Background Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations. Results We propose QuaDMutEx an QuadMutNetEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. QuaDMutNetEx is our proposed method that combines protein-protein interaction networks to the method elements of QuaDMutEx. In particular, QuaDMutEx incorporates three novel elements: a non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty. In the new method, we incorporated a new quadratic rewarding term that prefers gene solution set that is connected with respect to protein-protein interaction networks. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors. Conclusions Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx and QuaDMutNetEx are tools that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods.

Millions of somatic mutations in human cancers have been identified by sequenc- ing. Identifying and distinguishing cancer driver genes amongst the millions of candi- date mutations remains a major challenge. Accurate identification of driver genes and mutations is essential for the progress of cancer research and personalizing treatment based on accurate stratification of patients. Because of inter-tumor genetic hetero- geneity, numerous driver mutations within a gene can be found at low frequencies. This makes them difficult to differentiate from other non-driver mutations. Inspired by these challenges, we devised a novel way of identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, function, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In ad- dition to identifying known driver genes, we identify several novel candidate driver genes. We provide an external evaluation of the predicted genes using a dataset of 26 nasopharyngeal cancer samples that underwent whole exome sequencing. We find that the predicted driver genes have a significantly higher rate of mutation than non-driver genes, both in publicly available data and in the nasopharyngeal cancer samples we use for validation. Additionally, we characterize sub-networks of genes that are jointly involved in specific tumors.

Melanoma is a heterogeneous neoplasm at the histomorphologic, immunophenotypic, and molecular levels. Melanoma with extreme histomorphologic heterogeneity can pose a diagnostic challenge in which the diagnosis may predominantly rely on its immunophenotypic profile. However, tumor survival and response to therapy are linked to tumor genetic heterogeneity rather than tumor morphology. Therefore, understating the molecular characteristics of such melanomas become indispensable. In this study, DNA was extracted from 11 morphologically distinct regions in eight formalin-fixed, paraffin-embedded melanomas. In each region, mutations in 50 cancer-related genes were tested using next-generation sequencing (NGS). A tumor was considered genetically heterogeneous if at least one non-overlapping mutation was identified either between the histologically distinct regions of the same tumor (intratumor heterogeneity) or among the histologically distinct regions of the paired primary and metastatic tumors within the same patient (intertumor heterogeneity). Our results revealed that genetic heterogeneity existed in all tumors as non-overlapping mutations were detected in every tested tumor (n = 5, 100%; intratumor: n = 2, 40%; intertumor: n = 3, 60%). Conversely, overlapping mutations were also detected in all the tested regions (n = 11, 100%). Melanomas exhibiting histomorphologic heterogeneity are often associated with genetic heterogeneity, which might contribute to tumor survival and poor response to therapy.

One of the defining features of cancer is cell migration: the tendency of malignant cells to become motile and move significant distances through intervening tissue. This is a necessary precondition for metastasis, the ability of cancers to spread, which once underway permits more rapid growth and complicates effective treatment. In addition, the emergence and development of cancer is currently believed to be an evolutionary process, in which the emergence of cancerous cell lines and the subsequent appearance of resistant clones is driven by selection. In this thesis we develop minimal models of the relationship between motility, growth, and evolution of cancer cells. These should be simple enough to be easily understood and analysed, but remain realistic in their biologically relevant assumptions. We utilise simple simulations of a population of individual cells in space to examine how changes in mechanical properties of invasive cells and their surroundings can affect the speed of cell migration. We similarly examine how differences in the speed of migration can affect the growth of tumours. From this we conclude that cells with a higher elastic stiffness experience stronger resistance to their movement through tissue, but this resistance is limited by the elasticity of the surrounding tissue. We also find that the growth rate of large lesions depends weakly on the migration speed of escaping cells, and has stronger and more complex dependencies on the rates of other stochastic processes in the model, namely the rate at which cells transition to being motile and the reverse rate at which cells cease to be motile. To examine how the rates of growth and evolution of an ensemble of cancerous lesions depends on their geometry and underlying fitness landscape, we develop an analytical framework in which the spatial structure is coarse grained and the cancer treated as a continuously growing system with stochastic migration events. Both the fully stochastic realisations of the system and deterministic population transport approaches are studied. Both approaches conclude that the whole ensemble can undergo migration-driven exponential growth regardless of the dependence of size on time of individual lesions, and that the relationship between growth rate and rate of migration is determined by the geometrical constraints of individual lesions. We also find that linear fitness landscapes result in faster-than-exponential growth of the ensemble, and we can determine the expected number of driver mutations present in several important cases of the model. Finally, we study data from a clinical study of the effectiveness of a new low-dose combined chemotherapy. This enables us to test some important hypotheses about the growth rate of pancreatic cancers and the speed with which evolution occurs in reality. We test a moderately successful simple model of the observed growth curves, and use it to infer how frequently drug resistant mutants appear in this clinical trial. We conclude that the main shortcomings of the model are the difficulty of avoiding over-interpretation in the face of noise and small datasets. Despite this, we find that the frequency of resistant mutants is far too high to be explained without resorting to novel mechanisms of cross-resistance to multiple drugs. We outline some speculative explanations and attempt to provide possible experimental tests.

