January 4, 2021
Brain cancer linked to tissue healing, study finds
Researchers discover brain cancer may develop when tissue healing runs amok, uncovering new approaches to combat the deadly disease
The healing process that follows a brain injury, such as an infection or a stroke, could spur tumour growth when the new cells generated are derailed by mutations, Toronto scientists have found. This discovery could lead to new therapy for glioblastoma patients who currently have limited treatment options with an average lifespan of 15 months after diagnosis.
The findings, published today in Nature Cancer, were made by an interdisciplinary team of researchers from OICR, the University of Toronto’s Donnelly Centre for Cellular and Biomolecular Research, The Hospital for Sick Children (SickKids) and the Princess Margaret Cancer Centre who are also on the pan-Canadian Stand Up to Cancer (SU2C) Canada Dream Team that focuses on a common brain cancer known as glioblastoma.
“Our data suggest that the right mutational change in particular cells in the brain could be modified by injury to give rise to a tumour,” says Dr. Peter Dirks, senior author of the study, OICR-supported researcher, Dream Team co-leader, and Head of the Division of Neurosurgery and a Senior Scientist in the Developmental and Stem Cell Biology program at SickKids. “We’re excited about what this tells us about how cancer originates and grows and it opens up entirely new ideas about treatment by focusing on the injury and inflammation response.”
The research group, led in part by OICR and Princess Margaret’s Dr. Trevor Pugh, applied the latest single-cell RNA sequencing and machine learning technologies to map the molecular make-up of the glioblastoma stem cells (GSCs), which Dirks’ team previously showed are responsible for tumour initiation and recurrence after treatment.
Equipped with these single-cell analysis methods, the research group was able to accurately differentiate and study different types of tumour cells. Through analyzing 26 tumours and nearly 70,000 cells, they found new subpopulations of GSCs that bear the molecular hallmarks of inflammation.
This finding suggests that some glioblastomas may start to form when the normal tissue healing process is derailed by mutations, possibly even many years before patients become symptomatic, Dirks says. Once a mutant cell becomes engaged in wound healing, it cannot stop multiplying because the normal controls are broken and this spurs tumour growth, according to the study.
The study’s authors, including co-leading researcher, Dr. Gary Bader from the Donnelly Centre as well as graduate students including Owen Whitley and Laura Richards, are now working to develop tailored therapies target these different molecular subgroups.
“There’s a real opportunity here for precision medicine.” says Pugh, who is Director of Genomics at OICR and the Princess Margaret Cancer Centre. “To dissect patients’ tumours at the single cell level and design a drug cocktail that can take out more than one cancer stem cell subclone at the same time.”
In addition to funding from the Stand Up To Cancer Canada Cancer Stem Cell Dream Team: Targeting Brain Tumour Stem Cell Epigenetic and Molecular Networks, the research was also funded by Genome Canada, the Canadian Institutes for Health Research, the Ontario Institute for Cancer Research, Terry Fox Research Institute, the Canadian Cancer Society and SickKids Foundation.
November 30, 2020
Researchers find 3-D structure of the genome is behind the self-renewing capabilities of blood stem cells
OICR-funded researchers open a new path to discover drivers of chemotherapy resistance and cancer relapse
Stem cells have the capability to self-renew and create other types of cells, but not all stem cells are equal. OICR-supported researchers at the Princess Margaret Cancer Centre, Drs. Mathieu Lupien and John Dick, have discovered a new way to distinguish the self-renewing capabilities of stem cells, revealing new ways to study the origins of cancer and cancer recurrence.
In their recently published study in Cell Stem Cell, Lupien, Dick and collaborators identified how some blood – or hematopoetic – stem cells can self-renew but others lose that ability. They found differences in the three-dimensional structure of the genetic information between different stem cell types.
DNA within each human cell, including stem cells, is coiled and compacted in a highly regulated way into structures called chromatin. Depending on how DNA is compacted into chromatin, some regions of DNA are accessible to gene-expressing cellular machinery while some aren’t, influencing how genes are expressed and how a cell may behave. The study group identified that this chromatin accessibility is a key component of a cell’s self-renewing capabilities and “stemness”.
“Enabled by the latest technologies, we found that the pattern of closed – or inaccessible – regions of DNA and the open or accessible regions differ between the long-term self-renewing stem cells and other more mature blood cell populations” says Lupien, Senior Scientist at the Princess Margaret Cancer Centre, Associate Professor at the University of Toronto and OICR Investigator.
