Transcriptomic And Cell Morphological Profiles

High throughput screening (HTS) is essential in the process of finding a potential drug candidate, and automation is key for a good screen both in terms of robustness and reproducibility.

Ng, Rick. Drugs: From Discovery to Approval. 3rd ed. Hoboken, NJ: John Wiley & Sons, 2015.

The decrease in the rate at which novel medical products are reaching the market, despite major scientific achievements and investment that might have predicted otherwise, is causing much concern. Although this 'pipeline problem' has often been discussed in the context of drug development, it is also crucial to examine the unique characteristics of the pipeline for biomarkers and diagnostics.

Metastasis is the cause of death for millions of cancer patients annually worldwide. Spatiotemporal identification of resistant subclones using breakthrough technological genome systems and methods can predict and prevent therapeutic resistance and metastatic relapse before it clinically occurs on the basis of genotype-phenotype map.

\subsection{Identification of most significant cell morphological phenotype changes in response to treatment with a compound}

In this report, I will be describing a hypothetical independent bioinformatics group project with Dr. Ian Watson (McGill University). I have discussed with him previously about potential post-graduate projects I could take on. Dr. Watson’s research focuses on melanoma genomics which include sequencing the exomes and genomes of patient samples (of varying severity) and biomarker responses to immunotherapy (Watson, 2016). I will choose to focus on a project that will work on previously sequenced patient samples for brevity and practicality. Specifically, I will choose to analyze the pharmacological responses from the supplied tumour data via analyzing their genetic profiles. Melanoma-targeted therapies have shown promise with many drugs currently undergoing (Food and Drug Administration) FDA trial (Cancer Research Institute, 2016). Dr. Watson has access to several clinical trial phase biopsies whose genetic profiles can be analyzed. Ideally, I will aim to differentiate and distinguish groups of poor- and high-responders by their genetic signatures. For instance, immunocytokines are more effective in patients with killer immunoglobulin-like receptor (KIR)/KIR-ligand mismatch genotypes (Delgado et al., 2010). Another example is vemurafenib which inhibits mutant BRAF (a common mutation in melanoma patients which activates the RAS pathway, see Figure 1). Indeed, vemurafenib has a higher survival rate than traditional chemotherapy in patients with a

As shown in Figure 3 and Figure 4, OMICS-viewer provides options for displaying expression data on the cellular overview and on a pathway diagram, and generating a table of pathways in which one or more genes show differential expression across the samples. Users can customize the display of expression data by defining the cut-off value, and choosing an available color schema to depict up-

portfolio of drugs with an industrial experience significant which make it a perfect partner for the 'Watson Health Cloud".

Pharmacogenomics focuses on genes with the aim of personalized treatment, enabling optimal efficacy and minimized toxicity. Although genetic variation is fundamental, there is increasing recognition of the limitations of pharmacogenomics on the basis that this approach does not consider the environmental influences on drug absorption,

Due to these technological advances now practical work related to bioinformatics can be performed with ease and can be done quickly by running bioinformatics related programs. Moreover, I also want to work on drug designing and to achieve that goal, bioinformatics is the ultimate field. Over the past decades the drug discovery scenery has changed vividly, computer aided drug designing is the top most area in my research interests. I am most primarily interested in molecular modelling drug design by using primary data for formulation of new drugs. As it has been evident through research that the main obstacle in drug designing is the interaction of given molecule with target which is extremely difficult to predict , however,

This analysis follows a shift in genomics from identifying molecules to analyzing interactions in the molecular network [12]. The effectiveness of each application is found by comparing several features offered. Features compared include cost, availability, extensibility, difficulty, command line access, scripting, selection and documentation [14], [15]. By comparing these categories, I decide the most effective tool to use in structure-based drug discovery. In a classic approach to drug, discovery testing would begin in-vivo. This would need the analysis of millions of molecules. The molecular visualization of protein-ligand interaction analysis tools can cut the number of molecules that need screening by calculating their potential docking scores. I can narrow the scope down to only those that are most likely to show strong performance. The first tool I look at is Cytoscape [7]. A standalone application that has historically been successful in drug discovery. In addition, I discuss molecular pathway techniques, proteome scale maps and, chemogenomics which have all shown potential for drug discovery in the future.

Metabolite concentration in cells. The formation of metabolites critically depends on enzymes that act on a parent structure. As such, they represent defined chemical intermediates in a pathway that is designed to modify the parent compound. Because they are intermediates, metabolites tend to be present in small amounts, and for limited and variable periods of time. The importance of metabolites in drug development cannot be overstated. When an organism’s biological systems are disturbed by disease, genetic mutations or environmental factors, the profile of metabolites produced often changes. This makes metabolites excellent candidates for biomarkers; they are particularly useful for understanding disease states, toxicities, drug

We leverage the significant cross correlation between the $CMs$ and related transcriptomic profiles to predict cell morphological states for a transcriptomic profile of interest (Figure 1, Step IVa) \cite{11}. We hypothesize similar transcriptomic profiles that mimic a query gene expression pattern can be applied using appropriate computational model to predict changes in cell morphological features in response to a compound treatment \cite{13}.

Incorporation of prior pathway knowledge into microarray data has become a popular research area in bioinformatics. Ever since the advent of microarray technology in the field of biomedical research, it led to the development of numerous analytic methods to analyse gene expression data from microarray. However, most of the methods are single gene based which unable to detect subtle but coordinated differentially expressed genes and these genes often dropped during feature selection by strict cut-off threshold [1,2]. In contrast, pathway-based microarray analysis consider a set of biologically related genes and help to detect these subtle changes in gene expression. Many discussion has been done by researchers regard pathway-based analysis such as reviews done regarding computational approaches proposed for pathway-based analysis by Curtis et al. [3] and also Misman et al. [4], enrichment-approach using various Kolmogorov-Smirnov by Subramanian et al. [5], random forest-based pathway analysis by Pang et al. [6], and Harris et al. [7] who proposed gene grouping based on gene ontology.

The main goal of this study is the development of a method to associate gene expression changes in response to perturbations with alterations in cell morphological features. The proposed approach for cell morphology enrichment analysis is composed of five main steps which we briefly describe here, illustrate in Figure 1, and discuss in detail in the rest of the Materials and Methods section.