The Combination of Afatinib With Dasatinib or Miransertib Results in Synergistic Growth Inhibition of Stomach Cancer Cells
Bibliographic record
Abstract
Background: Of various human epidermal growth factor receptor (HER) inhibitors, only the anti-HER2 monoclonal antibody (mAb) Herceptin/trastuzumab and the antibody-drug conjugate trastuzumab deruxtecan (T-Dxd) has been approved for the treatment of patients with stomach cancer. However, the duration of response may be short in many patients, with tumor heterogeneity being one contributing factor. Methods: We investigated the effect of various types of targeted agents on growth in vitro and migration of a panel of human stomach cancer cells (HSCCLs) and the impact of cell proliferation rate on the anti-tumor activities of these agents. We also investigated the association between the cell surface expression of the HER family members, hepatocyte growth factor receptor (c-Met), anaplastic lymphoma kinase (ALK)7 and cancer stem cell markers CD44 and CD133, and the response to the targeted agents. Results: Of the 18 agents examined, the cyclin dependent kinase (CDK) 1/2/5/9 inhibitor dinaciclib was the most effective and inhibited the growth of all human HSCCLs at 50% inhibitory concentration (IC 50 ) values between 9 nM to 23 nM. Of various HER inhibitors, the irreversible pan-HER family inhibitors (e.g., afatinib) were more effective than the reversible dual epidermal growth factor receptor (EGFR)/HER2 tyrosine kinase inhibitor (TKI) lapatinib and the EGFR-specific TKI erlotinib in inhibiting the growth of HSCCLs. Of agents targeting different downstream cell signaling molecules, dasatinib targeting Ab1/Src/C-Kit, trametinib targeting MERK1/2 and miransertib targeting AKT1/2/3 inhibited growth of majority of HSCCLs, with the IC 50 values ranging from 2 nM to 7 µM. Many of these agents were more effective in inhibiting the growth of HSCCLs when they were proliferating at a slower rate. Treatment with neratinib, afatinib, dinaciclib, dasatinib, stattic, miransertib and paclitaxel significantly inhibited migration of stomach cancer cells. Interestingly, treatment with a combination of afatinib and dasatinib or afatinib and miransertib resulted in synergistic and additive growth inhibition of stomach cancer cells. Conclusions: These results suggest that treatment with a combination of these agents may be of therapeutic value in stomach cancer and warrants further investigations. World J Oncol. 2024;15(2):192-208 doi: https://doi.org/10.14740/wjon1769
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".