Discovery and Longitudinal Evaluation of Candidate Protein

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Discovery and longitudinal evaluation of candidate protein biomarkers for disease recurrence in prostate cancer Claire Tonry, Darren Doherty, Carmel O'Shea, Brian Morrissey, Lisa Staunton, Brian Flatley, Aoife Shannon, John Armstrong, and Stephen R. Pennington J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00041 • Publication Date (Web): 26 May 2015 Downloaded from http://pubs.acs.org on June 5, 2015

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Journal of Proteome Research

Discovery and longitudinal evaluation of candidate protein biomarkers for disease recurrence in prostate cancer Claire L. Tonry1*; Darren Doherty1; Carmel O’Shea2; Brian Morrissey1; Lisa Staunton1; Brian Flatley1; Aoife Shannon2; John Armstrong2; Stephen R. Pennington1 1

Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Co. Dublin 2 St. Luke’s Hospital, Rathgar, Dublin 6, Co. Dublin KEYWORDS: Prostate Cancer, Biomarkers, Serum, LC-MS/MS, MRM, Longitudinal Evaluation, ABSTRACT: When compared with hormonal therapy alone, treatment with combined hormone and radiation therapy (CHRT) gives improved disease-specific survival outcomes for patients with prostate cancer. However, a significant number of CHRT patients still succumb to recurrent disease. The purpose of this study was use longitudinal patient samples obtained as part of an on going noninterventional clinical trial (ICORG06-15) to identify and evaluate a potential serum protein signature of disease recurrence. Label-free LC-MS/MS based protein discovery was undertaken on depleted serum samples from CHRT patients who showed evidence of disease recurrence (n=3) and time-matched patient controls (n=3). A total of 104 proteins showed a significant change between these two groups. Multiple reaction monitoring (MRM) assays were designed for a subset of these proteins as part of a panel of putative prostate cancer biomarkers (41 proteins) for evaluation in longitudinal serum samples. This data revealed significant inter-patient variability in individual protein expression between time of diagnosis, disease recurrence and beyond and serves to highlight the importance of longitudinal patient samples for evaluating the use of candidate protein biomarkers in disease monitoring.

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INTRODUCTION Prostate cancer (PCa) is the second most common solid organ cancer diagnosis in men worldwide, with an estimated 1.11 million cases reported in 2012 (cruk.org/cancerstats). In Ireland, just over 500 men die from this disease each year (www.ncri.ie). Similar population adjusted figures for diagnosis and mortality are observed in most Western countries therefore PCa poses a significant global burden1. Generally, prostate cancer may be treated effectively with radical prostatectomy, androgen-deprivation therapy, radiotherapy or combinations thereof2. Combined hormone (androgen deprivation) and radiation therapy (CHRT) in men with high risk PCa demonstrates clinical benefits that appear to be related to the ability of androgen deprivation to make PCa more susceptible to radiation-induced tumor cell death. Such benefits have been demonstrated in a number of studies, including a significant retrospective study in which it was shown that CHRT significantly improved biochemical and clinical progression rates3,4. However, many patients treated with CHRT become resistant to androgen deprivation, thereby increasing their risk of developing metastatic disease. In these patients, the earliest indication of treatment failure is termed biochemical recurrence (BR) and is recognized by two successive serum PSA measurements 2ng/ml above the nadir, the nadir being defined as the lowest PSA levels following a patient’s treatment. However, PSA is considered a poor biomarker as a rise in PSA levels is not always associated with subsequent disease progression. Furthermore, treatment failure can occur without an increase in PSA levels5–9. It is evident therefore, that there is a significant need for more reliable biomarkers to indicate, as early as possible, the likelihood of treatment failure and in this way to improve personalized patient care 10.

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Proteomic-based investigations have become a central feature in biological and clinical research – especially for the identification of potential candidate biomarkers 11–13

. In the last decade, enormous progress has been made in mass spectrometry-

based proteomics both in instrumentation and software for data analysis. In fact, it is currently the only available method that allows systematic characterization of diseaseassociated alterations at the molecular level14. In contrast to most DNA-based techniques, proteomics strategies allow identification of the functional effectors of disease progression and can therefore be applied to pinpoint potential therapeutic targets as well as disease biomarkers15. Proteomics based on high-resolution mass spectrometry (MS) now allows the quantification of thousands of proteins as well as their modifications, localization, turn over and interaction partners16. This improvement in MS-based proteomic technology has supported enhanced discovery and validation of putative protein biomarker candidates in complex biological samples17,18,19. Indeed, these technological developments have contributed to the advances that have been made with the Human Proteome Project (HPP) initiative (www.HUPO.org) and those that have reported the initial mapping of the complete human proteome20,21. One MS focused element of the HPP is directed towards tracking the development and dissemination of robust multiple reaction monitoring (MRM)-based assays for proteotypic peptides corresponding to at least one product of all known genes20. MRM can measure 10's if not 100's of proteins in a single analysis (of approximately 30 to 40 min). In its simplest iteration MRM assays do not require antibodies and this makes it a much more attractive alternative to the current gold standard of ELISAs (and their derivatives) for biomarker verification and validation in patient samples22. Furthermore, it has been demonstrated that multiplexed MRM-



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based measurements can be optimized to fulfill requirements for pre-clinical evaluation of an extensive list of candidate protein biomarkers23,24–26. In the field of prostate cancer, numerous studies report the use of MS-based proteomics for the identification and verification of novel diagnostic markers. However, none have yet resulted in credible and validated biomarkers suitable for clinical use5,27–33. Ultimately, successful biomarker development is dependent on the availability of appropriate clinical samples, both for discovery and clinical validation34. Under the auspices and direction of the All Ireland Clinical Oncology Research Group (www.icorg.ie) we initiated (in 2006) a collaborative noninterventional clinical trial aligned to a cohort of patients receiving CHRT at St. Luke’s Hospital, Dublin. This trial was dedicated to the accrual, with fully informed consent, of sequential serum (and urine) samples from 60 patients enrolled under strict inclusion criteria and is associated with a database of detailed clinical information acquired according to the International Conference on HarmonisationGood Clinical Practice guidelines. This significant bio-resource (ICORG 06-15) provides samples for initial biomarker discovery experiments, as well as access to longitudinal samples from patients of interest. Previously, a study was undertaken to identify differentially expressed proteins between a PCa patient who had developed biochemical recurrence (BR) following treatment with combined hormone and radiation therapy (CHRT) and a time-matched control patient who had shown no evidence of BR. This investigation was undertaken using an LTQ-Orbitrap Velos mass spectrometer and resulted in the identification of 287 proteins in depleted serum samples31. Two additional patients within the ICORG trial have since shown evidence of BR and we now have access to one of the latest generation Orbitrap instruments – the Q-Exactive hybrid quadrupole-Orbitrap mass


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spectrometer. The Q-Exactive has previously been shown to outperform the LTQOrbitrap-Velos in terms of both peptide and protein group IDs35. Here, we report the analysis of depleted serum samples taken from 3 patients who have shown BR alongside samples taken from 3 control (no evidence of BR) patients with samples taken at two time points (onset of treatment and time of BR diagnosis). In this proof of principle study we used these samples accrued under strict clinical trial governance for label free LC-MS/MS biomarker discovery and confirmation by MRM to seek to identify a potential serum protein signature of disease (biochemical) recurrence in patients treated with CHRT.

