Tumour development has long been recognised as an evolutionary process during which cells accumulate mutations and evolve into a mix of genetically distinct cell subpopulations. The resulting genetic intra-tumour heterogeneity poses a major challenge to cancer therapy, as it increases the chance of drug resistance. To study tumour evolution in more detail, reliable approaches to infer the life histories of tumours are needed. This dissertation focuses on computational methods for inferring trees of tumour evolution from single-cell and multi-sample sequencing data. Recent advances in single-cell sequencing technologies have promised to reveal tumour heterogeneity at a much higher resolution, but single-cell sequencing data is inherently noisy, making it unsuitable for analysis with classic phylogenetic methods. The first part of the dissertation describes OncoNEM, a novel probabilistic method to infer clonal lineage trees from noisy single nucleotide variants of single cells. Simulation studies are used to validate the method and to compare its performance to that of other methods. Finally, OncoNEM is applied in two case studies. In the second part of the dissertation, a comprehensive collection of existing multi-sample approaches is used to infer the phylogenies of metastatic breast cancers from ten patients. In particular, shallow whole-genome, whole exome and targeted deep sequencing data are analysed. The inference methods comprise copy number and point mutation based approaches, as well as a method that utilises a combination of the two. To improve the copy number based inference, a novel allele-specific multi-sample segmentation algorithm is presented. The results are compared across methods and data types to assess the reliability of the different methods. In summary, this thesis presents substantial methodological advances to understand tumour evolution from genomic profiles of single cells or related bulk samples.

Les sarcomes à « génétique complexe » sont des tumeurs rares du tissu mésenchymateux. Ces tumeurs sont caractérisées par un nombre important de réarrangements chromosomiques et possèdent un génome complexe dans lequel aucune altération n’avait été retrouvée de manière spécifique et récurrente. Le but de ma thèse était donc d’identifier les conséquences de cette complexité chromosomique et de comprendre comment celle-ci pouvait être générée. Dans un premier temps, le transcriptome de 112 sarcomes à « génétique complexe » a été analysé. La recherche et la validation de transcrits chimériques a conduit l’identification de réarrangements fréquents au niveau du gènes TRIO. Ces transcrits permettent la formation soit d’une protéine tronquée et seraient des évènements issus du réarrangement global du génome de ces tumeurs. Dans un deuxième temps, nous avons alors recherché l’origine de la formation de ces altérations, en s’intéressant particulièrement à la fusion cellulaire comme mécanisme initiateur. Ce processus physiologique est observé dans des cellules mésenchymateuses comme les macrophages et les myoblastes et peut être détourné afin de permettre le développement et l’évolution tumorale. J’ai alors étudié les conséquences génomiques et phénotypiques de la fusion de fibroblastes à différents stades d’immortalisation ou à différentes phases du cycle cellulaire. Mes travaux ont alors permis de montrer que des mécanismes de fusion cellulaire conduisent à la formation d’altérations génétiques similaires à celles des sarcomes à « génétique complexe » et contribueraient à l’initiation et à la progression de ces tumeurs. / Sarcomas with a complex genetics are rare tumours from mesenchymal tissue. They are characterized by massive chromosomal without any recurrent and specific alteration. The objective of my thesis was to identify the consequences of this chromosomal complexity and mechanisms explaining how this could be generated. First, transcriptome of 112 sarcomas with a complex genetics have been analysed. Chimeric transcripts detection and validation permitted the identification of frequent rearrangements in TRIO gene. These transcripts lead to the formation of a truncated protein and they would originate from a global rearrangement of the tumour genomes. Second, we have sought the origin of these alterations, with a particular interest for the cell fusion as an initiator mechanism. This physiological process is observed in mesenchymal cells like macrophages and myoblasts and it can be hijacked to drive tumour inception and evolution. I consequently studied both genomic and phenotypic consequences of hybrids from fibroblasts at different immortalization steps or in the different cell cycle phases. This work permitted to demonstrate that cell fusion mechanism leads to the initiation of genetic alterations that mimics the ones in sarcomas with complex genetics and would contribute to their tumour initiation and progression.

A better understanding of tumour heterogeneity is central for accurate diagnosis, targeted therapy and personalised treatment of glioblastoma patients. This thesis aims to investigate whether pre-operative multi-parametric magnetic resonance imaging (MRI) can provide a useful tool for evaluating inter-tumoural and intra-tumoural heterogeneity of glioblastoma. For this purpose, we explored: 1) the utilities of habitat imaging in combining multi-parametric MRI for identifying invasive sub-regions (I & II); 2) the significance of integrating multi-parametric MRI, and extracting modality inter-dependence for patient stratification (III & IV); 3) the value of advanced physiological MRI and radiomics approach in predicting epigenetic phenotypes (V). The following observations were made: I. Using a joint histogram analysis method, habitats with different diffusivity patterns were identified. A non-enhancing sub-region with decreased isotropic diffusion and increased anisotropic diffusion was associated with progression-free survival (PFS, hazard ratio [HR] = 1.08, P < 0.001) and overall survival (OS, HR = 1.36, P < 0.001) in multivariate models. II. Using a thresholding method, two low perfusion compartments were identified, which displayed hypoxic and pro-inflammatory microenvironment. Higher lactate in the low perfusion compartment with restricted diffusion was associated with a worse survival (PFS: HR = 2.995, P = 0.047; OS: HR = 4.974, P = 0.005). III. Using an unsupervised multi-view feature selection and late integration method, two patient subgroups were identified, which demonstrated distinct OS (P = 0.007) and PFS (P < 0.001). Features selected by this approach showed significantly incremental prognostic value for 12-month OS (P = 0.049) and PFS (P = 0.022) than clinical factors. IV. Using a method of unsupervised clustering via copula transform and discrete feature extraction, three patient subgroups were identified. The subtype demonstrating high inter-dependency of diffusion and perfusion displayed higher lactate than the other two subtypes (P = 0.016 and P = 0.044, respectively). Both subtypes of low and high inter-dependency showed worse PFS compared to the intermediate subtype (P = 0.046 and P = 0.009, respectively). V. Using a radiomics approach, advanced physiological images showed better performance than structural images for predicting O6-methylguanine-DNA methyltransferase (MGMT) methylation status. For predicting 12-month PFS, the model of radiomic features and clinical factors outperformed the model of MGMT methylation and clinical factors (P = 0.010). In summary, pre-operative multi-parametric MRI shows potential for the non-invasive evaluation of glioblastoma heterogeneity, which could provide crucial information for patient care.