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Metastasis is the main cause of cancer morbidity and mortality. Despite its clinical significance, several fundamental questions about the metastatic process in humans remain unsolved. Does metastasis occur early or late in cancer progression? Do metastases emanate directly from the primary tumor or give rise to each other? How does heterogeneity in the primary tumor relate to the genetic composition of secondary lesions? Addressing these questions in representative patient populations is crucial, but has been difficult so far. Here we present a simple, scalable PCR assay that enables the tracing of tumor lineage in patient tissue specimens. Our methodology relies on somatic variation in highly mutable polyguanine (poly-G) repeats located in non-coding genomic regions. We show that poly-G mutations are present in a variety of human cancers. Using colon carcinoma as an example, we demonstrate an association between patient age at diagnosis and tumor mutational burden, suggesting that poly-G variants accumulate during normal division in colonic stem cells. We further show that poorly differentiated colon carcinomas have fewer mutations than well-differentiated tumors, possibly indicating a shorter mitotic history of the founder cell in these cancers. We collect multiple spatially separated samples from primary carcinomas and their metastases and use poly-G fingerprints to build well-supported phylogenetic trees that illuminate each patient's path of progression. Our results imply that levels of intra-tumor heterogeneity vary significantly among patients.

Le cancer du sein est le cancer le plus fréquent chez la femme et représente la principale cause de mortalité liée au cancer. Le décés est habituellement causé par le développement de résistance aux traitements et la propagation métastatique de la maladie. Malgré la pertinence clinique, la complexité moléculaire de la maladie et sa dynamique restent à ce jour peu connues.Depuis longtemps, l’hétérogénéité du cancer du sein a été observée au niveau histologique et du profil évolutif clinique, et ces différences ont servi de base pour la classification de la maladie. Avec le développement des technologies à haut débit, telles que les puces à damier (microarrays) et le séquençage à haut débit, cette classification a été affinée et une complexité génétique jusqu'alors inconnue a été révélée.Des études utilisant ces techniques ont montré que des différences moléculaires existent non seulement entre les différentes patientes atteintes d’un cancer du sein (hétérogénéité inter-tumorale), mais aussi chez la même patiente (hétérogénéité intra-tumorale). En outre, l'hétérogénéité intra-tumorale peut exister non seulement entre les différentes parties d'une tumeur (hétérogénéité intra-tumorale spatiale) mais elle peut aussi résulter de l’évolution moléculaire d'une tumeur au cours du temps (hétérogénéité intra-tumorale temporelle). Cette complexité pourrait avoir un impact important sur la façon dont les patientes atteintes d’un cancer du sein sont prises en charge et traitées.La recherche que j’ai menée dans le Breast Cancer Translational Research Laboratory sous la direction du Professeur Christos Sotiriou avait deux objectifs principaux. Le premier était de déterminer l'ampleur et les implications cliniques de l'hétérogénéité intra-tumorale dans deux scénarios cliniques courants, à savoir: les cancers du sein multifocaux (MFBCs) et les cancers du sein métastatiques ER positif / HER2 négatif. Le deuxième était d'étudier l'impact de l'édition de l'ARN dans la détermination de l'hétérogénéité inter-tumorale, phénomène encore peu caractérisé. Notre recherche a notamment montré que:1) Les lésions de tous les MFBCs que l’on a étudiés partagent une origine commune. Malgré cela, et malgré des caractéristiques pathologiques similaires, chez un tiers des patientes, les lésions multifocales d’une même patiente ne partageaient aucune substitution et aucune insertion/déletion. De plus, l’hétérogénéité inter-lésion a été observée pour des mutations oncogéniques dans des gènes tels que PIK3CA, TP53, GATA3 et PTEN;2) En se concentrant sur un nombre défini de gènes associés au cancer, une concordance substantielle des mutations et du nombre de copies des gènes a été observée entre les lésions primaires et métastatiques appariées de cancers du sein ER positif / HER2 négatif. Des différences entre les lésions appariées ont cependant été trouvées pour les niveaux d’expression de certains gènes. Dans les lésions primaires, seuls les niveaux d’expression de quelques gènes et un niveau élevé d'amplification de FGFR1 ont été associés à la survie;3) L'édition de l’ARN est une source généralisée de variation du transcriptome dans le cancer du sein. Dans ce cancer, et potentiellement dans tous les cancers, l'édition de l’ARN est principalement contrôlée par deux facteurs, à savoir l'amplification de 1q et l'inflammation, qui sont toutes deux très répandues parmi les cancers humains. La magnitude de l'édition de l’ARN, en combinaison avec la conservation des sites d'édition détectés dans les tissus et les patientes, suggère qu'il pourrait y avoir des implications cliniques et thérapeutiques pour un large éventail de patientes atteintes d’un cancer.Nos résultats suggèrent qu'une caractérisation moléculaire approfondie des cancers du sein multifocaux et métastatiques est importante pour apprécier leur complexité, et que dans la recherche