EXPERIMENTAL PROCEDURES Samples originating from the All-Ireland Co-Operative Oncology Research Group (ICORG), 06-15 clinical trial were authorized for use by the St. Luke’s Hospital Research Committee in accordance with the principals founded in the Declaration of Helsinki.

Sample collection Blood sample collection adhered to standard operating procedures developed in accordance with the International Conference on Harmonisation (ICH) “Harmonised Tripartite Guidelines for GCP and Clinical Trials on Products for Human Medicinal Use Regulations 2004 to 2006” (S1 190 of 2004 & S1 374 of 2006). Blood samples were collected in anti-coagulant free tubes and placed upright on ice for at least 30 min to allow for the complete coagulation of the sample. Blood samples were then centrifuged at 1300 x g for 10 min to separate the serum from the clotted material.



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Serum was then removed and placed at -80 C for storage. Samples were processed and frozen within 1 hour from the initial collection time.

Depletion of high abundant proteins Serum samples were depleted of high abundant protein (HAP) using the Agilent Multiple Affinity Removal System (MARS) comprising a Hu-14 column (4.6 x 100 mm; Agilent Technologies, 5188-6557) on a Biocad Vision Workstation. This system uses immunoaffinity interactions to deplete serum of the 14 most abundant proteins (albumin, transferrin, IgG, IgM, IgA, haptoglobulin, α-1 anti-trypsin, α-2 macroglobulin, α-1 glycoprotein, apolipoprotein A1, apolipoprotein A2, complement 3, transthyretin and fribrinogen) which together account for approximately 94% of the protein mass of serum. The remaining low abundant protein (LAP) fractions were retained for further analysis by LC-MS/MS. Briefly, 45 µl of serum was diluted in 135 µl MARS buffer A (Agilent Technologies, 5185-5987) and injected directly onto a MARS Hu14 column. LAP fractions were eluted from the column in buffer A. The HAP fractions were subsequently eluted from the column with MARS buffer B (Agilent Technologies, 5185-5998). The LAP fractions were pooled, as were HAP fractions. Both pools were then concentrated separately using 5kDa molecular weight cut-off filters (Sartorius Stedim Biotech, VS0413) with centrifugation at 3000 x g for 60 min. Protein concentrations were measured by Nanodrop (Thermo Scientific version ND_1000). To confirm the protein quantification and the successful depletion of the serum samples, 8 µg of protein from each LAP and HAP fraction was analysed by 1D SDS PAGE. 12% cross-linked polyacrylamide gels of 1.5 mm thickness and 7 cm length were prepared using a Mini Protean gel system (BioRad Laboratories).


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Samples were electrophoresed at a constant voltage of 120 V and then stained with Commassie blue as described previously36. Sample Preparation LAP fractions were digested with sequencing grade modified porcine trypsin (Promega) for subsequent LC-MS/MS analysis37. 50 µg of protein from each LAP sample was re-suspended in a 5kDa molecular weight cut-off filter (Sartorius Stedim Biotech, VS0413) to give final concentrations of 50 mM ammonium bicarbonate, 10 mM DTT and 50% trifluoroethanol. Samples were incubated for 30 min at room temperature to allow protein denaturation. Iodoacetomide was added to a final concentration of 20 mM and samples were incubated in the dark for a further 60 min at room temperature. Denatured and alkylated protein samples were diluted tenfold with 5% trifluoroethanol, 50 mM ammonium bicarbonate and concentrated using 5 kDa molecular weight cut-off filters with centrifugation at 3000 x g for 60 min at 4 C. Promega sequencing grade trypsin was re-suspended in 1:1 dilution of 5% trifluoroethanol/50 mM ammonium bicarbonate and added to each sample to achieve a protease to protein ratio of 1:50. To accelerate trypsin activity calcium chloride solution was added to a final concentration of 1 mM. Samples were incubated in a thermomixer at 500 rpm for 18-24 hr at 37oC. Digested samples were evaporated to dryness and stored at -20 C. For peptide purification, stage tips were prepared inhouse with commercially available C18 Empore Disks (3M, Minneapolis, MN). 10 µl of sample was re-suspended at 1 µg/µl in 0.1% TFA. C18 discs were activated by addition of 50 µl of 50% AcN/0.1% TFA and centrifugation at 4700 rpm for 12 min. Discs were equilibrated with 50µl of 1% TFA and centrifugation at 4700 rpm, 21oC for 12 min. Sample (10 µl) was passed through the prepared C18 discs with centrifugation at 4700 rpm, 21oC for 12 min. C18 discs were washed twice with 50 µl 7 ACS Paragon Plus Environment


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of 1% TFA and centrifugation at 4700 rpm, 21oC for 12 min. Bound peptides were eluted into clean lo-bind Eppendorf tubes with 50% AcN/0.1% TFA. Purified samples were completely dried down under vacuum for approximately 1 hour at 30oC and resuspended in 12 µl buffer A [3% ACN, 0.1% formic acid] prior to LC-MS/MS and MRM analysis. Label-free LC-MS/MS Samples were analysed on a Thermo Scientific Q-Exactive MS connected to a Dionex Ultimate 3000 (RSLCnano) chromatography system. To monitor technical reproducibility a reference pool sample was generated from a pool of all patient samples. The reference pool sample was run at the beginning, middle and end of the LC-MS/MS run. Each patient sample (2 µg) and reference pool sample (2 µg) was loaded onto a Biobasic Picotip Emitter (120 mm length, 75 µm ID) packed with Reprocil Pur C18 (1.9 µm) reverse phase media and was separated by an increasing ACN gradient over 150 min at a flow rate of 250 nL/min. The MS was operated in positive ion mode with a capillary temperature of 220°C, and a potential of 2300V applied to the frit. All data was acquired with the MS operating in automatic data dependent switching mode. A high resolution (70,000) MS scan (300-1600 m/z) was performed in a Q- Exactive to select the 12 most intense ions prior to MS/MS (resolution 1,750) analysis using HCD. The relative collision energy and dynamic exclusion was set at 27 eV and 40 s respectively. The charge state screening settings were set to collect data on all detected ions except from ions with an unassigned or singly charged state. Bioinformatic data analysis Data files from the label-free LC-MS/MS data were imported into PEAKS (version 6) 8 ACS Paragon Plus Environment

