An algorithm shedding light on the evolution of tumours helps individualise cancer therapies

Software that maps out the history of malignant tumours helps determine what makes the disease progress, offering potential for finding the best forms of treatment.

The genome of cancerous tumours grows much like a tree: a mutation occurs in a single tumour cell, resulting in different development paths for certain cells of the tumour. Over time, new mutations take place in the divergent cells. The tree grows.

This a genuine cause of concern for those developing cancer therapies, as a continuously transforming disease easily eludes drug treatment. Even within a single tumour, mutations can make cancer cells evolve along different paths, which makes it more difficult to find an efficacious therapy.

“It’s important to understand the current developmental stage of the tumour and its route to that stage. Once this is known, it becomes easier to find an individualised therapy for the patient that suits the cancer type and the developmental stage of the particular tumour they have,” explains researcher Alexandru Tomescu from the Department of Computer Science, University of Helsinki.

A multidisciplinary research group headed by Tomescu and Professor Martin Milanič from the Slovenian University of Primorska has developed a program that helps track the genetic evolutionary history of cancer tumours with a high degree of precision.

Historical data is important, because based on such data, it is possible to identify what are known as driver mutations in the tumour's DNA. These are gene mutations that drive the development of malignant tumours.

If driver mutations and their location in genes are identified, physicians have a better chance of choosing a drug that is effective against the specific disease of an individual patient.

Genome data reveals history

The algorithm employed by the program analyses sequencing data gained from tumour cell samples, reconstructing it into an evolutionary history of the tumour.

“In a sense, this is archaeology of tumour cells carried out using computational methods,” Tomescu says.

Several computational models for determining the evolutionary history of tumours are already in use, one of which Tomescu’s group utilised as the basis for their program.

However, the new program was able to describe the evolutionary history of tumours in more detail than any previous method. It also takes into account the possibility that the internal evolution of the tumour may have branched off into several different paths, and that these paths can be jumbled together in the tumor cell samples.

The method was successful with both data collected from a model simulating evolution and with data gained from actual cancer types.

“Through appropriate application, this tool can make a difference in patient care. In addition, our tool only needs a couple of minutes to complete its task, which may take several days for other methods,” Tomescu adds.

The program is openly available. For the time being, the method is not used in patient care, but the preconditions are favourable. The advantage of the program is its utilisation of input and output formats already well-established in bioinformatics, making its potential future deployment smooth.

Targeting specialised treatment

Tomescu points out that other such methods of analysis are constantly being developed, and their accuracy is definitely improving. Certain sequencing techniques are already able to interpret the genome of single cells, providing an even more precise picture of tumour evolution in the future.

“Ideally, we would be able to sequence a tumour immediately after the cancer diagnosis. Mutations could be identified, which, in turn, would enable the deduction of the tumour’s entire evolutionary history. Subsequently, the physician would be able to choose the treatment best suited to the individual patient. The test could then be repeated at a later date, adapting the cancer therapy when necessary.”

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Genome-Scale Algorithmics research group
Master's Programme in Life Science Informatics