Abstract A16: Induction of activating transcription factor 3 is associated with cisplatin responsiveness in NSCLC: A potential predictive biomarker of response.
Bibliographic record
Abstract
Abstract The goal of this study was to identify treatment induced biomarkers of platin activity that may predict response to this important class of chemotherapeutic agents. We employed RNAseq transcriptome analysis comparing two parental NSCLC cell lines Calu6 and H23 to their resistant sub-lines, Calu6cisR1 and H23cisR1, derived following exposure to high dose cisplatin. To this end, we identified a stress pathway consisting of GADD45α, ATF3 and DDIT3/CHOP that was induced specifically in cisplatin treated parental but not their resistant sub-lines. Furthermore, ATF3 in particular was not expressed in untreated parental or resistant clones but was robustly induced only in the parental sensitive cell lines following cisplatin treatment. Cisplatin-induced MAPKinase activation, particularly the JNK pathway was abrogated in Calu6cisR1 cells that regulates ATF3 induction in Calu6 cells. In ex-vivo NSCLC tumors, ATF3 was induced in 2/4 tumors evaluated but not in their corresponding normal adjacent lung tissue (0/4) following cisplatin treatment. Thus, the lack of significant expression in untreated NSCLC cells and normal lung tissue but a robust induction following cisplatin treatment in sensitive parental NSCLC cell lines and a cohort of ex-vivo tumor samples suggest potential utility as a predictive biomarker of platin response that requires further study. Citation Format: Jair Bar, David Stewart, Goss D. Glenwood, Patrick J. Villeneuve, Jim Dimitroulakos. Induction of activating transcription factor 3 is associated with cisplatin responsiveness in NSCLC: A potential predictive biomarker of response. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr A16.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".