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Record W1966036358 · doi:10.1158/1538-7445.am2013-895

Abstract 895: Activating transcription factor 3 (ATF3) down-regulation correlates with platinum resistance in non-small cell lung cancer (NSCLC).

2013· article· en· W1966036358 on OpenAlexaff
Jair Bar, Ivan Gorn‐Hondermann, Stephanie Reid, Patrícia Moretto, Iris Shiran, Shlomit Jessel, Marina Perelman, Eyal Heller, Iris Kamer, Inbal Daniel‐Meshulam, Glenwood D. Goss, Jim Dimitroulakos

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsOttawa Regional Cancer FoundationOttawa Hospital
Fundersnot available
KeywordsCisplatinVorinostatCancer researchCytotoxicityLung cancerCell cultureBiologyMolecular biologyMedicineOncologyHistone deacetylaseChemotherapyHistoneGeneticsGeneIn vitro

Abstract

fetched live from OpenAlex

Abstract Background: NSCLC is the most common cause of cancer-related death. Platinum-based chemotherapy is the mainstay of treatment, but a variety of mechanisms lead to platinum-resistance. ATF3 is a transcription factor, activated in response to a wide variety of stress signals including DNA damage and hypoxia. We recently demonstrated a role for ATF3 as an important regulator of platinum-induced cytotoxicity. In this study, we hypothesize that ATF3 expression correlates with platinum-sensitivity/resistance in NSCLC. Methods: ATF3 induction was examined by Western blots and real-time RT-PCR in isogenic sets of platinum- sensitive (S) or induced resistant (R) NSCLC cell lines. A 1200 compound library was screened to identify platinum-sensitizers in both sets of cell lines. Complete RNA sequencing (RNA-seq) was performed in parental (S) and resistant (R) derived cell lines comparing expression patterns of either untreated or platinum treated cells. Similarly, platinum R and S tumors were identified by screening NSCLC patients’ clinical records and expression patterns compared with RNA-seq analysis. Results: Both mRNA and potein levels of ATF3 were induced 35-120 fold (mRNA) by cisplatin treatment in S cell lines, but only 1-12 fold (mRNA) in the corresponding R lines. The 1200 compound library screen of FDA approved compounds identified several commonly used chemotherapeutic agents as sensitizers of platinum cytotoxicity, as well as vorinostat, a histone deacetylase inhibitor that sensitized only S but not R cell lines. An analogue of this agent, M344, enhanced ATF3 induction and cisplatin cytotoxicity in the S lines but not in the R lines. Comparing RNA-seq results of the S and R lines revealed up-regulation of GADD45A, ATF3 and DDIT3/CHOP in the S but not R cell lines. Comparing samples of S and R NSCLC tumors by RNA-seq demonstrated ATF3 to be among the five genes significantly up-regulated in the S tumors compared to the R tumors. Conclusions: Stress-induced ATF3 is correlated with platinum-sensitivity in NSCLC cells. Tumor ATF3 levels represent a potential predictive marker of response to platinum-based treatment in NSCLC. Revealing the mechanism of ATF3 induction by platinum may lead to the identification of novel platinum-sensitizing therapeutic targets. Citation Format: Jair Bar, Ivan Gorn-Hondermann, Reid Stephanie, Patricia Moretto, Iris Shiran, Shlomit Jessel, Marina Perelman, Eyal Heller, Iris Kamer, Inbal Daniel-Meshulam, Glenwood D. Goss, Jim Dimitroulakos. Activating transcription factor 3 (ATF3) down-regulation correlates with platinum resistance in non-small cell lung cancer (NSCLC). [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 895. doi:10.1158/1538-7445.AM2013-895

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.029
GPT teacher head0.317
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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