Long-Term Dynamics of Three Dimensional Telomere Profiles in Circulating Tumor Cells in High-Risk Prostate Cancer Patients Undergoing Androgen-Deprivation and Radiation Therapy
Bibliographic record
Abstract
Patient-specific assessment, disease monitoring, and the development of an accurate early surrogate of the therapeutic efficacy of locally advanced prostate cancer still remain a clinical challenge. Contrary to prostate biopsies, circulating tumor cell (CTC) collection from blood is a less-invasive method and has potential as a real-time liquid biopsy and as a surrogate marker for treatment efficacy. In this study, we used size-based filtration to isolate CTCs from the blood of 100 prostate cancer patients with high-risk localized disease. CTCs from five time points: +0, +2, +6, +12 and +24 months were analyzed. Consenting treatment-naïve patients with cT3, Gleason 8-10, or prostate-specific antigen > 20 ng/mL and non-metastatic prostate cancer were included. For all time points, we performed 3D telomere-specific quantitative fluorescence in situ hybridization on a minimum of thirty isolated CTCs. The patients were divided into five groups based on the changes of number of telomeres vs telomere lengths over time and into three clusters based on all telomere parameters found on diagnosis. Group 2 was classified as non-respondent to treatment and the Cluster 3 presented more aggressive phenotype. Additionally, we compared our telomere results with the PSA levels for each patient at 6 months of ADT, at 6 months of completed RT, and at 36 months post-initial therapy. CTCs of patients with PSA levels above or equal to 0.1 ng/mL presented significant increases of nuclear volume, number of telomeres, and telomere aggregates. The 3D telomere analysis of CTCs identified disease heterogeneity among a clinically homogeneous group of patients, which suggests differences in therapeutic responses. Our finding suggests a new opportunity for better treatment monitoring of patients with localized high-risk prostate cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".