Development of blood-based biomarker tests for early detection of colorectal neoplasia: Influence of blood collection timing and handling procedures.

Abstract

Introduction:

Blood-based, cancer-associated biomarkers are susceptible to a variety of well-known preanalytical factors. The influence of bowel preparation before a diagnostic colonoscopy on biomarker levels is, however, poorly investigated. The present study assessed the influence of bowel preparation on colorectal cancer-associated biomarkers. In addition, the effect of single versus double centrifugation of plasma biomarkers was assessed.

Methods:

Blood samples were collected pre- and post-bowel preparation from 125 subjects scheduled for first time diagnostic colonoscopy due to symptoms attributable to CRC. The samples were separated into serum and EDTA plasma, and analyzed by four independent collaborators for: 1) the proteins AFP, CA19–9, CEA, hs-CRP, CyFra21–1, Ferritin, Galectin-3 and TIMP-1, 2) the proteins BAG4, IL6ST, vWF, CD44 and EGFR, 3) the glycoprotein Galectin-3 ligand, and 4) cell-free DNA (cfDNA). Statistical analysis of biomarker data has been performed using mixed modelling, including repeated measures.

Results:

The biomarkers generally showed negligible variation between pre- and post-bowel preparation except for CyFra21–1, Ferritin, BAG4 and cfDNA. CyFra21–1 levels were systematically reduced with 29% (95% CI 21–36%) by bowel preparation (p≤0.0001). Ferritin was not significantly different between pre- and post-bowel preparation (p=0.07), however the estimated difference (increase) was 18%. BAG4 was systematically reduced by 12% (95% CI 1–22%, p=0.04), while cfDNA showed a significant increase of 28% (95% CI 17–39%, p<0.0001).

Double centrifugation compared to single centrifugation showed reduced vWF (ratio 0.86, p≤.0001) and CD44 (ratio 0.85, p=0.016), but increased IL6ST levels (ratio 1.18, p=0.014).

Conclusions:

Results of the present study demonstrated systematic, statistically significant differences between pre-bowel and post-bowel preparation levels for three independent blood-based biomarkers (BAG4, CyFra21–1, cfDNA), illustrating the importance of timing of sample collection for biomarker analyses.

Introduction

Although testing for occult fecal blood is considered to be the optimal population screening method for colorectal neoplasia in terms of cost-benefit, the compliance issues appear to be a dominant limitation for population screening. Procedures using fecal immunochemical testing (FIT) with sensitivities for colorectal cancer (CRC) of an average of 79%¹, are limited by compliance of 60–63%², which reduces the detection (clinical sensitivity) of subjects with CRC to 47.5% - 50%³. Corresponding results for detection of advanced neoplasia (combination of high-risk adenoma and CRC) appear to be in the range of 20% - 30%⁴.

Improvement of population screening for colorectal neoplasia needs to focus on at least two specific parameters – the test sensitivity and the compliance. Obviously, the test cut-off level plays a prominent role; results from major studies indicate that the higher the cut-off the higher the risk of missed lesions^5–7. Therefore, the optimal cut-off balancing sensitivity and yield at colonoscopy must be finally settled⁷. The issue of limited compliance² needs utmost attention, because less than two thirds of the screen relevant populations adhere to screening, even in countries where screening and possible subsequent colonoscopy procedures are free of charge.

In order to improve clinical sensitivities, simultaneously focusing on improvement of both the test sensitivity and the compliance issue appears to be critical. Screening tests which include blood-based, cancer-associated biomarkers may be one of the options to improve compliance and overall screening approaches^8–10, either as a front-line test or as part of a triage test in combination with fecal testing^6,11. At present, many studies appear to be based on blood samples collected non-uniformly from groups of subjects at risk of colorectal neoplasia and from patients with known colorectal neoplasia, or ultimately from a mix of these two groups^12–19. Therefore, it is unknown how current results would translate when implemented in routine screening approaches. Although guidelines including blood collection and handling for discovery, testing and validation of blood-based, cancer-associated biomarkers have been established^20–22 only very few reports include detailed presentation of blood collection, handling, storage and repository surveillance procedures. As biomarkers can be derived from proteomes, genomes, epigenomes and metabolomes, the handling procedures may also play a significant role, as it is well accepted that various biomolecules, including proteins and cell-free DNA, are released from white cells and platelets during handling, storage and thawing procedures^23–25. Consideration of the timing of blood collection may be critical, because recent results have indicated that bowel preparation may affect biomolecule levels and subsequent interpretation of the results^26,27.

Previous work on the effect of bowel preparation influencing cancer-associated biomarkers is limited. When conducting a thorough literature search (PubMed and EMbase) only a few reports exist, most of which concern the well-described and well-established biomarker Carcinoembryonic Antigen (CEA). One study found no effect of bowel preparation²⁸, yet two other studies found a small increase in serum CEA after bowel preparation^29,30. Thus, CEA levels appear to show limited variations of what might be expected from pre-analytical-, analytical- and biological variation. The changes of CEA levels would have negligible impact on clinically relevant variations.

