Project description
Affordable tool for early detection of multiple myeloma
Multiple myeloma (MM) is the second most common blood cancer and often goes undetected until the late stages of progression. By the time symptoms appear, treatment options are limited, and long-term survival rates are poor. Although next-generation DNA sequencing (NGS) has provided insights into disease progression, it is not yet used in clinical settings for prognosis. Current research methods are too costly due to the large genomic regions they require for analysis. The ERC-funded MYELOMA-RISK project aims to develop a low-cost bioinformatics tool to identify key mutation signatures, enabling early diagnosis, disease monitoring, and identification of high-risk patients, potentially improving treatment outcomes.
Objective
Multiple myeloma (MM) is the second most common haematological cancer which is symptomless until the later stages, with few treatment options and poor long-term prognoses. Next-generation DNA sequencing (NGS) has been used to identify and characterise the process of clonal evolution and disease progression in asymptomatic MM, however, there remains no viable prognostic NGS method which can be used by clinicians to leverage different mutations to predict progression. The scientific literature indicates the methods under research utilise large genomic regions and significant coverage in sequencing that would result in prohibitively high costs of testing if applied to the clinical domain. The aim of this PoC project is to develop a complete, low-cost prognostic bioinformatics tool able to characterise the driver mutation signatures of MM in order to allow early diagnosis, monitor progression and potentially identify at risk patients that may benefit from early treatment to improve outcomes.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC-POC - HORIZON ERC Proof of Concept GrantsHost institution
20122 Milano
Italy