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

这一小型企业技术转移(STTR)项目的更广泛影响/商业潜力将是开发一个分析软件包，通过帮助为每个患者选择正确的治疗方法，显著降低医疗成本，同时改善患者护理。据估计，每年有140万女性因乳腺癌接受不必要的治疗，给社会带来了322亿美元的成本。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，手术切除肿瘤后不建议常规进行化疗，尽管他们中的许多人会复发。正确识别疾病亚型和患者亚组的能力是区分哪些患者需要最积极的治疗，哪些患者永远不会进展或复发的先决条件。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2300项第三阶段临床试验，其中整整50%注定会失败，每年损失10亿美元。如果定义了正确的纳入标准，并且药物只给最有可能有反应的人使用，这种情况是可以避免的。这个STTR第一阶段项目建议开发一个新的软件包，能够基于多种类型的组学数据的集成来识别疾病的亚型。由于目前无法区分患者亚组和/或疾病亚类，许多候选药物失败，许多患者接受不适当的治疗。为了实现这一点，人们已经进行了许多仅基于基因表达签名的尝试，但到目前为止只取得了不大的成功。此外，很少有方法能够组合多种数据类型，而且大多数情况下，对每种数据类型的分析会导致很难解释的不同子组。这里提出的技术将能够通过集成多种类型的高通量数据，如mRNAs、miRNAs、甲基化等，发现临床相关的疾病亚型。该项目的目标是将这项技术作为一个软件包实施，将促进其大规模应用，与现实世界的使用一致。此外，该计划将使用来自12个不同研究的1800多个真实患者数据，通过与现有的前三种方法进行广泛比较来评估这项技术的可行性。这三种方法分别是共识集群法、相似性网络融合和iClusterPlus。

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.