Abstract S5-6: Activating HER2 mutations in HER2 gene amplification negative breast cancers.
Bibliographic record
Abstract
Abstract Background: Breast cancer genome sequencing projects, performed by the genome sequencing centers in the U.S., Canada, and the U.K., are elucidating the somatic mutations and other genomic alterations that occur in human breast cancer. These studies recently identified somatic HER2 mutations in breast cancers lacking HER2 gene amplification. Results: Compilation of data from seven sequencing studies documented 22 patients with somatic HER2 mutations. These mutations clustered in three regions. The first cluster was at amino acid (aa) 309–310 (exon 8), located in the extracellular domain. These aa residues form part of the HER2 dimerization interface. The second cluster was at aa 755–781, located in the kinase domain (exons 19–20). This was the most common location for HER2 mutations, with 17 out of 22 patients having somatic mutations here. The third region was at aa 835–896, also in the kinase domain (exons 21–22). Using multiple experimental approaches (cell line experiments, in vitro kinase assays, protein structure modeling, and xenograft experiments), we tested seven of these HER2 mutations and showed that 4 of them are activating mutations that are sensitive to lapatinib and trastuzumab. Another 2 mutations were found to be lapatinib resistant and we determined their sensitivity to neratinib, canertinib, and gefitinib. Conclusions: These findings biologically validate somatic HER2 mutations as good targets for breast cancer treatment, but the appropriate choice of targeted drug is dependent on the precise mutation present. This study is among the first to functionally characterize mutations identified by breast cancer genome sequencing. A prospective, multi-institutional clinical trial has been launched to screen for HER2 mutation positive patients and determine the clinical outcome of treatment with HER2 targeted drugs. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr S5-6.
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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) | 0.004 | 0.001 |
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".