The study discovered that the self-renewal capabilities are specifically linked to parts of the genome that bind a protein that is responsible for chromatin folding, called CTCF. As cancer researchers, Lupien and Dick are now applying these discoveries made in normal stem cells to study cancer stem cells. It is thought that if a cancer treatment cannot eliminate the cancer’s stem cells, these surviving self-renewing cells can give rise to recurrent tumours. With a better understanding of cancer stem cells, researchers can investigate the roots of cancer and how to potentially target or manipulate the mechanisms behind self-renewal.
This breakthrough study was made possible by Lupien’s expertise in epigenetics, the field that studies gene expression, Dick’s expertise in stemness and blood development, and the contributions of collaborators and trainees, including Drs. Naoya Takayama and Alex Murison who led the wet lab assays and bioinformatics analyses respectively.
“Understanding how stemness is controlled is key to being able to harness the power of stem cells for cell-based therapies, but also to understand how malignant cells perturb stemness to allow the cancer stem cells to continue to propagate tumor growth,” says Dick, Senior Scientist at the Princess Margaret Cancer Centre, Professor at the University of Toronto and lead of OICR’s Acute Leukemia Translational Research Initiative. “We look forward to furthering our understanding of hematopoiesis and bringing these insights closer to clinical application.”
October 15, 2020
Q&A with Morgan Taschuk, OICR’s new Director of Genome Sequence Informatics
Morgan Taschuk reflects on a decade of supporting critical cancer research and on her new role as OICR’s Director of Genome Sequence Informatics
Cancer genomics research depends on infrastructure and analysis tools that collect, process, analyze and annotate vast quantities of valuable sequencing data. Behind these systems at OICR is a team of individuals dedicated to enabling cancer research discoveries. OICR is proud to announce that Morgan Taschuk will now lead this essential team as Director of Genome Sequence Informatics (GSI).
Here, she reflects on her new role and her outlook on the next few years.
Your behind-the-scenes work is essential to the cancer research we do at OICR. How would you describe your work?
Our team makes sequencing data analysis and management easier for researchers and clinicians in several different ways. We create and maintain the infrastructure and computational tools that researchers need to process, analyze and annotate sequencing data, so they can spend their time working on other challenging research questions.
GSI also offers expert bioinformatics support services directly to researchers to collaborate on challenging research projects. In addition, OICR Genomics is pursuing clinical accreditation this year and so we have a team of clinical genome interpreters who can issue reports on a patient’s unique genome. There’s a lot going on!
How has your work evolved since you’ve been at OICR?
I began at OICR nearly a decade ago, when most of our bioinformatics work was custom for every project, and our sequencing instruments produced a fraction of the data of instruments today. Any kind of automation was quite limited and the amount of analysis we could scale up was limited by the number of people we could hire. Back then, the biggest projects were analyzing human whole genomes for research and participating in international consortia.
In the last nine years, I’ve seen us scale up our sequencing and analysis capabilities, expand a fantastic team of highly educated experts from computer science through data analysts to clinical genome interpreters, and reinforce our reputation of excellence in bioinformatics and computational biology. We’re doing everything we were doing a decade ago plus much more. We sequence single cells, cell free and circulating tumour DNA, analyze immune profiles, participate in international consortia like ICGC-ARGO, contribute to SARS-CoV-2 projects, and will soon produce clinically accredited genome reports, all while still sequencing whole genomes for research.
Today, our team oversees around two petabytes of data, and runs about 3,000 workflows and about 1.8 terabytes of analysis, per day. Genome Sequence Informatics is a team of about 20 people, including bioinformaticians, software developers and engineers, currently supporting 155 research projects – and their resulting research discoveries – each year.
As the Director of Genome Sequence Informatics, what are you most looking forward to?
This new role allows me to focus on the bigger picture rather than on technical challenges. I see this as an opportunity to unify efforts across teams, departments, and across the institution. For example, if one team that we support is doing a task differently than another, I can help bring them together to work towards a common solution for everyone so we can learn from each other, maintain more consistent quality control, and make the best use of the resources and funds we have. I’m looking forward to more productive interactions with the phenomenal teams at OICR and with other organizations around the country and world.
What are your top priorities over the next couple of years?
My goal over the next few years is to share what we’ve created with the community while growing our network. We’ll continue to create solutions to fulfill the evolving needs of researchers and clinicians. We’ll continue to publish our code so other bioinformaticians can confirm what we’ve done and start their own analysis pipelines. We’ll publish protocols and guidelines that we’ve created for our clinically accredited analysis as well as our core assays. And we’ll share our challenges and solutions with the community so we can build on our collective expertise. In addition, we’ll reach out to other teams and organizations to collaborate and learn from them. The bioinformatics, software development, and clinical genomics communities have vast knowledge that we want to take advantage of, improve and share.
The next couple of years for GSI are going to be about collaborative, open science so the scientific and clinical communities can all benefit from the progress made by Genome Sequence Informatics and OICR as a whole.
