Molecular Characterization of Circulating Tumor Cells Isolated from the Peripheral Blood of Prostate Cancer Patients
Bibliographic record
Abstract
Due to the high prevalence and morbidity associated with prostate cancer, and the absence of a reliable and effective screening tool, we have sought to evaluate the potential role of telomere profiles and circulating tumor cells (CTCs) in filling this void. CTCs can be isolated from the blood of patients and used to profile the molecular characteristics of the primary tumor they derive from. Utilizing a quantitative fluorescence in-situ hybridization (Q-FISH) protocol and our Teloview(TM) program, we can analyze the telomeres contained within these cells. It has been proven that increasing telomere dysfunction can be correlated with increasing aggressiveness in various cancers. In this study we were able to successfully isolate CTCs from all prostate cancer patients enrolled in our study irrespective of disease stage. We were also able to produce a unique telomere profile for each patient. When repeat analysis of telomere profiles was done, we demonstrated that some patients had stable profiles, some had minor changes, and some had substantial changes. We have linked these changes to the clinical interventions used to manage the patient’s disease. Using a statistical analysis system we categorized individual telomeres as low, medium or high intensity, and measured the peak telomere number (PTN) for each patient. Combining this information has allowed us to create a model to stratify patients based on their risk of disease progression. In the future this system may replace conventional screening, and prognostication methods, and aid in the development of a more personalized approach to treating 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".