sur le cancer du sein, plus d’importance devrait être accordée à l'édition de l'ARN, un phénomène encore peu étudié qui pourrait influencer notre connaissance sur le développement et l'évolution de la maladie. / Breast cancer still represents the most frequently diagnosed cancer and the leading cause of cancer-related mortality in women. Death is usually caused by the development of resistance to treatments and the resulting metastatic spread of the disease. Despite the clinical relevance, little is known about the molecular complexity of the disease and its dynamics.Breast tumor heterogeneity has been observed at the level of the histology and the natural history of the disease for a long time, and these differences have served as the basis for disease classification. With the advent of high-throughput technologies, such as gene expression microarrays and massively parallel sequencing, this classification has been refined and a previously unknown genetic complexity has been revealed. Studies implementing these technologies have shown that molecular dif¬ferences exist not only between different breast cancer patients (inter-tumor heterogeneity), but also within the same patient (intra-tumor heterogeneity). Furthermore, intra-tumor heterogeneity could occur either between different regions of a tumor (spatial intra-tumor heterogeneity), or as the result of the molecular evolu¬tion of a tumor over time (temporal intra-tumor heterogeneity). This complexity might have a profound impact on the way breast cancer patients are managed and treated. The research work that I carried out in the Breast Cancer Translational Research Laboratory under the direction of Prof Christos Sotiriou had two main aims. The first was to determine the extent and the clinical implications of intra-tumor heterogeneity in two common clinical scenarios, namely: multifocal breast cancers (MFBCs) and metastatic ER positive/HER2 negative breast cancers. The second was to investigate the potential impact of yet poorly characterized phenomenon, such as RNA editing, in determining inter-tumor heterogeneity. For this purpose, I have conducted three main projects, which resulted in three manuscripts.We showed that:1) The lesions of all the investigated MFBCs shared a common origin. Despite this, and despite having similar pathological features, in up to a third of the patients the lesions of the same MFBC didn’t share any substitution/indels, and inter-lesion heterogeneity was observed for oncogenic mutation(s) in genes such as PIK3CA, TP53, GATA3, and PTEN; 2) When focusing on a defined number of cancer-associated genes, a substantial concordance for mutations and copy number aberrations could be found between primary and matched metastatic lesions of ER positive/HER2 negative breast cancers. Differences between matched pairs could however be found for the level of expressions of few genes. In primary lesions, only the expression levels of few genes and high FGFR1 amplification levels were associated with OS;3) A-to-I RNA editing is a pervasive source of transcriptome variation in breast cancer. In breast and potentially all cancers, A-to-I editing is mainly controlled by two factors, namely 1q amplification and inflammation, both of which are highly prevalent among human cancers. The wide-spread editing observed, in combination with the conservation of editing sites detected across tissues and patients, suggests that there might be clinical and therapeutic implications for a wide range of cancer patients.Our results suggest both that a thorough molecular characterization of multifocal and metastatic breast cancers is important to appreciate their genomic complexity, and that in breast cancer research more relevance should be given to RNA editing, a yet poorly investigated phenomenon that has the potential to impact the development and the evolution of the disease. / Doctorat en Sciences médicales (Médecine) / info:eu-repo/semantics/nonPublished

abstract: Understanding intratumor heterogeneity and their driver genes is critical to designing personalized treatments and improving clinical outcomes of cancers. Such investigations require accurate delineation of the subclonal composition of a tumor, which to date can only be reliably inferred from deep-sequencing data (>300x depth). The resulting algorithm from the work presented here, incorporates an adaptive error model into statistical decomposition of mixed populations, which corrects the mean-variance dependency of sequencing data at the subclonal level and enables accurate subclonal discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer simulations and real-world data, this new method, named model-based adaptive grouping of subclones (MAGOS), consistently outperforms existing methods on minimum sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports subclone analysis using single nucleotide variants and copy number variants from one or more samples of an individual tumor. GUST algorithm, on the other hand is a novel method in detecting the cancer type specific driver genes. Combination of MAGOS and GUST results can provide insights into cancer progression. Applications of MAGOS and GUST to whole-exome sequencing data of 33 different cancer types’ samples discovered a significant association between subclonal diversity and their drivers and patient overall survival. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2019

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.

