Man K Chan

Tsz M Tsang

Laura W Harris

Paul Guest

Elaine Holmes

Dr Sabine Bahn

Evidence for disease and antipsychotic medication effects in post-mortem brain from schizophrenia patients Running title: Disease and medication effects in schizophrenia brain

Keywords: post-mortem brain, schizophrenia, antipsychotic medication, bipolar disorder, LC-MS E, 1 H NMR

Extensive research has been conducted on post-mortem brain tissue in schizophrenia, particularly the dorsolateral prefrontal cortex (DLPFC). However, to what extent the reported changes are due to the disorder itself and which are the cumulative effects of lifetime medication remains to be determined. In this study, we employed label-free liquid chromatography-mass spectrometry (LC-MS E ) based proteomic and proton nuclear magnetic resonance ( 1 H NMR) based metabonomic profiling approaches to investigate DLPFC tissue from two cohorts of schizophrenia patients grouped according to their lifetime antipsychotic dose, together with tissue from bipolar disorder subjects, and normal controls (n=10 per group). Both techniques showed profound changes in tissue from low-cumulative-medication schizophrenia subjects, but few changes in tissue from medium-cumulative-medication subjects. Protein expression changes were validated by Western blot and investigated further in a third group of subjects who were subjected to high-cumulative-medication over the course of their lifetime. Furthermore, key protein expression and metabolite level changes correlated significantly with lifetime antipsychotic dose. This suggests that the detected changes are present prior to antipsychotic therapy and moreover, may be normalized with treatment. Overall, our analyses revealed novel protein and metabolite changes in low-cumulative-medication subjects associated with synaptogenesis, neuritic dynamics, presynaptic vesicle cycling, amino acid and glutamine metabolism, and energy buffering systems. Most of these markers were altered specifically in schizophrenia as determined by analysis of the same brain region from bipolar disorder patients.

Introduction

Schizophrenia (SCZ) is a debilitating psychiatric disorder that affects approximately 1% of the world population [START_REF] Greally | The Relevance of Epigenetics to Major Psychosis[END_REF]. Extensive research has been conducted to investigate brain regions such as the dorsolateral prefrontal cortex (DLPFC) since this area is thought to represent the primary site of higher cognitive function impairment [START_REF] Frith | The role of the prefrontal cortex in higher cognitive functions[END_REF]. For example, cytoarchitectural, neurochemical [START_REF] Torrey | Neurochemical markers for schizophrenia, bipolar disorder, and major depression in postmortem brains[END_REF] and structural (4) abnormalities have been observed in this brain region of SCZ patients, such as decreased neuronal size and dendritic spine density [START_REF] Pierri | Decreased somal size of deep layer 3 pyramidal neurons in the prefrontal cortex of subjects with schizophrenia[END_REF][START_REF] Rosoklija | Structural abnormalities of subicular dendrites in subjects with schizophrenia and mood disorders: preliminary findings[END_REF][START_REF] Kolluri | Lamina-specific reductions in dendritic spine density in the prefrontal cortex of subjects with schizophrenia[END_REF][START_REF] Cotter | Reduced neuronal size and glial cell density in area 9 of the dorsolateral prefrontal cortex in subjects with major depressive disorder[END_REF]. Evidence in favour of abnormal neurotransmission, signal transduction (9), neural connectivity, GABAergic [START_REF] Guidotti | GABAergic dysfunction in schizophrenia: new treatment strategies on the horizon[END_REF] and glutamatergic activity [START_REF] Beneyto | Abnormal glutamate receptor expression in the medial temporal lobe in schizophrenia and mood disorders[END_REF] has also been demonstrated in the DLPFC of these patients.

There has been increasing interest in employing global protein profiling methodologies, including mass spectrometry (MS)-based methods, to examine the DLPFC of SCZ patients [START_REF] English | 2-D DIGE analysis implicates cytoskeletal abnormalities in psychiatric disease[END_REF][START_REF] Martins-De-Souza | Prefrontal cortex shotgun proteome analysis reveals altered calcium homeostasis and immune system imbalance in schizophrenia[END_REF][START_REF] Martins-De-Souza | Proteomic analysis of dorsolateral prefrontal cortex indicates the involvement of cytoskeleton, oligodendrocyte, energy metabolism and new potential markers in schizophrenia[END_REF][START_REF] Novikova | Identification of protein biomarkers for schizophrenia and bipolar disorder in the postmortem prefrontal cortex using SELDI-TOF-MS ProteinChip profiling combined with MALDI-TOF-PSD-MS analysis[END_REF][START_REF] Pennington | Prominent synaptic and metabolic abnormalities revealed by proteomic analysis of the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder[END_REF][START_REF] Prabakaran | Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress[END_REF][START_REF] Smalla | A comparison of the synaptic proteome in human chronic schizophrenia and rat ketamine psychosis suggest that prohibitin is involved in the synaptic pathology of schizophrenia[END_REF][START_REF] Behan | Proteomic analysis of membrane microdomain-associated proteins in the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder reveals alterations in LAMP, STXBP1 and BASP1 protein expression[END_REF][START_REF] Shao | Mitochondrial involvement in psychiatric disorders[END_REF]. Converging results from these studies have supported the case for synaptic, oxidative stress mitochondrial and metabolic dysfunction in the disorder. In support of this, the recent application of state-of-the-art metabonomic platforms and informatics tools have identified alterations in markers of energy and neurotransmitter metabolism in subregions of the DLPFC of SCZ patients [START_REF] Prabakaran | Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress[END_REF][START_REF] Khaitovich | Metabolic changes in schizophrenia and human brain evolution[END_REF], and in animal models of antipsychotic drug treatment [START_REF] Mcloughlin | Analysing the Effects of Psychotropic Drugs on Metabolite Profiles in Rat Brain Using 1H NMR Spectroscopy[END_REF]. However, most global profiling studies using post-mortem brain tissue have been performed on subjects who have been treated with varying lifetime antipsychotic medication doses. Although the confounding effects of factors such as post-mortem interval and tissue pH have been investigated in molecular profiling studies [START_REF] Vawter | Mitochondrial-related gene expression changes are sensitive to agonal-pH state: implications for brain disorders[END_REF][START_REF] Li | Systematic changes in gene expression in postmortem human brains associated with tissue pH and terminal medical conditions[END_REF][START_REF] Halim | Increased lactate levels and reduced pH in postmortem brains of schizophrenics: Medication confounds[END_REF][START_REF] Atz | Methodological considerations for gene expression profiling of human brain[END_REF], those associated with cumulative lifetime antipsychotic doses have not been addressed fully. This is likely to be an important factor since neuroimaging analyses of SCZ subjects have demonstrated a correlation between cumulative antipsychotic dose and specific anatomical and cellular alterations [START_REF] Cahn | Brain volume changes in firstepisode schizophrenia: a 1-year follow-up study[END_REF][START_REF] Gur | A follow-up magnetic resonance imaging study of schizophrenia. Relationship of neuroanatomical changes to clinical and neurobehavioral measures[END_REF].

With this in mind, we have attempted to identify the molecular signatures associated with the inherent pathological processes as well as antipsychotic drug-induced changes in DLPFC tissue from SCZ subjects who had received different cumulative lifetime antipsychotic doses. We conducted a parallel profiling study using label-free LC-MS E based proteomic and 1 H NMR spectroscopy based metabonomic approaches. We also included DLPFC tissue from bipolar disorder (BPD) subjects in the analysis, to determine the specificity of any observed biomarkers for SCZ given that there is an aetiological overlap between SCZ and BPD and, epidemiological and genetic linkage studies have also identified a number of shared susceptibility loci between these two disorders [START_REF] Berrettini | Evidence for shared susceptibility in bipolar disorder and schizophrenia[END_REF].

Materials and methods

Tissue samples

Sixty post-mortem DLPFC Broadmann Area 9 (BA9) gray matter samples were obtained from the Stanley Medical Research Institute (Bethesda, MD, USA). The average fluphenazine mg equivalent (FME) was used as an indicator of cumulative lifetime antipsychotic dose [START_REF] Fritzen | NO synthase-positive striatal interneurons are decreased in schizophrenia[END_REF]. SCZ samples were separated into three demographically-matched groups according to FME: Group 1) low-cumulative-medication [(L-SCZ), FME: 6515±6465, n=10 (including 2 drug-naive with FME=0 and 2 with FME≤600)], Group 2) mediumcumulative-medication [(M-SCZ), FME: 86200±47499, n=10], and Group 3) high-cumulative-medication [(H-SCZ), FME: 182500±123855, n=10]. The medication groups were chosen, to the best possible, to give balanced groups with the maximum number of patients in each group with the least overlap in FME dose.

The mean FME dose between the medication groups was significantly different (p=0.0005). In addition, 10 bipolar disorder (BPD) and 10 control (CT) subjects were matched and analysed. An additional batch of 10 controls was matched specifically to H-SCZ subjects as the latter was not matched to the previous control group with respect to gender. Unless stated otherwise, samples were matched for post-mortem brain interval (PMI), pH, age of onset, age of death, duration of illness and gender (summary of demographic details and statistical values are shown in Table 1; additional details are provided in Supplementary Tables S1 andS2).

Samples from H-SCZ patients and matched controls were used for Western blot validation analyses only.

Since the average brain pH of H-SCZ patients was significantly lower than that of the controls, Pearson correlation analyses were performed using GraphPad PRISM v5 (San Diego California, USA) to confirm that there were no effects of pH on protein expression (average r of -0.40 and 0.34, p>0.05). Approximately 70 mg of tissues were sectioned (15μm slices) using a Leica Cryostat tissue sectioning device (Milton Keynes, UK), collected into pre-chilled lysing matrix D tubes (MP Biomedicals, UK) and stored at -80ºC until use.

(Table 1 here)

Experimental design

Fractionation of post-mortem DLPFC tissue (n=60) from all subject groups (Table 1) was performed to increase proteome coverage of membrane-associated proteins. In the first stage of this study, membrane protein fractions from L-SCZ, M-SCZ, BPD and controls were analysed by LC-MS E . In the next stage, differentially expressed proteins were validated by Western Blot analysis using membrane and soluble fractions, and unfractionated total brain lysate samples across all 5 groups [L-SCZ, M-SCZ, H-SCZ, BPD and controls]. In the final stage, 1 H NMR spectroscopy metabonomic analysis was carried out for L-SCZ, M-SCZ and controls. The same analysis for BPD samples from this brain collection was conducted previously [START_REF] Lan | Metabonomic analysis identifies molecular changes associated with the pathophysiology and drug treatment of bipolar disorder[END_REF].

