A Machine Learning Approach to Connect Multiple Myeloma Complexity to Early Disease Recurrence

This is a non-interventional, national, multicenter prospective non-profit observational study aiming at improving the accuracy of risk prediction in multiple myeloma (MM) by applying machine-learning tools for data processing to develop model(s) predicting response to therapy and the probability of early relapse for MM patients.

Study Overview

• Status: Recruiting
• Conditions: Multiple Myeloma (MM)

Detailed Description

Improvements in the therapy of MM have prompted the achievement of very deep clinical responses, with significantly improved outcomes and survival [2]. However, despite significant therapeutic progress, MM remains a challenge, due to the composite pathogenesis and intricate networks of different interacting factors. As a result, a relatively high proportion of NDMM patients across the different therapeutic strategies have a higher risk of disease progression and worse outcomes, independently of the anti-MM regimen received. These patients currently represent un unmet clinical need, with consequent challenges in the management of the disease and identify these patients upfront is an important goal in MM.

At present, risk stratification scores rely on a limited set of clinical and biological variables, not always sufficient to identify patients at a high risk of early disease progression or relapse (i.e., within 12 months from start of first-line therapy). Recently, AI tools have been explored to improve the accuracy of risk prediction, showing that high-risk diseases might be upfront recognized, based on tumor and immune biomarkers [3-4].

By applying Machine Learning (ML) tools for data processing, clinical, genomic, and imaging data from MM patients will be integrated and employed in models aimed at improving the accuracy of MM risk prediction. In this way, ML models will aggregate all tumor- and microenvironment-related information obtained by high-throughput technologies and omics approaches to identify and describe clusters of MM patients that best correlate with the achievement of early progression.

Overall, this study will identify new knowledge to support clinical research and decision-making in MM: precise up-front stratification of patients, based on the whole landscape of MM-related features, could improve understanding of MM individual risk. Results from this study will have an impact on the possibility to access personalized treatment, with predictable overall repercussion on the effective management of MM patients and savings for the National Health System.

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

Contacts and Locations

• Study Contact: Name: Elena Zamagni, MD, PhD; Phone Number: +39 051 214 3680; Email: e.zamagni@unibo.it

Study Locations

• Italy
  • Bologna, Italy, 40138
    • Recruiting
    • IRCCS Azienda Ospedaliero-Universitaria di Bologna
    • Contact: Elena Zamagni, MD, PhD; Phone Number: +39 051 214 3680; Email: e.zamagni@unibo.it
    • Contact: Simona Barbato, Ph.D.; Phone Number: +39 051 214 3827; Email: simona.barbato3@unibo.it
    • Principal Investigator: Elena Zamagni
    • Sub-Investigator: Carolina Terragna
  • Cagliari, Italy, 09134
    • Not yet recruiting
    • ARNAS "G. Brotzu" di Cagliari
    • Contact: Daniele Derudas; Phone Number: +39 070 52965520; Email: daniele.derudas@aob.it
  • Napoli, Italy, 80131
    • Not yet recruiting
    • Azienda Ospedaliera Universitaria Federico II
    • Contact: Barbara Izzo; Phone Number: +39 081 3737869; Email: barbara.izzo@unina.it
  • Forlì-Cesena
    • Meldola, Forlì-Cesena, Italy, 47014
      • Not yet recruiting
      • Istituto Romagnolo per lo Studio dei Tumori "Dino Amadori" - IRST IRCCS
      • Contact: Martina Ghetti; Phone Number: +39 0543 739100; Email: martina.ghetti@irst.emr.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Study participants will be adult patients with newly diagnosed (ND) multiple myeloma.

Description

Inclusion Criteria:

• Age ≥ 18 years
• Signed Informed Consent form for study participation and personal data processing
• Diagnosis of active multiple myeloma

Exclusion Criteria:

• None

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Overall Response Rate	Overall Response Rate	12 months after the start of anti-MM therapy

Collaborators and Investigators

• Sponsor: IRCCS Azienda Ospedaliero-Universitaria di Bologna
• Investigators: Principal Investigator: Elena Zamagni, MD, PhD, IRCCS Azienda Ospedaliero-Universitaria di Bologna

Study record dates

Study Major Dates

• Study Start (Actual): October 30, 2024
• Primary Completion (Estimated): April 2, 2026
• Study Completion (Estimated): August 31, 2026

Study Registration Dates

• First Submitted: January 2, 2025
• First Submitted That Met QC Criteria: January 8, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 8, 2025
• Last Verified: November 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Pathologic Processes; Neoplasms; Disease Attributes; Immune System Diseases; Neoplasms by Histologic Type; Hematologic Diseases; Lymphoproliferative Disorders; Immunoproliferative Disorders; Hemostatic Disorders; Paraproteinemias; Blood Protein Disorders; Hemorrhagic Disorders; Recurrence; Multiple Myeloma; Neoplasms, Plasma Cell
• Other Study ID Numbers: AIMMer; PNRR-TR1-2023-12378246 (Other Grant/Funding Number: Italian Ministry of Health)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No