Abstract 1611: Epithelial-mesenchymal-epithelial transition induced by long term exposure to TGFB1 creates cellular heterogeneity
Bibliographic record
Abstract
Abstract Reversible and dynamic transitions between epithelial and mesenchymal cellular states (EMT/MET) contribute to cancer progression and dissemination. Whereas EMT facilitates initial steps of tumour cell detachment, MET is likely required for colonization at distant sites. Although MET is generally perceived as mirroring EMT, we hypothesize that MET entails its own set of novel and/or differentially active molecular circuitries, generating cells with features distinct from the original epithelial state. Using an in vitro TGFβ1-induced EMT/MET model, we demonstrated that MET generates co-existing heterogeneous cell populations (Reverted-Epithelial or RE-cells) with novel phenotypic and functional properties, such as increased self-renewal, in vivo increased tumourigenicity and distinct chemoresistance properties. Overall, our results indicate that MET is a permissive process, driving cellular plasticity towards heterogeneity and with it, creating novel biological signatures of relevance for cancer growth. Citation Format: Patricia Oliveira, Joana Carvalho, Sara Rocha, Mafalda Azevedo, Andre F. Vieira, Daniel Ferreira, Nuno Mendes, Ines Reis, Joao Vinagre, Alireza Heravi-Moussavi, Joana B. Nunes, Jorge Lima, Valdemar Máximo, Angela Burleigh, Calvin Roskelley, Joana Paredes, Fatima Carneiro, David Huntsman, Carla Oliveira. Epithelial-mesenchymal-epithelial transition induced by long term exposure to TGFB1 creates cellular heterogeneity. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1611.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".