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Drug repurposing in breast cancer

乳腺癌的药物再利用

基本信息

批准号：
10328975
负责人：
Rong Stephanie Huang
金额：
$ 43.52万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-02-13 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10328975
关键词：
Animal Experiments Biological Markers Breast Cancer Treatment Cancer Model Cancer Patient Cancer cell line Cell Line Cells Clinical Clinical Trials Computing Methodologies Consumption Data Data Set Databases Death Rate Development Disease Drug Combinations Drug usage Genes Genome Goals Growth Healthcare Systems In Vitro Individual Malignant Neoplasms Medical Metastatic breast cancer Methodology Methods Mission Molecular Molecular Profiling Morbidity - disease rate Mus Patient Care Patients Pharmaceutical Preparations Pharmacogenomics Pre-Clinical Model Process Public Health Publishing Refractory Regimen Relapse Research Resources Sampling Speed Taxonomy Testing The Cancer Genome Atlas Therapeutic Time Tissues Translating Treatment outcome United States National Institutes of Health Validation Xenograft procedure advanced breast cancer base biomarker discovery cancer genomics cancer therapy cancer type cost data mining disorder subtype drug development drug repurposing drug response prediction drug sensitivity drug testing efficacious treatment efficacy validation established cell line genome-wide genomic data high throughput screening improved in vivo individualized medicine innovation interest malignant breast neoplasm molecular subtypes mortality mouse model novel novel therapeutics optimal treatments patient derived xenograft model patient response phenotypic data pre-clinical precision medicine predictive modeling prospective test response small molecule sound standard of care survival outcome therapy development tool transcriptome triple-negative invasive breast carcinoma tumor whole genome

项目摘要

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.

项目总结/摘要降低晚期乳腺癌死亡率需要紧急开发更好的药物，治疗策略;然而，新药开发极其耗时且昂贵。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））和公开可用的高通量筛选数据的数千个小分子（其中许多具有已经获得至少一种医疗状况的监管批准），计算药物重新定位或再利用为精准医学提供了巨大的潜力，并可能为显着改善乳腺癌提供工具癌症治疗和结果。我们的假设是，最佳的治疗选择可以确定为难以治疗的乳腺癌，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在难以治疗的乳腺癌，即三阴性乳腺癌（TNBC）中的疗效，转移性乳腺癌（MBC）。为实现这一目标，该提案包含两个具体目标，即开发、应用、并改进预测药物敏感性的方法（作为单一药剂或组合）。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，并使用细胞基于TNBC和MBC的模型和体内（小鼠）模型。在目标1中，我们将重点确定有效的药物，目标2是确定和验证最佳治疗组合。我们的研究意义重大，因为它将加速难以治疗的新疗法的发展。通过重新利用现有的药物，从而避免了漫长而危险的新药开发，过程为特定疾病亚型定制治疗以及识别和验证新药的能力适应症将在对抗TNBC和MBC的斗争中提供有价值的治疗选择，降低相关死亡率。我们提出的研究是创新的，在这两个方法采用及其应用，因为我们基于转录组的药物敏感性预测代表了一种范式转变，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生物标志物发现，以定制个性化治疗，而且用于药物再利用。的将生物标志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因为肿瘤的全基因组表达谱将用于提供最佳治疗选择，在不同的癌症中，许多“老”药物可以在改善癌症治疗结果方面找到新的用途。

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Emerging role of long non-coding RNAs in cancer precision medicine.

长链非编码 RNA 在癌症精准医学中的新兴作用。

DOI：
10.1080/23723556.2019.1684130
发表时间：
2020
期刊：
Molecular & cellular oncology
影响因子：
2.1
作者：
Nath,Aritro;Huang,RStephanie
通讯作者：
Huang,RStephanie

Long Non-Coding RNA ANRIL as a Potential Biomarker of Chemosensitivity and Clinical Outcomes in Osteosarcoma.

DOI：
10.3390/ijms222011168
发表时间：
2021-10-16
期刊：
International journal of molecular sciences
影响因子：
5.6
作者：
Lee AM;Ferdjallah A;Moore E;Kim DC;Nath A;Greengard E;Huang RS
通讯作者：
Huang RS

Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling.

DOI：
10.3390/cancers13040885
发表时间：
2021-02-20
期刊：
Cancers
影响因子：
5.2
作者：
Gruener RF;Ling A;Chang YF;Morrison G;Geeleher P;Greene GL;Huang RS
通讯作者：
Huang RS

Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity.

DOI：
10.1186/s13059-018-1507-0
发表时间：
2018-09-11
期刊：
Genome biology
影响因子：
12.3
作者：
Geeleher P;Nath A;Wang F;Zhang Z;Barbeira AN;Fessler J;Grossman RL;Seoighe C;Stephanie Huang R
通讯作者：
Stephanie Huang R

Oncogene or tumor suppressor? Long noncoding RNAs role in patient's prognosis varies depending on disease type.