Detection of urothelial carcinoma using plasma cell-free methylated DNA.
Bibliographic record
Abstract
5046 Background: Methylation profiling of circulating cell-free DNA (cfDNA) is a promising approach for non-invasive tumor detection due to the presence of tissue-specific epigenetic signatures that are detectable in cfDNA. Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMedDIP-seq) is a sensitive, low-input, cost-effective, bisulfite-free approach to profiling cfDNA methylomes, capable of detecting and classifying various tumor types. We tested the feasibility of cfMeDIP-seq to detect urothelial carcinoma (UC) in plasma samples. Methods: We performed cfMeDIP-seq on plasma samples from 43 patients (pts): 18 metastatic UC (UC) pts, 12 pre-cystectomy non-metastatic UC pts, and 13 cancer-free controls. Six (50%) of pre-cystectomy cases were non-muscle invasive UC. cfDNA was immunoprecipitated and enriched using an antibody targeting 5-methylcytosine and PCR-amplified to create a sequence-ready library. The top differentially methylated regions (DMRs) between UC and control samples were used to train a regularized binomial generalized linear model using 80% of the samples as a training set. The 20% of withheld test samples were then assigned a probability of being UC or control. This process was repeated 100 times. Results: The average amount (standard deviation) of cfDNA isolated from 1 ml of UC plasma samples was 29.2 (27.4) ng/µL and 8.02 (3.58) ng/µL in cancer-free controls. We identified 9,826 DMRs in plasma samples at an adjusted p-value of < 0.01, which partitioned UC and control samples. Iterative training and classification of held out samples using the top 300 DMRs resulted in a mean AUROC of 0.987. Conclusions: cfMeDIP-seq is an interesting new approach for non-invasive detection of UC. cfMeDIP-seq demonstrates high sensitivity to detect UC across all stages of UC, including non-muscle invasive disease.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".