Targeting Kinesins for Therapeutic Exploitation of Chromosomal Instability in Lung Cancer
Bibliographic record
Abstract
New therapeutic approaches that antagonize tumour-promoting phenotypes in lung cancer are needed to improve patient outcomes. Chromosomal instability (CIN) is a hallmark of lung cancer characterized by the ongoing acquisition of genetic alterations that include the gain and loss of whole chromosomes or segments of chromosomes as well as chromosomal rearrangements during cell division. Although it provides genetic diversity that fuels tumour evolution and enables the acquisition of aggressive phenotypes like immune evasion, metastasis, and drug resistance, too much CIN can be lethal because it creates genetic imbalances that disrupt essential genes and induce severe proteotoxic and metabolic stress. As such, sustaining advantageous levels of CIN that are compatible with survival is a fine balance in cancer cells, and potentiating CIN to levels that exceed a tolerable threshold is a promising treatment strategy for inherently unstable tumours like lung cancer. Kinesins are a superfamily of motor proteins with many members having functions in mitosis that are critical for the correct segregation of chromosomes and, consequently, maintaining genomic integrity. Accordingly, inhibition of such kinesins has been shown to exacerbate CIN. Therefore, inhibiting mitotic kinesins represents a promising strategy for amplifying CIN to lethal levels in vulnerable cancer cells. In this review, we describe the concept of CIN as a therapeutic vulnerability and comprehensively summarize studies reporting the clinical and functional relevance of kinesins in lung cancer, with the goal of outlining how kinesin inhibition, or "targeting kinesins", holds great potential as an effective strategy for treating lung 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".