Subcellular proteome fractionation and protein purification/digestion

Brain tissue sections were processed using the Subcellular Proteome Extraction Kit (Merck; Nottingham, UK) as described previously [START_REF] Abdolzade-Bavil | Convenient and versatile subcellular extraction procedure, that facilitates classical protein expression profiling and functional protein analysis[END_REF][START_REF] Arndt | Cloning and functional characterization of a new Ski homolog, Fussel-18, specifically expressed in neuronal tissues[END_REF][START_REF] Sabio | p38gamma regulates the localisation of SAP97 in the cytoskeleton by modulating its interaction with GKAP[END_REF]. Briefly, tissues were homogenized in 1mL ice cold Extraction Buffer I containing 5μL Protease Inhibitor Cocktail (PI) at 4°C using the FastPrep FP120 cell disrupter (Qbiogene; Cambridge, UK) set at speed 4 for 5 seconds. Homogenates were incubated on a rotary shaker for 30 minutes at 4°C and centrifuged at 11000g for 10 minutes. The supernatants (fraction I, enriched soluble proteins) were collected and pellets were suspended in 1mL of ice cold Extraction Buffer II containing 5μL PI by first flicking the tubes and then mixing on a rotary shaker for 60 minutes at 4°C. Following centrifugation at 6000g for 10 minutes, the supernatants (fraction II, enriched membrane proteins) were collected.

Approximately 200μg of proteins from fraction II were precipitated using the ProteoExtract® Protein Precipitation Kit (Merck), reduced, alkylated and digested using the all-in-one trypsin digestion kit (MERCK). Trypsin was obtained from Promega (Madison, WI, USA).

LC-MS E analysis

LC-MS E was carried out and the reproducibility and quantitative reliability was assessed as described previously [START_REF] Ma | Antipsychotic treatment alters protein expression associated with pre-synaptic function and nervous system development in rat frontal cortex[END_REF]. To minimize systematic batch effects, sample preparation and MS acquisition were performed in a randomized order. The PLGS2.3 software was used for ion detection and clustering [START_REF] Hughes | Quantitative proteomic analysis of drug-induced changes in mycobacteria[END_REF][START_REF] Silva | Simultaneous qualitative and quantitative analysis of the Escherichia coli proteome: a sweet tale[END_REF][START_REF] Silva | Quantitative proteomic analysis by accurate mass retention time pairs[END_REF][START_REF] Vissers | Analysis and quantification of diagnostic serum markers and protein signatures for Gaucher disease[END_REF] and the embedded ion accounting algorithm employed for searching the H. Sapiens complete proteome fasta sequence Integr8 database (http://www.ebi.ac.uk/integr8/FtpSearch.do?orgProteome Id=25; version 25), appended with S.cerevisiae enolase sequence (P00924). Data were normalized using the Trackstats software (Waters; Milford MA, USA) and filtered for replication using R (www.r-project.org) with criteria described previously [START_REF] Ma | Antipsychotic treatment alters protein expression associated with pre-synaptic function and nervous system development in rat frontal cortex[END_REF]. To perform quantitation on the protein level, peptide correlation analyses (r>0.5) were performed for every detected protein [START_REF] Ma | Antipsychotic treatment alters protein expression associated with pre-synaptic function and nervous system development in rat frontal cortex[END_REF][START_REF] Schwarz | Peptide correlation: a means to identify high quality quantitative information in large-scale proteomic studies[END_REF]. To identify significant alterations in protein expression levels between different experimental groups, 2-tailed Student's t-tests were performed (p≤0.05) and false discovery rates (FDR) were calculated using the Benjamini & Hochberg correction approach [START_REF] Reiner | Identifying differentially expressed genes using false discovery rate controlling procedures[END_REF]. This approach calculates the proportion of statistically significant differences that are actually false positives.

Biological process grouping was carried out using the Human Protein Reference Database (HPRD; http://www.hprd.org/) and the Database for Annotation, Visualization and Integrated Discovery (DAVID; http://david.abcc.ncifcrf.gov/).

Western blot analysis

Protein samples (10μg) were resolved on 4-12% NuPAGE Novex Bis-Tris gels (Invitrogen, Paisley, UK) and transferred electrophoretically onto nitrocellulose membranes. Membranes were washed three times for 5 minutes in phosphate buffered saline containing 0.1% Tween-20 (PBS-T) followed by incubation for 1 hour in Odyssey blocking buffer (LI-COR Biosciences; Cambridge, UK) and then overnight at 4ºC with primary antibodies diluted in the same buffer containing 0.1% Tween-20 (OBB-T) (details of the primary antibodies are shown in the legend for Figure 2a and Supplementary Figure S1). Membranes were washed five times 20 minutes in PBS-T followed by incubation with the appropriate infrared dye-linked secondary antibodies (800CW goat anti-mouse, 680CW goat anti-rabbit and 800CW goat anti-rat; Licor Biosciences) in OBB-T in the dark. Images were acquired using the Odyssey Infrared Imaging System and analysed and quantified using the Odyssey v3.0 Software. Calmodulin was used as internal control for normalizing protein loading differences as this protein showed no significant differences in abundance between disease and control by LC-MS E [L-SCZ (p=0.75), M-SCZ (p=0.80), BPD: (p=0.28) and Western blot analysis (Figure 2b). For validation purposes, 2-tailed Student's t-tests were conducted and significance was regarded as p<0.05. Fold changes were calculated by dividing the average integrated intensity readings of disease by control samples.

H NMR spectroscopy

Separate tissue slices obtained from the same post-mortem brain specimens used in the above studies were homogenized while frozen in 1mL of methanol:deionised water (1:1) in 2mL microfuge tubes (Eppendorf; Loughborough, UK) containing a metal bead (Qiagen; West Sussex, UK), using a tissue grinder (Qiagen) set at 30 cycles/second for 5minutes. Chloroform (1mL) was added to produce aqueous and lipid phases.

Homogenates were vortexed and centrifuged at 1200g for approximately 7 minutes at 5°C. The upper aqueous phase containing hydrophilic metabolites was collected and left to dry overnight at room temperature to remove methanol before freeze drying. Samples were reconstituted in 700μL of deuterium oxide (D 2 O) and 600μL was transferred into NMR tubes for analysis. D 2 O provides deuterium field frequency lock for the NMR spectrometer. 1 H NMR spectra were generated at 600.13 MHz on a Bruker DRX-600 spectrometer (Bruker Avance, Bruker GmBH; Rheinstetten, Germany) as described previously [START_REF] Lan | Metabonomic analysis identifies molecular changes associated with the pathophysiology and drug treatment of bipolar disorder[END_REF]. Complete spectra (chemical shift range 0-10ppm) were processed using the Topspin v2.0 software (Bruker). Spectra were phase and baseline corrected and calibrated using the reference resonances of the CH 3 lactate doublet (1.33ppm). Any variation effect due to pre-saturation of water was removed by zeroing intensity values between 4.5 and 4.9ppm. To account for concentration differences between samples, data were normalized to the total integrated peak area intensity by converting the integral values into a percentage of the sum of the integral.

Chemometric modelling and interpretation of the 1 H NMR data

To identify metabolites differentiating disease and control groups, partial least squares discriminant analysis (PLS-DA) was performed on full resolution 1 H NMR spectral data using SIMCA P v12 (Umetrics AB; Umeå, Sweden). To remove confounding variation, orthogonal-projection to latent structures discriminant analysis (OPLS-DA) was used to model the data using MATLAB v6.5 (The Mathworks Inc, Natick; MA, USA) scripts proprietary to Imperial College London. This method represents an improvement to the traditional supervised PLS algorithm and enhances the interpretability of spectral variation between classes [START_REF] Eriksson | Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)[END_REF][START_REF] Trygg | Chemometrics in metabonomics[END_REF]. To confirm the differences identified by these multivariate analyses, univariate analyses (Wilcoxon-Mann-Whitney U tests (44)) were performed using GraphPad Prism v5. Significance was regarded as p<0.05 and fold changes were obtained by calculating the ratio between disease and control groups.

Results

Quality control

Membrane protein enrichment efficiency in fraction II was demonstrated by Western blot analysis of a DLPFC specimen using subcellular distribution markers. LC-MS E analysis resulted in the identification of 370 proteins in fraction I and 568 proteins in fraction II. Fractionation efficiency, as evaluated by analysis of cellular distribution of these proteins, was demonstrated by identification of a higher proportion of soluble and membrane proteins in fractions I and II, respectively (details are provided in Supplementary Figure S1).

Mass Spectrometry

Quantitative analysis of fraction II resulted in identification of approximately 30 differentially expressed proteins (p≤0.05) out of the approximate 500 identifiable proteins in L-SCZ and BPD patients, compared to the control group (Figure 1). None of the differentially expressed proteins overlapped across the diseases. No significant changes were observed for the M-SCZ patients compared to the same control group.

Differentially expressed proteins were assigned to biological process groups using HPRD and DAVID (Table 2 for L-SCZ and Supplementary Table S3 for BPD). Table 2 also indicates markers which have been reported previously in the literature in association with proteomic or transcriptomic analyses of SCZ DLPFC tissue, thereby providing an additional means of validation as many directions of change reported in the literature agree with our findings.

(Figure 1 and Table 2 here)

Western blot validation

Confirmation of mass spectrometry profiling results: Raw p-values for LC-MS E data were corrected for multiple hypothesis testing using the Benjamini-Hochberg false discovery rate method (Table 2). As corrected p-values for differentially expressed proteins were relatively high, Western blot analyses were conducted to confirm changes in L-SCZ and BPD membrane fraction samples. Calmodulin was used as a loading control since this protein was not changed in this study, as determined by LC-MS E and Western blot analyses. Antibodies were selected against 7 proteins to represent different functional networks and directional changes in L-SCZ and BPD subjects. These included MAG, MARCKS, NCAM, GAP43 and NFM. Neuronal nitric oxide synthase (nNOS) was also examined due to its regulatory action on NCAM [START_REF] Park | The inhibition of nitric oxide synthase enhances PSA-NCAM expression and CREB phosphorylation in the rat hippocampus[END_REF] and functional relevance in SCZ [START_REF] Baba | Expression of nNOS and soluble guanylate cyclase in schizophrenic brain[END_REF][START_REF] Tanda | Abnormal social behavior, hyperactivity, impaired remote spatial memory, and increased D1-mediated dopaminergic signaling in neuronal nitric oxide synthase knockout mice[END_REF]. β-tubulinIII (TUBB) was tested as an example of a protein altered in BPD patients. Western blot analysis validated the expression changes in these proteins, with the exception of MAG (Figure 2a and2b). The predicted change in nNOS was also confirmed in L-SCZ. These results confirmed the reliability of the label-free LC-MS E based profiling approach. Validation was also conducted using soluble fractions and unfractionated total brain lysates from the L-SCZ patient samples. Overall, most of the changes identified in the membrane fractions were confirmed in the corresponding soluble fractions and total brain lysates (Figure 2b).

