Mechanism-driven hypothesis generation support for a predictive adverse effect in colorectal cancer treatment

Diese bioinformatische Dissertation beschreibt die tumorbiologische Hypothesengenierung, insbesondere im Kontext des Kolorektalkarzinoms. Hintergrund der Studien ist eine Beobachtung aus der klinischen Praxis. Verschiedene Autoren berichten, dass bei der Behandlung mit Inhibitoren des Epidermalen Wachstumsfaktor Rezeptors (EGFR), speziell des therapeutischen Antikörpers Cetuximab, eine Minderheit der Patienten die übliche Nebenwirkung der Hauttoxizität nicht oder in deutlich verminderter Form zeigt. Bei diesen Patienten wird gleichzeitig eine reduzierte Wirksamkeit der Therapie beschrieben. Das Ausbleiben der Nebenwirkung wird somit als phänotypischer Biomarker genutzt, um gegebenenfalls die Therapie anzupassen. Nachteilig erscheint in diesem Kontext allerdings die präventive Hautpflege sowie die Tatsache, dass eine Cetuximab-Behandlung zunächst gestartet werden muss, um eine Information über die Wirksamkeit zu gewinnen. Dadurch, dass der zugrunde liegende molekulare Mechanismus unbekannt ist, kann keine Vorhersage anhand eines klinischen Test getroffen werden. In der vorliegenden Arbeit war es das Ziel, Hypothesen zu generieren, welche Proteine und zellulären Signalwege kausal für das unterschiedliche Ansprechverhalten der Patientengruppen sein könnten. Ausgehend von der Annahme, dass natürliche Keimbahnvarianten in der Erbinformation der Individuen im Behandlungskontext diskriminatorisch wirken, baut die Dissertation auf einem kleinen Datensatz von 23 Exomen von Teilnehmern klinischer Studien auf. Diese Sequenzierungsdaten wurden in genomische Varianten überführt und auf ihren potentiellen genetisch-mechanistischen Einfluss hin untersucht. Gezielte Einschränkungen wurden dabei anhand einer Modellierung des biomedizinischen Kontextes des Anwendungsfalls eingeführt, um die reduzierte Datenlage gezielt mit Informationen anzureichern. Die so erhaltenen Kandidatengene, welche in nachfolgenden praktischen Arbeiten validiert werden müssen, werden im Einzelnen beschrieben und bewertet. Methodisch ist das Ergebnis dieser Dissertation die „Molecular Systems Map“, eine in Cytoscape modellierte Netzwerkstruktur, die funktionelle Interaktionen zwischen Proteinen interaktiv visualisiert und gleichzeitig als Filter auf Basis des biologischen Kontexts dient. Ziel hierbei ist es, einen biomedizinisch ausgebildeten Fachanwender bei der Generierung von Hypothesen zu unterstützen, indem im Gegensatz zu sonst häufig anzutreffenden tabellarischen Ansichten die Ergebnisse aus der Sequenzanalyse in eben jenem funktionalen Kontext dargestellt werden. Darüber hinaus wird so die Anwendung von Graphenalgorithmen und die Integration weiterer Daten ermöglicht, z.B. solcher aus komplementären ‘omics-Experimenten.This bioinformatics thesis describes work and results from a study on a use case in the context of colorectal cancer. Background of the studies is an observation form the clinical practice. Various authors report that upon treatment with inhibitors of the Epidermal Growth Factor Receptor (EGFR), in particular with the therapeutic antibody Cetuximab, a minority of patients does not, or in a clearly reduced form, show common adverse effects of skin toxicity. For these patients, at the same time a reduced efficacy of the therapy is described. The lack of the adverse effect therefore gets used as a phenotypic biomarker for inducing a switch of therapy. However, preventive skin care during treatment, counteracting the biomarker signal, and the necessity to start the therapy first in order to gain the information, appear unfavorable. As the underlying molecular mechanisms remain elusive, predictions ahead of treatment, e.g. by a clinical test, are not possible yet. In the presented work, the aim was to generate hypotheses, which proteins and cellular signaling pathways might be causal for the differentiating response of the patient groups. Starting from the assumption that naturally occurring germline variations functionally discriminate individuals in the context of the treatment, the thesis builds up on a small dataset of 23 exomes of patients from a clinical study context. These sequencing data were processed to genomic variants and analyzed for their potential influence on the mechanistic level. Targeted restrictions were introduced by modeling the biomedical context of the use case in order to enrich the sparse individual data with further information. The obtained candidate genes, which are necessary to be validated in practical studies, are described and evaluated in detail. Methodologically, the result of the thesis is the „Molecular Systems Map“, a network data structure modeled in Cytoscape, interactively visualizing the functional interactions of proteins and simulatenously filtering the called variants upon the biological context. Here, the aim is to enable biomedical domain experts, beyond scrolling tabular information on called variants, to review their experimental data in the functional context and support them in the hypothesis generation process. Additionally, this provides the opportunity to apply graph algorithms and integrate further data, e.g. such from completary ‘omics experiments

