Dynamics of three-dimensional telomere profiles of circulating tumor cells in patients with high-risk prostate cancer who are undergoing androgen deprivation and radiation therapies
Bibliographic record
Abstract
INTRODUCTION: Accurate assessment and monitoring of the therapeutic efficacy of locally advanced prostate cancer remains a major clinical challenge. Contrary to prostate biopsies, circulating tumor cells (CTCs) are a cellular source repeatedly obtainable by blood sampling and could serve as a surrogate marker for treatment efficacy. In this study, we used size-based filtration to isolate and enumerate CTCs from the blood of 20 patients with high-risk (any one of cT3, Gleason 8-10, or prostate-specific antigen>20ng/ml), nonmetastatic, and treatment-naive prostate cancer before and after androgen deprivation therapy (ADT) and radiation therapy (RT). MATERIALS AND METHODS: We performed 3D telomere-specific quantitative fluorescence in situ hybridization on isolated CTCs to determine 3D telomere profiles for each patient before and throughout the course of both ADT and RT. RESULTS: Based on the distinct 3D telomere signatures of CTC before treatment, patients were divided into 3 groups. ADT and RT resulted in distinct changes in 3D telomere signatures of CTCs, which were unique for each of the 3 patient groups. CONCLUSION: The ability of 3D telomere analysis of CTCs to identify disease heterogeneity among a clinically homogeneous group of patients, which reveals differences in therapeutic responses, provides a new opportunity for better treatment monitoring and management of patients with high-risk prostate cancer.
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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".