A Non-Canonical p75HER2 Signaling Pathway Underlying Trastuzumab Action and Resistance in Breast Cancer
Bibliographic record
Abstract
Overexpression of HER2 occurs in 25% of breast cancer. Targeting HER2 has proven to be an effective therapeutic strategy for HER2-positive breast cancer. While trastuzumab is the most commonly used HER2 targeting agent, which has significantly improved outcomes, the overall response rate is low. To develop novel therapies to boost trastuzumab efficacy, it is critical to identify the mechanisms underlying trastuzumab action and resistance. We recently showed that the inhibition of breast cancer cell growth by trastuzumab is not through the inhibition of HER2 canonical signaling. Here we report the identification of a novel non-canonical HER2 signaling pathway and its interference by trastuzumab. We showed that HER2 signaled through a non-canonical pathway, regulated intramembrane proteolysis (RIP). In this pathway, HER2 is first cleaved by metalloprotease ADAM10 to produce an extracellular domain (ECD) that is released and the p95HER2 that contains the transmembrane domain (TM) and intracellular domain (ICD). p95HER2, if further cleaved by an intramembrane protease, γ-secretase, produced a soluble ICD p75HER2 with nuclear localization signal (NLS). p75HER2 is phosphorylated and translocated to the nucleus. Nuclear p75HER2 promotes cell proliferation. Trastuzumab targets this non-canonical HER2 pathway via inhibition of the proteolytic cleavage of HER2 by both ADAM10 and γ-secretase. However, p75HER2 pathway also confers resistance to trastuzumab once aberrantly activated. Combination of trastuzumab with ADAM10 and γ-secretase inhibitors completely blocks p75HER2 production in both BT474 and SKBR3 cells. We concluded that HER2 signals through the RIP signaling pathway that promotes cell proliferation and is targeted by trastuzumab. The aberrant HER2 RIP signaling confers resistance to trastuzumab that could be overcome by the application of inhibitors to ADAM10 and γ-secretase.
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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 | 0.000 | 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".