Pre-diagnosis plasma cell-free DNA methylome profiling up to seven years prior to clinical detection reveals early signatures of breast cancer
Bibliographic record
Abstract
Abstract Profiling of cell-free DNA (cfDNA) has been well demonstrated to be a potential non-invasive screening tool for early cancer detection. However, limited studies have investigated the detectability of cfDNA methylation markers that are predictive of cancers in asymptomatic individuals. We performed cfDNA methylation profiling using cell-free DNA methylation immunoprecipitation sequencing (cfMeDIP-Seq) in blood collected from individuals up to seven years before a breast cancer diagnosis in addition to matched cancer-free controls. We identified differentially methylated cfDNA signatures that discriminated cancer-free controls from pre-diagnosis breast cancer cases in a discovery cohort that is used to build a classification model. We show that predictive models built from pre-diagnosis cfDNA hypermethylated regions can accurately predict early breast cancers in an independent test set (AUC=0.930) and are generalizable to late-stage breast cancers cases at the time of diagnosis (AUC=0.912). Characterizing the top hypermethylated cfDNA regions revealed significant enrichment for hypermethylation in external bulk breast cancer tissues compared to peripheral blood leukocytes and breast normal tissues. Our findings demonstrate that cfDNA methylation markers predictive of breast cancers can be detected in blood among asymptomatic individuals up to six years prior to clinical 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 | 0.000 | 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".