(Figure 2 here)

Assessment of disease specificity and effects of lifetime antipsychotic medication in schizophrenia:

LC-MS E analysis revealed that none of the differentially expressed proteins identified in samples from L-SCZ subjects were found in those from M-SCZ subjects. This was also confirmed by Western blot analyses of membrane fractions from L-SCZ, M-SCZ, H-SCZ and CT samples (Figure 2a and2b). Overall, the results confirmed that the levels of MARCKS, GAP43 and nNOS were changed in L-SCZ but not in M-SCZ and H-SCZ. This suggested that abnormal expression of these proteins may be a primary disease change and not a medication effect. However, NFM did not show a clear pattern of expression relating to medication. NCAM was increased significantly in all SCZ medication groups and in BPD samples. This suggested that NCAM expression may not be affected by lifetime antipsychotic levels or mood stabilizer treatment and that it is not specific to SCZ. The LC-MS E results showed no overlap in the differentially expressed protein profiles of SCZ and BPD subjects (Table 2 and Supplementary Table S3), as confirmed by Western blot analysis (Figure 2b). Biological process grouping using HPRD and DAVID also showed no overlap (Figure 1). The most notable changes for SCZ were 15 differentially expressed proteins involved in cell communication and 7 metabolism proteins, whereas BPD was marked by alterations of 8 cell communication proteins and 7 cell growth proteins (Figure 1).

Correlation analysis

The LC-MS E profiling results showed changes in tissues from L-SCZ subjects but not in M-SCZ individuals. This suggests that these changes are associated with the primary disease state and, moreover, may be normalized with treatment. For this to be the case, expression changes should not only show a group difference according to treatment level, but also a direct relationship to lifetime antipsychotic dose within the group. In order to further investigate this possibility, FME and Western blot expression values from individual SCZ subjects were tested in correlation analyses. GAP43 (r=-0.77, p=0.025), MARCKS (r=-0.76, p=0.029) and, nNOS (r=-0.90, p=0.006) expression levels were significantly negatively correlated with FME, consistent with their identification as differentially expressed proteins in the L-SCZ group and their lack of change after more extensive drug treatment comprising the M-SCZ group (Supplementary Figure S2a). NCAM (r=0.64, p=0.085), on the other hand, was not significantly correlated with FME, consistent with the Western blot finding that this protein was altered in both the L-SCZ and the M-SCZ groups. The effects of lifetime antipsychotic dose were further confirmed by demonstrating a significant correlation between FME dose and levels of significantly altered metabolites [e.g. alanine (r=0.64, p=0.003) and branched amino acids (r=48, p=0.039)]. Similar to the case of NCAM, glutamine levels (r=-0.15, p=0. [START_REF] Van Kammen | Further studies of elevated cerebrospinal fluid neuronal cell adhesion molecule in schizophrenia[END_REF] were also not significantly correlated with FME dose, as it was altered in both medication groups (Supplementary Figure S2b). We also considered the possibility that the molecular alterations may be confounded by a shorter duration of illness in patients with low FME. Although this factor was well matched between the L-SCZ and M-SCZ groups (p=0.27), the mean duration of illness was higher in H-SCZ patients which were investigated separately. Correlation analyses show that duration of illness did not affect expression of key validated proteins or levels of significantly altered metabolites (Supplementary Figure S3).

H NMR spectroscopy data analysis

PLS-DA of 1 H NMR spectroscopy data revealed a separation between the L-SCZ or M-SCZ and CT groups.

O-PLS-DA showed the metabolites that are most influential to these separations (Figure 3a). Specifically, branched amino acids, alanine, creatine and glutamine were altered in L-SCZ patients. Most of the metabolites identified using multivariate analyses were confirmed by univariate statistical analysis (Figure 3b). Of the 4 metabolites altered in L-SCZ, 3 (branched chain amino acids, alanine and creatine) were not changed in M-SCZ and only glutamine was changed in both groups. In addition, taurine was altered in M-SCZ but not in L-SCZ.

(Figure 3 here)

Discussion

This is the first study employing label-free LC-MS E proteomic and 1 H NMR metabonomic profiling platforms to examine molecular effects of cumulative lifetime antipsychotic medication dose in post-mortem brain tissue (dorsolateral prefrontal cortex, BA9, gray matter) of SCZ patients. To control for non-specific effects of chronic psychiatric illnesses, the proteomic profile of BPD patients was also investigated to demonstrate that the changes reported are relatively specific for SCZ. Lifetime antipsychotic medication was found to have an effect on a number of proteins and metabolites in SCZ patients. The specificity of the protein expression changes identified in L-SCZ patients was also demonstrated relative to BPD providing evidence for diverging pathologies between the two disorders.

Disease signatures of schizophrenia and targets of drug effects

Out of the approximately 500 proteins and 16-20 metabolites identified per dataset, 34 proteins and 4 metabolites were altered in L-SCZ patients and only 1 protein (NCAM) and 2 metabolites (glutamate and taurine) were altered in M-SCZ patients. This suggested that a higher cumulative lifetime antipsychotic dose may exert neuroadaptive effects by normalizing some of the inherent molecular changes in SCZ patients, who were either medication-free or had been subjected to a lower cumulative antipsychotic medication dosage throughout life. Therefore, at least some of the differentially expressed proteins identified in L-SCZ patients may represent 'disease signatures' of SCZ as well as representing targets of antipsychotic drug action. These include effects on proteins associated with synaptogenesis, neuritic dynamics, presynaptic vesicle cycling, amino acid and glutamine metabolism, and energy buffering systems. Previously, we have identified molecular changes in proteomic [START_REF] Ma | Antipsychotic treatment alters protein expression associated with pre-synaptic function and nervous system development in rat frontal cortex[END_REF] and metabonomic [START_REF] Mcloughlin | Analysing the Effects of Psychotropic Drugs on Metabolite Profiles in Rat Brain Using 1H NMR Spectroscopy[END_REF] studies of rats treated with antipsychotic medications. Some of these markers are altered in opposite directions to those observed in L-SCZ patients, in the present study, providing further support for our findings. For example, antipsychotics affected components of the rat presynaptic vesicle machinery, neurite outgrowth dynamics and synaptogenesis. Nine proteins and 2 metabolites found in these antipsychotic-treated rats were shown here to be altered in L-SCZ patients (SYN2, GLUD1, GAPDH, NCAM1, ATP2B4, MAG, GNB1, GAP-43, NT, Gln and Cr). Synaptogenic alterations during the premorbid stage of illness (gestation, infancy, childhood and adolescence) have been proposed in the context of the neurodevelopmental hypothesis of SCZ [START_REF] Lieberman | The early stages of schizophrenia: speculations on pathogenesis, pathophysiology, and therapeutic approaches[END_REF]. We identified alterations in key regulators of synaptogenesis [START_REF] Oshima | SHPS-1, a multifunctional transmembrane glycoprotein[END_REF][START_REF] Muller | Role of NCAM in Spine Dynamics and Synaptogenesis[END_REF][START_REF] Rougon | New insights into the diversity and function of neuronal immunoglobulin superfamily molecules[END_REF] and synaptic plasticity in L-SCZ patients including neurofascin1 (NFS) and tyrosine-protein phosphatase non-receptor type substrate 1 (SIRPA), which have not been previously implicated in SCZ. We also identified changes in the expression of NCAM, which has been reported to be altered in the brain [START_REF] Behan | Proteomic analysis of membrane microdomain-associated proteins in the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder reveals alterations in LAMP, STXBP1 and BASP1 protein expression[END_REF][START_REF] Vawter | Abnormal expression of cell recognition molecules in schizophrenia[END_REF][START_REF] Honer | Cingulate cortex synaptic terminal proteins and neural cell adhesion molecule in schizophrenia[END_REF], serum [START_REF] Tanaka | Alteration in serum neural cell adhesion molecule in patients of schizophrenia[END_REF] and CSF (55-57) of SCZ patients.

Therefore, these findings support the case for synaptogenic and synaptic plasticity alterations in SCZ patients [START_REF] Stephan | Synaptic plasticity and dysconnection in schizophrenia[END_REF]. These findings are also supported by animal studies showing that an excess of NCAM at the synapse inhibits synaptogenesis [START_REF] Martin | Cell adhesion molecules, CREB, and the formation of new synaptic connections[END_REF]. The observation that NCAM was altered in all conditions examined (L-SCZ, M-SCZ, H-SCZ and BPD patients) suggests that it is not affected by either antipsychotic medication or mood stabilizers and that it is not specific to SCZ. Whether it is a non-specific marker of psychiatric illness or a key biological change underlying brain dysfunction in both disorders requires further studies.

Prominent alterations in components involved in synaptic function have been identified by transcriptomic and proteomic profiling studies of SCZ patients [START_REF] Pennington | Prominent synaptic and metabolic abnormalities revealed by proteomic analysis of the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder[END_REF][START_REF] Prabakaran | Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress[END_REF][START_REF] Behan | Proteomic analysis of membrane microdomain-associated proteins in the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder reveals alterations in LAMP, STXBP1 and BASP1 protein expression[END_REF]. Many of these findings were confirmed in our study and we also identified novel changes in proteins involved in synaptic vesicle recycling/endocytosis and exocytosis in the L-SCZ patients, again supporting the case for altered presynaptic vesicle cycling in SCZ.

