Breast Cancer Early Stage Breast Cancer (Stage 1-3)
Conditions
Keywords
microRNA, metastases, prognosis
Brief summary
This is a multicenter, observational validation study designed to evaluate the prognostic performance of the PORTENT algorithm in patients with early-stage breast cancer. The model integrates clinicopathological variables and the expression levels of two small non-coding RNAs (miR-3916 and miR-3613-5p) to estimate individual risk of developing distant metastases. The primary objective is to assess the discriminatory ability of the PORTENT algorithm for predicting distant metastasis at predefined time points after diagnosis.
Detailed description
This multicenter retrospective observational study aims to clinically validate the PORTENT prognostic algorithm for predicting the risk of distant metastases in women with early-stage breast cancer. Female patients with Stage I-III disease from three independent cohorts with available residual tumor tissue and follow-up data will be included. The primary endpoint of the study is the discriminatory performance of the PORTENT algorithm, assessed by the area under the receiver operating characteristic curve (AUC) for the prediction of distant metastases at 5 and 10 years from diagnosis. The algorithm integrates established clinicopathological prognostic factors (tumor stage, histological grade, and Ki67-MIB1) with the expression levels of miR-3916 and miR-3613-5p. MicroRNA expression and target protein expression will be evaluated using RT-qPCR and immunohistochemistry. Secondary and exploratory analyses will include model calibration assessed using calibration curves and the Integrated Calibration Index (ICI), as well as survival analyses (overall survival, progression-free survival, and metastasis-free survival) performed using Cox proportional hazards models.
Interventions
DIAGNOSTIC_TESTValidation of Prognostic tool
Collection of Clinical History: Clinical data, including medical history, clinicopathological features (e.g., age, histology, receptor and nodal status), and follow-up information (presence or absence of metastasis, patient vital status), will be collected and entered into a dedicated platform by. Follow-up updates are scheduled by Month 48 of the project (December 31, 2025) to ensure timely and accurate patient outcome data. Laboratory Analysis: Residual tumor sections prepared by the Pathology Unit of each participating center will be sent to the Oncology Laboratory at CSS-IRCCS, where RNA will be extracted using standardized experimental procedures. Expression levels of miR-3916, miR-3613-5p, and their target genes will be analyzed by quantitative real-time PCR (RT-qPCR). Protein expression of target genes will be assessed via immunohistochemistry. Additionally, the extracted RNA will be analyzed at the Gerobiomics and Exposomics Laboratory of IRST IRCCS.
Sponsors
Casa Sollievo della Sofferenza IRCCS
Istituto Tumori Giovanni Paolo II, BARI
IRCCS Centro di Riferimento Oncologico della Basilicata
Fondazione Humanitas per la Ricerca
Fondazione IRCCS Policlinico San Matteo di Pavia
Istituto Romagnolo per lo Studio dei Tumori Dino Amadori IRST S.r.l. IRCCS
Istituto Nazionale Tumori Regina Elena
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Istituto Nazionale Tumori IRCCS - Fondazione G. Pascale
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Stage I-III breast cancer * Residual tumor tissue available * Written informed consent
Exclusion criteria
* Age \<18 * Stage IV at diagnosis * Refusal or inability to provide informed consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Discriminatory Performance of the Prognostic Algorithm (AUC) for Prediction of Distant Metastasis Within 5 Years From Diagnosis | 5 years from diagnosis; interim analysis at March 2027. | Area under the Receiver Operating Characteristic (ROC) curve (AUC) with 95% confidence intervals for patient-level risk probabilities generated by the prognostic algorithm to predict the occurrence of distant metastases within 5 years after breast cancer diagnosis. Discrimination will be assessed using ROC curve analysis and DeLong's test. |
| Discriminatory Performance of the Prognostic Algorithm (AUC) for Prediction of Distant Metastasis Within 10 Years From Diagnosis | 10 years from diagnosis; final analysis after completion of 10-year follow-up for all participants (expected by 2029). | Area under the Receiver Operating Characteristic (ROC) curve (AUC) with 95% confidence intervals for patient-level risk probabilities generated by the prognostic algorithm to predict the occurrence of distant metastases within 10 years after breast cancer diagnosis. Discrimination will be assessed using ROC curve analysis and DeLong's test. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Calibration of the Prognostic Model at 5 Years (Integrated Calibration Index) | 5 years from diagnosis; interim analysis at March 2027. | Calibration of the prognostic algorithm will be evaluated at 5 years using the Integrated Calibration Index (ICI) and calibration plots to assess agreement between predicted and observed distant metastasis risk. |
| Calibration of the Prognostic Model at 10 Years (Integrated Calibration Index) | 10 years from diagnosis; final analysis after completion of 10-year follow-up (expected by 2029). | Calibration of the prognostic algorithm will be evaluated at 10 years using the Integrated Calibration Index (ICI) and calibration plots to assess agreement between predicted and observed distant metastasis risk. |
| Difference in Discriminatory Performance Between Prognostic Models (ΔAUC) at 5 Years | 5 years from diagnosis; interim analysis at March 2027. | Difference in AUC between the standard clinical prognostic model (clinicopathological variables only) and the extended model including miR-3916 and miR-3613-5p expression for prediction of distant metastases at 5 years. Statistical comparison will be performed using DeLong's test. |
| Difference in Discriminatory Performance Between Prognostic Models (ΔAUC) at 10 Years | 10 years from diagnosis; final analysis after completion of 10-year follow-up (expected by 2029). | Difference in AUC between the standard clinical prognostic model (clinicopathological variables only) and the extended model including miR-3916 and miR-3613-5p expression for prediction of distant metastases at 10 years. Statistical comparison will be performed using DeLong's test. |
| Classification Performance of the Prognostic Algorithm at 10 Years (Sensitivity, Specificity, PPV, NPV) | 10 years from diagnosis; final analysis after completion of 10-year follow-up (expected by 2029). | Estimation of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with 95% confidence intervals, for predefined probability thresholds corresponding to low (\<10%), intermediate (10-30%), and high (\>30%) 10-year distant metastasis risk categories. |
| Association Between miR-3916 and miR-3613-5p Expression and Overall Survival | Up to 10 years from diagnosis; final analysis after completion of follow-up (expected by 2029). | Association between miR-3916 and miR-3613-5p expression levels and overall survival, assessed using hazard ratios and 95% confidence intervals from Cox proportional hazards models. |
| Prognostic Algorithm Performance Within PAM50 Molecular Subgroups | 5 and 10 years from diagnosis; final analysis after completion of follow-up (expected by 2029). | Discriminatory performance and calibration of the prognostic algorithm within PAM50 molecular subgroups (Luminal A, Luminal B, HER2-enriched, Basal-like), evaluated using AUC and calibration metrics. |
| Analytical Validity of miRNA Expression Assessment | Data collection and analysis completed by March 2027. | Assessment of analytical validity of miR-3916 and miR-3613-5p expression measurement, including RNA yield, RT-qPCR amplification success rate, and assay reproducibility. This outcome assesses technical performance only. |
Countries
Italy
Contacts
PRINCIPAL_INVESTIGATORPaola Parrella, MD
Fondazione Casa Sollievo della Sofferenza, IRCCS
Outcome results
None listed