Network community cluster-based analysis for the identification of potential leukemia drug targets

Leukemia is a hematologic cancer which develops in blood tissue and causes rapid generation of immature and abnormal-shaped white blood cells. It is one of the most prominent causes of death in both men and women for which there is currently not an effective treatment. For this reason, several therapeutical strategies to determine potentially relevant genetic factors are currently under development, as targeted therapies promise to be both more effective and less toxic than current chemotherapy. In this paper, we present a network community cluster-based analysis for the identification of potential gene drug targets for acute lymphoblastic leukemia and acute myeloid leukemia.Peer ReviewedPostprint (author's final draft

Control of asymmetric Hopfield networks and application to cancer attractors

The asymmetric Hopfield model is used to simulate signaling dynamics in gene/transcription factor networks. The model allows for a direct mapping of a gene expression pattern into attractor states. We analyze different control strategies aiming at disrupting attractor patterns using selective local fields representing therapeutic interventions. The control strategies are based on the identification of signaling $bottlenecks$, which are single nodes or strongly connected clusters of nodes that have a large impact on the signaling. We provide a theorem with bounds on the minimum number of nodes that guarantee controllability of bottlenecks consisting of strongly connected components. The control strategies are applied to the identification of sets of proteins that, when inhibited, selectively disrupt the signaling of cancer cells while preserving the signaling of normal cells. We use an experimentally validated non-specific network and a specific B cell interactome reconstructed from gene expression data to model cancer signaling in lung and B cells, respectively. This model could help in the rational design of novel robust therapeutic interventions based on our increasing knowledge of complex gene signaling networks

Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design.

Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.Peer reviewe

MicroRNAs in cancer biology and therapy: Current status and perspectives

AbstractThe study of a class of small non-coding RNA molecules, named microRNAs (miRNAs), has advanced our understanding of many of the fundamental processes of cancer biology and the molecular mechanisms underlying tumor initiation and progression. MiRNA research has become more and more attractive as evidence is emerging that miRNAs likely play important regulatory roles virtually in all essential bioprocesses. Looking at this field over the past decade it becomes evident that our understanding of miRNAs remains rather incomplete. As research continues to reveal the mechanisms underlying cancer therapy efficacy, it is clear that miRNAs contribute to responses to drug therapy and are themselves modified by drug therapy. One important area for miRNA research is to understand the functions of miRNAs and the relevant signaling pathways in the initiation, progression and drug-resistance of tumors to be able to design novel, effective targeted therapeutics that directly target pathologically essential miRNAs and/or their target genes. Another area of increasing importance is the use of miRNA signatures in the diagnosis and prognosis of various types of cancers. As the study of non-coding RNAs is increasingly more popular and important, it is without doubt that the next several years of miRNA research will provide more fascinating results

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data

Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCAD

Transcriptomic data integration for precision medicine in leukemia

This thesis is comprised of three studies demonstrating the application of different statistical and bioinformatic approaches to address distinct challenges of implementing precision medicine strategies for hematological malignancies. The approaches focus on the analysis of next-generation sequencing data, including both genomic and transcriptomics, to deconvolute disease biology and underlying mechanisms of drug sensitivities and resistance. The outcomes of the studies have clinical implications for advancing current diagnosis and treatment paradigms in patients with hematological diseases. Study I, RNA sequencing has not been widely adopted in a clinical diagnostic setting due to continuous development and lack of standardization. Here, the aim was to evaluate the efficiency of two different RNA-seq library preparation protocols applied to cells collected from acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) patients. The poly-A-tailed mRNA selection (PA) and ribo- depletion (RD) based RNA-seq library preparation protocols were compared and evaluated for detection of gene fusions, variant calling and gene expression profiling. Overall, both protocols produced broadly consistent results and similar outcomes. However, the PA protocol was more efficient in quantifying expression of leukemia marker genes and drug targets. It also provided higher sensitivity and specificity for expression-based classification of leukemia. In contrast, the RD protocol was more suitable for gene fusion detection and captured a greater number of transcripts. Importantly, high technical variations were observed in samples from two leukemia patient cases suggesting further development of strategies for transcriptomic quantification and data analysis. Study II, the BCL-2 inhibitor venetoclax is an approved and effective agent in combination with hypomethylating agents or low dose cytarabine for AML patients, unfit for intensive induction chemotherapy. However, a limited number of patients responding to venetoclax and development of resistance to the treatment presents a challenge for using the drug to benefit the majority of the AML patients. The aim was to investigate genomic and transcriptomic biomarkers for venetoclax sensitivity and enable identification of the patients who are most responsive to venetoclax treatment. We found that venetoclax sensitive samples are enriched with WT1 and IDH1/IDH2 mutations. Intriguingly, HOX family genes, including HOXB9, HOXA5, HOXB3, HOXB4, were found to be significantly overexpressed in venetoclax sensitive patients. Thus, these HOX-cluster genes expression biomarkers can be explored in a clinical trial setting to stratify AML patients responding to venetoclax based therapies. Study III, venetoclax treatment does not benefit all AML patients that demands identifying biomarkers to exclude the patients from venetoclax based therapies. The aim was to investigate transcriptomic biomarkers for ex vivo venetoclax resistance in AML patients. The correlation of ex vivo venetoclax response with gene expression profiles using a machine learning approach revealed significant overexpression of S100 family genes, S100A8 and S100A9. Moreover, high expression ofS100A9was found to be associated with birabresib (BET inhibitor) sensitivity. The overexpression of S100A8 and S100A9 could potentially be used to detect and monitor venetoclax resistance. The combination of BCL-2 and BET inhibitors may sensitize AML cells to venetoclax upon BET inhibition and block leukemic cell survival.In this thesis, the aim was to utilize gene expression information for advanced precision medicine outcomes in patients with hematological malignancies. In the study, I, the contemporary mainstream library preparation protocols, Ribo-depletion and PolyA enrichment used for RNA sequencing, were compared in order to select the protocol that suffices the goal of the experiment, especially in patients with acute leukemias. In study II, we applied bioinformatics approaches to identify IDH1/2 mutation and HOX family gene expression correlated with ex vivo sensitivity to BCL-2 inhibitor venetoclax in acute myeloid leukemia (AML) patients. In study III, statistical and machine learning methods were implemented to identify S100A8/A9 gene expression biomarkers for ex vivo resistance to venetoclax in AML patients. In summary, this thesis addresses the challenges of utilizing gene expression information to stratify patients based on biomarkers to promote precision medicine practice in hematological malignancies

