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Prognostic and predictive effects of<i> KRAS</i> mutation subtype in completely resected non-small cell lung cancer (NSCLC): A LACE-bio study.

2012· article· en· W2600031158 on OpenAlexaff
Frances A. Shepherd, Abderrahmane Bourredjem, Élisabeth Brambilla, Caroline Domerg, Jean-Yves Douillard, Martin Filipits, Stephen L. Graziano, Pierre Hainaut, Pasi A. Jänne, Thierry Le Chevalier, Gwénaël Le Teuff, Jean‐Pierre Pignon, Robert Pirker, Lesley Seymour, Jean‐Charles Soria, Miquel Tarón, Ming‐Sound Tsao

Bibliographic record

VenueJournal of Clinical Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsQueen's UniversityPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsKRASMedicineHazard ratioColorectal cancerOncologyInternal medicineMutationCancerChemotherapyLung cancerCancer researchGeneticsGeneConfidence intervalBiology

Abstract

fetched live from OpenAlex

7007 Background: We reported previously that KRAS is weakly prognostic and not significantly predictive of benefit from adjuvant chemotherapy (ACT) in NSCLC (Tsao et al. Proc ESMO 2012, Abst 4217). In colorectal cancer (CRC), KRAS mutation predicts resistance to EGFR monoclonal antibodies, but recent reports suggest that this may not be true for all mutation subtypes (De Roock et al. JAMA 2010). Smoking-related KRAS mutations in NSCLC differ from those of CRC. To explore the influence of KRAS mutation subtype, we undertook an analysis of KRAS subtype in LACE-Bio. Methods: KRAS mutation was determined in blinded fashion in 3 laboratories by direct sequencing. Exploratory analyses were performed to identify relationships between mutation status and subtype, overall (OS) and disease-free survival (DFS) using a Cox model stratified by trial and adjusted for covariates. Results: KRAS subtype was available in 1,532 patients (756 observation [OBS], 776 ACT). There were 300 mutations (275 codon 12, 24 codon 13, 1 codon 14). In the OBS arm, there was no difference in prognosis for OS for codon 12 (Hazard Ratio [HR] mutation v wild-type [WT] KRAS 1.04, CI .77-1.4) or codon 13 (HR 1.01, CI 0.47-2.17) mutations. A trend for benefit from ACT was observed in WT (HR ACT v OBS 0.89 CI 0.76-1.04, p=0.15) but not in patients with codon 12 mutations (HR 0.95, CI .67-1.35, p=0.77). In patients with codon 13 mutations, chemotherapy was deleterious (HR 5.78, CI 2.06-16.2, p<0.001), test for equality among 3 HRs p=0.002. Results were similar for DFS. Among codon 12 mutations, there was no prognostic effect based on specific amino acid substitution. Patients with G12A or G12R mutations appeared to derive greater benefit from ACT (HR 0.66 p=0.48) compared to those with G12C or G12V (HR 0.94 p=0.77) or G12D or G12S (HR 1.39 p=0.48) or WT, but the differences were not significant (comparison of 4 HRs p=0.76). Conclusions: In this study, patients with KRAS codon 13 mutations had significantly poorer outcomes with ACT. These results require further confirmation and should be interpreted with caution in view of the small number of patients with codon 13 mutations. Supported by unrestricted grants from Sanofi Aventis and LNCC.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.051
GPT teacher head0.456
Teacher spread0.405 · 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

Citations17
Published2012
Admission routes1
Has abstractyes

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