̽��ֱ�� of Cambridge - James Brenton

Minister for AI and Digital Government visits Cambridge to tour the DAWN supercomputer

Anonymous — Mon, 13 Jan 2025 16:34:50 +0000

Feryal Clark MP was welcomed to the ̽��ֱ��’s DAWN supercomputer facility, located on the ̽��ֱ��’s West Cambridge Innovation District, by Pro-Vice-Chancellor for Research, Professor John Aston, and Dr Paul Calleja, Director of Research Computing Services at the ̽��ֱ��.

Together they toured the DAWN supercomputer and met with representatives from academia and industry partners who have ambitious plans for AI and supercomputing in Cambridge. ̽��ֱ��visit comes as the Government opens a UK-wide call for early access to the new AI Research Resource service, of which DAWN is part of.

Now up and running in its state-of-the-art Data Centre in Cambridge, DAWN is currently the most powerful AI supercomputer in the UK, with more than a thousand top-end Intel graphics processing units (GPUs) operating inside its server stacks. ̽��ֱ��supercomputer’s bespoke innovations in hardware and software result from a long-term co-design partnership between the Cambridge Open Zettascale Lab, directed by Dr Paul Calleja, and global tech leaders Intel and Dell Technologies, with support from the UK Atomic Energy Authority (UKAEA), StackHPC and UK Research & Innovation.

Our world-leading researchers are exploring how AI can benefit society.

̽��ֱ��Vice-Chancellor, Professor Deborah Prentice, also welcomed the Minister to the Wolfson Brain Imaging Centre on the Cambridge Biomedical Campus, where they were able to learn about the impact of DAWN and AI on patients, with a demonstration of the advances in healthcare.

Feryal Clark MP later toured the ̽��ֱ��’s latest brain imaging scanner and heard from leading ̽��ֱ�� researchers who are utilising DAWN supercomputing capabilities and AI to improve patient outcomes, and develop new and innovative treatments.

̽��ֱ��Minister moved on to meet with Professor Zoe Kourtzi, whose team are working on improving the early diagnosis of Alzheimer's, for which AI can help develop tools by combining diverse data sources that provider a richer picture of a patient’s brain health. Professor James Brenton also presented on his work developing a comprehensive clinical decision-making support platform that integrates and refines cancer patient data from multiple sources into a single, much more manageable tool. Feryal Clark MP further heard from researcher Bill McGough, who is working on a project to develop an AI tool to detect renal cancers in non-contrast and low-dose CT, to enable kidney screening in the UK.

̽��ֱ�� ̽��ֱ�� of Cambridge is home to world-leading researchers in AI, to students enthusiastic about the potential of AI, and to an innovation ecosystem that is successfully translating this research into innovative new start-ups and creating jobs. ̽��ֱ�� ̽��ֱ��’s flagship AI@Cam is harnessing the ̽��ֱ��'s interdisciplinary research to drive a new wave of AI innovation that delivers public value.

̽��ֱ��Minister for AI and Digital Government, Feryal Clark MP, visited the ̽��ֱ�� of Cambridge on the day the Government announced their new AI Action Plan.

Lloyd Mann / ̽��ֱ�� of Cambridge

Left to right: Nicola Ayton, Deputy Chief Executive of Cambridge ̽��ֱ�� Hospitals (CUH), Feryal Clark MP, Minister for AI and Digital Government, Vice-Chancellor of Cambridge ̽��ֱ��, Professor Deborah Prentice

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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AI versus cancer - the Cambridge researchers using machine intelligence to beat disease

sb726 — Mon, 15 Jul 2024 07:00:31 +0000

‘Game changing’ technology which can predict how patients will respond to cancer treatment is part of a wave of Cambridge research harnessing AI to fight the disease.

Cancer isn’t fair – but care should be

lw355 — Sun, 04 Feb 2024 07:50:57 +0000

Listening to people's lived experiences is helping to improve the awareness and uptake of cancer care. On World Cancer Day, we take a look at some of the ways researchers are working with communities to ‘close the cancer care gap’.

̽��ֱ��women helping to change the story of ovarian cancer

lw355 — Mon, 24 Jan 2022 13:40:02 +0000

Every patient with cancer has a story to tell of their journey through diagnosis and treatment. We meet a group of women who are at the centre of pioneering research in Cambridge that’s changing the outcome of ovarian cancer – helping to create treatments that are as unique as their stories.

