Abstract 4923: Myofibroblasts induce specific epithelial-to-mesenchymal transition (EMT) patterns in colorectal cancer cells by enhanced secretion of bone morphogenic protein (BMP) inhibitors
Bibliographic record
Abstract
Abstract Epithelial-to-mesenchymal transition (EMT) is dictated by both cell-autonomous and stromal oncogenic signals. We hypothesized that colon cancer desmoplasia provides multiple signals, inducing EMT in cancer cells. To address this issue, we first developed cell-contact cocultures of SW480 and SW620 cell lines with normal fibroblasts. Cocultured fibroblasts (myofibroblasts) acquired the myofibroblastic phenotype with increased alpha-SMA and MMP2 expression, which was reversed by TGF-beta disruption. We identified oncogenic signals from myofibroblasts, after subjecting mono- and cocultures to secretome analysis with mass spectrometry (LC-MS/MS). In a total of 1551 identified proteins (>2 peptides), a) secretome algorithms based on prediction of secretion, were deployed, allowing the removal of intracellular contaminants from the generated coculture lists and b) monoculture datasets were used as exclusion datasets to denote the non-specific-to-desmoplasia proteins. Subsequent bioinformatic analysis in this “desmoplastic” dataset (124 proteins) was used for protein clustering and recovery of a potential EMT cluster. We deployed: a) Ingenuity pathway analysis, b) Gene ontology, c) Smooth muscle enrichment analysis with Genevestigator d) Protein-protein interaction network analysis with STRING. Interestingly, this integration revealed GREM1, FST and FSTL3, three Bone Morphogenic Protein (BMP) inhibitors, as potent MET disruptors in colorectal cancer. We thus hypothesized that cancer cells recruit stromal fibroblasts to disrupt unfavorable BMP signaling (through generation of multiple BMP inhibitors), which supports a cancer cell undifferentiated state. To test this, a colon cancer cell line (HT29; active BMP signaling), was treated with human BMP7 and decreased cell migration (p<0.05) and proliferation (p<0.05) were shown. These lysates were further used for proteomic analysis (LC-MS/MS) with label-free quantitation to investigate differential protein expression. Using gene expression meta-analysis from fetal tissues, a significant (p<0.01) shift from mesenchymal to epithelial protein dominance was noticed, after 200 ng/mL BMP7 treatment. Differentially expressed proteins remain to be validated, to better characterize the “BMP-7-disrpuption” EMT signatures in colon cancer. In conclusion, integrative “systems biology” approaches may support the identification and validation of EMT signatures with important causative links to cancer. 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 4923. doi:10.1158/1538-7445.AM2011-4923
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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) | 0.002 | 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".