August 28, 2020
Understanding how cancer differs between sexes: A deeper dive
In the most comprehensive analysis of whole cancer genomes to date, OICR researchers identify novel sex-linked genomic differences that may be able to predict cancer severity and response to therapy
Cancer differs in males and females but the origins and mechanisms of these differences remain unresolved. A better understanding of sex-linked differences in cancer could lead to more accurate tests and allow sex to be included as a consideration when personalizing treatments for patients.
In a study, published in Nature Communications, OICR’s Constance Li and collaborators identify key genetic characteristics that differ between sexes. Here, Li describes what they found and what this means for patients.
Some studies have already hinted that cancer genomes differ between males and females. What is new about this study?
Previous studies focused on the exomes of patient tumours. That means that they were only looking at a small fraction of the genome that codes for proteins. This study allowed us to look at the entire genome – all of our DNA code – and take a dive deep into many aspects of the disease, like how tumours evolve over time.
By looking at the entire genome and in this ‘dark space’ that we hadn’t explored, we were able to confirm some previous findings but also find new differences between male and female tumour samples.
What sort of differences did you find?
We catalogued the differences we found across nearly 2,000 patient tumours representing more than two dozen different cancer types. Interestingly, we found that biliary cancers – like some liver, gall bladder and bile duct cancers – evolve differently in males than they do in females.
We also found that mutations in the TERT promoter – which is a hot topic in cancer research – occur much more often in men than in women, especially in thyroid cancers.
What does this mean for researchers who are looking into this subject?
Our findings suggest that there are underlying biological differences in the way that male and female tumours begin and progress. Overall, we need to be aware of these differences and consider the sex differences as we develop new tools that can match patients to appropriate treatments.
How else could this be helpful for cancer patients?
These findings are preliminary but powerful. It is important to note that more clinical data and research are needed to validate the differences we found. Ultimately, if we look deeper and find that a cancer progresses along one course in females and a different course in males, we can design roadblocks – or therapies – to stop the cancer along that specific course for that sex.
This paper is part of the Pan-Cancer Analysis of Whole Genomes Project. Read more about the Pan-Cancer project here.
February 5, 2020
Unprecedented exploration generates most comprehensive map of cancer genomes charted to date
Pan-Cancer Project discovers causes of previously unexplained cancers, pinpoints cancer-causing events and zeroes in on mechanisms of development
Toronto – (February 5, 2020) An international team has completed the most comprehensive study of whole cancer genomes to date, significantly improving our fundamental understanding of cancer and signposting new directions for its diagnosis and treatment.
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Project (PCAWG), known as the Pan-Cancer Project, a collaboration involving more than 1,300 scientists and clinicians from 37 countries, analyzed more than 2,600 genomes of 38 different tumour types, creating a huge resource of primary cancer genomes. This was then the launch-point for 16 working groups studying multiple aspects of cancer’s development, causation, progression and classification.
Previous studies focused on the 1 per cent of the genome that codes for proteins, analogous to mapping the coasts of the continents. The Pan-Cancer Project explored in considerably greater detail the remaining 99 per cent of the genome, including key regions that control switching genes on and off — analogous to mapping the interiors of continents versus just their coastlines.
The Pan-Cancer Project has made available a comprehensive resource for cancer genomics research, including the raw genome sequencing data, software for cancer genome analysis, and multiple interactive websites exploring various aspects of the Pan-Cancer Project data.
The Pan-Cancer Project extended and advanced methods for analyzing cancer genomes which included cloud computing, and by applying these methods to its large dataset, discovered new knowledge about cancer biology and confirmed important findings of previous studies. In 23 papers published today in Nature and its affiliated journals, the Pan-Cancer Project reports that:
- The cancer genome is finite and knowable, but enormously complicated. By combining sequencing of the whole cancer genome with a suite of analysis tools, we can characterize every genetic change found in a cancer, all the processes that have generated those mutations, and even the order of key events during a cancer’s life history.
- Researchers are close to cataloguing all of the biological pathways involved in cancer and having a fuller picture of their actions in the genome. At least one causal mutation was found in virtually all of the cancers analyzed and the processes that generate mutations were found to be hugely diverse — from changes in single DNA letters to the reorganization of whole chromosomes. Multiple novel regions of the genome controlling how genes switch on and off were identified as targets of cancer-causing mutations.
- Through a new method of “carbon dating,” Pan-Cancer researchers discovered that it is possible to identify mutations which occurred years, sometimes even decades, before the tumour appears. This opens, theoretically, a window of opportunity for early cancer detection.
- Tumour types can be identified accurately according to the patterns of genetic changes seen throughout the genome, potentially aiding the diagnosis of a patient’s cancer where conventional clinical tests could not identify its type. Knowledge of the exact tumour type could also help tailor treatments.
