Disitamab vedotin in preclinical models of HER2-positive breast and gastric cancers resistant to trastuzumab emtansine and trastuzumab deruxtecan
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it