This company develops diagnostic tests for early cancer detection through liquid biopsies and analysis of circulating tumor cells and genomic DNA. Their services aim to identify cancer at an earlier stage, enabling more effective treatment options for patients.
Funding
Funding not disclosed
Founders
Product
Problem
Current liquid biopsy methods for early cancer detection, primarily focused on circulating tumor DNA (ctDNA), often struggle to detect early-stage tumors due to insufficient ctDNA shedding. This limitation necessitates complementary approaches to improve early detection rates and enable timely intervention.
Solution
Serum Detect is developing a novel liquid biopsy technology that leverages the body's immune system to detect cancer early by analyzing T-cell receptor (TCR) sequences in circulating T cells. Their approach focuses on tumor immune surveillance, identifying T-cell proliferation in response to tumor antigens as an indicator of early-stage disease. By using next-generation sequencing (NGS) to assay TCR sequences, Serum Detect identifies TCR repertoire functional units (RFUs) associated with cancer. These cancer-associated RFUs are then analyzed using machine learning models to generate a cancer score, providing a comprehensive liquid biopsy approach for early cancer detection. This method offers an orthogonal principle of detection compared to ctDNA analysis, potentially amplifying subtle signals for improved sensitivity in detecting small, early-stage tumors.
Target Audience
The primary target audience includes clinical partners and liquid biopsy developers interested in improving early cancer detection rates, particularly for lung cancer, as well as individuals at high risk for cancer who may benefit from early screening.
Features
- Analyzes T-cell receptor (TCR) sequences in circulating T cells using next-generation sequencing (NGS).
- Employs proprietary computational methods to group TCRs into repertoire functional units (RFUs).
- Identifies cancer-associated RFUs by comparing TCR counts between cancer patients and healthy controls.
- Uses machine learning (ML) models to generate a cancer score based on cancer-associated RFUs.
- Integrates seamlessly into existing liquid biopsy workflows using the buffy coat fraction of a routine blood draw.
- Initially focused on early detection of lung cancer, with potential applications across various tumor types.