June 2017

MALDI-FTICR MS PROFILING AMONG BRCA1/2-MUTATION CARRIERS WITH AND WITHOUT OVARIAN CANCER: A CASE CONTROL STUDY.

THIALDA PAULINE GERHARDINE HARKEMA S2443449

SUPERVISED BY: PROF. DR. G.H. DE BOCK, ONCOLOGICAL EPIDEMIOLOGIST - DEPARTMENT OF EPIDEMIOLOGY, PROF. DR. M.J.E. MOURITS, ONCOLOGICAL GYNAECOLOGIST - DEPARTMENT OF ONCOLOGICAL GYNAECOLOGY, OVARIAN CANCER AND DR. W.E. MESKER, ASSISTANT PROFESSOR – DEPARTMENT OF SURGERY

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PREFACE

This master thesis is entitled: ‘MALDI-FTICR MS profiling among BRCA1/2-mutation carriers with and without ovarian cancer: a case control study’.

This thesis was written within a research clerkship as part of the master program of Medicine at the University of Groningen. The study was conducted from the 30th of December 2016 until the 30th of June 2017 under supervision of Prof. Dr. G.H. de Bock, Prof M.J.E. Mourits, from University Medical Centre Groningen (UMCG) and Dr. W.E. Mesker from Leids University Medical Centre (LUMC). Research activities took place in the UMCG, Groningen (study design, case selection and retrieval) and in the LUMC, Leiden (MALDI-FTICR).

My interest in this subject was sparked by the study population consisting of BRCA1/2 mutation carriers with and without ovarian cancer, since I aim to pursue a medical career in the gynecological field. Furthermore, the technical part and the research activities in the laboratory for this subject seemed to me a nice opportunity to broaden my horizon next to increasing my clinical knowledge. More specifically, I was not familiar with the mass spectrometry technique applied in this study. This resulted in a challenging project but above all a great contribution to my personal development.

The original design of this project was the comparison of protein profiles of BRCA1/2 carriers who had developed breast cancer with protein profiles of BRCA1/2 carriers who had developed ovarian cancer in order to evaluate whether the protein profiles found in breast cancer patients with a BRCA1/2 mutation were breast cancer specific, cancer specific, or mutation specific. However, the number of available samples of BRCA1/2 mutation carriers who had developed breast cancer without an ovarian cancer history was lower than expected. For that reason, we decided to focus on proteomic profiling in ovarian cancer instead of breast cancer among BRCA1/2 mutation carriers. In the beginning the switch to a new study design and research plan formed a small hurdle. Luckily I could count on my supervisors who gave me good advice when needed and their critical thinking brought this experience into a significant learning experience for me. Their experience based advice and support helped me to complete this thesis.

Thialda P.G. Harkema

Groningen, June, 2017

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ABSTRACT

Background: Ovarian cancer is the most lethal gynaecological malignancy and has an elevated risk on development among BRCA1/2 mutation carriers. Since there is currently no sufficient screening or non-invasive risk reducing approach to ovarian cancer, there is obvious need for the development of a new screening tool. We evaluated whether proteomics can provide an alternative or add-on for ovarian cancer screening in the high risk population of BRCA1/2 mutation carriers. Methods: We analysed 117 serum samples of 40 women with advanced stage ovarian cancer and 77 healthy controls by means of MALDI-FTICR mass spectrometry. All patients and controls were BRCA1/2 mutation carriers. To evaluate the diagnostic properties of MALDI-FTICR mass spectrometry, logistic ridge regression was applied, and sensitivity, specificity and Area Under the Curve (AUC) were estimated. Validation was performed by bootstrapping. To evaluate whether MALDI/FTICR has additional value to CA125, we calculated sensitivity and specificity of MALDI-FTICR combined with CA125 considering two definitions for a positive test outcome: both a positive MALDI-FTICR MS and a positive serum CA125 outcome, or either a positive MALDI-FTICR MS or a positive serum CA125 outcome. Results: MALDI-FTICR MS was able to distinguish BRCA1/2 associated advanced stage ovarian cancer cases from healthy BRCA1/2 mutation carriers with a sensitivity of 91%, a specificity of 42.5% and a AUC of 0.727. In comparison to serum CA125 alone, the addition of MALDI-FTICR to CA125 measurements was able to improve sensitivity from 62.9% to 78.8% with a specificity of 87.2%, in case of either a positive MALDI-FTICR or positive CA125 outcome as defined positive outcome. When stated a positive outcome when both MALDI-FTICR and CA125 outcome were positive, sensitivity and specificity were 24,2% and 97,4% respectively. Discussion: MALDI-FTICR is able to distinguish highly selected patients with advanced ovarian cancer from healthy controls among BRCA1/2 mutation carriers and to improve the sensitivity of CA125 alone when added to the CA125 measurements. However, these findings require further evaluation on identifying asymptomatic women with a low grade tumour. The ultimate goal is to discover a protein profile that can distinguish women with a small tumour volume in a healthy population, with a high positive- and negative predictive value.

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SAMENVATTING

Achtergrond: Ovariumcarcinoom is de dodelijkste gynaecologische maligniteit. Vrouwen met een BRCA1/2 mutatie hebben een verhoogd risico op de ontwikkeling van ovariumcarcinoom. Aangezien er geen effectieve screening methode of non-invasief risico-verlagende aanpak beschikbaar is, is er een urgentie voor de ontwikkeling van een nieuwe, sensitieve screenings methode voor vroeg-stadium ovariumcarcinoom. Wij onderzochten de waarde van proteomics als methode voor de identificatie van ovariumcarcinoom in een populatie van BRCA1/2 draagsters met en zonder ovariumcarcinoom en vergeleken de uitkomsten met die van CA125. Methode: Wij analyseerden 117 serum samples van 40 vrouwen met ovarium kanker en 77 gezonde controles door middel van MALDI-FTICR massa spectrometrie. Alle patiënten waren BRCA1/2 mutatie draagsters. Voor evaluatie van de diagnostische eigenschappen van MALDI-FTICR MS was een ridge logistische regressie uitgevoerd en de sensitiviteit, specificiteit en Area Under the Curve (AUC) zijn berekend. Validatie was uitgevoerd door middel van bootstrapping. Om te beoordelen of MALDI-FTICR een toegevoegde waarde heeft aan CA125, berekende we de sensitiviteit en specificiteit van MALDI-FTICR gecombineerd met CA125 in 2 scenario's: een positief gecombineerd positieve uitslag wanneer 1) zowel MALDI-FTICR als CA125 een positieve uitslag gaven, 2) of MALDI-FTICR of CA125 een positieve uitlag gaven. Resultaten: MALDI-FTICR MS onderscheidde BRCA1/2 draagsters met ovariumcarcinoom van gezonde BRCA1/2 mutatie draagsters met een sensitiviteit van 91%, een specificiteit van 42.5% en een AUC van 0.727. De toevoeging van MALDI-FTICR aan CA125 verbeterde de sensitiviteit van CA125 alleen van 62.9% naar 78.8% met een specificiteit van 87.2%. Hierbij werd ofwel een positieve MALDI-FTICR of een positieve CA125 meting als positieve uitslag gedefinieerd. Wanneer een positieve gecombineerde uitslag was gedefinieerd indien beide MALDI-FTICR en CA125 uitslagen positief, waren de sensitiviteit en specificiteit 24,2% en 97,4% respectievelijk. Conclusie: MALDI-FTICR is in staat vrouwen met gevorderd stadium ovariumcarcinoom van gezonde controles te onderscheiden in een populatie van BRCA1/2 mutatie draagsters. MALDI-FTICR kan de sensitiviteit van CA125 verbeteren, wanneer toegevoegd aan de CA125 metingen. Echter, deze bevindingen vereisen verder onderzoek. Om toepasbaar te zijn voor de vroege opsporing van asymptomatische vrouwen met beginnend ovariumcarcinoom is nog veel nader onderzoek nodig. Het ultieme doel is een proteïne profiel te identificeren dat met een hoge positief- en negatief voorspellende waarde vrouwen identificeert met een klein tumorvolume in een populatie van gezonde vrouwen.

