Publications

Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility

Basyuni, S., et al., Nature Communications, Sep 2024

Abstract

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The PARTNER trial of neoadjuvant olaparib with chemotherapy in triple-negative breast cancer

Abraham, J.E., et al., Nature, April 2024

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Automated Small Kidney Cancer Detection in Non-Contrast Computed Tomography

McGough, W., et al., IEE, August 2024

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Radiology and multi-scale data integration for precision oncology

Paverd, H., et al., NJP Precision Oncology, Jul 2024

Abstract

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Acceptability of adding a non-contrast abdominal CT scan to screen for kidney cancer and other abdominal pathology within a community-based CT screening programme for lung cancer: A qualitative study

Usher-Smith, J. A., et al., PLOS ONE, Jul 2024

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High-resolution and highly accelerated MRI T2 mapping as a tool to characterise renal tumour subtypes and grades

Horvat- Menih, I., et al., European Radiology Experimental, Jul 2024

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Fast and High-Resolution T2 Mapping Based on Echo Merging Plus k-t Undersampling with Reduced Refocusing Flip Angles (TEMPURA) as Methods for Human Renal MRI

Li, H., et al., Magnetic resonance in Medicine, May 2024

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Risk-stratified screening for the early detection of kidney cancer

Rossi, S. H., et al., The Surgeon, Feb 2024

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Deep learning-based segmentation of multisite disease in ovarian cancer

Buddenkotte, T., et al., European Radiology Experimental, Dec 2023

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Lithographically defined encoded magnetic heterostructures for the targeted screening of kidney cancer

Pichot, S. l., et al., Nanoscale Advances, Dec 2023

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A Mathematical Model of Blood Loss during Renal Resection

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Short-term psychosocial outcomes of adding a non-contrast abdominal computed tomography (CT) scan to the thoracic CT within lung cancer screening

Usher-Smith. J.A., et al., BJUI, Dec 2023

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The impact of imputation quality on machine learning classifiers for datasets with missing values

Shadbahr, T., Communications Medicine, Oct 2023

Background

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier’s performance.

Methods

We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data.

Results

The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised.

Conclusions

It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.

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Position statement on clinical evaluation of imaging AI

McCague, C., et al., The Lancet Digital Health, Jul 2023

Governments and medical associations across the world, including the US Food and Drug Administration, the UK Medicines and Healthcare products Regulatory Agency, the Royal College of Radiologists, and the European Society of Radiology, believe the advent of health technologies associated with artificial intelligence (AI) will be the most radical change in how medical care is delivered in our lifetime. At a time of unprecedented demand for medical imaging, when hospitals struggle with staffing shortages, AI tools could provide a solution.

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Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Delgardo-Ortet, M., et al, Frontiers in Oncology, Feb 2023

Background: High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.

Methods: In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.

Results: Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm³) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.

Conclusions: We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

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Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation

Buddenkotte, T., et al., Computers in Biology and Medicine, Sept 2023

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human–machine collaboration.

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Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Crispin-Ortuzar, M., et al Nature Communications, Oct 2023

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

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Comparison of tumor-informed and tumor-naïve sequencing assays for ctDNA detection in breast cancer

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Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression

Sciacovelli, M., Nature Communications, Dec 2022

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.

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Multiparameter single-cell proteomic technologies give new insights into the biology of ovarian tumors

Funingana, I. G., et al, Seminars in Immunopathology, Jan 23

High-grade serous ovarian cancer (HGSOC) is the most lethal gynecological malignancy. Its diagnosis at advanced stage compounded with its excessive genomic and cellular heterogeneity make curative treatment challenging. Two critical therapeutic challenges to overcome are carboplatin resistance and lack of response to immunotherapy. Carboplatin resistance results from diverse cell autonomous mechanisms which operate in different combinations within and across tumors. The lack of response to immunotherapy is highly likely to be related to an immunosuppressive HGSOC tumor microenvironment which overrides any clinical benefit. Results from a number of studies, mainly using transcriptomics, indicate that the immune tumor microenvironment (iTME) plays a role in carboplatin response. However, in patients receiving treatment, the exact mechanistic details are unclear. During the past decade, multiplex single-cell proteomic technologies have come to the forefront of biomedical research. Mass cytometry or cytometry by time-of-flight, measures up to 60 parameters in single cells that are in suspension. Multiplex cellular imaging technologies allow simultaneous measurement of up to 60 proteins in single cells with spatial resolution and interrogation of cell–cell interactions. This review suggests that functional interplay between cell autonomous responses to carboplatin and the HGSOC immune tumor microenvironment could be clarified through the application of multiplex single-cell proteomic technologies.

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Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker–driven learning framework

Rossi, S. H., et al, Science Advances, Sep 2022

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

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Hyperpolarized 13C-Pyruvate Metabolism as a Surrogate for Tumor Grade and Poor Outcome in Renal Cell Carcinoma—A Proof of Principle Study

Ursprung. S., et al, Cancers, Jan 2022

We evaluated renal cancer with varying aggressive appearances on histology, using an emerging form of non-invasive metabolic MRI. This imaging technique assesses the uptake and metabolism of a breakdown product of glucose (pyruvate) labelled with hyperpolarized carbon-13. We show that pyruvate metabolism is dependent on the aggressiveness of an individual tumor and we provide a mechanism for this finding from tissue analysis of molecules influencing pyruvate metabolism, suggesting a role for its membrane transporter.

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Multi-omic machine learning predictor of breast cancer therapy response

Sammut, S. J., et al, Nature, Dec 2021

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment¹. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy². Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery³ were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

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Hyperpolarized carbon-13 MRI for very early response assessment of neoadjuvant chemotherapy in breast cancer patients

Woitek, R., et al, Cancer Research, Sept 2021.

Hyperpolarized ¹³C-MRI is an emerging tool for probing tissue metabolism by measuring ¹³C-label exchange between intravenously injected hyperpolarized [1–¹³C]pyruvate and endogenous tissue lactate. Here, we demonstrate that hyperpolarized ¹³C-MRI can be used to detect early response to neoadjuvant therapy in breast cancer.

Media coverage for this publication in the Sunday Times.

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Reproducibility standards for machine learning in the life sciences

Heil, B.J. et al, Nature Methods, 30 Aug 2021.

To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.

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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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