Cancer is a complex disease and presents one of the greatest challenges in modern medicine. Despite remarkable advances in treatment of several cancer types, relapse and resistance to therapy remain recurring outcomes in patients, which underscores a need for personalized treatment approaches. These complications have been related to the high genetic diversity observed within tumors, termed intratumor heterogeneity (ITH). While specific mutational profiles have been associated with the development of heterogeneous tumors, the relationship between ITH and phenotype could unveil features that undergo selection and convey fitness. Features presented in the transcriptome, as markers of heterogeneity, might therefore be valuable biomarkers. In this project, these features are explored by assuming a linear relationship between genetic ITH measures and gene expression data from The Cancer Genome Atlas samples. By first reducing the number of variables among the transcriptome to the differentially expressed genes between low and high ITH samples, the association between specific gene expression profiles and ITH is sought with a linear model. By using two different methods for estimating ITH, called Expands and PhyloWGS, the association was modeled with each method. Interestingly, the model based on Expands captured the elevated expression of a chaperone gene DNAJC18 as being consistently associated with lower ITH in four cancer types. On the other hand, models based on PhyloWGS presented lower predictive power. These results demonstrate that the transcriptome can be used to predict genetic ITH, although this depends on the method used for characterizing ITH. / Cancer är en komplex sjukdom och en av de största utmaningarna i dagens medicin. Trots stora framsteg i behandlingen av flera cancerformer är återfall och terapiresistens återkommande problem vilket talar starkt för behov av individualiserad behandling. Dessa komplikationer har relaterats till den höga genetiska variabiliteten som observeras inom tumörer, även kallad intratumoral heterogenitet (ITH). Undersökning av relationen mellan ITH och fenotypisk data kan ta fram markörer som är involverade i cancerutvecklingen som bidragare till heterogenitet. Genom att modellera associationen mellan transcriptomen och ITH kan man även hitta kliniskt relevanta biomarkörer. I detta projekt undersöks relationen mellan genutryck och ITH genom att applicera linjär regression. Genom att först reducera antalet variabler i transkriptomen till de diferentiellt utryckta gener, används linjära modellen för att ta fram specifika gener vars utryck kan relateras till ändringar i ITH uppskattad för The Cancer Genome Atlas prover. ITH uppskattas med två algoritmiska metoder, kallade Expands och PhyloWGS. Resultaten visade att förhöjd uttryck an genen DNAJC18 är associerad med lägre ITH uppskattad med Expands bland fyra cancer typer. Trots detta visade inte genutryck och ITH uppskattat med PhyloWGS lika starkt linjärt samband.

abstract: No two cancers are alike. Cancer is a dynamic and heterogeneous disease, such heterogeneity arise among patients with the same cancer type, among cancer cells within the same individual’s tumor and even among cells within the same sub-clone over time. The recent application of next-generation sequencing and precision medicine techniques is the driving force to uncover the complexity of cancer and the best clinical practice. The core concept of precision medicine is to move away from crowd-based, best-for-most treatment and take individual variability into account when optimizing the prevention and treatment strategies. Next-generation sequencing is the method to sift through the entire 3 billion letters of each patient’s DNA genetic code in a massively parallel fashion. The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2015

Les tumeurs sont généralement constituées de différentes sous-populations de cellules cancéreuses génétiquement hétérogènes, responsables en grande partie de la capacité de la tumeur à évoluer rapidement et à s’adapter aux conditions environnementales. Cette diversité génétique a des conséquences majeures pour le patient, notamment au cours de la progression tumorale et pour l’acquisition d’une résistance aux traitements.Nous avons développé une nouvelle stratégie basée sur la technologie CRISPR/Cas9 qui consiste à introduire, en plus d’une altération de séquence voulue d’un gène d’intérêt, une série de mutations silencieuses, constituant une sorte d’étiquette génétique qui peut être détectée par PCR quantitative ou séquençage de nouvelle génération. En parallèle, un code-barres constitué exclusivement de mutations silencieuses