(EN) Humans and cells in their bodies are exposed to various mutagens in their lifetime that cause DNA damage and mutations, which affect the biology and physiology of the target cell, and can lead to the expansion of an immortalized cell clone. Genome-wide massively parallel sequencing allows the identification of DNA mutations in the coding sequences (whole exome sequencing, WES), or even the entire genome of a tumour. Mutational signatures of individual mutagenic processes can be extracted from these data, as well as mutations in genes potentially important for cancer development ('cancer drivers', as opposed to 'passengers', which do not confer a comparative growth advantage to a cell clone). Many known mutational signatures do not yet have an attributed cause; and many known mutagens do not have an attributed signature. Similarly, it is estimated that many cancer driver genes remain to be identified. This Thesis proposes a system based on immortalization of mouse embryonic fibroblasts (MEF) upon mutagen treatment for modelling of mutational signatures and identification and testing of cancer driver genes and mutations. The signatures extracted from WES data of 25 immortalized MEF cell lines, which arose upon treatment with a variety of mutagens, showed that the assay recapitulates the...

Somatische Mutationen sind eine Hauptursache für die Entstehung von Krebs. Allerdings tragen nicht alle Mutationen gleichermaßen zur Tumorentstehung bei. Ein wichtiges Ziel der personalisierten Medizin ist es daher, die für das Wachstum und Überleben des Tumors wesentlichen (sogenannte „Treiber“-Mutationen) von den zahlreichen biologisch neutralen Mutationen (sogenannte „Passagier“-Mutationen) zu unterscheiden. In der vorliegenden Studie etablierte ich einen CRISPR-basierten, genetischen Screen mit dessen Hilfe die funktionelle Rolle von Mutationen bei Krebs untersucht werden kann. Ich konnte nachweißen, dass diese mutationsselektive Strategie geeignet ist, um neue Krebstreibermutationen in der Kolorektalkarzinom- Zelllinie RKO zu identifizieren. Dazu verwendete ich 100 unterschiedliche sgRNAs, welche jeweils eine Krebsmutationssequenz spezifisch schneiden während die Wildtyp-Sequenz nicht verändert wird. Als Kontrolle nutzte ich die Kolorektalkarzinom- Zelllinie HCT116, welche die Zielmutationen nicht trägt. Interessanterweise ergab die Datenanalyse, dass zwei sgRNAs, welche die gleiche Mutation (UTP14A: S99del) schneiden, besonders rasch und ausschließlich in RKO-Zellen verloren gingen. Im Einklang mit den Screening-Ergebnissen führte die individuelle Infektion der Zellen mit diesen sgRNAs zu einem selektiven Verlust in RKO-, nicht aber HCT-Zellen, wodurch UTP14A: S99del als mutmaßliche Treiber-Mutation in RKO-Zellen identifiziert werden konnte. Die weitere Validierung und Charakterisierung dieser mutmaßlichen Treiber-Mutation wird diskutiert. Insgesamt zeigt dieser Ansatz, dass ein solches CRISPR-basiertes System ein leistungsfähiges Werkzeug auch für umfangreichere Untersuchungen von Krebsmutationen darstellt. Parallel dazu setzte ich die CRISPR-Cas-Technologie ein, um bekannte und bisher nicht therapierbare Treiber-Mutationen, wie z.B. innerhalb der Ras-Onkogen-Familie, zu untersuchen. Bemerkenswert ist in diesem Zusammenhang, dass jeder dritte Krebspatient ein durch Mutationen aktiviertes KRAS exprimiert, welches damit das am häufigsten mutierte Onkogen in menschlichen Tumorzellen ist. Im Gegensatz zu anderen Molekülen des MAPK-Signalweges konnte KRAS bisher nicht mittels kleiner, inhibitorischer Moleküle inaktiviert werden. Unter diesen Voraussetzungen birgt ein genomischer, CRISPR-basierter Ansatz das Potenzial, eine dringend benötigte therapeutische Alternative zur KRAS-Inaktivierung zu liefern. Ich entwarf daher drei mutationsselektive sgRNAs abzielend auf die häufigsten KRAS-Mutationen. Obwohl diese Strategie geeignet war, um KRAS-mutierte Tumorzellen in 3 unterschiedlichen Krebszelllinien effizient und spezifisch zu entfernen, führte die langfristige Cas9-Expression zur Bildung von onkogenen, resistenten Klonen. Dieses