{"paper_id":"0348d7e7-d875-4e31-b2ff-005f65e81067","body_text":"1 \n \nRunning title: Peritoneal endometriosis biomarkers 1 \nTitle: A pilot study of discovery and validation of peritoneal endometriosis biomarkers in 2 \nperitoneal fluid and serum  3 \nSee Ling Loy, Ph.D.,a,b Jieliang Zhou, B.SC.,c Liang Cui, Ph.D.,c,d Tse Yeun Tan, 4 \nF.R.A.N.Z.C.O.G.,a Tat Xin Ee, M.R.C.O.G.,a Bernard Su Min Chern, F.R.C.O.G.,b,e Jerry Kok 5 \nYen Chan, F.R.C.O.G., Ph.D.,a,b Yie Hou Lee, Ph.D.,b,c,d,1 6 \n 7 \naDepartment of Reproductive Medicine, KK Women’s and Children’s Hospital, Singapore, 8 \nSingapore 229899. loy.see.ling@kkh.com.sg (SLL); tan.tse.yeun@singhealth.com.sg (TYT); 9 \nee.tat.xin@singhealth.com.sg (TXE); jerrychan@duke-nus.edu.sg (JKYC) 10 \nbObstetrics and Gynecology-Academic Clinical Program, Duke-NUS Medical School, Singapore, 11 \nSingapore 169857. bernard.chern.s.m@singhealth.com.sg (BSMC); yiehou.lee@smart.mit.edu 12 \n(YHL) 13 \ncKK Research Centre, KK Women’s and Children’s Hospital, Singapore, Singapore 229899. 14 \nzhou.Jieliang@kkh.com.sg (JZ); liangcui@smart.mit.edu (LC) 15 \ndSingapore-MIT Alliance for Research and Technology, Singapore, Singapore 138602.   16 \neDepartment of Obstetrics and Gynecology, KK Women’s and Children’s Hospital, Singapore, 17 \nSingapore 229899. 18 \n 19 \nCorresponding author:  20 \nYie Hou Lee 21 \nKK Research Centre, KK Women’s and Children’s Hospital, 100 Bukit Timah Road, Singapore 22 \n229899 23 \nE-mail: yiehou.lee@smart.mit.edu  24 \nPhone: +65-63948122 25 \nFax: +65-63941618 26 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n2 \n \n1Present address: Singapore-MIT Alliance for Research and Technology, 1 CREATE Way, #04-27 \n13/14 Entreprise Wing, Singapore 138602  28 \n 29 \nCapsule: Discovery and validation of novel peritoneal endometriosis biomarker, known as 30 \nphenylalanyl-isoleucine in peritoneal fluid and serum, pointing its potential use as a diagnostic 31 \nbiomarker of peritoneal endometriosis.   32 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n3 \n \nObjective: To identify potential serum biomarkers in women with peritoneal endometriosis (PE) 33 \nby first looking at its source in the peritoneal fluid (PF).   34 \nDesign: Case-control pilot studies, comprising independent discovery and validation sets. 35 \nSetting: KK Women’s and Children’s Hospital, Singapore. 36 \nPatient(s): Women with laparoscopically confirmed PE and absence of endometriosis (control).  37 \nIntervention(s): None. 38 \nMain Outcome Measure(s): In the discovery set, we used untargeted liquid chromatography-39 \nmass spectrometry (LC-MS/MS) metabolomics, multivariable and univariable analyses to 40 \ngenerate global metabolomic profiles of PF for endometriosis and to identify potential 41 \nmetabolites that could distinguish PE (n=10) from controls (n=31). Using targeted 42 \nmetabolomics, we validated the identified metabolites in PF and sera of cases (n=16 PE) and 43 \ncontrols (n=19). We performed the area under the receiver-operating characteristics curve 44 \n(AUC) analysis to evaluate the diagnostic performance of PE metabolites. 45 \nResult(s): In the discovery set, PF phosphatidylcholine (34:3) and phenylalanyl-isoleucine were 46 \nsignificantly increased in PE than controls groups, with AUC 0.77 (95% confidence interval 0.61-47 \n0.92; p=0.018) and AUC 0.98 (0.95-1.02; p<0.001), respectively. In the validation set, 48 \nphenylalanyl-isoleucine retained discriminatory performance to distinguish PE from controls in 49 \nboth PF (AUC 0.77; 0.61-0.92; p=0.006) and serum samples (AUC 0.81; 0.64-0.99; p=0.004). 50 \nConclusion(s): Our preliminary results propose phenylalanyl-isoleucine as a potential 51 \nbiomarker of PE, which may be used as a minimally-invasive diagnostic biomarker of PE.   52 \nKeywords: biomarker, diagnosis, endometriosis, LC-MS/MS, metabolomics   53 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n4 \n \nINTRODUCTION 54 \nEndometriosis affects approximately 10% of reproductive aged women (1, 2) and is associated 55 \nwith substantial morbidity, including chronic pelvic pain and infertility (3, 4). Endometriosis is 56 \nrepresented by three main subphenotypes: ovarian endometriosis (OE), superficial peritoneal 57 \nendometriosis (PE) and deep infiltrating endometriosis (DIE) (5). To diagnose endometriosis, 58 \ntransvaginal ultrasonography can be used to detect OE and DIE (6, 7), with pelvic magnetic 59 \nresonance imaging to assess the extent of DIE (1). However, the detection of PE, characterized 60 \nby superficial endometrial lesions occurring on the peritoneum, remains challenging (8-11). 