In June, scientists from Cambridge University and the Madrid-based National Cancer Research Center described a novel framework tracking chromosomal instability and copy number changes in particularly deadly cancers. Analysing almost eight thousand tumours across 33 different cancers, researchers say this marks the first time that a framework was created to understand the role of internal factors in driving such genomic alterations. 

In the same space, a different team of Cambridge scientists used whole genome sequencing to map out the largest sample of mutational signatures yet. The team identified 58 entirely new signatures and developed a computer tool that can help oncologists spot these signatures in individual patients. 

Genomic research have greatly expanded our understanding of disease pathophysiology over the years. But the recent spate of published research has stood out given the breadth of samples used and the different ways in which researchers have tackled these questions. But while these new findings improve our understanding of how genomic instability and individual mutations could be linked to cancer, it is still challenging to use them to inform therapeutic use.

In a Nature publication, Florian Markowetz, PhD, and his fellow researchers analyzed copy number signatures across a number of cancers, and found 17 types of chromosomal instability while identifying 49 new drug targets. 

The team focused on a particular set of cancers with high chromosomal instability, which are frequently high-risk indications. The genomes in such cancers no longer have just two copies, but instead can have whole chromosomes missing or multiplied and folded, explains Markowetz, who is a senior group leader at the Cancer Research UK Cambridge Institute. 

In a separate study, a team of scientists led by Dr Serena Nik-Zainal at Cambridge University Hospitals shared their findings on mutational signatures from what is described as the “biggest study of its kind.” The group analysed 12,222 samples collected through whole genome sequencing efforts of the UK National Health Service as part of the 100,000 Genomes Project and added further data on 6,418 cancers from the International Cancer Genome Consortium and the Hartwig Medical Foundation. 

Both teams had the same underlying goal. In a nutshell, Markowetz says their study looked at mutations in a cancer genome, which were sampled from the US National Cancer Institute’s Cancer Genome Atlas. But while Nik-Zainal’s study looked at single-base and double-base substitutions, Markowetz and colleagues examined much broader copy number changes. In other words, the studies both looked at finding causes for the mutations, but the data and methods were different, explains Markowetz. 

While most causes of chromosomal instability are endogenous, owing to defects within cellular machinery, work such as Nik-Zainal’s looks at point mutations that are often caused by exogenous factors, says Geoff Macintyre, PhD, junior group leader at the Spanish National Cancer Institute. 

Studying point mutations gives valuable insight into the development of mutations in cancer. But researchers in Nik-Zainal’s study looked at both—mutations that drive cancers, and passenger mutations. “When you look at these cancers, you can start to see patterns,” says Andrea Degasperi, PhD, research associate at the Early Cancer Institute and Department of Medical Genetics at Cambridge University, who was part of the study.

At the same time, other patterns are more recent and unique to the cancer cell, Degasperi adds. 

These patterns hint at the factors playing a role in generating mutations, be it environmental risks such as UV light or smoking or DNA repair mechanism defects that cause internal mutations, giving a very individual picture of each cancer from a genomic point of view, says Degasperi. Eventually, this understanding can be used for better care, and drug development, as well as for unveiling the role of external factors affecting cancer growth.

Focusing on internal factors that drive genomic instability creates an opportunity to design drugs that can synergize with DNA mismatch repair, and be less harmful to healthy cells, says Degasperi. But this approach can even be used to exploit available treatments with greater precision. Indeed, copy number changes can be used as biomarkers that distinguish the efficacy of treatments in different patients, says Markowetz. 

The Nature publication touches on this in the case of platinum-based chemotherapy. There, copy number changes can be used to differentiate between patients who respond to platinum chemotherapy and those who do not, explains Markowetz. 

Macintyre says that some of the drug targets identified in the Nature study are novel, and relevant therapies will likely appear in the next decade. But other targets already have treatments in early development, which could potentially make early drug development more efficient, he says. 

The use of copy number changes is particularly noteworthy, Macintyre says, as it can be used to identify drug targets that can work across different cancers, which differentiates it from other precision therapies that often rely on gene mutations. Those are limited by their specificity and patient population, whereas copy number changes could benefit broader blocks of patients, he explains. 

Both Markowetz and Macintyre are now taking this technology to develop new treatments at the Cambridge spinoff Tailor Bio. Markowetz, who is the technology lead at the company, says it is now raising seed funds. 

Given the large number of sequenced samples, the Nik-Zainal study was able to detect new signatures, many of which are rare, states Degasperi. Even if only a few of these rare mutational signatures point to an appropriate therapy for a medical condition, this could benefit some patients, he adds. 

Indeed, focusing on mutational burden is very important for cancer and personalized medicine-related research, says Michael Snyder, PhD, professor of genetics at Stanford University. At the moment, high mutational burden can be used to determine response to immunotherapies. In the future, it could be used to design personalized cancer vaccines, some of which are based on very specific sequences, Snyder says. But he also says that the development of drugs utilizing such methods will lag behind use with existing treatments.

Still, some key questions remain unanswered. Indeed, the origins of many mutational signature patterns still remain unknown, says Degasperi. But if some of these signatures are due to specific DNA repair defects, then they can be reproduced in the laboratory and used for finding the right drug, he explains. 

Degasperi and his colleagues developed an algorithm titled Signature Fit Multi-Step that identifies both common and rare signatures. He describes the need to build infrastructure supporting such work as a challenge requiring financial investment, training, and potentially regulation in data safety. 

Similarly, while studying chromosomal instability, it is challenging to explain which cause is historical or current and quantify it, says Macintyre. In other words, some of these changes could have taken place 20 years ago and have no therapeutic relevance, while others are ongoing and would be better to target, he explains. In this sense, differentiating between the two would require the use of single-cell whole genome sequencing. 

Research funding is often focused on single-target approaches and can be challenging to access for such cancer-agnostic approaches that are less appealing to potential investors. 

“We have a very broad platform that has the potential to work across cancer types, and for many different drugs,” he says, but “getting this funded requires a shift in mindset for venture capital and funders, and that is a major issue.”

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