Circulating cell-free methylated DNA (cfmeDNA) to predict postoperative recurrence in patients with muscle-invasive bladder cancer (MIBC).
Bibliographic record
Abstract
454 Background: CfmeDNA is a promising non-invasive biomarker to assess solid tumor burden: i) CpG island methylation changes in cfDNA are stable ii) methylation is tissue- and tumor- specific iii) methylation target size is larger and more sensitively detected than genomic alterations. CfmeDNA Immunoprecipitation and high throughput sequencing (cfMeDIP-seq) is an innovative assay for genome-wide bisulfite-free plasma DNA methylation profiling, that permits CpG enrichment. We tested the feasibility of cfmeDNA to predict recurrence of MIBC post- radical cystectomy (RC). Methods: We selected 12 pts who underwent RC for MIBC: 6 pts who had recurrent disease within 2-3yrs after RC (A) and 6 pts who did not (B). 119 healthy pts without BC were controls. cfDNA isolated from 1ml of plasma samples collected after RC and before recurrence (A) or during follow-up in those who did not recur (B) was analyzed by the cfMeDIP-seq using 10ng cfDNA. The data were analyzed using the MEDIPS program and differentially methylated regions (DMR) between the cohorts were studied. ENCODE ChIP-seq analytical pipeline was used for fastq file processing and peak calling. Results: The average cfDNA isolated from 1ml of plasma was 13.1 ng (6.4-19.7) in A and 17.1 ng (13.6-21.2) in B. The median time from RC to plasma collection were respectively 9.3 mos (3.4-91.3) vs 12.3 mos (2.9-150). Median time from plasma collection to recurrence was 21.9 mos (0.25-141.3). We identified ~137,000 peaks in ≥1 sample. The supervised classification identified 61 DMR (FDR<0.050), predominantly located in intergenic region, which distinguished A from B. Randomized sample tests proved the discriminatory power of the identified set. Supervised analysis comparing the status of the identified DMRs relative to healthy controls showed 28 regions were differentially methylated (logFC > +/- 1, FDR < 0.05). The study is limited by retrospective design and sample size. Conclusions: This is the first study to demonstrate that cfmeDNA can be readily harvested from MIBC pts to detect cancer-specific methylation patterns and predict recurrence post-RC. Prospective validation will enable the selection of suitable pts for adjuvant therapy.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".