Disitamab vedotin in preclinical models of HER2-positive breast and gastric cancers resistant to trastuzumab emtansine and trastuzumab deruxtecan
Bibliographic record
Abstract
BACKGROUND: Most HER2-positive breast or gastric cancers eventually become resistant to the approved anti-HER2 antibody-drug conjugates (ADC) trastuzumab emtansine (T-DM1) and trastuzumab deruxtecan (T-DXd). Disitamab vedotin (DV) is a novel anti-HER2 ADC that binds to a different epitope on HER2 compared to trastuzumab. We assessed the efficacy of DV in breast and gastric cancer cell lines and xenografts, including tumor models resistant to T-DM1 and T-DXd. Additionally, we investigated whether combining two anti-HER2 ADCs could enhance the efficacy of the individual ADCs. METHODS: The efficacy of DV, T-DM1, and T-DXd, both as single agents and in combinations, was assessed using an AlamarBlue cell proliferation assay in HER2-positive breast and gastric cancer cell lines, including those resistant to T-DM1 and T-DXd. The efficacy of DV was evaluated also in breast and gastric cancer SCID mouse xenografts that had progressed on T-DM1 and/or T-DXd. ADC combinations were tested in breast and gastric cancer xenografts. RESULTS: DV was effective in cell lines resistant to T-DM1 and/or T-DXd, and it inhibited the growth of breast and gastric cancer xenografts that had progressed on T-DM1 and/or T-DXd. The combinations of DV plus T-DM1 and DV plus T-DXd showed greater efficacy than the corresponding single agents in both breast and gastric cancer cell lines and xenografts. CONCLUSIONS: DV was effective in treating breast and gastric cancer xenograft tumors resistant to T-DM1 and/or T-DXd. The combination of DV with T-DM1 or T-DXd demonstrated promising activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".