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Hacking cancer: Reprogramming the Immune system and decoding cancer therapies

Step-free access with accessible toilets
Mon 19 May Doors 7:00 pm
Event 7:30 pm to 9:30 pm
The Bedford, 77 Bedford Hill,
London SW12 9HD
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Standard £5.00
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Join us for an evening discussing different tools being used to fight cancer. Hearing from PhD student Abigail Shurr on reporgramming our own immune system to defend us against cancer as well as hearing from Matthew Griffiths on how we can use computer programs and biophysical simulations to advance cancer therapies

Reprogramming the immune system against cancer

Abigail Shurr (PhD student )
What if we could turn the body’s own defences into cancer-fighting weapons? This talk will explore how we are learning to guide and supercharge the immune system to detect and destroy cancer. We’ll uncover how immune cells can be trained to resist exhaustion, outsmart tumours, and operate in the hostile environments cancer creates. From understanding how tumours manipulate the immune system to diving into cutting-edge immunotherapies, we’ll explore why these treatments don’t always work—and what researchers at UCL are doing to improve them. Join us as we explore the innovative tools used to reprogram the immune system and bring the fight directly to cancer.
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Predicting therapeutic responses to cancer therapies using AI

Dr Matthew Griffiths (Chief Technology Officer)
The uncertainty of knowing whether a treatment will be effective for a cancer patient is one of the cruellest and most damaging aspects of the disease. Despite the nearly weekly news of advancements in cancer therapies and diagnostics, only in a handful of cases oncologists can accurately predict how individual patients are going to respond to cancer therapy. My company, Concr, was founded to address that problem. In my talk I will discuss the challenges in getting effective treatments to patients and how, together with astrophysicists we’ve developed and deployed world’s first Bayesian Digital Twin technology for individualised response and outcome prediction.
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