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

Advaita Corporation, Whitmore Lake MI

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be the development of an analysis method and software package to identify human disease subtypes using omics data. This technology will enable the ability to provide personalized treatment for patients, and more successful and cost-effective clinical trials, bring drugs to market more rapidly. The goal is identification of disease subtypes and patient subgroups, a prerequisite to the ability to distinguish between patients who are in danger and need the most aggressive treatments, and those who are less suited to treatment because they will never progress or recur or they will develop resistance. Currently, 70% of drugs entering Phase III clinical trials fail, leading to a loss of more than $1 trillion per year. This may be avoided by refining trial inclusion criteria and administering the drug only to the patients most likely to respond. The technology is designed to identify patient subgroups most likely to respond or not respond to a given treatment. This technology also may reduce the cost of prophylactic clinical trials by reducing the number of subjects and/or duration necessary to achieve sufficient power. The technology will significantly reduce drug development costs while simultaneously improving patient care by selecting the correct treatment for each patient. The intellectual merit of this SBIR Phase II project is to develop a novel analysis method and software package that is 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 (respondents vs. non-respondents) and/or subtypes of disease (aggressive vs. non-aggressive). The current unmet challenge is to discover the molecular subtypes of disease and subgroups of patients. Attempts to achieve this based solely on gene expression signatures have been undertaken but yielded only modest success (very few gene expression tests are FDA-approved to date). The technology proposed here may be used to discover clinically relevant disease subtypes by integrating multiple types of high-throughput data. In addition, the Phase I results obtained on real patient data demonstrated that the technology is able to distinguish between more and less aggressive types of cancer based on their molecular profiles alone. This Phase II project proposes to extend this technology to integrate genomic and clinical data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.