Publications

The use of hyperpolarised 13C-MRI in clinical body imaging to probe cancer metabolism

Woitek, R and Gallagher, F. A, British Journal of Cancer, 28 Jan 2021.

Metabolic reprogramming is one of the hallmarks of cancer and includes the Warburg effect, which is exhibited by many tumours. This can be exploited by positron emission tomography (PET) as part of routine clinical cancer imaging. However, an emerging and alternative method to detect altered metabolism is carbon-13 magnetic resonance imaging (MRI) following injection of hyperpolarised [1-¹³C]pyruvate. The technique increases the signal-to-noise ratio for the detection of hyperpolarised ¹³C-labelled metabolites by several orders of magnitude and facilitates the dynamic, noninvasive imaging of the exchange of ¹³C-pyruvate to ¹³C-lactate over time. The method has produced promising preclinical results in the area of oncology and is currently being explored in human imaging studies. The first translational studies have demonstrated the safety and feasibility of the technique in patients with prostate, renal, breast and pancreatic cancer, as well as revealing a successful response to treatment in breast and prostate cancer patients at an earlier stage than multiparametric MRI.

This review focuses on the strengths of the technique and its applications in the area of oncological body MRI including noninvasive characterisation of disease aggressiveness, mapping of tumour heterogeneity, and early response assessment.

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18F-C2Am: a targeted imaging agent for detecting tumor cell death in vivo using positron emission tomography

Bulat, F., et al, EJNMMI Research, 9 Dec 2020.

Trialing novel cancer therapies in the clinic would benefit from imaging agents that can detect early evidence of treatment response. The timing, extent and distribution of cell death in tumors following treatment can give an indication of outcome. We describe here an ¹⁸F-labeled derivative of a phosphatidylserine-binding protein, the C2A domain of Synaptotagmin-I (C2Am), for imaging tumor cell death in vivo using PET.

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Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer

Beer, L., Martin-Gonzalez, P., et al, European Radiology, 14 Dec 2020.

We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC.

Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy.

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Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer.

Martin-Gonzalez, P. et al., Insights Into Imaging, 17 Aug 2020.

In this review, we describe how these challenges might be overcome by integrating quantitative features extracted from medical imaging with the analysis of paired genomic profiles, a combined approach called radiogenomics, to generate virtual biopsies. Radiomic studies have been used to model different imaging phenotypes, and some radiomic signatures have been associated with paired molecular profiles to monitor spatiotemporal changes in the heterogeneity of tumours. We describe different strategies to integrate radiogenomic information in a global and local manner, the latter by targeted sampling of tumour habitats, defined as regions with distinct radiomic phenotypes.

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Hyperpolarized 13C MRI of Tumor Metabolism Demonstrates Early Metabolic Response to Neoadjuvant Chemotherapy in Breast Cancer

Woitek R. et al, Radiology: Imaging Cancer, July 2020

The purpose of this research was to compare hyperpolarized carbon 13 (13C) MRI with dynamic contrast material–enhanced (DCE) MRI in the detection of early treatment response in breast cancer. In this institutional review board–approved prospective study, a woman with triple-negative breast cancer (age, 49 years) underwent 13C MRI after injection of hyperpolarized [1–carbon 13 {13C}]-pyruvate and DCE MRI at 3 T at baseline and after one cycle of neoadjuvant therapy. The 13C-labeled lactate-to-pyruvate ratio derived from hyperpolarized 13C MRI and the pharmacokinetic parameters transfer constant (Ktrans) and washout parameter (kep) derived from DCE MRI were compared before and after treatment.

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3D-printed moulds of renal tumours for image-guided tissue sampling in the clinical setting

Mireia C.-O., et al., JCO Clinical Cancer Informatics, 2020

Spatial heterogeneity of tumours is a major challenge in precision oncology. We have developed an open source computational framework to automatically produce patient-specific 3D-printed moulds that can be used in the clinical setting and applied it to patients with renal cancer undergoing radical nephrectomy. Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumour heterogeneity on multiple spatial scales.

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ctDNA monitoring using patient-specific sequencing and integration of variant reads

Wan, J.C.M., et al., Sci Transl Med, 17 Jun 2020

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci.

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Unraveling tumor–immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy

Jiménez-Sánchez, A. et al, Nature Genetics, 2020

In this study we demonstrate that the tumour–immune microenvironment (TME) of advanced high-grade serous ovarian cancer (HGSOC) is intrinsically heterogeneous and that chemotherapy induces local immune activation. Therefore, exploring new combination therapies and therapeutic targets based on a greater understanding of the TME has the potential to change the current treatment paradigm and improve clinical outcomes in patients with HGSOC.

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Tissue-specific and Interpretable Sub-segmentation of Whole Tumour Burden on CT Images by Unsupervised Fuzzy Clustering

Rundo L. et al, Artificial Intelligence in Medicine, 2020

This is the first method for Computed Tomography tissue-specific image segmentation of whole tumours. In particular, our computational framework based on unsupervised fuzzy clustering techniques sub-segments tumour lesions into hypo-dense (cystic/necrotic), hyper-dense (calcified), and intermediately dense (soft tissue) tumour components. The results obtained on ovarian and renal cancer are accurate and reliable.

