Validation of a Personalised Medicine Tool for Multiple Myeloma That Predicts Treatment Effectiveness in Patients

NCT ID: NCT03409692

Last Updated: 2023-06-29

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

278 participants

Study Classification

OBSERVATIONAL

Study Start Date

2017-06-14

Study Completion Date

2022-07-25

Brief Summary

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The consortium aims to commercialise the MMpredictor as a personalised medicine tool that predicts the most effective treatment strategy for individual Multiple Myeloma (MM) patients. MM is the second most common form of blood cancer contributing to 15% of all blood cancers and \~1,5% and 2% of all cancer deaths annually in the EU and US, respectively.

Patients show a large variability in treatment response and side effects due to tumour heterogeneity and the patient's intrinsic characteristics. Therefore, not every treatment will be suitable for each patient, and treatment strategies are often based on trial-and-error. The availability of multiple (\>20) treatment options complicates treatment decision-making even more. With the current development of many more promising treatments, there is an urgent unmet clinical need for a diagnostic assay that supports personalised cancer treatment in order to improve patient health outcomes, prevent side effects and reduce healthcare costs.

SkylineDx has previously developed the MMprofiler, a microarray-based diagnostic test that can subtype MM patients and reliably predict MM patient survival (prognosis). In this project, the test's clinical value will be expanded to include the prediction of treatment effectiveness in individual patients based on Gene Expression Profiling. An addendum for new intended use will be filed to the current in vitro diagnostic (IVD) registration, while renaming the test to MMpredictor. The project will also focus on positioning the test as a cost-effective IVD test for personalised medicine, that will increase health outcome and quality of life of patients and reduce healthcare costs.

The consortium consists of a life science SME specialised in molecular diagnostics, clinical centres with world renowned KOLs, a leading health economic institute, and a European MM patient advocacy organisation combining all the required complementary expertise to successfully bring the MMpredictor to market.

Detailed Description

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Conditions

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Multiple Myeloma

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

RETROSPECTIVE

Interventions

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Gene Expression Profiling

SkylineDx has previously developed the MMprofiler, a microarray-based diagnostic test that can subtype MM patients and reliably predict MM patient survival (prognosis). In this project, the test's clinical value will be expanded to include the prediction of treatment effectiveness in individual patients based on Gene Expression Profiling.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* The bio-banked MM patient samples and clinical data will be obtained from previous European clinical trials that have been conducted over the past 5 years within the participating clinical centres and other clinical centers. The samples were not collected for the purposes of this project. The subjects from which the data was initially taken cannot be identified from the data/records. However, the patients from the above mentioned trial have explicitly consented for the use of their samples for other (future) clinical research purposes.

Exclusion Criteria

\-
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Mario Boccadoro

OTHER

Sponsor Role lead

Responsible Party

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Mario Boccadoro

Director of Department of Molecular Biotechnology and Health Sciences

Responsibility Role SPONSOR_INVESTIGATOR

Locations

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University of Turin

Turin, , Italy

Site Status

Countries

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Italy

Other Identifiers

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Horizon2020 - FTI Pilot Study

Identifier Type: -

Identifier Source: org_study_id

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