Insulin-Like Growth Factor Binding Protein-2 Is a Novel Therapeutic Target Associated with Breast Cancer
Bibliographic record
Abstract
PURPOSE: Insulin-like growth factor (IGF) binding proteins (IGFBP) modulate interactions of IGF ligands with the IGF-I receptor. The role of IGFBPs, and specifically IGFBP-2, in breast cancer progression has been poorly defined. This study assesses the effect of IGFBP-2 on the behavior of human breast cancer using clinical specimens as well as in vitro and in vivo experimental systems. EXPERIMENTAL DESIGN: 4,181 primary invasive breast cancers and 120 benign breast tissue samples were identified for tumor tissue microarray construction and immunostained with IGFBP-2 antibody. Estrogen receptor-negative MDA-MB-231 cells constitutively overexpressing IGFBP-2 (MDA-MB-231BP-2) were created to assess the effect of IGFBP-2 gain-of-function. MDA-MB-468 cells, naturally expressing IGFBP-2, were used to determine the effect of IGFBP-2 loss-of-function using OGX-225, an antisense oligonucleotide drug candidate. RESULTS: IGFBP-2 expression was significantly higher in breast cancer tissue compared with benign breast tissue. MDA-MB-231BP-2 cells grew more rapidly and were more resistant to paclitaxel both in vitro and in vivo compared with parental cells. OGX-225 decreased IGFBP-2 expression and attenuated the associated aggressive phenotype of MDA-MB-231BP-2 cells both in vitro and in vivo. Furthermore, OGX-225 inhibited the in vitro and in vivo growth of MDA-MB-468 cells. CONCLUSIONS: This study provides evidence that IGFBP-2 expression is associated with breast cancer. Novel therapeutics targeting IGFBP-2, such as OGX-225, merit further evaluation.
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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".