Machine Learning-Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer

[EN] Background: Estimation of the depth of myometrial invasion (MI) in endometrial cancer is pivotal in the preoperatively staging. Magnetic resonance (MR) reports suffer from human subjectivity. Multiparametric MR imaging radiomics and parameters may improve the diagnostic accuracy. Purpose: To discriminate between patients with MI ¿ 50% using a machine learning-based model combining texture features and descriptors from preoperatively MR images. Study Type: Retrospective. Population: One hundred forty-three women with endometrial cancer were included. The series was split into training (n = 107, 46 with MI ¿ 50%) and test (n = 36, 16 with MI ¿ 50%) cohorts. Field Strength/Sequences: Fast spin echo T2-weighted (T2W), diffusion-weighted (DW), and T1-weighted gradient echo dynamic contrast-enhanced (DCE) sequences were obtained at 1.5 or 3 T magnets. Assessment: Tumors were manually segmented slice-by-slice. Texture metrics were calculated from T2W and ADC map images. Also, the apparent diffusion coefficient (ADC), wash-in slope, wash-out slope, initial area under the curve at 60 sec and at 90 sec, initial slope, time to peak and peak amplitude maps from DCE sequences were obtained as parameters. MR diagnostic models using single-sequence features and a combination of features and parameters from the three sequences were built to estimate MI using Adaboost methods. The pathological depth of MI was used as gold standard. Statistical Test: Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, precision and recall were computed to assess the Adaboost models performance. Results: The diagnostic model based on the features and parameters combination showed the best performance to depict patient with MI ¿ 50% in the test cohort (accuracy = 86.1% and AUROC = 87.1%). The rest of diagnostic models showed a worse accuracy (accuracy = 41.67%¿63.89% and AUROC = 41.43%¿63.13%). Data Conclusion: The model combining the texture features from T2W and ADC map images with the semi-quantitative parameters from DW and DCE series allow the preoperative estimation of myometrial invasion. Evidence Level: 4 Technical Efficacy: Stage 3This study received funding from the Global Investigator Initiated Research Committee (GIIRC) research program by Bracco S.p.A (2015/0724). The funders had no role in study design, data collection and analysis and preparation of the manuscript.Rodriguez Ortega, A.; Alegre, A.; Lago, V.; Carot Sierra, JM.; Ten-Esteve, A.; Montoliu, G.; Domingo, S.... (2021). Machine Learning-Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer. Journal of Magnetic Resonance Imaging. 54(3):987-995. https://doi.org/10.1002/jmri.27625S98799554

Harnessing adaptive novelty for automated generation of cancer treatments

© 2020 The Authors Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of customization possibilities. However, it also leads to a rich design space which is difficult to investigate and optimize. An additional problem emerges when they are applied to cancer treatment. A heterogeneous and highly adaptable tumour can quickly become resistant to primary therapy, making it inefficient. To automate the design of potential therapies for such complex cases, we propose a computational model for fast, novelty-based machine learning exploration of the nanoparticle design space. In this paper, we present an evolvable, open-ended agent-based model, where the exploration of an initially small portion of the given state space can be expanded by an ongoing generation of adaptive novelties, whenever the simulated tumour makes an adaptive leap. We demonstrate that the nano-agents can continuously reshape themselves and create a heterogeneous population of specialized groups of individuals optimized for tracking and killing different phenotypes of cancer cells. In the conclusion, we outline further development steps so this model could be used in real-world research and clinical practice

The role of space in homeostasis and preneoplasia in stratified squamous epithelia

