Impact of<i>ERBB2</i>mutations on in vitro sensitivity of bladder cancer to lapatinib
Bibliographic record
Abstract
Lapatinib, a dual tyrosine kinase inhibitor of ErbB1 and ErbB2, shows a clinical benefit in a subset of patients with advanced urothelial bladder cancer (UBC). We hypothesized that the corresponding gene, ERBB2, is affected by mutations in a subset of UBC and that these mutations impact ErbB2 function, signaling, UBC proliferation, gene expression, and predict response to lapatinib. We found ERBB2 mutations in 5 of 33 UBC cell lines (15%), all of which were derived from invasive or high grade tumors. Phosphorylation and activation of ErbB2 and its downstream pathways were markedly enhanced in mutated cell lines compared with the ERBB2 wild-type. In addition, the gene expression profile was distinct, specifically for genes encoding for proteins of the extracellular matrix. RT112 cells infected with ERBB2 mutants showed a particular growth pattern ("mini-foci"). Upon treatment with lapatinib, 93% of these "mini-foci" were reversed. The sensitivity to lapatinib was greatest among cell lines with ERBB2 mutations. In conclusion, ERBB2 mutations occur in a subset of UBC and impact proliferation, signaling, gene expression and predict a greater response to lapatinib. If confirmed in the clinical setting, this may lead the way toward personalized treatment of a subset of UBC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".