“The incredible work of the Pan-Cancer Project team that was unveiled today is the culmination of a remarkable international collaboration that has enriched our understanding and provided new ways to approach the prevention, diagnosis and treatment of cancer,” said The Honourable Ross Romano, Ontario’s Minister of Colleges and Universities. “I congratulate the entire research group on this ground-breaking achievement in cancer research. Ontarians can be proud of the leading role OICR played in this initiative.”
“The findings we have shared with the world today are the culmination of an unparalleled, decade-long collaboration that explored the entire cancer genome,” says Dr. Lincoln Stein, member of the Project steering committee and Head of Adaptive Oncology at the Ontario Institute for Cancer Research (OICR). “With the knowledge we have gained about the origins and evolution of tumours, we can develop new tools to detect cancer earlier, develop more targeted therapies and treat patients more successfully.”
“The Pan-Cancer Project has generated a much-needed deeper understanding of the biology of cancer and how the elusive and untapped “dark matter” in the human genome drives cancer,” says Dr. Laszlo Radvanyi, OICR’s President and Scientific Director. “These discoveries can lead to totally new area of targets for cancer therapy. It is gratifying to know that OICR helped to lead the international effort, while also integrating a collaborative network of Ontario researchers to play a leading role in this global project. It is a further indication of the value of our strategic investments into data infrastructure, research and informatics expertise, as well as the value the Ontario government continues to create in supporting OICR. I congratulate Dr. Stein, his team and all Pan-Cancer researchers on this landmark achievement.”
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More information
Nature landing page – https://www.nature.com/collections/pcawg/
ICGC – International Cancer Genome Consortium (https://icgc.org/)
TCGA – The Cancer Genome Atlas (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga)
PCAWG – PanCancer Analysis of Whole Genomes (dcc.icgc.org/pcawg)
UCSC – University of California Santa Cruz (pcawg.xenahubs.net)
Expression Atlas (www.ebi.ac.uk/gxa/home)
PCAWG-Scout (pcawgscout.bsc.es)
Chromothripsis Explorer (compbio.med.harvard.edu/chromothripsis)
COSMIC – Catalogue of Somatic Mutations in Cancer (https://cancer.sanger.ac.uk/cosmic)
About the Ontario Institute for Cancer Research
OICR is a collaborative, not-for-profit research institute funded by the Government of Ontario. We conduct and enable high-impact translational cancer research to accelerate the development of discoveries for patients around the world while maximizing the economic benefit of this research for the people of Ontario. For more information visit www.oicr.on.ca.
Media contact
Hal Costie
Ontario Institute for Cancer Research
647-260-7921
hal.costie@oicr.on.ca
Related links
- 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
- Discovering cancer’s vulnerabilities: The whole may be greater than the sum of its parts
- New tumour-driving mutations discovered in the under-explored regions of the cancer genome
February 5, 2020
New clues to cancer in the genome’s other 99 per cent
OICR leads more than 1,300 researchers from around the world in an unprecedented investigation into the dark matter of the human cancer genome.
Adapted from a story in OICR’s 2018-2019 Annual Report.
Three billion letters of code make up our complete genetic blueprint, yet everything we know about cancer to date comes from only one per cent of those letters.
What about the other 99 per cent? Could those regions be holding clues to new cancer solutions and cures? What could we find if we looked into this dark matter? Dr. Lincoln Stein wanted to find out – and he wasn’t alone.
In the fall of 2015, more than 1,300 investigators from the International Cancer Genome Consortium (ICGC) expressed interest in exploring these uncharted regions. Four years and hundreds of terabytes of data analysis later, they’ve found ways to map the evolutionary history of cancer, identified traces of the disease long before it is diagnosed, and elevated the world’s standards for genomics data sharing and research.
A collective goal, a collaborative feat
“When this project was first announced, we were delighted by the overwhelming interest,” says Jennifer Jennings, Senior Project Manager of the ICGC. She says that was when the scientific leadership of ICGC realized that a concerted effort was needed to address common computational and logistical challenges, leverage the strengths of collaborators and develop shared infrastructure to achieve the ultimate goals of this research.
They named this project PCAWG, the Pan-Cancer Analysis of Whole Genomes Project, also known as the Pan-Cancer Project , which would soon become the largest ever pan-cancer analysis of whole genomes and one of the largest coordinated cancer research endeavors to date.
Stein and a small group of scientific leaders took on the challenge of synchronizing research groups with similar research goals, strategically rearranging expertise and coordinating collaboration on an international scale.
