Phosphorylated Insulin-Like Growth Factor-I/Insulin Receptor Is Present in All Breast Cancer Subtypes and Is Related to Poor Survival
Bibliographic record
Abstract
Drugs that target the insulin-like growth factor-I receptor (IGF-IR) and/or insulin receptor (IR) are currently under investigation for a variety of malignancies including breast cancer. Although we have previously reported that IGF-IR expression in primary breast tumors is common, the activation status of this receptor has not been examined in relation to survival. Phosphorylated IGF-IR/IR (P-IGF-IR/IR) and its downstream signaling partner phospho-S6 (P-S6) were evaluated immunohistochemically in tumor tissue microarrays representing 438 cases of invasive breast cancer. P-IGF-IR/IR (n = 114; P = 0.046) and total levels of IR (n = 122; P = 0.009) were indicative of poor survival, whereas total IGF-IR (n = 112; P = 0.304) was not. P-IGF-IR/IR and P-S6 were coordinately expressed in primary breast tumors (likelihood ratio, 11.57; P = 6.70 x 10(-4)). Importantly, P-IGF-IR/IR was detected in all breast cancer subtypes (luminal, 48.1%; triple negative, 41.9%; and HER2, 64.3%). In vitro, the IGF-IR/IR inhibitor BMS-536924 decreased phospho-RSK and P-S6, and significantly suppressed the growth of breast cancer cell lines MCF-7, SUM149, and AU565 representing the luminal, triple negative, and HER2 subtypes, respectively, in monolayer and soft agar. BMS-536924 also inhibited growth in tamoxifen resistant MCF-7 Tam-R cells while having little effect on immortalized normal breast epithelial cells. Thus, we can determine which patients have the activated receptor and provide evidence that P-IGF-IR/IR is a prognostic factor for breast cancer. Beyond this, P-IGF-IR/IR could be a predictive marker for response to IGF-IR and/or IR-targeted therapies, as these inhibitors may be of benefit in all breast cancer subtypes including those with acquired resistance to tamoxifen.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".