DOI: 10.7910/dvn/csnwrf
title: Specificity of Metabolic Colorectal Cancer Biomarkers in Serum Through Effect Size.

description: INTRODUCTION___ Colorectal cancer is one of the most diagnosed cancers, leading to a large number of deaths. In addition to existing screening methods, metabolic profiling can help both to diagnose and to understand the various states of the disease. 
Objectives We aimed to find candidate biomarkers (CB) of the active state of colorectal cancer that are specific to it, in comparison to the remission state of the disease evaluated on distinct patients (cross-sectional study).___
METHODS___ All serum samples were analyzed using comprehensive two-dimensional gas chromatography (GC×GC) coupled to high resolution time of flight mass spectrometry through an optimized and validated untargeted analytical method regulated by a quality control (QC) system. First, we used a specific multi-approaches data (pre)processing workflow to highlight, annotate and assess the performances of the most altered metabolites between the active state (CRC, n=18) and healthy control samples (HC, n=19) specifically matched for age and gender, two of the most influential bias factors. 
On the contrary, due to the difficulty to control and to match for all clinical and demographic traits when sampling small cohorts, we used samples from patients in remission (R-CRC, n=17) that were not matched for age and gender. Because of the consequent risk of bias, the usual null hypothesis significance tests (NHST) could not be applied reliably. Therefore, we compared the R-CRC samples to another specifically matched group of healthy controls (R-HC, n=17), and used this comparison to indirectly compare the two states of the disease (CRC vs R-CRC) through a measure called effect size (ES) whose methodological aspects were investigated.___
RESULTS___ We obtained 24 candidate biomarkers able to discriminate the CRC and HC samples efficiently (ROC AUC of 0.86, sensitivity and specificity of 0.72 and 0.78). 10 of those were found to have signals significantly returning to normal, near healthy levels in the R-CRC samples and were therefore specific to the active state of colorectal cancer. Regarding the effect size, we observed that, in a point biserial case, many statistical values led to identical ES, and r-like and d–like ES were directly convertible. Only linear and rank-based ES were different. Hedges’ g, Spearman’s rho and Kendall’s tau are therefore recommended, along with an unstandardized ES such as the percentage of variation of the raw analytical signals. The confidence intervals were of particular interest to consider the uncertainty of the measure and were well represented through scatterplots and distribution curves.___
CONCLUSION___
The candidate biomarkers found, along with their specificity, could, after proper validation on independent cohorts, complement the detection of colorectal cancer and help understand its occurrence and remission. The effect size, used and studied on a MS global profiling data set, is an ideal complement to NHST and a useful tool to compare and combine distinct cohorts, here within a study but also between studies (meta-analysis).
description: GC Image, 2.5
description: EXPERIMENTAL DESIGN.__________ SAMPLES__________ Patient recruitment and serum sample intakes were performed at the university hospital of Liège, Belgium. The project protocol received full approval of the institutional ethical reviewing board and each patient provided a signed informed consent (B707201213737). For all samples, the sample intake, processing and storage procedures were standardized and followed our biobanking guidelines developed for proteomics studies and utilized also for clinical trials. We analysed serum samples from colorectal cancer patients in the active (CRC) and remission (R-CRC) states (n= 18 and n=17) as well as controls (HC and R-HC) respectively matched for gender and age (n=19 and n=17). Based on endoscopic examination, patients with pTNM confirmed cancer lesions (cancer group) or with a previous history of cancer (cancer in remission group) were included. The (“healthy”) control groups consisted of patients screened by colonoscopy and diagnosed negative for CRC as well as for any other pathology affecting the bowel (as inflammatory bowel disease, diverticulitis...) or any other known cancer at the time of sample intake. The clinical data of the patients are summarized in the Supporting Information (SI S-2). The internal QC samples consisted in 30 µL aliquots of the study samples, made through one freeze-thaw cycle__________ PROTOCOLS __________ SAMPLE COLLECTION __________ For all samples, the sample intake, processing and storage procedures