Selective tyrosine kinase inhibition of insulin-like growth factor-1 receptor inhibits human and mouse breast cancer–induced bone cell activity, bone remodeling, and osteolysis
Bibliographic record
Abstract
Insulin-like growth factor 1 (IGF-1) plays an important role in both bone metabolism and breast cancer. In this study, we investigated the effects of the novel IGF-1 receptor tyrosine kinase inhibitor cis-3-[3-(4-methyl-piperazin-l-yl)-cyclobutyl]-1-(2-phenyl-quinolin-7-yl)-imidazo[1,5-a]pyrazin-8-ylamine (PQIP) on osteolytic bone disease associated with breast cancer. Human MDA-MB-231 and mouse 4T1 breast cancer cells enhanced osteoclast formation in receptor activator of NF-κB ligand (RANKL) and macrophage colony-stimulating factor (M-CSF) stimulated bone marrow cultures, and these effects were significantly inhibited by PQIP. Functional studies in osteoclasts showed that PQIP inhibited both IGF-1 and conditioned medium-induced osteoclast formation by preventing phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) activation without interfering with RANKL or M-CSF signaling. Treatment of osteoblasts with PQIP significantly inhibited the increase in RANKL/osteoprotegerin (OPG) ratio by IGF-1 and conditioned medium and totally prevented conditioned medium-induced osteoclast formation in osteoblast-bone marrow (BM) cell cocultures, thereby suggesting an inhibitory effect on osteoblast-osteoclast coupling. PQIP also inhibited IGF-1-induced osteoblast differentiation, spreading, migration, and bone nodule formation. Treatment with PQIP significantly reduced MDA-MB-231 conditioned medium-induced osteolytic bone loss in a mouse calvarial organ culture system ex vivo and in adult mice in vivo. Moreover, once daily oral administration of PQIP significantly decreased trabecular bone loss and reduced the size of osteolytic bone lesions following 4T1 intratibial injection in mice. Quantitative histomorphometry showed a significant reduction in bone resorption and formation indices, indicative of a reduced rate of cancer-associated bone turnover. We conclude that inhibition of IGF-1 receptor tyrosine kinase activity by PQIP suppresses breast cancer-induced bone turnover and osteolysis. Therefore, PQIP, and its novel derivatives that are currently in advanced clinical development for the treatment of a number of solid tumors, may be of value in the treatment of osteolytic bone disease associated with breast cancer.
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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".