A major subject of study in biological research is the dynamics of stem cells in squamous epithelia. Given that most common human cancers develop from epithelia, understanding the rules of cell fate decision in these systems is key to explaining not only healthy tissue growth and maintenance but also the processes of mutagenesis and cancer. The aim of my project was to investigate the dynamics in squamous epithelial tissues both in homeostasis and preneoplasia, using cellular automata (CA) models. Stem cell dynamics has been shown to be accurately described by a simple mathematical model, the single progenitor (SP) model. Reliable parameterisation of this model would give access to valuable quantitative information on epithelial tissue maintenance and enable investigating how mutations affect tissue dynamics. I initially identified the most appropriate method for accurately parameterising the homeostatic system. I then sought to account for the spatial patterning of cells by implementing the SP model in two-dimensional space. The spatial model was able to reproduce the key signatures of homeostatic dynamics, thus showing that restrictions imposed by tissue organization do not alter the neutral dynamics. Furthermore, I studied non-homeostatic dynamics in stratified squamous epithelial tissues by spatially modelling the growth and competition of non-neutral mutations as well as the effects of wounding in the tissue. The studied dynamics of Notch and p53 mutant clones in mouse epithelia has been found to be highly distinct, with the former fully colonizing the tissue whereas the latter only partially. I demonstrated that the two mutants’ tissue takeover dynamics can be recapitulated by two distinct spatial feedback rules, on the basis of response to crowding, providing a mechanistic explanation of the observed distinct growth modes. Finally, mutant competition was explored. A striking effect resulting from the spatial interaction of the two mutations in a wild-type background is that the p53 mutant cell population was always outcompeted by the Notch mutant population and appeared to shrink. Considering this consistent emergent behaviour in the competition simulations and given the paucity of Notch mutations in human cancer datasets, it is tempting to speculate that the aggressive fitness of Notch may offer a tumour-protective effect

Radiomics and Machine Learning in the Prediction of Cardiovascular Disease

Carotid atherosclerosis is a major risk factor for ischaemic stroke which is a leading cause of death worldwide. For stroke survivors, 1 in 4 will have another stroke within five years. Carotid CT angiography (CTA) is commonly performed following an ischaemic stroke or transient ischemic attack to help guide patient management in the secondary prevention of stroke. For example, carotid endarterectomy surgery plus medical therapy or medical therapy alone. The degree of carotid stenosis is the mainstay in making this decision and uses only one aspect of anatomical information that can be obtained from a carotid CTA scan. Radiomics, sometimes called ‘texture analysis’, is the extraction of quantitative data from medical images that may not be apparent to the naked eye and has already demonstrated clinical utility in oncology for applications ranging from lesion characterisation to tumour grading and prognostication. Machine learning refers to the process of learning from experience (in this case data), rather than following pre-programmed rules. This thesis presents the findings of a proof-of-principle study to assess the value of radiomics in identifying the ‘vulnerable plaque’ and the ‘vulnerable patient’ within the context of cerebrovascular events. To evaluate the potential of radiomic features as imaging biomarkers, their reproducibility and robustness to morphological perturbations were assessed, as well as their biological associations with both PET and immunohistochemistry data. The ability of radiomic features to classify different carotid artery types, namely, culprit, non-culprit and asymptomatic carotid arteries was assessed using several machine learning classifiers. This was subsequently compared with a deep learning approach, which has greater capacity for data mining than feature-based machine learning approaches. Overall, radiomics could extract further useful information from carotid CTA scans. Culprit versus non-culprit carotid arteries in symptomatic patients and asymptomatic carotid arteries from asymptomatic patients had different radiomic profiles that could be leveraged using machine learning for better classification performance than carotid calcification or carotid PET imaging alone. Reliable and robust CT-based carotid radiomic features were identified that were associated with the degree of inflammation underlying the carotid artery. If validated with future prospective studies, this has the potential to improve personalised patient care in stroke management and advance clinical decision-making.Cambridge School of Clinical Medicine, the Medical Research Council's Doctoral Training Partnership and the Frank Edward Elmore Fun

Potential neoplastic evolution of Vero cells: in vivo and in vitro characterization

Vero cell lines are extensively employed in viral vaccine manufacturing. Similarly to all established cells, mutations can occur during Vero cells in vitro amplification which can result in adverse features compromising their biological safety. To evaluate the potential neoplastic evolution of these cells, in vitro transformation test, gene expression analysis and karyotyping were compared among low- (127 and 139 passages) and high-passage (passage 194) cell lines, as well as transformed colonies (TCs). In vivo tumorigenicity was also tested to confirm preliminary in vitro data obtained for low passage lines and TCs. Moreover, Vero cells cultivated in foetal bovine serum-free medium and derived from TCs were analysed to investigate the influence of cultivation methods on tumorigenic evolution. Low-passage Vero developed TCs in soft agar, without showing any tumorigenic evolution when inoculated in the animal model. Karyotyping showed a hypo-diploid modal chromosome number and rearrangements with no difference among Vero cell line passages and TCs. These abnormalities were reported also in serum-free cultivated Vero. Gene expression revealed that high-passage Vero cells had several under-expressedand a few over-expressed genes compared to low-passage ones.Gene ontology revealed no significant enrichment of pathways related to oncogenic risk. These findings suggest that in vitro high passage, and not culture conditions, induces Vero transformation correlated to karyotype and gene expression alterations. These data, together with previous investigations reporting tumour induction in high-passage Vero cells, suggest the use of low-passage Vero cells or cell lines other than Vero to increase the safety of vaccine manufacturing