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LIST OF ABBREVIATIONS

Apo: Apolipoprotein

AUC: Area under the curve

β-Hb: beta- hemoglobin

BRCA1: Breast cancer susceptibility gene 1

BRCA2: Breast cancer susceptibility gene 2

CA125: Cancer antigen 125

CHCA: α-cyano-4-hydroxy-cinnamic acid

CTAP3: Connective tissue activating protein 3

ELISA: Enzyme-linked immunosorbent assay

FDA Food and Drug Administration

FIGO: International Federation of Gynaecology and Obstetrics

FTICR: Fourier transform ion cyclotron resonance

Hb: Hemoglobin

HE4: Human epididymis protein 4

hK11 Human Kallikrein 11

IQR: Interquartile range

RRSO: Risk reducing salpingo-oophorectomy

LOOCV: Leave-one-out-cross-validation

LUMC: Leids University Medical Centre

MALDI: Matrix assisted laser desorption/ionisation

MB: Magnetic bead

MOC: Family Cancer Clinic (for breast and ovarian cancer)

MS: Mass spectrometry

m/z: Mass-to-charge ratio

NCI: National Cancer Institute

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PTM: Post translational modifications

PVV: Positive predictive value

RBP4: Retinol binding protein 4

ROC: Receiver-operating characteristics

ROMA: Risk of Ovarian Malignancy Algorithm

RPPA: Reverse phase protein array

SEE-FIM: Sectioning and extensively examining the fimbriated end

SELDI: Surface enhanced laser desorption ionization

SLPI: Secretory leukocyte protease inhibitor

SPE: Solid-phase extraction

TF: Transferrin

TFA: Trifluoroacetic acid

TOF: Time-of-flight

TTR: Transthyretin

UMCG: University Medical Centre Groningen

VDBP: Vitamin D binding protein

WCX: Weak cation exchange

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TABLE OF CONTENTS

Preface p.2 Abstract p.3 Samenvatting p.4 List of Abbreviations p.5 Chapter 1: Introduction p.8 Chapter 2: Aim of the Study p. 18 Chapter 3: Material and Methods p. 19 Chapter 4: Results p. 25 Chapter 5: Discussion p. 30 Acknowledgements p. 33 References p. 34 Appendix p. 40

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CHAPTER 1: INTRODUCTION

Woman with a BRCA mutation are at a high risk to develop breast and ovarian cancer when compared to the general population (1–4). In case of ovarian cancer, most cases are discovered when already at a late stage at which point survival rates are low (5). Currently there is no effective screening method to detect ovarian cancer in an early stage and the only effective strategy to prevent women from dying of ovarian cancer is to remove the ovaries and fallopian tubes before the incidence rises (6,7). However, risk reducing salpingo-oophorectomy (RRSO) at premenopausal age induces acute menopause and many short and long term sequellae of which vasomotor and sexual symptoms are most striking (8,9). Serum CA125 seemed a promising screening tool until further research showed that this tool was not effective for early detection. Therefore, there is need for an alternative screening method to improve early detection and lower mortality. In this thesis we will evaluate whether proteomics can distinguish women with advanced ovarian cancer from healthy controls in a population of BRCA1/2 mutation carriers.

Below, the problem definition will be described in more detail followed by an overview of the relevant literature. In this literature overview a brief introduction of proteomics and proteomic research is given. At first, the modification of proteins in blood due to cancer processes and their value in cancer research will be illustrated. Subsequently, the meaning of proper biomarkers and their utility for screening will be elucidated followed by a summary of mass spectrometry tools for biomarker discovery by proteomic analysis. Finally, an overview of proteomics research in ovarian cancer is given. In this overview I will start with a summary of the discovered proteins and protein profiles, and after that I will focus on the most frequently studied proteins. After the introduction, the aim of the study will be displayed followed by an explanation of the methods used in this study. Additionally, the results will be presented and lastly, those results will be evaluated in a clinically relevant perspective in the discussion.

1.1. BRCA1 & BRCA2 mutation incidence

BRCA1 and BRCA2 are both tumour suppressor genes and play therefore an important role in cancer prevention (10,11). Mutations in those genes are associated with an increased risk of developing breast cancer and ovarian cancer (1–4). Mutations of BRCA1 and BRCA2 genes are carried by 0.1-0.2% of the general population. More precisely, the prevalence of a BRCA1 and BRCA2 mutation is one in 800 and one in 500 respectively, with a 5-10 times higher prevalence among people from Iceland and the Ashkenazi Jews (12–14).

1.2. BRCA1/2 mutation cancer risks

Carriers of a BRCA1-mutation have a cumulative life time risk of 35-83% of developing breast cancer by age 70, where for BRCA2-mutation carriers this risk is between 41-86% (15–17). This is much higher than the already high risk of breast cancer for Western women from the general population, where breast cancer is the most common type of cancer, and where the life time risk to develop breast cancer is one in eight (18). For ovarian cancer the average cumulative risks are estimated at 39% (95%CI: 18%-54%) in BRCA1 and 11% (95%CI: 2.4%-19%) in BRCA2-carriers (19). Although the percentages in ovarian cancer are not as high as the risk percentages in breast cancer, it is a tremendous increase of risk compared to the life-time risk

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of about 1% among woman without a genetic predisposition and no ovarian cancer family history (20). This increase is especially problematic when it is taken in consideration that ovarian cancer is the most lethal gynaecological malignancy. Besides this increased risk, BRCA1/2 mutation carriers are more likely to develop breast cancer and ovarian cancer at a younger age (21).

1.3. Ovarian cancer incidence and mortality rate

In the Netherlands, the incidence of ovarian cancer in 2015 was 1,360 women. In the same year, the mortality due to ovarian cancer was 1,002 women, with a European Standard Rate (ESR) of 7.31 per 100.000 (22,23). The mortality of ovarian cancer and prognosis of the patient is highly dependent of the International Federation of Gynaecology and Obstetrics (FIGO) stage of ovarian cancer at detection. Ovarian cancer only has a good prognosis with a 5-years survival of 80-95% when detected at an early stage (FIGO I-II) (24). However, over 70% of the ovarian cancer patients demonstrate symptoms late in the course of the disease, are detected at an advanced stage and have a poor 5-years survival of 10-30% (5).

1.4. Ovarian cancer screening program in the high risk population of BRCA1/2 carriers

The aim of cancer screening is to improve the outcome of the disease by detecting the malignancy at an early stage and by providing an early intervention in the preclinical phase. Thereby, screening can reduce mortality, morbidity and costs (25–27). Some years ago, screening for ovarian cancer consisted of periodic investigation of the adnexa starting from the age of 35 with annual gynaecological pelvic examination, trans-vaginal ultrasound and determination of serum CA125 (6). This approach appeared to be ineffective, not only in a population based screening, but also in a high risk population. Screening wasn’t effective due to lack of early stage detection and could not reduce ovarian cancer related mortality (6,28,29). In the study of van der Velde et al., the sensitivity and positive predictive values of all modalities were low and ovarian cancers were only detected in advanced stages (FIGO stage IIIc) (6). As a consequence of the low early detection rate and no proven benefit of ovarian screening, almost simultaneously in all parts of the world, the annual gynaecological screening of women with a BRCA1/2 mutation was retracted (6,29–31).

1.5. Current approach on ovarian cancer risk reduction and the disadvantages

As there is no effective screening for ovarian cancer at this point, the only effective approach to reduce both ovarian cancer risk and mortality in woman at high risk of developing ovarian cancer is bilateral salpingo-oophorectomy. This method is preferred to be carried out before the incidence of ovarian cancer rises, i.e. in BRCA1 mutation carriers before the age of 38 years and for BRCA2 carriers this increase of incidence starts from the age of 45 years (21). However, this approach results in acute menopause and numerous menopausal side-effects (8,9). Additionally, a small residual risk for pelvic high-grade serous cancer may exist after RRSO in BRCA1/2 carriers (32). The pathologic examination of the ovaries and fallopian tubes must be performed very carefully to prevent missed small (pre)malignant abnormalities in the distal fallopian tubes. Since 2006 the guideline ‘sectioning and extensively examining the fimbriated end' (SEE-FIM) was available for examination of the preventively removed ovaries and fallopian tubes (33).

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1.6. New hypothesis on the origin of ovarian cancer

There is a mounting evidence that the origin of ovarian cancer lays not in the ovaries themselves, since intraepithelial carcinomas were never identified in the ovarian surface epithelium so far (7). Since the publication of Piek et al in 2001 (34), a new hypothesis was launched, suggesting the fallopian tube being the primary site of origin of pelvic high grade serous cancer (34–36). This hypothesis was supported by a mouse model study and in several other studies from different research groups (33,36–40). Peritoneal cancer after RRSO is possibly the result of tubal intraepithelial carcinoma metastasis instead of originated from the peritoneum (41). As ovarian cancer screening is ineffective, RRSO is effective but has major side effects, and since the fallopian tube might be the primary site of origin of many if not all high-grade serous ovarian cancers, studies to prove this tubal hypothesis are urgently needed. However, randomized controlled trials are not feasible: to compare a proven effective strategy (RRSO) with a not-proven safe procedure (salpingectomy only) is unethical. This led to the currently ongoing study from the Radboud University Medical Centre, Nijmegen developing an alternative for RRSO in BRCA1/2 mutation carriers by evaluating if early salpingectomy with delayed oophorectomy improves quality of life, when compared to RRSO at the recommended age (42). The primary outcome of this study is quality of life, secondary outcome is incidence of pelvic high-grade serous cancer. A similar study is on-going in VS (NCT01907789) and in a clinical trial in France (NCT01608074) evaluating Radical Fimbriectomy in BRCA1/2 carriers. Until then, we still need a sensitive and specific tool to identify high-grade serous cancer patients at an early stage.

