需要有针对性的治疗， 生物标志物，为三阴性乳腺癌（TNBC）带来免疫检查点治疗的临床益处 承诺影响患者的结果，这是该提案的目标。我的核心假设是免疫系统 在适当生物标志物的帮助下，检查点治疗可以扩展到一些乳腺癌患者 来自临床前测试的选择。819例乳腺癌预测性生物信息学分析 通过对样本的分析，我发现有证据表明，基底细胞样TNBC患者的临床指标与其他癌症相似 从抗CTLA4和抗PD1治疗中获益。这表明有效治疗的可能性很高。 接受这些治疗的TNBC患者。通过建立适当的临床前模型，模拟临床试验 免疫检查点疗法是可以实现的。事实上，初步数据显示， 当在一种小鼠模型中结合抗PD1和常用化疗时， 基底样基因表达特征和关键免疫特征的表达。临床和翻译 使用基因工程小鼠模型（GEMM）模拟临床试验的价值在于能够测试 联合治疗，并利用反应数据来开发预测性生物标志物， 指导未来的临床试验。基于这些发现和目标，我制定了两个目标。在目标1中，我将测试 假设TNBC GEMM将对免疫检查点抑制剂呈现可变的反应， 使用敏感和耐药的GEMM，我将能够识别出反应的基因组生物标志物， 与人类癌症进行比较分析。我的目标是利用这种设置来识别生物标志物， 这些治疗策略将允许这些疗法用于治疗TNBC。在目标2中，我将检验假设 Apobec3的表达将导致许多新的突变和独特的DNA诱变特征， 潜在的许多新抗原，这将使肿瘤对免疫检查点抑制剂敏感。这项工作的结果 将确定人类同源物Apobec3B是否是免疫应用的分子预测因子。 检查点疗法本提案中的工作符合国家癌症研究所的使命，因为其成果是 预计将为未来使用免疫检查点疗法的临床试验提供信息，并提高乳腺癌的生存率 患者

Immune checkpoint therapies have led to remarkable clinical responses in human cancers such as melanoma, colorectal cancer, and non-small cell lung cancer. Key to efficacious use of these treatments, is patient selection based on molecular indicators that faithfully predict response. With a need for targeted therapy, identifying biomarkers to bring the clinical benefits of immune checkpoint therapy to triple negative breast cancer (TNBC) promises to impact patient outcomes and is the goal of this proposal. My central hypothesis is that immune checkpoint therapy can be extended to some breast cancer patients with the assistance of appropriate biomarker selection coming from pre-clinical testing. Using predictive bioinformatics analysis of 819 human breast cancer samples, I found evidence that patients with basal-like TNBC present similar clinical indicators as other cancers that benefit from anti-CTLA4 and anti-PD1 therapy. This indicates a high likelihood for efficacious treatment of TNBC patients with these therapies. By establishing the appropriate preclinical models, simulating clinical trials with immune checkpoint therapies can be accomplished. Indeed, preliminary data reveals a therapeutic benefit when combining anti-PD1 and common use chemotherapy in one of the mouse models that credentialed for basal-like gene expression features and expression of key immune signatures. The clinical and translational value in simulating clinical trials using genetically engineered mouse models (GEMMs) is the ability to test combination therapies and utilize response data to develop predictive biomarkers that can be carried over to guide future clinical trials. Based on these findings and goals, I have developed two aims. In aim 1, I will test the hypothesis that TNBC GEMMs will present variable responses to immune checkpoint inhibitors, and that by using sensitive and resistant GEMMs I will be able to identify genomic biomarkers of response that will be leveraged in comparative analyses with human cancers. My goal is to use this setting to identify biomarkers and treatment strategies that will allow these therapies to be used to treat TNBC. In aim 2, I will test the hypothesis that Apobec3 expression will lead to many new mutations and a unique DNA mutagenesis signature, and potentially many neoantigens, which will sensitize tumors to immune checkpoint inhibitors. The result of this work will determine whether the human homolog, Apobec3B, is a molecular predictor for application of immune checkpoint therapies. The work in this proposal aligns itself with the mission of the NCI as the outcomes are expected to inform future clinical trials using immune checkpoint therapies and improve survival for breast cancer patients.