Developing microstructural and metabolic magnetic resonance imaging to address the diagnostic and prognostic unmet needs in breast cancer

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2015 saw over 55,000 new cases of breast cancer in the UK. However, not all of these breast cancers are the same. Breast cancers that look identical on standard imaging tests like x-ray mammography and ultrasound incorporate both those that are slow-growing and those that are destined to spread rapidly and claim lives. The most life-threatening tumours need aggressive treatment early but the question is how to discriminate these from those that are slower growing? Today, we do this by biopsying the tumour and examining its grade, protein expression, and if hormone receptors are present. However, a biopsy does not represent the whole tumour and current predictive methods lack precision.Hyperpolarised C13 imaging is a very novel technique that combines magnetic resonance imaging (HPMRI) with metabolic information (i.e. how the cancer cells utilise energy, for example glucose). Specifically, HPMRI allows us to measure how cells convert pyruvate to lactate, which reflects metabolic activity. Cancers have more metabolic activity than normal cells and some cancers are more metabolically active than others. For example, we know from animal studies that the most aggressive tumours convert pyruvate faster and in greater volumes than less aggressive tumours. HPMRI may therefore identify the most aggressive cancers but the test is so novel that this has not been researched to any great degree in man. Furthermore, the equipment needed for HPMRI is extremely expensive and restricted to just a few centres worldwide.There is also another problem that needs addressing, namely that almost 60% of women that have biopsy to see if there is a tumour actually have a negative result, so we really need a better way to tell if something was tumour or not. We have developed novel MRI techniques (VERDICT, T2-mapping and Fat-fraction MRI) that we are using to find tumours and avoid unnecessary biopsies in patients being investigated for prostate cancer. We think these techniques could be applied to women being investigated for breast cancer and in a similar fashion could help avoid many unnecessary breast biopsies. Our research aims to develop these new imaging techniques in women with breast cancer. We will do so in a robust, safe, validated manner by following the "Imaging Biomarker Roadmap for Cancer Studies" (Nature Reviews Clinical Oncology 2017). We will do experiments to technically validate these biomarkers, followed by biological and clinical validation, to plan for clinical implementation. We will determine if these biomarkers are reproducible and what they tell us about a breast cancer. Ultimately, if they prove sufficiently promising, we ultimately aim to add their information to existing prognostic models to detect aggressive breast cancer. A prognostic model is a collection of information known about a patient that predicts what will happen to them in the future, i.e. whether their tumour will spread. We at UCL are especially well-placed to do this work. The Medical Research Council and Cancer Research UK and other funders have already invested >£30 million to support our imaging research. For example, an MRC Clinical Research Infrastructure award funded installation and associated equipment necessary for hyperpolarised metabolic imaging and digital histopathology. We have an assembled a world-class multidisciplinary research team that is working currently in prostate cancer. A successful CARP award will allow us to turn our attention to breast cancer by funding research time for an existing NHS consultant radiologist (Dr. Abeyakoon) to join our team. She has extensive prior research experience in breast cancer imaging (having gained a PhD; unusual in radiology).The results of our research will be disseminated to the scientific community via conference proceedings and peer reviewed papers, the public via public patient engagement forums at UCL/H, and funders via grant applications to support the next phase of research