Cell-free methylated DNA (cfMeDNA) immunoprecipitation and high throughput sequencing technology (cfMeDIP-seq) in patients with clear cell renal cell carcinoma (ccRCC).
Bibliographic record
Abstract
3052 Background: CfmeDNA is a promising biomarker for non-invasive assessment of solid tumors: i) MeDNA is tissue- and tumor-specific ii) cfDNA methylation changes are stable unlike DNA alterations iii) ‘methylation target size’ is larger than identifying specific genomic alterations and, therefore, more sensitive. CfMeDIP-seq is a sensitive assay for genome-wide bisulfite-free cfMeDNA profiling, that requires 1-10 ng input DNA. We tested the feasibility of cfMeDIP-seq to detect ccRCC across TNM stages. Methods: We evaluated plasma cfDNA collected prior to nephrectomy in 46 pts with ccRCC: 25 stage I, 7 stage II, 6 stage III, 8 stage IV. cfMeDIP-seq involves four steps: 1) cfDNA end-repair, A-tailing, and adapter ligation 2) cfMeDNA immunoprecipitation and enrichment using an Ab targeting 5-methylcytosine (quality control by qPCR to ensure <1% of unMeDNA and >99% reaction specificity) 3) adapter-mediated PCR to amplify cfMeDNA 4) high-throughput NGS for cfMeDNA data. A previously-derived model (Shen et al, Nature, 2018) was used to classify pts as having ccRCC or not based on cfMeDNA. cfMeDIP-seq paired end data was reduced to 300 bp windows of the genome that map to CpG islands, shores, shelves, and FANTOM5 enhancers; a classifier was then built using the top 1,000 most variable fragments between pts with ccRCC and cancer-free controls. Statistical comparisons were performed in the R statistical environment, with the caret package being used for classifier construction and evaluation. Results: The average amount of cfDNA isolated from 1 ml of ccRCC plasma was 19.8±39.8 ng/µL [1.95-260]. Greater than 99% specificity of reaction and <1% of unMeDNA was achieved in 46/46 samples (100%). The previously-derived classifier of ccRCC correctly predicted 46/46 pts (100%) as having ccRCC. Across 3 rounds of 5-fold cross-validation, the classifier performed with a Cohen’s Kappa of 0.93. Conclusions: CfMeDIP-seq is a non-invasive, cost-effective, and sensitive assay to detect cancer-specific cfmeDNA in ccRCC pts prior to nephrectomy. With further validation, cfmeDNA may detect minimal residual disease after nephrectomy for ‘precision’ adjuvant therapy.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".