Osteopontin increases breast cancer cell sensitivity to specific signaling pathway inhibitors in preclinical models
Bibliographic record
Abstract
Src kinase and Hsp90 play important roles in malignancy-promoting signaling pathways in a variety of cancers and related targeting agents are presently in clinical trials. To help improve the success of Src kinase and Hsp90 inhibitor therapies, identifying sensitive patient populations will be essential. Osteopontin (OPN), a secreted integrin-binding glycophosphoprotein, is associated with progression and metastasis in a variety of cancers and has been studied as a prognostic marker. Previous work of ours and other groups has indicated that OPN-induced signal transduction involves activation of both Src and Hsp90-dependent pathways. We thus hypothesized that over-expression of OPN could make tumor cells more vulnerable to these classes of inhibitors. This study used multiple in vitro assays to determine if OPN levels could predict breast cancer cell sensitivity to Hsp90 and/or Src kinase inhibitors. We used multiple derivatives of two unrelated of human breast cancer cell lines, high vs. low levels of OPN, to determine if OPN affects the response to two specific inhibitors, an Hsp90 inhibitor and a Src kinase inhibitor, in in vitro migration and colony formation assays. Cells had greater decreases in migration and colony forming ability after Hsp90 and Src kinase inhibitor treatments when OPN was present (either endogenous or exogenous). Decreasing OPN levels via shRNA knockdown decreased inhibitor effects. In rescue experiments, adding exogenous OPN to non-expressing cells increased inhibitor effects. These results suggest that OPN could potentially be useful clinically as a predictive marker in identifying patients who will benefit from either Hsp90 or Src kinase inhibitor therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.000 |
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 teacher head, 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".