Abstract 1162: Fibroblast RHAMM promotes breast cancer aggression by promoting expression of a subset of ERK1,2 target genes
Bibliographic record
Abstract
Abstract The microenvironment plays a key role in cancer progression and metabolism of the extracellular matrix component hyaluronan (HA), a glycosaminoglycan, is associated with breast tumor progression. High molecular weight HA is degraded by hyaluronidases and free radicals to fragments of heterogeneous size, which accumulate in the peritumor stroma and activate signaling pathways in both tumor and stromal cells. The HA receptor RHAMM is a multifunctional protein that is found on the cell surface, inside the nucleus and the mitotic spindles of mesenchymal cells. RHAMM expression and HA accumulation in the tumor stroma are linked to breast tumor progression, predicting that RHAMM/HA regulated signaling is key to breast cancer progression. An N-terminally truncated RHAMM isoform, RHAMMonc, is oncogenic when overexpressed in mesenchymal cells. Intracellular RHAMMonc directly binds to ERK1, and complexes with ERK1,2 and MEK1. Intracellular RHAMM is required for sustained ERK1,2 activation and for nuclear localization of a subset of active ERK1,2. We hypothesized that this function results in expression of oncogenic proteins that support breast cancer cell aggression. Study purpose: determine the mechanism for RHAMMonc supported breast cancer aggression. Experimental procedure: we used microarray analysis in combination with real time PCR and ERK1,2 inhibition to identify genes that are upregulated in response to RHAMMonc overexpression. To determine whether nuclear RHAMMonc is required for neoplastic transformation we added a nuclear export signal to the RHAMMonc cDNA (RHAMMNES-onc). Mesenchymal cells overexpressing RHAMMonc or RHAMMNES-onc were injected into immune compromised mice. To test whether these cells could support in vivo growth of breast cancer cells, MCF7 cells were co-injected with RHAMMonc or RHAMMNES-onc expressing cells into the mammary gland fat pad. Tumor growth was quantified as tumor wet weight. Conclusion: Microarray analysis in combination with real time PCR identified altered expression of genes in all categories of the Hallmarks of Cancer in fibroblasts overexpressing RHAMMonc compared to parental cells. A subset of these were shown to be regulated by ERK1,2. Fusion of a nuclear export signal to RHAMMonc (RHAMMNES-onc) resulted in export of this protein from the nucleus, significant reduction of nuclear active ERK1,2 and modified expression of a subset of ERK1,2 regulated genes (e.g. LOX, MMP9, CDH11). Importantly, forced nuclear export of RHAMMonc suppressed its oncogenic effect on fibroblasts. Furthermore, proliferation of human MCF7 breast cancer cell xenografts was strongly stimulated by co-injection with RHAMMonc fibroblasts but this stimulatory effect was lost when MCF7 tumor cells were co-injected with RHAMMNES-onc. This study suggests that nuclear RHAMMonc:ERK 1,2 interactions control an oncogenic program in fibroblasts that support breast cancer aggression. Citation Format: Cornelia Toelg, Patrick Telmer, Sara Hamilton, James McCarthy, Eva Turley. Fibroblast RHAMM promotes breast cancer aggression by promoting expression of a subset of ERK1,2 target genes. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1162. doi:10.1158/1538-7445.AM2014-1162
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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".