Interferon-α Promotes the Anti-Proliferative Effect of Gefitinib (ZD1839) on Human Colon Cancer Cell Lines
Bibliographic record
Abstract
OBJECTIVE: Interferon-alpha (IFN alpha) treatment is associated with up-regulation of epidermal growth factor receptor (HER 1/EGFR) expression and marked growth inhibition of colon cancer cell lines in vitro. We aimed to determine the effect of combining IFN alpha and gefitinib on colon cancer cell line growth. METHODS: A panel of nine colon cancer cell lines were characterised for expression of HER 1/EGFR and then treated with gefitinib alone, or IFN alpha alone, or IFN alpha plus gefitinib, following a pre-treatment using vehicle or IFN alpha. Crystal violet staining and flow cytometry were used to assess cell proliferation and expression of HER 1/EGFR. The indexes and statistical assays were used to evaluate significant differences between treatment groups against vehicle control. RESULTS: All cell lines except SW 620 were HER 1/EGFR positive. IFN alpha treatment was associated with significant up-regulation of cell surface HER 1/EGFR expression in all HER 1/EGFR-positive cell lines except KM 12 SM. Concurrent treatment with IFN alpha and gefitinib, or IFN alpha pre-treatment followed by gefitinib, or IFN alpha pre-treatment followed by a combination of IFN alpha plus gefitinib, additively or supra-additively/synergistically enhanced the sensitivity of the seven HER 1/EGFR-up-regulated cell lines. CONCLUSION: IFN alpha improves the anti-proliferative effect of EGFR inhibition in colorectal cancer cell lines. This approach may have clinical implications for improving treatment based on targeting of HER 1/EGFR.
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 | 0.000 | 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 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".