<i>ERBB2</i> /HER2 Alterations in ctDNA and Metachronous Tissues of Patients with Metastatic Urothelial Cancer
Bibliographic record
Abstract
PURPOSE: HER2 targeting is increasingly relevant in metastatic urothelial cancer (mUC) because of emerging antibody-drug conjugates, in which assessment of tumor HER2/ERBB2 status could aid interpretation of ongoing trials. We evaluated whether precise ERBB2 genotype can be determined from mUC ctDNA and the relationship with tissue status. EXPERIMENTAL DESIGN: ERBB2 genotype was determined through targeted sequencing of longitudinal samples from 226 patients with mUC. Heterogeneity was evaluated across ctDNA and metachronous tumor tissue ERBB2 genotype and compared with HER2 IHC. RESULTS: Activating ERBB2 mutation and/or amplification was identified in 16% and 29% of patients by ctDNA and tissue sequencing, respectively. Alternatively, 55% of tissue samples were HER2 IHC positive. Agreement between HER2 IHC and ERBB2 genotype (by either ctDNA or tissue) was 64%, in contrast to 87% between patient-matched ctDNA and tissue genotypes. Across serial samples, genotype and IHC revealed marked heterogeneity in ERBB2 status. Factors linked with heterogeneity included ERBB2 amplifications on extrachromosomal DNA, detected through whole-genome sequencing of ctDNA and FISH, and subclonal ERBB2 mutations, which were evident in half of ctDNA and one-third of tissue samples. CONCLUSIONS: We report a combined analysis of HER2 IHC and ERBB2 genotype in mUC ctDNA and archival tissue, revealing high spatiotemporal heterogeneity. Our data suggest that sole reliance on DNA- or protein-based HER2 assessment is insufficient to capture nuanced genomic indicators of HER2 pathway reliance and support a role for ctDNA alongside existing methods for characterizing HER2/ERBB2 status during biomarker development in mUC.
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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 | 0.001 |
| 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".