SBIR Phase I: A multi-omics data integration approach for precision medicine and improved clinical trial success

SBIR 第一阶段：用于精准医疗和提高临床试验成功率的多组学数据集成方法

• 批准号: 1721898
• 负责人: Calin Voichita
• 金额: $ 22.5万
• 依托单位: Advaita Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2017-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1721898&HistoricalAwards=false
• 关键词: SBIR; Phase; multi; omics; data

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project will be to develop an analysis software package to significantly reduce health care costs while simultaneously improving patient care by helping select the correct treatment for each patient. Every year an estimated of 1.4 million women undergo unnecessary treatments at a cost to society of $32.2 billion for breast cancer. At the same time, some patients do not receive the treatment they need. For instance, chemotherapy is not routinely recommended after surgical tumor removal for patients with early stage lung cancer, even though the disease will recur in a large number of them. The ability to correctly identify disease subtypes and patient subgroups is a pre-condition to the ability to distinguish between patients that need the most aggressive treatments, and those who will never progress or recur. Further, the proposed approach can improve the results of clinical trials. With an estimated 2,300 Phase III clinical trials per year in the US, a full 50% of them are destined for failure with a loss of $1 billion/year. This can be avoided if the correct inclusion criteria are defined, and the drug is administered only to the people most likely to respond.This STTR Phase I project proposes to develop a novel software package able to identify subtypes of disease based on the integration of multiple types of omics data. Many drug candidates fail and many patients receive inappropriate treatment because of the current inability to distinguish between subgroups of patients and/or subtypes of disease. Many attempts to achieve this based solely on gene expression signatures have been undertaken but yielded only modest success so far. In addition, very few approaches are able to combine multiple data types and most of the time the analysis of each data type leads to different subgroups that are very hard to interpret. The technology proposed here will be able to discover clinically relevant disease subtypes by integrating multiple types of high-throughput data such as mRNAs, miRNAs, methylation, etc. The goal of this project is to implement this technology as a software package that will facilitate its application in large scale consistent with real-world use. In addition, the plan is to assess the feasibility of this technology by performing an extensive comparison with the top three existing approaches: Consensus clustering, similarity network fusion, and iClusterPlus using over 1,800 real patient data from 12 different studies.

这个小企业技术转让（STTR）项目的更广泛的影响/商业潜力将是开发一个分析软件包，以显着降低医疗保健成本，同时通过帮助为每位患者选择正确的治疗方法来改善患者护理。据估计，每年有140万妇女接受不必要的治疗，社会因乳腺癌而付出的代价为322亿美元。与此同时，一些患者没有得到他们需要的治疗。例如，对于早期肺癌患者，在手术切除肿瘤后通常不建议进行化疗，尽管其中大部分患者的疾病会复发。正确识别疾病亚型和患者亚组的能力是区分需要最积极治疗的患者和永远不会进展或复发的患者的先决条件。此外，所提出的方法可以改善临床试验的结果。据估计，美国每年有2,300项III期临床试验，其中整整50%注定失败，每年损失10亿美元。如果定义了正确的入选标准，并且只给最有可能产生反应的人使用药物，就可以避免这种情况。STTR第一阶段项目提出开发一种新的软件包，能够基于多种类型的组学数据的整合来识别疾病的亚型。由于目前无法区分患者亚组和/或疾病亚型，许多候选药物失败，许多患者接受不适当的治疗。已经进行了许多尝试来实现这一点，但迄今为止只取得了适度的成功。此外，很少有方法能够联合收割机组合多种数据类型，并且大多数情况下，对每种数据类型的分析导致很难解释的不同子组。这里提出的技术将能够通过整合多种类型的高通量数据（如mRNA、miRNA、甲基化等）来发现临床相关的疾病亚型。该项目的目标是将该技术实现为一个软件包，以促进其与实际使用一致的大规模应用。此外，该计划通过与现有的三种方法进行广泛比较来评估该技术的可行性：共识聚类，相似性网络融合和iPhoneterPlus，使用来自12项不同研究的1，800多个真实的患者数据。