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Prognostic and predictive effects of a gene expression signature for NRF2 pathway activation in lung squamous cell carcinoma (SqCC).

2013· article· en· W3011197425 on OpenAlexaff
David W. Cescon, Desmond She, Chang‐Qi Zhu, Shingo Sakashita, Melania Pintilie, Frances A. Shepherd, Ming‐Sound Tsao

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsGeneGene expression profilingMicroarrayVinorelbineLung cancerOncologyGene signatureMicroarray analysis techniquesCisplatinCancer researchERCC1BiologyMedicineGene expressionInternal medicineGeneticsChemotherapy

Abstract

fetched live from OpenAlex

7517 Background: Genomic profiling of SqCC in TCGA identified somatic alterations that activate the NRF2 transcriptional program – a master regulator of the oxidative stress response – in ~35% of tumors (NFE2L2 mutations/amplifications, KEAP1 or CUL3 mutations/deletions). This pathway has been implicated in resistance to chemotherapy. To evaluate the clinical significance of this molecular subset, we developed a gene expression classifier and tested this signature as a predictor of adjuvant chemotherapy benefit with cisplatin/vinorelbine (cis/vin) in a subset of SqCC patients with microarray data from the NCIC JBR.10 Phase III clinical trial. Methods: Logistic regression (LR) and SAM analysis were independently applied to 104 TCGA SqCC cases that had both microarray gene expression and mutation data to identify genes associated with NRF2 pathway mutational status. Overlapping genes were used to define the signature, which was then tested in 3 independent SqCC datasets (62 JBR.10; 54 UHN; 129 UM) to evaluate the prognostic and predictive values of putative NRF2 pathway activation. Results: 29 genes comprising the signature were identified by overlap between LR (291 genes) and SAM (45 genes). The signature consistently separated SqCC into 2 groups in all datasets, corresponding to putatively activated and wild type (WT) NRF2 pathway tumors. No prognostic effect of the activated signature was observed in independent datasets (UHN HR 0.86, 95%CI 0.28 – 2.67; UM HR 1.43, 95%CI 0.82 – 2.48). Similarly, in JBR10, no prognostic effect was observed in the observation arm (n=24, HR 0.66, 95%CI 0.13 – 3.29). A trend toward improved survival with adjuvant chemotherapy was observed in patients with the WT signature (HR 0.34, 95%CI 0.08 – 1.78, p=0.13), but not in patients with the activated signature (HR 1.16, 95%CI 0.19 – 6.97, p=0.87; interaction p=0.18). Conclusions: A gene expression signature based on mutational activation of the NRF2 pathway may be predictive of benefit from adjuvant cis/vin in SqCC. Patients with NRF2 pathway activating somatic alterations may have reduced benefit from this therapy. Validation of this potentially "actionable" finding in additional datasets is necessary.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.397
Teacher spread0.372 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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