P056: Three-dimensional nuclear telomere remodeling defines mechanisms of recurrence in glioblastomas
Bibliographic record
Abstract
samples to the sequencing read counts for either "buccal" or "granulocyte" (normal tissue) samples.Survival outcomes were assessed using a Kaplan-Meier web tool at cBioPortal for Cancer Genomics.The amplified group consists of gene copy numbers "tumor count" / "buccal count" or "granulocyte count" (normal tissue) above two.Ratios two and below were deemed the non-amplified group.Additionally, the correlation of FASLG CNV with RNA expression levels was analyzed.Results: The survival analysis of FASLG shows the amplified group samples to have decreased survival probabilities compared to those in the non-amplified group (p = 0.07).TNF, TRAIL, MYC, and BCL6 survival analysis did not approach a significant difference between amplified and non-amplified groups, p = 0.4, p = 0.4, p = 0.5, p = 0.4 respectively.RNA analysis showed a higher average RNA Seq value in the FASLG CNV amplified group compared to the nonamplified group.Conclusion: Utilizing copy number variation to explore the heterogeneity of tumor genomics has the potential to discover genes that impact prognosis of cancer patients.The idea of cancers upregulating death receptors such as FASLG, TRAIL, and TNF to potentially induce apoptosis of infiltrating lymphocytes is not well researched.Future studies include expanding FASLG CNV analysis to other cancers where FASLG is upregulated.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".