Predicting potential drugs and drug-drug interactions for drug repositioning

The purpose of drug repositioning is to predict novel treatments for existing drugs. It saves time and reduces cost in drug discovery, especially in preclinical procedures. In drug repositioning, the challenging objective is to identify reasonable drugs with strong evidence. Recently, benefiting from various types of data and computational strategies, many methods have been proposed to predict potential drugs. Signature-based methods use signatures to describe a specific disease condition and match it with drug-induced transcriptomic profiles. For a disease signature, a list of potential drugs is produced based on matching scores. In many studies, the top drugs on the list are identified as potential drugs and verified in various ways. However, there are a few limitations in existing methods: (1) For many diseases, especially cancers, the tissue samples are often heterogeneous and multiple subtypes are involved. It is challenging to identify a signature from such a group of profiles. (2) Genes are treated as independent elements in many methods, while they may associate with each other in the given condition. (3) The disease signatures cannot identify potential drugs for personalized treatments. In order to address those limitations, I propose three strategies in this dissertation. (1) I employ clustering methods to identify sub-signatures from the heterogeneous dataset, then use a weighting strategy to concatenate them together. (2) I utilize human protein complex (HPC) information to reflect the dependencies among genes and identify an HPC signature to describe a specific type of cancer. (3) I use an HPC strategy to identify signatures for drugs, then predict a list of potential drugs for each patient. Besides predicting potential drugs directly, more indications are essential to enhance my understanding in drug repositioning studies. The interactions between biological and biomedical entities, such as drug-drug interactions (DDIs) and drug-target interactions (DTIs), help study mechanisms behind the repurposed drugs. Machine learning (ML), especially deep learning (DL), are frontier methods in predicting those interactions. Network strategies, such as constructing a network from interactions and studying topological properties, are commonly used to combine with other methods to make predictions. However, the interactions may have different functions, and merging them in a single network may cause some biases. In order to solve it, I construct two networks for two types of DDIs and employ a graph convolutional network (GCN) model to concatenate them together. In this dissertation, the first chapter introduces background information, objectives of studies, and structure of the dissertation. After that, a comprehensive review is provided in Chapter 2. Biological databases, methods and applications in drug repositioning studies, and evaluation metrics are discussed. I summarize three application scenarios in Chapter 2. The first method proposed in Chapter 3 considers the issue of identifying a cancer gene signature and predicting potential drugs. The k-means clustering method is used to identify highly reliable gene signatures. The identified signature is used to match drug profiles and identify potential drugs for the given disease. The second method proposed in Chapter 4 uses human protein complex (HPC) information to identify a protein complex signature, instead of a gene signature. This strategy improves the prediction accuracy in the experiments of cancers. Chapter 5 introduces the signature-based method in personalized cancer medicine. The profiles of a given drug are used to identify a drug signature, under the HPC strategy. Each patient has a profile, which is matched with the drug signature. Each patient has a different list of potential drugs. Chapter 6 propose a graph convolutional network with multi-kernel to predict DDIs. This method constructs two DDI kernels and concatenates them in the GCN model. It achieves higher performance in predicting DDIs than three state-of-the-art methods. In summary, this dissertation has proposed several computational algorithms for drug repositioning. Experimental results have shown that the proposed methods can achieve very good performance

Computational methods for personalized cancer genomics

In recent years, cancer treatment strategies have moved towards personalized approaches, specifically tailoring cancer treatments on a single-patient basis using molecular profiles from the patients’ tumor genomes. Knowledge of a patient’s molecular profile can be used to 1) identify the disease mechanisms and underlying cause of a single patient’s cancer, 2) assign patients into treatment groups based on the molecular prognosis, and 3) recommend potential treatments for individual patients based on the patient’s molecular signature data. However, the bottleneck of the personalized medicine approach lies in the challenge of translating the vast amount of sequencing data to meaningful clinical insights. This dissertation explores several computational methods that utilize molecular signature data to understand disease mechanisms of cancer, categorize patients into biologically relevant subtypes, and recommend drug treatments to patients. In the dissertation, we present a method, DawnRank, a patient-specific method that determines the potential driving genomic alterations (the drivers) of cancer. We expand on DawnRank’s capabilities by using the DawnRank scores in key driver mutations and copy number variants (CNVs) to identify breast cancer subtypes. We found 5 alternative subtypes based on potentially clinically relevant driver genes, each with unique defining target features and pathways. These subtypes correspond to and build upon our previous knowledge of breast cancer subtypes. We also identify disease mechanisms in identifying key novel cancer pathways in which driver genes interact. We developed a method, C3, which pinpoints patterns of cancer mutations in a pathway context from a patient population to detect novel cancer pathways that consist of significant driver genes. C3 improves on current methods in driver pathway detection both on a technical aspect and a results-oriented aspect. C3 can detect larger and more consistent pathways than previous methods as well as discovering more biologically relevant drivers. Finally, we address the issue of drug recommendation in the wake of molecular signature data. We develop a method, Scattershot, which combines genomic information along with biological insights on cancer disease mechanisms to predict drug response and prioritize drug treatments. Scattershot outperforms previous methods in predicting drug response and produces recommendations that largely comply with known medical treatment protocols.Scattershot recommends drugs to cancer patients that are in line with the actual drugs prescribed by the physician

