Liquid Biopsies for Improving the Pre-operative Diagnosis of Ovarian Cancer

Overview

An accurate preoperative diagnosis of an ovarian tumor is important for the patients' surgical work-up, proper referral to oncological centers and for the patients' mental wellbeing since uncertainty about the nature (benign vs malignant) of an ovarian tumor may cause anxiety. Currently, the Risk of Malignancy Index (RMI), with a cut-off value of 200, is often used in the Netherlands to select patients with an increased risk of ovarian cancer that should be referred to an oncologic center. However sensitivity and specificity of the RMI-score are far from optimal. Around 40% of the referred patients have benign disease in final pathological examination. Therefore, other models have been developed, such as the IOTA (International Ovarian Tumor Analysis) consortium algorithms, but these models require training, expertise and are subjective. To determine the nature of an ovarian tumor, histological examination is the golden standard. However, a pre-operative biopsy of an ovarian tumor is undesirable because of the risk of spill of tumor cells in the abdominal cavity. Therefore, there is an urgent need for non-invasive diagnostic tools to determine the nature of an ovarian tumor pre-operatively. Liquid biopsies could be such a non-invasive tool. Currently, circulating tumor DNA (ctDNA) circulating tumor cells (CTC), microRNA (miRNA) and tumor-educated platelets (TEPs) are available and can function as a potential blood-based biosource for (early) cancer diagnostics. Previous studies show promising results of liquid biopsies are used in (early) detection of cancer, also for ovarian cancer. Therefore, a diagnostic algorithm will be developed using ct-DNA and TEPs as liquid biomarkers in combination with the existing ultrasound models (RMI and IOTA-models) and tumor markers (CA125 and HE4) to differentiate between early ovarian cancer and benign ovarian tumors pre-operatively. Nature and extent of the burden and risks associated with participation, benefit and group relatedness. There is no extra burden/risk for the patients in this study. Five extra vials of blood will be collected from each participant and two questionnaires will be filled out.

Full Title of Study: “OVI-DETECT Liquid Biopsies for Improving the Pre-operative Diagnosis of Ovarian Cancer”

Study Type

  • Study Type: Observational [Patient Registry]
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: August 1, 2024

Detailed Description

In the Netherlands 7600 women are annually diagnosed with an ovarian tumor. Only 5% of these tumors are malignant in the final histology assessment. This means that a general gynecologist is confronted with a patient with low stage ovarian cancer less than once a year. Therefore an accurate preoperative diagnosis of an ovarian tumor is challenging. This accurate pre-operative diagnosis is important because patients with an ovarian carcinoma have to undergo extensive surgery in an oncology center, performed by a gynecologist-oncologist. The current preoperative possibilities to distinguish benign and malignant ovarian tumors are based on classification systems containing clinical, biochemical and ultrasound characteristics. For example, predictive ultrasound models developed by the IOTA (International Ovarian Tumor Analysis) consortium have been widely used. However, these models require training and expertise and are therefore not always easy to implement. The predictive value of current serum-biomarkers such as CA-125 is limited because this biomarker is not increased in 50% of early stage ovarian carcinoma and CA-125 may also have increased in benign gynecological conditions such as endometriosis. Current Dutch guidelines make use of the Risk of Malignancy Index (RMI) to determine whether the risk of ovarian carcinoma is increased. This score is based on the concentration of CA125, specific ultrasound characteristics and menopausal status. According to Dutch guidelines, patients with ovarian tumor are referred to oncology centers if the RMI is increased (>200). The published sensitivity and specificity of RMI in a non-selected population of patients with ovarian tumors is 72% and 92%, respectively. However, the population treated in the oncology centers is enriched with patients with RMI >200. In this selected population, our own prelimenary data show that the sensitivity is 84% for RMI and the specificity is only 51%. This means that the incidence of malignancy within this population is 40%. This is unacceptably low because this implies that half of the patients with benign tumors referred to oncology centers undergo unnecessarily extensive surgery and these patients become unnecessarily emotionally burdened with the possibility of getting cancer. Tissue biopsies are an important tool in the treatment of ovarian carcinoma because theses procedures can confirm or rule out the presence of a malignancy preoperative. At an early stage, however, tissue biopsy is considered as an unwanted invasive procedure, as this procedure can cause tumor spreading and has an invasive character for patients. In short, despite the development of numerous prediction models, accurate preoperative diagnosis of early stage ovarian carcinoma is a challenge. There is an urgent need to develop prediction models with a high degree of accuracy that are easy to implement in the clinic to maximize the number of malignant tumors treated in oncology centers. Blood-based biopsies, 'liquid' biopsies, as an alternative to traditional tissue biopsies, are emerging as these biopsies can provide accurate and comprehensive information on tumors. Examples of such include circulating tumor DNA (ctDNA) circulating tumor cells (CTC) and tumor-educated platelets (TEPs). These platelets are normally responsible for hemostasis, but appear to be able to include tumor signals found in the presence of mirco-tumor RNA. By using whole genome sequencing and detecting structural DNA and RNA changes in the ctDNA and TEPs, malignancies can be detected or excluded. These DNA changes were firstly demonstrated by coincidence when using the NIPT (non-invasive pregnancy test), which, in addition to assessing possible errors in fetal DNA, is also able to detect abnormalities in maternal DNA; in asymptomatic pregnant women, DNA changes were found in maternal DNA accounting for the presence of a malignancy. Research shows that within patients with early stage ovarian carcinoma (st I and II), ctDNA analysis showed a sensitivity of 69% and a specificity of >99%. The amount of ctDNA correlates with the tumor burden. Other studies conducted within small patient populations show that when ctDNA is combined with existing tumor markers, CA-125 for example, sensitivity and specificity both increase. From prelimanery data on TEP-analysis performed in the NKI-AvL a sensitivity of 76% and specificity of 98% for high-grade ovarian carcinoma was found and a sensitivity of 81% and specificity of 80% for low-grade carcinoma was found. Therefore, this study investigates the clinical value of longitudinal assessment of liquid biopsy-derived information for preoperative diagnosis in patients suspected of early ovarian cancer

