Through the use of whole genome sequencing (WGS) and other omics data, we redefine how to transform data into understanding knowledge.
More relevant data = Better understanding
Through the use of whole genome sequencing (WGS) and other omics data, we redefine how to transform data into understanding.
More relevant data = Better understanding
Over 90,000 custom genomic features are used as an input into the AI machinery, built by the combined expertise of biologists and data scientists.
Genomic features are numerical representations of molecular tumor characteristics - the key to understanding oncogenesis.
ARETEAI analyzes millions of genomic patterns by identifying genomic features correlated with DNA damage response activity. These patterns explain the malfunctioning of pathways, which result in genomic instability.
ARETEAI builds a pathway activity profile for each tumor by traversing the landscape of all DNA damage response pathway interactions. ARETEAI uses these profiles to identify pathway-dependent tumors. Inhibition of the pathway that the tumor is addicted to results in tumor death.
By understanding pathway interactions, we have created an approach to identify new drug targets through the analysis of genes that are rarely inactivated in real tumors, but are always preserved in the specific genomic context of a tumor.
ARETEAI delivers a ranking of drug target candidates by identifying genes that are synthetically lethal to the given genomic context of the real tumor.
For a given drug target candidate, ARETEAI pinpoints predictive biomarkers of response. ARETEAI is a high-throughput platform for the simultaneous discovery of drug targets and predictive biomarkers.
ARETEAI led us to identify MNM177, a first-in-class drug target, aimed at the replication of stress-high tumors.
Our new drug targets are validated via cell-based and in-vivo models. Selected candidates are at lead optimization stage.