Alternative splicing generates HER2 isoform diversity underlying antibody–drug conjugate resistance in breast cancer
Bibliographic record
Abstract
Breast cancer (BC) is a heterogeneous disease that can be molecularly classified based on the expression of the ERBB2 receptor (also known as HER2 ) and hormone receptors. Targeted therapies for HER2-positive BC, such as trastuzumab, antibody-drug conjugates (ADCs) and tyrosine kinase inhibitors, have improved patient outcomes, but primary/acquired resistance still poses challenges that can limit treatments’ 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), can produce proteins with varied functions, cellular localizations, or binding properties. Here, we comprehensively characterize the HER2 alternative splicing isoforms, assess their expression in primary BC patients and cell lines, and explore their role in resistance to anti-HER2 therapies. We expand the catalog of known HER2 protein-coding isoforms from 13 to 90, revealing distinct patterns of protein domains, cellular localizations, and protein structures, along with their antibody-binding sites. By profiling expression in 561 primary BC samples and mass spectrometry data, we discover a complex landscape of HER2 isoform, revealing novel transcripts that were previously unrecognized and are not assessed in routine clinical practice. Finally, the assessment of HER2 isoform expression in BC cell cultures sensitive or resistant to trastuzumab and ADCs reveals that drug-resistant cells shift their expression toward isoforms lacking antibody-binding domains. Our results broaden the understanding of HER2 isoforms, revealing distinct mechanisms of potential resistance to anti-HER2 therapies, particularly ADCs. This expanded landscape of HER2 isoforms emphasizes the crucial role of alternative splicing investigations in advancing precision-targeted cancer therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".