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software to determine the number of peptides and proteins identified in each sample. Database searching was performed using the “HumanUniprot” database [39,704 protein sequences] (downloaded 01/11/2013) with the following search parameters applied; enzyme: trypsin, maximum missed cleavages: 2, species: Homo Sapiens, variable modifications: oxidation methionine, 4-hydroxynonenal (4-HNE), lysine acetylation at N and C termini, amidation, ammonia loss at N and C termini, precursor ion tolerance: 10 ppm, product ion tolerance: 0-3Da and maximum variable post translational modifications per peptide: 3. The false discovery rate (FDR) was set to 0.1% as a means of filtering these results. To determine which proteins and peptides were identified in each sample, and their respective measured intensities, the LCMS/MS data files were uploaded into MaxQuant (V.1.4.1.2) software and processed through the Andromeda search engine. Database searching was performed using the Unirprot/SwissProt database [40,452 protein sequences] (downloaded 29/07/2014) with the FDR set to 0.5%. A minimum of two peptides was required for protein identification. This search provided a full list (.txt format) of peptide and protein identifications along with their respective label-free quantitation (LFQ) intensities. This list was used to manually calculate the fold change of commonly identified proteins between control and BR samples and identify proteins that were uniquely expressed in either control or BR samples. In addition, the percentage coefficient variance (%CV) was calculated for all proteins of interest based on their recorded LFQ intensity values across all of the reference pool samples. Following manual interrogation of the data, the .txt file generated as result of Andromeda processing, was imported into Perseus (V.1.5.0.9) software for statistical characterization of protein expression changes between control and BR samples. The data was filtered to remove all protein contaminants, reverse phase proteins and those proteins that were



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only identified by site - an automated data processing feature of the Perseus software. Numeric Venn diagrams were generated from the filtered dataset. Protein expression changes between control and BR samples were compared using Student’s t-test (p≤0.05), principal component analysis (PCA) and hierarchical clustering analysis using Perseus software. MRM design and data acquisition All MRM assay design and data analysis was conducted using Skyline (64-bit) software (V.2.5.0.6157). The selection criteria for proteotypic peptides were as follows: no missed cleavages or ‘ragged ends’ and sequence length within 7-25 amino acids. Where possible, peptides with reactive cysteine (C) or methionine (M) residues were avoided. A maximum of two peptides were selected for each protein. Four transitions were selected for each peptide. MRM data was acquired using an Agilent 6460 triple quadrupole `mn coupled to a ChipCube interface with a 150 mm x 75 µm C18 nano-LC Chip (Agilent G4240-62010). Dried peptide samples were reconstituted in buffer A (3% ACN/0.1% formic acid) at 1 µg/µl and centrifuged for 30 min at 4o C. 1 µg of sample was loaded per injection at a flow rate of 300 nL/min. Samples were analysed over 40 min from 0 to 95% buffer B [90% ACN 9.9% H20 0.1% formic acid] along the following gradient; [0 min 0% B, 5 min 10% B, 35 min 30% B, 37 min 95% B, 38 min 95% B 40 min 0% B]. Dwell time was set to 10 ms. Collision energy was calculated based on the equation ((Precursor Ion mass/100)*3.6-4.8) V. MS 1 and MS 2 resolution was set to Unit, fragmentor voltage was set to 130V and cell acceleration was maintained at 4. The duty cycle was kept under 3s. Following enrichment on a trapping column (160 nl), peptides were separated on a C18 analytical column [Zorbax 3005B C18 5 µm (150 mm x 75 µm)] before being passed into the QqQ through a nanospray needle also contained in the Chip. Prior to the start 10 ACS Paragon Plus Environment

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of the MRM runs, 5 µl of a six peptide BSA-based PepMixTM (Thermo Scientific) was loaded under the same experimental conditions with an 18 min gradient [0 min 0% B, 9 min 35% B, 12 min 95% B, 15 min 95% B, 18 min 0% B] as a means of assessing system suitability. Longitudinal patient samples were analysed in four randomised batches of 21 (x2) and 22 (x2) patient samples. To establish the reproducibility of MRM analysis across all runs, a technical replicate (TR) – made by combining 10 µl of all digested sample replicates (see results section) - was injected 5 times within each batch, twice at the beginning, twice in the middle and once at the end. The position of the ‘middle’ TR samples in the experimental run order was randomly allocated for each batch to avoid any possible technical bias. The same TR sample was used throughout analysis of all 86 longitudinal patient samples. All resulting data was analysed using Skyline software and subsequently published to the Panorama server. Analysis reports for each run were exported from Skyline for further evaluation of discriminating candidate biomarkers. RESULTS Accrual and preparation of patient serum samples Serum samples used in this study were collected as part of a non-interventional clinical trial (ICORG 06-15). This trial was established in 2006, with support from ICORG (www.icorg.ie), for sequential accrual of sequential serum and urine samples from high risk PCa patients being treated with CHRT. Patient enrollment followed strict eligibility criteria, which is outlined in Supplementary data Table S.1. A summary of sample collection regime for patients enrolled in the ICORG 06-15 trial is outlined in Figure 1. In a previous study using this patient cohort, 62 candidate biomarker proteins were discovered following label-free LC-MS/MS analysis of



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serum samples from a single patient with biochemical recurrence (n=1) and a timematched control patient. MRM assays were developed for 18 of these proteins and used to confirm the protein expression changes observed via LC-MS/MS31. As of September 2013, when this current study was initiated, two additional patients had been diagnosed with BR following two successive PSA recordings greater than 2ng/ml above the nadir. Using serum samples from these patients, an improved proteomic workflow was implemented for the identification and longitudinal evaluation of candidate protein biomarkers for disease recurrence in patients being treated with CHRT. For each of the BR patients, a time-matched patient, who had yet to show evidence of BR (and had remained disease free for a prolonged period) was included as a control. Serum samples taken at two different time points – baseline and reported time of BR – were used for each of the six patients (Figure 2). Depleting some of the most highly abundant proteins is considered a useful technique to reduce sample complexity and enhance the detection of lower abundant proteins

38

. Immunoaffinity depletion with

antibodies to the most highly abundant proteins is often used to increase the number of identifications possible in the discovery phase

39

. Here, the 14 most abundant

serum proteins were remove from patient serum samples using a MARS Hu-14 antibody affinity removal system. Supplementary data Figure S.1 shows the effective separation of high abundant proteins (HAP) and low abundant proteins (LAP) from a crude patient serum sample. The LAP fraction from each patient sample was retained for MS based analysis.


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LC-MS/MS based protein analysis To investigate the differences in serum protein expression between BR and control patients, LAP samples were tryptically digested and resulting peptides analysed by LC-MS/MS on a Q-Exactive mass spectrometer. The run order of patient samples was randomized prior to analysis on the mass spectrometer in order to minimize any instrument-related bias. A reference pool was generated from a combination of all stage-tipped patient serum samples. The reference pool sample was injected at four stages throughout the run – once at the beginning, twice in the middle and once at the end - to monitor technical reproducibility. Following MS data acquisition, data files (.d) files were uploaded onto PEAKS (version 6) software and searched de novo as well as against the human database. This combined search identified an average of 265 proteins between all reference pool and patient samples with 1% FDR and a minimum of 1 unique peptide per protein. All (.d) files were subsequently uploaded directly to MaxQuant and searched through the Andromeda search engine. Overall, 347 proteins were identified across all samples with an FDR of 0.1% and a minimum of 2 unique peptides for protein identification. Data was imported into Perseus software for further analysis of serum protein expression profiles. Here, the data was filtered for; valid values, contaminant proteins and reverse phase proteins. Empty columns were removed and the data was again filtered for valid values. Ultimately, this resulted in 226 proteins quantified by 2 unique peptides. Serum protein expression The technical reproducibility of the protein discovery approach was evaluated through numeric Venn diagram and multi-scatter plots of all reference pool samples. The Venn diagram indicated that 187 proteins out of 226 were identified common to all four