Interventions

  • Diagnostic Test: ctDNA – circulating tumor DNA
    • lcWGS and WGS of circulating tumor DNA
  • Diagnostic Test: TEP – Tumor Educated Platelets
    • sequencing miRNA from TEPs

Arms, Groups and Cohorts

  • Benign ovarian tumors
    • All histological proven benign ovarian tumors
  • Malignant ovarian tumors
    • All histological proven malignant ovarian tumors
  • Borderline ovarian tumors
    • All histological proven borderline ovarian tumors

Clinical Trial Outcome Measures

Primary Measures

  • The diagnostic accuracy of the developed algorithm
    • Time Frame: 3 -4 years
    • The diagnostic accuracy of the developed algorithm, displayed as sensitivity and specificity.

Secondary Measures

  • Cost-effective analysis (I)
    • Time Frame: 3 -4 years
    • The cost effectiveness of the algorithm in the differentiation between (early) OC and benign ovarian tumors will be measured by using different validated questionnaires. The EQ-5D-5L questionnaire will be used to measure cost-effectiveness modeling, QoL will be measured by means of utilities in order to derive quality adjusted life-years (QALYs). The utilities will reflect the preferences of society for length of life versus quality of life.
  • Cost-effective analysis (II)
    • Time Frame: 3 -4 years
    • The (Dutch) Medical Consumption Questionnaire (iMCQ) includes questions related to frequently occurring contacts with health care providers. The costs of medical consumption are calculated by multiplying the measured volumes of care by the cost price per unit of care. The higher the score more medical consumption is used.
  • Cost-effective analysis (III)
    • Time Frame: 3 -4 years
    • The (Dutch) Productivity Cost Questionnaire (iPCQ) will be used to measure the impact of disease on the ability of a person to perform work, either employed or unemployed. It is divided into 3 categories: absences of work measured in days from 0 to 28 days or more, either employed or unemployed; the cost of productivity loss for participants and the employees will be calculated by the friction cost method and the human capital method. Higher scores mean more production loss, for employed participants; The cost of productivity loss from unpaid work is calculated by multiplying the amount of productivity loss by a standard hourly rate of household care. Higher scores mean more production loss, for unemployed participants;
  • Psychological Burden (I)
    • Time Frame: 3 -4 years
    • The EQ-5D will be used to measure within five different domains the health status of the patient before and after the surgical intervention and between different ovarian tumor origins. For example a health status can be better, worse or the same. The self-reported visual scale standard, which is part of the EQ-5D, with a value of 80.6 is scored as a normal value for healthy individuals.
  • Psychological Burden (II)
    • Time Frame: 3 -4 years
    • The EORTC-QLQ-C30 will be used to measure cancer specific cancer-specific quality-of-life, using 5 functional scales, 3 symptom scales, a global health status/quality of life scale, and a number of single items assessing additional symptoms (dyspnea, sleep disturbance, constipation and diarrhea) and perceived financial impact. For ease of presentation and interpretation, all subscale and individual item responses are linearly converted to a 0 to 100 scale. For the functional and global quality of life scales, a higher score represents a better level of functioning. For the symptom scales and items, a higher score reflects a greater degree of symptoms. Individual item responses are linearly converted to a 0 to 100 scale. The EORTC QLQ-OV28 a 4-point Likert-type response scale is used, ranging from “not at all” to “very much. Results from patients with a benign and malignant ovarian tumor will be compared in the assessment of quality of life.
