Cancer mutation detection at ultra-low variant allele frequencies (VAFs) is an unmet challenge that is intractable with current state-of-the-art mutation calling methods. Specifically, the limit of VAF detection is closely related to the depth of coverage due to the requirement of multiple supporting reads in extant methods, precluding the detection of mutations at VAFs that are orders of magnitude lower than the depth of coverage.
The ability to detect cancer-associated mutations in ultra low VAFs is a fundamental requirement for low-tumor burden cancer diagnostics applications such as early detection, monitoring, and therapy nomination using liquid biopsy methods (cell-free DNA).
Bypassing dependency to the sequencing depth as a read centric method with keeping cancer related variables (Tumor Education) in consideration but not only variants, Our Combined approach would be able to detect cancer at great efficiency.
Benifits of Liquid Biopsy based method to detect cancer early
- Cancer patients have DNA from tumor cells (ctDNA) along with germline (normal) DNA in their bloodstream in the form of cell free DNA (cfDNA).
- Quantity of cfDNA increase in the bloodstream with the severity of cancer.
- Quantitative analysis of freely circulating cell free DNA in the bloodstream is used to diagnose cancer severity, Hence it make cancer diagnose possible at early stage in asymptomatic patients.
- Because ctDNA represents tumor cells, Hence somatic variations can be detected
Clinical Applications of Liquid Biopsy
EDCbyLB, an overview to detect cancer at early stage
- 2 layer model based on variants as well as methylation data could achieve higher sensitivity and specificity
- This gives more confident to patients and doctors to act on the disease even before any symptoms appear
Methodology
- Read-centric method to identify cancer causing variants without dependency of depth of sequencing.
- Apart from variants several factors or variables like DNA methylation, Gene Expression, smallRNA expression etc. provide a wide picture and classification followed by cross validation (CV)
- Target dataset or validation dataset (control + case samples) could be generated using pre-established cfDNA based open source databases
- EDCbyLB, a combined model can take both variant based as well as cancer variables based information in account and derive a more sensitive and specific result with significant accuracy.
More information to be published soon.
VALUE PROPOSITION
EDCbyLB is one of our seed concept projects. Our unique value proposition is as a product or service for screening of cancers at early stages with high efficiency and accuracy by leveraging the power of advanced ML techniques.