Abstract 628: Determinants of quality of next-generation sequencing output from the strand-specific TruSight Tumor Sequencing Panel in a clinical diagnostic setting
Bibliographic record
Abstract
Abstract The use of Next-Generation Sequencing (NGS) technologies is increasingly prevalent within diagnostic labs. As genomic regions are sequenced to greater depth in cancer diagnostics, it is critical to differentiate clinically actionable variants from artifacts arising from sequencing-errors, sample- processing or sample-age, and to identify samples that will be difficult to evaluate. We sought to determine whether strand-specific sequencing approaches, such as the TruSight Tumor Sequencing Panel (Illumina) could enable sample and variant triage in a clinical diagnostic settingTruSight Tumor Sequencing Panel allows for paired-end sequencing of individual strands of DNA and analyzing them either together (Paired) or separately (Pool A and Pool B). Variants identified in one pool, but not the other, are putative artifacts; variants identified in both pools are considered true calls. Combined analysis of both pools was performed in two ways: By summing variant calls across pools, and by informatically determining overlapping variant calls between pools. In a test cohort of 44 FFPE samples of varying age and tumor type, we assessed whether age of sample, strand bias, and fixation impacted the detection of high confidence variants using the TruSight Tumor Sequencing panel. Data were compared to the results of analysis of the same samples using the established Illumina TruSeq Amplicon Cancer Panel and/or Sanger Sequencing.Sample age, tumor cellularity, tumor type and template DNA quality were not found to be associated with quality of NGS output in our study. We also evaluated the overall transition/transversion (Ti/Tv) ratios for variants detected either uniquely in one pool or in combined analysis. Interestingly, for variants detected in both pools, the Ti/Tv ratio was 1.97, compared to 0.52-0.60 for those detected in only 1 pool (p < 0.001). Strikingly, samples that sequenced successfully but gave inconclusive and difficult to interpret variant lists were associated with%G>A:C>T transition > 62.5% and Ti/Tv ratios of > 4.0 (p < 0.001). G>A:C>T transitions were significantly over-represented in these samples. The overall%G>A:C>T transitions were equivalent (44-52%) in individual pools or in paired analysis. However, when inconclusive samples were accounted for, the%G>A:C>T transitions differed between the two analyses: 49.7% (paired) vs. 30.1-32.1% (individual pools). In summary, the Ti/Tv ratio can act as a critical determinant of variant call quality - Ti/Tv ratios ∼0.5 represent sequencing artifacts, while Ti/Tv ratios > 4.0 are indicative of inconclusive sequencing output.We conclude that variant Ti/Tv ratio as well as%G>A:C>T transition in variants detected by the TruSight Tumor Sequencing Panel may be helpful evaluators of quality and clinical utility of sequencing output for FFPE tumor samples tested in a clinical diagnostic setting. Citation Format: Swati Garg, Mahadeo A. Sukhai, Mariam Thomas, Michelle Mah, Tong Zhang, Trevor Pugh, Suzanne Kamel-Reid, Tracey L. Stockley. Determinants of quality of next-generation sequencing output from the strand-specific TruSight Tumor Sequencing Panel in a clinical diagnostic setting. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 628. doi:10.1158/1538-7445.AM2015-628
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".