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Evaluation of circulating VEGF based biomarkers in INTEGRATE: A randomized phase II double-blind placebo-controlled study of regorafenib in refractory advanced oesophagogastric cancer (AOGC)—A study by the Australasian Gastrointestinal Trials Group (AGITG).

2016· article· en· W2590057071 on OpenAlexaffabout
Sonia Yip, Rozelle Harvie, Andrew Martin, Katrin Marie Sjoquist, Eric Tsobanis, Yoon‐Koo Kang, Yung‐Jue Bang, Thierry Alcindor, Christopher J. O’Callaghan, Margot J. Burnell, Niall C. Tebbutt, Sun Young Rha, Jeeyun Lee, Y. Choi, Lara Lipton, Andrew Strickland, John Zalcberg, John Simes, David Goldstein, Nick Pavlakis

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsSaint John Regional HospitalMcGill University Health Centre
Fundersnot available
KeywordsMedicineInternal medicineVEGF receptorsRamucirumabInterleukin 8OncologyBevacizumabGastroenterologyPlaceboVascular endothelial growth factorRefractory (planetary science)Proportional hazards modelBiomarkerCancerPathologyCytokineChemotherapyBiology

Abstract

fetched live from OpenAlex

64 Background: The INTEGRATE study evaluated activity of regorafinib (REG) v placebo (PBO) in 147 eligible patients with refractory AOGC. REG was highly effective in prolonging progression free survival (PFS). Differences between regions (i.e. Australia New Zealand/Canada (ANZ/CAN) vs Korea) were found in the magnitude of effect, but REG was effective across all regions and subgroups. We report on an exploratory analysis of VEGF biomarkers to identify predictive/prognostic markers. Methods: Protein biomarkers IL8, VEGF-A,-B,-C-D, soluble(s)VEGFR-1,-2-3 were analysed in plasma at baseline (BL) by multiplex immunoassays (Bio-Plex,BioRad) or ELISA (Abnova). Spearman statistics were used to quantify correlations between markers. Wilcoxon Rank-Sum tests were used to compare markers across regions. The prognostic and predictive value of markers was determined using cox proportional hazards analysis of PFS. Results: There were moderate-to-strong correlations between BL levels of IL8 and VEGF-C (ρ = 0.68), IL8 and VEGF-D (ρ = 0.66), VEGF-A and VEGF-C (ρ = 0.68), VEGF-A and sVEGFR-1 (ρ = 0.54); and a modest negative correlation between VEGF-D and sVEGRF-1 (ρ = -0.33). The regions differed according to BL levels of: VEGF-A (higher in ANZ/CAN; p = 0.0015), VEGF-B (higher in Korea; p = 0.0003), VEGF-D (higher in Korea; p <.0001), and sVEGFR-1 (higher in ANZ/CAN; p <.0001). Adjusting for treatment group, there were statistically significant negative associations between PFS and BL IL8 (p = 0.047), VEGF-A (p = 0.037) and sVEGFR-1 (p = 0.045). There was no convincing statistical evidence that any BL plasma biomarker modified the effect of REG. The effect of region on effectiveness of REG was maintained when evaluated in conjunction with BL biomarkers individually and in combination. Conclusions: Highplasma IL8, VEGF-A and sVEGFR-1 may be adverse prognostic factors. A predictive VEGF blood based biomarker remains elusive. A broader biomarker study including markers beyond the VEGF axis and tissue based markers is ongoing. Clinical trial information: ACTRN12612000239864.

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
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 head0.228
GPT teacher head0.521
Teacher spread0.293 · 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 designRandomized trial
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

Citations3
Published2016
Admission routes2
Has abstractyes

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