Celestial bodies

cjb250 — Wed, 04 Feb 2015 14:05:52 +0000

Despite their red-brick finish, the corridors of the Institute of Astronomy can seem more like an art gallery than a research centre, so beautiful are the images of supernovae and nebulae hanging there. Dr Nic Walton passes these every day as he makes his way to his office to study the formation of the Milky Way and search for planets outside our solar system.

On the screen of Walton’s computer is what appears to be a map of stars in our Milky Way. In fact, it is something that is around 25 orders of magnitude smaller (that’s ten followed by 25 zeros).

It is an image of cells taken from a biopsy of a patient with breast cancer; the ‘stars’ are the cells’ nuclei, stained to indicate the presence of key proteins. It is the similarities between these patterns and those of astronomical images that he, together with colleagues at the Cancer Research UK (CRUK) Cambridge Institute, is exploiting in PathGrid, an interdisciplinary initiative to help automate the analysis of biopsy tissue.

“Both astronomy and cell biology deal with huge numbers: our Milky Way contains several hundred billion stars, our bodies tens of trillions of cells,” explained Walton.

PathGrid began at a cross-disciplinary meeting in Cambridge to discuss data management. Walton has been involved for many years with major international collaborations that, somewhat appropriately, amass an astronomical amount of data. But accessing data held by research teams across the globe was proving to be a challenge, with a lack of standardised protocols. Something needed to be done and Walton was part of an initiative to sort out this mess.

̽��ֱ��issue of data management in an era of ‘big data’ is not unique to astronomy. Departments across the ̽��ֱ�� – from the Clinical School to the Library – face similar issues and this meeting was intended to share ideas and approaches. It was at this meeting that Walton met James Brenton from the CRUK Cambridge Institute. They soon realised that data management was just one area where they could learn from each other: image analysis was another.

Walton and his colleagues in Astronomy capture their images using optical or near-infrared telescopes, such as the prosaically named Very Large Telescope or the recently launched Gaia satellite, the biggest camera in space with a billion pixels. These images must then be manipulated to adjust for factors including the telescope’s own ‘signature’, cosmic rays and background illumination. They are tagged with coordinates to identify their location, and their brightness is determined.

Analysing these maps is an immense, but essential, task. Poring over images of tens of thousands of stars is a laborious, time-consuming process, prone to user error, so this is where computer algorithms come in handy. Walton and colleagues run their images through object detection software, which looks for astronomical features and automatically classifies them.

“Once we start characterising the objects, looking at what’s a star, what’s a galaxy, then we start to see the really interesting bigger picture. Light is distorted by gravitational mass on its way to us, so the shapes of the galaxies, for example, can tell us about the distribution of dark matter towards them. When we start counting stars, we start to see structures, like tidal streams.”

Professor Carlos Caldas, one of Brenton’s colleagues at the CRUK Institute, and now a collaborator of Walton’s, says the problems faced by medical pathologists are very similar, if at the opposite extreme of measurements. Could the same algorithms help pathologists analyse images taken by microscopes?

When a patient presents with suspected breast cancer, a pathologist takes a core of the tumour tissue – a tiny sample, less than 1 mm in diameter. ̽��ֱ��tumour samples are arranged on a block, typically together with 200 other samples taken from different patients. Each sample needs to have its own ‘coordinates’ so that the researchers know that a particular tumour came from a particular patient.

“We then cut a slice of the 200 or so cores, mount it into a slide that is stained, and take a digital picture of this slide,” explained Caldas, “but each of these high-resolution images is a few gigabytes of data, so we quickly accumulate hundreds of terabytes of data.”

By adapting the astronomers’ image analysis software, the PathGrid collaborators are able to analyse the tumour images, for example to recognise the three types of cells in the tissue samples: cancer cells, immune cells and stromal cells. Just as object identification in astronomy reveals hidden patterns and information, so the information from the slides begins to tell researchers how the different cell types relate to each other. Staining the samples to highlight elements such as potentially important proteins could also help the researchers identify new biomarkers to aid in the diagnosis or prognosis of cancers.

