Evolving Therapeutic Strategies to Exploit Chromosome Instability in Cancer
Bibliographic record
Abstract
Cancer is a devastating disease that claims over 8 million lives each year. Understanding the molecular etiology of the disease is critical to identify and develop new therapeutic strategies and targets. Chromosome instability (CIN) is an abnormal phenotype, characterized by progressive numerical and/or structural chromosomal changes, which is observed in virtually all cancer types. CIN generates intratumoral heterogeneity, drives cancer development, and promotes metastatic progression, and thus, it is associated with highly aggressive, drug-resistant tumors and poor patient prognosis. As CIN is observed in both primary and metastatic lesions, innovative strategies that exploit CIN may offer therapeutic benefits and better outcomes for cancer patients. Unfortunately, exploiting CIN remains a significant challenge, as the aberrant mechanisms driving CIN and their causative roles in cancer have yet to be fully elucidated. The development and utilization of CIN-exploiting therapies is further complicated by the associated risks for off-target effects and secondary cancers. Accordingly, this review will assess the strengths and limitations of current CIN-exploiting therapies, and discuss emerging strategies designed to overcome these challenges to improve outcomes and survival for patients diagnosed with cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".