1.7. Protein modifications as a reflection of cancer and their application in cancer research

Cancer development is a result of multiple events on a molecular and genetic level. The result of DNA translation presents itself at the level of protein expression. Therefore, changes on the base of protein levels will occur during the development of a disease and cell transformation of a normal cell into a malignant cell. These changes include protein post-translational modifications (PTMs), inappropriate localization, activity changes and altered expression (27,43). Post translational modifications are of great importance in the regulation of signalling pathways considering that reversible modifications on proteins influences the characteristics and complexity of the proteome (44). Besides proteins, glycans also have disease- and malignancy-associated alterations. The glycosylation of proteins is a common posttranslational modification (PTM) and has four main classifications: N-linked glycosylation, O-linked glycosylation, C-mannosylation, and glyco-phosphatidlyinositol (45). Glycans themselves are fundamental in many cellular processes such as protein folding, protein secretion, cell adhesion and receptor binding and activation, and are described as potential biomarkers for liver, pancreatic, prostate, ovarian, breast and lung cancers (46,47). Due to these characteristics, proteomic analysis is a frequently used tool for multiple application in the oncological field to improve diagnosis and therapy and to obtain biological insights in the carcinogenesis processes such as the stratification of cancer, prognosis of cancer, cancer phenotypes on a molecular level and modification of proteins in malignancy. Strategies for obtaining this knowledge are identifying protein-protein interactions, mapping cancer associated pathways, discovering post translational modifications (PTMs) and discovering cancer specific biomarkers (44). Implications of research in cancer proteomics are cancer prognosis, diagnosis, monitoring and prediction of therapeutic response. However the greatest focus of proteomics research in oncology is the identification of glycan and protein biomarkers in peripheral blood serum for

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pre-symptomatic, early detection of cancer (44,48–50). Given the clinical presentation, ovarian cancer is a potentially attractive disease to study proteomics for early detection.

1.8. Biomarkers; their characteristics and value in screening

Biomarkers are indicators of changes in the cell due to disease. The National Cancer Institute (NCI) defines biomarkers as “a biological molecule found in blood, other body fluids or tissues, that is a signs of a normal or abnormal process or of a condition or disease” (51). A for tumour detection applicable biomarker is able to provide early detection or indicate progression or recurrence of the disease. Besides, they should be secreted only by tumour tissue, easily detectable in (non-invasively) obtained body fluids, measurable across different populations and identify high risk individuals (26,27). Biomarkers can be very helpful in screening for early stage cancer by signalling changes on the protein level during the preclinical phase.

1.9. Methods for proteomics analysis

To analyse these proteins and glycans, mass spectrometry (MS) is the method of choice. (52,53). Two commonly used types of mass spectrometry are surface enhanced laser desorption ionization-time-of-flight (SELDI-TOF) and matrix assisted laser desorption/ionisation-time-of-flight (MALDI-TOF). MALDI-TOF MS and SELDI-TOF MS are mostly used as a discovery platform for biomarkers, while antibody-based technologies, such as western blot, enzyme-linked immunosorbent assay (ELISA), immunohistochemistry, tissue microarray and reverse- phase protein array (RPPA), are frequently applied as a validation platform. These antibody-based technologies cannot compete with the tremendous high throughput and low labour intensity of mass spectrometry (54). Where SELDI-TOF makes use of a modified surface by a chip with a chemical functionality, in MALDI-TOF, samples are mixed with a light-absorbing matrix and dried on a target plate. In both strategies the sample proteins will crystallize with the matrix, where after desorption and ionisation will occur to generate charged molecules. Both methods analyse samples by using time-of-flight mass spectrometry (TOF-MS) and measuring their mass to charge ratios (m/z). Since MALDI-TOF is more sensitive, has a higher binding capacity and demands less serum for analysis than SELDI-TOF, MALDI-TOF is the most frequently used strategy in MS-based biomarker profiling studies (52).

A more sophisticated approach is the MALDI protein profiling by Fourier transform ion cyclotron resonance (FTICR) MS. In this strategy the generated ions are trapped in the ion cyclotron resonance (ICR) cell. In the ICR cell the ions are excited by a combination of a strong magnetic field with an axial weak magnetic field into a coherent orbit. Electrodes detect the image current from the ions moving near the electrodes and the time domain signal is converted by Fourier transform and digitized (55,56). FTICR mass spectrometry has the highest resolution, m/z measurement accuracy, isotopic accuracy and superior resolving power of all mass spectrometry techniques. Moreover, the FTICR has a more accurate spectrum alignment and a more robust peptides quantification compared to MALDI-TOF, which measures more broad protein peaks (57). To increase the likelihood of reproducibility and validation of MS profiling studies, a standardization of sample collection protocols and protein purification protocols is pivotal (52). Fully automated solid phase extraction showed to minimize variation. According to a recently published Dutch study (coordinated by LUMC), MALDI-TOF combined with solid-phase extraction (SPE) by magnetic bead (MB) fractionation seems a solid

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method to improve the early detection of cancer. In this study MS combined with magnetic bead clean-up has been applied to analyse peptide and protein profiles and gave good results for the classification of breast cancer with high sensitivity and specificity (58). Furthermore, the study of Nicolardi et al. (2014) stated that MALDI-FTICR MS combined with an automated SPE serum sample clean-up procedure provided a powerful, accurate fast and robust approach for obtaining biomarker signatures. This study showed a sensitivity and specificity both above 85% (57).

1.10. Biomarker research in relation to ovarian cancer

1.10.1. Biomarker sources and FDA approved biomarkers for ovarian cancer

For ovarian cancer biomarker discovery, the following body fluids have been utilized: serum/plasma, urine, ovarian cystic fluid, ascites and pleural effusion. Ascites is an attractive and easy available source for proteomic analysis, since most of the patients with ovarian cancer develop ascites, especially in advanced stage of ovarian cancer. The suitability of ovarian cyst fluid lays in its nearness to the site of the disease and has in that way the ability to reflect changes in tumour tissue. Both pleural effusion and urine, have been analysed and several studies have identified differentially expressed proteins as potential biomarkers for ovarian cancer, mentioned in a review by Elzek et al. (45). Nevertheless, most research focuses on sera and plasma for proteomic analysis, since blood is a suitable source of proteins and easily accessible. Moreover, the FDA approved protein markers for ovarian cancer and used in clinical practice are all blood-based. Those FDA approved protein markers are: CA125, HE4, Risk of Ovarian Malignancy Algorithm (ROMA) and OVA1 (44). From those protein markers, CA125 (MUC16) is the most useful and most thoroughly studied biomarker in endometroid and epithelial ovarian carcinomas. A level of CA125 > 35 U/mL is considered to be abnormal. However, CA125 is not particularly elevated in the early stages and has a low positive predictive value (PPV) of 4%. The study of Duffy et al. for example, demonstrated a sensitivity of CA125 in early stage of only 50-62% (59). Besides, not only ovarian malignancies but also benign diseases can cause elevated CA125 levels in the blood (59,60). Research in additional serum markers to complement CA125 was therefore necessary. Multiple serum markers have been assessed in combination with CA125 (61). Human epididymis protein 4 (HE4) received FDA approval as well as in combination with CA125 (ROMA) as solitary. HE4 is a glycoprotein that is elevated in most of the ovarian cancer subtypes except for mucinous tumours. Overexpression of HE4 is found in all endometrial (100%), 93% of the serous and 50% of the clear cell carcinomas (62). The ROMA was presented in 2009 by Moore et al. to assess the risk of serous epithelial ovarian cancer in women with a pelvic mass (63). The OVA1 test is multivariate, consisting of five biomarkers: APO-A1, transthyretin, transferrin, β-2 microglobulin, and CA125. The OVA1 test has been developed after identification of the differentially expressed serum proteins in early stage ovarian cancer by Zhang et al. and used for assistance in suspected pelvic mass triage (45,64). Despite those FDA approved biomarkers, there is still need for a solid biomarker with higher sensitivity and specificity and PPV than those currently available. Especially for early detection, improvement is necessary. Attempts have been made to develop a new biomarker for ovarian cancer detection of which some examples are given below.

