Drug repurposing in breast cancer

项目摘要/摘要 降低晚期乳腺癌死亡率迫切需要开发更好的药物和改进 治疗策略；然而，新药开发极其耗时和昂贵。与 大规模癌症基因组和表型数据的爆炸性增长(例如，癌症基因组图谱[TCGA]) 以及公开可用的数千个小分子的高通量筛选数据(其中许多小分子 已获得至少一种医疗条件的监管批准)、计算性药物重新定位或 重新调整用途具有精准医学的巨大潜力，并可能提供显著改善乳房的工具 癌症治疗和结果。 我们的假设是，对于难以治疗的乳腺癌，可以通过以下方式确定最佳治疗选择 应用基于转录组的药物敏感性预测方法。我们的长期目标是识别和验证 现有药物治疗难治性乳腺癌，即三阴性乳腺癌(TNBC)和 转移性乳腺癌。为了实现这一目标，这项提案包含两个具体目标：制定、应用、 并改进预测药物敏感性的方法(无论是作为单一药物还是联合使用)。我们还将 在其他大规模癌症基因组数据集中验证这些预测，并使用CELL翻译结果 基于和活体(小鼠)的TNBC和MBC模型。在目标1中，我们将重点确定有效的药物为 单一疗法，而目标2是确定和验证最佳治疗组合。 我们的研究具有重要意义，因为它将加速难治性疾病新疗法的开发 通过改变现有药物的用途来治疗乳腺癌，从而避免冗长且有风险的新药开发 进程。针对特定疾病亚型进行量身定制治疗的能力，以及新药的识别和验证 适应症将在与TNBC和MBC的斗争中提供有价值的治疗选择，并随后 降低他们的相关死亡率。我们提出的研究在两种方法上都是创新的。 以及它们的应用，因为我们基于转录组的药物敏感性预测代表了 药物敏感性预测；此外，我们正在将这些新的预测方法应用于患者肿瘤 数据不仅用于发现生物标记物，以便量身定做个体化治疗，而且还用于药物再利用。这个 将生物标记物发现和药物再利用结合在一起的能力将为癌症提供新的机会 治疗，因为肿瘤的整个基因组表达谱将被用来提供最佳的治疗选择 在不同的癌症中，许多老的药物可以在改善癌症治疗结果方面找到新的目的。

Project Summary/Abstract Reducing advanced breast cancer mortality requires urgent development of better drugs and improved therapeutic strategies; however, new drug development is extremely time-consuming and costly. With the explosive growth of large-scale cancer genomic and phenotypic data (e.g., the Cancer Genome Atlas [TCGA]) and publicly available high-throughput screening data for thousands of small molecules (many of which have already received regulatory approval for at least one medical condition), computational drug repositioning or repurposing holds great potential for precision medicine and may provide tools to significantly improve breast cancer treatment and outcomes. Our hypothesis is that optimal therapeutic choices can be identified for hard to treat breast cancers by applying transcriptome-based drug sensitivity prediction methods. Our long term goal is to identify and validate the efficacy of existing drugs in hard to treat breast cancers, namely triple negative breast cancer (TNBC) and metastatic breast cancer (MBC). Toward this goal, this proposal contains two specific aims to develop, apply, and improve methods to predict drug sensitivity (either as a single agent or in combination). We will also validate these predictions in additional large-scale cancer genomic datasets and translate the results using cell based and in vivo (mouse) models of TNBC and MBC. In Aim 1, we will focus on identifying effective drugs as monotherapy, while Aim 2 is to identify and validate optimal therapeutic combinations. Our study is significant because it will accelerate the development of novel therapies for hard to treat breast cancers by repurposing existing drugs, thus avoiding the lengthy and risky new drug development process. The ability to tailor therapy for specific disease subtypes and identification and validation of new drug indications will provide valuable therapeutic options in the battle against TNBC and MBC, and subsequently reduce their associated mortality. Our proposed research is innovative in both the methodologies employed and their applications, as our transcriptome-based drug sensitivity prediction represents a paradigms shift in drug sensitivity prediction; furthermore, we are applying these novel prediction approaches to patient tumor data not only for biomarker discovery in order to tailor individual therapy, but also for drug repurposing. The ability to bring biomarker discovery and drug repurposing together will present a new opportunity for cancer therapy, as the whole genome expression profile of a tumor will be used to provide optimal therapeutic options in different cancers, and many “old” drugs can find a new purpose in improving cancer treatment outcomes.