Equally important will be how the data is stored so that several years down the line, as researchers find new questions to ask, they can still access and analyse any of the 15,000 different tumours and their hundred stains. “We need to know that at some point in the future we can extract sample 53, for example, or find all tumours that were positive for a particular stain,” said Caldas. “Imagine if you had a million sheets of paper and you just threw them all into a room and asked someone to find page 53. They’d have to sort through all the papers to find the right one, but if you could make it glow, you’d be able to find it more easily. This is similar to what we do, except we do this digitally.”

As well as this technology allowing oncologists to ask new questions and at a much larger scale, Caldas believes that in the future it could be used as ‘digital pathology’, aiding diagnosis and prognosis even in regions with no specialist oncologists. “You could imagine a scenario where a clinician takes a biopsy and a pathologist processes and stains the slide, takes a picture and digitally relays it. This is then analysed by one of the algorithms to say if it is a tumour, identify the tumour type and say how aggressive it will be.”

Walton makes an interesting and unexpected comparison between his and Caldas’s work: “We deal with star deaths, they deal with patient deaths.” If PathGrid is successful, this might change: while the astronomers continue to watch star deaths, their collaborators will hopefully become even better at preventing many more patient deaths from cancer.

Inset image: Left - Carlos Caldas; right - Nic Walton

Astronomy and oncology do not make obvious bedfellows, but the search for new stars and galaxies has surprising similarities with the search for cancerous cells. This has led to new ways of speeding up image analysis in cancer research.

We deal with star deaths, they deal with patient deaths

Nic Walton

̽��ֱ��District

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Scientists develop simple blood test to track tumour evolution in cancer patients

bjb42 — Thu, 02 May 2013 09:06:52 +0000

By tracking changes in patients’ blood, Cambridge scientists have created a new way of looking at how tumours evolve in real-time and develop drug resistance. ̽��ֱ��research was published in the print edition of Nature today, 02 May.

Scientists at the Cancer Research UK Cambridge Institute at the ̽��ֱ�� of Cambridge used traces of tumour DNA, known as circulating tumour DNA (ctDNA), found in cancer patients’ blood to follow the progress of the disease as it changed over time and developed resistance to chemotherapy treatments.

For the study, which was co-directed by Dr James Brenton, Professor Carlos Caldas, and Dr Nitzan Rosenfeld from the ̽��ֱ��'s Cancer Research UK Cambridge Institute, they followed six patients with advanced breast, ovarian and lung cancers and took blood samples, over one to two years. They then focused analysis on those samples that contained relatively higher concentrations of tumour ctDNA,

By looking for changes in the tumour ctDNA before and after each course of treatment, they were able to identify changes in the tumour’s DNA that were likely linked to drug resistance following each treatment session.

Using this new method they were able to identify several changes linked to drug-resistance in response to chemotherapy drugs such as paclitaxel (taxol) which is used to treat ovarian, breast and lung cancers, tamoxifen which is used to treat oestrogen-positive breast cancers and transtuzumab (Herceptin) which is used to treat HER2 positive breast cancers.

̽��ֱ��researchers hope this new approach could facilitate research on how cancer tumours develop resistance to some of our most effective chemotherapy drugs as well as providing an alternative to current methods of collecting tumour DNA – by taking a sample direct from the tumour – a much more difficult and invasive procedure.

Dr Rosenfeld said: “Tumours are constantly changing and evolving which helps them develop a resistance to many of the drugs we currently give patients to treat their disease. We’ve shown that a very simple blood test can be used to collect enough tumour DNA to suggest to us what parts of the cancer’s genetic code is changing and creating tumour resistance to chemotherapy or biologically-targeted therapies.

“We hope that our discoveries can pave the way to helping us understand how cancers develop drug resistance as well as identifying new potential targets for future cancer drugs.”

Dr Brenton added: "Importantly, this advance means that we will be able to screen a much larger number of genes in the blood to test if specific genetic changes in the cancer explain resistance to treatment. ̽��ֱ��low cost and high acceptability of a blood sample means that this can be done across hundreds or thousands of patients. This is vital to discover reliable clinical biomarkers."

Professor Caldas said: " ̽��ֱ��tracking of different cancer clones in real time using a liquid biopsy will have enormous value to identify drug resistance in the clinic and adjust therapy accordingly."

