Molecular and immunohistochemical characterization of <i>ERBB2</i> activating mutations in low‐grade serous ovarian carcinoma
Bibliographic record
Abstract
AIMS: Low-grade serous carcinoma (LGSC) of the ovary presents unique therapeutic challenges due to its resistance to platinum-based chemotherapies and a tendency to present at an advanced stage. Approximately 50% of LGSC possess activating mutations in KRAS, NRAS, and BRAF, a finding associated with better overall survival. However, many tumours lack obvious driver alterations against which to direct targeted treatment strategies, necessitating further investigation into molecular drivers of LGSC and their impact on clinical outcomes. METHODS AND RESULTS: We conducted a retrospective analysis of 84 LGSC patients who underwent tumour-only targeted next-generation sequencing at our institution. Molecular data were correlated with clinical outcomes, HER2 immunohistochemistry, and supplemented with additional tumour sequencing data from the AACR GENIE cohort v15.1 (n = 295). Approximately 5% of LGSC cases across the combined cohort harboured activating alterations in ERBB2 (n = 17/369), which encodes the HER2 receptor tyrosine kinase. These alterations were mutually exclusive of other MAP kinase pathway mutations and included exon 20 insertions (n = 6), extracellular domain/transmembrane domain missense alterations (n = 4), and exon 16 skipping mutations (n = 7). ERBB2 exon 16 emerged as a mutational hotspot in LGSC when compared to other tumour types. Immunohistochemistry revealed variable HER2 expression patterns that were independent of ERBB2 mutational status. In our institutional cohort, patients with RAS/RAF mutant tumours (n = 38) showed better overall survival compared to RAS/RAF wildtype tumours (n = 35). No tumours in our internal cohort (n = 84) harboured ERBB2 amplifications. CONCLUSION: As the landscape of HER2-directed therapies continues to evolve, these findings suggest that ERBB2 alterations and HER2 expression may represent a potential therapeutic target in LGSC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".