Alterations in proteins involved in the synaptic endocytosis machinery such as clathrin coat assembly protein AP180 (SNAP91), Hsc70 interacting protein (Hip) and reticulon1 (RTN1) have not been reported in SCZ before, although synapsin 2 (SYN2) [START_REF] Dyck | Behavioral abnormalities in synapsin II knockout mice implicate a causal factor in schizophrenia[END_REF][START_REF] Chen | Family-based association study of synapsin II and schizophrenia[END_REF] and another reticulon isoform (RTN4) [START_REF] Martins-De-Souza | Prefrontal cortex shotgun proteome analysis reveals altered calcium homeostasis and immune system imbalance in schizophrenia[END_REF][START_REF] Novak | Schizophrenia and Nogo: elevated mRNA in cortex, and high prevalence of a homozygous CAA insert[END_REF] have been implicated previously. Though increases in syntaxin1A (STX1A) expression have been reported in SCZ [START_REF] Johnson | SNARE proteins and schizophrenia: linking synaptic and neurodevelopmental hypotheses[END_REF][START_REF] Sokolov | Levels of mRNAs encoding synaptic vesicle and synaptic plasma membrane proteins in the temporal cortex of elderly schizophrenic patients[END_REF][START_REF] Halim | Presynaptic proteins in the prefrontal cortex of patients with schizophrenia and rats with abnormal prefrontal development[END_REF], we identified novel alterations in other proteins involved in presynaptic exocytosis in L-SCZ patients, including a group of G i/o proteins (Galphai2, GNB1, GNB4, GNAO1), which are involved in the autoinhibitory feedback mechanism of exocytosis [START_REF] De Waard | How do G proteins directly control neuronal Ca2+ channel function?[END_REF][START_REF] Brown | Presynaptic signaling by heterotrimeric G-proteins[END_REF]. To our knowledge, only three recent proteomic studies have reported increase in GNB1 expression in the PFC of patients [START_REF] Martins-De-Souza | Proteomic analysis of dorsolateral prefrontal cortex indicates the involvement of cytoskeleton, oligodendrocyte, energy metabolism and new potential markers in schizophrenia[END_REF][START_REF] Behan | Proteomic analysis of membrane microdomain-associated proteins in the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder reveals alterations in LAMP, STXBP1 and BASP1 protein expression[END_REF][START_REF] Clark | A proteome analysis of the anterior cingulate cortex gray matter in schizophrenia[END_REF]. Significant decreases in neuronal size, number and dendritic spine density of the glutamatergic and GABAergic neurons have been observed in DLPFC [START_REF] Pierri | Decreased somal size of deep layer 3 pyramidal neurons in the prefrontal cortex of subjects with schizophrenia[END_REF][START_REF] Rosoklija | Structural abnormalities of subicular dendrites in subjects with schizophrenia and mood disorders: preliminary findings[END_REF][START_REF] Kolluri | Lamina-specific reductions in dendritic spine density in the prefrontal cortex of subjects with schizophrenia[END_REF][START_REF] Cotter | Reduced neuronal size and glial cell density in area 9 of the dorsolateral prefrontal cortex in subjects with major depressive disorder[END_REF] and several other brain regions [START_REF] Rosoklija | Structural abnormalities of subicular dendrites in subjects with schizophrenia and mood disorders: preliminary findings[END_REF][START_REF] Roberts | Reduced striatal spine size in schizophrenia: a postmortem ultrastructural study[END_REF][START_REF] Harrison | The neuropathology of schizophrenia. A critical review of the data and their interpretation[END_REF] of SCZ patients. We identified alterations in a number of regulators of growth cone path finding, cell shape, structural integrity, spine dynamics and neuronal morphology. Therefore, these findings suggest a link between the aberrant levels of regulators of neuritic dynamics and the macroscopic brain pathology in SCZ. While increased neuromodulin (GAP-43) expression has been reported in the brain of SCZ patients [START_REF] Halim | Presynaptic proteins in the prefrontal cortex of patients with schizophrenia and rats with abnormal prefrontal development[END_REF][START_REF] Perrone-Bizzozero | Levels of the growth-associated protein GAP-43 are selectively increased in association cortices in schizophrenia[END_REF][START_REF] Hakak | Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia[END_REF], this is the first report showing alterations in MARCKS, Ras-related protein Rap-2A (RAP2A) and NF-M at the protein level in the DLPFC of L-SCZ patients. In addition, altered MARCKS expression in SCZ has been reported only at the transcript level [START_REF] Hakak | Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia[END_REF] and NFM was found to be decreased in one study in a different brain region (anterior temporal lobe) of SCZ patients [START_REF] Martins-De-Souza | Alterations in oligodendrocyte proteins, calcium homeostasis and new potential markers in schizophrenia anterior temporal lobe are revealed by shotgun proteome analysis[END_REF]. Whether these changes are primary or secondary to the disease process remain to be addressed. Elevated glutamine (Gln) levels have been reported in the DLPFC [START_REF] Van Elst | Increased prefrontal and hippocampal glutamate concentration in schizophrenia: evidence from a magnetic resonance spectroscopy study[END_REF] and other brain regions of drug-naive and medicated SCZ patients [START_REF] Bartha | Measurement of glutamate and glutamine in the medial prefrontal cortex of never-treated schizophrenic patients and healthy controls by proton magnetic resonance spectroscopy[END_REF][START_REF] Theberge | Glutamate and glutamine measured with 4.0 T proton MRS in never-treated patients with schizophrenia and healthy volunteers[END_REF][START_REF] Theberge | Glutamate and glutamine in the anterior cingulate and thalamus of medicated patients with chronic schizophrenia and healthy comparison subjects measured with 4.0-T proton MRS[END_REF]. Our findings of increased Gln concentration and glutamate dehydrogenase expression in L-SCZ and/or M-SCZ patients are consistent with these results. Gln, or Glnderived ammonia in excessive levels has detrimental effects on mitochondrial function since this results in excessive production of free radicals, reactive oxygen species (ROS), oxidative stress and increased neuronal nitric oxide synthase (nNOS) expression [START_REF] Lockwood | The dynamics of ammonia metabolism in man. Effects of liver disease and hyperammonemia[END_REF][START_REF] Waagepetersen | Energy and Amino Acid Neurotransmitter Metabolism in Astrocytes. Astrocytes in (Patho)Physiology of the Nervous System[END_REF][START_REF] Waagepetersen | Glutamate, and GABA: Metabolic Aspects Handbook of Neurochemistry and Molecular Neurobiology Springer US[END_REF], as confirmed by Western blot analysis in this study. This suggests a possible link between Gln-induced neurotoxicity and mitochondrial dysfunction in patients [START_REF] Zoratti | Mitochondrial permeability transitions: how many doors to the house?[END_REF] and is consistent with the findings from our colleagues [START_REF] Prabakaran | Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress[END_REF] who reported mitochondrial dysfunction, energy metabolism defects and increased oxygen and ROS metabolism pathways in the DLPFC of SCZ patients.

Creatine has been found to be decreased in the anterior cingulate cortex and parieto-occipital cortices of medicated SCZ patients [START_REF] Ongur | Creatine abnormalities in schizophrenia and bipolar disorder[END_REF] and increased in the DLPFC (BA46) of medicated patients [START_REF] Khaitovich | Metabolic changes in schizophrenia and human brain evolution[END_REF]. We demonstrated that Cr was increased in the DLPFC (BA9) of L-SCZ patients and not in M-SCZ patients.

Elevations in Cr concentration may be indicative of a regulatory response to increased energy turnover since at times of significant energy demand or crisis (e.g. hypoxic damage in SCZ ( 17)), Phospho-Cr is rapidly dephosphorylated to enable transfer of the phosphoryl group to ADP to form ATP and Cr [START_REF] Saks | Metabolic control and metabolic capacity: two aspects of creatine kinase functioning in the cells[END_REF]. Evidence demonstrating overexpression of creatine kinase in patients [START_REF] Clark | A proteome analysis of the anterior cingulate cortex gray matter in schizophrenia[END_REF][START_REF] Martins-De-Souza | Proteome analysis of schizophrenia patients Wernicke's area reveals an energy metabolism dysregulation[END_REF] supports this possibility. Increased Cr levels may also reflect a neuroprotective response mechanism to mitochondrial damage and neurotoxicity [START_REF] Terao | Incidence and risk factors of benign creatine phosphokinase elevations in chronic psychiatric patients[END_REF] since it also acts as a direct anti-oxidant by scavenging ROS. Cr also regulates glycolysis, prevents reduced energy stores and improves neuronal function [START_REF] Klein | The Neuroprotective Role of Creatine Creatine and Creatine Kinase in Health and Disease[END_REF]. By identifying for the first time, that both Ala and branched amino acids (BAA's) were significantly decreased, we hypothesize that the uptake process and metabolism of these amino acids may be altered in SCZ, although future work must be conducted to confirm these findings. Reductions in these amino acids may have implications in the ammonia transport system [START_REF] Yudkoff | Brain amino acid requirements and toxicity: the example of leucine[END_REF], neurotransmitter synthesis [START_REF] Banos | The effect of age upon the entry of some amino acids into the brain, and their incorporation into cerebral protein[END_REF][START_REF] Hutson | Branched Chain Amino Acids (BCAAs) in Brain[END_REF] and energy metabolism [START_REF] Hutson | Branched Chain Amino Acids (BCAAs) in Brain[END_REF] given their indispensable roles in these processes.

Specificity of the changes in L-SCZ patients relative to BPD

We identified a different panel of differentially expressed membrane associated proteins in L-SCZ and BPD patients. In contrast to SCZ, BPD showed changes in a group of axon growth/branch promoting signal transduction proteins and structural proteins. These findings suggest abnormal signalling cascades regulating axon branching and cytoskeleton reorganization in BPD. This is consistent with previous studies which identified disruptions of second messenger cascades involving CAMKs and ERK/MAPK [START_REF] Bezchlibnyk | The neurobiology of bipolar disorder: focus on signal transduction pathways and the regulation of gene expression[END_REF], abnormal G protein function and expression in brain and peripheral tissues of BPD patients [START_REF] Vawter | Neuropathology of bipolar disorder[END_REF]. Increased β-tubulin expression has been found in a recent proteomic profiling study [START_REF] Pennington | Prominent synaptic and metabolic abnormalities revealed by proteomic analysis of the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder[END_REF] and reductions in glial fibrillary acidic protein (GFAP) have also been reported in various brain regions of BPD patients [START_REF] Johnston-Wilson | Disease-specific alterations in frontal cortex brain proteins in schizophrenia, bipolar disorder, and major depressive disorder. The Stanley Neuropathology Consortium[END_REF][START_REF] Webster | Glial fibrillary acidic protein mRNA levels in the cingulate cortex of individuals with depression, bipolar disorder and schizophrenia[END_REF]. These data demonstrate specificity of the detected changes in SCZ and BPD and suggest diverging pathological processes between the two disorders. However, as the BPD subjects were treated with mood stabilisers, effects of these drugs cannot be ruled out.

Methodological issues

The quantitative reliability of the label-free LC-MS E proteomic profiling approach for robust identification of expression differences was demonstrated by Western blot validation of soluble and membrane fractions as well as total brain lysates. Since the corrected p-values were relatively high for the SCZ and BPD datasets (ranging from 0.25 to 0.58), we propose a more relaxed corrected p-value cutoff threshold for brain studies using this label-free proteomic profiling platform. A microarray study conducted by Kaiser et al., 2004 (95) supports this view by demonstrating that the rate of confirmation of results by RT-QPCR was 100% for genes with a corrected p-value<0.1 and 82% for genes with a corrected p-value<0.5. Although careful matching of the brain tissue samples can increase confidence of the results obtained, the realistic fact is that there are a number of additional confounders which cannot be controlled for and remain inherent caveats to these studies. These include factors such as heterogeneity and phenotypic diversity of brain tissue, possible changes in small subpopulations of brain cells, and subtle differences in the cellular distribution of proteins across different brain and cellular compartments. Schizophrenia disease subtype may also represent a source of heterogeneity. Therefore, we propose validation of results using an independent brain series although obtaining further well-characterized post-mortem samples from low-cumulative-medication SCZ patients remains difficult. Although we have made some progress towards distinguishing the effects of antipsychotic medication and SCZ disease signatures, there are several limitations associated with the present study.