̽��ֱ��Cancer Research UK Cambridge Institute is a major research centre which aims to take the scientific strengths of Cambridge to practical application for the benefit of cancer patients. ̽��ֱ��Institute is a unique partnership between the ̽��ֱ�� of Cambridge and Cancer Research UK. It is housed in the Li Ka Shing Centre, a state-of-the-art research facility located on the Cambridge Biomedical Campus which was generously funded by Hutchison Whampoa Ltd, Cambridge ̽��ֱ��, Cancer Research UK, ̽��ֱ��Atlantic Philanthropies and a range of other donors. For more information visit www.cruk.cam.ac.uk.

Story adapted from CRUK press release.

For more information about this story, please contact: Genevieve Maul, Office of Communications, ̽��ֱ�� of Cambridge. Email: Genevieve.Maul@admin.cam.ac.uk; Tel: 01223 765542.

Research sheds light on how tumours develop drug resistance

We hope that our discoveries can pave the way to helping us understand how cancers develop drug resistance

Nitzan Rosenfeld

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Data mining the complex cancer landscape

bjb42 — Mon, 04 Jan 2010 14:27:17 +0000

̽��ֱ��advent of new technologies has enabled researchers to interrogate human genomes at unprecedented resolution. These technologies have led to a wealth of genomic information that has the capacity to shed new light on complex diseases, but they also generate far too much data to interpret by eye. Consequently, novel computational and statistical techniques have evolved to extract meaningful patterns from the data (‘data mining’) and to integrate this with other types of biological information in an attempt to make sense of what it all means.

In Cambridge, a collaboration between the Computational Biology group at the Cancer Research UK Cambridge Research Institute (CRI), led by Professor Simon Tavaré (also at the Department of Applied Mathematics and Theoretical Physics), and Professor Carlos Caldas and Dr James Brenton (who are both at the CRI and the Department of Oncology) seeks to do just this, demonstrating the power of combining expertise in statistics, computational and experimental biology, and clinical medicine to understand cancer.

METABRIC

Breast cancer, like other malignancies, is driven by the progressive acquisition of key genetic alterations that confer growth advantages on the cell. Most of the acquired aberrations are merely ‘passenger’ events that do not influence cancer progression, whereas the ‘driver’ events are critical mediators of disease. It is these ‘driver’ events and the effects they elicit that researchers seek to identify, the problem being akin to searching for a needle in a haystack.

METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) is an Anglo-Canadian effort aimed at finding the ‘needles’, and is funded by Cancer Research UK and the British Columbia Cancer Foundation. Given the substantial molecular heterogeneity among breast cancer patients, METABRIC seeks to interrogate the genomic and transcriptional landscape of over 2,000 clinically annotated breast tumour specimens to generate a robust molecular description of the disease.

Each sample is examined for differences in gene expression levels, as well as for alterations in the number of copies of each gene, by combining measurements from nearly two million probes that query the genome at sequential positions. Add to this a variety of other data types and five years of clinical history, and you wonder how the sheer volume of data can be processed,

let alone interpreted. In fact, a major challenge with such high-dimensional datasets is the identification of true differences amid inherent background noise. How can one see the forest for the trees in the cancer genome landscape?

Seeing the forest for the trees

Dr Christina Curtis in the Computational Biology group is leading the analysis of massively parallel technologies using computational and statistical methods to make sense of this vast data deluge. Much of her research relies on high-performance computing to process these and other large datasets. Fortunately, the CRI houses a computer cluster with nearly 500 cores and multiple high-memory nodes that will expand to meet the growing need for computational capacity.

Using novel analytical approaches for data integration and mining, Dr Curtis and her team have defined additional subtypes of breast cancer based on their unique molecular characteristics, providing fresh insight into mechanisms of breast cancer tumourigenesis. Future work will relate these molecular profiles to specific clinical phenotypes and identify markers that improve the classification of breast cancer. Importantly, the techniques employed for METABRIC are sufficiently general to be applied to a variety of other cancer datasets.

Computational biology is providing the means to navigate through large-scale, multi-dimensional datasets. In doing so, it can help to build a holistic, systems-level perspective on cancer that will shape future clinical practice.

For more information, please contact Professor Simon Tavaré (st321@cam.ac.uk) and Dr Christina Curtis (cc529@cam.ac.uk) at the Cancer Research UK Cambridge Research Institute/Li Ka Shing Centre.

Computational biology is helping scientists to navigate through the data deluge generated from the analysis of cancer genomes.

Christina Curtis

Data mining

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