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Implementation of the Methylome in the Characterization of Solid Tumors

Implementation of the Methylome in the Characterization of Solid Tumors

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07268066
Acronym
AMethiST
Enrollment
180
Registered
2025-12-05
Start date
2024-05-21
Completion date
2026-12-31
Last updated
2025-12-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Solid Tumor Cancer

Brief summary

This study aims to investigate the functional consequences of specific methylation changes and to examine the clinical implications of these alterations. Methylation analysis is a powerful tool for understanding the molecular mechanisms of brain tumors and other solid tumors and can help identify new diagnostic and therapeutic targets. By analyzing DNA methylation patterns in tumor tissue, it is possible to identify genes that are silenced or activated by methylation. This information can be used to develop new biomarkers for early diagnosis and to identify new therapeutic targets that can be used to reverse methylation-mediated gene silencing.

Detailed description

A total of 180 cases will be collected: 60 central nervous system tumors, 60 endometrial carcinomas, and 60 endocrine tumors (pituitary tumors, adrenal tumors, thyroid tumors). By following these practical steps, the project aims to develop and implement a methylation-based tumor classification system that improves clinical decision-making and patient care in the field of solid tumor oncology. 1\. Study design and sample collection: * Select a diverse cohort of patients with various types of solid tumors. * Collect tumor tissue samples for DNA extraction. * Select the neoplastic area and extract DNA. 2. NGS profiling: * Analyze molecular alterations using lab-developed NGS panels developed and in use at the IRCCS molecular pathology laboratory (sequencer: S5 Prime - Thermo Fisher Scientific). 3\. Methylation profile: * Methylation analysis using ChIP array (Next 550 - Illumina) to generate methylation data. 4\. Clinical correlation and interpretation of data: * Analyze the clinical relevance of tumor classification based on methylation profile. Define how these classifications relate to clinical parameters, to treatment outcome and survival 5. Development of diagnostic/therapeutic protocols: * Development of interdisciplinary protocols to implement methylation profile -based tumor classification in the clinical setting.

Interventions

None listed

Sponsors

IRCCS Azienda Ospedaliero-Universitaria di Bologna
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Confirmed diagnosis of one of the following types of cancer: gliomas, malignant thyroid neoplasms, or endometrial carcinomas (according to the WHO Nomenclature) * Availability of adequate material for analysis at the laboratory, specifically availability of at least 3 sections of 10 µm of FFPE

Exclusion criteria

* no

Design outcomes

Primary

MeasureTime frameDescription
Identify methylation profilesfrom January 2024 to December 2026The primary objective of this project is to perform a comprehensive analysis of methylation in glial tumors, endometrial carcinomas, and endocrine neoplasms, with the overall goal of improving their classification and translating the data obtained into clinical practice. Specifically, this project aims to: 1. Characterize DNA methylation patterns in the solid tumors under investigation: this research aims to clarify the specific DNA methylation profiles associated with different types of solid tumors, allowing the identification of unique epigenetic signatures that may contribute to tumor classification; 2. Develop robust predictive models for the classification of the tumors studied; 3. Validate classification models using clinical data; 4. Assess the relevance of methylation-based tumor classification to determine the clinical utility of classification models; 5. Developing an easy-to-use clinical implementation framework.

Countries

Italy

Contacts

Primary ContactGiovanni Tallini, Prof.
giovanni.tallini@ausl.bologna.it+39 338 9763329

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026