Abstract A055: Treatment and tumor heterogeneity – Effects of competition, variable response to treatment, and finite resources
Bibliographic record
Abstract
Abstract Background: Advanced sequencing techniques have shed light on the complex genotypic and phenotypic landscapes of tumors. In particular, it is known that in many tumors there exist multiple subclone populations. The presence of these subclone populations can greatly alter the efficacy of chemotherapeutic agents and lead to the return of therapy-resistant tumors if any one of the populations is resistant or immune to the treatment. If the resistant subclone population is small compared to the sensitive subclone populations, the tumor may initially shrink when exposed to treatment. However, the sensitive cells are being eliminated and the tumor in time will continue to grow while exhibiting greater resistance to treatment. As such, it is important to understand, if possible, how the heterogeneous make-up of subclonal populations within a tumor changes over time in the presence of therapy. Methods: To investigate the factors affecting tumor heterogeneity under treatment, we constructed a multi-type branching model incorporating effects of competition, fitness, finite carrying capacity, and treatment on a population of subclones. We studied this model system analytically and numerically for randomized subclone fitnesses for a range of competition and treatment effects. We also compared measures of population heterogeneity using the Simpson index and Shannon index with measurements of fitness variability. Results: In the weak competition case, population heterogeneity, regardless of metric, is sensitive to small perturbations in the fitness variability of the subclone populations. This sensitivity decreases as competition strength is increased, but for all competition values studied, there was no clear connection between fitness variability and population heterogeneity. Additionally, individual simulations show cases where population heterogeneity can be increased by treatment and other cases where population heterogeneity can be decreased by treatment. Conclusions: Given the sensitivity to fitness variability, changes in population heterogeneity of the genetic composition of tumors are not related to the outcome of treatment in a straightforward way. As an additional avenue of research, it may be worth examining whether combining tumor subclonal dynamics with genetic status facilitates inference about tumor response to treatment from changes in heterogeneity. Citation Format: Alexander L. Young, Timothy Ebinger, Madison Stoddard, T Ryan. Gregory, Arijit Chakravarty. Treatment and tumor heterogeneity – Effects of competition, variable response to treatment, and finite resources [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 A055.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".