The Effect of Roux-en-Y Gastric Bypass Surgery in Morbidly Obese Patients on Pharmacokinetics of (Acetyl)Salicylic Acid and Omeprazole: the ERY-PAO Study.

BACKGROUND: Data on the absorption of orally administered drugs following Roux-en-Y gastric bypass (RYGB) surgery in obese patients are limited and inconclusive. As it is difficult to predict changes in absorption, studies on frequently used drugs in this population are necessary. Acetylsalicylic acid (ASA) and omeprazole are two commonly prescribed drugs in obese patients. METHODS: In this repeated measures study, omeprazole and salicylic acid (SA) serum concentrations were measured before and after RYGB in 34 morbidly obese subjects. Time to maximum concentration (Tmax), lag time (Tlag), maximum concentration (Cmax), and area under the serum concentration versus time curve (AUC) were calculated for both drugs to determine possible differences in drug absorption after the procedure. RESULTS: For SA, Tmax significantly decreased after RYGB, while both Cmax and AUC0-24 significantly increased. For omeprazole, both Tmax and Tlag significantly decreased after RYGB, while Cmax significantly increased. Mean AUC0-12 significantly decreased post-surgery. The difference in AUC0-12 before and after surgery varied between subjects. CONCLUSIONS: Our study shows a faster absorption of both ASA and omeprazole after RYGB. The exposure to ASA is higher post-surgery, but the standard dose of 80 mg does not need to be modified, considering its range in effective dose. The exposure to omeprazole is, on average, decreased after surgery. Clinicians should be aware to increase the dose of omeprazole if symptoms suggest inadequate response.

Postoperative serum proteomic profiles may predict recurrence-free survival in high-risk primary breast cancer.

PURPOSE: Better breast cancer prognostication may improve selection of patients for adjuvant therapy. We conducted a retrospective longitudinal study in which we investigated sera of high-risk primary breast cancer patients, to search for proteins predictive of recurrence-free survival. METHODS: Sera of 82 breast cancer patients obtained after surgery, but prior to the administration of adjuvant therapy, were fractionated using anion-exchange chromatography, to facilitate the detection of the low-abundant serum peptides. Selected fractions were subsequently analysed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS), and the resulting protein profiles were searched for prognostic markers by appropriate bioinformatics tools. RESULTS: Four peak clusters (i.e. m/z 3073, m/z 3274, m/z 4405 and m/z 7973) were found to bear significant prognostic value (P ≤ 0.01). The m/z 3274 candidate marker was structurally identified as inter-alpha-trypsin inhibitor heavy chain 4 fragment(658-688) in serum. Except for the m/z 7973 peak cluster, these peaks remained independently associated with recurrence-free survival upon multivariate Cox regression analysis, including clinical parameters of known prognostic value in this study population. CONCLUSION: Investigation of the postoperative serum proteome by, e.g., anion-exchange fractionation followed by SELDI-TOF MS analysis is promising for the detection of novel prognostic factors. However, regarding the rather limited study population, validation of these results by analysis of independent study populations is warranted to assess the true clinical applicability of discovered prognostic markers. In addition, structural identification of the other markers will aid in elucidation of their role in breast cancer prognosis, as well as enable development of absolute quantitative assays.

Searching for early breast cancer biomarkers by serum protein profiling of pre-diagnostic serum; a nested case-control study.

BACKGROUND: Serum protein profiles have been investigated frequently to discover early biomarkers for breast cancer. So far, these studies used biological samples collected at or after diagnosis. This may limit these studies' value in the search for cancer biomarkers because of the often advanced tumor stage, and consequently risk of reverse causality. We present for the first time pre-diagnostic serum protein profiles in relation to breast cancer, using the Prospect-EPIC (European Prospective Investigation into Cancer and nutrition) cohort. METHODS: In a nested case-control design we compared 68 women diagnosed with breast cancer within three years after enrollment, with 68 matched controls for differences in serum protein profiles. All samples were analyzed with SELDI-TOF MS (surface enhanced laser desorption/ionization time-of-flight mass spectrometry). In a subset of 20 case-control pairs, the serum proteome was identified and relatively quantified using isobaric Tags for Relative and Absolute Quantification (iTRAQ) and online two-dimensional nano-liquid chromatography coupled with tandem MS (2D-nanoLC-MS/MS). RESULTS: Two SELDI-TOF MS peaks with m/z 3323 and 8939, which probably represent doubly charged apolipoprotein C-I and C3a des-arginine anaphylatoxin (C3adesArg), were higher in pre-diagnostic breast cancer serum (p = 0.02 and p = 0.06, respectively). With 2D-nanoLC-MS/MS, afamin, apolipoprotein E and isoform 1 of inter-alpha trypsin inhibitor heavy chain H4 (ITIH4) were found to be higher in pre-diagnostic breast cancer (p < 0.05), while alpha-2-macroglobulin and ceruloplasmin were lower (p < 0.05). C3a(desArg) and ITIH4 have previously been related to the presence of symptomatic and/or mammographically detectable breast cancer. CONCLUSIONS: We show that serum protein profiles are already altered up to three years before breast cancer detection.