61 \nLaparoscopic visualization remains as the standard for definitive diagnosis of PE, the most 62 \ncommon subphenotype which accounts for ~80% of all endometriosis (11-13).  63 \nUsing laparoscopic visualization as the first line diagnostic tool poses a number of 64 \nchallenges, including its invasive nature, associated risks and potential complications of surgery 65 \n(11). Laparoscopy is appropriate when symptoms reach a level of severity to justify the surgical 66 \nrisk (5), yet clinical symptoms has a poor correlation with disease burden (10). Indeed, accuracy 67 \nof diagnosis is dependent on practitioners’ laparoscopic skills due to the diversity of 68 \nendometriotic appearances and locations, insofar that endometriosis may be inadvertently 69 \nmissed with less obvious or microscopic endometriotic lesions (14). Consequently, there is often 70 \na delay with an average of eight years in the diagnosis of endometriosis (6). Thus, there is a 71 \ngreat need to identify a less invasive method for PE diagnosis, which would have a 72 \ngroundbreaking impact in preventing or delaying disease progression, improving patients’ 73 \nquality of life and the efficacy of available treatments. This is particularly important in women for 74 \nwhom fertility is a priority whereby hormonal treatment is not appropriate (13). 75 \nTo date, despite the evaluation of numerous potential biomarkers, a reliable biomarker 76 \nspecifically for the diagnosis of PE has yet to be identified (10). Advances of high-throughput 77 \nbioanalytical technologies in omics have made metabolomics a powerful tool for biomarker 78 \ndiscovery (15, 16). Metabolites are intermediates to a wide range of biological processes and 79 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n5 \n \nsignaling axes such as mammalian target of rapamycin (mTOR), peroxisome proliferator-80 \nactivated receptor (PPAR) and mitogen-activated protein kinase (MAPK) pathways (17-19). The 81 \nassociation of aberrant metabolism and endometriosis has emerged in recent years (20-22). 82 \nDifferences in metabolism and metabolite levels reflecting endometriosis subphenotypes 83 \npathophysiology potentially forms the basis for identifying novel biomarkers for PE.  84 \nThe peritoneal fluid (PF) is notably rich in proteins and lipids including cytokines, 85 \nchemokines, growth factors and matrix metalloproteinases, and serves an important 86 \nenvironment where endometriotic lesions reside and communicate with surrounding tissues 87 \nincluding nerve cells and ovaries (23-25). The PF is therefore a source for the assessment of 88 \nthe dysregulated peritoneal cavity, and for reflecting dysregulated metabolic state of the 89 \nsubphenotypes. Thus, in this study, we aimed to identify potential biomarkers for the diagnosis 90 \nof peritoneal endometriosis by first looking at its source in the peritoneal fluid. Through 91 \nuntargeted metabolomics, we characterized global metabolomics alterations in the peritoneal 92 \nfluid samples, and multivariable and univariable statistics were used to discover potential 93 \nbiomarkers that resolve women with a laparoscopic diagnosed peritoneal endometriosis and 94 \nwithout endometriosis. To verify the identified potential biomarkers in peritoneal fluid, we 95 \nperformed targeted metabolomics on peritoneal fluid and serum samples of women from 96 \nindependent case-control sets. 97 \n 98 \nMATERIALS AND METHODS 99 \nStudy design 100 \nIn this case-control pilot study, investigation of biomarkers was conducted in two phases 101 \n(Supplemental Figure 1, available online). Phase I, defined as the discovery set, untargeted 102 \nmetabolomics approach was employed to generate PF-specific global metabolomic maps of 103 \nendometriosis. Phase II, defined as the validation set, where two separate groups of women 104 \nwere independently enrolled to verify the identified PF-metabolites for PE diagnosis. We 105 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n6 \n \nperformed targeted metabolomics analysis using both PF (invasive) and serum samples 106 \n(minimally-invasive) among women with PE and controls. This study was conducted according 107 \nto the Helsinki Declaration, and all procedures were approved by the Singhealth Centralized 108 \nInstitutional Review Board (reference 2010-167-D).    