Abstract SY02-02: Interaction of MMPs with oncogenic signaling: Disruption of dynamic reciprocity and tissue polarity
Bibliographic record
Abstract
Abstract Whether or not epithelial cells organize into three-dimensional structures defines their normal or invasive and malignant status. In a model of human breast morphogenesis, we have shown that inhibiting key signaling pathways in breast cancer cells leads to phenotypic reversion of the malignant cells. Using intact architecture or invasive behavior as an endpoint, we report that in all cases, signaling through Raf/MEK/ERK disrupts tissue polarity via MMP9 activity. Induction of Raf or activation of an engineered functionally inducible MMP9 in nonmalignant cells leads to loss of tissue polarity and reinitiates proliferation. Conversely, inhibition of Raf or MMP9 with small molecule inhibitors or shRNAs restores the ability of cancer cells to form polarized quiescent structures. Silencing MMP9 expression reduces tumor growth dramatically in a murine xenograft model. LC-MS/MS analysis comparing secreted proteins from nonmalignant cells – with or without active MMP9 – reveals laminin 111 (LN1) as a prominent target of MMP9. LN1 is shown to be necessary for acinar morphogenesis; thus its degradation by MMP9 provides the molecular mechanism by which tissue polarity is lost and growth and invasion reinitiated. These findings underscore the essential dynamic reciprocity between ECM integrity, tissue polarity, and suppression of Raf/MEK/ERK and MMP9 activities: the correct balance is essential to homeostasis, whereas imbalance leads to malignant progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr SY02-02. doi:10.1158/1538-7445.AM2011-SY02-02
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".