The Study of Massively Parallel Sequencing in Early Detection for Gynecologic Malignant Tumor

As human aging, 50% of women will be faced with the threat of cancer, especially gynecological malignancies. Ovarian, cervical and endometrial cancer are three major gynecological malignancies. In recent years, the incidence of cervical cancer, especially in young women significantly increased; ovarian cancer, although the incidence of malignant tumors in the female reproductive system ranked second, but its mortality rate already in the first place; and the morbidity and mortality of endometrial cancer is also rising.

The key to gynecologic malignancies is how to early diagnose and treat. With the advancement of science and technology, such as molecular biology techniques widely used in the medical field, early diagnosis, proper treatment and other aspects in gynecologic malignancies is expected to achieve a breakthrough. Genome-wide scan strategy, making the whole genome for linkage analysis for gynecological malignancies possible. Therefore, the investigators intend to find specific mutations to provide a new early screening approach for gynecological malignancies, which in later result in early diagnosis and specific treatment for gynecological malignant tumors.

Study Overview

• Status: Unknown
• Conditions: Gynecologic Malignant Tumor

• Study Type: Observational
• Enrollment (Anticipated): 200

Contacts and Locations

Study Locations

• China
  • Chongqing
    • Chongqing, Chongqing, China, 400038
      • Department of Obstetrics and Gynaecology, Southwest Hospital, Third Military

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 16 years to 75 years (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: Female

• Sampling Method: Probability Sample

Study Population

The experiment group are patients with cervical cancer, ovarian cancer or endometrial cancer.While the control group are volunteers on the basis of informed consent.

Description

Inclusion Criteria:

• Female aging from 16 to 75-year old;
• Preoperative diagnosis of gynecologic malignancies;
• Have the ability to comply with Research programs;
• Voluntarily participate in the study and signed an informed consent form

Exclusion Criteria:

• The cases do not meet the inclusion criteria should be removed from,indicating the reason for excluding.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
experiment group; patients with cervical cancer, endometrial cancers or ovary cancer
control group; postmenopausal women with benign tumor

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Gene mutation from species of cervical cancer, endometrial cancers and ovary cancer	We expect to found spcific DNA mutation in the species (peripheral blood, liquid Pap smear and cancer tissue) from cervical cancer, endometrial cancers and ovary.	4 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
the difference between detection sensitivity	Comparing sensiitivity of the three kinds of samples on early detection of gynecologic malignancies	4 years

Collaborators and Investigators

• Sponsor: Zhiqing, Liang
• Collaborators: Shenzhen Huada Genomics Co., Ltd.

Study record dates

Study Major Dates

• Study Start: October 1, 2013
• Primary Completion (Anticipated): October 1, 2016
• Study Completion (Anticipated): October 1, 2017

Study Registration Dates

• First Submitted: February 10, 2014
• First Submitted That Met QC Criteria: June 17, 2014
• First Posted (Estimate): June 18, 2014

Study Record Updates

• Last Update Posted (Estimate): June 18, 2014
• Last Update Submitted That Met QC Criteria: June 17, 2014
• Last Verified: June 1, 2014

More Information

Terms related to this study

• Keywords: endometrial cancer; ovarian cancer; cervical cancer; high-throughput sequencing
• Additional Relevant MeSH Terms: Neoplasms
• Other Study ID Numbers: 001 (NavyGHB); fck001 (Registry Identifier: ZLiang)