Farnesyltransferase inhibitor SCH‐66336 downregulates secretion of matrix proteinases and inhibits carcinoma cell migration
Bibliographic record
Abstract
The ras oncogenes are among those most frequently found in human cancers. Blocking Ras farnesylation is a promising strategy for arresting cancer growth. Ras activates several signaling pathways with key roles in cellular proliferation, invasion, metastasis and angiogenesis. Furthermore, proteolytic activities of matrix proteinases such as urokinase-type plasminogen activator (uPA) and matrix metalloproteinases (MMPs) are regulated by Ras isoforms. Thus, we investigated the effects of SCH-66336, a farnesyltransferase inhibitor, on secretion of components of the plasminogen activation system as well as on the gelatinases MMP-2 and MMP-9, which play pivotal roles in matrix remodeling. SCH-66336 up to 5 microM did not significantly alter the viability of prostate (PC-3) and renal (Caki-1) cancer cells incubated in serum-depleted medium. SCH-66336 partly inhibited the processing of H-Ras, while levels of mature N-Ras and K-Ras remained unaffected. Under these noncytotoxic conditions, uPA and tPA levels were lowered in culture medium but raised in cell lysates, suggesting inhibition of trafficking pathways. In contrast, SCH-66336 had no effect on uPAR expression or on secreted PAI-1 levels. As expected, the reduction of uPA and tPA activities by SCH-66336 inhibited the conversion of plasminogen to plasmin by about 25% in PC-3 cells. SCH-66336 also inhibited the levels of secreted pro-MMP-2 and pro-MMP-9 as well as the release of their inhibitors TIMP-1 and TIMP-2. SCH-66336 decreased both the adhesion and even more so the migration of PC-3 cells on gelatin. Thus, SCH-66336 inhibited farnesylation in both cancer cell types, and H-Ras functions should be reduced by the drug. In addition, the lower levels of secreted proteinases in the presence of SCH-66336 suggest that reduced matrix remodeling and cell migration should occur in treated tumors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".