were standardized and followed our biobanking guidelines utilized also for clinical trials. These conditions, originally developed from proteomics studies are quite strict concerning the collection and conservation of samples. In brief, Venosafe (Terumo, NV, EU) 10 mL dry tubes were used for serum collection. The gel separators belonged to different batches due to the total collection time that lasted longer than the batch validity. Clotting time was set at 30 minutes at room temperature before a centrifugation step at 2,000 RCF for 10 minutes at 4 °C. Aliquoting of sera was performed in polypropylene cryotubes. The delay between sample intake and storage (-80 °C) was kept inferior to 2 hours. All patients recruited for the proof of concept study were controlled, for IBD or any known digestive cancer by an endoscopic examination of the bowel (rectum, colon and, when possible, last of the ileum) performed by a trained endoscopist. When applicable, the diagnosis was confirmed by an anatomopathological examination of the biopsies or specimen resected. __________ SAMPLE PREPARATION __________ All samples were stored at -80 °C until use. After thawing at 4 °C for 1h, 30 µL of sample were put in hole-shaped vials to which 3 µL of glycine-2,2-d2 and succinic acid-2,2,3,3-d4 were added as isotopically labelled internal standards. Metabolites were extracted with methanol (3:1) before cooling at -20 °C for 10 min and centrifugation at 9,300 RCF for 10 min. Fumaric acid-2,3-d2 (3 µL) was then added, before the samples were dried under gentle N2 flow while heated at 40 °C. They were stored again at -80 °C until use. Before injection, the metabolites were made volatile with 15 µL of MeOX and 10 µL of MSTFA. Both reactions were performed at 40 °C for 1 hour. The samples were homogenized after each step by 1 min vortexing. The medium was recovered and 3 µL of 4,4'-dibromooctafluorobiphenyl were finally added. All steps were performed manually. __________ CHROMATOGRAPHY __________ Capillary column set Rxi-5/Rxi-17 (non polar/semi-polar) of 30 m and 1 m respectively, both 0.25 mm ID and 0.25 µm film thickness. Helium as carrier gas in constant flow mode (1 ml/min). __________ MASS SPECTROMETRY __________ Instrumentation consisted in a JMS-T100GCVV GC×GC-TOF-MS (Jeol Ltd, Tokyo, Japan), using an Agilent 6890 GC (Santa Clara, California, USA) coupled to a CTC Autosampler (CTC Analytics AG, Zwingen, Switzerland). ZX1 loop cryogenic modulator using liquid nitrogen (Zoex Corporation, Houston, USA). Initial temperature of 50 °C held for 1 min. Temperature ramp of 3 °C/min until 240 °C then 15 °C/min to 300 °C held for 5 min. Modulation period of 3.5 s coupled to a 700 ms hot jet and a 250 °C splitless injection of 1 µL using a ‘precision’ liner. Electron ionization (EI) in positive mode, 70 eV, 300 µA. Acquisition rate of 25 Hz at m/z 50-700. Ion source and transfer line temperatures of 250 °C. For exact mass (metabolite identification) : same conditions using a Pegasus GC-HRT 4D (LECO Corporation, Saint-Joseph, Michigan, USA; resolution of 25,000) with the secondary oven kept 15 °C higher than the main one. __________ DATA TRANSFORMATION __________ The raw data acquired were preprocessed through centroidization (Data Manager software, JEOL Ltd, Japan; .cdf and .gci data formats). GC Image 2.5 software (LLC, Lincoln, Nebraska, USA) automatically performed a process blank substraction and baseline correction. The multiple chromatograms were aligned and used to automatically create a composite template that included all detected features. The template was manually verified for artefacts, degradation zones, co-elutions and multiple peaks of single compounds (amino acids, sugars and wraparounds) that were respectively removed, split and merged. Then it was applied to each individual chromatogram to generate consistent peak lists examined for transcription errors. Only metabolites present in at least half the samples of any class were kept. The remaining missing values were replaced by the lowest signal measured for the metabolite divided by two. Peak volumes were normalized to the internal standard giving the lowest relative standard deviation (RSD) in the QC samples. Outliers were sought for by calculating robust z-scores, according to the median absolute deviation of the variable in all concerned samples. When &gt; 50, the outlier was replaced by the closest value divided or multiplied by two (maximum or minimum outlier). Finally, the peak volumes were corrected for analytical variation measured in the QC samples through a locally estimated scatterplot smoothing (LOESS) procedure. Only the metabolites with a corrected analytical variation (RSD) lower than 30% were kept. __________