February 18, 2020
The Terry Fox Research Institute (TFRI) announced today that Dr. Juri Reimand, OICR Investigator, has been granted the Terry Fox New Investigator Award to support his research into the evolution of glioblastoma, a deadly brain cancer that often recurs after treatment, with no long-term cure.
“This is a terrible disease with a dismal prognosis. It is usually fatal within a year or two after diagnosis and current therapies mostly fail to halt its recurrence and progress,” says Reimand. “We are taking a data-driven approach to see if we can change the tide on this disease by mapping the evolutionary history of each tumor and identifying genes and pathways that could be targeted through new or existing drugs.”
Backed by TFRI support, Reimand and collaborators are creating a robust multi-omics dataset derived from samples of glioblastoma tumours, including those that have returned after initial treatment. The dataset will incorporate many types of layered data from each sample including whole genome sequencing data, RNA sequencing data and proteomic data.
Reimand, who has expertise in integrating complex datasets, will develop machine learning strategies to identify new potential targets for treatment. The tools and methodologies will be designed to be applicable to other cancer types and will be made freely available for the research community to use.
“We hope that our expertise in computational biology can help shed new light on glioblastoma recurrence by analyzing tens of thousands of genes, proteins and RNAs in complex interaction networks, and ultimately provide a small number of high-confidence targets for further experimental work and therapy development,” said Reimand.
This research is enabled in large part by Reimand’s partnership with Dr. Sheila Singh, a clinician-scientist at McMaster University in Hamilton.
“We are routinely generating large amounts of complementary data utilizing different platforms that are difficult to compare,” says Dr. Singh. “This is why we are so excited to collaborate with Dr. Reimand to decipher GBM recurrence, as he brings invaluable expertise in computational biology, bioinformatics and machine learning. Dr. Reimand’s multi-omics integrative analysis will deliver our PPG with target genes, pathways and drug interactions that will help us to identify new therapies and understand the complex mechanisms of GBM recurrence.”
This post has been adapted from the original announcement made by TFRI.
February 5, 2020
OICR and Pan-Cancer Project researchers map key cancer pathways, signposting new directions for its diagnosis and treatment
What works in a lab experiment doesn’t always work in the complex human body. But as technology advances, researchers are gaining the ability to study different features of a cancer cell and the interactions, mechanisms and pathways between them. As more data become available, however, it is becoming increasingly difficult to find the most important molecular pathways that, when blocked, can stop the progression of the disease.
Dr. Jüri Reimand’s lab specializes in this area.
“Researchers often collect molecular data on one aspect of a cancer cell at a time, like its DNA, RNA or proteins,” says Reimand, who is an OICR Investigator. “If we can weave these complex molecular datasets together into a bigger picture, we can gain a more thorough understanding of cancer and potentially find new ways to tackle the driving mechanisms behind the disease.”
Decoding the donors’ data
Thanks to more than 2,500 patient donors from around the world, the Pan-Cancer Project presented one of the largest cancer datasets to date. The Project made hundreds of terabytes of data available to the global cancer research community in a coordinated effort to advance our understanding of the disease.
To help interpret these data, the Reimand Lab developed ActivePathways – a statistical method that can discover significant pathways across multiple molecular omics datasets. These methods, published today in Nature Communications, allow researchers to characterize the cell at a systems-level, decipher how the components interact and tease out the most important pathways.
“We designed a simplified approach to tackle one of the largest cancer genomics datasets to date,” says Reimand. “With these methods we can now chart important interactions that we wouldn’t have recognized by looking at one component or dataset alone.”
The power of the ensemble
The Reimand Lab teamed up with researchers in Belgium, Norway, Spain, Switzerland and across the U.S. who were also interested in analyzing the important pathways within the Pan-Cancer Project dataset. They combined their methods and expertise and identified nearly 200 important driver pathways across 38 different cancer types.
Their findings showed that cancer cells often have related or coordinated mutations in the coding regions and the non-coding regions of the genome.
