Abstract 5297: Chromosomal instability as a prognostic marker in cervical cancer
Bibliographic record
Abstract
Abstract Previous studies have demonstrated that chromosomal instability (CIN) is a consistent feature of the majority of solid tumors. In this current study, we sought to examine the published CIN70 gene signature in a cohort of cervical cancer patients treated at the Princess Margaret (PM) Cancer Centre (n = 79) and an independent cohort of The Cancer Genome Atlas (TCGA) cervical cancer patients (n = 130). Patients with a high CIN70 score had a higher number of copy number alterations (Spearman's correlation coefficient (r) = 0.28, p<0.001), and a higher percentage of genome altered (r = 0.19, p = 0.016). According to Kaplan-Meier analysis, the CIN70 signature achieved borderline significance for para-aortic nodal or distant relapse, with a hazard ratio of 3.02 and Wald p-value of 0.05, but not significant for overall, disease-free survival, or local relapse. In summary, these findings demonstrate that chromosomal instability plays an important role in cervical cancer, and is significantly associated with patient outcome. For the first time, this CIN70 gene signature provided prognostic value for patients with cervical cancer. Citation Format: Christine How, Jeff Bruce, Jonathan So, Melania Pintilie, Benjamin Haibe-Kains, Angela Hui, Blaise Clarke, David Hedley, Richard Hill, Michael Milosevic, Anthony Fyles, Kenneth W. Yip, Fei-Fei Liu. Chromosomal instability as a prognostic marker in cervical cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5297. doi:10.1158/1538-7445.AM2015-5297
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 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".