Chemical Systems Biology Studies of Triple Negative Breast Cancer Cell Lines

Triple negative breast cancer (TNBC) is a highly aggressive type of breast cancer that accounts for 15-20% of breast cancer cases. Targeted therapy remains to be established for TNBC that lacks estrogen receptors, progesterone receptors and human epidermal growth factor receptor HER2. This limits the therapy to traditional chemotherapy, radiation and surgery, which is only beneficial to a fraction of TNBC patients. Transcriptomics-based subtyping of TNBC into six classes, but it is unclear how the transcriptomics-based subtypes link to effective therapeutic strategies, resulting in a poor clinical prognosis in comparison to other breast cancer subtypes. Hence, there is an imminent need for identifying molecular markers and druggable targets against TNBC. This study is focused on the establishment of functional profiling of TNBC cell lines based on their drug vulnerabilities, and to identify novel druggable signaling nodes. We studied a panel of 16 TNBC cell lines using a functional profiling approach in which we measured the responses of TNBC cells to 304 oncology compounds and 355 GSK published kinase inhibitors. The clustering analysis based on overall drug-responses did not match the transcriptomics-based subtypes, suggesting the presence of extensive heterogeneity in TNBC and that the genomic or transcriptomic profiles do not always reflect the functional behavior of these cells. First, to go beyond standard anti-proliferative drug effects, we established a multiplexed readout for both cell viability and cytotoxicity. We identified many drug classes (such as anti-mitotics, anti-metabolites), which generally are assumed to have cytotoxic effects, mostly exhibited strong effects on cell viability but failed to kill the cells. However, in a subset of the cell lines, they induced a selective cell death. In those cases, we identified differential levels of protein markers linked to the cytotoxic responses (e.g. high level PAI-1 linked to anti-mitotics), suggesting their potential use in clinics for therapeutic decision. These results highlighted that simple multiplexed cell viability and cytotoxicity measurements provide more insight in cellular responses towards the treatment and thereby may help in providing better translationally predictive readouts. Second, we devised a novel drug response metric, called normalized drug response (NDR), which accounts for many kinds of screening artifacts such as signal growth rate differences in positive and negative control, as well as in drug-treated conditions. We found that the NDR metric is a time-independent method and it significantly improved the drug response curve fitting. Our NDR will be of great value in cell-based high throughput drug screening approaches as it cuts down the cost and time for the replicate experiments and further validation with cytotoxicity assay. Lastly, we used computational approach to decipher the kinase signal addiction of breast cancer cell lines by integrating vulnerabilities to kinase inhibitors and their polypharmacology data. We developed the kinase inhibition sensitivity score (KISS) to predict single and combinatorial signal addictions. For this study, we used 40 kinase inhibitors with well-defined target selectivities. With this approach, we predicted and validated novel synergistic inhibitor combinations against TNBC cells, such as dasatinib with axitinib, bosutinib with foretinib combinations for HCC1937 cells. This study suggests that drug sensitivity profiling is a powerful strategy for de-convolving cancer cell specific target addictions.N

Network-driven strategies to integrate and exploit biomedical data

[eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited. In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca d’una millor comprensió dels sistemes biològics complexos, la comunitat científica ha estat aprofundint en la biologia de les proteïnes, fàrmacs i malalties, poblant les bases de dades biomèdiques amb un gran volum de dades i coneixement. En l’actualitat, el camp de la biomedicina es troba en una era de “dades massives” (Big Data), on la investigació duta a terme per ordinadors se’n pot beneficiar per entendre i caracteritzar millor les entitats químiques i biològiques. No obstant, la heterogeneïtat i complexitat de les dades biomèdiques requereix que aquestes s’integrin i es representin d’una manera idònia, permetent així explotar aquesta informació d’una manera efectiva i eficient. L’objectiu d’aquesta tesis doctoral és desenvolupar noves estratègies que permetin explotar el coneixement biomèdic actual i així extreure informació rellevant per aplicacions biomèdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal d’integrar i explotar el coneixement biomèdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoòmics per tal d’ajudar accelerar el procés de descobriment de nous fàrmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratègia per identificar grups funcionals de gens associats a la resposta de línies cel·lulars als fàrmacs, (ii) creat una col·lecció de descriptors biomèdics capaços, entre altres coses, d’anticipar com les cèl·lules responen als fàrmacs o trobar nous usos per fàrmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biològics corresponen a una associació biològica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors químics i biològics rellevants pel procés de descobriment de nous fàrmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina

