Induction of Activating Transcription Factor 3 Is Associated with Cisplatin Responsiveness in Non–Small Cell Lung Carcinoma Cells
Bibliographic record
Abstract
Non-small cell lung carcinoma (NSCLC) is the most common cause of cancer deaths, with platin-based combination chemotherapy the most efficacious therapies. Gains in overall survival are modest, highlighting the need for novel therapeutic approaches including the development of next-generation platin combination regimens. The goal of this study was to identify novel regulators of platin-induced cytotoxicity as potential therapeutic targets to further enhance platin cytotoxicity. Employing RNA-seq transcriptome analysis comparing two parental NSCLC cell lines Calu6 and H23 to their cisplatin-resistant sublines, Calu6cisR1 and H23cisR1, activating transcription factor 3 (ATF3) was robustly induced in cisplatin-treated parental sensitive cell lines but not their resistant sublines, and in three of six tumors evaluated, but not in their corresponding normal adjacent lung tissue (0/6). Cisplatin-induced JNK activation was a key regulator of this ATF3 induction. Interestingly, in both resistant sublines, this JNK induction was abrogated, and the expression of an activated JNK construct in these cells enhanced both cisplatin-induced cytotoxicity and ATF3 induction. An FDA-approved drug compound screen was employed to identify enhancers of cisplatin cytotoxicity that were dependent on ATF3 gene expression. Vorinostat, a histone deacetylase inhibitor, was identified in this screen and demonstrated synergistic cytotoxicity with cisplatin in both the parental Calu6 and H23 cell lines and importantly in their resistant sublines as well that was dependent on ATF3 expression. Thus, we have identified ATF3 as an important regulator of cisplatin cytotoxicity and that ATF3 inducers in combination with platins are a potential novel therapeutic approach for 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".