Yet in theory, other biomarkers might be susceptible to changes since bowel preparation solutions unquestionably change the inner homeostatic set-point for some electrolytes and molecules. Certain electrolytes and molecules seem to increase after use of bowel preparation solutions (calcium, creatinine) and some decrease (sodium, potassium, chloride, testosterone)^31–33.

The next step in development of blood-based tests should focus on blood samples and data collected from population-based screening that uses tests for occult fecal blood, including FIT. Indeed, the timing of the blood collection might be extremely important, because such studies include subjects that may have positive or negative FIT test results. Differentiations between those two groups have to consider the influence of bowel preparation before colonoscopy among the FIT-positive subjects, a procedure that definitely will not be applied to FIT-negative subjects. Therefore, the primary aim of the present study was to evaluate the influence of bowel preparation on results of various cancer-associated biomarkers. A secondary aim was to evaluate the effect of single- versus double-centrifugation, which is often used in studies involving nucleic acids, on results achieved by analyses of plasma samples.

Methods

125 subjects (Danish citizens) scheduled to undergo diagnostic colonoscopy due to symptoms attributable to CRC were consecutively included in the study from September 2014 to February 2015. All eligible subjects ≥18 years of age were invited at the first out-patient visit, where the colonoscopy procedure was scheduled. According to the approval from the Ethics Committee of The Capital Region of Denmark (H-1–2014-068) all signed a consent form before entering into the study, which was performed according to the Helsinki Declarations. The first set of blood samples were collected at room temperature using light tourniquet at one of the forearms into 5 × 10 ml DNA’se-, RNA’se- and endotoxin-free Vacutainer collection tubes (Becton Dickinson, Wokingham, UK), one for serum and 4 for EDTA plasma, at the out-patient visit. According to a validated standard operative procedure (SOP), the tubes for serum were left for 20–30 mins at room temperature for complete clotting and thereby hindering fibrin clots, while the four tubes for EDTA plasma were centrifuged immediately at 3,000 G, 10 mins at 21°C. The supernatant plasma from two of the tubes were pipetted into 1.5 ml storage tubes (Dacos A/S, Esbjerg, Denmark); leucocyte contamination was minimized by leaving 0.5 cm supernatant above the buffy-coat untouched. The supernatant EDTA plasma from the remaining two EDTA collection tubes were pipetted into one re-centrifugation tube without additives (Dacos A/S, Esbjerg, Denmark). Subsequently, the collection tube for serum and the re-centrifugation tube with EDTA plasma were centrifuged at 3,000 G, 10 mins at 21°C and finally pipetted into 1.5 ml storage tubes. Those tubes were marked with unique barcodes for identification, which was performed using the FreezerWorks® (Seattle, WA, USA) pc-based management and tracking system. All storage tubes were then frozen and stored at −80°C freezers that were under 24/7 electronical surveillance in a separately locked repository. The entire blood collection, handling and storage procedures were finalized within two hours.

The included subjects underwent bowel preparation, and the second set of similar blood samples were collected just before any sedation in association with the subsequent colonoscopy procedure. These blood samples were also handled according to the very same SOP as used at the first set of blood samples. All blood-samples were handled by the same person to minimize pre-analytical variation due to difference in handling procedure. Also, all blood-samples (both pre – and post bowel preparation were obtained between 7:30 AM – 02:00 PM) Demographic data and data on findings at colonoscopy or at subsequent intervention, including operation, were entered into a pc-based database.

At termination of the inclusion of subjects, the frozen sample sets were transferred to various collaborators for analysis. Levels of AFP, CA19–9, CEA, hs-CRP, CyFra21–1, Ferritin, Galectin-3, and TIMP-1 were determined in a complete set of EDTA plasma samples by using the Abbott ARCHITECT® automated immunoassay platform utilizing a two-step dual monoclonal immunoassay. The determinations were performed at the Abbott Center of Excellence at Vrije University Medical Center (VUMC), Amsterdam, The Netherlands. Abbott in-house research TIMP-1 prototype and on-market reagents for all other ARCHITECT markers were used for the determinations.

