Abstract 3053: Stability and stemness of the hybrid epithelial-mesenchymal phenotype
Bibliographic record
Abstract
Abstract Transitions between epithelial and mesenchymal phenotypes – EMT and MET – are hallmarks of cellular plasticity during embryonic development and cancer metastasis. During these transitions, cells can also adopt a hybrid epithelial/mesenchymal (hybrid E/M) phenotype enabling them to migrate collectively as observed during gastrulation, wound healing, and clusters of Circulating Tumor Cells (CTCs). The hybrid E/M phenotype has largely been tacitly assumed to be 'metastable', i.e. transient state. Here, we integrate mathematical modeling with in vitro experiments to identify certain 'phenotypic stability factors' (PSFs) - GRHL2, OVOL2 and ΔNP63α that can stabilize a hybrid E/M phenotype. We show that H1975 (NSCLC cell line) cells can display a hybrid E/M phenotype stably and migrate collectively, a behavior that is impaired by knockdown of GRHL2 or OVOL2. Further, our computational model predicts that these PSFs can also associate hybrid E/M phenotype with high tumor-initiating potential, a prediction strengthened by the observation that the higher levels of one or more of these PSFs may predict poor patient outcome. Overall, our results suggest that a hybrid E/M phenotype need not be 'metastable', and bolster the notion that a hybrid E/M phenotype, but not necessarily full EMT, associates with aggressive tumor progression. Citation Format: Mohit Kumar Jolly, Dongya Jia, Satyendra C. Tripathi, Steve Mooney, Muge Celiktas, Samir M. Hanash, Sendurai A. Mani, Kenneth J. Pienta, Eshel Ben-Jacob, Herbert Levine. Stability and stemness of the hybrid epithelial-mesenchymal phenotype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3053. doi:10.1158/1538-7445.AM2017-3053
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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 | 0.000 |
| 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) | 0.005 | 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".