UNDERSTANDING the RAREST GYNECOLOGICAL CANCERS: a MULTI -OMICS PLATFORM for IMPROVED PATIENTS MANAGEMENT

NCT ID: NCT06582108

Last Updated: 2024-09-03

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 190 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-09-15
• Study Completion Date: 2026-12-31

Brief Summary

Rare gynecological cancers including uterine sarcomas, vulvar and non-epithelial ovarian cancers, are under-studied diseases and the absence of standardized diagnostic and therapeutic approaches or tailored clinical guidelines led to low survival rates and/or poor quality of life outcomes.

The ROAR project aims at increasing the molecular understanding of these diseases in a multidisciplinary, interinstitutional setting including gynecologic oncologists, medical oncologists, phase I researchers, pathologists, molecular pathologist, research nurses, genetists, bioinformatics, psychologists and patients' advocacy groups.

The project will pursue this aim through the following activities:

1. Harmonizing procedures and enabling safe and easy data sharing across all involved institutions; this will be achieved by reviewing the available clinical and molecular data (WP1), establishing an interinstitutional second-opinion board (WP2/Task1), developing a dedicated electronic customized research form (WP2/Task2) and deploying a genomics platform (WP2/Task3).

2. Performing comprehensive somatic and transcriptional profiling as well as immunological landscape assessments on high quality annotated samples stored in a biobank dedicated to rare gynecological cancers; this will require systematic and standardized clinical data and biological samples collection (WP3/WP4), standardization of pre-analytical and sequencing procedures (WP5/6). The board will tailor indications and modalities for genomics and liquid biopsy assessments based on clinical features.

3. Evaluating the clinical impact of the board on rare gynecological cancers management and, through the evidence gathered, implementing diagnostic and therapeutic strategies; this will include analyzing the activities of the board (WP7/Task 1.1), integrating -omics data in the board report (WP7/Task 1.2), evaluating liquid biopsy role in identifying minimal residual disease post-surgery, disease monitoring over time, capability of capturing tumor heterogeneity at baseline and identifying hot spot actionable molecular alterations. Finally, the evidence gathered from the ROAR project, will allow update of clinical guidelines or provide new recommendations for each rare gynecological cancer included. The results of ROAR will also be disseminated through direct access to the integrated platform developed.

Conditions

Uterine Sarcoma; Vulvar Cancer; Non Epithelial Ovarian Cancers (NEOC)

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Interventions

DNA, RNA, ctDNA, metabolomics

The use of specific assays or global molecular profiling to identify novel biomarkers based on DNA, RNA, protein, lipids, or metabolites obtained from tumor tissue or body fluids

Intervention Type: GENETIC

Eligibility Criteria

Inclusion Criteria

• >18 years old

Histological diagnosis of:

Non-epithelial ovarian tumors Uterine sarcomas Vulvar cancer

Exclusion Criteria

• Active HIV, HBV, or HCV infection Synchronous neoplasms Non-rare histologies of gynecological malignancies

• Minimum Eligible Age: 18 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

OTHER

Sponsor Role: lead

Responsible Party

SCAMBIA GIOVANNI

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Fondazione Policlinico Gemelli IRCCS

Rome, , Italy

Countries

Italy

Central Contacts

Giovanni Scambia

Role: CONTACT

giovanni.scambia@policlinicogemelli.it

0630151

Facility Contacts

Role: primary

0630161

valentina Iacobelli

Role: backup

valentina.iacobelli@guest.policlinicogemelli.it

Other Identifiers

ID 6868

Identifier Type: -

Identifier Source: org_study_id