Another complete set of EDTA plasma samples were used for determination of BAG4, IL6ST, vWF, CD44, and EGFR at Fred Hutchinson Cancer Research Center, Seattle, WA, USA using a modified Luminex assay, as described previously¹⁴. Briefly, plasma samples were depleted of IgG and albumin using ProteoPrep Immunoaffinity columns. Depleted samples were then reacted with a 20x molar excess of sulfo-NHS-Biotin (ThermoFisher) at RT for 30 minutes. Subsequently, free biotin was quenched with a 10x molar excess of ethanolamine (Sigma) on ice for 2 hours. Antibodies to BAG4 (Novus Biologicals, Centennial, CO 80112, USA, Catalogue number, 21080002), IL6ST (Novus Biologicals, 20480002), VWF (Abcam, Cambridge, UK, ab6994), CD44 (R&D Systems, Minneapolis, MN 55413, USA, BBA25), and EGFR (Novus Biologicals, 31700002) were each paired with a nonmagnetic, COOH bead (Bio-Rad, Hercules, CA, USA) that is uniquely labeled with two fluorescent dyes. Beads were activated with 0.5M Sulfo-HNS and 0.2M EDC (ThermoFisher) in 0.1M NaH₂PO₄, pH 6.2, for 20 minutes at room temperature in the dark (as are all subsequent steps). Beads were washed with 50μM MES, and primary antibodies were reacted to activated beads for 2 hours. After washing with PBS, beads were then incubated with 1% BSA for 30 minutes. Beads were then washed and stored in 1% BSA at 4°C. Subsequently, 5,000 of each unique antibody-coupled bead were added to individual wells of a filter plate (Millipore) and washed with PBST (PBS with 0.05% Tween-20). 50μL of biotinylated sample (10μg/mL total protein) was added to individual wells and shaken for 1 hour at RT. Beads were then washed 3 times with PBST and incubated with 50μL of a 1:1000 Streptavidin-R-Phycoerythrin conjugate (BD Biosciences, San Jose, CA, USA) for 30 minutes. Beads were again washed 3 times with PBST and 125μL of 1% BSA was added to each well. Fluorescent signal was read on a Bio-Rad Luminex 100 system, counting 50 beads per region.

A third complete set of EDTA plasma samples were used for determination of cell-free (cf)DNA. The cfDNA was extracted from 1 mL of plasma using the QIAsymphony DSP Virus/Pathogen Midi kit (Qiagen). cfDNA was quantified using a duplex droplet digital PCR (ddPCR) reaction measuring the number of circulating DNA templates at two independent reference genomic regions, located on chromosomes 3 and 7; the two assays are termed Chr3 and gCYC³⁴. The cfDNA quantity was calculated as the mean number of copies of the two reference regions. DdPCR was performed on a QX200 Droplet Digital PCR system following manufacturer’s instructions (Bio-Rad). Purification efficiency and analysis for contamination with DNA from lysed lymphocytes were assessed by ddPCR as previously described (28). In brief, a fixed amount of soybean CPP1 DNA fragments, was added to the plasma samples prior to extraction. Purification efficiency was calculated as the percent recovery of CPP1 fragments following cfDNA extraction. Lymphocyte DNA contamination was estimated by an assay targeting the VDJ rearranged IGH locus specific for B cells. For each reaction 9 uL cfDNA eluate was used. The final results are given as cfDNA copies per mL plasma after adjustment for purification efficiency.

Finally, a subset of serum samples from 63 subjects were used for analysis of Galectin-3 ligand, an aberrantly glycosylated haptoglobin glycoform³⁵, using a magnetic bead assay based on the Luminex platform (Luminex, Austin, TX). Magnetic beads were coupled to a rabbit α-haptoglobin antibody and used to perform a bead-based ELISA assay. Antibody binding was reported by a dual system of Biotinyl Erythrina Cristagalli Lectin (Biotinyl ECL, Vector, Burlingame, CA) and Streptavidin Phycoerythrin (SA-PE, Invitrogen, Thermo Fisher Waltham, MA). The detection was performed by measuring fluorescence with a Bio-Rad Bio-Plex system (Bio-Rad, Hercules, CA). Samples and haptoglobin standard (Sigma, St. Louis, MO) were desialylated by hydrolysis with 0.5N H₂SO₄ for 60 minutes at 80˚C. The mixture was then neutralized by 0.5N NaOH. Double distilled H₂O was added up to 1ml. The sample was diluted into a final concentration of 100,000-fold in 1x PBST. Then 0.1 ml of magnetic beads (Luminex, Austin TX) were coupled with rabbit anti-haptoglobin (Sigma, St. Louis, MO). Uncoupled magnetic beads were washed with wash buffer PBST (PBS, 0.05% Tween-20, pH 7.4) and re-suspended in activation buffer (0.1 M NaH₂PO₄, pH 6.2). To activate the COOH beads, 10 μL of 50 mg/mL Sulfo-N-hydroxysulfosuccinimide (S-NHS) and 10 μL of 50 mg/mL N-(3-Dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC) were prepared in activation buffer and added to the beads. The coupling reaction tube was covered with aluminum foil and agitated on a shaker for 20 minutes. Then 150 μL of PBS, pH 7.4, was added and the beads was washed two more times. After resuspension, the 9 μg of the antibody diluted in PBS was added to the activated beads. The coupling reaction was incubated for 2 hours with mixing on a shaker. After incubation, the beads were then washed with PBS, pH 7.4. Then blocking buffer (PBS-TBN, 0.1% BSA, 0.02% Tween-20, 0.05% Azide, pH 7.4) were added, and incubated for 30 minutes on a shaker. After incubation, the beads were washed with blocking buffer and were diluted to the final concentration. The coupled beads were then stored and refrigerated in 2–8 ˚C wrapped in aluminum foil. The magnetic beads were added to each well of the 96-well plate. The samples and standards in triplicate were also added to each well using a multi-channel pipette. A triplicate of each sample was performed to ensure reliability. The plate was vortexed and incubated at room temperature for 1 hour on a shaker while covered with aluminum foil. After the incubation, the plate was washed using a protocol on the Biorad Bio-Plex Pro™ Wash Station (Bio-Rad, Hercules, CA). After completion, 50 μL of 2.5 μg/mL Biotinyl ECL (Vector), diluted in staining buffer (1x PBS, 1% BSA, pH 7.4), was added to each well. The plate was vortexed, covered, and incubated at room temperature for one hour. After the incubation, the plate was again washed using the washer. 50 μL of 2.5 μg/mL SA-PE (Invitrogen,Thermo Fisher Inc., Waltham, MA) was prepared in staining buffer. Using a multi-channel pipette, the mixture was added to all wells on the plate. The plate was vortexed and left on the shaker for 10 minutes. After the final incubation, storage buffer (1x PBS, 0.1% BSA, 0.02% Tween-20, 0.05% Azide, pH 7.4) was added into each well. Fluorescence was measured by the Bio-rad Bio-Plex machine.