The absolute quantification of eight inter-α-trypsin inhibitor heavy chain 4 (ITIH4)-derived peptides in serum from breast cancer patients.

PURPOSE: Various studies exploring the potential of the low-molecular-weight serum peptidome have identified proteolytic cleavage products of inter-α-trypsin inhibitor heavy chain-4 (ITIH(4)) as potential markers for different types of cancer, presumably generated by tumor-associated exoproteases. However, further elucidation of the discriminative properties of such peptides requires specific quantitative analytical methods. EXPERIMENTAL DESIGN: Using a recently developed and fully validated liquid chromatography-tandem mass spectrometric method, we have compared absolute serum concentrations of eight peptides derived from ITIH(4 [658-687]) to ([667-687]) (ITIH(4)-30 to -21) between breast cancer patients (n=45) and controls (n=78). Furthermore, serum samples obtained before and after surgical removal of the tumor were analyzed (n=30). RESULTS: The inter-individual variability in measured serum concentrations was high. Nevertheless, most peptides showed a tendency toward elevated levels in the presence of the breast cancer tumor. Significantly increased serum concentrations were observed in the breast cancer group for ITIH(4)-25 (p=0.036) and -29 (p=0.015). Intra-individual comparisons of serum obtained before and after surgery showed significantly decreased serum levels after surgery for seven of the ITIH(4)-derived peptides (p<0.02). CONCLUSIONS AND CLINICAL RELEVANCE: The obtained results particularly suggest potential for these ITIH(4)-derived peptides in the follow-up of breast cancer after surgery.

Serum degradome markers for the detection of breast cancer.

Many proteins have been proposed as potential biomarkers for breast cancer. Yet, validation of their discriminative value using quantitative methods has scarcely been performed. In this study, we investigated the discriminative value of six peptides that were previously proposed to be generated by breast cancer specific exoproteases: bradykinin, des-Arg(9)-bradykinin, Hyp(3)-bradykinin, and fragments of fibrinogen alpha-chain (Fib-alpha ([605-629])), complement component 4a (C4a ([1337-1350])), and interalpha trypsin inhibitor heavy chain 4 (ITIH4 ([666-687])). Their absolute serum concentrations were measured with a completely validated liquid chromatography-tandem mass spectrometric assay (LC-MS/MS) and compared between 62 newly diagnosed breast cancer patients and 62 controls matched for age and sample storage duration. Both ITIH4 ([666-687]) and des-Arg(9)-bradykinin showed statistically significantly higher median concentrations in breast cancer samples than in matched control samples. Additionally, we analyzed serum samples collected after surgical removal of the tumor, in which median ITIH4 ([666-687]) and des-Arg(9)-bradykinin concentrations were significantly decreased and not statistically significantly different from concentrations in the controls anymore. In a combined analysis, ITIH4 (666-687]) and des-Arg(9)-bradykinin independently contributed to the discrimination between cases and controls. In this study, we confirmed that the exoprotease breakdown peptides, ITIH4 (666-687]) and des-Arg(9)-bradykinin, differed between breast cancer cases and controls, supporting the potential of degradome markers for the diagnosis of breast cancer.

Detection of breast cancer by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry tissue and serum protein profiling.

AIM: Novel diagnostic breast cancer markers have been extensively searched for in the proteome, using, among others, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Thus far, the majority of SELDI-TOF MS studies have investigated samples originating from biorepositories, which hampers biomarker discovery as they likely suffer from variable adherence to collection protocols. MATERIAL AND METHODS: We investigated breast cancer (n=75) and control (n=26) serum and tissue samples, collected prospectively by rigorous adherence to a strictly defined protocol. Sera were collected preoperatively and postoperatively, and serum and tissue samples were analyzed by SELDI-TOF MS using the IMAC30 Ni and Q10 pH 8 array. RESULTS: Three serum peaks were significantly associated with breast cancer, while in tissue, 27 discriminative peaks were detected. Several peak clusters gradually increased or decreased in intensity from healthy to benign to cancer, or with increasing cancer stage. The constructed classification trees had a tenfold cross-validated performance of 67% to 87%. Two tissue peaks were identified as N-terminal albumin fragments. These are likely to have been generated by (breast) cancer-specific proteolytic activity in the tumor microenvironment. CONCLUSIONS: These albumin fragment scan potentially provide insights into the pathophysiological mechanisms associated with, or underlying, breast cancer, and aid in improving breast cancer diagnosis.

