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Completed
MYELOMA-RISK
Personalized prognosis in smoldering myeloma through automated analysis of mutational signatures
FunderEuropean Commission
Funder Contribution €150,000.00
Duration01 Jul 2023 - 31 Dec 2024
Funded underHE | ERC | HORIZON-ERC-POC
Call for proposalERC-2023-POC
Project code101123230
Identifiers
Project timeline100%
Description
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.
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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.
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