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Executable cancer models: successes and challenges

Fisher, J. Nature Reviews Cancer, 2020

This perspective discusses how executable computational models, integrating various data sets derived from preclinical models and cancer patients, can be used to represent the dynamic biological behaviours inherent in cancer. The article argues that these models might be used as patient avatars to improve personalised treatments.

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Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

Beer, L., et al, European Radiology, April 2020

This study investigated the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC).  It provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra- and inter-site heterogeneity, and the abundance of several proteins.

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Comprehensive characterization of cell-free tumor DNA in plasma and urine of patients with renal tumors

Smith, C.G. et al, Genome Med, 2020

New research detecting tumour DNA in blood and urine of kidney cancer patients opens up potential for clinical management.

The detection of ctDNA in blood or urine – known as ‘liquid biopsies’ – has been established as a method for non-invasive monitoring for many cancers, such as breast, lung and colorectal. This study is the most extensive analysis to date in patients with kidney cancers.

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Imaging mass cytometry and multi-platform genomics define the phenogenomic landscape of breast cancer

Ali, R. et al, Nature Cancer, 2020

Zooming in on breast cancer reveals how mutations shape the tumour landscape. Scientists have created one of the most detailed maps of breast cancer ever achieved, revealing how genetic changes shape the physical tumour landscape.

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Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma - a systematic review and meta-analysis

Ursprung, S. et al, European Radiology, 2020

Radiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures.

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Imaging breast cancer using hyperpolarized carbon-13 MRI

Gallagher F., Woitek R. et al, PNAS, 2020

A new type of scan that involves magnetising molecules allows doctors to see in real-time which regions of a breast tumour are active. This is the first time that researchers have demonstrated that a scanning technique, called carbon-13 hyperpolarised imaging, can be used to monitor breast cancer.

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Variational Autoencoders for Cancer Data Integration Design Principles and Computational Practice

Simidjievski N. et al., Frontiers in Genetics, 2019

The rapid technological developments in cancer research yield large amounts of complex heterogeneous data on different scales—from molecular to clinical and image data. To capitalize on the inter-dependencies and relations across heterogeneous types of data about each patient, integrating multiple types and sources of data is essential. We provide a clear methodological and computational framework for designing and systematically analysing several deep-learning approaches for data integration based on Variational Autoencoders (VAEs), that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these deep-learning approaches can be designed, built, and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data.

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Rethinking drug design in the artificial intelligence era

Schneider P. et al., Nature Reviews Drug Discovery, 2019

Artificial Intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the ‘grand challenges’ in small-molecule drug discovery with AI and the approaches to address them.

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Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modelling

Kreuzaler P. et al., PNAS, 2019

Cells with higher levels of Myc proliferate more rapidly and supercompetitively eliminate neighbouring cells. Nonetheless, tumour cells in aggressive breast cancers typically exhibit significant and stable heterogeneity in their Myc levels, which correlates with refractoriness to therapy and poor prognosis. This suggests that Myc heterogeneity confers some selective advantage on breast tumour growth and progression. To investigate this, we created a traceable MMTV-Wnt1–driven in vivo chimeric mammary tumour model comprising an admixture of low-Myc– and reversibly switchable high-Myc–expressing clones. Our study illustrates the power of executable models in elucidating mechanisms driving tumour heterogeneity and offers an innovative strategy for identifying combination therapies tailored to the oligoclonal landscape of heterogeneous tumours.

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Computed Tomography-Derived Radiomic Metrics Can Identify Responders to Immunotherapy in Ovarian Cancer

Himoto Y. et al., JCO Precision Oncology, 2019

We set out to determine if radiomic measures of tumour heterogeneity derived from baseline contrast-enhanced computed tomography (CE-CT) are associated with durable clinical benefit and time to off-treatment in patients with recurrent ovarian cancer (OC) enrolled in prospective immunotherapeutic trials. We found that fewer disease sites and lower intra- and intertumour heterogeneity modelled from the baseline CE-CT may indicate better response of OC to immunotherapy.

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Modeling breast cancer progression to bone: how driver mutation order and metabolism matter

Ascolani G. and Lio P., BMC Medical Genomics, 2019

We introduce a quantitative model in the framework of Cellular Automata to investigate the effects of metabolic mutations and mutation order on cancer stemness and tumour cell migration from breast, blood to bone metastasised sites. Our work provides a quantitative basis of how the order of driver mutations and the number of mutations altering metabolic processes matter for different cancer clones through their progression in breast, blood and bone compartments. This work is innovative because of multi compartment analysis and could impact proliferation of therapy-resistant clonal populations and patient survival.

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Quantifying normal human brain metabolism using hyperpolarized [1-13C]pyruvate and magnetic resonance imaging

Grist. J. T. et al, Neuroimage, 2019

Hyperpolarized 13C Magnetic Resonance Imaging (13C-MRI) provides a highly sensitive tool to probe tissue metabolism in vivo and has recently been translated into clinical studies. We report the cerebral metabolism of intravenously injected hyperpolarized [1-13C]pyruvate in the brain of healthy human volunteers for the first time. Imaging normal brain metabolism with hyperpolarized [1-13C]pyruvate and subsequent quantification, have important implications for interpreting pathological cerebral metabolism in future studies.

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Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups

Oscar, R.M. et al, Nature, 2019

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data.  We present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.

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