All achieved analysis results were transferred to the study statisticians at Hvidovre Hospital, Denmark and included in the database of demographic and findings data.

Statistics

Analysis of marker data has been done using mixed modelling, including repeated measures. All markers have been log transformed for statistical analysis in order to ensure a normal distribution. The results are then back transformed for data presentation. The explanatory variable are single/double centrifugation and sampling before or after bowel evacuation. Appropriate tests for interaction have been performed, the results for these are not shown as none were significant. Results are presented by the ratio of the level post evacuation against the level prior to evacuation and double centrifugation against single centrifugation. All results are shown with 95% confidence intervals (CI) and p-values. In addition, relevant Pearson correlations and coefficients of variation ($100\times \sqrt{\left(e^{{\sigma }^2}-1\right)}$) ³⁶ are included. The significance level has been set to 5%. Statistical analysis has been done using SAS (v9.4, SAS Institute, Cary, N.C. USA).

Results

Demographic characteristics for the 125 included subjects were a median age of 69 years (20–88, first quartile 61 years, third quartile 79 years) and 54 (43%) females and 71 (57%) males. The median BMI was 24.8 (17.2–43.7). Five subjects were diagnosed with colorectal cancer and 46 with adenomas.

Descriptive statistics for all biomarkers are shown in Table 1 (values given as the median level with the minimum and maximum as well as the 1^st and 3^rd quartiles).

Table 1.

Descriptive statistics for all biomarkers stratified by pre/post bowel evacuation, single/double centrifugation on the original scale.

			Median	Min	Q1	Q3	Max
Sample	Centrifuge	Marker
POST	Double	BAG4	429.00	66.00	325.50	632.50	8037.50
		EGFR	395.00	65.00	313.00	504.50	13799.00
		vWF	3433.75	985.50	2928.00	4212.50	23541.00
		IL6ST	816.75	81.00	562.50	1234.50	7393.00
		CD44	298.75	14.00	212.50	408.50	7059.50
		AFP	3.85	0.80	2.70	6.30	31.60
		CEA	2.10	0.50	1.40	3.40	103.50
		Ferritin	110.50	5.00	65.00	173.00	931.00
		Galectin-3	14.00	6.00	12.00	18.00	34.00
		CyFra21 −1	0.75	0.10	0.60	1.10	12.10
		CA19–9	6.80	2.00	3.90	11.90	70240.00
		TIMP-1	91.50	47.00	82.00	111.00	241.00
		hs-CRP	1.77	0.10	0.90	3.57	25.36
		cfDNA	2743.48	728.27	1977.39	3474.50	15170.57
		Galectin-3 ligand	1.61	0.19	1.18	2.29	6.34
	Single	BAG4	464.00	24.50	256.75	645.00	8624.50
		EGFR	373.50	4.00	272.00	550.50	15571.50
		vWF	4225.00	252.50	3374.25	5224.75	24055.00
		IL6ST	698.50	3.00	427.75	1107.50	10647.00
		CD44	365.50	4.50	239.50	508.00	15825.00
		AFP	4.05	0.80	2.60	6.30	33.60
		CEA	2.10	0.50	1.40	3.30	104.00
		Ferritin	105.50	5.00	65.00	166.00	983.00
		Galectin-3	14.00	6.00	12.00	17.00	33.00
		CyFra21 −1	0.70	0.20	0.60	1.10	12.70
		CA19–9	6.90	2.00	3.80	12.20	72924.00
		TIMP-1	94.00	48.00	84.00	114.00	230.00
		hs-CRP	1.77	0.10	0.86	3.54	25.32
		cfDNA	3016.99	700.73	2254.03	3668.75	16397.63
PRE	Double	BAG4	431.75	126.50	346.50	622.50	3757.50
		EGFR	402.00	93.00	322.00	520.00	4977.00
		vWF	3521.25	1655.00	2993.00	4078.00	11105.00
		ILST	751.00	170.50	558.00	1132.50	4358.50
		CD44	279.00	92.00	206.00	415.50	3748.50
		AFP	3.70	0.90	2.60	6.20	34.20
		CEA	2.20	0.50	1.40	3.40	105.70
		Ferritin	90.00	5.00	50.00	160.00	793.00
		Galectin-3	14.00	7.00	11.00	16.00	27.00
		CyFra21 −1	1.10	0.40	0.80	1.40	10.10
		CA19–9	7.40	2.00	3.70	12.00	79992.00
		TIMP-1	96.00	39.00	85.00	113.00	314.00
		hs-CRP	1.50	0.10	0.84	3.54	27.26
		cfDNA	2133.60	646.21	1603.46	2806.36	13221.14
		Galectin-3 ligand	1.64	0.36	1.07	2.24	6.50
	Single	BAG4	506.75	50.00	391.75	762.75	4679.00
		EGFR	409.50	13.00	293.00	546.00	7743.00
		vWF	4330.50	874.50	3609.00	5131.00	17463.50
		IL6ST	674.50	49.00	451.50	1260.00	8456.00
		CD44	348.00	18.00	253.50	510.00	5157.50
		AFP	3.70	0.80	2.60	6.20	36.10
		CEA	2.10	0.50	1.40	3.40	107.60
		Ferritin	88.00	5.00	49.00	165.00	890.00
		Galectin-3	14.00	7.00	11.00	17.00	26.00
		CyFra21 −1	1.10	0.30	0.80	1.40	10.40
		CA19–9	7.30	2.00	3.50	12.30	84593.00
		TIMP-1	99.00	38.00	86.00	114.00	297.00
		hs-CRP	1.50	0.10	0.85	3.41	27.58
		cfDNA	2334.61	540.18	1675.87	2914.51	13894.96

