Scientists are mapping the genetic landscape of drug resistance in cancers

All mutations in cancer that cause drug resistance fall into one of four categories. New research has detailed each type, helping to uncover targets for drug development and identify potentially effective second-line treatments.

In a new large-scale study, researchers from the Wellcome Sanger Institute, EMBL’s European Bioinformatics Institute (EMBL-EBI), Open Targets and colleagues used CRISPR gene editing to map the genetic landscape of drug resistance in cancers, focusing in the colon, in the lung. and Ewing sarcoma. The team explains how known mutations affect drug resistance and highlights new DNA changes that could be explored further.

The research, published today (October 18) in Genetics of Natureinvestigated the effect of mutations on sensitivity to 10 anticancer drugs, also identifying potentially effective second-line treatments based on an individual’s genetic makeup.

By understanding the mechanisms of how cancers become resistant to treatment, researchers can identify new targets for personalized therapies, help treat patients based on the genetic makeup of their cancer, give second-line treatment options to those without currently none and help develop further research into next-generation cancer drugs that could prevent the emergence of drug resistance.

One of the biggest challenges in cancer treatment is drug resistance. Mutations in cancer cells mean they become less responsive to treatments over time. After the cancer becomes resistant to the initial treatment, the following treatments are known as second-line treatments, and the options for these may be limited. Understanding the molecular changes that cause resistance, and what can be done to counter it, can help reveal new options and inform clinical pathways for specific mutations.

However, current methods for identifying drug-resistant mutations require multiple samples from patients collected over a long period of time, making this a time-consuming and difficult process.

To gather large-scale information about cancer mutations, the team from the Wellcome Sanger Institute, EMBL-EBI, Open Targets and colleagues used cutting-edge CRISPR gene editing and single-cell genomic techniques to investigate the impact of several drugs on man. cancer cell lines and organoid cell models. By combining these techniques, the researchers were able to create a map showing drug resistance in different cancers, focusing on colon, lung and Ewing’s sarcoma. The map reveals more about drug resistance mechanisms, highlights DNA changes that may be potential treatment biomarkers, and identifies promising combination or second-line therapies.

The team found that cancer mutations fall into four different categories depending on the impact of the DNA change. Drug resistance mutations, otherwise known as normal drug resistance mutations, are genetic changes in the cancer cell that cause the drug to be less effective. For example, changes that mean the drug can no longer bind to its target in the cancer cell.

Drug addiction mutations cause some of the cancer cells to use the drug to help them grow, rather than destroy them. This research supports the use of drug breaks in addiction transitions, which are periods without treatment. This could help destroy cancer cells with this type of mutation, as the cells are now dependent on the treatment.

Driver mutations are gain-of-function genetic changes that allow cancer cells to use a different signaling pathway to grow, avoiding the pathway that the drug may have blocked.

Finally, drug-sensitizing variants are genetic mutations that make cancer more sensitive to certain treatments and could mean that patients with these genetic changes in their tumors will benefit from certain drugs.

The research focused on colon, lung and Ewing sarcoma cancer cell lines, all of which are prone to developing resistance and have limited second-line treatments available. The team used 10 cancer drugs that are either currently prescribed or undergoing clinical trials to see if any of them could be repurposed or used in combination to tackle resistance, reducing the time it would take to get potential treatments in the clinic.

Understanding more about the four different types of DNA changes can help support clinical decisions, explain why treatments don’t work, support the idea of ​​drug holidays in some patients, and help develop new treatments. This knowledge is also helping to accelerate drug companies’ research into next-generation cancer inhibitors that could better avoid drug resistance.

Cancer cells developing resistance to treatments is a huge problem, and having a quick way to identify these mutations in patients and understand how to fight them is key to treating cancer. Our study details how the mutations fall into four different groups, which may require different treatment plans. For example, if drug addiction mutations are present, taking a break from treatment may help. Using cutting-edge genetic techniques, we have begun to build a large-scale and rapid way to understand drug resistance and hope to find new targets for second-line therapies.”

Dr. Matthew Coelho, first author from the Wellcome Sanger Institute and Open Targets

Dr Magdalena Strauss, study author formerly of EMBL’s European Bioinformatics Institute (EMBL-EBI) and now at the University of Exeter, said: “By combining cutting-edge CRISPR gene editing and single-cell techniques with statistical machine learning, we have in position to gain a detailed picture of the specific mechanisms by which each of the individual mutations we studied affects drug response. Adding to our collective knowledge, it also highlights mutations that could be used as biomarkers, highlighting cancer cells that are more sensitive to certain treatments, which could help inform future clinical trials.

Dr Mathew Garnett, senior author from the Wellcome Sanger Institute and Open Targets, said: “Before this study, it was difficult to understand on a large scale why and how drug resistance develops in cancer. This research brings us a step closer to being able to match combination or second-line therapies to a person’s genetic makeup, to try to ensure that treatments are as effective and personalized as possible.Furthermore, we believe that our new systematic approach will be important to understanding the genetic mechanisms of resistance to new drugs in the future. This could help even before resistance emerges in the clinic, and this early knowledge will improve the development of cancer treatments.”