Abstract 4416: Rapid, low-input, targeted NGS workflow for DNA methylation
Bibliographic record
Abstract
Abstract DNA methylation is a fundamental epigenetic process that regulates human gene expression. In cancer, methylation changes promote proliferation networks and metastasis. Development of biomarkers for methylation will be enabled by flexible, fast, low-input next generation sequencing workflows. We describe the first Ion AmpliSeq™ Methylation targeted panel and protocol on the turn-key Genexus™ Integrated Sequencer as part an ongoing collaboration with Ontario Institute for Cancer Research (OICR) to detect and predict response in early stage breast cancer and improve diagnostics for Black and Asian women. A 2-pool Ion AmpliSeq™ Methylation Panel for Breast Cancer Research was developed as a demonstration of design, workflow, and reporting for targeted, low-input methylation assessment in a multiplex setting across a variety of samples sources including FFPE. The panel contains 327 amplicons and was designed to target both strands and 10ng DNA input into bisulfite conversion was used for controls. A complete workflow begins at bisulfite conversion and progresses through the Genexus™ Integrated Sequencer which combines library construction, template preparation, and sequencing into a single run. The bioinformatics pipeline provides DNA methylation calls on both Watson and Crick strands at single base resolution and methylated:unmethylated ratios for each CpG. The entire end-to-end workflow was completed in 2 days, including a full analysis software solution. The panel was evaluated using 2 control gDNA samples. The first had an expected average methylation state across all CpGs of >98% and the second <5%. An equal mixture of these two samples was also tested. The Methylation Panel for Cancer Research performed well on control samples. The single lane Loading, Total Bases, Final Reads, and Raw Read Accuracy were >92%, >1G, >14 Million, and >90% respectively. The workflow was also demonstrated on research FFPE cancer samples. This Methylation Panel protocol offers a 2-day, end-to-end workflow with high resolution, targeted and quantitative methylation analysis from DNA input as low as 10ng into bisulfite conversion. The option to design custom methylation panels for interrogation of targets of interest without the need for whole genome methylation is now available. Citation Format: Loni Pickle, Andrew Hatch, Gokhan Yavas, Melanie Spears, Louis Gasparini, Vida Talebian, Anna Ying-Wah Lee, Mathieu Lariviere, Jane Bayani, Seth Sadis, Jeffrey M. Smith. Rapid, low-input, targeted NGS workflow for DNA methylation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4416.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.032 |
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".