A systems view of epithelial–mesenchymal transition signaling states
Bibliographic record
Abstract
Epithelial-mesenchymal transition (EMT) is an important contributor to the invasion and metastasis of epithelial-derived cancers. While considerable effort has focused in the regulators involved in the transition process, we have focused on consequences of EMT to prosurvival signaling. Changes in distinct metastable and 'epigentically-fixed' EMT states were measured by correlation of protein, phosphoprotein, phosphopeptide and RNA transcript abundance. The assembly of 1167 modulated components into functional systems or machines simplified biological understanding and increased prediction confidence highlighting four functional groups: cell adhesion and migration, metabolism, transcription nodes and proliferation/survival networks. A coordinate metabolic reduction in a cluster of 17 free-radical stress pathway components was observed and correlated with reduced glycolytic and increased oxidative phosphorylation enzyme capacity, consistent with reduced cell cycling and reduced need for macromolecular biosynthesis in the mesenchymal state. An attenuation of EGFR autophosphorylation and a switch from autocrine to paracrine-competent EGFR signaling was implicated in the enablement of tumor cell chemotaxis. A similar attenuation of IGF1R, MET and RON signaling with EMT was observed. In contrast, EMT increased prosurvival autocrine IL11/IL6-JAK2-STAT signaling, autocrine fibronectin-integrin α5β1 activation, autocrine Axl/Tyro3/PDGFR/FGFR RTK signaling and autocrine TGFβR signaling. A relatively uniform loss of polarity and cell-cell junction linkages to actin cytoskeleton and intermediate filaments was measured at a systems level. A more heterogeneous gain of ECM remodeling and associated with invasion and migration was observed. Correlation to stem cell, EMT, invasion and metastasis datasets revealed the greatest similarity with normal and cancerous breast stem cell populations, CD49f(hi)/EpCAM(-/lo) and CD44(hi)/CD24(lo), respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".