“Organizing and bringing these researchers together was the greatest challenge,” says Stein, who is the Head of Adaptive Oncology at OICR. “Working with others may be slower at first and the benefits aren’t always evident, but the rigour of the resulting science and the progress made is greater than what any of us could do on our own.”
Turning data into discoveries
PCAWG researchers went on to investigate more than 2,600 cancer whole genomes from ICGC patient donors across more than 20 primary disease sites such as the pancreas and the brain. They created the computational tools and established the necessary infrastructure to process and analyze more than 800 terabytes of genomic data in a standardized, accurate and timely fashion.
Powered by these tools, they were able to order the progression of genetic changes that lead to certain types of cancer and showed that these events may occur decades before diagnosis.
“For exceptional cases like in certain ovarian cancers, we were able to see these early events happening 10 to 20 years before the patient has any symptoms,” says Stein. “This opens up a much larger window of opportunity for earlier detection and treatment than we thought possible.”
Understanding the order of genetic changes that lead to cancer – or the probability that one will occur after another – may allow researchers to outsmart how a tumour evolves. This knowledge could help devise new strategies to treat these changes as they occur or prevent them from occurring in the first place, Stein says.
PCAWG researchers have also discovered common patterns in the distribution of genetic mutations that may point to new causes of cancer. Similar to the common genetic signatures associated with smoking and ultraviolet radiation, these patterns may point to unknown environmental or behavioural causes that, once fully understood, could be used to change course and help prevent cancer.
“The biological insights discovered through PCAWG have tremendously advanced our understanding of cancer genomics and we’re approaching a place where we know all the molecular pathways involved with cancer,” says Stein. “We’ve discovered the causes of two thirds of cancers that were previously unexplained — but this is just the beginning.”
Setting new standards for the future
Last July, PCAWG data were officially made available for the scientific community to use as a resource for future cancer research. The key PCAWG findings were recently published in a collection of more than 20 scholarly papers in Nature and its affiliated journals. An expected 40 additional papers relying on PCAWG data will be published within the next year alone.
PCAWG methodologies are now the world’s gold standard for whole genome data processing and analysis. They will continue to be used for years to come as more patient samples are collected and sequenced around the world. All related computational tools, including the data exploration and discovery tools, have been made publicly available.
“We made both the genomic data, and the computational pipelines to analyze it, free to use for the global cancer research community,” says Stein. “Now, others can analyze these data – or new data – at the same level as we have in the pursuit of new cancer research discoveries.”
Related links
- Unprecedented exploration generates most comprehensive map of cancer genomes charted to date
- 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
- Discovering cancer’s vulnerabilities: The whole may be greater than the sum of its parts
- New tumour-driving mutations discovered in the under-explored regions of the cancer genome
February 5, 2020
AI algorithm classifies cancer types better than experts
Pan-Cancer Project researchers develop deep learning system that can determine where a cancer originates with better accuracy than human experts
If doctors know where a patient’s cancer started, they can better treat the disease. Unfortunately, this is not always possible, but AI could play a role in solving that.
In a study published today in Nature Communications, a Toronto-based researcher group developed a deep learning system that can accurately classify cancers and identify where they originated based on patterns in their DNA. The system could potentially help clinicians differentiate difficult-to-classify tumours and help recommend the most appropriate treatment option for their patients.
“We reasoned that there was something within the cancer’s DNA that could help us classify these tumours,” says Dr. Quaid Morris, OICR Senior Investigator and co-lead author of the study1. “But I didn’t expect our system to work at well as it does – in some cases, far better than pathologists.”
The team
The initiative began with the dataset: 2,600 whole genomes across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes Project, also known as the Pan-Cancer Project or PCAWG.
Dr. Lincoln Stein, Head, Adaptive Oncology at OICR and member of the Pan-Cancer Project Steering Committee, and his team began to work with these data to identify patterns in a cancer’s genetic material that could help classify these tumours. To them, this was a perfect problem for AI.
When we started to collaborate, We realized we had something amazing.
– Wei Jiao
“Deep learning models excel when they’re trained on large amounts of data,” says Wei Jiao, Research Associate in the Stein Lab and co-first author of the study. “We had an incredibly large dataset to work with, the most comprehensive dataset of whole cancer genomes to date, but we also needed the machine learning expertise.”
The Stein Lab posted their progress on bioRxiv, an open-access repository for biology publications that have not yet been peer-reviewed, which in turn sparked the collaboration between his team and the Morris Lab – a group with deep machine learning expertise.
The system
The development of their deep learning system was not simple. They mined through terabytes of data looking for patterns in the type of mutations, the source of mutations and where mutations occurred in the genome, among other factors.
To their surprise, they found that patterns in driver mutations – the changes in DNA that are thought to ‘drive’ the development of cancer – were not useful in determining where the tumour originated. Instead, they found that patterns in the distribution of mutations and the type of mutation within a patient’s sample could better classify the patient’s disease.
