Periodic Reporting for period 1 - MYELOMA-RISK (Personalized prognosis in smoldering myeloma through automated analysis of mutational signatures)
Okres sprawozdawczy: 2023-07-01 do 2024-12-31
Multiple myeloma (MM) is a blood plasma cell cancer, which is often asymptomatic until late stages when there are limited options for effective treatment. At present, several biomarkers of the disease are used but risk models to identify which patients will develop the more aggressive forms of the disease are not reliable, leading to barriers for both treatment and research.
The PI has developed through the ERC CoG, a method of DNA mutation signature analysis which can identify risk of plasma cell clonal evolution and disease progression from asymptomatic smouldering multiple myeloma (SMM) to MM using whole genome sequencing. The MYELOMA-RISK PoC project has demonstrated that the same mutation signature methodology could be successfully applied to DNA sequence data from a targeted cancer gene panel, which is routinely performed as the standard of care for MM patients. This method results in a more cost-efficient quantification of mutation activity, which significantly reduces the costs of the analysis. A bioinformatics software tool was developed to perform this specific analysis and resulted in very high agreement with software based on whole genome and exome sequencing. This new approach shows great potential as a precision clinical tool for early identification of disease progression in early-stage MM patients where none are currently available.
Engagement with clinicians and market research conducted within the PoC indicated that there is a high demand for such a diagnostic method for this disease and there was a high acceptance of the proposed software-based approach. A business plan and IPR strategy for the exploitation of the PoC research outcomes was developed and agreed between the consortium partners.
Context and overall objectives
Current clinical assessment of MM and its pre-cursor stages are performed through a series of blood tests and medical imaging techniques usually following the onset of key signs and symptoms. Analysis of mutational signatures has the potential to accurately differentiate stable and progressive precursor conditions at an early stage or low disease burden clinical states. Our approach for mutation signature analysis could substantially reduced through optimising the amount of sequencing data required and streamlining the bioinformatics analysis pipeline. This could reduce the costs from €3-10,000 per sample for whole genome or exome sequencing that is used currently, down to approximately €500 per sample. This compares very favourably with the costs of current molecular approaches such as fluorescence in-situ hybridization (FISH) whilst providing improved prognostic information for risk of progression.
Our analysis focused on the early identification of high-risk SMM patients that will lead to improvements in patient outcomes and quality of life by initiating early treatment to those who will benefit from it, whilst those who are unlikely to progress to MM can avoid potentially toxic treatments and the psychological burden of not knowing how their disease will progress. In addition, improved diagnostic capabilities should promote the development of new treatment options by allowing early and more robust patient stratification. Healthcare costs of treating SMM patients will be reduced by early identification of those patients at highest risk of MM progression who can then be targeted for treatment. The mutation signature analysis technique is relevant to a number of other cancers and potentially has applications as biomarkers of cancer progression, tumour features and responses to treatment and is likely to be a key area of future cancer research.
The PI has developed through the ERC CoG, a method of DNA mutation signature analysis which can identify risk of plasma cell clonal evolution and disease progression from asymptomatic smouldering multiple myeloma (SMM) to MM using whole genome sequencing. The MYELOMA-RISK PoC project has demonstrated that the same mutation signature methodology could be successfully applied to DNA sequence data from a targeted cancer gene panel, which is routinely performed as the standard of care for MM patients. This method results in a more cost-efficient quantification of mutation activity, which significantly reduces the costs of the analysis. A bioinformatics software tool was developed to perform this specific analysis and resulted in very high agreement with software based on whole genome and exome sequencing. This new approach shows great potential as a precision clinical tool for early identification of disease progression in early-stage MM patients where none are currently available.
Engagement with clinicians and market research conducted within the PoC indicated that there is a high demand for such a diagnostic method for this disease and there was a high acceptance of the proposed software-based approach. A business plan and IPR strategy for the exploitation of the PoC research outcomes was developed and agreed between the consortium partners.
Context and overall objectives
Current clinical assessment of MM and its pre-cursor stages are performed through a series of blood tests and medical imaging techniques usually following the onset of key signs and symptoms. Analysis of mutational signatures has the potential to accurately differentiate stable and progressive precursor conditions at an early stage or low disease burden clinical states. Our approach for mutation signature analysis could substantially reduced through optimising the amount of sequencing data required and streamlining the bioinformatics analysis pipeline. This could reduce the costs from €3-10,000 per sample for whole genome or exome sequencing that is used currently, down to approximately €500 per sample. This compares very favourably with the costs of current molecular approaches such as fluorescence in-situ hybridization (FISH) whilst providing improved prognostic information for risk of progression.
