Abstract C006: Fitness landscape modeling indicates decreased effectiveness of microtubule assembly targeting drugs in tumors demonstrating chromosomal instability (CIN)
Bibliographic record
Abstract
Abstract Introduction: Tumor cells often display aberrant chromosome counts, or aneuploidy, which can be exacerbated by increased chromosome missegregation rates in a condition called chromosomal instability (CIN). Despite the prevalence of CIN in solid tumors and its correlation to poor patient prognosis, understanding how CIN status affects cancer aggression remains a contested issue. In this work, we present a novel mathematical model linking missegregation levels and degree of aneuploidy to the underlying fitness landscape, and examine how these fitness landscapes can affect drug efficacy. Methods: To model how chromosome counts can shift over time, we use a partial differential equation that contains a diffusion term corresponding to chromosome missegregation and a growth term proportional to the relative fitness of a particular chromosomal distribution. Using this framework, a fitness landscape that connects chromosome count to relative fitness can be inferred from a steady-state cellular population. Fluorescence in situ hybridization (FISH) was used to track chromosome counts in both CIN and non-CIN cell lines over the course of 2 months, with data collected for chromosome 10 individually and for the group of chromosomes 1, 5, and 19. Experimental results were then analyzed using our mathematical framework to determine fitness landscapes for both CIN and non-CIN cells. Results: A partial differential equation was developed to model how chromosome counts change over time based on a diffusion-like term corresponding to lagging chromosome rate and a growth rate based on a chromosome count’s contribution to overall cellular fitness. The CIN lines exhibited chromosome count distributions that were statistically closer to normal distributions than non-CIN lines. When connected to our mathematical model, these distributions imply that the CIN lines have a flatter fitness landscape than their non-CIN counterparts. This finding reinforces the notion that CIN lines are tolerant of highly varying degrees of aneuploidy. Conclusion: Our work suggests an underlying mechanism for the clinical observation that CIN tumors are more resilient to treatment with taxanes and other microtubule or mitotic targeting drugs. The flatter fitness landscapes associated with CIN provide an explanation for why these drugs are less effective, since increasing or decreasing the number of chromosomes in a CIN line does not lead to a relatively large change in fitness. Citation Format: Kelly Brock, Marianna Kleyman, Madison Stoddard, T. Ryan Gregory, Andrew Chen, Arijit Chakravarty. Fitness landscape modeling indicates decreased effectiveness of microtubule assembly targeting drugs in tumors demonstrating chromosomal instability (CIN) [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C006.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".