SyzOnc develops the STEM 3 platform, an integrated, systems-level oncology solution designed to address the complexity of solid tumors. This platform maps, models, and modulates the tumor ecosystem to identify triple-threat protein targets controlling cancer cell growth, immune function, and matrix architecture. The goal is to rapidly discover and compare compound phenotypes to generate improved drug candidates for historically difficult-to-treat solid tumors.
Funding
$800K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Matrix-rich solid tumors, such as liver and pancreatic cancers, are difficult to treat due to the protective ecosystem surrounding the cancer cells. Current treatment options are often ineffective, leading to poor survival rates for patients with these aggressive cancers. A lack of effective methods for identifying druggable targets within the tumor microenvironment hinders the development of improved therapies.
Solution
SyzOnc's STEM3 platform is a systems-level oncology solution designed to identify druggable targets in matrix-rich solid tumors. The platform utilizes multi-modal human tissue data and AI-driven ecosystem models to map the tumor landscape, model cancer type-specific ecosystems, and rapidly compare genetic and compound phenotypes. By integrating data from across the tumor ecosystem, STEM3 facilitates the discovery of novel biology and the identification of improved drug candidates, ultimately aiming for better clinical-stage results and improved patient outcomes. The platform's approach allows for targeting the tumor microenvironment, which protects cancer cells and contributes to treatment resistance.
Target Audience
The primary target audience includes pharmaceutical companies and research institutions focused on developing novel cancer therapeutics, particularly for matrix-rich solid tumors like liver, pancreatic, and glioblastoma cancers.
Features
- Multi-modal human tissue data integration for comprehensive tumor mapping
- AI/ML models incorporating data from across the tumor ecosystem to create cancer type-specific models
- Rapid comparison of genetic and compound phenotypes to identify compound starting points
- Identification of novel biology within the tumor microenvironment
- Validation using gold-standard in vivo models
- Focus on matrix biology and phenomics to explore potential druggable matrix targets