The primary analysis was mixed modelling for each biomarker and the results are presented in Table 2. The ratios with 95% CI are shown for each group of biomarkers. The Pearson moment correlations between the pre-bowel preparation biomarker levels and that of the post-bowel preparation levels are shown for samples with a single centrifugation and double centrifugation. A ratio of 1 implies no systematic difference of levels before and after bowel preparation.

Table 2.

Results of mixed modelling showing the ratios between post/pre evacuation and double/single centrifugation with 95% confidence intervals. In addition, the Pearson correlation coefficients between pre and post evacuation levels are given.

		Ratio	Lower limit	Upper limit	P-value	Pearson-r/double	Pearson-r/single
Marker
BAG4	Double cent. vs single	0.98	0.87	1.10	0.7154	.	.
	Post level vs pre level	0.88	0.78	0.99	0.0397	0.48	0.24
EGFR	Double cent. vs single	1.11	0.98	1.26	0.0991	.	.
	Post level vs pre level	0.94	0.83	1.07	0.3366	0.63	0.28
vWF	Double cent. vs single	0.86	0.80	0.93	<.0001	.	.
	Post level vs pre level	0.97	0.89	1.04	0.3650	0.63	0.32
IL6ST	Double cent. vs single	1.18	1.03	1.35	0.0141	.	.
	Post level vs pre level	0.97	0.85	1.11	0.6744	0.29	0.16
CD44	Double cent. vs single	0.85	0.75	0.97	0.0162	.	.
	Post level vs pre level	0.94	0.82	1.06	0.3106	0.51	0.17
AFP	Double cent. vs single	1.00	0.90	1.12	0.9877	.	.
	Post level vs pre level	1.02	0.91	1.14	0.7131	0.92	0.99
CEA	Double cent. vs single	1.01	0.88	1.17	0.8707	.	.
	Post level vs pre level	1.00	0.87	1.16	0.9780	0.99	0.99
Ferritin	Double cent. vs single	1.00	0.84	1.19	0.9740	.	.
	Post level vs pre level	1.18	0.99	1.40	0.0705	0.97	0.97
Galectin-3	Double cent. vs single	1.00	0.95	1.05	0.9963	.	.
	Post level vs pre level	1.04	0.99	1.10	0.0883	0.91	0.92
CyFra21 −1	Double cent. vs single	0.99	0.89	1.11	0.8568	.	.
	Post level vs pre level	0.71	0.64	0.79	<.0001	0.62	0.66
CA19–9	Double cent. vs single	1.00	0.81	1.25	0.9645	.	.
	Post level vs pre level	0.97	0.79	1.21	0.8137	0.99	0.99
TIMP-1	Double cent. vs single	0.98	0.94	1.03	0.4529	.	.
	Post level vs pre level	0.98	0.93	1.02	0.2951	0.93	0.93
hs-CRP	Double cent. vs single	1.01	0.84	1.23	0.8797	.	.
	Post level vs pre level	1.07	0.89	1.30	0.4628	0.82	0.83
cfDNA	Double cent. vs single	0.93	0.86	1.02	0.1115	.	.
	Post level vs pre level	1.28	1.17	1.39	<.0001	0.65	0.66
Galectin-3 ligand	Post level vs pre level	0.99	0.79	1.23	0.8987	0.73	.

