Agent‐based modelling reveals strategies to reduce the fitness and metastatic potential of circulating tumour cell clusters
Bibliographic record
Abstract
Metastasis-the ability of cancer cells to disperse throughout the body and establish new tumours at distant locations-is responsible for most cancer-related deaths. Although both single and clusters of circulating tumour cells (CTCs) have been isolated from cancer patients, CTC clusters are generally associated with higher metastatic potential and worse prognosis. From an evolutionary perspective, being part of a cluster can provide cells with several benefits both in terms of survival (e.g. protection) and reproduction (group dispersal). Thus, strategies aimed at inducing cluster dissociation could decrease the metastatic potential of CTCs. However, finding agents or conditions that induce the dissociation of CTC clusters is hampered by the fact that their detection, isolation and propagation remain challenging. Here, we used a mechanistic agent-based model to (a) investigate the response of CTC clusters of various sizes and densities to different challenges-in terms of cell survival and cluster stability, and (b) make predictions as to the combination of factors and parameter values that could decrease the fitness and metastatic potential of CTC clusters. Our model shows that the resilience and stability of CTC clusters are dependent on both their size and density. Also, CTC clusters of distinct sizes and densities respond differently to changes in resource availability, with high-density clusters being least affected. In terms of responses to microenvironmental threats (such as drugs), increasing their intensity is, generally, least effective on high-density clusters. Lastly, we found that combining various levels of resource availability and threat intensity can be more effective at decreasing the survival of CTC clusters than each factor alone. We suggest that the complex effects that cluster density and size showed on both the resilience and stability of the CTC clusters are likely to have significant consequences for their metastatic potential and responses to therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".