Patients were medicated differently up to the time of death and for different periods of time. In addition, since patients were treated by a mixture of typical and atypical antipsychotic drugs, the differential effects of these drugs cannot be addressed in the present study. Though we have previously addressed this effect in rat animal models [START_REF] Ma | Antipsychotic treatment alters protein expression associated with presynaptic function and nervous system development in rat frontal cortex[END_REF], future studies are warranted to investigate this effect in patients. Although the effects of medication were demonstrated by comparing L-SCZ and M-SCZ patients, further confirmation of these effects by direct statistical comparison of L-SCZ and H-SCZ (matched to their own controls) patients was not possible due to their differences in the mean duration of illness. Duration of illness is an important confounding factor (97) which challenges post-mortem studies investigating drug naive or low-cumulativemedication patient cohorts, as these patients often have short durations of illness up to the time of death or may be in an earlier disease stage compared to chronically ill patients which live longer to receive treatments. Furthermore, patient records assume compliance in taking the prescribed medication and, compliance is known to be poor in the case of schizophrenia [START_REF] Mcintosh | Compliance therapy for schizophrenia[END_REF]. The fluphenazine mg. equivalent is a proxy measure of drug exposure based on historical records and does not take these factors into account.

Moreover, as antipsychotic medication is the mainstay of treatment for SCZ, patients who have never received medication may have additional variation distinct from those seen in medicated patients such as higher social functioning. A limitation associated with the BPD patients was that cumulative drug dosage (antidepressants and mood stabilizers or antipsychotic drug) information for these patients is not available and thus may represent another confounding factor.

Conclusion

The present study demonstrates the utility of subproteome and metabonome profiling approaches for providing new insights into the pathophysiology and therapeutic mechanisms in schizophrenia. We found that the cumulative lifetime antipsychotic medication exerts a considerable effect on a number of proteins and metabolites in the DLPFC of patients. This suggests careful interpretation of data available to account for the confounding effects of lifetime medication levels. We identified novel protein and metabolite disease signatures of schizophrenia and targets of antipsychotic drug effects involved in synaptic connection, neuritic dynamics, presynaptic vesicle cycling, amino acid metabolism and energy buffering systems. Finally, with the exception of NCAM, we found no changes in common between SCZ and BPD, indicating the specificity of the changes and distinct pathologies. Altogether, our results may impact development of new drug discovery strategies. Furthermore, knowledge that most of the biomarkers identified are released into a variety of body fluids and peripheral tissues may facilitate translation of these markers to blood-based assays.

FiguresFigure 1 .Figure 2 .

 12 Figures

Figure 3 .

 3 Figure 3. a) Multivariate analysis of 1 H NMR spectra generated from post-mortem brain extracts. Plots on the left show the separation for the PLS-DA scores for A) L-SCZ vs control and B) M-SCZ vs control. Plots on the right C) and D) represent the corresponding O-PLS-DA loading coefficient plots, showing spectral descriptors which distinguish the disease from the control groups. Direction of change is indicated by signal orientation (positive values indicate spectral regions co-varying with disease while negative correlation values specify spectral regions co-varying with controls). The range of spectral colour is proportional to the strength of the correlation with disease (red indicates highest correlation and blue indicates no correlation) based on R 2 values. b) List of metabolite changes in schizophrenia. Numbers in parentheses indicate fold changes; significance at *p<0.05 and **p<0.01.

Table 2 . Differentially expressed proteins identified in L-SCZ patients

 2

	Gene name	UniprotKB accession	Protein name	p-value	Corrected p-value	FC (fold change)	Described in DLPFC of SCZ (*=transcriptomic and #=proteomic profiling/assay)
	Cell communication / signal transduction
	▼ST13	P50502	Hsc70-interacting protein (Hip)	0.03	0.58	-1.32	------------
	▲Q6B6N3	Q6B6N3	Galphai2 protein	0.05	0.58	1.15	------------
	▲NCAM1	P13591	Neural cell adhesion molecule 1 (140 kDa)	0.03	0.58	1.14	▲#(19), ▲#(52), ▲ #(53)
	▲NFASC	O94856	Neurofascin	0.05	0.58	1.16	------------
	▲GNB1	P62873	Guanine nucleotide-binding protein subunit beta-1	0.05	0.58	1.17	▲#(14), ▲#(68), ▲#(69)
	▲RTN1	Q16799	Reticulon-1	0.03	0.58	1.18	▲*(73)
	▲SIRPA	P78324	Tyrosine-protein phosphatase non-receptor type substrate 1	0.02	0.58	1.20	▼#(13)
	▲SNAP91	O60641	Clathrin coat assembly protein AP180	0.02	0.58	1.21	------------
	▲RAP2A	P10114	Ras-related protein Rap-2a precursor	0.03	0.58	1.21	------------
	▲STX1A	Q16623	Syntaxin-1A	0.04	0.58	1.24	▲#(53), ▲#(99), ▲#(100), ▲*(64)
	▲OPA1	O60313	Dynamin-like 120 kDa protein	0.03	0.58	1.25	▲#(13)
	▲SYN2	Q92777	Synapsin-2	0.04	0.58	1.26	▼*(101)
	▲GAP43	P17677	Neuromodulin	0.04	0.58	1.27	▲#(72),▲*(73)
	▲GNB4	Q9HAV0	Guanine nucleotide-binding protein subunit beta-4	0.01	0.58	1.28	------------
	▲GNAO1	P09471	Guanine nucleotide-binding protein G(o) subunit alpha	0.05	0.58	1.30	------------
	Cell growth and/or maintenance
	▼NEFM	P07197	Neurofilament medium polypeptide (NF-M)	0.02	0.58	-2.06	▼#(74), ▼# (102)
	▲MARCKS	P29966	Myristoylated alanine-rich C-kinase substrate	0.00	0.58	1.51	▲*(73)
	Immune response
	▼MAG	P20916	Myelin-associated glycoprotein precursor	0.05	0.58	-2.35	▼*(73), ▼*(103), ▼*(104)
	▼SCRN1	Q12765	Secernin-1	0.03	0.58	-1.21	▲#(102)
	Metabolism / energy pathways
	▼PADI2	Q9Y2J8	Protein-arginine deiminase type-2	0.03	0.58	-1.28	------------
	▼GAPDH	P04406	Glyceraldehyde-3-phosphate dehydrogenase (EC 1.2.1.12)	0.02	0.58	-1.24	▼#(17), ▲#(13)
	▼PRDX6	P30041	Peroxiredoxin-6	0.03	0.58	-1.20	▲#(13),
	▲GLUD1	P00367	Glutamate dehydrogenase 1 (EC 1

Acknowledgements

This research was supported by the Stanley Medical Research Institute and the donations of the Stanley brain collection courtesy of Drs. Michael B Knable, E Fuller Torrey and Robert H Yolken. Special thanks to Dr Maree Webster for providing post-mortem brain samples. We thank Drs. Dan Ma, Lan Wang and Xiaoping Yang for intellectual input. Many thanks to Drs. Yishai Levin and Emanuel Schwarz for LC-MS E instrument tuning and statistical advice. Thanks to Miss Sandra Pietsch for sectioning the schizophrenia brain samples for 1 H NMR analysis. Dr. Man Chan was funded by Fundação para a Ciência e a Tecnologia, Portugal. Most of all, thanks to all patients and healthy volunteers for donating samples used in this study.

Conflict of interest

SB is Chief Scientific Officer for Psynova Neurotech Ltd. PG acts as a consultant for Psynova Neurotech Ltd. MKC, TMT, LWH, EH declare no conflict of interest. Supplementary information is available at the Molecular Psychiatry's website. Table 1.

Tables


Cyril Voyant
email: cyrilvoyant@hotmail.com

Katia Biffi

Delphine Leschi

Jerome Briancon

Celine Lantieri Marcovici

Jérome Briançon

Céline Lantieri

Dosimetric uncertainties related to the elasticity of bladder and rectal walls: Adenocarcinoma of the prostate

Abstract.

Background

Radiotherapy is an important treatment of prostate cancer. During the sessions, bladder and rectal repletion is difficult to quantify and cannot be measured with a single and initial CT scan acquisition. Some methods, such as IGRT (Image Guided Radiation Therapy) and DGRT (Dose Guided Radiation Therapy), aim to compensate this missing information through periodic CT acquisitions. The aim is to adapt the patient"s position, the beam configuration or the prescribed dose for a dosimetric compliance.

Methods

We evaluated organ motion (and repletion) for 54 patients after having computed the original ballistic on a new CT scan acquisition. A new delineation was done on the prostate, bladder and rectum to determine the new displacements and define organ doses mistakes (EUD, average dose and HDV).

Results

The new CT acquisitions confirmed that the bladder and rectal volumes were not constant during the sessions. Some cases showed that once validated treatment plan became unsuitable. A proposed solution is to correct the dosimetries when the bladder volume modifications are significant. The result is an improvement for the stability of the bladder doses, the D50 error is reduced by 25.3%, mean dose error by 5.1% and EUD error by 2.6%. For the rectum this method decreases the errors by only 1%. This process can reduce the risk of mismatch between the initial scan and the following treatment sessions..

Conclusion

For the proposed method, the cone beam CT is necessary to properly position the isocenter and to quantify the bladder and rectal volume variation and the deposited doses. The dosimetries are performed in the event that bladder (or rectum) volume modification limits are exceeded. To identify these limits, we have calculated that a tolerance of 10% for the EUD (compared to the initial value of the first dosimetry), this represents 11% of obsolete dosimetries for the bladder, and 4% for the rectum.

Résumé.

Objectif La radiothérapie est un traitement important du cancer de la prostate. Durant les séances, les réplétions rectales et vésicales sont difficiles à quantifier, une unique tomodensitométrie initiale ne peut procurer cette information.

Des méthodes comme l"IGRT (Image Guided Radiation Therapy) ou la DGRT (Dose Guided Radiation Therapy), ont pour but de compenser cette perte d"information, en utilisant des acquisitions CT périodiques. La finalité étant de proposer une radiothérapie adaptative et plus réelle.

Méthode

Nous avons évalués le mouvement (et la réplétion) des organes de 54 patients, après avoir calculé la balistique initiale sur une nouvelle tomodensitométrie. De nouvelles délinéations furent effectuées pour la prostate, la vessie et le rectum afin de déterminer les déplacements occasionnés, ainsi que les erreurs de doses générées sur les différents organes (EUD, dose moyenne and HDV).

Résultats

Les nouvelles acquisitions confirment que la vessie et le rectum ne sont pas de volume identique durant les séances. Dans certains cas la dosimétrie validée initialement n"est plus correcte et doit être retravaillée. Une solution proposée ici, est de ne s"attarder que sur les importantes modifications de volume vésical (5% des patients). Avec cette méthodologie, on constate une nette amélioration concernant la stabilité des doses vésicales, l"erreur sur le D50 est réduite de 25.3%, la dose moyenne de 5.1% et l"erreur sur l"EUD de 2.6%. Pour le rectum, cette méthode diminue toutes les erreurs de 1%. Ce processus tend à réduire le risque de non-respect de la dosimétrie validée, entre la simulation et les séances de radiothérapie.