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reference pool samples, while both R-squared and Pearson analysis of multi-scatter plots gave values ≥0.98 (Figure 3). Principal component analysis (PCA) of the 226 quantified proteins was undertaken to determine, in an unbiased manner, the overall differences/similarities between the individual patient samples. Again, the four reference pool samples grouped together. PCA analysis revealed that there was no definitive grouping of BR and control patient samples (Supplementary data Figure 2). This may not be surprising considering the inherent variability associated with each individual patient in this study, e.g. their advanced age, the stage of their disease and other potential comorbidities. It seems apparent that these differences may mask the discrimination between patients who are responsive to CHRT and those who are not. Hierarchal clustering of all samples confirmed the observations made from the PCA analysis. There were minimal changes in protein expression between reference pool samples, while patient samples showed varying patterns of protein expression. It seems, again, that there was no discrimination between BR and control samples (Supplementary data Figure S.2). To determine if any of the identified proteins could potentially be included as part of a candidate biomarker panel for BR, relative changes in protein expression between each BR/control pair, at both time points, were assessed. Figure 4 shows the proteins that were changed between each BR and control pair at both baseline and time of disease recurrence. A Student’s t-test (p-value ≥0.05) was also performed on each BR and control pair. The number of significantly changing proteins for each patient sample pair is shown in Figure 4. In addition, proteins that were uniquely expressed in at least 2 of the BR samples compared with their respective controls were identified. The reference pool samples were used to calculate the % CV values of all proteins selected as result of these analyses. The following criteria were thus applied for the selection of potential biomarker


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candidates: proteins ‘unique’ to at least 2 BR samples; proteins showing a significant change in expression in a least two BR vs control comparisons – as indicated by a fold change value ≥1.5 and Student’s t-test analysis; proteins with a calculated CV ≤20% based on reference pool data. This led to the identification of 28 candidates at baseline and 37 at time of recurrence, a total of 65 protein biomarker candidates. Many of these overlapped with results from previous PCa related discovery experiments performed previously in our research group (unpublished data). Importantly, all proteins previously identified as significant our initial CHRT biomarker discovery experiments and as reported in Morrissey et al.31 were again identified here. Longitudinal evaluation of protein expression changes by MRM The PCa biomarker candidates discovered as part of this study were combined with previously measured PCa candidates for development of a putative serum protein signature of disease recurrence in PCa patients. The list of proteins of interest selected for measurement by MRM, and the corresponding peptides used for MRM development are detailed in Supplementary data Table S.2. Samples taken from each of the six study patients (BR and control) from their time of diagnosis to a most recent sample, were used for longitudinal evaluation of candidate biomarker expression throughout the course of CHRT treatment. In total, 86 time-point patient samples were analysed by MRM (Figure 5). The 86 samples were randomized and subjected to trypsin digestion in four batches of either 21 or 22 samples. Sample replicates (SR), made up of pools of an additional PCa patient sample which was not included as part of this study, were also digested in each batch. In total, 24 samples were digested in each batch. All SR samples were then analysed on a Q-Tof mass spectrometer to



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confirm reproducibility of the sample digestion across all batches prior to MRM analysis. Development and optimisation of the MRM assays was undertaken using Skyline software. MRM assays for 41 proteins were developed successfully (Figure 6). The final method consisted of 59 peptides and 236 transitions (4 transitions per peptide) with a 40 min gradient and dwell time of 10 ms. Patient samples were again randomized and run in 4 sets with 21 or 22 samples in each. To establish the MRM reproducibility and monitor instrument performance across analysis of all 86 samples, SR samples were combined to form a technical replicate (TR) sample. Five TR samples were analysed with the same 41-protein MRM method at the beginning, middle and end of each experimental run as outlined in Supplementary data Figure S.3. As such, a total of 27 samples were analysed in each run. At the end of each run, the total ion chromatograms for the TR samples were assessed visually, using Agilent Mass Hunter software (version B.06.00), to confirm that they all showed similar signal intensity and peak distribution across the 40 min gradient. This information provided initial reassurance that each batch had been analysed successfully (Supplementary data Figure S.3). All resulting (.d) files were directly imported into Skyline for further inspection of the data quality and to ensure correct peak integration for all measured peptides. The peak identity was assessed using information for the retention time and .dot product of each measured peptide. Data from the TR samples provided knowledge on the expected retention time range, while the peak area data from the 4 transitions measured for each peptide was used to calculate the dot product, which was always greater than 0.9. The peak area for the most intense transition was used to quantify each peptide. The recorded intensity averages for all TR samples across the entire experiment indicated that, across the entire experiment, there was no drop-off in signal intensity and no increase in the %


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CV. This information provided confidence in the technical performance of the QqQ mass spectrometer (data not shown). Following peak inspection in Skyline, data was exported to Excel. Prior to further analysis, the measured protein intensities were normalized against the total protein concentration that was subsequently determined for each sample. Total protein for each patient sample was determined using area measurements for four high abundant housekeeping proteins that were included in the MRM method – Haptoglobin, Complement C3, Apolipoprotein A-I and Transthyretin. All measured intensities were summed to give a ‘total’ protein concentration for each of the 20 TR samples analysed across all batches. %CV and area values for all housekeeping peptides and proteins are indicated in Table 1. The summed intensity for the peptides representing the 4 housekeeping proteins for each sample was divided against the mean intensity for the TR sample. This individual sample ratio was then used to normalize the MRM data collected the patient sample. With the ‘normalised’ values, longitudinal line plots were made for each individual peptide to show the change in peptide expression over time in each BR v control patient pair. PSA measurements from these patients were included in these plots to show time of BR for the BR patients. Patterns in biomarker expression, which may prelude treatment failure, were difficult to identify in both the BR and control samples. As an example, we show a plot of the longitudinal expression of DFDFVPPVVR (Complement C3 protein) in Figure 8. Similar plots for all measured peptides are shown in Supplementary data Figure S.4. To further investigate potentially significant changes in peptide expression the data from each BR and control patient pair were compared by one-way ANOVA at four ‘stages’ of recurrence – baseline, biochemical recurrence (BR – 3 patients), local recurrence (LR – 2 patients) and bone metastasis (BM – 1 patient). In all cases, the one-way ANOVA