The biomarkers analyzed using the Architect platform generally showed negligible variation (with ratios close to 1) except for CyFra21–1 and ferritin regarding pre- vs post-bowel preparation. Plots of the post-bowel preparation biomarker levels regressed on the pre-bowel preparation levels are shown in Figure 1a (only double centrifugation). CyFra21–1 levels were reduced with 29% (95% CI 21–36%) by bowel preparation (p≤0.0001) and the plot demonstrates a systematic difference as well as substantial variation. Ferritin was not statistically significant before and after bowel preparation (p=0.07), however the estimated difference was 18%. The levels of all eight protein biomarkers were similar in samples, which underwent either single or double centrifugation. The Pearson moment correlations were substantial for all the proteins with the exception of CyFra21–1 (r=0.62).

Figure 1.

Results for all biomarkers regressing the post bowel evacuation levels on the pre evacuation levels. The regression line (solid) and the 95% limits of detection (dotted) are shown. The blue dots indicate the 5 cancer patients included. The results are shown on the log scale (natural). Only results for double centrifugation are show.

The analyses performed at the Fred Hutchinson Center exhibited large variations (Table 2). However, only BAG4 was systematically reduced (12%, p=0.04) by bowel preparation. The regression plots are shown in Figure 1b (double centrifugation only), these indicate larger variance with the highest correlation being 0.63 (vWF and EGFR). The results show that levels with double centrifugation are reduced compared to single centrifugation for vWF (ratio 0.86, p≤0.0001) and CD44 (ratio 0.85, p=0.016), but are increased for IL6ST (ratio 1.18, p=0.014). The estimated correlation coefficients are considerably lower than those seen in the Architect results, and indicate a larger variance between pre- and post-bowel preparation biomarker levels. In addition, there was a significant (except for EGFR) difference between those samples with double centrifugation versus those with single centrifugation.

The results for cfDNA showed a significant increase of 28% (95% CI 17–39%, (p<0.0001) between pre- and to post-bowel preparation samples as shown in Table 2, with Pearson r=0.65; that demonstrates a substantial variation, which is in agreement with the plot shown in Figure 1c.

The Galectin-3 ligand from MD Anderson did not demonstrate a systematic difference from pre- to post-bowel preparation levels with the ratio equal to 0.99 (p=0.90) with a moderate variation (Pearson r =0.73).

For each biomarker, plots (only double centrifugation) illustrate the change from the pre-bowel to post-bowel preparation in Figure 2 for each individual. Increase in biomarker levels are shown by black lines and decrease by red lines. The figures show large increases as well as decreases for some biomarkers, including CyFra21–1 and IL6ST. Estimates of the coefficient of variation (CV) between pre- and post-bowel preparation biomarker levels are shown in Table 3. The results show resembling CV’s for the biomarkers except from the Fred Hutchison Center, which show a substantial difference between single and double centrifuged samples with those double centrifuged having lower CV’s.

Figure 2.

The pre/post evacuation levels plotted for each biomarker and individual. The black lines denote those showing an increase and the red lines those decreasing. Only data for double centrifugation are shown.

Table 3.

Table showing coefficients of variation for the ratio post/pre evacuation for all biomarkers.

	centrifuge
	Double	Single
	Coefficient of Variation (%)	Coefficient of Variation (%)
BAG4	69.5	120.6
EGFR	55.3	124.7
vWF	31.2	65.9
IL6ST	91.4	161.5
CD44	72.7	142.9
AFP	26.2	10.0
CEA	12.7	10.3
Ferritin	26.6	25.4
Galectin-3	12.3	11.0
CyFra21–1	60.3	58.1
CA19–9	19.6	17.7
TIMP-1	10.3	10.0
hs-CRP	70.5	70.7
cfDNA	42.5	40.3
Galectin-3 ligand	48.1	.

Discussion

The present study evaluated the potential effect of bowel preparation and single- versus double-centrifugation on blood-based biomarker levels in 125 patients undergoing diagnostic colonoscopy. The 15 biomarkers included in the study was chosen due to their association with colorectal malignancy. While cfDNA has been related to poor prognosis and reduced overall survival on metastatic disease^37,38quantification of cfDNA alone is insufficient to discriminate early-stage CRC patients from healthy individuals because the level of cfDNA is influenced by physiological conditions other than cancer. However, knowledge of how bowel preparation affects the level of cfDNA is very important as the level of cfDNA in the circulation influences the detection of circulating tumor-derived DNA (ctDNA) and results in studies where ctDNA are reported as variant allele frequencies.

The effect of bowel preparation on blood biomarker levels is not well described. Of the included biomarkers, the effect of bowel preparation has only been investigated for CEA.

