Can pharmacological receptor tyrosine kinase inhibitors sensitize poor outcome breast tumors to immune-based therapies?
Bibliographic record
Abstract
Receptor tyrosine kinases (RTKs) drive breast cancer progression, particularly in human epidermal growth factor receptor 2 and basal tumors, the two worst prognosis subtypes. Tumor cells recruit host stromal components, including immune cells, which strongly influence disease progression. This has been studied in human breast cancer and translated to murine models of breast cancer. Stromal immune components including cytotoxic T lymphocytes (CTLs) and natural killer cells, destroy cancer cells through a process termed immune surveillance. Unfortunately, clinically detectable tumors escape these immune protective effects through their ability to limit the infiltration, activation, and/or survival of CTLs in breast tumors. The immunosuppressed state of established tumors limits the success rate of immune-based therapies, and possibly other therapeutic modalities that depend on host immunity. Published studies demonstrate that RTKs facilitate breast cancer progression, in part, by establishing immune suppression. This raises the intriguing possibility that pharmacological RTK inhibitors may be exploited to sensitize breast cancer patients to immune-based therapies.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".