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returned a critical value of 5.6. Those peptides with a fold change ≥5.6 at all 4 time points are indicated in Table 2. In accordance with observations made based on visualization of the line graphs generated from the MRM data, protein expression changes varied widely between all BR and control pairs. Only one peptide – AGEVQEPELR representing zinc-alpha-2-glyoprotein was shown to be consistently down regulated in BR patients at baseline. The peptide TGLQEVEVL was shown to be consistently up regulated at the time of local recurrence in the two BR patients who progressed to this disease stage. DISCUSSION In this study 347 proteins were identified in depleted patient serum samples. Significant proteins which had previously been identified in an n=1 study based on samples from the ICORG 06-15 trial were again identified here31. Following filtering of the data and the application of strict selection criteria, 65 candidate protein biomarkers were identified. These overlapped greatly with proteins that have previously been identified in our group in other PCa studies. Moreover, many of the candidates have been referenced in recent literature as having association with PCa progression40–42. Forty one (41) of these candidates were measured longitudinally by MRM using crude serum samples from BR and control patients. Patient amples were analysed in batches of 21 or 22 with (i) each sample being interspersed with blank samples to prevent sample carry-over and, (ii) technical replicates (TR) to measure analytical reproducibility. In total, 27.33 hours of instrument time was required for analysis of each sample run of patient and technical replicate samples. Analysis of the TR samples indicated that over the course of the entire experiment (almost 130 hours instrument time) there was minimal drop off in signal intensity and the average % CV of peptide intensities did not change (data not shown). Of the 59 peptides that were 18 ACS Paragon Plus Environment

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measured as part of the final assay, 26 peptides (corresponding to 23 proteins) were measured endogenously in serum samples, across all four batches, with CV values ≤25% (Figure 6). This indicates the feasibility of measuring medium-low abundant multi-protein panels in patient samples with minimal sample pre-treatment. All of these peptides measurements were plotted to show longitudinally changing expression from the onset of patient treatment with CHRT up to the most recent sample. However, unlike with PSA expression, there was no consistent pattern of increasing or decreasing candidate biomarker expression which was common to all BR or control samples. This result may not be surprising when one considers the likely heterogeneity in the modest number of patients included in this study all of who have been treated for prostate cancer. Moreover, those who were diagnosed with BR were diagnosed at different times following treatment and their disease recurrence has since progressed to varying degrees, unfortunately, in one case to metastatic disease. To further elucidate the data, statistical analysis was performed to compare BR and control samples at certain ‘milestones’, which occurred over the course of patient treatment with CHRT - these being baseline and time of biochemical recurrence, local recurrence and bone metastasis. Among the peptides that were found to have a prominent change in expression (fold change ≥ 5.6) was LSEPAELTDAVK – the peptide measured for Prostate Specific Antigen (PSA). This peptide was found to be greatly up regulated at the time of BR in two of the three BR patients, which is in agreement with the expression changes observed for total PSA at this time point. Numerous studies have demonstrated a link between zinc-alpha-2-glyoprotein expression and prostate cancer43–47. The results obtained here correlate to those of previous immunohistochemistry-based studies, in which lower levels of zinc-alpha-2glycoprotrein expression were found to correlate with more aggressive PCa46,47. As



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such, this protein in particular holds much potential for use as a serum biomarker for disease recurrence. The full protein list was imported into the open software source STRING (version 10) for basic pathway analysis. Unsurprisingly, the emerging pathways were found to be those with associations to complement and coagulation cascades and cancer (Table 3). In addition, there are a number of publications which report associations of these proteins with cancer, metabolic diseases and cardiovascular disorders 48–54 CONCLUSIONS The results from this study confirm the feasibility of multiplexed measurements of protein panels in crude serum sample with minimal sample handling. This study, being the first to longitudinally assess changes in candidate protein biomarker expression over a prolonged post-CHRT treatment period, highlights the inherent patient variability among PCa patients. These observations add further weight to the requirement of large sample numbers for statistically robust biomarker validation. Despite these limitations the proteins found to be significant here, with the combined used of relatively elementary statistics and pathway analysis, included proteins that have previously been shown to be potentially involved in PCa40–42. It is important to note that one of the three BR patients in this cohort had not shown two successive increases in PSA when BR was indicated. However, since this study was initiated, this patient went on to develop the local recurrence that is the next identifiable indication of disease progression. So, although in this case changes in PSA alone did not meet the criteria for BR the individual did have disease recurrence. This highlights the need for additional biomarkers to support PSA in being able to make definitive assessment of a patient’s disease status. Future studies will be directed towards supplementing the current biomarker panel of 41 proteins with additional candidate biomarkers both 20 ACS Paragon Plus Environment

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novel and those that ma have an existing association with PCa progression. Clinical evaluation of MRM assays that are developed for the measurement of such PCa biomarker panels will require additional longitudinal sample cohorts of substantial size for robust assessment of their predictive and/or diagnostic capacity for monitoring disease progression in individual patients. ASSOCIATED CONTENT Supporting Information This material is available free of charge via the internet http://pubs.acs.org. The supplementary data associated with this manuscript consists of (i) validation of successful protein depletion (Figure S.1), (ii) PCA and hierarchical clustering analysis for all samples analysed on the Q-Exactive mass spectrometer (Figure S.2), (iii) run order and mass hunter analysis of TR samples for MRM experiments (Figure S.3), (iv) longitudinal expression of all measured candidate peptides in patient samples (Figure S.4), (v) ICORG 06-15 patient inclusion criteria (Table S.1) and, (vi) the final transition list for MRM analysis (Table S.2).

AUTHOR INFORMATION Corresponding Author Claire Tonry Present Address Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland Tel: 00353 861997996 E-mail: [email protected] Web: www.biomedicalproteomics.org 21 ACS Paragon Plus Environment


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Author Contributions All authors have given approval to the final version of the manuscript

Funding Sources This work was supported by grants from the St Luke’s Institute of Cancer Research. The UCD Conway Institute is funded by the programme for research in Third Level Institutions as administered by the Higher Education Authority of Ireland.

ACKNOWLEDGEMENT We thank all patients at St. Luke’s and UPMC Beacon hospitals who enrolled in ICORG06-15 and so generously supported the accrual of samples and, in particular, for their dedication in enabling repeated sample collection which has made this study possible. Dr. Pierre Thirion, Dr. Alina Mihai, Dr. Gerard McVey, Siobhan Pullan, Marie Finn, Mary Dunne, Valerie Owens, Leon Flannagan, Anne Halpin, Eugene Molumby, Siobhan O’Keefe, June D’Alton, Eilis Carr, Elizabeth Martin, Sinead Callinan and Carol McSherry, all of St. Lukes’ and UPMC Beacon hospitals are thanked for their on-going support.

