Overview
The detection sensitivity for low-frequency variants from a limited
amount of sample is of a great importance of ctDNA analysis kit.
research with Seoul National University Hospital (SNUH) since 2017. We have integrated our market leading proprietary technologies including probe design
algorithms, noise removal techniques, and reagents optimization. The panel is thoroughly validated and ready to use for clinical diagnosis.
Features &
Benefits
-
- Detects ctDNA for Colorectal cancer, Breast cancer, and Lung cancer
- Assess 16 key genes for Colorectal cancer, 14 for Breast cancer,
15 for Lung cancer
-
- Highly optimized panel for clinical testing with exceptional accuracy
- Complete validated panel performance conducted with patient samples
through collaborative research with Seoul National University Hospital
-
- Provides Unique Molecular Identifiers (UMI) and Bioinformatics Software
- Receive high-quality data supported by Celemics proprietary UMI algorithms
and analysis software, enabling efficient duplication removal
and minimizing sequencing noise
-
- Algorithms to reduce sequencing background noise to
accurate analysis of low-frequency variants - Celemics applies algorithms for reducing the sequencing noise
to provide accurate and reliable sequencing results to our customers
- Algorithms to reduce sequencing background noise to
Panel
Performance
-
Colorectal Cancer Sensitivity Freq. 0.5% 100% Freq. 1.0% 100% Specificity 97.9% 
-
Breast Cancer Sensitivity Freq. 0.5% 94.4% Freq. 1.0% 100% Specificity 96.3% 
-
Lung Cancer Sensitivity Freq. 0.5% 100% Freq. 1.0% 100% Specificity 100% 
Every three ctDNA panels of Celemics showed over 94% of sensitivity and 96% of specificity which fitted at ctDNA research and clinical field
Workflow
Workflow of Celemics’ Circulating Tumor DNA Panel
- Able to assess ctDNA with ultra-low variant allele frequency (VAF)
- Modular algorithm to be applied in the existing pipeline.
- Retrieves more unique reads than that from conventional duplication
removal algorithm reducing sequencing costs - Noise removal and accurate calls due to proprietary consensus sequence
generation algorithm
Bioinformatics SW for noise reduction and duplicate read recovery
- Minimizes the noise for accurate analysis of variants with ultra-low VAF from ctDNA
- Generates consensus read to support noise suppression
- Continuous improvement of the noise removal technology by data accumulation
Applications
-
Early diagnosis of cancers
-
Observation of the prognosis