Phänomen wird durch DNA-Doppelstrangbrüche und die nachfolgend einsetzende, endogene DNA-Reparaturmaschinerie begünstigt. Ich konnte zeigen, dass der Adenin-Basen-Editor im Gegensatz dazu nicht nur in der Lage ist, die KRAS-Mutation ohne Doppelstrangbruch zu inaktivieren, sondern diese auch zur Wildtyp-Sequenz reparieren kann. Mit Hilfe dieses Ansatzes erreichte ich insbesondere bei Vorliegen der G12D-Mutation, einen fast vollständigen Abbau der KRAS-korrigierten Zellen. Die Validierung in patienten-abgeleiteten KRAS-G12D-Organoiden bestätigte die effiziente Korrektur sowie die daraus resultierende erhöhte Sensitivität, wenn auch in einem geringeren Maße als in Zelllinien. Somit konnte in dieser Studie erstmals gezeigt werden, dass Basen-Editierung sowohl in Zelllinien als auch in Organoiden, welche aus Tumorzellen der Patienten stammen, erfolgreich eingesetzt werden kann. Darüber hinaus ist dieses System gut verträglich und induziert weder in Zelllinien noch in Organoiden bei Vorliegen des KRAS Wildtyps unerwünschte Nebeneffekte (sogenannte „Off-target-Effekte“). Langfristig kann die Anwendung von CRISPR-basierten- und Basen-Editierungs-technologien zum Ausschalten von KRAS-Mutationen nicht nur zu einem besseren Verständnis der RAS-Biologie führen, sondern zusammen mit neuen Verabreichungsformen und Technologien die Grundlage für eine dringend benötigte KRAS-Therapie bilden.

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La leucémie aiguë lymphoblastique (LAL) est une maladie complexe à l’étiologie multifactorielle. Elle représente la forme la plus commune de cancer pédiatrique et malgré une augmentation significative du taux de survie des patients, près de 15% d’entre eux ne répondent pas aux traitements classiques et plus de 2/3 subissent les effets du traitement à long terme. Réduire ces chiffres passe par une meilleure compréhension des causes sous-jacentes de la LAL. À travers l’analyse des données de séquençage de nouvelle génération (SNG) de la cohorte QcALL du CHU Sainte-Justine, je me suis intéressé aux déterminants génomiques contribuant aux différents aspects de la LAL (prédispositions, développement/progression et rechutes). Dans un premier temps, j’ai développé un outil d’analyse (SNooPer) basé sur un algorithme d’apprentissage intégrant les données SNG normales et tumorales des patients, permettant d’identifier les mutations somatiques au sein de données à faible couverture (low-pass). Cet outil, couplé aux analyses prédictives in silico et aux validations fonctionnelles adéquates, nous a permis de caractériser les événements rares ou récurrents impliqués dans le processus leucémogène. En analysant les données de LALs pré-B, j’ai pu mettre en évidence une série de mutations drivers rares au niveau de gènes (ACD, DOT1L, HCFC1) qui n’avaient jamais été associés à la LAL. L’étude fonctionnelle de la mutation identifiée au niveau d’ACD, membre du complexe shelterin, a démontré qu’elle conduit à une réduction de l’apoptose et une augmentation de la taille des télomères. Outre l’intérêt de la découverte de ces nouveaux drivers, je souhaitais démontrer l’importance des mutations somatiques rares afin d'établir la spécificité interindividuelle, généralement sous-estimée, et d’identifier l’ensemble des fonctions cellulaires impliquées. Au cours de ces travaux, j'ai également mis en évidence de nouveaux évènements récurrents de la LAL à cellules T (LAL-T), en particulier au niveau de patients présentant un phénotype immature encore mal caractérisé. J'ai démontré l’influence d'une mutation dans le gène codant pour U2AF1, membre de la machinerie d’épissage (spliceosome), sur l’épissage de gènes d’intérêt et ainsi confirmer l’importance du dysfonctionnement de l’épissage dans le développement de la leucémie. J'ai également identifié deux suppresseurs de tumeurs portés par le chromosome X, MED12 et USP9X, qui n’avaient jamais été associés à la LAL-T auparavant et qui représentent un intérêt particulier étant donné le débalancement de l'incidence en fonction du sexe (ratio garçon:fille =1.22). Enfin, grâce à