Caractériser précisément l’hétérogénéité tumorale constitue un enjeu majeur en cancérologie. Le calcul de biomarqueurs de cette hétérogénéité directement à partir des données d’imagerie présente de nombreux avantages : il est non-invasif, répétable plusieurs fois au cours du traitement, ne nécessite pas d’examen supplémentaire et permet de caractériser la tumeur toute entière et ses éventuelles métastases. Mon projet de recherche visait à développer et évaluer des méthodes pour une caractérisation plus complète de l’activité métabolique des tumeurs. L’analyse de texture des images TEP nécessite un protocole de calcul des index plus complexe que celui des paramètres conventionnels utilisés en clinique. Afin de déterminer l’influence des étapes préliminaires au calcul de ces index, une étude méthodologique a tout d’abord été menée. Cette analyse a montré que certains index de texture étaient redondants et qu’il existait une forte corrélation entre certains d’entre eux et le volume métabolique. Elle a également mis en évidence l’impact de la formule et du taux de discrétisation sur les valeurs des paramètres de texture et permis de clarifier l’interprétation des indices. Après avoir établi un protocole de calcul strict, une seconde partie de ce travail a consisté à évaluer la capacité de ces index pour la caractérisation des tumeurs. L’analyse de texture a ainsi permis de différencier les tissus sains des tissus tumoraux et de distinguer les types histologiques pour les tumeurs mammaires, les lésions pulmonaires ou encore les gliomes.Afin de comprendre le lien entre l’hétérogénéité tumorale quantifiée sur les images TEP et l’hétérogénéité biologique des lésions, nous avons comparé l’analyse de texture réalisée à différentes échelles sur un modèle animal. Cette étude a révélé que la texture mesurée in vivo sur les images TEP reflétait la texture mesurée ex vivo sur les images autoradiographiques. / The precise characterization of the biological heterogeneity of a tumor is a major issue in oncology. The calculation of biomarkers reflecting this heterogeneity directly from imaging data offers a number of advantages: it is non-invasive, can be repeated during the therapy, does not require supplementary examinations and the whole tumor and possible metastases can be investigated from the images. My research project was to develop and assess methods to characterize the metabolic activity distribution in tumors.Texture analysis based on PET images requires a protocol to compute index that is somehow more sophisticate than when simply measuring the conventional index used in clinical practice. To determine the role of the different steps that are involved in the computation of texture index, a methodological study was conducted. This study demonstrated that some texture parameters were redundant and that there existed a strong correlation between some of them and the metabolic volume. We have also shown that the formula and the rate of discretization impact the texture analysis and clarified the interpretation of these metrics. After the protocol of texture index computation has been established, the second part of this work was to assess the interest of these indices for the tumor characterization. We showed that some texture indices were different in tumor and in healthy tissue and could identify histological types such as the triple-negative breast tumors, the squamous cell carcinoma from adenocarcinoma in lung tumors, as well as the grade of gliomas.To understand the links between the tumor heterogeneity as measured from PET images and the biological heterogeneity of lesions, we compared the texture analysis based on different scales in a mouse model. This study revealed that the texture measured in vivo based on PET images reflects the texture measured ex vivo from autoradiographic images.