109 \n  110 \nClinical participants  111 \nPatients were recruited from the subfertility clinic in the KK Women’s and Children’s Hospital, 112 \nSingapore, between June 2010 and May 2013 for Phase I, and between June 2010 and August 113 \n2016 for Phase II. Laparoscopy was scheduled for suspected endometriosis, infertility, 114 \nsterilization procedures, and/or pelvic pain. Exclusion criteria included menstruating patients, 115 \npost-menopausal patients, patients on hormonal therapy (e.g. norethisterone, combined oral 116 \ncontraceptive pill) for at least three months before laparoscopy, and other potentially 117 \nconfounding diseases such as diabetes, adenomyosis or any other chronic inflammatory 118 \ndiseases (rheumatoid arthritis, inflammatory bowel disease, systemic sclerosis). All eligible 119 \npatients provided written informed consent upon recruitment.    120 \nDuring diagnostic laparoscopy, a detailed inspection of the uterus, fallopian tubes, 121 \novaries, pouch of Douglas and the pelvic peritoneum was performed by senior gynecologists 122 \nsubspecialized in reproductive endocrinology and infertility. Patients with laparoscopically 123 \nconfirmed PE were defined as the cases, and staged according to the revised American Fertility 124 \nSociety classification of endometriosis (AFS, 1985; ASRM, 1997). The possible overlapping of 125 \nthe three lesion subphenotypes led us to classify the patients according to the worst lesion 126 \nfound in each subject, based on endometriosis subphenotype grouping by Somigliana et al. (26) 127 \nand Chapron et al. (27). Patients from the same clinic but laparoscopically observed to be 128 \nwithout endometriosis were defined as the controls, including those with benign gynecological 129 \npresentations such as tubal occlusion, uterine fibroids, benign ovarian cysts and polycystic 130 \novary.    131 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n7 \n \n 132 \nSample collection and sample processing 133 \nFor the collection of PF, clinical staff collected samples via aspiration with a syringe attached to 134 \nan irrigation/suction tube from the Pouch of Douglas, with 1% protease inhibitor added (Roche, 135 \nSwitzerland) as previously described (28), and which is in line with Endometriosis Phenome and 136 \nBiobanking Harmonization Project Standard Operating Procedures (29). The aspirates were 137 \nthen centrifuged (1,000×g, 4°C) for 10 min and the clear supernatants were transferred to 15 mL 138 \ntubes. For the collection of peripheral venous blood, samples were collected into the BD 139 \nVacutainer® SST II tubes. After 10 min centrifugation (1,200×g, 4°C), the top yellowish layers 140 \nwere transferred to 15 mL tubes, followed by centrifuging for another 10 min (3,600×g, 4°C) 141 \nwhere the supernatants were transferred to 1 ml aliquots. Both PF and serum samples were 142 \nstored at -80°C until further use (28).  143 \nPrior to LC-M/S analysis, 100 μ L from PF or serum sample was thawed at 4°C and 144 \nproteins were precipitated with 400 μ L ice-cold methanol. After vortexing for 1 min, the mixture 145 \nwas centrifuged at 17,000×g for 10 min at 4 °C and the supernatant was collected and 146 \nevaporated to dryness in a vacuum concentrator. The dry extracts were then resuspended in 147 \n100 μ L of 98:2 water/methanol or in 100 µL of 0.1% formic acid in methanol for untargeted or 148 \ntargeted metabolomics, respectively. Quality control (QC) samples were prepared by mixing 149 \nequal amounts of reconstituted extracts from all the samples and processed as per other 150 \nsamples. All samples were kept at 4°C and analyzed in a random manner. QC samples are 151 \ninterspersed through the analytical runs and ran after each 10th sample to monitor the stability of 152 \nthe system.  153 \n 154 \nUntargeted mass spectrometry analysis 155 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n8 \n \nWe performed metabolomics analysis as previously described with modifications (30). 