Unique treatmentsfor unique patients.
Shifting away fromthe one-size-fits-allapproach.
Historically, cancer treatment has relied on a one-size-fits-all approach where drugs and other therapies designed to target large groups of individuals are prescribed based on population parameters, but not on the individual patient’s likelihood of positive response. Precision oncology is here to change that.
Providing valuableinsights throughour Drug Activityand ResistanceTest (DARTⓇ).
We’re developing technologies combining deep biology to replicate patients’ disease and AI to unlock accurate predictions of their response to a range of cancer therapies.
PlatformCapabilities
We’re leveraging our technology to enable unique approaches to treatment from bench to bedside.
Through partnerships from the early stages of drug development to actual prescription at the clinic, we’re seeking to improve the lives of current and future cancer patients.
Your’re in goodcompany
OncoPrecision’s Team is driven by a strong conviction and belief that each person is unique and therefore requires tailored care.
Gastón Soria, PhD
Candelaria Llorens, PhD
Gerardo Gatti, PhD
Tarek A. Zaki
Our Partners
Select Papers
Identification of PLK1 as a therapeutic target in BRCA1-deficient cancers
In this work we developed a flow-cytometry-based co-culture screening technology for drug discovery, and we used to explore a public kinase inhibitor set.
Bioprospecting South American flora for synthetic lethal lead compounds
In this work we used our flow-cytometry-based co-culture screening technology to screen a collection of 50 plants species from South America in a wide dose-response scheme.
Screening of regulatory partners of ZEB1 to inhibit its pro-metastatic properties
In this work we performed In Silico screenings to identifty phosphor regulatory sites of the EMT factor ZEB1 and validated PKCα as a novel partner capable of modulating its premetastatic properties.
Repurposing of breast cancer transcriptomic signature for pan-cancer applications
In this work we perform studies of correlations of drug sensitivity of a cell line database with transcriptomic classes of cancer cells derived from a commercial breast cancer signature.