Soft Tissue Sarcomas (STS) are among the most dangerous diseases, with a 50% mortality rate in the USA in 2016. Heterogeneous responses to the treatments of the same sub-type of STS as well as intra-tumor heterogeneity make the study of biopsies imprecise. Radiologists make efforts to find non-invasive approaches to gather useful and important information regarding characteristics and behaviors of STS tumors, such as aggressiveness and recurrence. Quantitative image analysis is an approach to integrate information extracted using data science, such as data mining and machine learning with biological an clinical data to assist radiologists in making the best recommendation on clinical trials and the course of treatment. The new methods in “Radiomics" extract meaningful features from medical imaging data for diagnostic and prognostic goals. Furthermore, features extracted from Convolutional Neural Networks (CNNs) are demonstrating very powerful and robust performance in computer aided decision systems (CADs). Also, a well-known computer vision approach, Bag of Visual Words, has recently been applied on imaging data for machine learning purposes such as classification of different types of tumors based on their specific behavior and phenotype. These approaches are not fully and widely investigated in STS. This dissertation provides novel versions of image analysis based on Radiomics and Bag of Visual Words integrated with deep features to quantify the heterogeneity of entire STS as well as sub-regions, which have predictive and prognostic imaging features, from single and multi-sequence Magnetic Resonance Imaging (MRI). STS are types of cancer which are rarely touched in term of quantitative cancer analysis versus other type of cancers such as lung, brain and breast cancers. This dissertation does a comprehensive analysis on available data in 2D and multi-slice to predict the behavior of the STS with regard to clinical outcomes such as recurrence or metastasis and amount of tumor necrosis. The experimental results using Radiomics as well as a new ensemble of Bags of Visual Words framework are promising with 91.66% classification accuracy and 0.91 AUC for metastasis, using ensemble of Bags of Visual Words framework integrated with deep features, and 82.44% classification accuracy with 0.63 AUC for necrosis progression, using Radiomics framework, in tests on the available datasets.

<p>Glioblastoma multiforme is a malignant brain tumor characterized by heterogeneity.Interactions between heterogeneous tumor cells are supposed to affect the behavior of awhole tumor cell population. In this thesis an <i>in vitro</i> model system of clonal glioma celllines originating from one glioblastoma tumor was used, and the behavior of cells incocultures was studied and compared the behavior of cells grown separately. The resultsindicate the presence of two types of interactions. In one, paracrine signals acted via extra-cellular media. This was associated with increased growth of the whole co-culture followedby a selective force driving one clone to dominance. In the other type, the cell clones grewside by side without signs of paracrine signalling, in a balance resulting in an increasedterminal cell density. Further investigations focused on mechanisms of interactions in thiscombination.</p><p>Two cell clones were chosen, a GFAP⁺ and a GFAP^-, for further experiments. Withdifferential display PCR it was possible to investigate their specific gene expressionpatterns. Seventeen cDNA fragments were differentially expressed, among them twocorresponded to known transcription factors, ATF3 and prox-1, one to a cytoskeletal protein,α-tropomyosin. The collection also contained eight ESTs (Expressed Sequence Tags) wherethe corresponding genes are unknown at present. Expression of the isolated sequences werealso analyzed in a panel of 12 different glioma cell lines and the results illustrate thecomplexity of gene expression and of tumor heterogeneity. Genes, the expression levels ofwhich were modulated in co-cultures and/or were cell density dependent, were alsoidentified.</p><p>PDGF B is suggested to play a role in sarcomas. The gene codes for an mRNA transcriptwith long UTRs, parts of which are deleted in the homologous oncogene <i>v-sis</i>. The UTRs ofPDGF B mRNAs in human sarcomas were investigated for deletions similar to <i>v-sis</i> thatmight result in increased protein levels. A new transcript variant was identified, lacking a149 base region in the 3'UTR, but its presence was not associated with increased levels ofprotein. Alterations in the 5'UTR were found more likely to be associated with increasedprotein levels.</p>

