Researchers have developed a new tool to help improve the accuracy of whole genome sequencing (WGS) analysis for patients with haematological cancers, which affect the blood, bone marrow or lymph nodes.
In cancer, WGS can be used to identify genetic changes that drive cancer development by comparing DNA from a patient’s tumour to the DNA in their healthy or ‘normal’ tissue. To do this, two samples need to be taken, one directly from the tumour as well as a ‘normal’ blood sample.
The new tool, developed by researchers from Genomics England, University of Trieste and Great Ormond Street Hospital for Children NHS Foundation Trust, aims to help address challenges in interpreting WGS cancer data when contamination of ‘normal’ samples occurs.
Bioinformatics pipelines designed to help analyse WGS data from patients with cancer can run into issues if a patient’s ‘normal’ sample has been contaminated by tumour cells, affecting the accuracy of the results generated from these pipelines. This problem is particularly relevant in blood cancers due to the natural spread of tumour cells within the bloodstream.
To help solve this challenge, the research team developed a new tool, known as TINC, to estimate the level of tumour contamination in normal samples based on an existing machine learning model used to understand tumour evolution. The results are published today in Nature Communications.
The TINC tool generates an easily interpretable score for the percentage of tumour cells in the normal sample, so that if a high level of contamination is detected it can trigger an alternative analytical workflow. This helps ensure clinical scientists using WGS to support the diagnosis and treatment of patients with cancer are provided with accurate data.
Genomics England supports the NHS to deliver whole genome sequencing for a number of cancer types via the NHS Genomic Medicine Service. The TINC tool has now been implemented into Genomics England’s clinically accredited bioinformatics pipelines to support WGS analysis for patients with haematological cancers.
The researchers validated the TINC tool using participant data from the 100,000 Genomes Project as well as against standard technologies used for minimal residual disease testing in blood cancers, which checks the number of cancer cells that remain in patient’s blood after treatment.
