Abstract C073: Heritable resistance and phenotypic stochasticity are likely drivers of irreproducibility in experimental determination of <i>in vitro</i> potency of anticancer agents
Bibliographic record
Abstract
Abstract Background: The estimation of the in vitro potency of anticancer agents against tumor cell lines in tissue culture is known to be fraught with a high degree of variability. While quality control of experimental conditions has been demonstrated by others to reduce this variability, other sources of variability remain even after standardization of experimental conditions. In this poster, we assess two potential mechanisms for this residual variability: phenotypic stochasticity in response and heritable resistance to treatment. Methods: We developed three agent-based mathematical models representing different potential scenarios for cellular responses to anticancer therapy: (1) all cells have an equal probability of survival under treatment (phenotypic stochasticity) and future division; (2) sensitive cells deterministically die under treatment while resistant cells survive and may divide to produce resistant offspring (heritable resistance); (3) sensitive subclones have a greater probability of death under treatment compared to resistant subclones (both mechanisms). We implemented these models in MATLAB, running simulated in vitro experiments consisting of large (106) or small (500 or 5000) populations of virtual cells under treatment. The final size of these virtual cell populations serves as the readout of the virtual experiments. To assess the effect of phenotypic stochasticity and heritable resistance on variation in experimental outcomes, we simulated these experiments 10,000 times and report the coefficient of variation (CV). Results: Variability in response to treatment was low under most simulated conditions. In the first model simulating phenotypic stochasticity alone, high coefficients of variation in assay outcome were observed only when the probability of survival under treatment was less than 10%. In simulations of deterministic heritable survival under treatment, the coefficient of variation was generally low. However, simulations incorporating both mechanisms exhibited substantial variability in experimental outcome, consistent with the observed phenomenon. Conclusions: Stochastic cellular response to treatment or heritable binary resistance alone are insufficient explanations for the variation in in vitro experimental results. On the other hand, the high degree of variation in experimental outcomes is consistent with a combination of heritable resistance and phenotypic stochasticity. Our findings are consistent with a role for Darwinian evolution in the response to anticancer treatment for cancer cells in tissue culture. Citation Format: Alexander L. Young, Liam Noronha, Madison Stoddard, T Ryan. Gregory, Arijit Chakravarty. Heritable resistance and phenotypic stochasticity are likely drivers of irreproducibility in experimental determination of in vitro potency of anticancer agents [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 C073.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".