156 \nReversed-phase liquid chromatography-MS analyses were performed using the Agilent 1290 157 \nultrahigh pressure liquid chromatography system (Waldbronn, Germany) equipped with a 6520 158 \nQ-TOF mass detector managed by a MassHunter workstation. The column used for the 159 \nseparation was rapid resolution HT Zorbax SB-C18 (2.1×100 mm, 1.8 μ m; Agilent Technologies, 160 \nUSA), and the mobile phase was (A) 0.1% formic acid in water and (B) 0.1% formic acid in 161 \nmethanol. The initial condition of the gradient elution was set at 2% B for 2 min with a flow rate 162 \nof 0.4 ml/min. A 7 min linear gradient to 70% B was then applied, followed by a 5 min gradient to 163 \n100% B which was held for 3 min. The sample injection volume was 2 μ L and the oven 164 \ntemperature was set at 40°C. The electrospray ionization mass spectra were acquired in both 165 \npositive and negative ion mode. Mass data were collected between m/z 100 and 1000 at a rate 166 \nof two scans per second. The ion spray voltage was set at 4,000 V for positive mode and 3,500 167 \nV for negative mode. The heated capillary temperature was maintained at 350°C. The drying 168 \ngas and nebulizer nitrogen gas flow rates were 12.0 L/min and 50 psi, respectively. Two 169 \nreference masses were continuously infused to the system to allow constant mass correction 170 \nduring the run: m/z 121.0509 (C5H4N4) and m/z 922.0098 (C18H18O6N3P3F24). 171 \n 172 \nTargeted mass spectrometry analysis 173 \nThe targeted LC-MS/MS analysis was performed in multiple reaction monitoring mode via Triple 174 \nQuadrupole 6460 mass spectrometer with Jet Stream (Agilent Technologies). Chromatographic 175 \nseparation was achieved by using Eclipse Plus column C18 (2.1×50 mm; Agilent, US) with 176 \nmobile phases (A) 10 mM ammonium formate and 0.1% formic acid in water and (B) 0.1% 177 \nformic acid in methanol. The initial condition was set at 100% A for 3 min at a flow rate of 178 \n0.3ml/min. A 3 min linear gradient to 100% B was then applied and held for 3 min. Then the 179 \ngradient returned to starting conditions over 0.1 min and maintain at the initial condition for 3 180 \nmin. The column was kept at 45°C and the flow rate was 0.3 mL/min. The auto-sampler was 181 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n9 \n \ncooled at 4°C and an injection volume of 2 μ L was applied. Mass transition and collision energy 182 \nwere optimized for each compound by direct infusion of individual standard solutions. Both 183 \npositive and negative electrospray ionization modes were performed with the following source 184 \nparameters: drying gas temperature at 250°C with a flow of 5 L/min, nebulizer gas pressure at 185 \n40 psi, sheath gas temperature at 400°C with a flow of 11 L/min, capillary voltage 4,000 and 186 \n3,500 V for positive and negative mode respectively, and nozzle voltage 500 V for both positive 187 \nand negative modes. Data acquisition and processing were performed using MassHunter 188 \nsoftware (Agilent Technologies, US). 189 \n 190 \nData analysis and Compound identification  191 \nFor metabolomics analysis, raw spectrometric data were converted to mzData (LC-MS/MS) and 192 \nNetCDF (GC-MS) formats via Masshunter (Agilent, US) and input to open-source software 193 \nMZmine 2.0 for peak finding, peak alignment and peak normalization across all samples. The 194 \nstructure identification of the differential metabolites was based on a previously described 195 \nstrategy (31). First, the element composition C13H25NO4 of the m/z 260.18 ion was calculated 196 \nbased on the exact mass, the nitrogen rule and the isotope pattern by Masshunter software 197 \nfrom Agilent. Then, the elemental composition and exact mass were used for open source 198 \ndatabase searching, including LIPIDMAPS (http://www.lipidmaps.org/), HMDB 199 \n(http://www.hmdb.ca/), METLIN (http://metlin.scripps.edu/) and MassBank 200 \n(http://www.massband.jp/). Next, MS/MS experiments were performed to obtain structural 201 \ninformation via the interpretation of the fragmentation pattern of the metabolite. 