Detection of tumor-related DNA methylation biomarkers in liquid biopsies from metastatic castration resistant prostate cancer patients to improve treatment decisions
Bibliographic record
Abstract
Background: Liquid biopsies are emerging as an important source of minimally invasive biomarkers, especially in metastatic castration resistant prostate cancer (mCRPC), where tumors are often inaccessible for biopsy based strategies. In particular, circulating cell free nucleic acids, such as cfDNA, can harbor tumor specific genomic and epigenomic changes. Tumor related DNA methylation markers are detectable in circulation of mCRPC patients; however, genome wide changes in the cfDNA methylome of mCPRC patients undergoing current androgen targeting therapies have not been extensively investigated. Methods/Results: In collaboration with the University Health Network Genitourinary Biobank (Toronto, Canada), we prospectively collected a cohort of mCRPC patients that received treatment with either enzalutamide or abiraterone acetate. Plasma cfDNA was isolated at baseline (prior to starting treatment), week-12 and clinical progression. As cfDNA methylation detection can be challenging due to low yield and quality, we optimized a protocol that involves methylated DNA immuno precipitation (MeDIP) followed by next generation sequencing (NGS). Overall, we are able to obtain good quality NGS data with high mappability to the genome as well as >5x coverage of 46-51% CpGs in the genome. We applied this MeDIP-seq protocol to cfDNA samples from 11 enzalutamide treated and 5 abiraterone treated patients that completed all study visits. We performed within patient analysis to identify differentially methylated regions (DMRs) associated with treatment and clinical progression. Overall, there were a number of DMRs identified through our established pipeline, with known mCRPC genes implicated, such as members of the HOX family of transcription factors and Wnt pathway members. Conclusions: Overall, we are able to detect methylation signals from low yields of cfDNA and potentially tumor specific methylation markers. We are currently performing pathway analysis and correlation with clinical parameters. Validation of these methylation markers in mCRPC could further shed light on underlying disease mechanisms and novel biomarkers. Biography Madonna R Peter is currently a PhD student at the University of Toronto, Department of Laboratory Medicine & Pathobiology and under the Supervision of Dr. Bharati Bapat (Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada). Previously, she completed her MSc in the Department of Immunology (University of Toronto)
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".