Sussex Researchers use AI to Personalise Cancer Patient Treatments

Researchers at the University of Sussex are using Artificial Intelligence (AI) technology to analyse different types of cancer cells to understand different gene dependencies, and to identify genes that are critical to a cell's survival. Sussex researchers have done this by developing a prediction algorithm that works out which genes are essential in the cell, by analysing the genetic changes in the tumour. This can be used to identify actionable targets that in time could guide oncologists to personalise cancer patient treatments

Dr Frances Pearl, Senior Lecturer in Bioinformatics in the School of Life Sciences at the University of Sussex says: "Our vision is to take advantage of the decreasing cost of DNA sequencing and to harness the power of AI to understand cancer cell differences and what they mean for the individual patient’s treatment. Through our research, we were able to identify cell-specific gene dependencies using only the DNA sequence and RNA levels in that cell, which are easily and cheaply obtainable from tumour biopsy samples.

"This is an incredibly exciting step in our research which means that we can now work to improve the technology so that it can be offered to oncologists and help in the treatment pathways for their patients."

Cancer treatments are primarily prescribed on the basis of the location and type of cancer. Genetic differences in tumours can make standard cancer treatments ineffective. Using a personalised approach to guide treatment could improve life expectancy, quality of life and reduce unnecessary side effects of cancer patients.

In each cell, there are around 20,000 genes that contain the information needed to make proteins. Around 1,000 of those genes are essential, meaning they are required for the cell to survive. When normal cells become cancer cells, oncogenes (that is, those genes with the potential to cause cancer) become activated and tumour suppressor genes become inactivated, causing a rewiring of the cell. This causes the cell to become dependent on a new set of genes to survive, and this can then be exploited to kill the cancer cells.

By using this new technology to target protein products of tumour-specific dependent genes, cancer cells can be killed, leaving the normal cells which are not dependent on these genes relatively unharmed. Although dependencies can be determined using intensive laboratory techniques, it is costly and time consuming and would not be feasible to analyse all tumour samples in this way.

Benstead-Hume G, Wooller SK, Renaut J, Dias S, Woodbine L, Carr AM, Pearl FMG.
Biological network topology features predict gene dependencies in cancer cell-lines.
Bioinform Adv. 2022 Nov 10;2(1):vbac084. doi: 10.1093/bioadv/vbac084

Most Popular Now

MEDICA 2024 + COMPAMED 2024: Adapted Hal…

11 - 14 November 2024, Düsseldorf, Germany. The final preparations for MEDICA 2024 and COMPAMED 2024 in Düsseldorf have begun. A total of more than 5,500 exhibitors from approximately 70 countries...

AI does Not Necessarily Lead to more Eff…

The use of artificial intelligence (AI) in hospitals and patient care is steadily increasing. Especially in specialist areas with a high proportion of imaging, such as radiology, AI has long...

Commission Joins Forces with Venture Cap…

The Commission has launched a Trusted Investors Network bringing together a group of investors ready to co-invest in innovative deep-tech companies in Europe together with the EU. The Union's investment...

Why the NHS is Seeking to Make Media Ser…

Opinion Article by Dean Moody, Healthcare Services Director, Airwave Healthcare. Tim Kelsey and Martha Lane Fox called for WiFi to be made available free of charge throughout the NHS back in...

An AI-Powered Pipeline for Personalized …

Ludwig Cancer Research scientists have developed a full, start-to-finish computational pipeline that integrates multiple molecular and genetic analyses of tumors and the specific molecular targets of T cells and harnesses...

Wearable Cameras Allow AI to Detect Medi…

A team of researchers says it has developed the first wearable camera system that, with the help of artificial intelligence (AI), detects potential errors in medication delivery. In a test whose...

Philips and Medtronic Advocacy Partnersh…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, and Medtronic Neurovascular, a leading innovator in neurovascular therapies, today announced a strategic advocacy partnership. Delivering timely stroke...

AI could Transform How Hospitals Produce…

A pilot study led by researchers at University of California San Diego School of Medicine found that advanced artificial intelligence (AI) could potentially lead to easier, faster and more efficient...

New AI Tool Predicts Protein-Protein Int…

Scientists from Cleveland Clinic and Cornell University have designed a publicly-available software and web database to break down barriers to identifying key protein-protein interactions to treat with medication. The computational tool...

Great Start for Ideas and Innovations: D…

8 - 10 April 2025, Berlin, Germany. From 15 October to 15 November 2024, the DMEA invites experts from business, science, politics and practice to actively participate in shaping the congress...

Start-Ups will Once Again Have a Starrin…

11 - 14 November 2024, Düsseldorf, Germany. The finalists in the 16th Healthcare Innovation World Cup and the 13th MEDICA START-UP COMPETITION have advanced from around 550 candidates based in 62...

AI for Real-Rime, Patient-Focused Insigh…

A picture may be worth a thousand words, but still... they both have a lot of work to do to catch up to BiomedGPT. Covered recently in the prestigious journal Nature...