Abstract 3681: A tumor-naive method for estimating tumor fraction using 5-hydroxymethylation cytosine in cell-free DNA
Bibliographic record
Abstract
Abstract Accurate measurement of the tumor fraction (TF) in circulating cell-free DNA (cfDNA) allows estimation of tumor burden non-invasively, potentially useful in guiding treatment decisions, monitoring treatment response, and detecting minimal residual disease. 5-hydroxymethylation of cytosine (5hmC) marks activation of cancer drivers and downstream gene targets, and has been shown to positively correlate with gene expression and gene regulation. Here we trained a machine learning (ML) model to predict TF in a tumor-naive way from 5hmC-enriched sequencing data together with low-pass whole genome sequencing data. In cross-validation the ML model showed excellent performance, showing high correlation (R > 0.8) and low composite absolute error (CAE < 5%) compared to copy-number variation (CNV) based estimates. The high correlation was confirmed in an independent test set which also showed R > 0.8, with only a modest increase in CAE (<10%) using targeted point mutation-based TF estimates as an external reference comparator. Using in silico cancer mixtures that created contrived cfDNA samples targeting concentrations below 10%, our 5hmC-based TF prediction method achieved detection sensitivity of approximately 90% with a specificity of 97%. Finally, we observed that 5hmC-based predicted TF was significantly associated with overall survival (HR = 1.04, p < 0.001) in an independent prostate patient cfDNA cohort when controlling for other covariates. In summary, we find that the 5hmC-based TF estimate is a reliable metric of tumor burden irrespective of tumor mutational status. The 5hmC-based TF estimate may be used either alone or in conjunction with mutation and CNV-based estimates to provide more accurate measures of survival risk and therapy response during treatment. Citation Format: Ceyda Coruh, Onur Sakarya, Yuhong Ning, Yuan Xue, Michael Cipriano, Kyle Hazen, Alexander W. Wyatt, David Quigley, Felix Feng, Martin Sjostrom, Gulfem Guler, Wayne Volkmuth, Samuel Levy. A tumor-naive method for estimating tumor fraction using 5-hydroxymethylation cytosine in cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3681.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".