ABBREVIATIONS CHRT, combined hormone and radiation therapy; PCa, prostate cancer; PSA, prostate specific antigen; BR, biochemical recurrence; MRM, multiple reaction monitoring; LC-MS/MS, liquid chromatography mass spectrometry; ACN, acetonitrile; FDR, false discovery rate; LFQ, label free quantitation; reference pool, reference pool; ANOVA, analysis of variance; CV, coefficient variance; SR, sample replicate; TR, technical replicate


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Table 1 (a): In-batch %CV values of the 4 housekeeping proteins Table 1 (b): Calculation of the mean ‘total protein’ across all TR samples Table 1 shows the area recorded for each housekeeping peptide. The sum of areas was taken as a ‘total protein’ measurement. The mean ‘total protein’ across all technical replicate (TR) samples was used to normalize the patient data. Table 2: Significantly changing peptides identified using one-way ANOVA. Table 2 shows the peptides (and proteins) that were significantly up/down regulated at four notable patient progression milestones - Baseline, Biochemical Recurrence (BR), Local Recurrence (LR) and Bone Metastasis (BM). Table 3: Pathways associated with candidate protein biomarkers Table S.1: Patient selection criteria for ICORG 06-15 clinical trial Table S.1 outlines the strict inclusion and exclusion criteria with which all perspective patients had to meet before inclusion in the ICORG 06-15 trial Table S.2 MRM parameters for measurement of 41 protein biomarkers Table S.2 displays full details of peptides selected for MRM measurement, as well as the parameters set for their detection on the Agilent triple quadruple mass spectrometer



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Lapolla, A.; Porcu, S.; Traldi, P. Mass spectrometry for diabetic nephropathy monitoring: new effective tools for physicians. ISRN Endocrinol. 2012, 2012, 768159.

(54)

Alexander, H.; Stegner, A. L.; Wagner-mann, C.; Bois, G. C. Du; Alexander, S.; Sauter, E. R. Proteomic Analysis to Identify Breast Cancer Biomarkers in Nipple Aspirate Fluid Proteomic Analysis to Identify Breast Cancer Biomarkers in Nipple Aspirate Fluid. Clin. Cancer Res. 2004, 7500–7510.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 43

Table 1 (a): In-batch %CV values of the 4 housekeeping proteins

Haptoglobin

Complement C3

Apolipoprotein-‐A1

Apolipoprotein-‐A1

Transthyretin

VTSIQDWVQK

DFDFVPPVVR

DYVSQFEGSALGK

LLDNWDSVTSTFSK

AADDTWEPFASGK

%CV

%CV

%CV

%CV

%CV

Batch 1

5.5

14.3

12.8

21.7

9.9

Batch 2

9.1

24.9

24.7

31.9

8.4

Batch 3

8.7

5.5

10.8

18.7

2.7

Batch 4

8.5

11.4

8.7

16.1

7.0

Table 1 (b): Calculation of the mean ‘total protein’ across all TR sample

VTSIQDWV QK

DFDFVPPVV R

DYVSQFEGS ALGK

Area

Area

Area

Area

Area

SUM

46228

29599

423153

230816

7891

737687

770805.1

0.96

TR2

47371

35226

426778

261023

8009

778407

770805.1

1.01

TR3

49696

38087

404744

243607

7640

743774

770805.1

0.96

TR4

49999

27582

549698

246038

9600

882917

770805.1

1.15

TR5

53304

37808

466764

139123

7758

704757

770805.1

0.91

TR1

50980

41982

665792

351904

7549

1118207

770805.1

1.45

TR2

47103

28207

449350

377239

7636

909535

770805.1

1.18

TR3

51529

20852

732165

155683

8935

969164

770805.1

1.26

TR4

45027

30195

447886

421245

8334

952687

770805.1

1.24

TR5

56963

32081

464254

420484

9024

982806

770805.1

1.28

TR1

44634

23898

297508

126016

8045

500101

770805.1

0.65

TR2

42954

24244

382374

165780

7566

622918

770805.1

0.81

TR3

37029

22626

393138

206675

7722

667190

770805.1

0.87

TR4

45204

24853

362927

203874

7520

644378

770805.1

0.84

TR5

46833

26285

336573

188070

7683

605444

770805.1

0.79

TR1

40941

33446

412568

258331

9443

754729

770805.1

0.98

TR2

46850

26555

351891

208588

8405

642289

770805.1

0.83

TR3

46404

27308

403813

173416

9087

660028

770805.1

0.86

TR4

52114

28426

450632

235269

8030

774471

770805.1

1.00

TR5

46886

33639

414844

259873

9371

764613

770805.1

0.99

47402.45

29644.95

441842.6

243652.7

8262.4

770805

154653. 5 20.1

Replicate Name Batch 1 TR1

Batch 2

Batch 3

Batch 4

LLDNWDSV TSTFSK

AADDTWEP FASGK

MEAN

StDv

4477.5

5637.0

103743.9

87106.3

713.0

%CV

9.4

19.0

23.5

35.8

8.6

Mean

Ratio

Table 1 shows the area recorded for each housekeeping peptide. The sum of areas was taken as a ‘total protein’ measurement. The mean ‘total protein’ across all technical replicate (TR) samples was used to normalize the patient data.


Page 31 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2: Significantly changing peptides identified using one-way ANOVA.

TIME:

Protein

Peptide

Alpha-‐1 antitrypsin

Baseline

BR

LSITGTYDLK

C1 v BR1 p=0.27

C2 v BR2 p=0.45

C3 v BR3 p=0.59

C1 v BR1 P=0.32

SVLGQLGITK

Alpha-‐1-‐ antichymotrypsin

ADLSGITGAR

Alpha-‐2-‐ microglobulin

C2 v BR2 P=0.81

LR

BM

C3 v BR3 P=0.67

C1 v BR1 P=0.69

C2 v BR2 P=0.7

C1 v BR1 P=0.73

+25.55

-‐198.5

+150.28

+9.7

-‐23.3

NEDSLVFVQTDK

+372.4

+102.7

+11.43

AMBP

ETLLQDFR

+14.2

+11.3

Antithrombin-‐III

TSDQIHFFFAK

Apolipoprotein A-‐II

SPELQAEAK

+9.16

EPCVESLVSQYFQTVTDYGK

Apolipoprotein A-‐IV

IDQNVEELK

+6.10

-‐57.0

-‐8.86

+5.69

+85.36

+5.93

+30.61

+78.48

LEPYADQLR

Apolipoprotein C-‐III

DALSSVQESQVAQQAR

Apolipoprotein E

VQAAVGTSAAPVPSDNH

Apolipoprotein A-‐I

LLDNWDSVTSTFSK

DYVSQFEGSALGK

Apoloipprotein C

GWVTDGFSSLK

Beta-‐2-‐ glycoprotein

EHSSLAFWK

+5.96

+12.62

ATVVYQGER

+5.90

+6.91

Caveoilin-‐1

ASFTTFTVTK

+20.81

Clusterin

IDSLLENDR

ASSIIDELFQDR

Complement C-‐ 1s subcomponent Complement C-‐ 4A

LLEVPEGR

+20.4

GLEEELQFSLGSK

+13.2

+19.0

Complement C1q subcomponent subunit B

GDPGIPGNPGK

+19.3

+14.0

-‐42.9

-‐6.47

-‐35.8

-‐7.4

-‐7.52

+28.88

-‐7.55

+79.33

+13.03

+9.18



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

TIME:

Protein

Peptide

Complement C3

Baseline

Page 32 of 43

BR

LR

BM

DFDFVPPVVR

C1 v BR1 p=0.27 +42.9

C2 v BR2 p=0.45 +45.7

C3 v BR3 p=0.59

C1 v BR1 P=0.32 +12.59

C2 v BR2 P=0.81 +14.62

C3 v BR3 P=0.67

C1 v BR1 P=0.69

C2 v BR2 P=0.7

C1 v BR1 P=0.73

TGLQEVEVK

+675.35

+28.80

+16.90

+7.55

Complement C4

VGDTLNLNLR

+10.8

+5.40

Complement component C6

SEYGAALAWEK

Complement component C9

LSPIYNLVPVK

+13.5

+12.6

+6.97

Complement factor 1

IVIEYVDR

+168.5

+21.9

+9.83

+14.12

-‐38.63

+6.31

Complement factor H

EQVQSCGPPPELLNGNVK

+20.0

-‐16.89

Ficolin-‐3

YGIDWASGR

Galectin-‐3 binding protein

SDLAVPSELALLK

Glutathione peroxidase 3

FLVGPDGIPIMR

Haptoglobin

TEGDGVYTLNNEK

Hemopexin

Insulin-‐like growth factor binding protein Inter alpha-‐ trypsin inhibitor

Kininogen

Leucine-‐rich alpha 2 glycoprotein

Monocyte differenciation antigen CD14 Pigment epithelium derived factor

+14.07

-‐6.30

+9.12

-‐10.59

-‐59.5

-‐145.5

-‐19.26

+685.85

VTSIQDWVQK

+75.4

+28.6

+7.90

+7.56

SGAQATWTELPWPHEK

+93.1

+14.4

NFPSPVDAAFR

+12.1

+6.0

FLNVLSPR

ILDDLSPR

NVVFVIDK

+19.4

IASFSQNCDIYPGK

TVGSDTFYSFK

+14.4

VAAGAFQGLR

+19.94

+4.29

DLLLPQPDLR

LTVGAAQVPAQLLVGALR

DTDTGALLFIGK

TVQAVLTVPK

+10.60

+18.4

+15.2

+11.00

+7.37


+7.98

Page 33 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


TIME:

Protein

Peptide

Plasminogen

Baseline

BR

LSSPAVITDK

C1 v BR1 p=0.27 +17.7

C2 v BR2 p=0.45

C3 v BR3 p=0.59 -‐15.8

C1 v BR1 P=0.32

EAQLPVIENK

+42.3

+14.1

Prostate-‐specific antigen

LSEPAELTDAVK

Serotransferrin

YLGEEYVK

+31.9

+12.0

Serum Amyloid-‐P Component

DNELLVYK

+8.9

+16.0

Tetranectin

LDTLAQEVALLK

Transthyretin

AADDTWEPFASGK

Vitamin-‐D binding protein Vitronectin

Zinc-‐alpha-‐2-‐ glycoprotein

BM

C3 v BR3 P=0.67 +6.03

C1 v BR1 P=0.69

C2 v BR2 P=0.7

+9.37

+25.59

+27.42

+48.25

VPTADLEDVLPLAEDITNILSK

DVWGIEGPIDAAFTR

+21.0

+11.7

FEDGVLDPDYPR

+32.9

+28.9

-‐6.0

-‐50.3

AGEVQEPELR

EIPAWVPFDPAAQITK

-‐8.4

C2 v BR2 P=0.81 +226.79

LR

+7.14

-‐35.32

+50.78

-‐7.69

-‐9.98

C1 v BR1 P=0.73 +6.44

+13.33

Table 2 shows the area recorded for each housekeeping peptide. The sum of areas was taken as a ‘total protein’ measurement. The mean ‘total protein’ across all technical replicate (TR) samples was used to normalize the patient data.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 43

Table 3: Pathways associated with candidate protein biomarkers KEGG Pathway

Number Of Genes

P-‐value

Complement and coagulation cascades Staphylococcus aureus infection Pertussis

6

0.00

5

0.00

3

0.00

Systemic lupus erythematosus Prion diseases

3

0.00

2

0.00

Chagas disease (American trypanosomiasis) Herpes simplex infection

2

0.01

2

0.02

Leishmaniasis

1

1.00

Pathways in cancer

1

1.00

Rap1 signaling pathway

1

1.00

Fat digestion and absorption Vitamin digestion and absorption Neuroactive ligand-‐ receptor interaction Influenza A

1

1.00

1

1.00

1

1.00

1

1.00

MAPK signaling pathway

1

1.00

Glutathione metabolism

1

1.00

Ras signaling pathway

1

1.00

Viral carcinogenesis

1

1.00

Proteoglycans in cancer

1

1.00

Tuberculosis

1

1.00

Legionellosis

1

1.00

Regulation of actin cytoskeleton Thyroid hormone synthesis Arachidonic acid metabolism Alzheimer's disease

1

1.00

1

1.00

1

1.00

1

1.00

Phagosome

1

1.00

PI3K-‐Akt signaling pathway Melanoma

1

1.00

1

1.00

Neurotrophin signaling pathway PPAR signaling pathway

1

1.00

1

1.00


Page 35 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60




Abstract/Table of contents Clinical evaluation of candidate protein biomarkers of prostate cancer disease recurrence Q-Exactive Mass Spectrometer Identification of 65 protein biomarker candidates Control Versus Biochemical Recurrence Longitudinal MRM Sample Timeline

Alpha-1 antiichymotrypsin ADLSGITGAR

Triple Quadrupole Mass Spectrometer

Control versus Biochemical Recurrence

1

Patient Number

17

Intensity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 43

5


15


4 2 0

RT

MRM development for 41 proteins, 59 peptides

500

1000

1500

2000

2500

3000

3500

Number of days on CHRT

Longitudinal measurement of panel of protein biomarkers

Label-‐free LC-‐MS/MS based protein discovery was undertaken on depleted serum samples from patients undergoing combined hormone and radiation therapy (CHRT) who showed evidence of disease recurrence (n=3) and time-‐ matched patient controls (n=3). Multiple reaction monitoring (MRM) assays were designed using Skyline software for a subset of identified protein biomarkers (65). These were included as part of a putative panel of prostate cancer biomarkers (41 proteins) which were measured longitudinally in patient samples collected from time of diagnosis, through to time of BR and beyond.


Page 37 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1: Schematic representation of the patient sample accrual regime Patient meets inclusion criteria, written informed consent obtained

1st Sample Patient receives 4-8 months hormone treatment (LHRH agonist: Decapeptyl and bicalutamide: Casodex

2nd Sample Patient receives 74-81 Grays of radiation to the prostate gland by EBRT

3rd Sample Patient follow-up

4th Sample (6 weeks after radiotherapy ends)

Follow up samples collected every 3 months for 3 years and thereafter 6 monthly for the duration of the trial. PSA levels measured at each sample collection

The first serum sample was collected once a patient has agreed to be included in the trial and met the inclusion criteria for the study (Supplementary data Table S.1). The second sample is taken after the patient has completed 4-8 months of hormone treatment. The third sample is taken after the patient has undergone 74-81 Grays of radiation to the prostate gland by external beam (EBRT). The fourth sample is taken 6 weeks following the end of radiotherapy. Serum samples are then collected every 3 months for the first 3 years and every 6 months thereafter. Figure adapted from Morrissey et al31.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 43

Figure 2: Patient samples selected for LC-MS/MS discovery and longitudinal biomarker evaluation by MRM.

