Osteopontin is a therapeutic target that drives breast cancer recurrence
Bibliographic record
Abstract
Recurrent breast cancers often develop resistance to standard-of-care therapies. Identifying targetable factors contributing to cancer recurrence remains the rate-limiting step in improving long-term outcomes. In this study, we identify tumor cell-derived osteopontin as an autocrine and paracrine driver of tumor recurrence. Osteopontin promotes tumor cell proliferation, recruits macrophages, and synergizes with IL-4 to further polarize them into a pro-tumorigenic state. Macrophage depletion and osteopontin inhibition decrease recurrent tumor growth. Furthermore, targeting osteopontin in primary tumor-bearing female mice prevents metastasis, permits T cell infiltration and activation, and improves anti-PD-1 immunotherapy response. Clinically, osteopontin expression is higher in recurrent metastatic tumors versus female patient-matched primary breast tumors. Osteopontin positively correlates with macrophage infiltration, increases with higher tumor grade, and its elevated pathway activity is associated with poor prognosis and long-term recurrence. Our findings suggest clinical implications and an alternative therapeutic strategy based on osteopontin's multiaxial role in breast cancer progression and recurrence.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".