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1.10.2. Biomarker identification studies in ovarian cancer

Table 1 shows an overview of discriminating protein peaks found in several proteomics studies. These protein peaks are: α1-antitrypsin, apolipoprotein (Apo) A-IV, ApoA-1, retinol binding protein 4 (RBP4), secretory leukocyte protease inhibitor (SLPI), vitamin D binding protein (VDBP), transthyretin (TTR), TTR fragment, TTR truncated form, beta- hemoglobin (β-Hb), transferrin (TF), afamin, inhibitor heavy chain H4, inter-α-inhibitor H4 fragment, Fibrinogen α, Kallikrein sensitive glycoprotein and human Kallikrein 11 (hk11). Most of the proteins are analysed in more than one study. For example α1-antitrypsin, ApoA-1 and ApoA-IV are frequently studied proteins but with different protein combinations in Lorkova et al., Timms et al., Dieplinger et al. and Zhang et al. (48,65–67). However, sometimes there are conflicting results. For example, the findings in Lorkova et al. by MALDI-TOF analysis (65). The results of α-1-antitrypsin and apolipoprotein A-IV in this study corresponded with results from other studies. However the up-regulation of RBP4 in this study showed discrepancy with the results from Cheng et al. where RBP4 was down-regulated (68). Cheng et al. mentioned different sample preparation conditions, changes in mass spectrometry stabilities and experimental bias as possible explanations for this contradiction. Besides, both Cheng et al. and Lorkova et al. used a relatively low sample size of only 20 women per group and 10 women per group respectively. Remarkable is the extensive use of CA125 for comparison of the study results or a marker combination model. Following studies all involved CA125: Kozak et al., Timms et al. (2010), Timms et al. (2014), Dieplinger et al., Zhang et al., McIntosh et al. and Lopez et al. (64,67,69,70,66,71,72).

Table 1. Characteristics of protein profiling studies in ovarian cancer biomarker research.

Article Study size (N)

Method Biomarker/ protein-peaks

Compared/combined with CA125

Sensitivity / specificity

External validation

Lorkova et al. (2011)

10 OC 10 HC

MALDI-TOF Verification: ELISA + Western blotting

α1-antitrypsin ↑ ApoA-IV ↓ RBP4 ↓

No NR No

Timms et al. (2014)

22 OC 45 BD 64 HC

SELDI-TOFF Verification: ELISA

α1-antitrypsin ↑ SLPI ↑ VDBP ↑ ApoA-IV ↓

Yes 82 % / 91% No

Kozak et al. (2005)

43 OC 31 HC

SELDI-TOF Vericifaction: ELISA + Western blotting

TTR fragment (12,9 kDa) ↓ TTR (13.9 kDa) ↓ β-Hb (15,9 kDa)↑ ApoA1 (28kDa) ↓ TF (79 kDa) ↓

Yes 86% / 86% No

Cheng Y. et al. (2014)

20 OC 20 BD 20 HC 20 myoma

1D-Gel LC-MS/MS + ELISA Verifiation: Western blotting

RBP4 ↑ No NR No

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Dieplinger et al. (2009)

181 OC 339 BD 177 HC

ELISA Afamin ↓ ApoA-IV ↓

Yes 90% / 42% 90% / 41%

Validation study

Zhang et al. (2004)

41 OC 41 HC 20 OM

SELDI-TOF ApoA-1 ↓ TTR truncated form ↓ inter-α-trypsin inhibitor heavy chain H4 ↑

Yes

74% / 97%

Yes

McIntosh et al. (2007)

34 OC 36 HC 21 BD 24 CC

ELISA hK 11 ↑ Yes NR Validation study

Lopez et al. (2007)

453 OC 110 HC

MALDI-TOF TTR, Kallikrein sensitive glycoprotein, inter-α-inhibitor H4, fibrinogen α, complement C3,

Yes 93% / 93% 93% / 97% 77% / 85%

No

Timms et al. (2010)

70 OC 89 BD 173HC 11 BO

MALDI-TOF Fibrinogen α chain precursor, inter-α-inhibitor H4 fragment

Yes 99% / 57% Yes

OC = ovarian cancer, HC = healthy controls, BD = benign disease, OM = other malignancies, CC = chirurgical controls, BO = Borderline, ↑ = up-regulated, ↓ = down-regulated, NR = not reported

In ovarian cancer proteomic research, like in other cancer proteomic research, ELISA is commonly used as an internal validation tool for mass spectrometry results or in validation studies as McIntosh et al.(71) This study validated the study of Borongo et al. where human Kallikrein 11 (hK11) is considered to be a novel independent biomarker (73). This study was already internal validated in the study of Diamandis et al. (74). Moreover, identified candidate markers have also been found in other cancer types like discovery of apolipoproteins and complement C3 in both breast cancer and ovarian cancer, in particular the ApoA-1. For example An et al. showed a most different protein profile expression among the serous subtype compared to the normal profile in healthy controls while using MALDI-TOF, with the least differing profile showed in the mucinous subtype (75). Another study suggested clinical relevance of proteins to the disease progression, hence they demonstrated differentially expressed proteomics between early stage and advanced stage of ovarian cancer (76). In the following paragraph the most frequently studied biomarkers will be discussed.

1.10.3. Most frequently studied ovarian cancer biomarkers.

The most frequently studied proteins in proteomics biomarker research for ovarian cancer are: ApoA-1, ApoA-IV, α1-antitrypsin, TTR, TF and inter-α-inhibitor H4. Those proteins are studied in several studies in different combined sets of proteins (table 2). They were found discriminating for ovarian cancer in different studies and with different study populations by as well as SELDI-TOF or MALDI-TOF technique. In almost all studies protein m/z peaks were analyzed as a set of combined markers.

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1.10.3.1. Apolipoprotein A1(Apo-A1)

Apolipoprotein A1 was identified in combination with TTR, TF and Hb in the study of Kozak et al. In this study the combined protein peaks included CA125 detected mucinous ovarian tumors with a greater receiver operator characteristic (ROC 0.955) than CA125 alone (ROC 0.613) (69). In the study of Zhang et al., Apo-A1 in combination with TTR and inter-α-inhibitor heavy chain H4 gave a specificity of 94% at an fixed sensitivity of 83% in a multivariate model with CA125 (64). This was a significant improvement to CA125 alone with a specificity of 52% and this fixed sensitivity of 83% (64). Moore et al. intended to reproduce the results of this study by measuring Apo-A1 and six posttranslational forms of TTR in addition to CA125 (77). The sensitivity of Apo-A1 and TTR together was 52.4% with a specificity of 96.5%. In combination with CA125 the sensitivity increased to 78.6% with an specificity of 94.3% (77). Apo-A1 combined with TF, TTR, hepcidine, beta-2 microglobulin, inter-α-trypsin inhibitor H4 and connective tissue activating protein 3 (CTAP3) were found to be specific for ovarian cancer in the study of Hogdall et al. Also in this study the model in which the biomarkers were added to CA125 gave a higher sensitivity and specificity compared to CA125 alone (78). Moreover, in Clarke et al. Apo-A1 in combination with other biomarkers gave an increased sensitivity of CA125 from 68% up to 88%. In this study the combined model consisted of Apo-A1, TTR and CTAP3. This model alone gave a sensitivity of 54% and a specificity of 98% (79). All studies compared their results with the outcome of CA125 and/or combined their model with CA125 with results of improvement. Also remarkably is the presence of both TTR and Apo-A1in all studies.

Table 2. The most often identified proteins in protein profiling studies and the corresponding studies.

Protein

Article Study population

Method Combined model

Upregulated/ downregulated

Sensitivity/ Specificity

Apolipoprotein A-I

Kozak et al. (2005)

43 OC 31 HC

SELDI-TOF yes ↓ 86% / 86%*

Zhang et al. (2004)

41 OC 41 HC 20 OM

SELDI-TOF yes ↓ 83% / 94%*

Moore et al. (2006)

42 OC 65 BD 122 HC

SELDI-TOF yes ↓ 52.4% / 96.5% 78.6% / 94.3%*

Hogdall et al. (2011)

144 OC 40 BD 469 BD

SELDI-TOF yes NR 95% / 81%*

Clarke et al. (2011)

92 OC 40 BD 99 HC

SELDI-TOF yes ↓ 84% / 98%*

Apolipoprotein A-IV

Lorkova et al. (2011)

10 OC 10 HC

MALDI-TOF

yes ↓ NR

Timms et al. (2014)

22 OC 45 BD 64 HC

SELDI-TOFF

yes ↓ NR

Dieplinger et al. (2009)

181 OC 339 BD 177 HC

ELISA (Validation study)

no ↓ 40.8% / 90%

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Li Li et al. (2012)

21 OC 16 BD 20 HC

LC-MS/MS no ↓ NR

Transthyretin (TTR)

Lopez et al. (2007)

453 OC 110 HC

MALDI-TOF

yes NR 93% / 97%

Zhang et al. (2004)

41 OC 41 HC 20 OM

SELDI-TOF yes ↓ 83% / 94%*

Kozak et al. (2005)

43 OC 31 HC

SELDI-TOF yes ↓ 86% / 86%*

Moore et al. (2006)

42 OC 65 BD 122 HC

SELDI-TOF yes ↓ 52.4% / 96.5% 78.6% / 94.3%*

Hogdall et al. (2011)

144 OC 40 BD 469 BD

SELDI-TOF yes NR 95%/ 81%*

Clarke et al. (2011)

92 OC 40 BD 99 HC

SELDI-TOF yes ↓ 84% / 98%*

Transferrine (TF)

Kozak et al. (2005)

43 OC 31 HC

SELDI-TOF yes ↓ 86% / 86%*

Clarke et al. (2011)

92 OC 40 BD 99 HC

SELDI-TOF yes ↓ 84% / 98%*

Hogdall et al. (2011)

144 OC 40 BD 469 BD

SELDI-TOF yes NR 95%/ 81%*

Inter-α-inhibitor H4

Lopez et al. (2007)

453 OC 110 HC

MALDI-TOF

yes NR 93% / 97%

Hogdall et al. (2011)

144 OC 40 BD 469 BD

SELDI-TOF yes NR 95%/ 81%*

Zhang et al. (2004)

41 OC 41 HC 20 OM

SELDI-TOF

yes ↑ 83% / 94%*

* Sensitifity and specificity results with CA125 included in the protein set.

