SiNVICT: ultra-sensitive detection of single nucleotide variants and indels in circulating tumour DNA
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".