Early Cancer Detection in Hereditary Breast and Ovarian Cancer Syndrome with Cell-Free DNA
Bibliographic record
Abstract
Abstract Early cancer detection for individuals with Hereditary Breast and Ovarian Cancer syndrome (HBOC) remains limited by the low sensitivity of available tests and lack of clinical surveillance methods for many cancers beyond breast cancer. To investigate cell-free DNA (cfDNA) sequencing as a pan-cancer surveillance modality, we analyzed 194 blood plasma samples from 88 BRCA1 and/or BRCA2 pathogenic variant carriers ( BRCA1/2-carriers ) using a multimodal assay integrating genomic and epigenomic (fragmentomic & DNA methylation) features. Cancer-associated signals were detected in 71% (43/61) of carriers with active cancers detected by conventional surveillance, as well as 30/54 patients (56%) with negative surveillance findings. Of the negative patients 43% (13/30) subsequently developed cancer within 2 years (12 non-breast cancers), suggesting early detection of occult cancers. These findings demonstrate the value of integrating multiple cfDNA analyses and support the potential of longitudinal, multimodal liquid biopsy analysis to improve early detection and risk stratification in BRCA1/2-carrier s. Significance Statement Improved clinical surveillance methods are urgently needed for BRCA1/2 germline carriers. We show that integrating cell-free DNA genomic and epigenomic (fragmentomic and DNA methylation) based assays identifies cancer-associated signals not captured by standard methods, supporting its use as a complementary non-invasive strategy for longitudinal monitoring in high-risk individuals.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".