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Solving the cancer puzzle: the emergence of multi-omics
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Solving the cancer puzzle: the emergence of multi-omics

Advances in technology are allowing scientists to better understand the complex traits of cancer cells so that they can design better treatments
Illustration of a computer analysing a genetic sequence
Representative image | Shutterstock, Canva

The field of cancer research has undergone a major transformation in recent years, thanks to the advent of multi-omics approaches. But what is multi-omics and how will it enable faster detection and more effective treatments for cancer? Happiest Health answers this.

Multi-omics refers to the simultaneous analysis of multiple molecular data types, including metabolites, proteins, and RNA, within cancer cells. This approach has revolutionized the way scientists understand the disease, and has paved the way for new, more precise treatments.

“Focusing only on cancer cells has turned out to be a dead end,” says Dr Rudi Schmidt, Managing Director of !mmunetrue GmbH in Germany.  The company uses AI to develop oncology treatments. “Nowadays, the tumour microenvironment and other factors, such as the immune system itself, are also of great importance.”

Initially, cancer cells were simply identified as cells that kept growing. But in the 1920s, Otto Warburg found that cancer cells had a different metabolic pathway for obtaining energy than normal cells, and that cancer cells were also diverse among different individuals. In pursuit of better treatments, scientists continue to work on their understanding of the complex traits of cancer cells.

“No one technology can expose the complexity of cancer cells,” says Dr Federico Bernuzzi, a research scientist and biostatistician at the Quadram Institute Bioscience in England. “The heterogeneity of cancer cells can be uncovered by coupling metabolomics with cancer genome sequencing to identify that cancer from different tissues harbour different mutations.”

A comprehensive understanding of cancer

Metabolomics is a technique that assesses the biosynthetic pathways by which cancer cells derive their energy to multiply and spread. By combining this with cancer genome sequencing, researchers can gain a more comprehensive understanding of the different types of cancer cells, and how they interact with the rest of the body.

To gain an even more complete picture of cancer cells, researchers are now using multi-omics to analyze multiple layers of omics data. This includes high-throughput technologies that can generate large amounts of data at once. “Multi-omics can generate an inclusive interpretation of the flow of information, from knowing the cause of the disease to the potential outcome of the crosstalk happening between the cancer cells,” says Bernuzzi.

One of the biggest advances in cancer research has been the advent of next-generation sequencing. Researchers now have the ability to obtain a large amount of data in a single experiment. This has launched the modern era of cancer research, furthering the development of advanced genomics techniques that can uncover the intercommunication between cancer cells and the immune system.

“The single-cell multi-omic study can uncover every cancer cell’s unknown and uncertain architecture at different sites,” says Bernuzzi. “The single-cell resolution can help us understand the disease points and show us how tumours have been suppressing immunity and then adapting, growing, and circulating across the body, modifying their survival pattern cell by cell.”

The use of multi-omics has given a new dimension to oncology, allowing scientists to identify and classify tumours at their site, and to develop strategies for precision medicine. “The inclusion of multiple layers of omics has given immense scope to medical science to develop strategies for precision medicine,” says Bernuzzi.

Personalising cancer treatments

One of the ways that multi-omics is helping to advance cancer treatment is through personalized therapy. One example is the identification of tumor mutation burden (TMB) as a potential biomarker for predicting the response to immunotherapies, such as in lung cancer. “There is much movement in this area. A few years ago, the FDA approved a cancer therapy based on a biomarker,” says Schmidt.

Another emerging area is in using the gut microbiome, the colony of trillions of bacteria in our guts, as a prognostic marker for cancer. Studies have shown that modulating gut microbiota can impact the efficacy of anticancer treatments. These interventions could include dietary modification, like the introduction of probiotics and prebiotics, and faecal microbiota transfer (FMT).

“An altered gut microbiota (GM) is associated with resistance to chemotherapy drugs or immune checkpoint inhibitors,” says Bernuzzi.

Other personalised therapies include CAR-T Cell therapy, which uses genetically-modified immune cells to recognise tumour and kill tumour cells but not healthy cells. CAR-T is currently used for treating blood cancers such as leukaemia but is being researched to treat other types of cancers as well.

“Clinical trials are also being carried out for mRNA-based cancer vaccines,” added Bernuzzi.

But challenges still exist. It is essential to develop therapies that work in the real world and not only in small cohorts of selected trial individuals. Another challenge is integrating real-world data from clinical routines into research and upcoming therapies to battle cancer.

“With the different cellular function levels (genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes), data integration and data analysis are critical,” says Schmidt, adding that developing precision medicine in cancer therapies will also need to look at inter and intra-tumoral heterogeneity.

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