  • Psychological Burden (III)
    • Time Frame: 3 -4 years
    • The EORTC QLQ-OV28 a 4-point Likert-type response scale is used, ranging from “not at all” to “very much. Results from patients with a benign and malignant ovarian tumor will be compared in the assessment of quality of life.
  • Psychological Burden (IV)
    • Time Frame: 3 -4 years
    • The Cancer Worry Scale will be used to assess the worries among the included patients for having ovarian cancer. For all items of the Cancer Worry Scale a 4-point Likert-type response scale is used, ranging from “not at all” to “very much”.
  • Psychological Burden (V)
    • Time Frame: 3 -4 years
    • The Intolerance of Uncertainty Scale (Dutch version IUS-12), will be used to measures both anxious and avoidance components of intolerance of uncertainty. For this study it will be used to measure the coping patients have when faced uncertainty before the final diagnosis is known. It makes use of on 5-point Likert-type response scale is used, ranging from “very strongly disagree” to “very strongly agree”.
  • Psychological Burden (VI)
    • Time Frame: 3 -4 years
    • Decision of regret scale is a 5-point scale to measure the “distress or remorse after a (health) care decision”. Participants will be asked to reflect on a specific past decision, and then will be asked to indicate the extent to which they agree or disagree on the regret scale by indicating a number from 1 (Strongly Agree) to 5 (Strongly Disagree) that best indicates their level of agreement. The amount of regret is measured at a point of time when the participant can reflect on the effects of the decision. This scale will be used to measure the regret patients could have on being referred to an oncological center, independently of the final origin of tumor.

Participating in This Clinical Trial

Inclusion Criteria

1. Age ≥18 years 2. Presence of a ovarian tumor and referred to specialized center for surgery based on: 1. Any ultrasound model e.g. RMI-scoring model ; IOTA-rules 2. Subjective assessment of the referring gynecologist 3. Normal Glomerular Filtration Rate (GFR): >60ml/min/1,73m2 3. General criteria: a. Understanding of Dutch language b. Fit for surgery (WHO 1-2) c. Written informed consent Exclusion Criteria:

1. Suspicion of advanced-stage of disease, e.g. ascites or peritoneal depositions 2. History of cancer (excl. BCC) within 5 years prior to inclusion 3. Multiple malignancies at the same time

Gender Eligibility: Female

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: Accepts Healthy Volunteers

Investigator Details

  • Lead Sponsor
    • The Netherlands Cancer Institute
  • Collaborator
    • Leiden University Medical Center
  • Provider of Information About this Clinical Study
    • Sponsor
  • Overall Official(s)
    • C.A.R. Lok MD, PhD, Principal Investigator, Dutch Cancer Institute
    • C.D. de Kroon, Principal Investigator, Leiden University Medical Center / Gynecology
    • J.M.J. Piek, Principal Investigator, Catharina Ziekenhuis Eindhoven / Gy-necology
  • Overall Contact(s)
    • C.A.R. Lok MD, PhD, 00 31 20 512 2975, c.lok@nki.nl

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