“We knew that we could distinguish between two different types of healthy cells by looking at how the DNA within the cell types are packaged,” says Stein, who is a co-lead author of the study. “We were surprised and gratified that we could do the same using cancer cells.”
“We saw that the tightly-packaged sections – also known as the closed chromatin – would have many more mutations than the loosely wound sections,” says Gurnit Atwal, PhD Candidate in the Morris Lab and co-first author of the study. “It was like the normal cell was casting a shadow on the cancer cell, and we just had to read the shadows.”
To achieve the highest accuracy, the research group developed a deep learning neural network-based system, a type of system that is loosely modeled after the human brain and commonly used to recognize patterns in images, audio and text. Their system achieved an accuracy of 91 per cent – roughly double the accuracy that trained pathologists can achieve using traditional methods when presented with a primary tumour and no clinical information.
Further, they tested their model on an additional 2,000 tumours from patients in the Netherlands who donated their cancer genomic data to the Hartwig Medical Foundation and the system still performed with a remarkably high level of accuracy.
“As more cancer genomes are sequenced, we can gain the ability to classify rarer cancers,” says Atwal. “Where we are now is great, but there is more work to be done.”
The potential
This study presents a deep learning system that could potentially improve how cancers are classified, enhancing the accuracy of current diagnostic tests and the treatment decisions they inform.
For some patients, this system could tell them where their cancer began, giving them valuable information about which course of treatment to choose. The system also could serve as a tool to help doctors identify whether a tumour in a patient who has been treated for cancer in the past is an entirely new tumour or a recurring tumour that has spread.
“A treatment plan for a cancer that originated in the throat may be very different than one for that originated in the breast, and the treatment for a cancer that has returned is different than for one that has metastasized,” says Atwal. “One day, our tool could help give doctors the power to distinguish these classes of tumours, giving patients valuable information that wouldn’t have been available otherwise.”
The authors of the study suggest that their system could start helping patients soon. They plan to further refine their system for patients with rare cancers before moving towards clinical studies.
“The potential impact of the system we’ve developed is encouraging,” says Morris. “We look forward to turning this system into a tool that can help clinicians and future cancer patients tackle this disease.”
1Morris is also a Canada CIFAR AI Chair, Faculty Member at the Vector Institute, and Professor at the University of Toronto’s Donnelly Centre for Cellular and Biomolecular Research.
Related links
- 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
- Discovering cancer’s vulnerabilities: The whole may be greater than the sum of its parts
- Finding the roots of cancer, ‘It’s a needle in a haystack’
- Dr. Lincoln Stein talks about the Pan-Cancer Project
- 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
February 5, 2020
Whole-genome analysis generates new insights into viruses involved in cancer
OICR researchers scan more than 2,600 whole cancer genomes for traces of known and potentially unknown cancer-causing viruses, identifying new ways that these pathogens may eventually lead to the disease
It is estimated that viruses cause nearly 10 per cent of all cancers. These cancer-causing viruses – also known as oncoviruses – can make changes to normal cells that may eventually lead to the disease. As researchers better understand how oncoviruses cause cancer, they can develop new therapies and vaccines to prevent them from doing so.
In the most extensive exploration of cancer genomes to date, OICR researchers and collaborators discovered new insights into the mechanisms behind the seven known oncoviruses, and provided strong evidence that there are no other human cancer-causing viruses in existence.
Their study was published today in Nature Genetics, alongside more than 20 related publications from the Pan-Cancer Analysis of Whole Genomes Project, also known as the Pan-Cancer Project or PCAWG. The research group analyzed whole genome data from more than 2,600 patient tumours representing 35 different tumour types.
“The Pan-Cancer Project is one of the largest cancer genome projects to date,” says Dr. Ivan Borozan, Scientific Associate at OICR and leading co-author of the study. “This project allowed us to search for viruses in the most comprehensive collection of cancer genomes using the latest and most advanced techniques. To analyze this extensive dataset, we first had to develop computational tools and analysis pipelines that can efficiently process large-scale sequencing data and – at the same time – extract accurate information about minute amounts of the viral genome present in each individual sample. The results generated using these tools were then integrated to decipher molecular mechanisms that lead to the development of cancer.”
Our research points towards a future where these cancers can be treated more effectively, and potentially prevented in the first place.
– Dr. Ivan Borozan
The group discovered that an individual’s immune system, while trying to protect itself from a certain strain of the well-known human papillomavirus (HPV), may cause damage to normal DNA that lead to the development of bladder, head, neck and cervical cancers.
