Novel <i>ERBB2</i> Variant Potentially Associated with Resistance against Anti-HER2 Monoclonal Antibody–Based Therapy in <i>ERBB2</i> -Amplified Metastatic Colorectal Cancer
Bibliographic record
Abstract
PURPOSE: HER2-targeted therapies in ERBB2-amplified metastatic colorectal cancer (mCRC) are effective; however, a notable portion of patients do not respond to treatment, and secondary resistance occurs in most patients receiving these treatments. The purpose of this study was to investigate determinants of treatment efficacy and resistance in patients with ERBB2-amplified mCRC who received HER2-targeted therapy by analyzing multiomics data. EXPERIMENTAL DESIGN: We investigated genomic data from a nationwide large cancer genomic screening project, the SCRUM-Japan project. We analyzed paired genome and transcriptome data of tissue and genomic data of ctDNA collected pre- and postprogression in patients enrolled in the related trial, TRIUMPH, in ERBB2-amplified mCRC. RESULTS: In 155 patients with ERBB2-amplified solid tumors who received HER2-targeted therapy based on the SCRUM-Japan project, the objective response rate was 50%, 51%, and 35% in ERBB2 wild-type, variant of unknown significance, and pathogenic variant groups, respectively. In the paired genome and transcriptome data analyses in TRIUMPH, we identified the novel splicing-associated variant c.644-66_-2del in one of the 11 patients with paired whole-exome sequencing and whole-transcriptome sequencing data sets, which lacks the binding domain of pertuzumab, in progressed metastatic tumor as a variant with potential pathogenicity. The time-course ctDNA analysis detected c.644-66_-2del as an acquired variant. CONCLUSIONS: This study highlighted the importance of ERBB2 genomic status when evaluating the efficacy of HER2-targeted therapies in ERBB2-amplified mCRC. The identification of a novel splicing-associated variant may provide insights into potential mechanisms of treatment resistance. Furthermore, we demonstrated the utility of ctDNA to follow the acquired genomic status of mCRC tumors.
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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 | 0.000 | 0.001 |
| 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".