Increased serum insulin‐like growth factor‐1 levels are associated with prolonged response to dasatinib‐based regimens in metastatic prostate cancer
Bibliographic record
Abstract
BACKGROUND: Dasatinib, an inhibitor of Src-family kinases, combined with docetaxel in men with castrate-resistant prostate cancer (CRPC), affects bone turnover markers in a phase I/II clinical trial in metastatic CRPC. 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 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 measured serum IGF-1 levels in men with CRPC treated with dasatinib plus docetaxel. To investigate the source of IGF-1, we utilized two different mouse models harboring human prostate cancer 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 with dasatinib and docetaxel is associated with a higher response rate and longer duration of treatment. Xenograft experiments with subcutaneous and intratibial injection of prostate cancer cells suggest that direct interaction of prostate cancer 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. CONCLUSION: Our results support a role for serum IGF-1 as a potential biomarker for benefit from dasatinib-based combination treatments in CRPC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".