The results showed that the majority of markers (12 out of 15) were unaffected by bowel prep. Only 3 markers showed a systematic, statistically significant difference: CyFra21–1 (mean reduction 29%), BAG4 (mean reduction 12%) and cfDNA (mean increase 28%). Ferritin’s estimated difference was 18% increase, however that was not statistically significant (p=0.07). Given the lack of previous publications on potential mechanisms leading to such level changes induced by bowel preparation we can only speculate on the cause.

CyFra21–1 is a fragment of the acidic protein cytokeratin-19, which is part of the intracellular cytoskeleton of epithelial cells; it is specifically abundant in bronchial and intestinal epithelium³⁹. One mechanism leading to decreased levels on post-bowel preparation samples may be due to the osmotic pressure caused by the large molecules of the bowel preparation solutions, which may lead to a pressure gradient from the epithelial cells into the bowel preparation solution and CyFra21–1 subsequently flushed out. Thereby, the release of CyFra21–1 to the circulation may be transiently, significantly reduced.

There are different mechanisms by which cfDNA can enter the circulation, such as necrosis, apoptosis and/or active secretion. Different situations may increase the levels of cfDNA including blunt traumas, major operations and extreme exercise. One study showed a 12-fold increase in cfDNA in patients with acute lung injury⁴⁰, and a 17-fold increase of plasma cfDNA were observed after long-distance running⁴¹. Bowel preparation may have caused a subtle inflammation and/or cell death and increased measures may be due to this impact. But perhaps the increase observed are not even related to increased cfDNA release. It could also be due to dehydration and a slightly reduced blood volume or reduced cfDNA clearance.

Ferritin is a protein, which stores and releases iron, and it’s often used as a measure of total amount of body iron. It is mostly found in liver-cells, and it reacts as an acute phase protein. Elevated blood levels, due to increased release to the circulation, are commonly observed when cell death or cell turnover increases. Such situations include acute and chronic inflammation, also in cancer diseases. If the observed increase of 18% is correct, and an indication of increased cell death, then this would be consistent with of the observed increase in cfDNA.

BAG4 is a protein widely expressed by many different cells. It is mostly localized in the nucleus and cytoplasm. The protein is involved in interactions with a variety of other proteins affecting cell-apoptosis and growth. In gastric cancers it has been shown that BAG4 promoted proliferation, migration and invasion. Reduced post-bowel preparation levels may be caused by similar mechanisms as those for CyFra21–1, as the increased cell turn-over activity of bowel epithelial cells may require BAG4 actitivity.

AFP is closely related to the protein albumin and abundant in early fetal life. It has no known biological function after birth, but the protein can be present in some cancer diseases (Germ cell tumors and CRC). No affection by bowel preparation were shown.

CEA is in post-fetal life mostly found in colonic mucosa tissue but are often observed to be elevated in various cancer diseases. In CRC, 50% of patients with lymph node metastasis show elevated CEA levels. Even though mostly found in colonic mucosa, there was no effect of bowel preparation.

Galectin-3 is one of a family of at least 14 galectin proteins. It is ubiquitous expressed and localized broadly (nucleus, cytoplasm, cell-surface and extracellular). It’s shown to be overexpressed in fibrosis conditions, cardiovascular diseases and in cancers. Bowel preparation was not shown to influence the levels.

CA19–9 is expressed on cell-surfaces of red blood cells, endothelium and epithelium (especially pancreas- and gastrointestinal epithelium). Increased levels are seen in both non-malignant situations linked to tissue inflammation, but also in malignant situations (mostly pancreatic cancer but also CRC). Bowel preparation was not shown to influence the levels.

TIMP-1 is an intracellular glycoprotein in different cell-types. In addition of functions to inhibit matrix metalloproteinases (MMPs), TIMP-1 also have roles regarding cell growth and inhibition of apoptosis and can therefore act as a functional regulator of increased malignancy. Bowel preparation didn’t reveal influence on this biomarker.

EGFR is a transmembrane receptor, which activates an intracellular cascade and ultimately influence cell growth and proliferation. When overexpressed it can also inhibit apoptosis, promote angiogenesis and invasion, therefore making it associated with a variety of cancers. Bowel preparation was not shown to have influence on the protein levels.

vWF is a large glycoprotein produced and stored predominantly in endothelial cells. In many situations of vascular stress, vWF is released to the bloodstream to bind and format platelet plugs. Bowel preparation was not shown to have influence on the protein levels.

IL6ST is a glyco- and transmembrane protein, which form complexes with IL-6 receptor after binding of IL-6 ligand resulting in a downstream signal cascade ultimately leading to regulation of immune response and hematopoiesis. The protein level has been shown increased in certain cancers. Bowel preparation was not shown to have influence on the protein levels.

CD44 is a cell-surface glycoprotein, which is ubiquitous expressed and involved in cell-cell, cell-matrix interactions and cell migration. CD44 expression is induced by pro-inflammatory cytokines such as TNF. Increased plasma levels of DC44 in a CRC may be a factor for unfavorable prognosis. Bowel preparation was not shown to have influence on the protein levels.