Control Patients

BR Patients Age 78

Age 81

Age 67

Age 69

Age 63

Age 63 3000

2500

2000

1500

1000

500

0

500

1000

1500

2000

2500

Number of days treated with CHRT before BR diagnosis Two samples for each Biochemical Recurrence (BR) and Control patient were used for LC-MS/MS-based discovery – the patient’s baseline sample and the sample collected at time of BR. The number of days for which BR patients were treated with CHRT prior to diagnosis of BR is indicated on the right. Red arrows on the left indicate the control samples used to compare against failure patients at time of BR.


Page 39 of 43

Figure 3: Multi-Scatter plots for reference pool samples.

(ii)

20 30 30

30

20

30

30

(i)

30

30

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Pearson Correlation 30

20

30

30

30

R-Squared 30

20

30

30

30

Multi-scatter plots generated using Perseus software show Pearson (i) and R-Squared (ii) values of ≥0.98 for reference pool samples



Figure 4: Protein changes between BR and Control samples.

+3 +2 +1.5

1

6

12

12

22

Baseline

BR

B(i)

A (ii) >3

+2

20 0

Control

BR

5

25

21

68 66 64 t-test 62 significnance 60 58 56

A (iii)

BR3 v C3

1

10

B(ii)

Control v BR

40

BR2 v C2

1

+3

+1.5

60

Baseline BR1 v C1

Fold Change

>3

Total Control v BR Fold Change

A (i) Fold Change

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 43

5

16

Time of Recurrence

>3 +3 +2 +1.5

B(iii)

Time of Recurrence BR1 v C1

BR2 v C2

1

2

9

20

11

35

25

32

Baseline

60 40 t-test significance BR1 v C1 BR2 v C2 BR3 v C3

t-test significance

20 0

BR1 v C1 BR2 v C2 BR3 v C3

The number of proteins showing fold changes ≥1.5 between each BR and Control pair at baseline and time of BR is shown in A (i) – (iii). The number of proteins showing statistical significance following Student’s t-test analysis (p≤0.05) of BR and Control patients at both baseline and time of BR is shown in B (i) – (iii).


Page 41 of 43

Figure 5: Sample collection timeline for longitudinal biomarker evaluation.

Longitudinal MRM Sample Timeline Control versus Biochemical Recurrence

1 17

Patient Number

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


5


15


4 2 1

0

2

3

500

4

5

6

1000

7

8

9

10

1500

11

12

2000

13

14 15

16

2500

3000

3500


Time-matched BR (patient number 2, 15 and 17) and control (patient number 4, 5 and 1) samples were collected at times indicated vertically. Arrows indicate the time at which biochemical recurrence (BR, circle), local recurrence (LR, square) and bone metastasis (BM, triangle) was identified.



Figure 6. MRM assays for selected candidate biomarker proteins Alpha-2-microglubulin NEDSLVFVQTDK

RT

15.1%

25.6%

RT

Intensity Intensity

Intensity

14.0%

RT

RT

RT Haptoglobin VTSIQDWVQK

Intensity

Glutathione peroxidase 3 FLVGPDGIPIMR

RT

23.4%

RT

8.0%

Kininogen IASFSQNCDIYPGK

15.8%

Plasminogen EAQLPVIENK

13.3%

RT

20.7%

Inter alpha-trypsin inhibitor NVVFVIDK

Pigment epithelium derived factor TVQAVLTVPK

Intensity

Intensity

RT

RT

RT Complement C3 DFDFVPPVVR

Kininogen TVGSDTFYSFK

Intensity

19.2%

14.3%

RT

11.1%

20.7%

RT Transthyretin AADDTWEPFASGK

Serotransferrin YLGEEYVK

Intensity

18.5%

Vitronectin FEDGVLDPDYPR

7.3%

RT

Insulin-like growth factor binding protein FLNVLSPR

Intensity

RT

Pigment epithelium derived factor DTDTGALLFIGK

21.6%

13.9%

Intensity

Intensity

8.2%

RT Complement factor 1 IVIEYVDR

Intensity

RT Hemopoexin NFPSPVDAAFR

24.3%

Intensity

Intensity

Intensity

23.3%

RT Complement C9 LSPIYNLVPVK

RT

Complement C1q subcomponent subunit B GDPGIPGNPGK

Intensity

22.9%

Complement C4 VGDTLNLNLR

22.1%

RT Clusterin IDSLLENDR

Intensity

Intensity

RT

17.8%

Apolipoprotein C-III DALSSVQESQVAQQAR

Intensity

9.8%

RT

Intensity

Intensity

5.9%

Intensity

RT Apolipoprotein C GWVTDGFESSLK

Apolipoprotein A-I DYVSQFEGSALGK

Apolipoprotein A-I LLDNWDSVTSTFSK

AMBP ETLLQDFR

Intensity

23.7%

Intensity

Intensity

Alpha-1 anti trypsin LSITGTYDLK

RT

7.0%

RT

Serum amyloid-p component DNELLVYK

Intensity

Intensity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 43

RT

15.2%

RT

MRM data for peptides measured endogenously with CV ≤25%, across the entire experiment, are shown. Skyline software was used to integrate peaks and retain information on the dot product, intensity and retention time of each measured peptide. The %CV values for each peptide are indicated in red.


Page 43 of 43

Figure 7: Longitudinal expression of PSA and candidate biomarker for patients and time-matched controls, over course of treatment with combined hormone and radiation therapy (CHRT).

10.00

BR

LR

(B) Complement C3: DFDFVPPVVR BR LR 400000.0 BM

BM

8.00 6.00 4.00

PSA_BR1

2.00

PSA_Control1

0.00 0

500

1000

1500

2000

2500

Peptide Intensity

PSA Measurement

(A) PSA

300000.0 200000.0

Control Peptide 1

100000.0

BR Peptide 1

0.0 0

3000

500

350000.0

8.00 6.00 4.00

PSA_BR2

2.00

PSA_Control2

0.00 0

500

1000

1500

2000

2500

Peptide Intensity

PSA Measurement

LR

BR

10.00

6.00 PSA_BR3

4.00 2.00

PSA_Control3

0.00 0

500

1000

1500


2000

2500

Peptide Intensity

BR

8.00

1500

2000

BR

250000.0

2500

3000

LR Control Peptide 2

150000.0

BR Peptide 2

50000.0 -50000.0 0

Number of days on CHRT 10.00

1000

Number of Days on CHRT


PSA Measurement

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


500

1000 1500 Number of Days on CHRT

350000.0

2000

2500

BR

250000.0 150000.0

Control Peptide 3 BR Peptide 3

50000.0 -50000.0 0

500

1000 1500 Number of Days on CHRT

2000

2500

The candidate biomarker shown here is DFDFVPPVVR, a proteotypic peptide for Complement C3. The arrows indicate points of biochemical recurrence (BR), local recurrence (LR) and bone metastasis (BM). This figure is representative of all measured candidates when compared to PSA expression (see supplementary data figure S.4)


Discovery and Longitudinal Evaluation of Candidate Protein

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