202 \n 203 \nStatistical analysis 204 \nWe used IBM SPSS statistics, version 19 (USA), the Unscrambler and GraphPad Prism, version 205 \n7.0 (USA) for statistical analyses. For the untargeted metabolomics, we used partial least 206 \nsquares analysis and volcano plots to preliminarily select significant metabolites. Mann-Whitney 207 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n10 \n \nU-test and Fisher’s exact test were used to compare continuous and categorical subject 208 \ncharacteristics respectively between PE versus controls. Significantly different metabolites 209 \n(SDMs) were defined by a fold change of >1.50 for increased metabolites and <0.67 for 210 \ndecreased metabolites. We performed the area under the receiver-operating characteristics 211 \n(ROC) curve (AUC) analysis to evaluate the diagnostic performance of PE metabolites. 212 \nAUC>0.70 was defined as optimal diagnostic performance value. Sensitivity and specificity were 213 \ndetermined at maximum Youden Index. For the discovery datasets, a two-sided p<0.05 was 214 \nconsidered statistical significance. For the validation datasets, a two-sided p<0.025 (0.05/2 215 \noutcomes) to account for multiplicity was considered statistically significant. 216 \n 217 \nRESULTS 218 \nCharacteristics of participants 219 \nFor the discovery set, PF samples were analyzed for 10 women with PE (mean age 33.3 years 220 \nold) and 31 women who served as controls (mean age 33.9 years old). Majority of women with 221 \nPE were at minimal-mild stage of endometriosis (rAFS stage I-II). No differences were observed 222 \nin terms of age, ethnicity and cycle phase between women with PE and controls (Supplemental 223 \nTable 1, available online).  224 \nFor the validation set, women with PE and controls were compared and analyzed. These 225 \nincluded PF samples for 19 PE (mean age 34.0 years old) and 20 controls (mean age 35.9 226 \nyears old). Serum samples were available for 16 PE and 19 controls, as illustrated in 227 \nSupplemental Figure 1 (available online). Similar to the discovery set, majority of women with 228 \nPE (89.5%) were at minimal-mild stage of endometriosis. No differences in age, ethnicity and 229 \ncycle phase were shown between women with PE and controls.  230 \n 231 \nDifferent PF metabolomic profiles between women with PE and controls 232 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n11 \n \nMultivariable partial least squares regression (PLSR) model was constructed to reveal the PF 233 \nmetabolome differences between women with PE and controls from the discovery set (Phase I). 234 \nThe PLSR score plots unbiasedly yielded good separation between metabolomic profiles of 235 \nwomen with PE and controls (Figure 1A).  Next, we projected the metabolomic profiles on the 236 \nendometriosis subphenotypes to identify metabolites that drive the subphenotype separation. 237 \nThe PLSR loadings plot yielded biochemically diverse metabolites for the separation of women 238 \nwith PE and controls. This discrimination was primarily driven by the top 20 metabolites shown 239 \nin Figure 1B. These metabolites comprised of 7 phospholipids, 5 free fatty acids, 4 amino acids, 240 \n1 carnitine, 1 sphingolipid, 1 dipeptide and 1 tricarboxylic acid cycle intermediate. The list of PF 241 \nmetabolites for PE and controls are shown in Supplemental Table 2 (available online). To 242 \nfurther ascertain the relationship of key metabolites to the subphenotypes, fold change was 243 \napplied to determine metabolites that are significantly different in PE compared with controls.   244 \n   245 \nDiscovery of PF metabolites that distinguished PE from controls  246 \nUsing volcano plots, 13 PF metabolites showed significant fold change differences (p<0.05) 247 \nwhen compared PE with controls. Of these 13 metabolites, five were identified as SDMs with 248 \nfold change >1.50 or <0.67. Compared with the controls, women with PE exhibited higher levels 249 \nof 5-tetradecenoylcarnitine (2.33-fold), phosphatidylcholine C34:3 (1.66-fold), phenylalanyl-250 \nisoleucine (1.79-fold) and tetracosahexaenoic acid (1.72-fold), but lower level of ceramide d34:0 251 \n(0.44-fold) (Figure 2). When diagnostic performance was evaluated by ROC analysis, 252 \nphenylalanyl-isoleucine and phosphatidylcholine C34:3 demonstrated diagnostic potential in 253 \ndistinguishing women with PE from controls, as indicated by AUC 0.98 (95% confidence interval 254 \n(CI) 0.95-1.02; p<0.001; sensitivity 100%; specificity 95.8%) and by AUC 0.77 (95% CI 0.61-255 \n0.92; p=0.018; sensitivity 77.8%; specificity 71.4%), respectively (Figure 3).  