Sensitive detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes.
Bibliographic record
Abstract
728 Background: Improving early cancer detection has the potential to significantly reduce cancer-related mortality. Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMedDIP-seq) is a highly sensitive, low-input, cost-efficient and bisulfite-free assay capable of detecting and classifying various tumor types. We tested the feasibility of cfMeDIP-seq to detect RCC in plasma samples and, for the first time, in urine cell-free DNA (cfDNA), with an emphasis on early-stage disease. Methods: We performed cfMeDIP-seq on 117 samples (72 plasma and 45 urine samples): 68 stage I-IV RCC cases pre-nephrectomy, 21 stage IV urothelial bladder cancer (UBC) plasma samples from 15 patients, and 28 healthy cancer-free controls. 60.5% of plasma samples and 66.7% of urine samples came from patients with TNM Stage I/II disease. cfDNA was immunoprecipitated and enriched using an antibody targeting 5-methylcytosine and amplified to create a sequence-ready library. The top differentially methylated regions (DMRs) which partitioned RCC and control samples or UBC 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 RCC or control. This process was repeated 100 times. This was performed using both plasma and urine cfDNA samples. Results: We identified 89,799 DMRs in plasma samples and 38,462 DMRs in urine samples. Iterative training and classification of held out samples, using the 300 DMRs which partitioned RCC and control samples, resulted in a mean AUROC of 0.990 (95% CI 0.984-0.997) in plasma samples and 0.791 (95% CI 0.759-0.823) in urine samples. Classification performance between tumor types was evaluated comparing plasma cfDNA from patients with RCC and UBC, resulting in a mean AUROC of 0.954 (95% CI 0.940-0.969). Conclusions: cfMeDIP-seq is a powerful tool for genome-wide discovery of non-invasive DNA methylation biomarkers. This is the first independent validation of plasma cfMeDIP-seq, demonstrating near-perfect classification of RCC in a cohort enriched for patients with early-stage disease and the potential of urine cfDNA methylome-based biomarkers for cancer detection.
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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 | 0.001 | 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) | 0.001 | 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".