Translational Oncogenomics and Human Cancer Interactome Networks

An overview of translational, human oncogenomics, transcriptomics and cancer interactomic networks is presented together with basic concepts and potential, new applications to Oncology and Integrative Cancer Biology. Novel translational oncogenomics research is rapidly expanding through the application of advanced technology, research findings and computational tools/models to both pharmaceutical and clinical problems. A self-contained presentation is adopted that covers both fundamental concepts and the most recent biomedical, as well as clinical, applications. Sample analyses in recent clinical studies have shown that gene expression data can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general, ‘personalized medicine’. Several cancer detection techniques are currently under development both in the direction of improved detection sensitivity and increased time resolution of cellular events, with the limits of single molecule detection and picosecond time resolution already reached. The urgency for the complete mapping of a human cancer interactome with the help of such novel, high-efficiency / low-cost and ultra-sensitive techniques is also pointed out

Leveraging big data resources and data integration in biology: applying computational systems analyses and machine learning to gain insights into the biology of cancers

Recently, many "molecular profiling" projects have yielded vast amounts of genetic, epigenetic, transcription, protein expression, metabolic and drug response data for cancerous tumours, healthy tissues, and cell lines. We aim to facilitate a multi-scale understanding of these high-dimensional biological data and the complexity of the relationships between the different data types taken from human tumours. Further, we intend to identify molecular disease subtypes of various cancers, uncover the subtype-specific drug targets and identify sets of therapeutic molecules that could potentially be used to inhibit these targets. We collected data from over 20 publicly available resources. We then leverage integrative computational systems analyses, network analyses and machine learning, to gain insights into the pathophysiology of pancreatic cancer and 32 other human cancer types. Here, we uncover aberrations in multiple cell signalling and metabolic pathways that implicate regulatory kinases and the Warburg effect as the likely drivers of the distinct molecular signatures of three established pancreatic cancer subtypes. Then, we apply an integrative clustering method to four different types of molecular data to reveal that pancreatic tumours can be segregated into two distinct subtypes. We define sets of proteins, mRNAs, miRNAs and DNA methylation patterns that could serve as biomarkers to accurately differentiate between the two pancreatic cancer subtypes. Then we confirm the biological relevance of the identified biomarkers by showing that these can be used together with pattern-recognition algorithms to infer the drug sensitivity of pancreatic cancer cell lines accurately. Further, we evaluate the alterations of metabolic pathway genes across 32 human cancers. We find that while alterations of metabolic genes are pervasive across all human cancers, the extent of these gene alterations varies between them. Based on these gene alterations, we define two distinct cancer supertypes that tend to be associated with different clinical outcomes and show that these supertypes are likely to respond differently to anticancer drugs. Overall, we show that the time has already arrived where we can leverage available data resources to potentially elicit more precise and personalised cancer therapies that would yield better clinical outcomes at a much lower cost than is currently being achieved

Exploring the Role of Molecular Dynamics Simulations in Most Recent Cancer Research: Insights into Treatment Strategies

Cancer is a complex disease that is characterized by uncontrolled growth and division of cells. It involves a complex interplay between genetic and environmental factors that lead to the initiation and progression of tumors. Recent advances in molecular dynamics simulations have revolutionized our understanding of the molecular mechanisms underlying cancer initiation and progression. Molecular dynamics simulations enable researchers to study the behavior of biomolecules at an atomic level, providing insights into the dynamics and interactions of proteins, nucleic acids, and other molecules involved in cancer development. In this review paper, we provide an overview of the latest advances in molecular dynamics simulations of cancer cells. We will discuss the principles of molecular dynamics simulations and their applications in cancer research. We also explore the role of molecular dynamics simulations in understanding the interactions between cancer cells and their microenvironment, including signaling pathways, proteinprotein interactions, and other molecular processes involved in tumor initiation and progression. In addition, we highlight the current challenges and opportunities in this field and discuss the potential for developing more accurate and personalized simulations. Overall, this review paper aims to provide a comprehensive overview of the current state of molecular dynamics simulations in cancer research, with a focus on the molecular mechanisms underlying cancer initiation and progression.Comment: 49 pages, 2 figure