l’étude longitudinale de patients LAL-B ayant subi une ou plusieurs rechutes, j'ai analysé l'architecture et l'évolution clonales des tumeurs. J’ai ainsi identifié 2 profils évolutifs distincts gouvernant les rechutes précoces et tardives: d'un côté, une dynamique élevée alimentée par un dysfonctionnement des mécanismes de réparation de l'ADN et conduisant à l'émergence rapide de clones mieux adaptés – de l'autre, une dynamique réduite, quasi-inerte, suggérant l'échappement de cellules en dormance épargnée par la chimiothérapie. De manière générale, cette thèse a permis de contribuer à la caractérisation des déterminants génomiques qui constituent la variabilité inter- et intra-tumorale, participent au processus leucémogène et/ou aux mécanismes de résistance au traitement. Ces nouvelles connaissances contribueront à un raffinement de la stratification des patients et leur prise en charge personnalisée. / Acute lymphoblastic leukemia (ALL) is a complex disease with a multi-factorial etiology. It represents the most frequent pediatric cancer and despite a significant increase of survival rate, about 15% of the patients still do not respond to current treatment protocols and over 2/3 of survivors experience long-term treatment related side effects. To reduce these numbers, a better understanding of the underlying causes of ALL is needed. Through the analysis of next-generation sequencing (NGS) data obtained from the established Quebec cALL (QcALL) cohort of the Sainte-Justine hospital, I have been particularly concerned about the genomic determinants that contribute to different phases of ALL (predispositions, onset/progress and relapses). First, I developed an analysis tool (SNooPer) based on a machine learning algorithm integrating both normal and tumor NGS data of the patient to identify somatic mutations from low-pass sequencing. This tool, combined to in silico predictive analysis and to adequate functional validations, allowed us to characterize rare or recurrent events involved in the leukemogenesis process. Through the analysis of pre-B ALLs, I have been able to identify several rare driver genes which had never been associated to ALL before (ACD, DOT1L, HCFC1). The functional study of the identified mutation in ACD, a member of the shelterin complex, showed a concomitant lengthening of the telomeres and decreased apoptosis levels in leukemia cells. Besides the interest aroused by the discovery of these new drivers, I wanted to demonstrate the importance of low-frequency somatic events to establish the generally underestimated interindividual specificity and identify all cellular functions involved. During this work, I also identified new recurrent driver events in T-cell ALL (T-ALL), particularly among poorly characterized immature T-ALL patients. For example, I demonstrated the impact of a recurrent mutation in U2AF1, member of the spliceosome, on alternative splicing of cancer-relevant genes, further suggesting the importance of aberrant splicing in leukemogenesis. I also identified two new X-linked tumor suppressors, MED12 and USP9X, never associated to T-ALL before and obtained results supporting a potential role for these genes in the male-biased sex ratio observed in T-ALL (ratio male:female =1.22). Finally, through the longitudinal study of pre-B cALLs who suffered one or multiple relapses, I analyzed the clonal architecture and evolution of the tumors. I identified two distinct evolution patterns governing either early or late relapses: on one hand a highly dynamic pattern, sustained by a defect of DNA repair processes, illustrating the quick emergence of fitter clones - and on the other hand, a quasi-inert evolution pattern suggesting the escape from dormancy of neoplastic stem cells likely spared from initial cytoreductive therapy. Overall, this thesis contributed to the characterization of genomic determinants that constitute the inter- and intra-tumor variability, participate in leukemogenesis and/or in resistance mechanisms. This new knowledge will contribute to refine patient stratification and treatment.