Dissecting Tumor Heterogeneity in Lung Cancer

Lung cancer is a heterogeneous disease composed of genetically and phenotypically distinct tumor cells as well as a heterogeneous microenvironment consisting of non-cancer cells and extracellular matrix. Constant interactions among these components ultimately leads to a complex tumor tissue that is ever evolving and poses a therapeutic challenge for sustained benefit. Strategies for targeting lung cancers are largely guided by the genetic alterations identified in the tumor specimens. However, in order to gain a better understanding of lung cancer progression and develop effective treatment modalities, studying tumor in context of its microenvironment is crucial. The first aim of this project was to establish an experimental model to capture tumor heterogeneity. We developed an Ex Vivo Tumor system that preserved tumor composition and allowed the introduction of specific modifications in the tumor microenvironment to investigate their role in tumor progression. We utilized this system to demonstrate the role of extrinsic as well as intrinsic alterations that modify tumor cell behavior. Next, we explored the biological phenomenon epithelial-to-mesenchymal transition as a source of tumor cell heterogeneity and therapeutic resistance. Genetically identical KRAS mutant lung cancer cells displayed different phenotypic states that were associated with distinct survival pathways that allowed cancer cells to escape therapeutic targeting. With the use of extensive in vitro, ex vivo and in vivo models, we identified that a combinatorial approach of utilizing CDK4 and MEK inhibitors to effectively control tumor growth by targeting distinct tumor subpopulations within lung cancer and prevented emergent resistance to either single agent

Recent Developments in Cancer Systems Biology

This ebook includes original research articles and reviews to update readers on the state of the art systems approach to not only discover novel diagnostic and prognostic biomarkers for several cancer types, but also evaluate methodologies to map out important genomic signatures. In addition, therapeutic targets and drug repurposing have been emphasized for a variety of cancer types. In particular, new and established researchers who desire to learn about cancer systems biology and why it is possibly the leading front to a personalized medicine approach will enjoy reading this book

Image informatics approaches to advance cancer drug discovery

High content image-based screening assays utilise cell based models to extract and quantify morphological phenotypes induced by small molecules. The rich datasets produced can be used to identify lead compounds in drug discovery efforts, infer compound mechanism of action, or aid biological understanding with the use of tool compounds. Here I present my work developing and applying high-content image based screens of small molecules across a panel of eight genetically and morphologically distinct breast cancer cell lines. I implemented machine learning models to predict compound mechanism of action from morphological data and assessed how well these models transfer to unseen cell lines, comparing the use of numeric morphological features extracted using computer vision techniques against more modern convolutional neural networks acting on raw image data. The application of cell line panels have been widely used in pharmacogenomics in order to compare the sensitivity between genetically distinct cell lines to drug treatments and identify molecular biomarkers that predict response. I applied dimensional reduction techniques and distance metrics to develop a measure of differential morphological response between cell lines to small molecule treatment, which controls for the inherent morphological differences between untreated cell lines. These methods were then applied to a screen of 13,000 lead-like small molecules across the eight cell lines to identify compounds which produced distinct phenotypic responses between cell lines. Putative hits from a subset of approved compounds were then validated in a three-dimensional tumour spheroid assay to determine the functional effect of these compounds in more complex models, as well as proteomics to determine the responsible pathways. Using data generated from the compound screen, I carried out work towards integrating knowledge of chemical structures with morphological data to infer mechanistic information of the unannotated compounds, and assess structure activity relationships from cell-based imaging data

