Insulin-Like Growth Factor-I Receptor Signaling and Resistance to Trastuzumab (Herceptin)
Bibliographic record
Abstract
BACKGROUND: Trastuzumab (Herceptin), an anti-HER2/neu receptor monoclonal antibody that inhibits growth of ErbB2-overexpressing breast cancer, is used to treat such cancers. Development of resistance to trastuzumab, however, is common. We investigated whether insulin-like growth factor-I (IGF-I), which activates cell survival signals, interferes with the growth-inhibitory action of trastuzumab. METHODS: MCF-7/HER2-18 and SKBR3 human breast cancer models were used to assess cell proliferation, colony formation in soft agar, and cell cycle parameters. Throughout, we used trastuzumab at a dose of 10 microg/mL and IGF-I at a dose of 40 ng/mL. All statistical tests were two-sided. RESULTS: Trastuzumab inhibited the growth of MCF-7/HER2-18 cells, which overexpress HER2/neu receptors and express IGF-I receptors (IGF-IRs), only when IGF-IR signaling was minimized. For example, in 1% fetal bovine serum (FBS), trastuzumab reduced cell proliferation by 42% (P =.002); however, in 10% FBS or IGF-I, trastuzumab had no effect on proliferation. In SKBR3 cells, which overexpress HER2/neu receptor but express few IGF-IRs, trastuzumab reduced proliferation by 42% (P =.008) regardless of IGF-I concentration. When SKBR3 cells were genetically altered to overexpress IGF-IRs and cultured with IGF-I, trastuzumab had no effect on proliferation. However, the addition of IGF-binding protein-3, which decreased IGF-IR signaling, restored trastuzumab-induced growth inhibition. CONCLUSIONS: In breast cancer cell models that overexpress HER2/neu, an increased level of IGF-IR signaling appears to interfere with the action of trastuzumab. Thus, strategies that target IGF-IR signaling may prevent or delay development of resistance to trastuzumab.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".