This is an innovative computational approach designed to revolutionize precision oncology.
Description
This technology leverages multi-OMIC sequencing data to predict clinical treatment outcomes more accurately. Unlike traditional methods that are prone to sequencing errors and biases, iGenSig offers a robust, white-box solution that enhances cross-dataset applicability and tolerance to sequencing bias. This groundbreaking method has demonstrated superior performance in predicting therapeutic responses, marking a significant advancement in the field of precision medicine.
Applications
• Cancer Therapeutics
• Personalized Medicine
• Drug Development
• Clinical Trials
Advantages
Firstly, it utilizes the complete set of genomic correlates for a given therapeutic response, reducing the impact of sequencing errors and biases. This comprehensive approach ensures more accurate predictions of therapeutic outcomes. Secondly, iGenSig's white-box design allows for transparency and interpretability, addressing the common issue of the "black box" nature of AI-based methods. The technology has demonstrated outstanding cross-dataset applicability, as evidenced by its performance in predicting drug responses across different datasets and its potential for clinical translation. For example, iGenSig has shown a high area under the receiver operating characteristic curve (AUROC) in predicting responses to drugs like lapatinib, erlotinib, and sorafenib.
Invention Readiness
iGenSig is currently at the prototype stage, with significant progress made in its development and validation. The technology has been tested using drug sensitivity data from the Genomic Datasets of Drug Sensitivity (GDSC) and the Cancer Cell Line Encyclopedia (CCLE), demonstrating high predictive performance. Further validation has been conducted using patient-derived xenograft (PDX) tumors and clinical trial data from the BATTLE trial. The next steps in its development include securing funding for further clinical validation, refining the computational algorithms, and exploring commercial partnerships for technology licensing and company formation. The support from NIH/NCI grants underscores the potential and credibility of this innovative solution.
IP Status
Copyright