The study also found that the hepatitis B virus (HBV), which is linked to some liver cancers, causes damage in normal cells by integrating into human DNA close to TERT, a well-understood cancer-driving gene.
Spinoffs of this research initiative have led to important discoveries about the Epstein-Barr Virus (EBV) and how it can promote the development of stomach cancer.
“These findings can help us develop new vaccines or therapies that target these mechanisms,” says Borozan. “Our research points towards a future where these cancers can be treated more effectively, and potentially prevented in the first place.”
As new sequencing research initiatives emerge, the research group’s computational tools and pipelines – which are available for the research community to use – will help further explain the mechanisms behind this complex disease.
Related links
- 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
- 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
- Discovering cancer’s vulnerabilities: The whole may be greater than the sum of its parts
- New tumour-driving mutations discovered in the under-explored regions of the cancer genome
February 5, 2020
Finding the roots of cancer, ‘It’s a needle in a haystack’
OICR’s Dr. Shimin Shuai and Pan-Cancer Project collaborators identify new cancer-causing mutations in the non-coding region of the cancer genome
Cancer begins with a ‘driver’ mutation – a DNA abnormality that may cause mutations to accumulate and give rise to the disease. These mutations are key targets for cancer therapies but most research to date has focused on the driver mutations within a small portion of the genome – the one per cent of our DNA that codes for proteins.
Now, researchers from the Pan-Cancer Project have explored the other 99 per cent.
In their paper, published today in Nature, the research team detailed a new set of potential driver mutations within the vast non-coding regions of the human genome. These driver mutations could point to new therapeutic approaches or new ways to personalize cancer treatment decisions in the future. The group’s analysis confirms previously reported drivers and raises doubts about others.
It’s amazing that we can use computational tools and algorithms to find important clues that direct us towards a future where precision medicine is a reality.
– Dr. Shimin Shuai
“We looked into the whole genomes of nearly 2,600 patients and some samples had tens of thousands of mutations,” says Dr. Shimin Shuai, leader of OICR’s contribution to the Pan-Cancer Project driver working group. “Driver mutations are really rare in the non-coding regions of the genome so we needed to design computational tools to find a needle in a haystack.”
A key tool behind these discoveries was a computational algorithm called DriverPower, developed by Shuai under the supervision of Dr. Lincoln Stein, Head of Adaptive Oncology at OICR. DriverPower, as described in a complementary publication in Nature Communications, can help differentiate driver mutations from other ‘passenger’ mutations across whole genomes.
“We now have a remarkably powerful computational tool for future driver discovery,” says Shuai, who is the first author of the Nature Communications publication. “It’s amazing that we can use computational tools and algorithms to find important clues that direct us towards a future where precision medicine is a reality.”
DriverPower identified nearly 100 potential driver mutations which will be evaluated in future studies. As more whole genome sequencing data are collected in the future, DriverPower will continue to be used for driver discovery.
“The findings we have shared with the world today are the culmination of an unparalleled, decade-long collaboration that explored the entire cancer genome,” says Stein. “With the knowledge we have gained about the origins and evolution of tumours, we can develop new tools and therapies to detect cancer earlier, develop more targeted therapies and treat patients more successfully.”
This work was part of the Pan-cancer Analysis of Whole Genomes Project (known as the Pan-Cancer Project or PCAWG), which was led in part by OICR.
Related links
- 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
- Unraveling the story behind the cancers we can’t explain
- TrackSig: Unlocking the history of cancer
- Discovering cancer’s vulnerabilities: The whole may be greater than the sum of its parts
- New tumour-driving mutations discovered in the under-explored regions of the cancer genome
February 5, 2020
TrackSig: Unlocking the history of cancer
Toronto-based machine learning experts map the changes that lead to cancer, revealing opportunities for earlier diagnosis and new approaches to outmaneuver the disease
A tumour is often made up of different cells, some of which have changed – or evolved – over time and gained the ability to grow faster, survive longer and potentially avoid treatment. These cells, which have an ‘evolutionary advantage’, are thought to cause the vast majority of cancer deaths but researchers now have a new tool to tackle tumour evolution: TrackSig.
TrackSig – which was developed by Dr. Quaid Morris and his team at the University of Toronto, the Vector Institute and OICR – is a novel computational method that can map a cancer’s evolutionary history from a single patient sample and in turn help researchers thwart the disease’s next move.
“We combined sequencing with evolutionary theory and mathematical modeling to understand how cancers develop and adapt to resist treatment,” says Yulia Rubanova, PhD Candidate in the Morris Lab and lead author of the study. “This understanding lays the foundation for us to be able to predict – and impede – tumour evolution in future cancer patients.”
