Effect of food intake on 92 oncological biomarkers by the Proseek Oncology II panel

Objective To evaluates the effect of food intake on 92 oncological biomarkers to evaluate whether the timing of blood sampling could be relevant. Twenty-two healthy subjects were investigated. A total of 92 biomarkers were measured before a standardised meal as well as 30 and 120 min afterwards with the Proseek Multiplex Oncology II kit. Results The levels of 6 biomarkers decreased significantly (P < 0.001) 30 min after food intake, and 4 biomarkers remained decreased (P < 0.001) 120 min after food intake. One biomarker was significantly increased (P < 0.001) at both 30 and 120 min after food intake. Some changes were less than 10%. Those biomarkers that showed a difference of more than 10% include: Granzyme H (13%), Methionine aminopeptidase 2 (14%), Secretory carrier-associated membrane protein 3 (39%), FAS-associated death domain protein (41%), and Pancreatic prohormone (79%). This study shows that food intake has a very modest effect on 92 different oncological biomarkers. Trial registration National Library of Medicine trial registration number NCT01027507 (retrospectively registered on December 8, 2009)


Introduction
Cancer is major cause of death and disability in the world and its prevalence is increasing [1]. The ongoing identification of novel biomarkers is critical to facilitating early detection of oncological disease. The timing of blood sampling, however, could be relevant as food digestion is known to have hemodynamic and metabolic effects [2][3][4][5][6][7]. In this study, we investigate whether certain biomarkers are affected by food intake. We have previously reported the influence of food intake on biomarkers analysed with the Proseek Multiplex CVD III, Proseek Multiplex CVD II and Proseek Multiplex Neurology I kits, and now advance our efforts with the Proseek Oncology II panel [8][9][10]. In this study, we assess the effect of food intake on 92 emerging oncological biomarkers. To our knowledge, this is the first study to do this.

Study population
The trial was registered retrospectively at the National Library of Medicine (trial registration number NCT01027507, retrospectively registered on December 8,2009). The study was approved by the Regional Ethical Review Board in Lund, Sweden. All subjects gave their written informed consent. The study investigated 22 healthy Caucasians (11 male and 11 female aged 25.9 ± 4.2 years). This is an exploratory study, first of its kind, and the data for a power calculation were not available. The data from the present study could be used for power calculations in future studies. None of the subjects took cardiovascular medication, showed symptoms of cardiovascular disease, or had a history of cardiovascular or other chronic disease. The subjects were examined between 7.30 and 11.00 after a minimum 8-hour fast. Their height and weight was measured and body mass index (BMI) calculated. The subjects then consumed a standardised meal consisting of 300 g rice pudding (AXA Goda Gröten Risgrynsgröt; Lantmännen AXA, Järna, Sweden). The total caloric value of the meal was 330 kcal: 10% from protein (9 g), 58% from carbohydrates (48 g), and 32% from fat (12 g).

Statistical analysis
Data are presented as mean ± standard deviation (SD). Statistical analyses were carried out using Statistica 12 (StatSoft Inc, Tulsa, OK, USA). Comparison between fasting values versus 30 and 120 min after food intake for any given biomarker was analysed for significance with the Wilcoxon matched pairs test. Statistical significance was set at P < 0.001 to counteract the problem of multiple comparisons.

Results and discussion
Six biomarkers showed a significant decrease in levels (P < 0.001) 30 min after food intake, and of those, 4 remained significantly decreased (P < 0.001) 120 min after food intake (Table 1). One biomarker (Pancreatic prohormone) showed a significant increase (P < 0.001) both 30 min and 120 min after food intake. The changes were most often less than 10%. Some biomarkers showed a difference of more than 10%: Granzyme H (13%), Methionine aminopeptidase 2 (14%), Secretory carrier-associated membrane protein 3 (39%), FASassociated death domain protein (41%), and Pancreatic prohormone (79%). A summary of our results can be found in Table 2.
To our knowledge, no study has previously evaluated the effect of food intake on plasma proteins measured by the Proseek Multiplex Oncology II kit. Our results show that of the 92 biomarkers investigated, only 9 were affected by food intake, and the changes were modest: less than 10%. Standardising food intake, therefore, is generally not required when using this kit. There are several exceptions. The greatest changes in observed levels were for Granzyme H (13%), Methionine aminopeptidase 2 (14%), Secretory carrier-associated membrane protein 3 (39%), FAS-associated death domain protein (41%), and Pancreatic prohormone (79%). Our results point to the need to standardise food intake when evaluating these biomarkers.
In our study, we were able to simultaneously measure 92 plasma proteins using the Proseek Multiplex Oncology II kit. The biomarkers were selected because of their oncological relevance. Twenty-one of these biomarkers are defined as cell proliferation, 11 tumorspecific, 11 general oncology, 10 angiogenesis specific, 9 immune response, 8 cell adhesion, 7 exploratory, 6 apoptosis, 5 invasion and metastasis and 4 cell differentiation. This new technology has been used in several studies [13][14][15][16][17][18][19][20][21], and our investigation adds methodological data to this developing field.
We observed most of the changes 30 min after food intake. A total of 7 biomarkers decreased, and one increased, 30 min after the meal. This could be due to the changes in hemodynamics. We have previously reported a 20% increase in stroke volume and a 28% increase in cardiac output 30 min after food intake in this cohort [4]. There was one obvious exception.
Pancreatic prohormone, also known as Pancreatic polypeptide, is secreted by the pancreatic islets of Langerhans and is known to be released by food intake, particularly fat-rich food [22]. Moreover, Pancreatic prohormone has been known to decrease both appetite and food intake [22]. Our finding is not novel concerning postprandial changes of this biomarker. We are not aware of any study that has investigated postprandial levels of Methionine aminopeptidase 2, Secretory carrier associated membrane protein 3, or FAS-associated death domain protein. Granzyme H is part of a group of proteases that are found mainly in cytotoxic immune cells, and has been investigated in a variety of diseases including infectious diseases, multiple sclerosis, large granular lymphocyte leukemia, lymphoma, and breast cancer [23]. Methionine aminopeptidase 2 has been suggested to have an important role in angiogenesis, which is pivotal for the progression of solid tumours [24]. Secretory carrier associated membrane protein 3 functions as a carrier to the cell surface and is also involved in other intracellular protein trafficking, and has to our knowledge only been studied on a cellular or tissue level and our findings may not be relevant [25,26]. FAS-associated death domain protein has a known role in apoptosis, but has also been implicated in cell proliferation, cell cycle regulation, and cell development. It has also been investigated in squamous cell carcinoma of the head and neck and in non-small cell lung cancer [27,28].
To our knowledge, only one study has previously assessed the impact of food intake on multiple biomarkers. Jahn et al. evaluated Kallikrein-11, Xaa-Pro aminopeptidase 2 (mAmP), Hepatocyte growth factor, Endothelial cell specific molecule 1, and mucin 16 (Cancer antigen-125) [29]. Their results, for the most part, coincided with ours: food intake showed no effect on biomarker levels. Kallikrein-11, however, was the exception. Jahn et al. found a decrease in Kallikrein-11 levels after food intake, which we did not observe. The variance could be explained by a difference in observation time and/or meal composition.

Limitations
The limitations of the study include the study population, which consisted of young, healthy Caucasian subjects. Additional studies are recommended in older healthy subjects from different ethnic groups and in patients with disease to determine whether these findings are reproducible. Moreover, the potential interaction effect of different medications should also be considered. Further research is also recommended to evaluate the effect of different diets such as high or low fat [30].