Adaptive Resistance to an Inhibitor of Chromosomal Instability in Human Cancer Cells
Bibliographic record
Abstract
Karyotype diversity is a hallmark of solid tumors that contributes to intratumor heterogeneity. This diversity is generated by persistent chromosome mis-segregation associated with chromosomal instability (CIN). CIN correlates with tumor relapse and is thought to promote drug resistance by creating a vast genomic landscape through which karyotypically unique clones survive lethal drug selection. We explore this proposition using a small molecule (UMK57) that suppresses chromosome mis-segregation in CIN cancer cells by potentiating the activity of the kinesin-13 protein MCAK. Sublethal doses of UMK57 destabilize kinetochore-microtubule (k-MT) attachments during mitosis to increase chromosome segregation fidelity. Surprisingly, chromosome mis-segregation rebounds in UMK57-treated cancer cells within a few days. This rapid relapse is driven by alterations in the Aurora B signaling pathway that hyper-stabilize k-MT attachments and is reversible following UMK57 removal. Thus, cancer cells display adaptive resistance to therapies targeting CIN through rapid and reversible changes to mitotic signaling networks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".