Bioinformatics and Machine Learning for Cancer Biology

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer

Metastatic Progression and Tumour Heterogeneity

Improved understanding of the cellular and molecular makeup of tumors in the last 30 years has unraveled a previously unexpected level of heterogeneity among tumor cells as well as within the tumor microenvironment. The concept of tumor heterogeneity underlines the realization that different tumors can display significant differences in their genomic content as well as in their overall behavior. Our capacity to better understand the heterogeneous make up of tumors has very important consequences on our ability to design efficient therapeutic strategies to improve patient survival. This book highlights several aspects of tumor heterogeneity in the context of metastatic development and summarize some of the challenges posed by heterogeneity for tumor diagnostics and therapeutic management of tumors

Functional Genomics of Germ Cell Tumors: from balls to bytes and back again

__Abstract__ The work discussed in this thesis explains the role of the functional genome in germ cell tumor (GCT) pathogenesis by applying newly developed and existing computational methods to (genome-wide) functional genomic datasets. Specifically, epigenetic and (post-)transcriptional regulation in GCTs was studied to gain a deeper understanding of disease pathogenesis, also aiming at clinical application of the findings. GCTs are a unique class of neoplasms originating from (fetal) developing germ cells. Five subtypes can be distinguished (I-V), which are related to physiological germ cell development. Type I GCTs, also called infantile or pediatric GCTs, are rare and generally benign. Type II GCTs, also called germ cell cancer (GCC), include a heterogeneous set of histological subtypes with clearly defined totipotent stem cell components. GCC accounts for 60% of all cancers in Caucasian males between the ages of 20 and 40. Type III, IV and V GCTs are generall

Pathophysiological role of MicroRNA-29 in pancreatic ductal adenocarcinoma

Indiana University-Purdue University Indianapolis (IUPUI)Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy and responds poorly to current therapies. Thus, it is imperative to develop novel treatments for PDAC. Dense fibrotic stroma associated with PDAC abrogates drug perfusion into the tumor, and pancreatic stellate cells (PSCs) are the major stromal cells responsible for fibrosis. Activated PSCs produce pro-inflammatory factors and secrete an excessive amount of extracellular matrix (ECM) proteins, the major stromal proteins in PDAC. MicroRNAs (miRNAs) are conserved small non-coding RNAs that regulate gene expression by binding to the 3′UTR of target mRNA transcripts, causing translational repression or degradation. A single miRNA regulates several targets within intracellular networks and can have a profound impact on normal physiology. miR-29 has been previously reported to have anti-fibrotic and tumor suppressive roles in various cancers. We found miR-29 expression was significantly decreased in activated PSCs and pancreatic cancer cells in vitro, in vivo models, as well as in PDAC patient biopsies. Through in vitro studies in activated PSC, we found that miR-29 inhibited the expression of ECM proteins and reduced cancer growth when co-cultured with pancreatic cancer cells. miR-29 overexpression in pancreatic cancer cells decreased their invasive potential and sensitized chemoresistant cancer cells to gemcitabine treatment by inhibiting autophagy through the direct targeting of two essential, autophagy related genes, TFEB and ATG9A. In developing therapies and for in vivo functional studies, viral-based gene delivery is a powerful tool to target the pancreas. We tested various self-complementary recombinant adeno-associated virus (scAAV) serotypes in normal mice (C57BL/6) and in a KrasG12D-driven pancreatic cancer mouse model via systemic and intraductal delivery methods. We found that retrograde intraductal delivery of scAAV6 safely targeted the pancreas/neoplasm with the greatest efficiency. Our findings provide a better understanding of miR-29 in pancreatic cancer and demonstrates its potential therapeutic use to target PDAC

New Advances in Melanoma

Melanoma is a very aggressive tumor which is derived from the transformation of pigment-producing cells termed the melanocytes. This cancer type accounts for most of the deaths associated with skin cancer as well as its incidence and is in constant evolution. Because of the rapid and very high metastatic potential of this tumor, melanoma prognosis has been quite poor for a long time. In the past decade, groundbreaking discoveries in the melanoma research field have led to the development of two main treatment strategies: combination therapies targeting specific kinases or combination therapies focused on immune checkpoint inhibitors (ICIs). These treatment approaches have become the standard of care in most cancer centers and significantly improved the prognosis and overall survival of advanced melanoma patients. Nevertheless, many patients do not benefit from or even respond to these treatments. It is therefore essential to better comprehend the phenomenon of drug resistance, immune escape mechanisms, as well as to search for alternative treatment strategies. In addition, strong predictive biomarkers are desperately needed to improve clinical efficacy. The aim of this Special Issue is to present recent advances in the field of melanoma research, in which the abovementioned areas represent the primary focus, and other relevant themes are also discussed