The startup develops a lab-on-a-chip device for in-vitro diagnostics that analyzes mutational profiling of tumor tissues and circulating tumor cells from blood samples. This technology enables early cancer diagnosis and personalized treatment monitoring, providing critical insights into treatment efficacy.
Funding
$600K 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
Current methods for cancer diagnosis and treatment monitoring often rely on invasive tissue biopsies, which may not always capture the full heterogeneity of the tumor or be feasible for regular monitoring. Traditional approaches can also be slow and may not provide timely insights into treatment efficacy or the emergence of drug resistance.
Solution
Oncophenomics offers a multi-analyte liquid biopsy platform that analyzes circulating tumor cells and tumor DNA from blood samples, providing a comprehensive genomic profile of the cancer. This non-invasive approach enables early cancer detection, personalized treatment selection, and real-time monitoring of treatment response. By leveraging advanced genomic profiling and machine learning algorithms, the platform identifies genetic alterations, monitors minimal residual disease, and helps overcome drug resistance. The platform's custom bioinformatics pipelines and GPU-accelerated servers handle large genomic datasets, delivering actionable insights to clinicians.
Target Audience
The primary customers are biotech and biopharma companies, cancer hospitals, and oncologists seeking advanced diagnostic tools for personalized cancer care, clinical trials, and translational cancer research.
Features
- Multi-analyte liquid biopsy technology for comprehensive genomic profiling from blood samples
- Analysis of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA)
- Custom bioinformatics data analysis pipelines built with Nextflow
- GPU-accelerated servers for handling large genomics datasets
- Machine learning algorithms for clinical interpretation of genomic findings (Variant-GPT, Bio-GPT)
- Identification of genetic alterations for personalized treatment selection
- Monitoring of minimal residual disease (MRD)
- Support for patient selection for clinical trials and biomarker identification