Cancer no more

We have discovered that the dark "junk" tumor genome hides a huge amount of information relevant to understanding cancer biology.

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

Genomic features

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 understand oncogenesis.

Patterns

ARETE^AI analyzes millions of genomic patterns by identifying genomic features correlated with DNA damage response activity. These patterns explain pathways malfunctioning resulting in genomic instability. 

Pathway Deconvolution

By traversing the landscape of all DNA damage response pathway interactions, ARETE^AI builds a pathway activity profile for each tumor. ARETE^AI uses these profiles to identify pathway-dependent tumors. Inhibition of the pathway that the tumor is addicted to, results in tumor death.

Drug target identification

By understanding pathway interactions, we have created an approach to identify new drug targets through analysis of genes that are rarely inactivated in real tumors, but are always preserved in the specific genomic context of a tumor.

ARETE^AI delivers ranking of drug target candidates by identifying genes synthetically lethal to given genomic context of the real tumor.

Cohort selection

For a given drug target candidate, ARETE^AI pinpoints predictive biomarkers of response. ARETE^AI is a high-throughput platform for the simultaneous discovery of drug targets and predictive biomarkers.