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Updated: Mar 16, 2026
Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
Published on: June 8, 2019
SiNVICT: ultra-sensitive detection of single nucleotide variants and indels in circulating tumour DNA
Can Kockan1,2, Faraz Hach1,3, Iman Sarrafi1
1School of Computing Science.
Motivation:
Successful development and application of precision oncology approaches require robust elucidation of the genomic landscape of a patient's cancer and, ideally, the ability to monitor therapy-induced genomic changes in the tumour in an inexpensive and minimally invasive manner. Thanks to recent advances in sequencing technologies, 'liquid biopsy', the sampling of patient's bodily fluids such as blood and urine, is considered as one of the most promising approaches to achieve this goal. In many cancer patients, and especially those with advanced metastatic disease, deep sequencing of circulating cell free DNA (cfDNA) obtained from patient's blood yields a mixture of reads originating from the normal DNA and from multiple tumour subclones-called circulating tumour DNA or ctDNA. The ctDNA/cfDNA ratio as well as the proportion of ctDNA originating from specific tumour subclones depend on multiple factors, making comprehensive detection of mutations difficult, especially at early stages of cancer. Furthermore, sensitive and accurate detection of single nucleotide variants (SNVs) and indels from cfDNA is constrained by several factors such as the sequencing errors and PCR artifacts, and mapping errors related to repeat regions within the genome. In this article, we introduce SiNVICT, a computational method that increases the sensitivity and specificity of SNV and indel detection at very low variant allele frequencies. SiNVICT has the capability to handle multiple sequencing platforms with different error properties; it minimizes false positives resulting from mapping errors and other technology specific artifacts including strand bias and low base quality at read ends. SiNVICT also has the capability to perform time-series analysis, where samples from a patient sequenced at multiple time points are jointly examined to report locations of interest where there is a possibility that certain clones were wiped out by some treatment while some subclones gained selective advantage.
Results:
We tested SiNVICT on simulated data as well as prostate cancer cell lines and cfDNA obtained from castration-resistant prostate cancer patients. On both simulated and biological data, SiNVICT was able to detect SNVs and indels with variant allele percentages as low as 0.5%. The lowest amounts of total DNA used for the biological data where SNVs and indels could be detected with very high sensitivity were 2.5 ng on the Ion Torrent platform and 10 ng on Illumina. With increased sequencing and mapping accuracy, SiNVICT might be utilized in clinical settings, making it possible to track the progress of point mutations and indels that are associated with resistance to cancer therapies and provide patients personalized treatment. We also compared SiNVICT with other popular SNV callers such as MuTect, VarScan2 and Freebayes. Our results show that SiNVICT performs better than these tools in most cases and allows further data exploration such as time-series analysis on cfDNA sequencing data.
Availability And Implementation:
SiNVICT is available at: https://sfu-compbio.github.io/sinvictSupplementary information: Supplementary data are available at Bioinformatics online.
Contact:
cenk@sfu.ca.
Insights
SiNVICT accurately detects low-frequency mutations in circulating tumor DNA (ctDNA) using liquid biopsies. This computational method improves cancer genomic analysis and therapy monitoring.
Area of Science:
- Genomics
- Computational Biology
- Oncology
Background:
- Precision oncology relies on detailed cancer genome analysis and monitoring treatment-induced genomic changes non-invasively.
- Liquid biopsy, using circulating cell-free DNA (cfDNA), offers a minimally invasive approach for cancer detection and monitoring.
- Detecting low-frequency single nucleotide variants (SNVs) and indels in cfDNA is challenging due to sequencing errors and complex tumor heterogeneity.
Purpose of the Study:
- To introduce SiNVICT, a computational method designed to enhance the sensitivity and specificity of SNV and indel detection in cfDNA.
- To enable accurate detection of mutations at very low variant allele frequencies, crucial for early cancer detection and monitoring.
- To provide a tool for time-series analysis of cfDNA, allowing tracking of clonal evolution during cancer treatment.
Main Methods:
- Development of SiNVICT, a computational algorithm for SNV and indel detection from cfDNA.
- SiNVICT's ability to handle data from multiple sequencing platforms and correct for platform-specific artifacts.
- Implementation of time-series analysis for joint examination of patient samples over time.
Main Results:
- SiNVICT demonstrated high sensitivity in detecting SNVs and indels down to 0.5% variant allele frequency on both simulated and real patient data.
- Successful detection using low DNA input amounts (2.5 ng on Ion Torrent, 10 ng on Illumina).
- SiNVICT outperformed popular SNV callers (MuTect, VarScan2, Freebayes) in accuracy and offered advanced analysis capabilities like time-series analysis.
Conclusions:
- SiNVICT significantly improves the accuracy and sensitivity of detecting low-frequency mutations in cfDNA, crucial for precision oncology.
- The method's ability to handle various sequencing platforms and perform time-series analysis makes it a valuable tool for clinical applications.
- SiNVICT facilitates personalized cancer treatment by enabling precise monitoring of treatment response and resistance mechanisms through liquid biopsies.
