Main Menu
Main Menu
Menu

SYNERGx: a computational framework for drug combination synergy prediction

by

Overview

When just one drug is used to treat cancer, the patient may not respond, or may develop resistance to it. Combination therapy, where two or more drugs are used in treatment, is more likely to be successful. Yet, it is impossible to test all drug combinations in clinical trials due to the high cost of required resources and certain ethical considerations. Computational techniques are therefore required to model the large amount of available data to improve current cancer treatment strategies and propose more efficient combinations of drugs.

Dr. Benjamin Haibe-Kains of the Princess Margaret Cancer Centre is developing SYNERGx, a new computational platform that will integrate multiple pharmacogenomic datasets. These datasets will be used to predict possible combinations of known drugs that can act in synergy, meaning that their combined therapeutic efficacy is greater than the sum of their individual effects.

The platform will implement analytic tools to improve modeling of synergistic drug effects. Users will have access to highly curated drug-combination pharmacogenetics data and an open-source machine-learning pipeline for drug synergy prediction. SYNERGx will also implement a new way to optimize drug-screening studies to identify novel synergistic combinations that can be further validated in preclinical studies and then in clinical trials.

SYNERGx will provide an efficient way to leverage massive investments in pharmacogenomics studies by allowing the integration of otherwise disparate datasets. It represents a major step forward in the design of new therapeutic strategies for cancer.

Computational tools for Data-Independent Acquisition (DIA) for quantitative proteomics and metabolomics

by

Overview

When cells lose control over their own behaviour or communication with other cells, diseases such as diabetes or cancer can arise. Protein and small molecule metabolites are responsible for cells’ behaviour, so identifying and quantifying these molecules is key to understanding how disease happens and how to prevent it.

Mass spectrometry has become the workhorse for proteomics and metabolomics. Drs. Anne-Claude Gingras of the Lunenfeld-Tanenbaum Research Institute and Hannes Röst of the Donnelly Centre for Cellular & Biomolecular Research at the University of Toronto are working with a technology called Data-Independent Acquisition (DIA), in which the mass spectrometer systematically identifies and quantifies the proteins and metabolites present in a sample. DIA has been shown to improve quantitative accuracy, reproducibility and throughput over other methods. Since its introduction, however, this approach has only been applied to small-scale studies and in a relatively small number of laboratories. Limitations to this method are due to the lack of user-friendly software that could enable a scalable analysis of the complex data generated in large-scale biomedical and medical research.

The project builds on the team’s proven strength in DIA data analysis and software development and will result in an integrated set of tools available under an open-source license. To encourage uptake of these tool, documentation, webinars and workshops will be made available to potential users. The results of the project could have long-lasting impact on the health sector in Canada by facilitating research into the root causes of disease and assisting with clinical questions such as patient stratification.

BridGE-SGA: A novel computational platform to discover genetic interactions underlying human disease

by

Overview

The ability to sequence the entire human genome at increasingly lower cost has led to a fundamental change in biomedical research. But there is a gap between the amount of data available and our ability to understand and interpret that data. Addressing this gap is essential to realize the promise of precision medicine.

Dr. Charles Boone and Dr. Brenda Andrews of the Donnelly Centre for Cellular and Biomolecular Research at the University of Toronto, and Dr. Chad Myers of the University of Minnesota, have worked together to discover that a significant part of our inability to interpret genomic data likely stems from the reality that disease generally arises from complex genetic interactions. While all humans essentially have the same set of genes, most have around five million unique genetic variants. The effect of any one variant depends on its interactions with other variants. So we need to understand not just the millions of genetic differences that affect gene function, but also how all those genes interact with each other. Current computational methods and technologies lack the statistical power to do so.

Drs. Boone, Andrews, Myers have developed the first complete genetic interaction map for any organism, and have built a computational method, BridGE, to discover genetic interactions. The team is now working to develop an innovative computational platform for genome sequencing data, BridGE-SGA, to enable the discovery of disease-associated genetic interactions from large-scale human genotype data. Their goal is to discover genetic interactions for a variety of diseases. Identifying and understanding these key genetic interactions will improve our ability to interpret data from whole genome sequencing and identify novel gene targets for drug discovery and development.

Applying genomic signal processing methods to accelerate crop breeding

by

Overview

Selective breeding improves plant and animal products by identifying desirable traits such as quality, yield, and ability to grow in difficult conditions, ensuring that that there is sufficient production for food, fuel and raw materials. Factors like climate change and population growth are making selective breeding more important than ever. One of the largest challenges facing the plant research community is identifying the suite of genes that make organisms well adapted to their environment and using this information in breeding programs.

Drs. Lewis Lukens, Cortland Griswold and their team are using bioinformatics tools to understand how organisms that adapt well to their environments can be selected to accelerate the development of new plant varieties.