Glioblastoma multiforme is a malignant brain tumor characterized by heterogeneity.Interactions between heterogeneous tumor cells are supposed to affect the behavior of awhole tumor cell population. In this thesis an in vitro model system of clonal glioma celllines originating from one glioblastoma tumor was used, and the behavior of cells incocultures was studied and compared the behavior of cells grown separately. The resultsindicate the presence of two types of interactions. In one, paracrine signals acted via extra-cellular media. This was associated with increased growth of the whole co-culture followedby a selective force driving one clone to dominance. In the other type, the cell clones grewside by side without signs of paracrine signalling, in a balance resulting in an increasedterminal cell density. Further investigations focused on mechanisms of interactions in thiscombination. Two cell clones were chosen, a GFAP+ and a GFAP-, for further experiments. Withdifferential display PCR it was possible to investigate their specific gene expressionpatterns. Seventeen cDNA fragments were differentially expressed, among them twocorresponded to known transcription factors, ATF3 and prox-1, one to a cytoskeletal protein,α-tropomyosin. The collection also contained eight ESTs (Expressed Sequence Tags) wherethe corresponding genes are unknown at present. Expression of the isolated sequences werealso analyzed in a panel of 12 different glioma cell lines and the results illustrate thecomplexity of gene expression and of tumor heterogeneity. Genes, the expression levels ofwhich were modulated in co-cultures and/or were cell density dependent, were alsoidentified. PDGF B is suggested to play a role in sarcomas. The gene codes for an mRNA transcriptwith long UTRs, parts of which are deleted in the homologous oncogene v-sis. The UTRs ofPDGF B mRNAs in human sarcomas were investigated for deletions similar to v-sis thatmight result in increased protein levels. A new transcript variant was identified, lacking a149 base region in the 3'UTR, but its presence was not associated with increased levels ofprotein. Alterations in the 5'UTR were found more likely to be associated with increasedprotein levels.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast is a widely used non-invasive approach to gather information about the underlying physiology of breast tumors. Recent studies indicate that breast tumor heterogeneity may reflect the presence of different levels of cellular aggressiveness or habitats within the tumor. This heterogeneity has been correlated to the variations in the contrast enhancement patterns within the tumor apparent on gadolinium-enhanced DCE-MRI. Although pathological and qualitative (based on contrast enhancement patterns) studies suggest the presence of clini- cal and molecular predictive tumor sub-regions, this has not been fully investigated in the quantitative domain. The new era of cancer imaging emphasizes the use of Radiomics to provide in vivo quan- titative prognostic and predictive imaging biomarkers. Thus Radiomics focuses on apply- ing image analysis techniques to quantify tumor radiographic properties to create mineable databases from radiological images. In this research work, the Radiomics approach was ap- plied to develop a novel computer aided diagnosis (CAD) model for quantifying intratumor heterogeneity not only within the tumor as a whole, but also within tumor habitats with an intent to build predictive models in breast cancer. The process of building these predictive models started with 2-D tumor segmentation followed by habitat extraction (based on vari- ations in contrast patterns and geometry) and textural kinetic feature extraction to quantify habitat heterogeneity. A new correlation based random subspace ensemble framework was developed to evaluate the textural kinetics from the individual tumor habitats. This new CAD framework was applied to predict two clinical and prognostic factors: Axillary lymph node (ALN) metastases and Estrogen receptor (ER) status. An AUC of more than 0.8 was achieved for classifying breast tumors based on number of ALN involvement. The highest AUC of 0.91 was achieved for classifying tumors with no ALN metastases from tumors with 4 or more ALN metastases. For classifying tumors based on ER status the highest AUC of 0.87 was achieved. These results were acquired by utilizing the textural kinetic features from the tumor habitat with rapid delayed washout. The results presented in this work showed that the heterogeneity within the tumor habitats which showed rapid contrast washout in the delayed phase, correlated with aggressive cellular phenotypes. This work hypothesizes that successfully quantifying these prognostic factors will prove to be clinically significant as it can improve the diagnostic accuracy. This, in turn, will im- prove the breast cancer treatment paradigm by providing more tailored treatment regimens for aggressive tumors.