Personalized prognosis in smoldering myeloma through automated analysis of mutational signatures

Introduction

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.

Current Challenges

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.

Limitations of Existing Methods

The scientific literature indicates that the methods under research utilise large genomic regions and significant coverage in sequencing. This would result in prohibitively high costs of testing if applied to the clinical domain.

Project Aim

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. This tool will allow for:

1. Early diagnosis
2. Monitoring of progression
3. Potential identification of at-risk patients who may benefit from early treatment to improve outcomes.