Plasma Cell–Free DNA Chromatin Immunoprecipitation Profiling Depicts Phenotypic and Clinical Heterogeneity in Advanced Prostate Cancer
Bibliographic record
Abstract
Cell phenotype underlies prostate cancer presentation and treatment resistance and can be regulated by epigenomic features. However, the osteotropic tendency of prostate cancer limits access to metastatic tissue, meaning most prior insights into prostate cancer chromatin biology are from preclinical models that do not fully represent disease complexity. Noninvasive chromatin immunoprecipitation of histones in plasma cell-free DNA (cfDNA) in humans may enable the capture of disparate prostate cancer phenotypes. In this study, we analyzed activating promoter- and enhancer-associated H3K4me2 from cfDNA in metastatic prostate cancer enriched for divergent patterns of metastasis and diverse clinical presentation. H3K4me2 density across prostate cancer genes, accessible chromatin, and lineage-defining transcription factor-binding sites correlated strongly with ctDNA fraction-demonstrating capture of prostate cancer-specific biology and informing the development of a statistical framework to adjust for ctDNA fraction. Chromatin hallmarks mirrored synchronously measured clinicogenomic features: bone- versus liver-predominant disease, serum PSA, biopsy-confirmed histopathologic subtype, and RB1 deletions convergently indicated phenotype segregation along an axis of differential androgen receptor activity and neuroendocrine identity. Detection of lineage switching after sequential progression on systemic therapy in select patients indicates potential use for individualized resistance monitoring. Epigenomic footprints of metastasis-induced normal tissue destruction were evident in bulk cfDNA from two patients. Finally, a public epigenomic resource was generated using a distinct chromatin marker that has not been widely investigated in prostate cancer. These results provide insights into the adaptive molecular landscape of aggressive prostate cancer and endorse plasma cfDNA chromatin profiling as a biomarker source and biological discovery tool. Significance: Plasma cell-free chromatin immunoprecipitation sequencing enables phenotypic dissection of lethal prostate cancer and is a practical tool for biomarker discovery while overcoming prior limitations of access to relevant tissue and reliance on model systems.
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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 | 0.000 |
| 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".