Systematic Review of Genetic Variation in Chromosome 5p15.33 and Telomere Length as Predictive and Prognostic Biomarkers for Lung Cancer
Bibliographic record
Abstract
Lung cancer remains the leading cause of cancer mortality worldwide. Known histomolecular characteristics and genomic profiles provide limited insight into factors influencing patient outcomes. Telomere length (TL) is important for genomic integrity and has been a growing area of interest as agents targeting telomerase are being evaluated. Chromosome 5p15.33, an established cancer susceptibility locus, contains a telomerase-regulatory gene, TERT, and CLPTM1L, a gene associated with cisplatin-induced apoptosis. This review offers a summary of the clinical utility of 5p15.33 polymorphisms and TL. A total of 621 abstracts were screened, and 14 studies (7 for 5p15.33, 7 for TL) were reviewed. Endpoints included overall survival (OS), progression-free survival (PFS), therapy response, and toxicity. Of the 23 genetic variants identified, significant associations with OS and/or PFS were reported for rs401681 (CLPTM1L), rs4975616 (TERT-CLPTM1L), and rs2736109 (TERT). Both shorter and longer TL, in tumor and blood, was linked to OS and PFS. Overall, consistent evidence across multiple studies of 5p15.33 polymorphisms and TL was lacking. Despite the potential to become useful prognostic biomarkers in lung cancer, the limited number of reports and their methodologic limitations highlight the need for larger, carefully designed studies with clinically defined subpopulations and higher resolution genetic analyses. Cancer Epidemiol Biomarkers Prev; 25(12); 1537-49. ©2016 AACR.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".