MEMORANDUM FOR: Science Writers and Editors on the Journal Press List: Insulin-Like Growth Factor Receptors May Be Involved in Development of Resistance to the Breast Cancer Drug Herceptin
Bibliographic record
Abstract
December 13, 2001 (EMBARGOED FOR RELEASE 4 P.M. EST December 18) New research suggests that insulin-like growth factor-I (IGF-I) receptors may be involved in the development of resistance to the breast cancer drug Herceptin. Herceptin (Genentech, San Francisco) is a monoclonal antibody that has important therapeutic activity against breast cancers that overexpress the protein HER2. However, most breast cancers develop a resistance to Herceptin in less than a year through mechanisms that are poorly understood. Yuhong Lu, M.D., and Michael Pollak, M.D., Jewish General Hospital and McGill University, Montreal, Canada, and colleagues found that an increased level of IGF-I receptor (IGF-IR) signaling appears to interfere with Herceptin action. Therefore, they believe that strategies that target IGF-IR signaling may prevent or delay development of resistance to Herceptin. These results appear in the Dec. 19 issue of the Journal of the National Cancer Institute. The authors investigated the ability of Herceptin to inhibit the growth of two different breast cancer cell lines. One of these cells lines—MCF-7/HER2-18—overexpresses HER2/neu receptors and expresses IGF-IRs. When these cells were grown under conditions where IGF-I signaling was minimized, Herceptin reduced cell proliferation by 42%. However, in the presence of IGF-I, Herceptin failed to inhibit proliferation.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.088 | 0.082 |
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".