Our analysis focused on the early identification of high-risk SMM patients that will lead to improvements in patient outcomes and quality of life by initiating early treatment to those who will benefit from it, whilst those who are unlikely to progress to MM can avoid potentially toxic treatments and the psychological burden of not knowing how their disease will progress. In addition, improved diagnostic capabilities should promote the development of new treatment options by allowing early and more robust patient stratification. Healthcare costs of treating SMM patients will be reduced by early identification of those patients at highest risk of MM progression who can then be targeted for treatment. The mutation signature analysis technique is relevant to a number of other cancers and potentially has applications as biomarkers of cancer progression, tumour features and responses to treatment and is likely to be a key area of future cancer research.
APOBEC mutation activity within specific genome regions was explored to see if these locations were associated with a risk of progression to MM. Whilst high mutation regions were noted, they were not associated with the disease and fitted the expected distribution of APOBEC mutations found by other researchers. Therefore, we focused on reducing mutation signature analysis to genome regions contained within common oncogenes used in gene panels for haematological cancers. We explored the relationship of sequence depth and mutation detection to obtain minimum requirements for mutation signature analysis whilst retaining adequate sensitivity and specificity to identify MM progression.
A novel bioinformatics tool was developed in C++ specifically for mutation signature analysis from FASTQ files. This removed the need to rely on third-party software and allowed optimisation of the analytic process. The tool was evaluated using low-input sequencing data from a small clinical dataset.
Market research and engagement with relevant clinicians indicated deficiencies in the current stratification of MM patients and the need for more advanced prognostic tools. In addition, there was evidence of a high demand for digital tools and methods which can complement existing approaches and clinical workflows.
A complete business plan for bringing the bioinformatics tool to market was developed based on the market research parameters. We constructed a financial model based on preliminary projections in order to assess the potential profitability and viability of the product. The basic scenario of the study has provided a net present value (NPV) of €1,783,564 and an internal rate of return (IRR) of 19.36%.
A novel bioinformatics tool was developed in C++ specifically for mutation signature analysis from FASTQ files. This removed the need to rely on third-party software and allowed optimisation of the analytic process. The tool was evaluated using low-input sequencing data from a small clinical dataset.
Market research and engagement with relevant clinicians indicated deficiencies in the current stratification of MM patients and the need for more advanced prognostic tools. In addition, there was evidence of a high demand for digital tools and methods which can complement existing approaches and clinical workflows.
A complete business plan for bringing the bioinformatics tool to market was developed based on the market research parameters. We constructed a financial model based on preliminary projections in order to assess the potential profitability and viability of the product. The basic scenario of the study has provided a net present value (NPV) of €1,783,564 and an internal rate of return (IRR) of 19.36%.
The project has successfully demonstrated a method for significantly reducing the costs of identifying progressive MM from DNA sequencing data. The method aligns with the normal standard of care for these patients and could be used to stratify those at high-risk of disease progression, thereby improving treatment options and quality of life.
A novel bioinformatics tool was developed to advance the methodology and utilise raw DNA sequencing files to produce complete mutational signature profiles for the sequence inputted. The tool was optimised to use the reduced sequence data from gene panels generated in normal clinical workflows. Further research is required to validate the software in larger datasets and investigate its potential use for other mutational signature profiles and disease applications.
A business plan for bringing the prognostic applications of this bioinformatics tool to market was developed based on market research carried out in the project. We conclude there is very high potential for commercialisation of the tool, although consideration needs to be given to the regulatory requirements for software as a medical device.
A novel bioinformatics tool was developed to advance the methodology and utilise raw DNA sequencing files to produce complete mutational signature profiles for the sequence inputted. The tool was optimised to use the reduced sequence data from gene panels generated in normal clinical workflows. Further research is required to validate the software in larger datasets and investigate its potential use for other mutational signature profiles and disease applications.
A business plan for bringing the prognostic applications of this bioinformatics tool to market was developed based on market research carried out in the project. We conclude there is very high potential for commercialisation of the tool, although consideration needs to be given to the regulatory requirements for software as a medical device.