Now, we have better methods and stronger evidence to move forward as we investigate how to block these pathways, and further, block the progression of the disease.
– Dr. Jüri Reimand
“Together, we came to a consensus list of frequently mutated molecular pathways, processes and target genes,” says Reimand. “Now, we have better methods and stronger evidence to move forward as we investigate how to block these pathways, and further, block the progression of the disease.”
All tools, methods and data related to the collaboration are freely available for the research community to use for future research.
“We’re proud of this progress,” says Reimand. “We look forward to the future research that will build on these findings towards better cancer diagnostic tests and treatment options.”
- Unprecedented exploration generates most comprehensive map of cancer genomes charted to date
- New clues to cancer in the genome’s other 99 per cent
- AI algorithm classifies cancer types better than experts
- Whole-genome analysis generates new insights into viruses involved in cancer
- Dr. Lincoln Stein talks about the Pan-Cancer Project
- Finding the roots of cancer, ‘It’s a needle in a haystack’
- Unraveling the story behind the cancers we can’t explain
- TrackSig: Unlocking the history of cancer
- New tumour-driving mutations discovered in the under-explored regions of the cancer genome
January 20, 2020
OICR researchers identify novel causes of cancer progression in the non-coding genome, opening new lines of investigation for several cancer types
Toronto – (January 20, 2020) In an unprecedented pan-cancer analysis of whole genomes, researchers at the Ontario Institute for Cancer Research (OICR) have discovered new regions of non-coding DNA that, when altered, may lead to cancer growth and progression.
The study, recently published in Molecular Cell, reveals novel mechanisms of disease progression that could lead to new avenues of research and ultimately to better diagnostic tests and precision therapies.
Although previous studies have focused on the two per cent of the genome that codes for proteins, known as genes, this study analyzed mutation patterns within the vast non-coding regions of human DNA that control how and when genes are activated.
We found evidence of new molecular mechanisms that may cause cancer and give rise to more-aggressive tumours.
“Cancer-driver mutations are relatively rare in these large non-coding regions that often lie far from genes, presenting major challenges for systematic data analysis,” says Dr. Jüri Reimand, investigator at OICR and lead author of the study. “Powered by novel statistical tools and whole genome sequencing data from more than 1,800 patients, we found evidence of new molecular mechanisms that may cause cancer and give rise to more-aggressive tumours.”
The research group analyzed more than 100,000 sections of each patient’s genome, focusing on the often-overlooked non-coding regions that interact with genes through the three-dimensional genome. One of the 30 key regions discovered was predicted to have a significant role in regulating a known anti-tumour gene in cancer cells, despite being more than 250,000 base pairs away from the gene in the genome. The group performed CRISPR-Cas9 genome editing and functional experiments in human cell lines to explore the cancer-driving properties of this non-coding region.
“We characterized several non-coding regions potentially involved in oncogenesis, but we’ve just scratched the surface,” says Reimand. “With our algorithms and the rapidly growing datasets of patient cancer genomes and epigenetic profiles, we look forward to enabling future discoveries that could lead to new ways to predict how a patient’s cancer will progress and ultimately new ways to target a patient’s disease or diagnose it more precisely.”
Reimand’s research group developed the statistical methods behind this study and made them freely available for the research community to use. These methods have been rigorously tested against other algorithms from around the world.
We’ve shown that our method, called ActiveDriverWGS, can excavate these regions and pinpoint specific areas that are important to cancer growth.
“Looking into the non-coding genome is really important because these vast sections regulate our genes and can switch them on and off. Mutations in these regions can cause these regulatory switches to act abnormally and potentially cause – or advance – cancer,” says Helen Zhu, student at OICR and co-first author of the study. “We’ve shown that our method, called ActiveDriverWGS, can excavate these regions and pinpoint specific areas that are important to cancer growth.”
