In the last decade, several computational methods have been successfully applied to understand cancer initiation and progression through integration of genomic and epigenomic data. However, despite recent advances in discovering a wide array of novel chemotherapy agents, identification of patients with poor and favorable response prior to treatment administration remains a major challenge in clinical oncology and cancer management.
Researchers at Rutgers university have developed a novel generalizable genome-wide computational framework that identifies biomarkers via gene enrichment analysis. The framework reveals an interplay between genomic and epigenomic mechanisms that explain the complexity of a cancer patient’s response to chemotherapy. This novel computational algorithm uses integrative approach, wherein, genomic (i.e. mRNA expression) and epigenomic (i.e. DNA methylation) patient profiles are used to uncover molecular pathways with significant (epi) genomic alterations that distinguish favorable from poor treatment response. To demonstrate generalizability of the approach, the Rutgers scientists have applied the algorithm across additional chemotherapy regimens and cancer types in order to demonstrate the tools ability to accurately predict the patients’ treatment response.
A novel integrative computational algorithm which can be utilized to identify (epi) genomically altered pathways implicated in primary chemo-response and effectively classify patients who would benefit from specific chemotherapy regimens or are at risk of resistance, which will significantly improve personalized therapeutic strategies and informed clinical decision making.
- Only computational approach that predicts whether a patient will be sensitive or resistant to chemotherapy.
- Builds a strong foundation for improving personalized therapeutic options.
- Potentially helps avoid harmful side effects and improve disease course for all cancer types.
Intellectual Property & Development Status
Patent Pending. Software available. Intellectual property available for licensing and/or research collaboration