Sulfasalazine Suppresses Drug Resistance and Invasiveness of Lung Adenocarcinoma Cells Expressing AXL
Bibliographic record
Abstract
Metastasis and drug resistance are the major causes of mortality in patients with non-small cell lung cancer (NSCLC). Several receptor tyrosine kinases (RTKs), including AXL, are involved in the progression of NSCLC. The AXL/MER/SKY subfamily is involved in cell adhesion, motility, angiogenesis, and signal transduction and may play a significant role in the invasiveness of cancer cells. Notably, no specific inhibitors of AXL have been described. A series of CL1 sublines with progressive invasiveness established from a patient with NSCLC has been identified that positively correlates with AXL expression and resistance to chemotherapeutic drugs. The ectopic overexpression of AXL results in elevated cell invasiveness and drug resistance. Nuclear factor-kappaB (NF-kappaB) signaling activity is associated with AXL expression and may play an important role in the enhancement of invasiveness and doxorubicin resistance, as shown by using the NF-kappaB inhibitor, sulfasalazine, and IkappaB dominant-negative transfectants. In the current study, sulfasalazine exerted a synergistic anticancer effect with doxorubicin and suppressed cancer cell invasiveness in parallel in CL1 sublines and various AXL-expressing cancer cell lines. Phosphorylation of AXL and other RTKs (ErbB2 and epidermal growth factor receptor) was abolished by sulfasalazine within 15 min, suggesting that the inhibition of NF-kappaB and the kinase activity of RTKs are involved in the pharmacologic effects of sulfasalazine. Our study suggests that AXL is involved in NSCLC metastasis and drug resistance and may therefore provide a molecular basis for RTK-targeted therapy using sulfasalazine to enhance the efficacy of chemotherapy in NSCLC.
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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".