Selection and competition of somatic mutations in normal epithelia

Tumourigenesis occurs when a series of genome alterations occur in the same group of cells and cause uncontrolled cell proliferation. Therefore, to understand the journey from healthy to cancerous tissue, it is important to study the accumulation and spread of mutations in pre- cancerous normal tissues. Recent studies have shown that apparently normal epithelium contains a high burden of mutations in cancer-associated genes. This thesis explores the behaviour of mutant clones in normal epithelium and how this affects cancer development. The nature of mutant clonal growth and competition in normal epidermis has been a subject of debate. A study found that mutant clone sizes inferred from DNA sequencing of normal human eyelid skin were consistent with a mathematical model of neutral cell dynamics, appearing to contradict a genetic analysis of the same dataset that found several genes under positive selection. I investigate this debate using computational modelling that takes into account the tissue structure and experimental tissue-sampling methods. The results show that mutant clone sizes in skin and oesophagus are consistent with non-neutral clonal competition. Further evidence for non-neutral selection in normal epithelium is found in patterns of mutations detected by DNA sequencing. By adapting a statistical method used for driver gene discovery, I look for enrichment or depletion of structural categories of missense mutations and find biologically meaningful patterns of selection in several proteins. The method can associate changes to protein structure or function with cell fitness, even in the absence of hotspot mutations and in the presence of passenger mutations. I demonstrate how the method is flexible and could be widely applicable, but can also produce misleading results if confounding sources of selection are not accounted for. Clonal competition in normal oesophageal epithelium is dominated by Notch1 loss-of- function mutations. I fit stochastic models of clonal dynamics to lineage tracing data to show that haploinsufficiency greatly accelerates Notch1 mutant expansion and that the loss of the second Notch1 allele provides a further strong selective advantage, consistent with the high frequency of NOTCH1 loss-of-heterozygosity events observed in human oesophagus. Finally, I examine a consequence of the spread of these highly fit mutant clones in the normal tissue. I use a mathematical model to analyse the results of a series of experiments in mutagen-treated mouse oesophagus, finding that microscopic tumours can be eliminated by highly fit clones in the surrounding normal tissue.Harrison Watson Fund at Clare College, Cambridg

Evolutionary games between epithelial cells: the impact of population structure and tissue dynamics on the success of cooperation

Cooperation is usually understood as a social phenomenon. However, it also occurs on the cellular level. A number of key mutations associated with malignancy can be considered cooperative, as they rely on the production of diffusible growth factors to confer a fitness benefit. Evolutionary game theory provides a framework for modelling the evolutionary dynamics of these cooperative mutations. This thesis uses evolutionary game theory to examine the evolutionary dynamics of cooperation within epithelial cells, which are the origin point of most cancers. In particular, we consider how the structure and dynamics of an epithelium affect cooperative success. We use the Voronoi tessellation model to represent an epithelium. This allows us much greater flexibility, compared to evolutionary graph theory models, to explore realistic dynamics for population updating. Initially, we consider a model where death and division are spatially decoupled. We analyse pairwise social dilemma games, focussing on the additive prisoner’s dilemma, and multiplayer public goods games. We calculate fixation probabilities, and conditions for cooperative success, by simulation, as well as deriving quasi-analytic results. Comparing with results for graph structured populations with spatially coupled birth and death, or well-mixed populations, we find that in general cooperation is promoted by local game play, but global competition for offspring. We then introduce a more realistic model of population updating, whereby death and division are spatially coupled as a consequence of contact inhibition. The strength of this coupling is positively correlated with the strength of contact inhibition. However, the extent to which strong spatial coupling inhibits cooperation depends on mechanical properties of the tissue