Conclusion

La méthode proposée nécessite l"utilisation du cone beam CT pour bien positionner les volumes cibles mais aussi pour quantifier les variations de volume du rectum et de la vessie. Les distributions de dose sont retravaillées dés que les volumes vésicaux ou rectaux sont considérablement modifiés (limite fixée d"après la présente étude). Nous avons identifiés que pour une tolérance de 10% concernant les EUD (comparée à la valeur initiale établie sur le premier scanner), cela correspondait à 11% de dosimétrie obsolète pour la vessie, et 4%

Introduction

Prostate cancer is the most frequent cancer affecting men over 50 years of age and second in terms of men mortality (25% of diagnosed cancers in France). Radiotherapy is an important treatment of prostate cancer. In some cases, it is the exclusive type of therapy. The current practice is dose escalation which limits the development of the tumour. The dosimetric limiting factor is the dose received by non-cancerous tissues, especially the organs at risk (OARs), such as the bladder, the rectum, femoral heads, the small intestine or the penis bulb [START_REF] Koper | Impact of volume and location of irradiated rectum wall on rectal blood loss after radiotherapy of prostate cancer[END_REF][START_REF] Peeters | Acute and late complications after radiotherapy for prostate cancer: results of a multicenter randomized trial comparing 68 Gy to 78 Gy[END_REF][START_REF] Tucker | Dose-volume response analyses of late rectal bleeding after radiotherapy for prostate cancer[END_REF][START_REF] Tucker | Comparison of rectal dose-wall histogram versus dose-volume histogram for modeling the incidence of late rectal bleeding after radiotherapy[END_REF]. In practice, rectum or bladder complications (late effects) often reduces the dose to the prostate. Dose escalation is really possible when the CTV motion during radiotherapy sessions (daily displacements or modifications) is controlled. The proposed study aims to, first, determine the mechanisms of organ interactions in the pelvic area and, second, estimate the consequences of these interactions on prostate treatment by radiation.

In fact, for all the different methodologies used in prostate cancer treatment [START_REF] Hatano | Current status of intensity-modulated radiation therapy (IMRT)[END_REF][START_REF] Zhu | Comparison of three radiotherapy treatment planning protocols of definitive external-beam radiation for localized prostate cancer[END_REF] (conformation, intensity modulation, arc-therapy, etc.), dosimetric planning uses data from the initial simulation alone [START_REF] Keros | Qualitative estimation of pelvic organ interactions and their consequences on prostate motion: study on a deceased person[END_REF]. To take into account organ repletion and internal morphology variation during the sessions, there are control methods such as IGRT [START_REF] Zhu | Comparisons of the impact of systematic uncertainties in patient setup and prostate motion on doses to the target among different plans for definitive external-beam radiotherapy for prostate cancer[END_REF][START_REF] Koseoglu | Assessment of setup accuracy in patients receiving postmastectomy radiotherapy using electronic portal imaging[END_REF] (image guided radiation therapy) and DGRT (dose guided radiation therapy) requiring periodic CTacquisitions. The purpose is to adapt the patient"s position, the beams and the prescription doses. These techniques decrease the errors due to patient positioning and ensure the proper location of the targets and OARs.

In the proposed study, we chose to concentrate on the dosimetric consequences generated by bladder and rectum morphological changes. The first consequence is that initial skin markers (often tattoos for pelvic treatment) are no longer adapted and generate a loss of tumour control [START_REF] Boubaker | Finite element simulation of interactions between pelvic organs: Predictive model of the prostate motion in the context of radiotherapy[END_REF][START_REF] Frank | Quantification of prostate and seminal vesicle interfraction variation during IMRT[END_REF][START_REF] Thongphiew | Comparison of online IGRT techniques for prostate IMRT treatment: adaptive vs repositioning correction[END_REF]. The same process can arrive to the tissues near the prostate. The physician and physicist validations (isodoses, organ dose, DVH…) related to the toxicity to the OARs cannot be adapted to the reality of treatment. The stochastic transformations which caused these patient setup errors are essentially related to the repletion of the bladder and rectum. These organs have a particular visco-elasticity that the radiotherapy team must quantify and control so as to increase the therapeutic dose (dose escalation). Elasticity is the quality of an object to be deformed and then resume its original shape when the stress applied disappears. These transformations are observable on pelvic organs, especially bladder or rectum muscles [START_REF] Hoogeman | Quantification of local rectal wall displacements by virtual rectum unfolding[END_REF][START_REF] Hoogeman | Strategies to reduce the systematic error due to tumor and rectum motion in radiotherapy of prostate cancer[END_REF][START_REF] Hoogeman | Strategies to reduce the systematic error due to tumor and rectum motion in radiotherapy of prostate cancer[END_REF]. Firstly, we will describe the tools used and the clinical trials implemented. Then, we will reveal the statistical interaction between ballistic parameters and dosimetric values. Thirdly, we will consider the clinical solutions proposed to select the morphological modifications requiring a new dosimetry. Lastly, we will look at the impact of the daily CT acquisitions and the time required to control the dose to the organs.

Materials and methods

Methodology

The CT scan is a random snapshot of a changing patient. Organ position and repletion are fixed arbitrarily. One method of adaptive radiotherapy is the use of multiple scans (preferably one a day) to estimate the organ position and to adapt the beams. To protect the patient from excessive irradiation, we have only taken two CT scans per patient. One made during the treatment and another during the simulation, before the treatment began (necessary for treatment planning). With the new scanner, we evaluated the impact of time on the dosimetry, and verified that the ballistic was still appropriate. It is evident that, as has shown Van-Herk with analyses of organ motion from repeated CT [START_REF] Van Herk | Quantification of organ motion during conformal radiotherapy of the prostate by three dimensional image registration[END_REF], the intrinsic error of the dose on the irradiated volume has two distinct origins. One systematic and the other random, he suggests a necessary patient set-up margin which ensures a minimum CTV dose of 95% for 90% of the patient population, calculated as 2.5+ 0.7 ( is the standard deviation of the patient mean errors, and the quadratic mean of the patient standard deviations). In the proposed study, the error due to movement is more "macroscopic" and the difference between the random ( and the systematic ( term becomes negligible. As shown in Figure 1, evaluating organ motion and patient setup errors is done after computing the original ballistic on the new anatomical configuration; the dose to different organs can then be evaluated. The phase following the acquisition is the delineation on the new scan of the prostate, the bladder and the rectum by a welltrained person who has already delineated the first scan.

Patients eligibility and ballistics

From 2008 to 2009, 54 patients with organ-confined prostate cancer were prospectively enrolled on a conformational radiation treatment in the radiotherapy centre of Ajaccio, the CHD Castelluccio. Each patient was assigned a pre-treatment (virtual simulation) with a CT scan of 2-mm slice thickness, followed by a threedimensional treatment plan. The initial CTV1 included the prostate and seminal vesicles and the second target volume (CTV2) included the prostate only. The PTV1 and PTV2 margins were done with CTV with a 1-cm uniform margin and 0.5-cm in posterior. The PTV-to-field-edge margin was 7-mm everywhere else, but 12-mm at the superior and inferior borders of the PTV. These margins take into account the dosimetric penumbra of the multileaf collimator and jaws. The selection criteria for this study were patients with non-operated prostate cancer at stage T2-T3aN0M0, with Gleason score less than or equal to 7 and with concentration of serum PSA less than or equal to 20 ng/ml. These patients were generally those for which the dose escalation treatment seemed most interesting. The majority of patients received a dose of 46 Gy on the PTV1 (4-beam-boxtechnique) and between 26 and 30 Gy boost on the PTV2 (generally 6 fields with different orientations). In the current study, normal tissues were delineated as follows: the rectal wall with a 0.5-cm wide ring (the extremities of the contouring were 1-cm below and above the CTV1 [START_REF] Voyant | Dose comparison between three planing prostate: 3D conformational radiotherapy, coplanar arctherapy and non-coplanar arctherapy[END_REF]); the bladder wall with a ring of 0.7-cm over the whole visible volume; the Treatment Planning System (TPS) used was Pinnacle® (Philips-AdacTM) equipped with the software version 8.0m with three-dimensional calculation with heterogeneity. Patients were in the supine position with the immobilization device Combifix® of SimMedTM. Patients were treated with a full bladder and an empty rectum. Only one patient was prescribed a laxative due to the presence of gas in the rectum. In addition, patients were not notified in advance of the day they received their second scan.

Comparison Criteria

Geometric critera

Organ movement induces dosimetric repercussions during the treatment. One or more beams can become unsuitable to the patient"s anatomy, generating dosimetric errors on the target volumes and OARs. The moment at which these errors take place is unpredictable, and no prior action can help quantify this "bad" conformation.

Many studies propose on-line or off-line solutions to take into account the morphological noise and properly target the PTV whatever the irradiation sessions [START_REF] Poulsen | Residual set-up errors and margins in on-line image-guided prostate localization in radiotherapy[END_REF][START_REF] Smitsmans | Automatic prostate localization on cone-beam CT scans for high precision image-guided radiotherapy[END_REF]2021,[START_REF] Yan | An off-line strategy for constructing a patientspecific planning target volume in adaptive treatment process for prostate cancer[END_REF][START_REF] Nuver | An adaptive off-line procedure for radiotherapy of prostate cancer[END_REF]. Rare are the solutions which can really be used on the influence of this noise on the OARs. Some authors believe that it is possible to calculate in real time the dose at any location on the patient [START_REF] Langen | Evaluation of ultrasound-based prostate localization for image-guided radiotherapy[END_REF][START_REF] Lu | Real-time motion-adaptiveoptimization (MAO) in TomoTherapy[END_REF]. This idea is not entirely realistic in a lot of radiotherapy unit. CT scans must be made frequently for all patients (maybe for all sessions) and the images acquired must be exported and merged in the planning system for organ delineation (~30 associated acquisitions). The next step must be the dose calculation with real volumes and real MU (Monitor Unit). It"s only with the compilation of these plans (one plan per CBCT) that the true patient dosimetry can be obtained. In order for this process to be clinically possible, it is important to find shortcuts to avoid any unnecessary action. The idea is to determine a threshold value, below which no dosimetric correction is necessary, and above which, corrections are applied. Before making any dosimetric considerations, we will quantify the anatomic shifts and changes affecting the position of other organs. The purpose here is to find any interaction between the different pelvic volumes. We chose to consider the centroid (center of gravity) displacement and volume modification of bladder, rectum and prostate.

Dosimetric criteria

To finalize a dosimetric study, it"s necessary to find and use the most relevant and objective parameters. In our case, we selected the most frequently used dosimetric criteria (in our unit) although more relevant ones could have been used instead. We chose the average dose, the Equivalent Uniform Dose (EUD) and dose to the volume for the OARs (D30 and D50 for the rectum and bladder respectively). Our lack of experience in 2008 concerning the parameter like V60, V70 and V74 for the rectum and the V60, V70 for the bladder (from "guide des procedures de 2007, SFRO" available on http://www.sfro.org/), forced us to did not use these tolerances. 3 Results and interpretation

Centroid displacement

In this study, we found a very surprising setting: although the bladder volume was often completely different between the first and second scan, the repercussion on prostate displacement was often minimal. Table 1 shows the Mean Absolute Percentage Error (MAPE), which is defined in Equation 4; as the average of the absolute percentage deviation of the parameters in the two different scans ( i CT V 1 represents the volume of the considered organ measured on the CT1 and for the patient i).

