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Record W2612399534 · doi:10.1016/j.lungcan.2017.05.010

Evaluation of the VeriStrat ® serum protein test in patients with advanced squamous cell carcinoma of the lung treated with second-line afatinib or erlotinib in the phase III LUX-Lung 8 study

2017· article· en· W2612399534 on OpenAlexaff
S. Gadgeel, Glenwood Goss, Jean‐Charles Soria, Enriqueta Felip, Vassilis Georgoulias, Shun Lü, Manuel Cobo, Konstantinos Syrigos, Ki Hyeong Lee, Erdem Göker, Salih Zeki Güçlü, Dolores Isla, Alessandro Morabito, Nicholas Dupuis, Claudia Bühnemann, Nicole Krämer, Flavio Solca, E. Ehrnrooth, Andrea Ardizzoni

Bibliographic record

VenueLung Cancer · 2017
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsAfatinibErlotinibMedicineInternal medicineLung cancerOncologyClinical endpointPopulationGastroenterologyAdenocarcinomaRandomized controlled trialCancerEpidermal growth factor receptor

Abstract

fetched live from OpenAlex

Objectives Identification of biomarkers associated with clinical benefit may be crucial in establishing optimal treatment choice for patients with squamous cell carcinoma (SCC) of the lung after first-line chemotherapy. In this study, the ability of the VeriStrat serum protein test to predict differential clinical benefit with afatinib versus erlotinib, and the association of VeriStrat status with clinical outcomes irrespective of EGFR-TKI used, was assessed in a retrospective analysis of the phase III LUX-Lung 8 trial. Materials and methods Pretreatment plasma samples were analyzed using matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Spectra were evaluated to assign a VeriStrat ‘Good' (VS-G) or VeriStrat ‘Poor' (VS-P) classification. Overall survival (OS), progression-free survival, and other endpoints were assessed with respect to pretreatment VeriStrat status; OS was the primary efficacy variable. Results Of 795 patients randomized in LUX-Lung 8, 675 were classified (VS-G: 412; VS-P: 263). In the VS-G group, OS was significantly longer with afatinib versus erlotinib (HR 0.79 [95% CI: 0.63–0.98]). In the VS-P group, there was no significant difference in OS between afatinib and erlotinib (HR 0.90 [0.70–1.16]). However, there was no interaction between VeriStrat classification and treatment group for OS ( p interaction =0.5303). OS was significantly longer in VS-G versus VS-P patients, both in the overall VeriStrat-classified population (HR 0.41 [0.35–0.49]) and afatinib-treated patients (HR 0.40 [0.31–0.51]). Multivariate analysis showed that VeriStrat was an independent predictor of OS in afatinib-treated patients, regardless of ECOG PS or best response to first-line chemotherapy. Outcomes with other efficacy endpoints were similar. Conclusion VS-G classification is strongly associated with favorable survival outcomes with either afatinib or erlotinib compared with VS-P classification. In VS-G patients, survival outcomes with afatinib are superior to those with erlotinib. VeriStrat classification may guide treatment decisions in patients with SCC of the lung. ClinicalTrials.gov registration number: NCT01523587.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.018
GPT teacher head0.350
Teacher spread0.332 · 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

Citations28
Published2017
Admission routes1
Has abstractno

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