256 \n 257 \nValidation of potential PF and serum metabolites that distinguished PE from controls 258 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n12 \n \nIn Phase II, to more precisely quantify the relationship of the candidate biomarkers in PE 259 \nrelative to controls, we next developed a targeted metabolomics assay to validate biomarker 260 \ncandidates phenylalanyl-isoleucine and phosphatidylcholine C34:3. Analyzing the PF samples 261 \nfrom an independent set of women using our developed targeted metabolomics assay, 262 \nphenylalanyl-isoleucine retained its diagnostic potential in distinguishing PE from controls (AUC 263 \n0.77, 95% CI 0.61-0.92; p=0.006; sensitivity 73.7%; specificity 72.2%) (Figure 4A). By contrast, 264 \nphosphatidylcholine C34:3 did not hold up to its initial diagnostic value (AUC 0.65, 95% CI 0.47-265 \n0.83; p=0.121) and hence not considered for further analysis. Next, we investigated the utility of 266 \nphenylalanyl-isoleucine as a minimally-invasive biomarkers using serum samples. We found 267 \nthat circulating phenylalanyl-isoleucine consistently distinguished PE from controls with AUC 268 \n0.81 (95% CI 0.64-0.99; p=0.004; sensitivity 71.4%; specificity 100%) (Figure 4B). 269 \n 270 \nDISCUSSION 271 \nIn this pilot study, we identified PF metabolites that differentiated women with PE from controls. 272 \nUsing untargeted metabolomic LC-MS/MS that characterized the global metabolomic alterations 273 \nin the PF, a diverse PF metabolomes was shown in these women. The PF metabolomic profiles 274 \npertaining to the PE subphenotype were then linked the pathophysiological changes to the 275 \nserum for biomarker discovery using a combination of untargeted and targeted metabolomics. 276 \nWe provided evidence showing a novel PF metabolite, phenylalanyl-isoleucine, has the 277 \npotential as a biomarker of PE and was subsequently validated. Importantly, this metabolite was 278 \nreflected in circulation, with a diagnostic performance value of 81.4% in serum (sensitivity 279 \n71.4%; specificity 100%), suggesting its potential use as a minimally-invasive diagnostic 280 \nbiomarker of PE.  281 \nPF is proximal to endometriotic lesions and thus, forms an environment that reciprocally 282 \ncommunicates with the lesions (32, 33). This indicates that PF can be a potential useful source 283 \nfor biomarker discovery of PE. Intriguingly, the diagnostic characteristics of PF biomarkers for 284 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n13 \n \nPE using metabolomics approach have not been assessed. Majority of earlier studies have 285 \nfocused on identifying potential biomarkers of endometriosis in general without subphenotypes 286 \nspecificity (21, 28, 34-37), with few focused on OE (22, 38, 39) but limited on PE (40). 287 \nImportantly, validation of proposed biomarkers which is critical to demonstrate biomarker 288 \nrobustness has been rarely conducted (41). In biomarker discovery studies, differential levels of 289 \nmetabolites such as cancer antigen 125 (CA-125), carnitines, phosphatidylcholine and 290 \nsphingomyelin in PF were observed in women with endometriosis (28, 38, 42), but there is 291 \nseldom linkage of these biomarkers to the circulating levels. CA-125 is a commonly investigated 292 \nbiomarker for endometriosis despite its undefined role in primary diagnosis (3, 10). As 293 \nsuggested, we performed additional analysis for CA-125 in serum of women from the validation 294 \nset and compared with its performance with serum phenylalanyl-isoleucine. Serum CA-125 295 \nshowed poor diagnostic performance (AUC 0.66; 95% CI 0.47-0.84; p=0.120) in differentiating 296 \nPE (n=19) from controls (n=16) (Supplemental Figure 2, available online). Our findings are 297 \nconsistent with international guidelines, such as the National Institute for Health and Care 298 \nExcellence (NICE) and the European Society of Human Reproduction and Embryology 299 \n(ESHRE), which have made recommendations to not use serum CA-125 as biomarker for 300 \nendometriosis diagnosis due to its limited diagnostic performance (13, 43). 