          N i i CT i CT N i i CT i CT i CT V V V V V MAPE 1 2 1 1 1 2 1 / 1 1 Equ 4
It"s shown that bladder volume was dramatically reduced by 50% on the second CT scan. This change didn"t affect the prostate centroid which moved by only 0.5 cm; the displacement is isotropic in all three directions. The confidence interval for this organ is very low as the dispersion of values is so small. The question is, why was there such a large change in bladder volume between the two scans? We have worked on this variation and can conclude that the waiting time before and during the simulation must be reduced. From the 30 th patient, we changed our methodology and acquired the CT scan maximum 1hr after the patient began to drink (results are not statistically significant but bladder volume ratio MAPE is equal to 51% for patients between the first and the 30 th and 48% for others).

C/C * R/L* A/P* 3d Vol (MAPE) Prostate 0,36 ± 0,09 0,24 ± 0,06 0,33 ± 0,08 0,54 13,06% Bladder 0,92 ± 0,31 0,27 ± 0,07 0,79 ± 0,16 1,24 49,63% Rectum 0,47 ± 0,10 0,28 ± 0,07 0,31 ± 0,06 0,63 17,69%

) -1/2 )
Table 1 shows the volume change of the bladder which induced a centroid change in the C / C (cranial / caudal) and A / P (anterior / posterior) directions, but not in the L / R direction (left-right). We can see that for the three organs studied, the L / R orientation is not very conducive to the changes (0.2 -0.3 cm). The results

shown in this section are similar to those found in the literature [START_REF] Poulsen | Residual set-up errors and margins in on-line image-guided prostate localization in radiotherapy[END_REF]. The repletion phenomenon in the pelvis is very complex and to better understand its characteristics, it"s necessary to test each organ correlation with other organ parameters, such as the displacement in the three directions or the organ volumes.

Bladder

Table 1 reveals a decrease in bladder volume at the second scan, as introduced in the previous section. In fact, the modification of the bladder"s centroid follows a displacement axis upper anterior oblique.

Figure 2. Displacement of the bladder centroid

The Figure 2 shows that for the bladder, the displacements are very important in the C/C and the A/P axis (> 0.8 cm for lots of patients). The correlation between the bladder volume ratio and the centroid offset in the A / P direction is equal to 0.28 (p = 0.06) and 0.58 for the C / C axis (p <0.001).

Bladder

Table 2: correlation coefficients (CC) for the bladder, rectum and prostate parameters

Table 2 shows that a change in the A / P axis is often followed by one in the C / C axis (p = 0.03). These two parameters are linked, and given this characteristic, instructions were given to the manipulation team to regularly ask patients to come to radiotherapy with full bladders. A change in repletion follows a change in volume, which then incurs a centroid change in the C /C and the A /P directions. The bladder repletion compliance must ensure the stability of the bladder centroid. In this case the displacement of the isocenter will of about 0.3 cm in all directions, (equal to the shift observed in the R / L direction which is not correlated with organ volume).

Rectum

For the rectum, the displacement in the C / C axis (Fig. 3) is only an indication because the delineation is linked to the fixed limits of the CTV1 and CTV2.

Figure 3: Rectum centroid displacements

Although the distributions aren"t Gaussian, we realized that the averages for the R / L and A / P offsets are approximately 0.5 cm. Table 2 reveals a significant correlation between the volume of the rectum and the C / C rectum displacement (correlation coefficient CC = 0.38, p < 0.02).

Prostate

Margins applied to our CTV are 1 cm across and 0.5 cm posteriorly.

Figure 4: Prostate centroid displacement (cm). The line is the average, and the dotted line the average + 2

Fig. 4 shows that for many patients, the centroid offsets can exceed these values (~ 5%). It is important to understand the mechanisms to better control and predict an eventual dosimetric drift. The volume ratio is distributed around 1, and certainly must be due to the uncertainty of intra-operative delineation (white noise, Fig. 5).

Figure 5 : CTV volume ratio between the two scans, line is the average and dotted line the average +/-2 (standard deviation)

Table 2 does not allow to draw any conclusion because many of the correlations are not significant for the selected patient pool. There is a noticeable phenomenon related to the patients" age (Table 2), which affects the position of the isocenter between the two scans spaced in time (p = 0.05). It"s difficult to determine the exact causes of this interaction, but it"s possible that the sphincters are damaged and become fragile with age. Thus, bladder and rectum repletion cannot be maintained during the sessions and the prostate is then displaced. To test this hypothesis we will test the cross-interaction between volumes and centroids for the bladder, rectum and prostate.

Cross-interactions

The cross-interaction study allows to determine the most relevant factors among all organs. The results of these interactions are shown in Table 3.

Prostate parameters

Bladder

Table 3: cross interactions between prostate and bladder and rectum parameters (top), and cross correlation between bladder and rectum parameters (bottom)

The analysis reveals that when the centre of the bladder and rectum is shifted, the isocentre of the prostate is consequently shifted. In the case of the bladder, p = 0.02 for a shift in the C / C axis, p = 0.03 for an A / P axis shift, and p < 0.001 for an R / L axis shift. In the case for the rectum, p < 0.001 for a shift in all three axes. However, due to our sample size and methodology, we failed to uncover a correlation such as the one existing between an A / P displacement of the prostate and the rectal volume, although [START_REF] Van Den Heuvel | Independent verification of ultrasound based image-guided radiation treatment, using electronic portal imaging and implanted gold markers[END_REF] reveal a significant correlation equal to 0.62. This interaction is therefore masked by other phenomena. Maybe there are manipulation errors related to metal markers or tattoos, which are sometimes not well positioned or the CTfusion is not well done. The next section focuses on the dosimetric aspect related to organ movement. The question is: is the dosimetric validation set by physicists and physicians at the initial time t0, still valid with pelvic organ changes during radiotherapy sessions?

Dosimetric modifications

Considering the previous sections about the organ motion, it seems necessary to use the CBCT in all radiotherapy session in order to target the PTV. This is time-consuming for technicians (dosimetrists, physicians and physicists ) as well as for LINAC. Is it possible to make real-time dosimetries with volumes changing on a daily basis? First, we need to consider that there is a threshold level on the first dosimetry, which is considered for the parameters studied in the previous section (bladder or rectum repletion). If the idea is to re-delineate all contours when patients have large repletion modifications, it would be necessary to quantify the change that is considered significant. A simple approximation would be to redo only 5% of the dosimetries, as we try to limit the amount of redundant work. A wide range of rectum and bladder volumes would be collected from the samples of 54 patients. A CT acquisition during the treatment (scanner or CBCT) describes an increase or decrease of repletion. If the change is out of the 95% interval, it is decided to redo the dosimetry. The use of CBCT is relevant to position the isocentre of the prostate, but also to identify the critical cases where repletion is not conforming to the initial scan. In case of doubt, the CT image dataset must be exported to TPS, merged to delineate the volume and applied the ballistic, then the dosimetry will need to be revalidated.

Elimination of bladder parameter Outliers

In this first part, only patients with an important change in bladder volume were considered. A simple approximation is made by considering the volume ratio distribution as a Gaussian distribution. We know it is wrong given the nature of the definition of this parameter (ratio). This means that 0.5 and 2 are symmetric (increase or decrease by a factor 2), which does not correspond to a Gaussian distribution. The assumption of normality is strongly compromised, but we accept this approximation knowingly. In the cohort of the 54 patients, the MAPE of bladder volume between the two CT acquisitions is computed from the bladder volume ratio of each patient (see Equation 4). The average bladder volume ratio is 1.27 between the volume of the first and the second scan, and the standard deviation is 0.63. It was decided to refuse patients whose ratio is above 2.53 (= 1.27 + 2x 0.63), which corresponds to two patients in our case. Table 5 shows that the elimination of the bladder outliers, which induced an important gain on the bladder dosimetric error (MAPE). For the rectum and prostate, there was no change and the same errors were observed.

In detail we found that the ratio of D50 (between first and second scan) is significantly decreased (from 66.6% to 39.7%), the bladder average dose significantly reduced and the bladder EUD ratio decreased (from 19.4% to 17.2%). Hence, the process of eliminating data outliers doesn"t seem to affect the dose to the target volume and the prostate centroid displacement (0.3cm) is compensated by the PTV margins.

Elimination of rectum parameter Outliers

Using the same process as previously described, approximating the normal distribution of rectal volume ratio, we obtained an average volume ratio of 1.02 with a standard deviation of 0.205. We will quantify the impact of eliminating all rectum modifications with volume ratio outside [0.613 - 7, it is interesting to note that the outliers elimination method does not improve the mean dose ratio, D30, nor the EUD for the rectum. In fact, rectal dose modifications are not only related to the rectal volume (shift in the A / P direction), but also to the movements caused particularly by gas (local contraction and expansion). The simple ratio of volumes is not sufficient to establish a dosimetric rule. The absolute volume of the rectum may be more specific and could act as a predictive parameter.

Elimination of bladder and rectum parameter Outliers

In this case the largest bladder and rectum volume ratios were eliminated (5% of bladders and rectums). 8 shows clearer results than for the two previous cases. Improvements are undoubtedly related to the bladder, with the D50 ratio (-27.3%) followed by the medium dose (-5.1%) and the EUD (-2.6%). The gain to the rectum is also interesting but less important, with about 1% gain for all parameters. The best method consists in repeating only 5% of the dosimetries for patients with a large change in bladder volume (in the present study, 5% of outliers represent a ratio superior to 250%). This process can reduce the risk of mismatch between the initial scan and those of the following days. The use of this process for the rectal volume ratio is less interesting, and is only a secondary correction.

Ratio Rectum

A global solution

We have seen that by repeating 5% of the dosimetries (and 10% if we consider both bladder and rectum cases), it is possible to improve the quality of the radiotherapy. It is now of interest to find the link between the number of repeated dosimetries and the gain generated (D30, D mean ….). Firstly, we need to find the parameters, such as organ volume or isocentre shift, that are most highly correlated to the EUD ratio for the bladder and rectum. The selected parameters should be strictly orthogonal and not correlated with each other as they provide the basis for a linear regression.

