Abstract 4308: 3D nuclear telomeric signatures define circulating tumor cells (CTCs) and characterize CTC subpopulations in intermediate risk prostate cancer patients
Bibliographic record
Abstract
Abstract Intermediate risk prostate cancer is a current medical challenge since the patients in this group can either be stable or progress, and there is no predictor for individual risk. Stratifying these patients would spare the patients unnecessary treatment or indicate the start of treatment immediately without losing precious time. Currently, prostate specific antigen (PSA) is used as evidence for biochemical progression. A more sensitive biomarker is needed to detect disease progression earlier and more reliably. We have isolated circulating tumor cells (CTCs) using a filtration-based device (ScreenCell). We subsequently performed the genetic characterization of the captured CTCs on a single cell basis. Using three-dimensional (3D) nuclear telomere imaging and quantitative analysis of 3D telomeric signatures of CTCs, we have characterized CTCs in the blood of the patients and have identified patient subgroups with one or more groups of CTCs. 380 samples of intermediate risk prostate cancer all revealed the presence of CTCs. However, the CTCs found were not genetically identical. Marked heterogeneity was seen, and three main groups of CTC profiles were defined based on TeloView software our group developed earlier. Repeat samples taken 6 months intervals define stable, mildly changing and significantly altered 3D profiles indicative of disease stability vs. progression. Citation Format: Awe A. Julius, Adam Yan, Nidhi Shah, Klewes Ludger, Alexandra Kuzyk, Michael Xu, Ramy Boles, Jeff Saranchuk, Darrel Drachenberg, Sabine Mai. 3D nuclear telomeric signatures define circulating tumor cells (CTCs) and characterize CTC subpopulations in intermediate risk prostate cancer patients. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4308. doi:10.1158/1538-7445.AM2014-4308
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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 | 0.001 | 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) | 0.006 | 0.001 |
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".