Serum insulin-like growth factor-1 levels in response to dasatinib-based regimens in bone-metastatic prostate cancer.
Bibliographic record
Abstract
166 Background: Dasatinib, an inhibitor of Src-family kinases, combined with docetaxel in men with castrate-resistant prostate cancer (CRPC), affects bone turnover markers. Only a subset of men benefit from this therapy, and predictive markers are lacking. We hypothesized a role for insulin-like growth factor-1 (IGF-1) as a predictive marker, since IGF-1 is important in both prostate cancer (PCa) progression and bone development. Hence, we determined the association of IGF-1 expression to treatment response, and whether this expression resulted from tumor cells, the microenvironment, or their interactions. Methods: We first measured serum IGF-1 levels in men with CRPC treated with dasatinib plus docetaxel. To investigate the source of IGF-1, we utilized different mouse models harboring human PCa cells, and used species-specific IGF-1 ELISA kits (mouse vs. human). Results: In men with CRPC, an increase in IGF-1 levels after one cycle of treatment is associated with a higher response rate and longer duration of treatment with docetaxel and dasatinib. Xenograft experiments with subcutaneous, and intratibial injection of PCa cells and treatment of mice with dasatinib-based combinations suggest that direct interaction of PCa cells with bone microenvironment is necessary for IGF-1 induction, is entirely host-derived, and occurs only in mice that respond to dasatinib-based therapy. Conclusions: Our results support a role for serum IGF-1 as a potential biomarker for dasatinib-based combination treatments in CRPC. Inoculation of human PCa cells into murine hosts was essential in determining that IGF-1 results from the bone microenvironment. Further studies are warranted to validate these findings in a larger cohort of patients in a prospective manner.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".