The bladder case

To complete the statistical study, the EUD ratio needs to be transformed by values superior to 1, so that a ratio of 0.9 becomes 1.11 (= 1/0.9). This modification prevents any compensation of the errors. To determine the parameters having most influence on the bladder EUD, we used the classical correlation coefficients associated to a Student T-test. We obtained parameters which have a CC statistically not equal to zero, and so which are significant: the D50 bladder (CC = 0.84), the average dose to the bladder (CC = 0.97), the ratio of the bladder volume (CC = -0.7), the 3-D displacement of the bladder (CC = 0.77) and the 3-D movement of the prostate (CC = 0.5). Note that the average dose is more representative of the risk (EUD bladder) than the D50, which could allow us to change our methodology to validate the bladder dose (actually D50 < 62 Gy). None of the dosimetric parameters (average dose to the bladder, D50…) are interesting for the proposed study, and we instead chose the two parameters, volume ratio of the bladder and 3-D displacement of the prostate. We used the fact that the bladder centroid displacement is a linear combination of displacement of the prostate"s isocentre and bladder volume. If the volume ratio is large then the isocentre moves, and if it is not properly positioned on the prostate, it may also cause a shift on the bladder. We find a regression for bladder EUD (F-Fisher = 11.5 and Figure 6) as such:

EUD bladder = 0.937 (p<0.001) + 0.26 x 3d_prostate (p<0.001) + 0.062 x ratio_bladder (p=0.04)

If we consider the use of the daily CBCT for the treatment and a systematic focus on the prostate"s isocentre (3d_prostate=0), the second term of this equation can be ignored as it is equal to zero. The objective is to say that if we fixed the problem of the isocentre (actual IGRT), we can only work on the volume of the bladder. The regression is limited to:

EUD bladder = 0.937 + 0.062 x ratio_bladder So the bladder volume ratio can be imposed by the difference on the EUD that we allow. On Figure 7, we can see that a threshold at 10% for the EUD (representing a tolerance of 10% for EUD compared to the initial value on the first dosimetry), represents an interval of between 2.63 and 0.38 for the bladder volume ratio, and corresponds to 11% of the misadjusted dosimetries. For a threshold at 5% (for bladder EUD error), there is an interval of between 0.54 and 1.8, thus 33% of dosimetries need to be remade, and for a threshold at 1%, there is a margin of 1.18 -0.85 which corresponds to 76% of dosimetries to adapt.

The rectum case

Using the same methodology as previously described, we obtained for the rectal volume the parameters that most influence the rectum EUD, namely the D30 (CC = 0.85), the average dose (CC = 0.84), the volume ratio (CC = 0.57), the 3-D displacement (CC = 0.3) and the 3-D movement of the prostate (CC = 0.31).

1%). The line represents the equation y=x

We get a regression (F = 7.97 and Figure 8) as follows:

EUD rectum = 0.86 (p<0.001) + 0.05 x 3d_prostate (p=0.05) + 0.144 x ratio_rectum (p=0.003)

As in the previous case, the use of daily CBCT cancels the 3-D prostate coefficient, which tends to near 0 when the number of CBCT increases and the new relationship becomes:

EUD rectum = 0.86 + 0.144 x ratio_rectm Fig. 9 shows that for 10% of EUD ratio (tolerance of 10% for EUD compared to the initial value on the first dosimetry), there is an interval in the bladder volume ratio of between 1.67 and 0.6, which represents 4% of the dosimetries which need to be remade. For 5% of EUD ratio, there is an interval of between 1.32 and 0.76 (19% of dosimetries to remake), and for 1%, there is an interval of between 1.04 and 0.96 (83% of dosimetries to redo).

Conclusion and perspectives

Organ movement is an important aspect to consider in 21 th century radiotherapy. Many authors have already studied this problem and have developed the IGRT process, which consists in positioning the beams in the target volume with a cone beam acquisition. This approach ensures that tumor control is maximal. In the proposed study, the approach is different as both the target volume and the organs at risk are studied, a methodology called the DGRT. The cone beam CT is necessary to better position the beam isocentre and the dosimetry is updated throughout the radiotherapy session. This method is based on bladder and rectal volume modifications, and if this variation is important, the dosimetry must be corrected. The future of radiotherapy must consider organ movements, and tools such as CBCT generate many images which need to be interpreted. Our methodology is dedicated to image data selection in order to eliminate all unnecessary manipulations. However, it will be necessary to complete the results shown in the present study (only 2 CT acquisitions for each patient) with studies including many CT acquisitions per patient. Some results like the cross-correlations will be certainly identical, but other like the relation between the EUD ratio and the volume ratio (see section 3.2.4), must be verified in this case.

Figures legends

Figure 1: Methodology for CT acquisitions Figure 2. Displacement of the bladder centroid Figure 3: Rectum centroid displacements Figure 4: Prostate centroid displacement (cm). The line is the average, and the dotted line the average + 2 Figure 5 : CTV volume ratio between the two scans, line is the average and the dotted line the average +/-2 (standard deviation) Figure 6: Modelised and measured bladder EUD ratio (normalized Root Mean Square Error=12,5%). The line represents the equation y = x Figure 7: Link between the number of dosimetry to remake, and the bladder EUD difference authorized Figure 8: Modelised and measured rectal EUD ratio (normalized Root Mean Square Error=5.1%). Line is the graph of y=x Figure 9: Link between the number of dosimetry to remake, and the rectum EUD difference authorized

Légendes des Figures

Figure 1: Methodologie pour les acquisitions CT Figure 2. Déplacement de la véssie (centroide) Figure 3: Déplacement du rectum (centroide) Figure 4: Déplacement de la prostate (cm). Le trait plein représente la moyenne, et les pointillés la moyenne+2 Figure 5 : Ratio du volume du CTV entre les 2 scans. Le trait plein représente la moyenne, et les pointillés la moyenne +/-2 (écat type) Figure 6: Ratio de l"EUD vésicale mesuré et modélisé (normalized Root Mean Square Error=12,5%). La ligne est la droite d"équation y=x Figure 7: Relation entre le nombre de dosimétries à réévaluer et la différence de l"EUD vesicale souhaitée Figure 8: Ratio de l"EUD rectale mesuré et modélisé (normalized Root Mean Square Error=12,5%). La ligne est la droite d"équation y=x Figure 9: Relation entre le nombre de dosimétries à réévaluer et la différence de l"EUD rectale souhaitée

Tables legends

Table 1: absolute difference for the centroids (in cm), and the volumes between the first and second CT acquisition (*is the mean value and 95% confidence interval=mean±2.Std_dev.(54) -1/2 ) Table 2: correlation coefficients (CC) for the bladder, rectum and prostate parameters Table 3: cross interactions between prostate and bladder and rectum parameters (top), and cross correlation between bladder and rectum parameters (bottom) Table 4: geometrical impact for bladder of the 95% bladder outliers methodology Table 5: dosimetric impact of the 95% bladder outliers methodology Table 6: geometrical impact for rectum of the 95% rectum outliers methodology Table 7: dosimetric impact of the 95% rectum outliers methodology Table 8: dosimetric impact of the 95% bladder and rectum outliers methodology

Légende des tableaux

Table 1: différence absolue pour les isocentres (cm), et différence entre les volumes de la première et de la seconde acquisition CT (* représente la moyenne suivi de l"intervalle de confiance 95% )

Table 2 : coefficients de corrélation (CC) pour les paramètres de la vessie, du rectum et de la prostate Table 3 : interactions croisées entre les paramètres de la prostate, et ceux de la vessie et du rectum (en haut), ainsi que les corrélations croisées entre les paramètres du rectum et de la vessie (en bas) Table 4 : impact géométrique pour la vessie de la méthodologie des « 95% bladder outliers » Table 5 : impact dosimétrique de la méthodologie « 95% bladder outliers » Table 6 : impact géométrique pour le rectum de la méthodologie des « 95% rectum outliers » Table 7 : impact dosimétrique de la méthodologie « 95% rectum outliers » Table 8 : impact dosimétrique de la méthodologie « 95% rectum and bladder outliers »

Figure 1 :

 1 Figure 1: Methodology for CT acquisitions

  For the target volume, we only used the mean dose and D95. All TCP and NTCP values were inapplicable because a large dose difference is required to produce a significant difference between the probability values. It is possible to determine the EUD with some of the values available on the differential Histogram Dose Volume (HDV), and with the Pinnacle TPS (Equation1). The parameters a and p (a= 1/p) being specific constants related to the tissue considered, d i the dose of each bin i (voxel i), the maximum of d i is noted d max , V i the volume fraction associate to the voxel i and vol ref the volume of the organ in question. It is necessary to compute the effective volume of the organ, called voleff, to determine the EUD (Equation 2 and 3).

Figure 6 :

 6 Figure 6: Modelized and measured bladder EUD ratio (normalized Root Mean

Figure 7 :

 7 Figure 7: Link between the number of dosimetry to remake, and the bladder EUD

Figure 8 :

 8 Figure 8: Modelized and measured rectal EUD ratio (normalized Root Mean Square

Figure 9 :

 9 Figure 9: Link between the number of dosimetry to remake, and the rectum EUD

Table 1 : absolute difference for the centroids (in cm), and the volumes between the first and second CT acquisition (*is the mean value and 95% confidence interval =mean±2.Std_dev.(54

 1

Table 4 : geometrical impact for bladder of the 95% bladder outliers methodology

 4

Table 4

 4 shows the effect of the elimination of these outliers. The impact is not apparent on the centroid displacement, but the gain is 1.5% for the MAPE of bladder volume ratio.

			Ratio Rectum			Ratio Bladder		Ratio CTV2
		D30	Dmean	EUD	D50	Dmean	EUD	D95	Dmean
	Average (without) 1,016	1,015	1,019	1,125	1,064	1,035	1,010	1,002
	Average (with)	1,019	1,016	1,021	1,179	1,097	1,059	1,010	1,002
	MAPE (without)	8,7%	7,8%	5,7%	66,6%	27,9%	19,4%	1,0%	0,3%
	MAPE (with)	8,6%	7,5%	5,8%	39,7%	23,3%	17,2%	1,0%	0,3%

Table 5 : dosimetric impact of the 95% bladder outliers methodology

 5

Table 6 :

 6 geometrical impact for rectum of the 95% rectum outliers methodology As in the case for the bladder, Table6shows that this induces very little displacements.

			Ratio Rectum			Ratio Bladder		Ratio CTV2
		D30	Dmean	EUD	D50	Dmean	EUD	D95	Dmean
	Average (without) 1,016	1,015	1,019	1,125	1,064	1,035	1,010	1,002
	Average (with)	1,002	1,006	1,010	1,143	1,076	1,045	1,010	1,002
	MAPE (without)	8,7%	7,8%	5,7%	66,6%	27,9%	19,4%	1,0%	0,3%
	MAPE (with)	8,0%	7,4%	5,2%	67,2%	27,6%	19,1%	1,0%	0,3%

Table 7 : dosimetric impact of the 95% rectum outliers methodology On Table

 7

Table 8 : dosimetric impact of the 95% bladder and rectum outliers methodology Table

 8

Acknowledgments

The authors thank the MV2A association members (M. de Mari, S. Cardi, V. Leandri, F. Savigny and C. Yousse) for their help during the CT acquisitions.