<i>ERBB2</i> mutations define a subgroup of endometrial carcinomas associated with high tumor mutational burden and the microsatellite instability‐high ( <scp>MSI</scp> ‐H) molecular subtype
Bibliographic record
Abstract
Anti-HER2 therapy is indicated for erb-b2 receptor tyrosine kinase 2 (ERBB2)-amplified/overexpressing endometrial carcinoma (EC). Mutations constitute another mode of ERBB2 activation, but only rare ERBB2-mutated ECs have been reported. We sought to characterize the clinicopathologic and genetic features of ERBB2-mutated EC. From an institutional cohort of 2638 ECs subjected to clinical tumor-normal panel sequencing, 69 (2.6%) with pathogenic ERBB2 mutation(s) were identified, of which 11 were also ERBB2-amplified. The most frequent ERBB2 hotspot mutations were V842I (38%) and R678Q (25%). ERBB2 mutations were clonal in 87% of evaluable cases. Immunohistochemistry revealed low HER2 protein expression in most ERBB2-mutated ECs (0/1+ in 66%, 2+ in 27%); all 3+ tumors (7.3%) were also ERBB2-amplified. Compared to ERBB2-wildtype ECs (with or without ERBB2 amplification), ERBB2-mutated/non-amplified ECs were enriched for the microsatellite instability-high (MSI-H) and, to a lesser extent, DNA polymerase epsilon, catalytic subunit (POLE) molecular subtypes, and associated with high tumor mutational burden and low chromosomal instability. Survival outcomes were similar between patients with ERBB2-mutated/non-amplified versus wildtype EC, whereas ERBB2 amplification was associated with worse prognosis on univariate, but not multivariate, analyses. In conclusion, ERBB2 mutation defines a rare subgroup of ECs that is pathogenically distinct from ERBB2-wildtype and ERBB2-amplified ECs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".