This understanding lays the foundation for us to be able to predict – and impede – tumour evolution in future cancer patients
– Yulia Rubanova
TrackSig was published today in Nature Communications alongside nearly two dozen other publications in Nature and its affiliated journals related to the Pan-Cancer Analysis of Whole Genomes Project, also known as the Pan-Cancer Project or PCAWG.
Previous tumour evolution studies focused on identifying the most frequent changes – or mutations – in a patient sample, where the most common mutations represent changes that came earlier in the tumour’s development and less common mutations represent more recent changes. Instead, Morris’ TrackSig charts different types of mutations over time, generating maps of a tumour’s evolutionary history in finer detail and with better accuracy than ever before.
This level of resolution enabled the discovery that many cancer-causing genetic changes occur long before the disease is diagnosed.
“For exceptional cases like in certain ovarian cancers, we were able to see these early events happening 10 to 20 years before the patient has any symptoms,” says Dr. Lincoln Stein, Head of Adaptive Oncology at OICR and member of the Pan-Cancer Project Steering Committee. “This opens up a much larger window of opportunity for earlier detection and treatment than we thought possible.”
The tools and findings from the Pan-Cancer Project are changing the way we think about cancer
– Dr. Quaid Morris
With their new detailed maps of tumour evolution, the research group plans to further investigate novel cancer treatment strategies and design new therapies that can better anticipate, prevent and overcome evolution and drug resistance.
“The tools and findings from the Pan-Cancer Project are changing the way we think about cancer,” says Morris. “We’ve uncovered new opportunities to improve diagnosis and treatment, and we’ll continue to strive towards getting the best treatment to patients at the right time.”
TrackSig is freely available for the research community to use at https://github.com/morrislab/TrackSig.
Related links
- 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
- Discovering cancer’s vulnerabilities: The whole may be greater than the sum of its parts
- New tumour-driving mutations discovered in the under-explored regions of the cancer genome
February 5, 2020
Unraveling the story behind the cancers we can’t explain
The Pan-Cancer Analysis of Whole Genomes Project has shown that despite cancer’s complexities, researchers are close to cataloguing all of the biological mechanisms that lead to the disease.
Today, Nature released a special collection of 23 publications related to the analysis, one of which presents the most comprehensive catalogue of RNA alterations in cancer to date.
We sat down with Dr. Philip Awadalla, OICR investigator and National Scientific Director of the Canadian Partnership for Tomorrow Project, and Dr. Fabien Lamaze, Postdoctoral Fellow in the Awadalla Lab, to discuss.
What can RNA show us about cancer?
PA: Cancer is thought to be a disease of the genome, where changes – or mutations – in an individual’s DNA accumulate and eventually lead to the development of the disease. Often, we can identify the mutations that drive this development, figure out the related mechanisms and design new therapies with that information, but sometimes no such ‘driver mutation’ exists.
We believe that RNA can help us unravel the story behind these cancers that we can’t yet explain.
What did the study find?
FL: In this study, we took a deep dive into the transcriptome – the RNA – of nearly two thousand tumour samples donated by patients from around the world, representing 27 different types of tumours. The group found more than 1.5 million different RNA alterations and related mechanisms in these samples, exposing the true complexity of the disease.
Interestingly, the study found key RNA alterations in patient samples with no DNA driver mutation. This suggests that some of the cellular changes that lead to cancer may manifest in RNA rather than DNA mutations.
What does this mean for the future of cancer research?
PA: We see that cancer is complex and we need even more data to fully understand it, but we’ve also shown that we can make this happen by working together.
FL: The Pan-Cancer Analysis of Whole Genomes Project was the product of an enormous international study that was only made possible by the dedication and true collaboration between thousands of researchers from around the world. For this study, in particular, I’d like to recognize the scientific leadership of Dr. Angela Brooks and collaborators from the University of California, Santa Cruz.
PA: As more patient samples are collected and sequenced, we look forward to using the software tools and infrastructure from the Pan-Cancer Project to gain further insights into cancer biology.
How can this help cancer patients?
FL: Understanding the changes that lead to cancer can help us design better tests and new treatments for future cancer patients. This study, for example, discovered six interesting gene fusions involved with cancer, where two genes come together, join in an abnormal way and wreak havoc. In the future, we could potentially develop new drugs that target the downstream products of these fusions and stop them from causing further damage in the cell.
PA: With the knowledge we’ve gained in this study, we look forward to furthering diagnostic and therapeutic research and development so we can ultimately treat patients more successfully. Work is already underway to make this happen.
Related links
- 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
- Discovering cancer’s vulnerabilities: The whole may be greater than the sum of its parts
- New tumour-driving mutations discovered in the under-explored regions of the cancer genome
February 5, 2020
Discovering cancer’s vulnerabilities: The whole may be greater than the sum of its parts
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.”
Related links
- 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
