Image source: Shutterstock/ktsdesign

Tool to identify tumour mutations

Machine learning fuels personalised cancer medicine

The Biomedical Genomics laboratory at the Institute for Research in Biomedicine (IRB) Barcelona has developed a computational tool that identifies cancer driver mutations for each tumour type.

This and other developments produced by the same lab seek to accelerate cancer research and provide tools to help oncologists choose the best treatment for each patient. The study has been published in the journal Nature.

Each tumour—each patient—accumulates many mutations, but not all of them are relevant for the development of cancer. Researchers led by ICREA researcher Dr. Núria López-Bigas at IRB Barcelona have developed a tool, based on machine learning methods, that evaluates the potential contribution of all possible mutations in a gene in a given type of tumour to the development and progression of cancer.

This information helps us to understand how a tumour is caused at the molecular level and it can facilitate medical decisions regarding the most appropriate therapy for a patient

Núria López-Bigas

In previous work that is already available to the scientific and medical community, the laboratory developed a method to identify those genes responsible for the onset, progression, and spread of cancer. "BoostDM goes further: it simulates each possible mutation within each gene for a specific type of cancer and indicates which ones are key in the cancer process. This information helps us to understand how a tumour is caused at the molecular level and it can facilitate medical decisions regarding the most appropriate therapy for a patient," explains Dr. López-Bigas, head of the Biomedical Genomics lab. In addition, the tool will contribute to a better understanding of the initial processes of tumour development in different tissues.

The new tool has been integrated into the IntOGen platform, developed by the same group and designed to be used by the scientific and medical community in research projects, and into the Cancer Genome Interpreter, also developed by this group and which is more focused on clinical decision-making by medical oncologists. BoostDM currently works with the mutational profiles of 28,000 genomes analysed from 66 types of cancer. The scope of BoostDM will grow as a result of the foreseeable increase in publicly accessible cancer genomes.

To identify the mutations involved in cancer, the scientists based themselves on a key concept in evolution, namely positive selection. Mutations that drive the growth and development of cancer are found in higher numbers in distinct samples, compared to those that would occur randomly. "We started from the premise that we only get to observe some mutations because the tumour cells with this mutation guide the development of the tumour, and we questioned what distinguishes these mutations from other possible mutations," says Dr. Ferran Muiños, postdoctoral researcher and co-first author of the work. "Doing this analysis manually would be excessively laborious, but there are computational strategies that allow it to be organised systematically and efficiently," he adds.

The research team: Dr. Ferran Muiños , Dr. Francisco Martinez-Jimenez, Dr....
The research team: Dr. Ferran Muiños , Dr. Francisco Martinez-Jimenez, Dr. Abel González-Pérez, Dr. Oriol Pich and Dr. Núria López-Bigas

Image source: IRB Barcelona

From the data, the proposed method learns what attributes are distinctive of the mutations that favour the development of cancer and this information is useful for the development of new therapeutic approaches. 

The tool that the researchers have developed has already generated 185 models to identify mutations in a specific gene in a given type of cancer. For example, it has produced a model that has identified all the possible mutations in the EGFR gene that trigger tumour development in some lung cancers, another model for the same gene in cases of glioblastoma (a type of cancer that affects the brain), etc.

As sequencing data on tumours become publicly accessible, it can be incorporated into the system, allowing it to generate new models for all cancer genes in the coming years. When a model has been developed, researchers can interrogate each possible mutation of a cancer gene in a tissue type (in a process known as saturation mutagenesis) and determine whether it is relevant for the development of the disease. This process produces a map of key mutations, which is valuable for both cancer research and personalised cancer medicine, and medical decision-making. The authors have demonstrated that this prediction model tool, BoostDM, is more efficient and accurate than experimental approaches.


Source: IRB Barcelona

30.07.2021

Read all latest stories

Related articles

Photo

Identificación de mutaciones tumorales

El aprendizaje automático impulsa la medicina personalizada del cáncer

El laboratorio de Genómica Biomédica del IRB Barcelona (Institute for Research in Biomedicine) ha desarrollado un método computacional que identifica las mutaciones causantes del cáncer para cada…

Photo

First full genome of a living organism assembled

Researchers in Canada and the U.K. have for the first time sequenced and assembled de novo the full genome of a living organism, the bacteria Escherichia Coli, using Oxford Nanopore's MinIONTM…

Photo

Functional diagnostics

New assay could advance personalized cancer treatment

A new study from the University of Helsinki shows that cells that are freshly isolated from lung cancers can be used to create robust drug response data. This approach can identify actionable or…

Related products

Agfa - Smart XR

Accessories/ Complementary Systems

Agfa - Smart XR

Agfa HealthCare
Canon - Advanced Intelligent Clear-IQ Engine for CT

Artificial Intelligence

Canon - Advanced Intelligent Clear-IQ Engine for CT

Canon Medical Systems Europe B.V.
Canon – Advanced intelligent Clear-IQ Engine for MR

Artificial Intelligence

Canon – Advanced intelligent Clear-IQ Engine for MR

Canon Medical Systems Europe B.V.
Canon - Aquilion Exceed LB

Oncology CT

Canon - Aquilion Exceed LB

Canon Medical Systems Europe B.V.
Canon - HIT Automation Platform

Artificial Intelligence

Canon - HIT Automation Platform

Canon Medical Systems Europe B.V.
Canon Medical - CT Scan Unit

Mobile CT Solutions

Canon Medical - CT Scan Unit

Canon Medical Systems Europe B.V.
Subscribe to Newsletter