Abstract 2734: Integrating hybrid spatiotemporal models and multiscale data for the study of cancer progression in 3D cultures
Bibliographic record
Abstract
Abstract Tumour progression consists of various stages including migration to the surrounding tissues, leading to metastasis [1]. In this work, we investigate the multiscale quantitative characteristics of the spatial organization and migration of cancer in 3D cultures. For this study, we combined 3D cell culture experiments of Triple Negative Breast Cancer (TNBC) cells, hybrid spatiotemporal models, and longitudinal RNA-sequencing data analysis. The experiments included two experimental conditions; simple growth experiments, and experiments of growth in presence of the migrastatic drug Paclitaxel. The spatial distributions of the cells enabled us to calibrate and validate a hybrid Keller-Segel model, incorporated into a novel computational framework capable of interpreting the relation between morphological patterns and the underlying mechanisms of cancer growth [2]. The RNA-seq data included different time-points with and without treatment. The results suggested that cancer cells exhibited biased movement towards the bottom of the space, a movement that was inhibited in the presence of Paclitaxel. The calibrated model was able to describe the overall characteristics of the experimental observations, and suggested that cancer cells exhibited chemotactic migration and cell accumulation, as well as random motion throughout the period of development. The spatial pattern analysis revealed transient, non-random spatial distributions of cancer cells that consisted of clustered patterns across a wide range of neighbourhood distances, as well as dispersion for larger distances. The RNA-seq data exhibited coherence with the chemotactic migration hypothesis of the mathematical model, indicating significant under-representation of the Gene Ontology (GO) terms related to chemotactic migration in presence of the migrastatic drug. Overall, this study provided an insightful quantitative characterization of the spatiotemporal organization and progression of TNBC cells in 3D cultures. We anticipate that these developments will enable us to expand our studies to more realistic conditions, including the introduction of heterogeneic cell populations. References [1] Friedl, P., Locker, J., Sahai, E., & Segall, J. E. (2012). Classifying collective cancer cell invasion.Nature Cell Biology, 14(8), 777-783. [2] Dimitriou, N. M., Flores-Torres, S., Kinsella, J. M., & Mitsis, G. D. (2021). Validating hybrid spatiotemporal models of tumour growth with 3D cell culture data. 3rd International Symposium on Mathematical and Computational Oncology. Citation Format: Nikolaos Dimitriou, Salvador Flores-Torres, Maria Kyriakidou, Joseph Matthew Kinsella, Georgios Mitsis. Integrating hybrid spatiotemporal models and multiscale data for the study of cancer progression in 3D cultures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2734.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".