Serum protein profiling for diagnosis of breast cancer using SELDI-TOF MS.

In search for novel markers for breast cancer, we aimed to identify and validate novel serum protein profiles specific for breast cancer, and assess the influence of clinical (subjects age) and pre-analytical (sample storage duration) variables on the constructed classifiers. To this end, sera of breast cancer patients (n=152) and healthy controls (n=129), randomly divided into a training and test set, were analysed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). In the training set, 14 peak clusters were found to differ significantly in expression between cases and controls. None of the peak clusters were influenced by subjects age and sample storage duration. Ten peak clusters were also found significantly discriminative in the test set. Peak clusters were structurally identified as C3a des-arginine anaphylatoxin, (tentative) inter-alpha-trypsin inhibitor heavy chain 4 fragments and a fibrinogen fragment. Logistic regression analyses on the training set yielded a classification model with a moderate performance on the test set, corresponding to those reported in previously performed validation studies. Most likely originating from the highly heterogeneous nature of breast cancer, selection of breast cancer subgroups for comparison with healthy controls is expected to improve results of future diagnostic SELDI-TOF MS studies.

Influence of sample storage duration on serum protein profiles assessed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS).

BACKGROUND: Issues have been raised concerning the robustness and validity of alleged serum markers discovered by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). Pre-analytical variables have been shown to exert a profound effect on protein profiles, irrespective of true biological variation. However, little is known about the possible effects of sample storage duration on protein profiles. We, therefore, aimed to investigate the effects of extended storage duration on the serum protein profile. METHODS: Archival sera from 140 breast cancer patients, stored at -30 degrees C for 1-11 years, were profiled by SELDI-TOF MS using immobilised metal affinity capture (IMAC) arrays, a condition applied in the majority of breast cancer biomarker discovery studies. RESULTS: Fourteen peak clusters, structurally identified as C3a des-arginine anaphylatoxin and multiple fragments of albumin and fibrinogen, were found to be significantly associated with sample storage duration by five distinct patterns. These proteins have been described previously as potential cancer markers, rendering them specific to both disease and sample handling issues. CONCLUSIONS: To prevent experimental variation being interpreted erroneously as disease associated variation, assessment of potential confounding pre-analytical parameters is a pre-requisite in biomarker discovery and validation studies. In addition, with respect to the different (non-)linear patterns observed in the current study, simply performing linear corrections to account for sample storage duration will not necessarily suffice.

Analysis of mass spectrometry data using sub-spectra.

BACKGROUND: Spectra resulting from Surface-Enhanced Laser Desorption/Ionisation (SELDI) mass spectrometry measurements are constructed by combining sub-spectra, each of which are the result of a single firing of the laser responsible for the process of desorption/ionisation. These firings are performed at different locations of the spot on which the sample is analysed. The final spectrum is then constructed by summing over all these sub-spectra. This process is sub-optimal in that it can average out peaks from peptides that are present in low abundance or are unevenly distributed across the spot, particularly because the amount of noise varies considerably between sub-spectra. This argues for analysing sub-spectra separately and combining results afterwards. RESULTS: Here, we propose to analyse these sub-spectra one-by-one and combine the results using a framework which includes a significance test. This allows one to, for the first time, attach a confidence measure to detected peaks, based on the signal strength of a peak across sub-spectra. In a comparison with three other approaches the sub-spectral approach achieves a higher sensitivity and a low FDR. We further introduce the notion of peak-bags, which provide rich information about the sub-spectral contributions to a given peak. CONCLUSION: The proposed procedure offers better control over the process of distinguishing signal from noise, resulting in an improved performance over other available methods. Moreover, our method provides an implicit deconvolution of peaks, yielding insight in the actual shape of a peak, potentially aiding in a deeper understanding of peak distribution. AVAILABILITY: Implementations of the algorithm in R are available upon request.

Validation of previously identified serum biomarkers for breast cancer with SELDI-TOF MS: a case control study.

