Abstract 425: Linked Target Capture: Rapid and high-performance NGS target enrichment for clinical sequencing applications
Bibliographic record
Abstract
Abstract With the continued decrease of DNA sequencing costs, targeted next-generation sequencing (NGS) is seeing broad adoption in many research, commercial and clinical settings. However, current target capture methods typically have long, multiday workflows that can limit their clinical utility. In addition, small panels for applications such as liquid biopsy typically have high off-target rates or low uniformity, resulting in an increased cost of sequencing required to reach the desired coverage for variant detection. We have developed a novel library preparation and enrichment method named “Linked Target Capture” that replaces typical multiday "PCR-capture-PCR" workflows with a single, linked "PCR/capture" step. This approach, which uses physically linked capture probes and universal PCR primers, enables users to work from extracted DNA to loaded sequencer in less than a single day with minimal hands-on time. The linked PCR and capture step is cycled multiple times to reduce off-target DNA and increase uniformity, ultimately reducing the cost of sequencing. Additionally, the universal primers and captures probes can be combined to enable detection of DNA fusions with unknown partners. Using this method, we evaluated the ability to detect multiple mutation types from both tissue and cell free DNA, as a function of sequencing usage. Unique Molecular Identifiers (UMIs) were integrated and enabled the detection of variants below 0.1%, represented on both senses of the starting DNA (duplex sequencing). Fusion detection was also demonstrated without a priori knowledge of the fusion partner. Using a 30-gene pan-cancer panel, on-target fraction was measured to be above 95%, and the fraction of the panel covered at greater than 25% of the mean depth was greater than 99%. In summary, we have developed a method that enables rapid and cost-effective detection of clinically relevant cancer variants, such as SNVs and gene fusions. This method holds promise for many NGS areas, including clinical applications requiring rapid and simple workflows, or groups new to NGS where simple workflows and repeatable performance help remove barriers to technology adoption. Citation Format: Joel Pel, Wendy Choi, Milenko Despotovic, Laura Gelinas, Amy Leung, Lloyd Ung, Andre Marziali. Linked Target Capture: Rapid and high-performance NGS target enrichment for clinical sequencing applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 425.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".