1.10.3.2. Apolipoprotein A4

Apolipoprotein A4 was studied in different studies and in combination with different protein biomarkers, such as α1-antitrypsin (Timms et al., Lorkova et al), RBP4 (Lorkova et al.) or as a biomarker alone (Dieplinger et al., Li Li et al.) (65,67,66,80). In the study of Dieplinger et al. the application of only ApoA-IV gave a specificity of 90% and a low sensitivity of 40.8%, and did ApoA-IV not add independently diagnostic information to CA125 (66). ApoA-IV in combination with other biomarkers was as well unable to give a higher diagnostic accuracy than CA125 (67). This was the result of the study of Timms et al where the four biomarkers α1-antitrypsin, ApoA-IV, VDBP and SLPI were combined. No additional improvement of ApoA-IV to CA125 was shown in the different studies.

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1.10.3.3. Transthyretin (TTR) and Transferrine (TF)

Tranthyretin was studied in the previously discussed studies: Zhang et a., Kozak et al., Moore et al., Hogdall et al. and, Clarke et al. in combination with TF, Hb, Apo-A1, inter-α-inhibitor heavy chain H4 hepcidine, beta-2 microglobulin and CTAP3. Additionally, the study of Lopez et al. showed a sensitivity and specificity of 93% and 97%, respectively. In this research the included peptides were complement component 3, complement component 4A, inter-α-inhibitor H4, fibrinogen and transthyretin, but no biomarker in combination with CA125 was implemented (72). Remarkably is that all studies that included transferrin, also contained Apo-A1 and transthyretin in their models (69,78,79). Those three proteins are also most frequently combined in different studies compared to the other proteins described above. Besides the three studies who contain all three TF, TTR and Apo-A1 showed their biomarker models to be an improving addition to CA125 alone(69,78,79). All three biomarkers are part of the already FDA approved OVA-1 test.

It is shown that a combined set of biomarkers provide good results, especially in combination with serum CA125. The different studies gave same results per protein concerning up-regulation and down-regulation. Combinations of most the discriminating peaks differ throughout the implemented research. However, a combination included the three proteins: TF TTR and APO-A1 was shown the most in different studies. The studies gave matching results regardless the fact that different techniques (MALDI-TOF and SELDI-TOF) were used. These facts are favourable when realized that discriminating peaks are influenced by bias in biological variation, pre-analytical variation and analytical reproducibility (52).

In conclusion, there is an obvious need for a more specific and safe and non-invasive alternative for ovarian cancer screening, since there is still no sufficient screening tool for early stage ovarian cancer. To improve ovarian cancer screening and offer young women a safe alternative to risk reducing surgery, changes in blood due to the early start of cancer are topic of research. Further research in biomarker discovery for ovarian cancer is needed, hence newly discovered biomarkers lack validation and reproducibility, have low sensitivity or have not yet succeeded in improving the current clinically approved biomarkers. For obtaining biomarker signatures, MALDI-FTICR MS combined with an automated SPE serum sample clean-up procedure by magnetic beads provides a powerful, accurate, fast and robust approach and may provide a minimally-invasive, relatively inexpensive screening tool. In this study, we focus on ovarian cancer biomarkers by means of MALDI-FTICR MS detection in a high risk population of BRCA1/2 mutation carriers by evaluating whether proteomics can distinguish BRCA1/2 associated ovarian cancer cases from healthy BRCA1/2 mutation carriers.

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CHAPTER 2: AIM OF THE STUDY

2.1. Aim: The clinical goal is to evaluate whether proteomics can be of value in the early detection of ovarian cancer in women with a high risk due to a BRCA1/2 mutation, as an improvement to the current ineffective screening methods and to offer an alternative for risk reducing salpingo-oophorectomy at premenopausal age. 2.2. Objectives: - To evaluate whether MALDI-FTICR MS can distinguish BRCA1/2 associated ovarian cancer cases from healthy BRCA1/2 mutation carriers. - Secondary objectives; to evaluate whether proteomics analysis by MALDI-FTICR MS can improve the sensitivity and specificity of CA125 measurements. Additionally, to evaluate whether MALDI-FTICR MS combined with CA125 can improve the sensitivity and specificity of CA125 measurements alone. For this evaluation, the two following definitions for a positive test outcome were utilized: both a positive MALDI-FTICR MS and a positive serum CA125 outcome, or either a positive MALDI-FTICR MS or a positive serum CA125 outcome. 2.3. Research question: Can proteomics, by means of MALDI-FTICR MS analysis, distinguish women with a BRCA1/2 mutation who currently have advanced stage ovarian cancer from women with a BRCA1/2 mutation who have no ovarian cancer or ovarian cancer history? 2.4. Hypothesis: We hypothesized that with proteomic biomarkers we can distinguish women with a BRCA1/2 mutation with advanced stage ovarian cancer from women with a BRCA1/2 mutation who have no ovarian cancer or ovarian cancer history.

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CHAPTER 3: MATERIAL EN METHODS

3.1. Study design

This study is a retrospective case control study. In this study two groups of women were compared. The first group consisted of 40 women with a BRCA1/2-mutation who had developed ovarian cancer and whose serum samples had been collected and stored for analyses. The second group consisted of 77 women with a BRCA1/2-mutation who had no history of breast cancer or ovarian cancer and whose serum samples had been collected and stored for analyses. From each patient, one blood sample was analysed for the protein profiles by the laboratory of the LUMC by means of MALDI-FTCIR Mass Spectrometry. All women gave written informed consent for storing clinical data and serum samples at data entry.

3.2. Setting

The study population consisted of 117 women with a genetic predisposition for breast and ovarian cancer by a BRCA1 or BRCA2 mutation. Women with a genetic predisposition for breast and ovarian cancer are subject to special monitoring. This counselling takes place in the surveillance program for hereditary breast and ovarian at the Family Cancer Clinic of the University Medical Centre Groningen (UMCG), The Netherlands. This surveillance program is multidisciplinary counselling by a team consisting of clinical geneticists, gynaecologist oncologists, oncology nurses, surgical oncologist, plastic surgeons, radiologists, a pathologist and a psychologist supported by epidemiologists. Women are offered to enrol this program when, by means of a positive family history or a personal history, a strongly increased risk for breast and/or ovarian is assumed. All indications to suspect an increased risk for breast and ovarian cancer are stated in national guidelines (14). BRCA1 and BRCA2 mutations can be detected by means of a genetic blood test. Before the women qualify for this blood test, they are counselled by a genetic counsellor. The clinical geneticist assesses whether there is an indication for DNA testing. Concerning ovarian cancer, women are counselled on risk reducing strategies for ovarian cancer, RRSO timing and family planning by a gynaecological oncologist in case of a positive family history or a proven BRCA1/2 mutation in the women herself. All women in this study were enrolled in the surveillance programme at the Family Cancer Clinic of the University Medical Centre Groningen (UMCG), The Netherlands. During consultation at the Family Cancer Clinic (MOC), approval for blood collection for the purpose of scientific research was asked. Their data have been collected in the MOC-database. All patients in this study gave their consent for use of these data and blood samples in research purposes.

3.3. Patient and sample selection

The database of stored samples consisted of 2,536 women from the family cancer clinic of whom 11,467 samples had been collected in total (Figure 1). Woman without a BRCA1/2 mutation were excluded. The remaining 570 BRCA1/2 mutation carriers were divided into 2 groups: 85 women with a history of ovarian cancer for the ovarian cancer group and 485 women without ovarian cancer history for the healthy control group. From the 85 women with ovarian cancer women, all women who had no sample collected before the date of primary surgery were excluded. The remaining 53 women were verified in the patient files on surgery date,

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chemotherapy data and BRCA mutation status, where after 2 women were excluded who had no proven BRCA1/2 mutation according to the patient files. At last, one sample with a time until storage longer than 60 hours was excluded to maintain equal sample quality among all samples. This resulted in 50 ovarian cancer cases. In the healthy control group, all 181 women who had a history of breast cancer were excluded. The remaining 304 healthy controls (HC) were matched with the ovarian cancer cases (OC) based on age at time of sample collection and based on storage time in years in the ratio of 1 (OC): 2 (HC). The selection of samples consisted of 50 ovarian cancer cases and 100 healthy controls with a total of 150 samples. During retrieval of the samples from the serum archive, 10 samples of ovarian cancer cases were not available. All 20 healthy control samples of the corresponding triplets were excluded. Altogether, 120 samples were selected for mass spectrometry in Leiden.

