{"id":1197,"date":"2024-10-25T00:01:59","date_gmt":"2024-10-24T22:01:59","guid":{"rendered":"https:\/\/event.eortc.org\/ena2024\/?p=1197"},"modified":"2024-11-13T09:26:15","modified_gmt":"2024-11-13T08:26:15","slug":"digital-twin-prediction","status":"publish","type":"post","link":"https:\/\/event.eortc.org\/ena2024\/2024\/10\/25\/digital-twin-prediction\/","title":{"rendered":"Scientists create cancer patients\u2019 \u2018digital twins\u2019 to predict how well treatments may work"},"content":{"rendered":"

Friday 25 October 2024, Barcelona, Spain: <\/strong>Researchers have shown that they can accurately re-create clinical trials of new treatments using \u2018digital twins\u2019 of real cancer patients. The technology, called FarrSight\u00ae-Twin, which is based on algorithms used by astrophysicists to discover black holes, will be presented today (Friday) at the 36th EORTC-NCI-AACR [1]<\/sup> Symposium on Molecular Targets and Cancer Therapeutics in Barcelona, Spain.<\/p>\n

The researchers say that this approach could be used by cancer researchers to run virtual clinical trials before testing new treatments on patients. It could also be used alongside clinical trials with a digital twin for each patient taking part, which together could form a control group for any trial. Ultimately, it might mean that patients could have different treatments tested on their digital twin to help select the most suitable treatment ahead of time.<\/p>\n

The research is presented by Dr Uzma Asghar, Co-founder and Chief Scientific Officer at Concr and a consultant medical oncologist, currently working at The Royal Marsden NHS Foundation Trust, London, UK. She said: \u201cAround the world, we spend billions of dollars on developing new cancer treatments. Some will turn out to be successful, but most will not.<\/p>\n

\u201cWe can use digital twins to represent individual patients, build clinical trial cohorts and compare treatments to see if they are likely to be successful before testing them out with real patients.\u201d<\/p>\n

Each digital twin is created from biological data from thousands of patients with cancer who have been treated in different ways. This information is combined to recreate the cancer of a real patient with molecular data on their tumour. This digital twin makes it possible to predict how a patient is likely to respond to a treatment.<\/p>\n

Dr Asghar and her colleagues used this approach to recreate published clinical trials with a digital twin representing each real patient who took part in the trial. Overall, the digital trials accurately predicted the outcome of the actual clinical trials in all simulated clinical studies. Further testing showed that where patients received the treatment predicted by FarrSight\u00ae-Twin to be best, they had a 75% response rate, compared to 53.5% response where patients received a different treatment. \u2018Response rate\u2019 means the proportion of patients whose tumours shrank following treatment.<\/p>\n

The trials they used in the study presented at the Symposium were in patients with either breast, pancreatic or ovarian cancer. They were phase II or III trials that compared two different drug therapies, including anthracyclines, taxanes, platinum-based drugs, capecitabine and hormone treatments.<\/p>\n

Dr Asghar said: \u201cWe are excited to apply this type of technology by simulating clinical trials across different tumour types to predict patients\u2019 response to different chemotherapies and the results are encouraging.<\/p>\n

\u201cThis technology means that researchers can simulate patient trials at a much earlier stage in drug development and they can re-run the simulation multiple times to test out different scenarios and maximise the likelihood of success. It is already being used to simulate patients to act as controls for comparing the effect of a new treatment with the existing standard of care.<\/p>\n

\u201cWe are currently developing this technology so that it can predict treatment response for individual patients in the clinic and help doctors understand which chemotherapy will or will not be helpful, and this work is ongoing.\u201d<\/p>\n

Dr Asghar and her colleagues are testing the technology to see if it could help predict which available treatments will work best for patients with triple-negative breast cancer, in an observational collaborative trial between Concr, The Institute of Cancer Research, Durham University and the Royal Marsden Hospital.<\/p>\n

Professor Timothy A Yap from the University of Texas MD Anderson Cancer Center, Houston, USA, is co-chair of the EORTC-NCI-AACR Symposium and was not involved in the research. He said: \u201cDespite major improvements in cancer treatment, there are still many types of cancer where treatment options are limited. Designing and testing new cancer treatments is challenging, time-consuming and costly. If we can exploit digital tools to make this process quicker and easier, that should help us find better treatments for patients more efficiently in the future.\u201d<\/p>\n

(ends)<\/p>\n

Abstract no: 11<\/strong> \u201cTransforming cancer care and therapeutic development by predicting individual patient responses to treatment\u201d, by Uzma Asghar, presented in the Late Breaking Abstracts and Proffered Papers: Novel discoveries in drug development session, 15:00-16:30 CEST, Friday 25 October, Room 111 + 112<\/p>\n

[1] EORTC [European Organisation for Research and Treatment of Cancer, NCI [National Cancer Institute], AACR [American Association for Cancer Research]. The Symposium takes place in Barcelona from 23-25 October 2024.<\/sup><\/p>\n

Funding: Concr Ltd<\/sup><\/p>\n

The European Organisation for Research and Treatment of Cancer (EORTC), the USA\u2019s National Cancer Institute (NCI) and the American Association for Cancer Research (AACR) will join forces on 23-25 October 2024 to provide a platform for presenting recent advances in pre-clinical and clinical research. Around 1,300 international experts in the field will be discussing innovations in drug development, ground-breaking new data, the selection of new targets for anti-cancer drugs, and the impact of new discoveries in molecular biology, in order to improve the survival and quality of life of cancer patients.<\/sup><\/p>\n<\/div><\/div><\/div>

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