Meta-analysis of genomic and proteomic features to predict synthetic lethality of yeast and human cancer

A major goal in cancer medicine is to find selective drugs with reduced side-effect. A pair of genes is called synthetic lethality (SL) if mutations of both genes will kill a cell while mutation of either gene alone will not. Hence, a gene in SL interactions with a cancer-specific mutated gene will be a promising drug target with anti-cancer selectivity. Wet-lab screening approach is still so costly that even for yeast only a small fraction of gene pairs has been covered. Computational methods are therefore important for large-scale discovery of SL interactions. Most existing approaches focus on individual features or machine learning methods, which are prone to noise or overfitting. In this paper, we propose an approach of meta-analysis that integrates 17 genomic and proteomic features and the outputs of 10 classification methods. It thus combines the strengths of existing methods. It also adjusts relative contributions of multiple methods with weights learned from the training data. Running on a dataset of the yeast strain of S. cerevisiae, our method achieves AUC (area under ROC curve) of 87.2% the highest among all competitors. Moreover, through orthologous mapping from yeast to human genes, we predicted a list of SL pairs in human that contain top mutated genes in lung and breast cancers recently reported by The Cancer Genome Atlas (TCGA). Our method and predictions would shed light on mechanisms of SL and lead to discovery of novel anti-cancer drugs.MOE (Min. of Education, S’pore)Accepted versio

Using interspecies biological networks to guide drug therapy

The use of drug combinations (DCs) in cancer therapy can prevent the development of drug resistance and decrease the severity and number of side effects. Synthetic lethality (SL), a genetic interaction wherein two nonessential genes cause cell death when knocked out simultaneously, has been suggested as a method of identifying novel DCs. A combination of two drugs that mimic genetic knockout may cause cellular death through a synthetic lethal pathway. Because SL can be context-specific, it may be possible to find DCs that target SL pairs in tumours while leaving healthy cells unscathed. However, elucidating all synthetic lethal pairs in humans would take more than 200 million experiments in a single biological context – an unmanageably large search space. It is thus necessary to develop computational methods to predict human SL. In this thesis, we develop connectivity homology, a novel measure of network similarity that allows for the comparison of interspecies protein-protein interaction networks. We then use this principle to develop Species-INdependent TRAnslation (SINaTRA), an algorithm that allows us to predict SL between species using protein-protein interaction networks. We validate it by predicting SL in S. pombe from S. cerevisiae, then generate over 100 million SINaTRA scores for putative human SL pairs. We use these data to predict new areas of cancer combination therapy, and then test fifteen of these predictions across several cell lines. Finally, in order to better understand synergy, we develop DAVISS (Data-driven Assessment of Variability In Synergy Scores), a novel way to statistically evaluate the significance of a drug interaction

Exploiting patterns in genomic data for personalised cancer treatment and new target discovery

In response to a global requirement for improved cancer treatments a number of promising novel targeted cancer therapies are being developed that exploit vulnerabilities in cancer cells that are not present in healthy cells. In this thesis I explore different ways of identifying the vulnerabilities of cancer cells, with the ultimate aim of providing personalised therapies to cancer patients on an individual basis. I first investigate approaches that utilise the concept of synthetic lethality. Therapies that exploit synthetic lethality are suitable where a specific tumour suppressor has been inactivated by a cancer and an identified synthetic lethal (SSL) pair for that gene may be therapeutically targeted. Mainly due to the constraints of the experimental procedures, relatively few human SSL interactions have been identified. Here I describe computational systems approaches for predicting human SSL interactions by identifying and exploiting conserved patterns in protein-protein interaction (PPI) network topology both within and across model species. I report that my classifiers out-perform previous attempts to classify human SSL interactions. Experimental validation of my predictions suggest they may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development. All predictions from this study have been made available via a new online database that I designed, built and published. As an extension to this approach I used similar network features to predict gene dependencies, otherwise known as acquired essential genes, in specific cancer cell lines. Genetic alterations found in each individual cell line were modelled using the novel approach of removing protein nodes to reflect loss of function mutations and changing the weights of edges in each protein-protein interaction network to reflect gain of function mutations and gene expression changes. I report that base PPI networks can be used to successfully classify human cell line specific gene dependencies within individual cell lines, between cell lines and even across tissue types. Furthermore, my personalised PPI network models further improve prediction power and show improved sensitivity to rarer gene dependencies, an improvement which offers opportunities for personalised therapy. In a therapeutic context these essential genes would be suitable as individual drug targets for each specific patient. Finally, I analyse copy number variance and ploidy in a set of cancers from kidney patients. Using clustering algorithms I investigate patterns in cancer cell line arm-wise ploidy and identify factors that may be driving this genomic instability