BACKGROUND: Serum protein profiling seems promising for early detection of breast cancer. However, the approach is also criticized, partly because of difficulties in validating discriminatory proteins. This study's aim is to validate three proteins previously reported to be discriminative between breast cancer cases and healthy controls. These proteins had been identified as a fragment of inter-alpha trypsin inhibitor H4 (4.3 kDa), C-terminal-truncated form of C3a des arginine anaphylatoxin (8.1 kDa) and C3a des arginine anaphylatoxin (8.9 kDa). METHODS: Serum protein profiles of 48 breast cancer patients and 48 healthy controls were analyzed with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Differences in protein intensity between breast cancer cases and controls were measured with the Mann-Whitney U test and adjusted for confounding in a multivariate logistic regression model. RESULTS: Four peaks, with mass-to-charge ratio (m/z) 4276, 4292, 8129 and 8941, were found that were assumed to represent the previously reported proteins. M/z 4276 and 4292 were statistically significantly decreased in breast cancer cases compared to healthy controls (p < 0.001). M/z 8941 was decreased in breast cancer cases (p < 0.001) and m/z 8129 was not related with breast cancer (p = 0.87). Adjustment for sample preparation day, sample storage duration and age did not substantially alter results. CONCLUSION: M/z 4276 and 4292 both represented the previously reported 4.3 kDa protein and were both decreased in breast cancer patients, which is in accordance with the results of most previous studies. M/z 8129 was in contrast with previous studies not related with breast cancer. Remarkably, m/z 8941 was decreased in breast cancer cases whereas in previous studies it was increased. Differences in patient populations and pre-analytical sample handling could have contributed to discrepancies. Further research is needed before we can conclude on the relevance of these proteins as breast cancer biomarkers.

Clinical proteomics in breast cancer: a review.

Breast cancer imposes a significant healthcare burden on women worldwide. Early detection is of paramount importance in reducing mortality, yet the diagnosis of breast cancer is hampered by the lack of an adequate detection method. In addition, better breast cancer prognostication may improve selection of patients eligible for adjuvant therapy. Hence, new markers for early diagnosis, accurate prognosis and prediction of response to treatment are warranted to improve breast cancer care. Since proteomics can bridge the gap between the genetic alterations underlying cancer and cellular physiology, much is expected from proteome analyses for the detection of better protein biomarkers. Recent technical advances in mass spectrometry, such as matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and its variant surface-enhanced laser desorption/ionisation (SELDI-) TOF MS, have enabled high-throughput proteome analysis. In the current review, we give a comprehensive overview of the results of expression proteomics (i.e. protein profiling) research performed in breast cancer using these two platforms. Many protein peaks have been reported to bear significant diagnostic, prognostic or predictive value, however, only few candidate markers have been structurally identified yet. In addition, although of pivotal importance in preventing overfitting of data and systematic bias by pre-analytical parameters, validation of biomarker candidates by other, quantitative, methods and/or in new populations is very limited. Moreover, none of the identified candidate biomarkers has been investigated for their utility as breast cancer markers in large, prospective, clinical settings. As such, the candidate biomarkers discussed in this overview have not been validated sufficiently to be used for clinical patient care. Nonetheless, regarding the promising results up to now, MALDI- and SELDI-TOF MS protein profiling studies could eventually fulfil the great promise that protein biomarkers have for improving cancer patient outcome, provided that these studies are performed with adequate statistical power and analytical rigour.

Haptoglobin phenotype is not a predictor of recurrence free survival in high-risk primary breast cancer patients.

BACKGROUND: Better breast cancer prognostication may improve selection of patients for adjuvant therapy. We conducted a retrospective follow-up study in which we investigated sera of high-risk primary breast cancer patients, to search for proteins predictive of recurrence free survival. METHODS: Two sample sets of high-risk primary breast cancer patients participating in a randomised national trial investigating the effectiveness of high-dose chemotherapy were analysed. Sera in set I (n = 63) were analysed by surface enhanced laser desorption ionisation time-of-flight mass spectrometry (SELDI-TOF MS) for biomarker finding. Initial results were validated by analysis of sample set II (n = 371), using one-dimensional gel-electrophoresis. RESULTS: In sample set I, the expression of a peak at mass-to-charge ratio 9198 (relative intensity 20), identified as haptoglobin (Hp) alpha-1 chain, was strongly associated with recurrence free survival (global Log-rank test; p = 0.0014). Haptoglobin is present in three distinct phenotypes (Hp 1-1, Hp 2-1, and Hp 2-2), of which only individuals with phenotype Hp 1-1 or Hp 2-1 express the haptoglobin alpha-1 chain. As the expression of the haptoglobin alpha-1 chain, determined by SELDI-TOF MS, corresponds to the phenotype, initial results were validated by haptoglobin phenotyping of the independent sample set II by native one-dimensional gel-electrophoresis. With the Hp 1-1 phenotype as the reference category, the univariate hazard ratio for recurrence was 0.87 (95% CI: 0.56 - 1.34, p = 0.5221) and 1.03 (95% CI: 0.65 - 1.64, p = 0.8966) for the Hp 2-1 and Hp 2-2 phenotypes, respectively, in sample set II. CONCLUSION: In contrast to our initial results, the haptoglobin phenotype was not identified as a predictor of recurrence free survival in high-risk primary breast cancer in our validation set. Our initial observation in the discovery set was probably the result of a type I error (i.e. false positive). This study illustrates the importance of validation in obtaining the true clinical applicability of a potential biomarker.