3.4. Serum Sampling

The blood samples were obtained at the blood collection department of the UMCG and distributed at the General Laboratory Haematology and Cytology of the Laboratory Department of the UMCG from January, 2001 up to December, 2016. The pre-processing was carried out at the Medical Oncological laboratory (MOL) of the Laboratory Department of the UMCG following the protocol (Appendix 1). After processing, the samples were stored at -80°C at the serum archive until sample retrieval.

3.5. Sample retrieval and sample aliquoting

Samples were retrieved from the -80°C freezer and kept frozen in a dry ice box until aliquoting. After retrieval, samples were thawed for aliquoting. Of each sample, 60 µL was pipetted in sterile 500-II barcode-labeled polyprolylene tubes (Thermo Trakmate, Matrix TechCorp) according to the plate design mentioned below. The 2D barcodes of the tubes were scanned with the Thermo scientific VisionMate and documented in the database along with the study numbers of the corresponding samples. Until transport to the LUMC for SPE and mass spectrometry, the matrix 2D barcode racks were kept in -80°C storage. The residual sample sera were refrozen and restored in the serum archive.

21

Figure 1. A flowchart of the patient selection of the women from the serum archive database.

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3.6. Plate design

We made use of the Thermo TrakMate Matrix TechCorm plates. Each plate consisted of 96 sterile 500-II barcode-labeled polypropylene tubes. For this study, 2 plates were needed to analyze the 120 serum samples. Each ovarian cancer case formed a triplet with the 2 matched healthy controls, based on age at sample collection and storage time, with a total of 50 triplets. To prevent design-bias, the following strategy was applied: each triplet was given a triplet number in order to smallest storage time in years up to the largest storage time in years with within each year of storage time samples ordered from eldest age at sample collection to youngest age at sample collection, numbered 1-50. For each sample, the triplet number was documented in the database. All odd triplet-numbers were assigned to plate number 1 and even triplet-numbers to plate number 2. This resulted in 25 triplets on each plate which equals 75 samples per plate. In this way we obtained an equal distribution of the samples storage times and age at time of simple collection between the two plates. Therewithal, with this strategy we ensured that all three samples of the same triplet were analyzed on the same plate, since they were divided to the two plates by triplet number. After excluding the missing ovarian cancer samples and corresponding triplet samples of the healthy controls, 120 triplets were left. The original plate design was maintained, resulting in 19 triplets (57 samples) on plate 1 and 21 triplets (63 samples) on plate 2.

3.7. Peptide Isolation by solid phase extraction and MALDI spotting

The solid phase extraction (SPE) of the peptides was carried out by the Hamilton Microlab Star Plus pipetting robot by means of C18 magnetic beads. For each sample 10 µL RCP18-beads were used. The magnetic beads were activated by washing with a 0.1 % trifluoroacetic acid (TFA) solution. This washing procedure was carried out four times. Subsequently, 15 µL 0.1% TFA and 10 µL of the sample were added to the activated beads for protein binding. The samples were left incubating for 5 minutes at room temperature, after which the beads were washed with 0.1% TFA for 3 times in total. Then the peptides were eluted with a 50% acetonitrile solution. From each eluate (containing the isolated peptides), 2 µL per sample was mixed with a 15 µL MALDI matrix in a 384-well PCR plate. This MALDI matrix is a solution of α-cyano-4-hydroxy-cinnamic acid (CHCA) matrix dissolved in a 1:2 mixture of acetone and ethanol with a final matrix concentration of 0,3 mg/ml. Finally, this mixture was spotted in quadruplicate onto the AnchorChipTM MALDI target plate (Bruker Daltonics, Germany) with an amount of 1µL per spot.

3.8. MALDI-FTICR Mass Spectrometry

MALDI-FTICR MS experiments were performed on a Bruker 15 tesla solariXTM mass spectrometer equipped with a CombiSource and a ParaCell (Bruker Daltonics, Germany). The MALDI-FTICR system, equipped with a Bruker Smartbeam-IITM laser system operating at 500 Hz, was operated by ftmscontrol software. Each spectrum was acquired in the m/z-range from 1011.86 to 4996.40 with 512 K data points. For each MALDI spot, 10 scans of 200 laser shots were averaged. The ParaCell parameters were as follows: the DC bias RX0, TX180, RX180, and TX0 were 9.1, 9.3, 9.2, 9.2 V, respectively; the trapping potentials were set at 9.5 and 9.4 V. The transfer time of the ICR cell was 1.2 ms, and the quadrupole mass filter was set at m/z

23

850. FTICR system was externally calibrated prior to each MALDI plate measurement using a commercially available peptide mix and a protein mix (Bruker Daltonics). In total 272 MALDI-spots were measured in approximately 3.5 hours. For visualizing and calibrating the spectra, Data Analysis Software 4.2 (Bruker Daltonics) was used.

3.9. Peak quantification

Protein and/or peptide signals were quantified according to the following strategy. First, peaks were selected for further analysis based on visual inspection of the profiles. Following the m/z-value, a peak number and an m/z-window were reported for each selected peak in a reference file. Then, the intensity of each user-defined peak was determined using the Xtractor tool which open source tool generates uniform data (peak) arrays regardless of spectral content. The MALDI-FTICR profiles were exported as XY (.xy) files, containing m/z values and corresponding intensities. As a final remark, 3 samples were excluded after the profiling process due to low quality spectra (figure 1). The quality of these profiles was not sufficient for further statistical analysis. The most likely cause of the low quality spectra is failed MALDI spotting.

3.10. Statistical Analysis

3.10.1. Descriptive statistics

IBM SPSS statistics 23 software was used for performing data analysis. Descriptive statistics were used for demographic characteristics and expressed as frequencies and percentages for discrete data. Continues data were expressed as median and range. The differences in characteristics between the two study groups and between the two MALDI-plates were tested two-sided with the independent T-test for normally distributed continuous data. The non-normally distributed continuous data were tested by the Mann-Whitney U-test. The χ2-test was used for testing the discrete data. P-values <0.05 were considered to be significant.

3.10.2. Quantitative statistics

The logarithmic function was used to transform the absolute peak intensities and thereafter the median (log-transformed) peak intensity was calculated from the two replicate spectra for each individual ἰ. To calibrate the classification rule, ridge logistic regression was used combined with a leave-one-out-cross-validation (LOOCV) approach in order to obtain unbiased estimates of the class probabilities for each individual ἰ. The model was fitted to both log-transformed data and standardized data. Standardized data were formed by subtracting the median and dividing by the interquartile range (IQR). For class assignments a cut-off threshold of 0.5 was used. Values equal or greater than 0.5 were stated as cases and values below 0.5 were stated as controls. From this point, brier score, error rate, sensitivity, specificity and area under the curve (AUC) were calculated. The brier score measures the distance between the model estimates and the true outcome values. The error rate is an indication of the proportional misclassification. The receiver-operating characteristics (ROC) curve was plotted for different parameter cut-off points in the false-positive rate (1-specificity) function. The area under the ROC curve is a reflection of the distinguishing performance of the diagnostic test between the cases and

24

controls. Sensitivity en specificity of CA125 alone and the sensitivity and specificity of MALDI-FTICR combined with CA125 were calculated in the 73 women of whom serum CA125 levels were available in the database. For the combination of both tests, sensitivity and specificity were calculated using the two following definitions for a positive test outcome: both a positive MALDI-FTICR MS and a positive serum CA125 outcome, or either a positive MALDI-FTICR MS outcome or a positive serum CA125 outcome.

25

CHAPTER 4: RESULTS

4.1. Characteristics of the study population

Despite careful selection, there were some differences between the two study groups (see table 3). The ovarian cancer group had a higher median age and with 32 women (80%) a higher percentage of postmenopausal than the healthy control group, with a postmenopausal definition of 52≥ years. The percentages of the mutation status were similar among both groups and no significant difference between the groups was shown. Also for both the storage time variables, there was no significant difference found. The samples of the women in this study were stored for an average of 6.2 years. De median storage time of the samples was 5 years for both the ovarian cancer group and the healthy controls. The levels of CA125 was determined and recorded in the database for 79 women. From 41 of the 120 samples, no CA125 was measured and stated in the database. The median levels of CA125 were 12 (range, 5-199) and 89 (range, 10-17776) for the healthy controls and ovarian cancer cases respectively.

