Algorithms for Analysis of Heterogeneous Cancer and Viral Populations Using High-Throughput Sequencing Data

Next-generation sequencing (NGS) technologies experienced giant leaps in recent years. Short read samples reach millions of reads, and the number of samples has been growing enormously in the wake of the COVID-19 pandemic. This data can expose essential aspects of disease transmission and development and reveal the key to its treatment. At the same time, single-cell sequencing saw the progress of getting from dozens to tens of thousands of cells per sample. These technological advances bring new challenges for computational biology and require the development of scalable, robust methods to deal with a wide range of problems varying from epidemiology to cancer studies. The first part of this work is focused on processing virus NGS data. It proposes algorithms that can facilitate the initial data analysis steps by filtering genetically related sequencing and the tool investigating intra-host virus diversity vital for biomedical research and epidemiology. The second part addresses single-cell data in cancer studies. It develops evolutionary cancer models involving new quantitative parameters of cancer subclones to understand the underlying processes of cancer development better

Planning for steerable needles in neurosurgery

The increasing adoption of robotic-assisted surgery has opened up the possibility to control innovative dexterous tools to improve patient outcomes in a minimally invasive way. Steerable needles belong to this category, and their potential has been recognised in various surgical fields, including neurosurgery. However, planning for steerable catheters' insertions might appear counterintuitive even for expert clinicians. Strategies and tools to aid the surgeon in selecting a feasible trajectory to follow and methods to assist them intra-operatively during the insertion process are currently of great interest as they could accelerate steerable needles' translation from research to practical use. However, existing computer-assisted planning (CAP) algorithms are often limited in their ability to meet both operational and kinematic constraints in the context of precise neurosurgery, due to its demanding surgical conditions and highly complex environment. The research contributions in this thesis relate to understanding the existing gap in planning curved insertions for steerable needles and implementing intelligent CAP techniques to use in the context of neurosurgery. Among this thesis contributions showcase (i) the development of a pre-operative CAP for precise neurosurgery applications able to generate optimised paths at a safe distance from brain sensitive structures while meeting steerable needles kinematic constraints; (ii) the development of an intra-operative CAP able to adjust the current insertion path with high stability while compensating for online tissue deformation; (iii) the integration of both methods into a commercial user front-end interface (NeuroInspire, Renishaw plc.) tested during a series of user-controlled needle steering animal trials, demonstrating successful targeting performances. (iv) investigating the use of steerable needles in the context of laser interstitial thermal therapy (LiTT) for maesial temporal lobe epilepsy patients and proposing the first LiTT CAP for steerable needles within this context. The thesis concludes with a discussion of these contributions and suggestions for future work.Open Acces

Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI

The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Lineage-Based Subclonal Reconstruction of Cancer Samples

Sundermann LK. Lineage-Based Subclonal Reconstruction of Cancer Samples. Bielefeld: Universität Bielefeld; 2019.Cancer is caused by the accumulation of mutations, leading to genetically heterogeneous cell populations. The characterization of a cancer sample in terms of a subclonal reconstruction is essential. The subclonal reconstruction informs about the co-occurrence of mutations per population, as well as the proportion of cells belonging to each population, and the ancestral relationships among populations. Typical mutations used to infer a subclonal reconstruction are simple somatic mutations (SSMs) and copy number aberrations (CNAs). Methods building subclonal reconstructions only with SSMs use the concept of lineages instead of populations. In contrast to a population, which comprises only cells with the same genotype, a lineage comprises all cells that are descendant from the same founder cell. In a lineage-based subclonal reconstruction, mutations are assigned to the lineage in which they arose. The lineage frequency indicates the proportion of cells in which mutations assigned to this lineage can be found. Methods building subclonal reconstructions with CNAs are population-based. In contrast to the lineage-based approach, mutations are assigned to all populations in which they occur, not just to the one in which they arose. In order to calculate the mutation frequencies, the ancestor-descendant relationships between all populations have to be inferred. Hence, multiple subclonal reconstructions are needed to model ambiguous population relationships. Two population-based subclonal reconstruction methods working with SSMs and CNAs are PhyloWGS and Canopy. In contrast to Canopy, PhyloWGS does not infer CNAs but needs them as input. In this thesis, we present the first lineage-based model that builds subclonal reconstructions from SSMs and CNAs of bulk-sequenced tumor samples. Modeling CNAs as relative copy numbers, so copy number changes, instead of absolute copy numbers allows us to assign them to lineages. Another special feature of our method is that we infer present or absent ancestor-descendant relationships between lineages only if they can be observed in the data, modeling them as ambiguous relationships otherwise. This enables us to combine multiple ambiguous subclonal reconstructions within a single subclonal reconstruction. As input, our method uses the variant allele frequencies of SSMs, as well as the average allele-specific major and minor copy numbers of genome segments where the genome is segmented in a way that consecutive regions with the same copy number profile belong to the same segment. Furthermore, the number of lineages needs to be given as input. We present a joint likelihood function for SSMs and CNAs and show a linear relaxation of our model as a mixed integer linear program that can be solved with state-of-the-art solvers. Given subclonal reconstructions of the same dataset inferred with different lineage numbers, we use the minimum description length principle to choose the subclonal reconstruction with the best lineage number. An extensive analysis of the chosen subclonal reconstruction allows us to classify the ancestor-descendant relationships between each pair of lineages as either present, absent or ambiguous. We implemented our method in a software called Onctopus. We evaluate Onctopus extensively on simulated data, analyzing its run time and memory usage as well as its performance when the mathematically optimal solution cannot be proved in the given time and space. We present different approaches to improve Onctopus’ performance, such as by clustering mutations, fixing CNAs or fixing lineage frequencies. Finally, we compare the performance of Onctopus against the performance of PhyloWGS and Canopy on simulated datasets and a deep sequenced breast cancer dataset. On the simulated datasets, we evaluate different aspects of the inferred subclonal reconstructions and show that Onctopus is superior in inferring the number of lineages and the lineage relationships. For the breast cancer dataset, we follow an analysis by Deshwar et al., comparing the inferred mutation assignment to a gold standard assignment. Here, Onctopus and PhyloWGS reach a comparable performance

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis