Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| University of Rome Tor Vergata | OTHER |
| Casa Sollievo della Sofferenza-IRCCS, San Giovanni Rotondo | UNKNOWN |
| Universita degli Studi di Palermo | OTHER |
Not provided
Not provided
Not provided
Not provided
Prediction of preoperative endometrial biopsy: the evolution from hyperplasia to cancer, the prognosis and the risk of recurrence. Intelligence methods artificial risk will be used to redefine the current risk classes including our profile immuno-mutational to provide a more precise characterization and closer to the real prognosis of the patient.
Identify new risk factors for endometrial cancer, using an integrated multi-omics approach linked to a specific immune pattern (called MOMIMIC score) useful for improving oncology and surgery precision. The aim is to evaluate the predictive value of the MOMIMIC score for early identification of progression from precancerous lesions to endometrial carcinoma, prognosis and relapses, to help the clinician in the decision to treatments. Through the identification during hysteroscopy of the most appropriate site for biopsies targeted endometrials, through an artificial intelligence algorithm applied to the video system hysteroscopic which, by comparing the information from the omics approach and the hysteroscopic image combined with radiogenomic information, it could help the gynecologist in the procedure and provide information on the prognosis through the omics-iconographic profile in order to calculate a preoperative predictive score. Furthermore by modulating the surgical radicality, according to the information obtained, there will be a tendency to preserve fertility in young patients with a low-risk profile (since currently the risk factors are not sufficient to discriminate for a non-treatment radical). This will help the surgeon through an artificial intelligence algorithm applied to the system robotic/laparoscopic video, will guide the operator in decision-making procedures regarding the resection margins tumor, metastasis localization, pathological lymph node detection, and imaging driven by biomolecular information.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Retrospective cohort | Fresh tissue samples stored at -80°C, collected at the Institute's IRE Biobank (a starting from 2019) and tissue preserved in paraffin at the biobank at 4°C at the UOC Pathological Anatomy archive, for carrying out WES, RNA-seq, scRNA-seq, spatial transcriptomics, metabolomics, proteomics, digital pathology, immune infiltrate characterization (e.g. FACS, immunohistochemistry) | ||
| Prospective cohort | Collection of tissue samples obtained at the time of surgery and verified by the anatomical pathologist for the actual availability and adequacy, for the purpose of the creation of organoids (Patient-Derived Organoids, PDO), cell lines and co-cultures (created with the patient's own peripheral immune cells, collected and processed), in the context of which secretomics analyzes will be conducted using Olink and Luminex. |
Not provided
| Measure | Description | Time Frame |
|---|---|---|
| OS (overall survival) | The study will evaluate the predictive value of the MultiOMics-IMmune-Iconographic model (global mutational profiling, RNA-seq of single cells coupled with the Spatial transcriptomics, proteomic and metabolomic profile) following the data obtained from the identification of new risk factors for endometrial carcinoma, in patients at high or low risk. They will be tested from Random Survival Forest to determine how capable a feature is discriminate between the 4 groups in terms of OS (overall survival). The selected features will be used in combination with the known prognostic clinical and histopathological risk factors described by ESMO-ESGO-ESTRO. | 24 months |
| DSF (disease-free survival) | The study will evaluate the predictive value of the MultiOMics-IMmune-Iconographic model (global mutational profiling, RNA-seq of single cells coupled with the Spatial transcriptomics, proteomic and metabolomic profile) following the data obtained from the identification of new risk factors for endometrial carcinoma, in patients at high or low risk. They will be tested via Random Survival Forest to determine how capable a feature is discriminate between the 4 groups in terms of impact on progression to cancer, recurrence, DFS (disease-free survival). | 24 months |
| Measure | Description | Time Frame |
|---|---|---|
| Area under the curve (AUC) | In order to obtain a more robust estimate of accuracy of the MultiOMics-IMmune predictive signature, for validation, we will use two groups of patients composed of a minimum of 200 cases (100 high risk and 100 low risk), at a reduction from 30% confidence interval to 95% when signature performance are kept constant. Considering the area under the curve (AUC). | 24 months |
Not provided
Inclusion Criteria:
Exclusion Criteria:
All exclusion criteria adopted in the surgical protocols will be applied to the study. In particular:
Not provided
Patients suffering from endometrial cancer.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Enrico Vizza, Doctor | Contact | 06 52666974 | +39 | enrico.vizza@ifo.it |
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| IRCCS National Cancer Institute "Regina Elena" | Recruiting | Rome | 00144 | Italy |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D016889 | Endometrial Neoplasms |
| ID | Term |
|---|---|
| D014594 | Uterine Neoplasms |
| D005833 | Genital Neoplasms, Female |
| D014565 | Urogenital Neoplasms |
| D009371 | Neoplasms by Site |
Not provided
Not provided
Not provided
Not provided
Not provided
Tissue samples and peripheral venous blood samples
| Accuracy (ACC) | In order to obtain a more robust estimate of accuracy of the MultiOMics-IMmune predictive signature, for validation, we will use two groups of patients composed of a minimum of 200 cases (100 high risk and 100 low risk), at a reduction from 30% confidence interval to 95% when signature performance are kept constant. Considering Accuracy (ACC). | 24 months |
| D009369 |
| Neoplasms |
| D014591 | Uterine Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D000091662 | Genital Diseases |