Table 3 – Characteristics of the study cohort

Ovarian cancer (OC)

Healthy controls (HC)

Total Statistics

Number of patients 40 80* 120 Age at sample collection Median (min-max), year a Mean (SD), years b

60 (41-75) 59 (8.9)

52 (27-69) 51 (8.4)

53 (27-75) 54 (9.2)

p < 0.001 p < 0.001

Menopausal state, N (%) Premenopausalc Postmenopausalc

8 (20%) 32 (80%)

38 (47.5%) 42 (52.5%)

46 (38.3%) 74 (61.7%)

P = 0.003 P = 0.003

BRCA mutation status, N (%) BRCA1c BRCA2c

27 (67.5%) 13 (32.5%)

52 (65.0%) 28 (35.0%)

79 (65.8%) 41 (34.2%)

P = 0.785 P = 0.785

CA125 level Median (min-max)b

89 (10-17776)

12 (5-199)

17 (5-17776)

p <0.001

BC history, N (%) Time before OC, Median (min-max), years Time before OC, IQR (0.25-0.75), years

14 (35%) 2.5 (1-39) 2 - 6.75

0 0 0

14 (35%) 2.5 (1-39) 2 - 6.75

Storage time, years Median (min-max)a Mean (SD)b

5 (1-16) 6.3 (4.3)

5 (1-16) 6.2 (4.1)

5 (1-16) 6.2 (4.1)

p = 0.890 p = 0.915

Storage time, months Median (min-max)a Mean (SD)b

64 (16-194) 81.1 (52.95)

66 (15-193) 79.1 (49.05)

65 (15-194) 79.8 (50.2)

p = 0.838 p = 0.922

a Statistical analysis by means of independent T-test (Sig. 2-tailed). b Statistical analysis by means of Mann-Whitney U-test (Sig. 2-tailed). c Statistical analysis by means of Chi-Square test (Sig. 2-tailed). * During the MALDI FTICR MS analysis, three samples were excluded due to low quality spectra. All were healthy controls and aged 38,62 and 69 (mean, 54) of whom 2 were BRCA2 mutation carriers and one a BRCA1 mutation carrier. Their mean storage time was 8,7 years.

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The tumour characteristics of the women with ovarian cancer history are shown in table 4. The tumours were mainly high grade serous ovarian cancers with an advanced FIGO stage as is often the case in BRCA mutation carriers (5). Of all 40 tumours, 33 (82.5%) were classified as serous type of ovarian cancer. The others were classified as endometrioid tumours (7.5%) transitional type (2.5%) and mixed endometrioid/serous type (2.5%). One tumour was stated as undefined and there was one borderline tumour in this study. Almost half of the tumours were of FIGO stage IIIC with an amount of 19 tumours (47.5%). As mentioned earlier, most of the woman (80%) had a grade 3 tumour. Of the 40 total cases, 3 cases remained undefined for grading since they could not be reliably classified due to lack of a sufficient amount of material or for clinical reasons.

Table 4 – Tumor characteristics of the ovarian cancer cases

Category N (%) Tumour type

Serous 33 (82.5%) Endometrioid 3 (7.5%) Transitional 1 (2.5%) Endometrioid 75% + serous 25%

1 (2.5%)

Borderline 1 (2.5%) Undefined 1 (2.5%)

FIGO stage

Stadium IA 2 (5%) Stadium IB 2 (5%) Stadium IC 3 (7.5%) Stadium IIB 1 (2.5%) Stadium IIC 2 (5%) Stadium IIIB 1 (2.5%) Stadium IIIC 19 (47.5%) Stadium IV 10 (25%)

Tumour Grade Grade 1 1 (2.5%) Grade 2 4 (10%) Grade 3 32 (80%) Undefined 3 (7.5%)

4.2. Quality of the plate design

Plate 1 consisted of 19 ovarian cancer cases and 38 healthy controls where the distribution on plate 2 was 21 ovarian cancer cases versus 42 healthy controls (Table 5). For both variables, age at sample collection and storage time, no significant difference between both plates was shown.

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Table 5 – Distribution of the characteristics of the study cohort and samples among the two MALDI plates.

Plate 1 Plate 2 Total Statistics Number of samples 57 63 120

Cases/Controls, N Ovarian cancer cases Healthy controls

19 38

21 42

40 80

Age at diagnosis Median (min-max), yearsa

53 (27-75)

53 (31-74)

53 (27-75)

p = 0.757

Storage time, years Median (min-max)a Mean (SD)b

5 (1-13) 5.6 (3.7)

5 (1-16) 6.8 (4.5)

5 (1-16) 6.2 (4.1)

p = 0.182 p = 0.137

Storage time, months Median (min-max)a Mean (SD)b

64 (15-164) 72.9 (45.4)

66 (19-194) 86.1 (53.7)

65 (15-194) 79.8 (50.2)

p = 0.200 p = 0.151

a Statistical analysis by means of independent T-test (Sig. 2-tailed). b Statistical analysis by means of Mann-Whitney U-test (Sig. 2-tailed).

4.3. Outcome of MALDI-FTICR MS

The signal intensities of all peaks were determined in all serum profiles as described in the materials and methods section and are shown in figure 2. These signals were obtained from low mass data. In order to display peak intensities per group without overlapping of peaks, ovarian cancer cases values were put negative and so appeared underneath the x-axes with the healthy controls above the x-axes. The obtained peak intensity values were used for calibrating and validating a discriminate model to obtain class probability scores, of which a boxplot is shown in figure 3.The median estimated class probabilities were 0.443 and 0.214 for the ovarian cancer cases and the healthy controls respectively. Among the healthy controls, there were 6 outliers. From those outliers, 4 cases had no elevated levels of CA125 (range,7-16). However, one case had a CA125 level of 199, which was also the maximum of the range of CA125 levels among the healthy control group (table 1). From one outlier, data on CA125 level was not available. This class probability score analysis resulted in the ROC curve (Figure 4). The area under the ROC curve (AUC) was calculated 0.726. The sensitivity of the test was 0.909 with a specificity of 0.425. The brier (indication of the level of difference between the estimated value and the observed value) was 0.198 and the error rate was 0.256, indicating an incorrect classified outcome of 26%. For CA125 measurements, the sensitivity and specificity were 62.9% and 97.7 respectively with an area under the curve of 0.803. The levels of CA125 were stated abnormal when above >35 kU/L for premenopausal women and when >25 kU/L for postmenopausal women (6). The combination of the two (MALDI-FTICR MS and CA125) gave the following results: a sensitivity of 24.2% and a specificity of 97.4% when stated a positive ovarian cancer outcome in case of both a positive CA125 outcome and a positive MALDI-FTICR MS outcome. A sensitivity of 78.8% and a specificity of 87.2% were shown when stated a positive test in case of either a positive CA125 outcome or a positive MALDI-FTICR MS outcome. A summary of the sensitivities and specificities are given in table 6.

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Figure 2. Display of the peak intensities of healthy controls in blue and ovarian cancer cases in red. The intensities are displayed in order of mass to charge ratio values, starting with the lowest value.

Figure 3. Boxplot of the estimated class probabilities.

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Figure 4. Receiver Operating Characteristic (ROC) curves generated from the case–control classification of the serum samples using low mass (LM) data as a reflection of the distinguishing performance of the test. The greater the area under the curve The better the ability of the test to distinguish cases from controls.

Table 6 – Performance of the MALDI-FTICR MS compared to and combined with CA125.

Test N Sensitivity

Specificity

AUC

MALDI-FTICR MS 117 90.9% 42.5% 0.73.

CA125* 73 62.9% 97.7% 0.80

MALDI FTICR MS + CA125* (in case of both a positive outcome)

73 24.2% 97.4% 0.61

MALDI FTICR MS + CA125* (in case of either one a positive outcome)

73 78.8% 87.2% 0.83

* sensitivity and specificity were calculated in the 73 women who had available data on CA125 measurements only.

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CHAPTER 5: DISCUSSION

5.1. Main results

In our study population of 117 women with a proven BRCA1/2 mutation, MALDI-FTICR MS was able to distinguish BRCA1/2 associated ovarian cancer cases from healthy BRCA1/2 mutation carriers with a sensitivity of 91%, a specificity of 42.5% and a AUC of 0.727. For CA125 measurements, the sensitivity and specificity 62.9% and 97.7% respectively. In the combination of the two (MALDI-FTICR MS and CA125), the results were 78.8% sensitivity and 87.2% for specificity, when considered either a positive CA125 or a positive MALDI-FTICR MS test as a positive outcome.

5.2. Study design and characteristics

These findings are unlikely to be explained by other differences in either the study population characteristics or samples characteristics between the 2 study groups, because we matched the study groups on age and storage time to keep the differences as low as possible. The results showed indeed no significant difference between the characteristics, except for the age at sample collection and menopausal status. Healthy controls were overall aged younger, since the lifetime risk for ovarian cancer increases with age. Age is stated as a factor that may influence serum protein profiles (81). The difference in age at sample collection was significant but relatively small, for what reason we think it would not have had a great influence on our outcome. Also differences in characteristics between both plates were kept as low as possible by the developed plate design. The results in table 5 reflects the reached goal of the plate design. Since no significant difference was found in characteristics between both plates, we can ensure that the plate design cannot have influenced the data. The three excluded samples with low quality spectra did not differ much in characteristics from those of the total study population, excepting the slightly longer storage time in the excluded samples. Given the small number of excluded samples we think this would be of minimal impact on the results. One difference between the study groups that might be of influence on the outcome is the presence of women with breast cancer history among the ovarian cancer cases. This was inevitable due to the highly elevated risk on both developing breast cancer and developing ovarian cancer among this population of BRCA1/2 mutation carriers. Most of the breast cancers occurred several years (median, 2.5) before the ovarian cancer diagnosis for what reason we think the impact of breast cancer is small. But since there is no data on the influence of breast cancer history and the duration of influence after breast cancer diagnosis and treatment on the protein profiles in the blood, we cannot ensure an absence of influence of breast cancer history on our outcome.

