Abstract B19: Three-dimensional nuclear telomere organization and clinical significance in non-small cell lung cancer patients.
Bibliographic record
Abstract
Abstract Non-small cell lung cancer (NSCLC) is the leading cause of cancer related death worldwide. Survival of NSCLC patients remains unfavorable prompting a better understanding of its molecular biology and causality, along with advances in diagnostic methods and multidisciplinary therapeutic approaches. Aberrant three-dimensional (3D) nuclear telomere organization can identify patient subgroups in various cancers and its correlation with tumor and patient characteristics may play a major role as a predictive and prognostic factor in NSCLC of different subgroups. This study aimed to explore the clinical application of 3D nuclear telomeric organization in NSCLC patients of different EGFR mutational status and smoking backgrounds. Paraffin-embedded NSCLC tissue specimens from non-smoking (N=6) and smoking (N=4) patients who underwent surgical resections at the British Columbia Cancer Research Centre of Canada were examined using the 3D fluorescent in situ hybridization (FISH). 3D nuclear-telomeric architecture analyses were performed using the TeloView program (Vermolen et al., 2005). The 3D nuclear telomere organization of 100 interphase nuclei per specimen was quantified by telomere numbers, telomere signal intensities, and frequencies of telomeric aggregates. Comparisons of 3D nuclear telomere organization between patient subgroups are shown below. In the smoking group, more telomeres, higher signal intensities, and greater numbers of aggregates were observed, compared to the non-smokers. The same pattern of 3D nuclear telomere organization was observed in the EGFR-negative/unknown group, compared to the EGFR-positive patients. This study provides a better understanding of 3D telomeric organization in NSCLC patients with different smoking backgrounds and EGFR mutational status, leading to a rationale of further exploration of this molecular profile in this cancer. 3D Telomeric parameters Average number of telomere per cell : Non-smokers 77.71, Smokers 80.37 Average number of aggregates per cell : Non-smokers 21.36, Smokers 27.59 Percentage of cells with aggregates : Non-smokers 2.16, Smokers 3.03 Average number of telomere per cell : Pos. EGFR 73.69, Neg./Unknown EGFR 80.95 Average number of aggregates per cell : Pos. EGFR 19.97, Neg./Unknown EGFR 25.52 Percentage of cells with aggregates : Pos. EGFR 1.96, Neg./Unknown EGFR 2.75 Citation Format: Patrapim Sunpaweravong, Chirawadee Sathitruangsak, Kelsie Thu, Wan L. Lam, Sabine Mai. Three-dimensional nuclear telomere organization and clinical significance in non-small cell lung cancer patients. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr B19.
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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) | 0.004 | 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".