Comparing the old and new generation SELDI-TOF MS: implications for serum protein profiling.

BACKGROUND: Although the PBS-IIc SELDI-TOF MS apparatus has been extensively used in the search for better biomarkers, issues have been raised concerning the semi-quantitative nature of the technique and its reproducibility. To overcome these limitations, a new SELDI-TOF MS instrument has been introduced: the PCS 4000 series. Changes in this apparatus compared to the older one are a.o. an increased dynamic range of the detector, an adjusted configuration of the detector sensitivity, a raster scan that ensures more complete desorption coverage and an improved detector attenuation mechanism. In the current study, we evaluated the performance of the old PBS-IIc and new PCS 4000 series generation SELDI-TOF MS apparatus. METHODS: To this end, two different sample sets were profiled after which the same ProteinChip arrays were analysed successively by both instruments. Generated spectra were analysed by the associated software packages. The performance of both instruments was evaluated by assessment of the number of peaks detected in the two sample sets, the biomarker potential and reproducibility of generated peak clusters, and the number of peaks detected following serum fractionation. RESULTS: We could not confirm the claimed improved performance of the new PCS 4000 instrument, as assessed by the number of peaks detected, the biomarker potential and the reproducibility. However, the PCS 4000 instrument did prove to be of superior performance in peak detection following profiling of serum fractions. CONCLUSION: As serum fractionation facilitates detection of low abundant proteins through reduction of the dynamic range of serum proteins, it is now increasingly applied in the search for new potential biomarkers. Hence, although the new PCS 4000 instrument did not differ from the old PBS-IIc apparatus in the analysis of crude serum, its superior performance after serum fractionation does hold promise for improved biomarker detection and identification.

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data.

BACKGROUND: Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result. RESULTS: In this paper, we describe the first systematic comparison of a wide range of normalisation methods, using two objectives that should be met by a good method. These objectives are minimisation of inter-spectra variance and maximisation of signal with respect to class separation. The former is assessed using an estimation of the coefficient of variation, the latter using the classification performance of three types of classifiers on real-world datasets representing two-class diagnostic problems. To obtain a maximally robust evaluation of a normalisation method, both objectives are evaluated over multiple datasets and multiple configurations of baseline correction and peak detection methods. Results are assessed for statistical significance and visualised to reveal the performance of each normalisation method, in particular with respect to using no normalisation. The normalisation methods described have been implemented in the freely available MASDA R-package. CONCLUSION: In the general case, normalisation of mass spectra is beneficial to the quality of data. The majority of methods we compared performed significantly better than the case in which no normalisation was used. We have shown that normalisation methods that scale spectra by a factor based on the dispersion (e.g., standard deviation) of the data clearly outperform those where a factor based on the central location (e.g., mean) is used. Additional improvements in performance are obtained when these factors are estimated locally, using a sliding window within spectra, instead of globally, over full spectra. The underperforming category of methods using a globally estimated factor based on the central location of the data includes the method used by the majority of SELDI users.

Clinical proteomics: searching for better tumour markers with SELDI-TOF mass spectrometry.

Recently, the focus of cancer research has expanded from genetic information in the human genome to protein expression analyses. Because this 'proteome' reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumour markers for disease diagnosis and therapy monitoring. Some current proteomic technologies are particularly suitable for protein profiling in the search for new biomarkers. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry has been used frequently, highlighting many new proteins as biomarkers (e.g. for ovarian, breast, prostate and colorectal cancer). However, it is becoming increasingly recognized that reproducibility and validation of these biomarkers should be addressed carefully, as should their origin and identity. If these efforts are made, protein profiling can contribute to the better diagnosis of patients and the optimization of their treatment.