est utilisé comme contrôle interne pour les effets non spécifiques potentiels qui peuvent être engendrés suite au clivage hors-cible par le système CRISPR/Cas9. Cette approche, que nous avons appelée CRISPR-barcoding, permet de générer et de suivre l’émergence d’un petit groupe de cellules cancéreuses contenant une mutation voulue au sein d’une population de cellules non modifiées, représentant ainsi un nouveau modèle expérimental d’hétérogénéité génétique intratumorale. Grâce à une série de preuves de concept, nous avons montré que CRISPR-barcoding est une nouvelle approche qui permet d’étudier de façon simple et rapide les conséquences fonctionnelles de différents types de modifications génétiques apportées directement au niveau de la séquence génomique.Dans la deuxième partie de ma thèse, nous avons utilisé cette nouvelle approche pour l'étude de la résistance du cancer bronchique non à petites cellules (CBNPC) à la thérapie ciblée. Les patients de CBNPC dont la tumeur présente une mutation activatrice de l'epidermal growth factor receptor (EGFR) sont généralement traités avec des inhibiteurs de ce récepteur. Malheureusement, malgré une réponse initiale, presque invariablement ces patients rechutent, suite au développement d’une résistance causée, dans la majorité des cas, par l’apparition de mutations secondaires ou tertiaire de l'EGFR. Grâce à un criblage réalisé en utilisant un modèle de CBNPC basé sur la stratégie CRISPR-barcoding, nous avons pu identifier l’inhibiteur multikinase sorafenib pour sa capacité à prévenir la résistance de ces cellules aux inhibiteurs d’EGFR. Ce composé présente un mécanisme d’action original, impliquant une diminution précoce du niveau de phosphorylation de STAT3, suivie par une baisse considérable de l’expression de l’EGFR, aboutissant à une inhibition des voies intracellulaires en aval de ce récepteur, telles que RAS-MAPK et PI3K-AKT-mTOR. Ces données ont été confirmées in vivo en utilisant un modèle de xénogreffe de cellules de CBNPC modifiées par CRISPR-barcoding.En conclusion, l’ensemble de nos résultats montre que le sorafenib peut prévenir l’émergence de cellules de CBNPC résistantes aux inhibiteurs d’EGFR, indiquant que ce composé pourrait représenter une nouvelle stratégie thérapeutique pour le traitement de ce type de tumeur. / Individual tumors are composed of multiple and genetically distinct subpopulations of transformed cells that can adapt and evolve in a different way based on environmental conditions. This genetic diversity has major consequences for the patient, particularly during tumor progression and for cancer treatment.We devised a new strategy based on CRISPR/Cas9 technology in which a potentially functional modification in the sequence of a gene of interest is coupled with a series of silent point mutations, functioning as a genetic label for cell tracing. In parallel, a second barcode consisting of distinct silent mutations is inserted in the same cell population and used as a control for CRISPR/Cas9 off-target cleavage. This approach, that we named CRISPR barcoding, enables detection of cells containing the mutation of interest within a mass population of unmodified cells using real-time quantitative PCR or deep sequencing. Through a series of proof-of-concept studies, we demonstrated that CRISPR-barcoding is a fast and highly flexible strategy to investigate the functional consequences of a specific genetic modification in a broad range of assays.In the second part of my thesis, we used CRISPR-barcoding to investigate non-small cell lung cancer (NSCLC) resistance to targeted therapy. Some NSCLCs harbor activating mutations of the epidermal growth factor receptor (EGFR) and are addicted to this signaling pathway. These tumors initially show a good response to EGFR inhibitors (EGFRi), but they almost invariably relapse, due to the acquisition of a resistance, as a result of additional genetic alterations, including secondary and tertiary EGFR mutations. Using a CRISPR-barcoding model, we identified the multikinase inhibitor sorafenib for its ability to prevent EGFRi resistance in NSCLC cells. This compound acts through an original mechanism that involves early reduction of STAT3 phosphorylation and late down-regulation of EGFR, resulting in the inhibition of different downstream pathways activated by this receptor, including, RAS-MAPK and PI3K-AKT-mTOR. These results were confirmed in vivo, using a CRISPR-barcoding xenograft model for NSCLC.Altogether, our data indicate that sorafenib can prevent NSCLC resistance to EGFRi through a novel mechanism, thus providing a new potential therapeutic strategy for the treatment of this type of cancer.