“Although these candidate driver mutations are rare, we now have the first experimental evidence that one of the mutated regions regulates cancer genes and pathways in human cell lines,” says Dr. Liis Uusküla-Reimand, Research Associate at The Hospital for Sick Children (SickKids) and co-first author of the study. “As the research community collects more data, we plan to look deeper into these regions to understand how the mutations alter gene regulation and chromatin architecture in specific cancer types to enable the development of new precision therapies to patients with these diseases.”
This study was supported by OICR through funding provided by the Government of Ontario, and by the Canadian Institutes of Health Research (CIHR), the Cancer Research Society (CRS), the Estonian Research Council, and the Natural Sciences and Engineering Research Council of Canada (NSERC).
Whole genome sequencing data used in this study was made available by the International Cancer Genome Consortium’s Pan-cancer Analysis of Whole Genomes Project (ICGC PCAWG), also known as the PCAWG Project or the Pan-Cancer Project.
February 21, 2019
Expert group develops comprehensive guide for the interpretation and visualization of gene lists, replacing outdated, decade-old protocols
The importance of understanding biological pathways – or how our genes work together – is becoming increasingly evident, but pathway analysis remains a major challenge for many basic and biomedical researchers. Current computational tools can help simplify this analysis, but there is no established guide or standard for using these tools in practice.
To fill this gap, a team of experts from OICR and the Bader Lab at the University of Toronto recently published a comprehensive, step-by-step guide to pathway enrichment analysis that brings together their highly-recommended tools into one protocol. The complete protocol, which is now published in Nature Protocols, can be performed in less than five hours and can be used by researchers with no prior training in bioinformatics or computational biology.
“These days, almost every omics study needs to include pathway enrichment analysis, but it has been over a decade since a comprehensive protocol for these analyses has been published,” says Dr. Jüri Reimand, Principal Investigator at OICR and co-lead author of the protocol. “Our new methods are designed to guide researchers through their analyses and serve as a practical resource for their studies.”
Each step of the protocol is supported with detailed instructions and valuable troubleshooting information, which were designed in large part by Ruth Isserlin, co-lead author and Senior Bioinformatics Analyst in the Bader Lab.
Recently, the methods were used to identify a therapeutic target for ependymoma, a prevalent type of childhood brain cancer that is notoriously difficult to treat. The pathway analysis, as described in Nature, led to a better understanding of why most ependymoma treatments are not effective and revealed a new treatment option that could stop the progression of the disease.
“Future cancer research discoveries rely on our understanding of biological pathways,” says Reimand. “This protocol provides a resource from which we can build our understanding and explore previously uncharted relationships between our genes.”
August 3, 2018
OICR researchers have contributed to major open source projects available to the global research community in order to accelerate cancer research. Click the link below to read about more of OICR’s open source software projects.
August 1, 2018
In the effort to bring better disease prevention and treatment to patients faster, cancer researchers are thinking more creatively about ways to conduct high-quality scientific research. Concerns about the quality, efficiency and reproducibility of research have motivated the open science movement – the growing trend of making data, methods, software and research more accessible to the greater scientific community.
Open source software (OSS), a major component of open science, enables research groups to reduce redundant efforts in software engineering by sharing software code and methods. In addition to improving efficiency, OSS promotes high-quality research by enabling collaboration, and helps make research easier to reproduce by making it more transparent.
December 8, 2015
There are many ways to tell a story, but Dr. Jüri Reimand likes to tell stories in a different way – with data. Reimand, a new OICR Investigator in the Informatics and Bio-computing Program, is a computer scientist by training who developed a keen interest in human biology and disease.
While growing up in Estonia, Reimand always had an interest in computers and understanding how they work. This led him to the University of Tartu to pursue a degree in computer science. When nearing the end of his undergraduate studies Reimand was looking for a thesis topic when he happened to meet a “young, kind of cool professor” who was working to set up a bioinformatics program from scratch. Reimand joined his team and worked to interpret large-scale data and gene lists.