301 \nOur results demonstrate that phenylalanyl-isoleucine was increased in PF and serum of 302 \nPE women, with a high discriminatory ability to distinguish between women with PE and other 303 \ngynecological disorders requiring laparoscopic diagnosis (controls). Thus, data obtained from 304 \nthis study have connected pathophysiology in the dysregulated peritoneal cavity to the 305 \ncirculation in endometriosis, in line with reports showing correlations between biomarkers in PF 306 \nand serum (44, 45). Phenylalanyl-isoleucine (C15H22N2O3) is a dipeptide composed of 307 \nphenylalanine and isoleucine (46), which has been shown to play an essential role in 308 \nintracellular signal transduction (47). However, its functional role in endometriosis 309 \npathophysiology remains unclear. To the best of our knowledge, phenylalanyl-isoleucine is a 310 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n14 \n \nnovel metabolite which has not been reported for endometriosis. Increased phenylalanyl-311 \nisoleucine in PF might indicate an altered metabolic state that would potentially contribute to 312 \nfurther growth and development of peritoneal lesions (48). Compared to healthy controls, 313 \nwomen with early stage of endometriosis had previously found to exhibit decreased 314 \nphenylalanine and isoleucine in endometrial tissue and serum, respectively (20, 37). In contrast, 315 \nphenylalanyl-isoleucine appeared to be relatively abundant in PF of women with PE, which 316 \ncould be explained by the proximity of PF with endometriotic lesions implanted in the peritoneal 317 \ncavity. It is possible that increased phenylalanyl-isoleucine reflected shedding into the PF due to 318 \nhigh levels of cell division, cell death and protein degradation. This is supported by Li et al. (49) 319 \nwhich found upregulations of various amino acids in the eutopic endometrium of women with 320 \nearly endometriosis. Further research is required to elucidate the association of phenylalanyl-321 \nisoleucine with PE pathophysiology. 322 \nSeveral studies have classified women with endometriosis according to their disease 323 \nstage, where greater altered metabolomic profiles and metabolite levels were seen in women 324 \nwith later stages (III-IV) of endometriosis as compared with the controls (44, 45). Importantly, 325 \ndifferences in the levels and types of metabolites were also shown for women with stage I and II 326 \nendometriosis in relation to the controls (37, 49), indicating their potential for early disease 327 \ndiagnosis and prognosis. In this study, PE cases were mostly classified at minimal-mild stages 328 \n(stage I-II), suggesting the candidate metabolite in women with PE might be additionally useful 329 \nto reflect the early stage of the disease. 330 \nStrengths of this study included quantitative and structured methodological framework to 331 \nidentify the potential biomarker of PE, with the applications of untargeted, unbiased 332 \nmetabolomics in the discovery process and targeted metabolomics with exquisite assay 333 \nsensitivity and precision in the validation process. Importantly, this study captured the most 334 \ncomprehensive catalogued metabolome space of PF in women with laparoscopic diagnosed PE 335 \nto-date. By treating the identification of PF biomarker as a pre-selection procedure for 336 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n15 \n \nsubsequent study for serum marker, we successfully demonstrated the presence of candidate 337 \nbiomarker in sera of PE women from an independent group, with optimal diagnostic 338 \nperformance for PE. The procedure of validating biomarker using separate, independent sets of 339 \nsamples by applying same analytical approach has rarely been carried out in most previous 340 \nstudies. However, an important limitation of this study was the small sample size. This has 341 \nrestricted our capability to stratify women with PE into individual stage of disease for further 342 \nassessing the prognostic applications of phenylalanyl-isoleucine. However, preliminary findings 343 \ngenerated from this pilot study serves as important baseline supportive evidence for conducting 344 \nsubsequent larger scale studies to confirm the reliability and validity of phenylalanyl-isoleucine 345 \nfor PE diagnosis.  346 \n 347 \nCONCLUSIONS 348 \nIn conclusion, we have identified a signature metabolite known as phenylalanyl-isoleucine in PF 349 \nof women with PE through untargeted and targeted metabolomics in independent datasets, 350 \nsuggesting its involvement in the pathophysiology of PE. The same metabolite was identified in 351 \nthe sera of women with PE. Large scale metabolomics studies validating this biomarker across 352 \ndifferent populations will further determine its diagnostic robustness. More studies are also 353 \nwarranted to test for the robustness of this biomarker in distinguishing PE from other 354 \nsubphenotypes of endometriosis and pelvic inflammatory diseases that exhibit similar clinical 355 \nsymptoms. 