<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.
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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 | 0.001 | 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".