Exploiting natural and induced genetic variation to study hematopoiesis

PUZZLING WITH DNA Blood cell formation can be studied by making use of natural genetic variation across mouse strains. There are, for example, two mouse strains that do not only differ in fur color, but also in average life span and more specifically in the number of blood-forming stem cells in their bone marrow. The cause of these differences can be found in the DNA of these mice. This DNA differs slightly between the two mouse strains, making some genes in one strain just a bit more or less active compared to those same genes in the other strain. The aim of part I of this thesis was to study the influence of genetic variation on gene expression and how this might explain the specific characteristics of the mouse strains. One of the findings in this study was that the influence of genetic variation on gene expression is strongly cell-type-dependent. Additionally, blood cell formation can be studied by introducing genetic variation into the system. In part II of this thesis genetic variation was introduced into mouse blood-forming stem cells by letting random DNA sequences or “barcodes” integrate into the DNA of these cells. Thereby, these cells were provided with a unique and identifiable label that was heritable from mother- to daughter cell. In this manner the fate of blood-forming stem cells and their progeny could be tracked following transplantation in mice. This technique is very promising for monitoring blood cell formation in future clinical gene therapy studies in humans. PUZZELEN MET DNA Bloedvorming kan bestudeerd worden door gebruik te maken van natuurlijke genetische variatie tussen muizenstammen. Zo bestaan er bijvoorbeeld twee muizenstammen die niet alleen verschillen in vachtkleur, maar ook in gemiddelde levensduur en meer specifiek in het aantal bloedvormende stamcellen dat zich in hun beenmerg bevindt. De oorzaak van deze verschillen kan gevonden worden in het DNA van deze muizen. Dat DNA verschilt net iets tussen de twee muizenstammen, waardoor sommige genen in de ene stam actiever of juist minder actief zijn dan diezelfde genen in de andere stam. In deel I van dit proefschrift is onderzocht hoe genetische variatie de expressie van genen beïnvloedt en hoe dit de specifieke eigenschappen van de muizenstammen zou kunnen verklaren. Er is onder andere gevonden dat de invloed van genetische variatie op de expressie van genen sterk celtype-afhankelijk is. Daarnaast kan bloedvorming bestudeerd worden door genetische variatie te introduceren in het systeem. In deel II van dit proefschrift is genetische variatie in bloedvormende stamcellen van muizen geïntroduceerd door random DNA volgordes of “barcodes” te laten integreren in het DNA van deze cellen. Dit resulteert erin dat elke cel voorzien wordt van een uniek label dat overgegeven wordt van moeder- op dochtercel. De DNA volgorde van het label kan gelezen worden met behulp van een zogenaamde sequencing techniek. Op deze manier kan het lot van bloedvormende stamcellen en hun nakomelingen gevolgd worden na transplantatie in muizen. Deze techniek is zeer veelbelovend voor het monitoren van bloedvorming in toekomstige klinische gentherapie studies in de mens.