Epirubicin, oxaliplatin, and capecitabine with or without panitumumab for patients with previously untreated advanced oesophagogastric cancer (REAL3): a randomised, open-label phase 3 trial.

BACKGROUND: EGFR overexpression occurs in 27-55% of oesophagogastric adenocarcinomas, and correlates with poor prognosis. We aimed to assess addition of the anti-EGFR antibody panitumumab to epirubicin, oxaliplatin, and capecitabine (EOC) in patients with advanced oesophagogastric adenocarcinoma. METHODS: In this randomised, open-label phase 3 trial (REAL3), we enrolled patients with untreated, metastatic, or locally advanced oesophagogastric adenocarcinoma at 63 centres (tertiary referral centres, teaching hospitals, and district general hospitals) in the UK. Eligible patients were randomly allocated (1:1) to receive up to eight 21-day cycles of open-label EOC (epirubicin 50 mg/m(2) and oxaliplatin 130 mg/m(2) on day 1 and capecitabine 1250 mg/m(2) per day on days 1-21) or modified-dose EOC plus panitumumab (mEOC+P; epirubicin 50 mg/m(2) and oxaliplatin 100 mg/m(2) on day 1, capecitabine 1000 mg/m(2) per day on days 1-21, and panitumumab 9 mg/kg on day 1). Randomisation was blocked and stratified for centre region, extent of disease, and performance status. The primary endpoint was overall survival in the intention-to-treat population. We assessed safety in all patients who received at least one dose of study drug. After a preplanned independent data monitoring committee review in October, 2011, trial recruitment was halted and panitumumab withdrawn. Data for patients on treatment were censored at this timepoint. This study is registered with ClinicalTrials.gov, number NCT00824785. FINDINGS: Between June 2, 2008, and Oct 17, 2011, we enrolled 553 eligible patients. Median overall survival in 275 patients allocated EOC was 11.3 months (95% CI 9.6-13.0) compared with 8.8 months (7.7-9.8) in 278 patients allocated mEOC+P (hazard ratio [HR] 1.37, 95% CI 1.07-1.76; p=0.013). mEOC+P was associated with increased incidence of grade 3-4 diarrhoea (48 [17%] of 276 patients allocated mEOC+P vs 29 [11%] of 266 patients allocated EOC), rash (29 [11%] vs two [1%]), mucositis (14 [5%] vs none), and hypomagnesaemia (13 [5%] vs none) but reduced incidence of haematological toxicity (grade ≥ 3 neutropenia 35 [13%] vs 74 [28%]). INTERPRETATION: Addition of panitumumab to EOC chemotherapy does not increase overall survival and cannot be recommended for use in an unselected population with advanced oesophagogastric adenocarcinoma. FUNDING: Amgen, UK National Institute for Health Research Biomedical Research Centre.

Multivariate analysis of regional-scale geochemical data for environmental monitoring

A compositional multivariate approach is used to analyse regional scale soil geochemical data obtained as part of the Tellus Project generated by the Geological Survey Northern Ireland (GSNI). The multi-element total concentration data presented comprise XRF analyses of 6862 rural soil samples collected at 20cm depths on a non-aligned grid at one site per 2 km2. Censored data were imputed using published detection limits. Using these imputed values for 46 elements (including LOI), each soil sample site was assigned to the regional geology map provided by GSNI initially using the dominant lithology for the map polygon. Northern Ireland includes a diversity of geology representing a stratigraphic record from the Mesoproterozoic, up to and including the Palaeogene. However, the advance of ice sheets and their meltwaters over the last 100,000 years has left at least 80% of the bedrock covered by superficial deposits, including glacial till and post-glacial alluvium and peat. The question is to what extent the soil geochemistry reflects the underlying geology or superficial deposits. To address this, the geochemical data were transformed using centered log ratios (clr) to observe the requirements of compositional data analysis and avoid closure issues. Following this, compositional multivariate techniques including compositional Principal Component Analysis (PCA) and minimum/maximum autocorrelation factor (MAF) analysis method were used to determine the influence of underlying geology on the soil geochemistry signature. PCA showed that 72% of the variation was determined by the first four principal components (PC’s) implying “significant” structure in the data. Analysis of variance showed that only 10 PC’s were necessary to classify the soil geochemical data. To consider an improvement over PCA that uses the spatial relationships of the data, a classification based on MAF analysis was undertaken using the first 6 dominant factors. Understanding the relationship between soil geochemistry and superficial deposits is important for environmental monitoring of fragile ecosystems such as peat. To explore whether peat cover could be predicted from the classification, the lithology designation was adapted to include the presence of peat, based on GSNI superficial deposit polygons and linear discriminant analysis (LDA) undertaken. Prediction accuracy for LDA classification improved from 60.98% based on PCA using 10 principal components to 64.73% using MAF based on the 6 most dominant factors. The misclassification of peat may reflect degradation of peat covered areas since the creation of superficial deposit classification. Further work will examine the influence of underlying lithologies on elemental concentrations in peat composition and the effect of this in classification analysis.