Das MYCN-Onkogen als Marker für minimale Resterkrankung und therapeutisches Ziel beim Neuroblastom

Neuroblastoma, the most common extracranial solid childhood cancer, arises from precursors of the developing sympathetic nervous system. MYCN oncogene amplification is a determinant of high risk and occurs in ~25% of neuroblastomas. Despite intensive treatment, more than half these patients succumb to their disease, implying persistence of therapy-resistant MYCN-amplified minimal residual neuroblastoma cells. This thesis proposes a comprehensive concept for the specific diagnostic detection of the MYCN amplicon and evaluates new treatment options for MYCN-amplified neuroblastoma. Disease-relevant nucleotide changes, structural gene rearrangements and copy number alterations were detected in tumor material by next-generation sequencing of a customized hybrid capture-based targeted panel. Unique MYCN amplicon breakpoints in the rearranged gene constitute a target sequence for a personalized minimal residual disease (MRD) PCR diagnostic. MYCN amplicon breakpoints in neuroblastoma cell lines and tumors were identified and recovered by individual, semi-quantitative PCR assays and Sanger sequencing. The assay was further developed for highly sensitive, real-time quantitative and droplet digital PCR detection for selected MYCN breakpoints in cell lines. MRD level detected in bone marrow aspirates collected during therapy outlined different disease courses in patients, including MRD persistence until relapse and good response to the first treatment course. Combining multi-agent chemotherapy in current high-risk protocols with indirect MYCN inhibitors provides a potential route to improve poor cure rates for MYCN-amplified neuroblastomas. Different hyperactive biological networks in MYCN-amplified neuroblastoma were tackled using small molecule inhibitors of the bromodomain and extra-terminal (BET) domain-containing protein BRD4, phosphoinositide 3-kinase (PI3K) and polo-like kinase 1 (PLK1). BET (JQ1, OTX015 and TEN-010) and kinase (alpelisib, volasertib and rigosertib) inhibitors demonstrated anti-cancer activity by diminishing viability in cell line-based drug screens at nanomolar to low micromolar concentrations. Rigosertib treatment altered PLK1 and PI3K signaling and strongly impaired the cellular ability for wound healing and colony formation. In line with in vitro observations, rigosertib reduced tumor growth in patient-derived neuroblastoma xenografts in mice. Combining OTX015 and volasertib produced synergistic anti-tumor responses in two MYCN-amplified neuroblastoma cell lines. To prevent MYCN-driven proliferation of tumor cells, further indirect MYCN targets are also being considered. This is exemplified by a substrate of PLK1, ASPM, which is elevated in MYCN-amplified primary neuroblastomas. Knockdown of ASPM, a microtubule-associated protein involved in mitotic spindle assembly, in MYCN-amplified neuroblastoma cell lines reduced viability and proliferation, accompanying a neuronal differentiation phenotype with neurite-like outgrowth, cytoskeletal changes and increased expression of differentiation markers. This study presents clinical implementable molecular diagnostics to pinpoint unique MYCN-amplified neuroblastoma cells within non-invasively accessible biopsy material, and proposes indirect small molecule-based MYCN therapies and potentially new drug targets for a personalized treatment.Das Neuroblastom, der häufigste extrakranielle solide Krebs im Kindesalter, entsteht aus Vorläuferzellen des sich entwickelnden sympathischen Nervensystems. Eine Amplifikation des MYCN-Onkogens ist ein bestimmender Faktor für ein hohes Risiko und