356 \n 357 \nAcknowledgements 358 \nWe thank the participants of the study. This work was supported by the National Research 359 \nFoundation Singapore under its National Medical Research Council Centre Grant Program 360 \n(NMRC/CG/M003/2017) and administered by the Singapore Ministry of Health’s National 361 \nMedical Research Council, and Duke-NUS Office of Academic Medicine (Clinical and 362 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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Available at: 498 \nhttps://pubchem.ncbi.nlm.nih.gov/compound/Phenylalanylisoleucine; 2020. Accessed 499 \nJuly 30, 2020. 500 \n47. Simmen T, Nobile M, Bonifacino JS, Hunziker W. Basolateral sorting of furin in MDCK 501 \ncells requires a phenylalanine-isoleucine motif together with an acidic amino acid cluster. 502 \nMol Cell Biol 1999;19:3136-44. doi:10.1128/mcb.19.4.3136. 503 \n48. Koninckx PR, Ussia A, Adamyan L, Wattiez A, Gomel V, Martin DC. Heterogeneity of 504 \nendometriosis lesions requires individualisation of diagnosis and treatment and a 505 \ndifferent approach to research and evidence based medicine. Facts Views Vis Obgyn 506 \n2019;11:57-61. 507 \n49. Li J, Guan L, Zhang H, Gao Y, Sun J, Gong X, et al. Endometrium metabolomic profiling 508 \nreveals potential biomarkers for diagnosis of endometriosis at minimal-mild stages. 509 \nReprod Biol Endocrinol 2018;16:42. doi: 10.1186/s12958-018-0360-z.  510 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n22 \n \nFigure 1. PLSR analysis of peritoneal fluid metabolites from women with PE and 511 \ncontrols. (2A) Score plots of the discovery set samples. (2B) Loading plots of the 512 \ndiscovery set samples. CN, control; PE, peritoneal endometriosis; PLSR, partial least 513 \nsquares regression.  514 \n 515 \nFigure 2. Volcano plots showing peritoneal fluid differential metabolites in women with 516 \nPE relative to controls from the discovery set. Red and blue dots represent significantly 517 \ndifferent metabolites with fold change >1.5 (increased) and <0.67 (decreased), 518 \nrespectively (p<0.05). Metabolites were identified using untargeted LC-MS/MS 519 \nmetabolomics. LC-MS, liquid chromatography-mass spectrometry; PE, peritoneal 520 \nendometriosis.  521 \n 522 \nFigure 3. Receiver-operating characteristic curves of significantly different metabolites 523 \nfor peritoneal endometriosis (versus controls). Metabolites were identified using 524 \nuntargeted metabolomics in the discovery set. AUC of 0.5 suggests no discrimination. 525 \n 526 \nFigure 4. Receiver-operating characteristic curves of phenylalanyl-isoleucine for 527 \nperitoneal endometriosis (versus controls) using peritoneal fluid (5A) and sera (5B). 528 \nPhenylalanyl-isoleucine was identified using targeted metabolomics in the validation set. 529 \nAUC of 0.5 suggests no discrimination.530 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n23 \n \nSupplementary data 531 \nSupplemental Figure 1. Flow diagram illustrating the study design. Phase II comprised of an 532 \nindependent set of patients from phase I which was used to validate the PE metabolites. LC-533 \nMS/MS, liquid chromatography-mass spectrometry; PE, peritoneal endometriosis; ROC, 534 \nreceiver operating characteristics; SDMs, significantly different metabolites 535 \n 536 \nSupplemental Figure 2. Receiver-operating characteristic curves of serum cancer 537 \nantigen 125 (CA-125) for peritoneal endometriosis (versus controls) in women from 538 \nvalidation dataset. 539 \n 540 \nSupplemental Table 1. Participants’ characteristics according to types of endometriosis in the 541 \ndiscovery and validation sets. 542 \n 543 \nSupplemental Table 2. Peritoneal fluid metabolites of women with peritoneal endometriosis 544 \nand controls, identified using untargeted metabolomics analysis in the discovery set.  545 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint \n\n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 14, 2020. ; https://doi.org/10.1101/2020.10.13.20211789doi: medRxiv preprint","source_license":"CC0","license_restricted":false}