Galectin-3 ligand analyzed at University of Texas, MD Anderson Cancer Research (there exist several other galectin-3 ligands i.e. synexin, Bl2, Ras, Gemin-4, CD29 and CD7) are found elevated in individuals with CRC. Bowel preparation was not shown to have influence on the protein levels.

It can be argued that use of bowel preparation solutions that may affect blood-based biomarker levels could be easily avoided by blood collection at an early time-point. However, since some bowel evacuation procedures are often performed at home, this might involve multiple visits to the out-patient clinic or prolonged hospital stays. Also, there may be certain situations where such collections need to be performed at the time at which the included subjects are at the hospital or the clinic for the colonoscopy; at that time the bowel preparation has already been performed. Indeed, current legislation may also play a significant role in that aspect. Danish legislation requires that all subjects with symptoms attributable to colorectal cancer or a FIT-positive result (cut-off 100ng/ml) at the current bowel screening program must be offered colonoscopy within 10 working days. Due to such restrictions it may be almost impossible or at least extremely cumbersome to collect blood samples before bowel preparation. Indeed, such procedures may require that the included subjects present in the out-patient clinic for the blood collection and data recording. In addition to constraints with that it may certainly also lead to biases, because some self-selection by the subjects with sufficient time to adhere will occur, while subjects that are still part of the work force may be significantly reduced. On the other hand, the results of the present study are urgent to evaluate comparisons of results between FIT-positives and FIT-negatives, because subjects with a FIT negative screening result will not undergo colonoscopy.

The secondary aim of the study was to evaluate whether centrifugation might affect the biomarker levels or presence. Centrifugation is a well-known preanalytical factor that has influence on measured molecular values when different g-force settings are used (for example 300g vs 2700g)⁴². Information on centrifugal influence is to some extent described for many biomarkers.

The cfDNA were reduced by double centrifugation by approximately 7%, though the change was non-significant (p=0.1115). The cfDNA is a measure of the genome equivalents of the DNA in the plasma and originates from two sources: 1) The DNA release into the circulation when cells die, and 2) DNA from intact blood cells that were not removed during plasma isolation. By performing double centrifugation, the likelihood of whole blood cell contamination of the plasma is reduced. Thus, the observed reduction in cfDNA level was expected. Whether double centrifugation has an impact on the level of cfDNA also depends on the quality of the plasma isolation after the first centrifugation. If this did not leave any contaminating cells, then the second centrifugation would make no difference.

vWF and CD44 levels were decreased when double centrifuged compared to single, while IL6ST levels were increased by that maneuver. However, those biomarker levels also showed large variation between the samples, which, were reduced among the double centrifuged samples. In general, the biomarkers analyzed by the Luminex platform appeared less stable compared to the biomarkers analyzed by the Architect platform. Possible explanatory factors may be due to methodological variability or biological variation.

Since the pre- and post-bowel preparation samples were collected from subjects’ days apart, it is possible that this could lead to some potential attributable variation of uncertain magnitude due to normal biological variation. However, the intra-individual CV’s have been reported to be 26.7% for AFP⁴³, 9.9% for CEA⁴⁴, 17.9% for ferritin⁴⁵, 13.1% for Galectin-3⁴⁶, 27.2% for CA19–9⁴³, 10.7% for TIMP-1⁴⁷ and 77.7% for hs-CRP⁴⁸, which are in reasonable accordance within the findings of the present study. A recent study my Madsen et al.⁴⁹ showed intra-individual CV for cfDNA = 25.0%, but more importantly they showed no difference in the level of cfDNA between 9AM and 3 PM. Since, our pre and post bowel preparation blood samples were drawn in the approximately same time interval (7.30 AM - 2:00 PM), we do not expect the observed variation in cfDNA levels between pre- and post-bowel preparation samples to be due to within day variance, but may be due to the abovementioned explanations. Intra-individual CV for vWF have been reported to be 15.8%⁴⁸, showing no accordance to CV’s found in the present study (double centrifugation 31.2% and single centrifugation 65.9%). No comparable data on CV’s for BAG4, EGFR, IL6ST, CD44, CyFra21–1 and Galectin-3 ligand were available and therefore cannot be compared.

There is lack of information in the literature on the biological variation for BAG4, EFGR, IL6ST and CD44 and further investigation into these matters are needed.

In conclusion, the results of the present study have demonstrated systematic, statistically significant differences between pre-bowel vs post-bowel preparation levels for three independent blood-based biomarkers (BAG4, CyFra21–1, cfDNA). In addition, the study also showed several biomarkers were impacted by succeeding another centrifugation (double centrifugation vs single centrifugation). Various questions remain to be answered in future studies: 1) the mechanisms leading to significant changes of post-bowel preparation biomarker levels/presence; 2) duration of post-bowel preparation influence on biomarker levels/presence; 3) biological variations for some of the chosen biomarkers and 4) influence of various diseases on such variations.

Highlights:

• Bowel preparation solutions impact on blood-based, cancer-associated biomarkers have been poorly investigated.

• This study showed that three independent biomarkers (BAG4, CyFra21–1 and cell-free DNA) were influenced.