tritt bei ~25% der Neuroblastome auf. Trotz intensiver Behandlung erliegt mehr als die Hälfte dieser Patienten ihrer Krankheit, was die Persistenz therapieresistenter, MYCN-amplifizierter minimaler Restneuroblastomzellen impliziert. Diese Arbeit stellt ein umfassendes Konzept für den spezifischen, diagnostischen Nachweis des MYCN-Amplikons vor und evaluiert neue Behandlungsoptionen für MYCN-amplifizierte Neuroblastome. Krankheitsrelevante Nukleotidveränderungen, strukturelle Genrearrangements und Kopienzahl-veränderungen wurden im Tumormaterial mit Hilfe eines maßgeschneiderten, zielgerichteten hybrid-capture-basierten Next Generation Sequencing (NGS) Assays nachgewiesen. Einzigartige MYCN-Amplikon-Bruchpunkte im rearrangierten Gen stellen eine Zielsequenz für eine personalisierte PCR-Diagnostik der minimalen Resterkrankung (MRD) dar. MYCN-Amplikon-Bruchpunkte in Neuroblastom-Zelllinien und Tumoren wurden durch individuelle, semi-quantitative PCR-Assays und Sanger Sequenzierung identifiziert und wiedererkannt. Der Assay wurde für den hochsensitiven, quantitativen Echtzeit- und digitalen Tröpfchen-PCR-Nachweis für ausgewählte MYCN-Bruchpunkte in Zelllinien weiterentwickelt. Die MRD Level, die in den während der Therapie gesammelten Knochenmarkaspiraten nachgewiesen wurden, skizzierten die verschiedenen Krankheitsverläufe bei den Patienten, einschließlich der MRD-Persistenz bis zum Rezidiv und des guten Ansprechens auf den ersten Behandlungsabschnitt. Die Kombination der Multi-Wirkstoff-Chemotherapie in den aktuellen Hochrisikoprotokollen mit indirekten MYCN-Inhibitoren stellt einen möglichen Weg dar, die schlechten Heilungsraten für MYCN-amplifizierte Neuroblastome zu verbessern. Verschiedene, hyperaktive biologische Netzwerke in MYCN-amplifizierten Neuroblastomen wurden mit niedermolekularen Inhibitoren der Bromdomäne und des extra-terminalen (BET) domänenhaltigen Proteins BRD4, der Phosphoinositid-3-Kinase (PI3K) und der polo-ähnlichen Kinase 1 (PLK1) behandelt. BET (JQ1, OTX015 und TEN-010) und Kinase-Inhibitoren (Alpelisib, Volasertib und Rigosertib) zeigten eine krebshemmende Wirkung, indem sie die Viabilität in zelllinienbasierten Wirkstoff-Screens bei nanomolaren bis niedrigen mikromolaren Konzentrationen verminderten. Die Behandlung mit Rigosertib veränderte die PLK1- und PI3K-Signalübertragung und beeinträchtigte die zelluläre Fähigkeit zur Wundheilung und Koloniebildung stark. In Übereinstimmung mit In-vitro-Beobachtungen reduzierte Rigosertib das Tumorwachstum in von Patienten stammenden Neuroblastom-Xenografts bei Mäusen. Die Kombination von OTX015 und Rigosertib erzeugte synergistische antitumorale Aktivität in zwei MYCN-amplifizierten Neuroblastom-Zelllinien. Um die MYCN-gesteuerte Proliferation von Tumorzellen zu verhindern, werden weitere indirekte MYCN-Targets in Betracht gezogen. Ein Beispiel hierfür ist ein Substrat von PLK1, ASPM, das in MYCN-amplifizierten, primären Neuroblastomen erhöht ist. Das Herunterregulieren von ASPM, einem Mikrotubuli-assoziierten Protein, das an der mitotischen Spindelanordnung beteiligt ist, führte in MYCN-amplifizierten Neuroblastom-Zelllinien zu einer verminderten Viabilität und Proliferation, was mit einem neuronalen Differenzierungsphänotyp mit neuritenartigem Auswuchs, zytoskelettalen Veränderungen und erhöhter Expression von Differenzierungsmarkern einherging. Diese Studie stellt eine klinisch umsetzbare, molekulare Diagnostik vor, um einzigartige MYCN-amplifizierte Neuroblastomzellen in nicht-invasiv zugänglichem Biopsiematerial zu detektieren, und schlägt indirekte, niedermolekular-basierende MYCN-Therapien und potenziell neue Zielmoleküle für eine personalisierte Krebsbehandlung vor