5.3. Mass spectrometry and statistical methods

An asset of the study is the use of solid phase extraction by means of magnetic beads. This automatized tool provides a minimized variation by uniform MALDI-spots and increases the likelihood of reproducibility and validation of MS profiling (52,82). Besides, the use of MALDI-FTICR MS seems to provide a most accurate spectrum alignment and a more robust peptides quantification by measuring more broad protein peaks (57) compared to other mass spectrometry methods. We were limited to an maximum amount of available ovarian cancer cases in this study design. The number of included samples was too small to use a validation

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set for validating the ridge logistic regression analysis. This forced us to use the leave-one-out-cross-validation (LOOCV) strategy (bootstrapping approach). Discovery of discriminating peaks are the key to develop a disease identifying protein fingerprint or biomarker. Due to the statistical method used and due to high correlation inherent we were not able to determine the most discriminating peaks in our dataset. For that reason we were less able to compare our results with previous studies. In addition, the calculation of the sensitivity en specificity of CA125 measurements alone and combined with MALDI was limited to 79 out of the 117 samples, due to missing data on CA125 levels. Those missing data made the outcomes less comparable but still provides an indication of the power of the tools in certain scenarios.

5.4. In comparison to previous studies

Most studies define and evaluate specific proteins by means of mass spectrometry. Since we were unable to determine the most discriminating peak in our dataset, we unable to compare our findings with proteins found in previous studies. However, the studies discussed in the introduction, had also the sensitivity and specificity as major outcomes. The sensitivity of these studies ranged from 40.8% to 95% with a median of 84%. Our findings are corresponding to the literature with a relatively high sensitivity of 91%. Our specificity outcome of 42.5%, does not fit the range of sensitivity (81%-98%) in those studies (64,65,69,70,66,72,77–80). An explanation could be a biological and pre-analytical variation. Our study differs from the studies mentioned in the introduction at several components, starting with our study population that consisted of BRCA1/2 mutation carriers only. It is currently not known whether the protein profiles in the blood related to ovarian cancer are comparable between the high risk population of BRCA1/2 mutation carriers and the general population. Another explanation could be the fact that we used the MALDI-FTICR mass spectrometry method while previous studies analysed their samples by means of SELDI-TOF or MALDI-TOF. Use of different techniques can cause differences in results. Besides, pre-analytical variance often occurs between different laboratories. It is stated that sample handling is of great importance and can affect the outcome (81), for that reason most studies show lack of external validation. Finally, it is noticeable that most sensitivity and specificity scores stated in table 2. are results from models with CA125 measurements included instead of the mass spectrometry analysis alone. When we compared our findings of the MALDI- FTICR MS combined with CA125 measurements, the sensitivity of 78.8% and specificity of 87.2% did fit the range.

It is noticeable that our study population consisted of specific and mostly advanced cancer patients versus healthy controls. While the studies Moore et al., Hogdall et al., Clarke et al., Timms et al., Dieplinger et al., Lili et al., had taken benign diseases into account by including them in the study population (67,66,77–80). The MALDI-FTICR mass spectrometry might be less powerful in distinguishing ovarian cancer from no ovarian cancer in a population mix consisting of also low grade tumours and benign diseases. This has to be taken in consideration when putting our results in perspective and comparing our results with the results of those studies. The study of Zhang et al. had besides ovarian cancer, also other malignancies included in the study population as a validation set (64). They were able to verify that the levels of ApoA1 and TTR were not altered in blood samples of breast cancer or colon cancer and breast cancer or prostate cancer patients respectively. This approach is a step towards obtaining an ovarian cancer specific protein set instead of a cancer specific protein set.

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5.5. Future perspectives

MALDI-FTICR is able to distinguish OC from HC in the high risk population and to improve the sensitivity of the current approach with CA125 when added to the CA125 measurements. These results make MALDI/FTICR mass spectrometry a candidate for developing a biomarker for the detection of ovarian cancer. However, further research on reproducibility and clinical value is needed. Before any clinical value can be concluded, evaluation of the most discriminating peaks is needed to develop a set of proteins for targeted mass spectrometry in order to obtain a specific protein fingerprint. Moreover, there is room for improvement regarding to the sensitivity and specificity of the MALDI-FTICR analysis. Future studies should focus on a more detailed analysis to determine whether a specific protein profile could identify ovarian cancer with a higher sensitivity and specificity than our approach. Since our study population consisted of highly selected advanced ovarian cancer cases versus healthy controls, require these findings further evaluation on identifying asymptomatic women with a low grade tumour in a mixed study population including benign disease. Here-after, internal and external validation in prospective studies and in particular in different laboratories is needed, before proteomic biomarkers can be implemented in clinical use.

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ACKNOWLEDGEMENTS

I would like to take this opportunity to acknowledge all who have contributed to the implementation of this multidisciplinary project.

In particular, a sincere thank you to my supervisors from the UMCG: prof. dr. G.H. de Bock and prof. dr. M.J.E. Mourits. Thank you for your great guidance and support on both scientific and personal field. I really appreciate how you created an environment that was open for questions and the discussion of difficulties, but also giving me the opportunity to try to discover solutions myself. Thank you to dr. G.B.A Wisman for providing me with the data from the blood samples database and helping out with retrieving missing data. Also thank you to H. Pijper for assisting me with the sample retrieval.

I would also like to express my gratitude to all people from the LUMC, whom I was relying on for obtaining the results of this project. A special thanks to dr. W.E. Mesker and dr. Y.E.M van der Burgt for being a great contact and arranging logistic matters. Dr. S. Nicolardi, dr. M.R. Bladergroen thank you for performing and showing me the MALDI process step by step and being pleased to answer all my questions. I would also like to thank dr. B.J.A Mertens and A.A. Kakourou for performing the statistical analysis and share their thoughts on the best approach to reach the most unbiased results.

Lastly, thank you to all the patients who made this research possibly by their willingness to donate their blood for scientific purposes.

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APPENDIX – PROTOCOL SERUM ARCHIVE

Protocolserumarchive–RWvL.docx

Onadailybasiswereceivebloodsamplesfromwomenwhovisitourgynaecological(outpatient)clinic.Thewomenwhowishtoparticipateintheresearchareaskedtodonateblood.ThebloodsamplesarenormallysenttoY1.116(Laboratoriumgeneeskunde – Laboratorium Alg H&C en Bloedtransfusies)andareplacedinabeigetuberacklabelled“gynonco”.Onlythesamplesthatwereobtainedfromthe“CINpoli”(onFridays)aresentdirectlytotheMOLbyHarryPijperandplacedonthelabdeskofHarryKlip.

Procedure

1. Collecttubes(orangecapandcontainingaficoll-likematter)andensurethattheclottingtime(i.e.incubationatroomtemperatureandnotat4°C)isbetween2hoursand8hours.Ifthisisnotfeasible,thenyoucandenotetheaberrantclottingtime(Ct<2orCt>8)onHarry’sform.

2. ThetopdraweratMOLlab(tissuebench)deskcontainsaformstatingwhichserumarchivesamplenumbersareusedforwhichpatient.Wenormallyreceive2tubesofbloodfromeachpatientandtheserumofeachtubeisstoredinseparate2mLmicrocentrifugetubes.Ifonly1tubeisobtained,theserumisdistributedovertwo2mLtubes.

3. Centrifugethebloodtubesinthe“lawaairuimte”atfullspeed(4000RPM≈2396RCF),roomtemperaturefor10minutes.

4. Doublechecktheformtoensurethedate,thepatientname,UMCG-number,dateofbirthandclottingtimesarecarefullydenoted.Alsowritedowntheserumarchivenumberonthelidofthetubes.Ifyoulacklabelled2mLtubes,youcansearchthetopdrawerformoreserumarchivestickers.E.g.thesamplename15-0967is2mLtubenumber967collectedintheyear2015.

5. Whenuncappingthebloodtubes,useatissuetopreventthespreadofaerosols.Decanttheserumintothecorrect2mLtube.Storetheseruminthe-20°Cfreezerinthe“lawaairuimte”(inthetopleftdrawerlabelled“serumbank”).

6. Optional:checkifthetemperatureofthefreezeractuallywas-20°Casdisplayedonthefrontandfillouttheformwhichstickstotherightsideofthefreezer.

7. Whenapageisfilledcompletely,itshouldbeputinthefolder“serumbank”,sothedatamanagercanentertheinformationintotheappropriatedatabase.

VersionApril2015 page40 RolandvanLeeuwen