Beyond HER2 expression in breast cancer: Investigating alternative splicing profiles as a mechanism of resistance to anti-HER2 therapies
Bibliographic record
Abstract
Breast cancer is a heterogeneous disease that can be molecularly classified based on the expression of hormone receptors and the overexpression of the HER2 receptor (ERBB2). Targeted therapies for HER2-positive breast cancer, including trastuzumab, antibody drug conjugates (ADCs) and tyrosine kinase inhibitors, have significantly improved patient outcomes. However, both primary and acquired resistance to these treatments pose challenges that can limit their long-term efficacy. Addressing these obstacles is vital for enhancing therapeutic strategies and patient care. Alternative splicing, a post-transcriptional mechanism that enhances transcript diversity (isoforms) within a cell, can result in isoform-encoded proteins with varied functions, cellular localizations, or binding properties. In this study, we undertook a comprehensive characterization of the alternative splicing isoforms of HER2, assessed their expression levels in primary breast tumors and cell lines, and explored their role in resistance to anti-HER2 therapies. Our results have significantly expanded the catalog of known HER2 protein-coding isoforms from 13 to 90, revealing distinct patterns of protein domains, cellular localization, and protein structures, as well as mapping their antibody-binding sites. Additionally, by profiling expression in 561 primary breast cancer samples and analyzing mass spectrometry data for translation evidence, we discovered a complex landscape of splicing isoform expression in primary tumors, revealing novel isoforms that were previously unrecognized and are not evaluated in routine clinical practice. This extends beyond the traditional profile based solely on HER2 gene expression and translation. Finally, by assessing HER2 isoform expression in cell cultures that are either sensitive or resistant to trastuzumab and ADCs (T-DM1 or T-DXd), we found that drug-resistant tumor cells shifted their expression toward splicing isoforms that lack the antibody-binding domains. Our results substantially broaden the understanding of HER2 protein-coding isoforms, revealing distinct mechanisms of potential resistance to anti-HER2 therapies, particularly ADCs, by uncovering a new dimension of splicing isoform diversity. This expanded landscape of HER2 isoforms, marked by unique domain patterns and altered antibody-binding sites, emphasizes the crucial role of alternative splicing investigations in advancing precision-targeted cancer therapies.
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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 | 0.001 |
| 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".