Artificial Intelligence Takes On Cancer: AI Analysis of Mutations Could Lead to Improved Therapy

Digital DNA Concept

The researchers have devised a computational analysis method to detect and distinguish the functional impacts of somatic structural variations.

Cancer is a complex and diverse disease, and its range of associated mutations is vast. The combination of these genomic changes in an individual is referred to as their “mutational landscape.” These landscapes vary based on the type of cancer, and even individuals with the same type of cancer can have very different mutation patterns.

Scientists have already documented the mutational landscapes of several forms of cancer. Somatic structural variations (SVs) have been found to account for over 50% of all cancer-causing mutations. These mutations occur in cells over time, such as through copying errors in DNA during cell division, resulting in alterations to the chromosome structure.

They are not inherited and are found only in affected cells and in their daughter cells. As we age, such genomic alterations become more numerous, and a person’s mutational landscape increasingly comes to resemble a unique mosaic.

Although somatic SVs play a crucial role in cancer development, relatively little is known about them. “There is a lack of methods that analyze their effects on cell function,” explains Dr. Ashley Sanders, who heads the Genome Stability and Somatic Mosaicism Lab at the Max Delbrück Center. That’s changing thanks to new research findings, which Sanders recently published in the journal Nature Biotechnology along with the European Molecular Biology Laboratory (EMBL).

“We developed a computational analysis method to detect and identify the functional effects of somatic SVs,” she reports. This enabled the team to understand the molecular consequences of individual somatic mutations in different leukemia patients, giving them new insights into the mutation-specific alterations. Sanders says it may also be possible to use these findings to develop therapies that target the mutated cells, adding that “they open up exciting new avenues for personalized medicine.”

Even more detailed than conventional single-cell analyses

Their calculations are based on data from Strand-seq – a special single-cell sequencing method that Sanders played an instrumental role in developing and that was first introduced to the scientific community in 2012. This technique can examine a cell’s genome in much greater detail than conventional single-cell sequencing technologies. Thanks to a sophisticated experimental protocol, the Strand-seq method can independently analyze the two parental DNA strands (one from the father and one from the mother).

With conventional sequencing methods, distinguishing such homologs – chromosomes that are similar in shape and structure but not identical – is nearly impossible. “By resolving the individual homologs within a cell, somatic SVs can be identified much better than with other methods,” explains Sanders. The approach used for doing this was described by the researcher and her colleagues in a paper that appeared in Nature Biotechnology in 2020.

The research team is part of the joint research focus “Single-Cell Approaches for Personalized Medicine” of the Berlin Institute of Health at Charité (BIH), Charité – Universitätsmedizin Berlin, and the Max Delbrück Center.

Building on this work, they are now able to also determine the positions of nucleosomes in each cell. Nucleosomes are units of DNA wrapped around protein complexes called histones, and play a crucial role in organizing chromosomes. The position of nucleosomes can change during gene expression, with the type of wrapping revealing whether or not a gene is active. Sanders and her colleagues developed a self-learning algorithm to compare the gene activity of patient cells with and without somatic SV mutations, allowing them to determine the molecular impact of the structural variants.

New targets for cancer therapy

“We can now take a sample from a patient, look for the mutations that led to the disease, and also learn the signaling pathways that the disease-causing mutations disrupt,” explains Sanders. For example, the team was able to identify a rare but very aggressive mutation in a leukemia patient. The nucleosome analysis provided the researchers with information about the signaling pathways involved, which they used to specifically inhibit the growth of cells containing the mutation. “This means that a single test tells us something about the cellular mechanisms involved in cancer formation,” says Sanders. “We can eventually use this knowledge to develop personalized treatments, guided by each patient’s unique condition.”


“Functional analysis of structural variants in single cells using Strand-seq” by Hyobin Jeong, Karen Grimes, Kerstin K. Rauwolf, Peter-Martin Bruch, Tobias Rausch, Patrick Hasenfeld, Eva Benito, Tobias Roider, Radhakrishnan Sabarinathan, David Porubsky, Sophie A. Herbst, Büşra Erarslan-Uysal, Johann-Christoph Jann, Tobias Marschall, Daniel Nowak, Jean-Pierre Bourquin, Andreas E. Kulozik, Sascha Dietrich, Beat Bornhauser, Ashley D. Sanders and Jan O. Korbel, 24 November 2022, Nature Biotechnology.
DOI: 10.1038/s41587-022-01551-4

“Single-cell analysis of structural variations and complex rearrangements with tri-channel processing” by Ashley D. Sanders, Sascha Meiers, Maryam Ghareghani, David Porubsky, Hyobin Jeong, M. Alexandra C. C. van Vliet, Tobias Rausch, Paulina Richter-Pechańska, Joachim B. Kunz, Silvia Jenni, Davide Bolognini, Gabriel M. C. Longo, Benjamin Raeder, Venla Kinanen, Jürgen Zimmermann, Vladimir Benes, Martin Schrappe, Balca R. Mardin, Andreas E. Kulozik, Beat Bornhauser, Jean-Pierre Bourquin, Tobias Marschall and Jan O. Korbel, 23 December 2019, Nature Biotechnology.
DOI: 10.1038/s41587-019-0366-x

Be the first to comment on "Artificial Intelligence Takes On Cancer: AI Analysis of Mutations Could Lead to Improved Therapy"

Leave a comment

Email address is optional. If provided, your email will not be published or shared.