January 8, 2021

Q&A with new OICR Investigator Dr. Shraddha Pai on uncovering the hidden differences between cancers

OICR is proud to welcome Dr. Shraddha Pai to its Computational Biology Program as a Principal Investigator. Here, Pai discusses current challenges in understanding diseases and what motivates her to tackle some of the biggest challenges in biomedical research.

What are some of the research questions you’re interested in?

I’m very driven to understand why different people with the same cancer type, have different outcomes and respond differently to the same treatment. As genomic assays get cheaper, we learn more about molecular interplay in different cells, and our population datasets become larger and mature, we are able to integrate different layers of the genome and cell types, to try to get at this question. For example, we now believe there are four main types of medulloblastoma with different underlying molecular networks and outcomes. This field of research is called ‘precision medicine’: using patient profiles to match them with the most effective treatment. But really this is just a new phrase to describe what doctors have been doing since the dawn of medicine; it just means that now we’re using powerful computers and algorithms to find patterns in much larger and complex genomic datasets. The principle is the same.

As a trainee in Dr. Gary Bader’s group, I led the development of an algorithm that integrates several types of patient data to classify patients by outcome.  Our method – called netDx – adapts the idea of recommender systems, used by Netflix and Amazon, to precision medicine. Just as one would ask Netflix to “find movies like this one”, netDx helps identify patients “with a treatment profile like this”. In a benchmark, netDx out-performed most other methods in predicting binary survival in four different types of cancer. Importantly, netDx is interpretable, and recognizes biological concepts like pathways. This makes it a useful tool to get mechanistic insight into why a predictor is doing well, and provides a way to understand the underlying biology and perhaps drive rational drug design.

I also have a special interest in understanding the link between epigenetics and disease, particularly as this pertains to the brain. Epigenetics refer to molecular changes that change how the genome behaves – for example, turning a gene on or off in a given cell type. My own previous research in mental illness has found epigenetic biomarkers related to psychosis, which explain the distinctive features of this condition. The same may be the case in certain types of cancers, particularly those of developmental origin.

How do you plan to unravel these complex layers of biology?

My research program has two main goals. The first is to build models for precision medicine – predicting disease risk, treatment response – starting with population-scale datasets that have several types of patient data. I’m hoping to use existing and emerging data such as UK BioBank, CanPath, ICGC-ARGO and the Terry Fox Research Institutes’ datasets, and ongoing clinical trials, to identify which clinical outcomes are easily amenable to our approaches. The models my group builds will incorporate prior knowledge about genome organization and regulation, so that these are interpretable. For example, we will use epigenomic maps of specific tissue types, or data from single-cell resolution maps, pathway information, to find and organize relevant needles in the genomic haystack. This feature will give us interpretability, which is key to increasing confidence in a model, as well as to improving the understanding of cellular pathways that affect disease and eventual drug development.

My second goal is to understand the epigenomic contributions – particularly developmental changes – to cancer risk, using a combination of molecular biological, genomic and analytic techniques.

As I work toward these goals, I hope to collaborate on complementary projects, such as identifying DNA methylation changes in circulating tumour DNA and improving how we subtype adult tumours. These projects will hopefully lead to new biomarkers, and ultimately improvements to how we diagnose and treat cancer.

Importantly, the software that my team builds will also be openly available to the research community, so others can apply my methods to different types of diseases. I’m excited to get started.

Your work applies beyond cancer. How do you traverse these different disease areas?

The reclassification of disease based on molecular or other biomarkers, and how disease subtype affects risk and treatment response, isn’t unique to cancer – the same research questions extend to other types of disease such as metabolic diseases, autoimmune diseases and mental illness. At the end of the day, we are looking at the same system organized at the molecular, cellular and organ-level, with similar principles of genomic regulation and perhaps similar considerations for drug discovery. Our algorithms are based on these general principles and can therefore be used to answer similar questions for different disease applications, or very different types of cancer. Of course, it’s important to collaborate with teams that have domain expertise to make sure the algorithms are “fine-tuned” for a particular application, and I look forward to benefitting from those partnerships.

What excites you about this type of work?

I’m excited to join a community where basic research is so strongly connected to clinical purpose. Personally, I am very motivated by the prospect of a positive impact on patients within my lifetime and feel that my group’s work is more likely to have a valuable impact in an environment that combines basic and translational research. That said, we’re only just beginning to see the benefits of precision medicine and many challenges remain to bring genomic knowledge into practice. I hope that I can create more useful methods and models for precision medicine and improved clinical decision-making in the coming decade.

I’m especially excited to be at OICR because of the Institute’s access to clinical trials, strong genomics and computational biology program, and pharmacology team. If my group can find promising biomarkers and leads, we can work with OICR collaborators in the Genomics and Drug Discovery groups to move from basic research to application.

Read more about Dr. Shraddha Pai.

February 18, 2020

Tackling brain cancer from all angles

Dr. Jüri Reimand

The Terry Fox Research Institute (TFRI) announced today that Dr. Jüri 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.”

Read more about Dr. Jüri Reimand’s work.

This post has been adapted from the original announcement made by TFRI.

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.”



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

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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

Jennifer Jennings

“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

Dr.Lincoln Stein

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.”

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February 5, 2020

AI algorithm classifies cancer types better than experts

Gurnit Atwal and Wei Jiao

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.

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February 5, 2020

Whole-genome analysis generates new insights into viruses involved in cancer

Dr. Ivan Borozan

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.

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February 5, 2020

Finding the roots of cancer, ‘It’s a needle in a haystack’

Dr. Shimin Shuai

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.

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February 5, 2020

TrackSig: Unlocking the history of cancer

Yulia Rubanova
Yulia Rubanova

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.

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February 5, 2020

Unraveling the story behind the cancers we can’t explain

Dr. Philip Awadalla
Dr. Philip Awadalla

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?

Dr. Fabien Lamaze
Dr. Fabien Lamaze

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.

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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.”

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October 21, 2019

Internationally-recognized computational biologist, Dr. Anna Panchenko joins OICR as Senior Investigator

Dr. Anna Panchenko. Tier I Canada Research Chair and OICR Senior Investigator

OICR welcomes Dr. Anna Panchenko, Tier I Canada Research Chair, to Ontario’s cancer research community as OICR’s newest Senior Investigator

Recently recruited to Canada as a Tier I Canada Research Chair and OICR Senior Investigator, Dr. Anna Panchenko has chosen to establish her lab at the Department of Pathology and Molecular Medicine, Queen’s University School of Medicine. OICR is proud to support Panchenko and her research endeavors with a Senior Investigator Award, which is given to researchers who have achieved national and international excellence and spent more than 10 years as independent investigators. 

Panchenko joins the local research community with nearly two decades of experience at the National Institutes of Health’s National Center for Biotechnology Information. She is internationally recognized for her expertise in using computational biology to study cancer genomics and epigenetics, protein-protein interactions and nucleosome dynamics. Her methods have been widely used by thousands of scientists from around the world.

Here, she discusses her work and the opportunities that Ontario provides.

What is your research about?

Generally, there are two prongs to my research focus. The first is investigating cancer-related mechanisms. We’re looking at how mutations accrue – or accumulate – in cancer cells, which mutations are driving carcinogenesis and how these mutations may affect proteins and their interactions. The second is looking into how chromatin is dynamically regulated at a molecular level.

Both of these avenues are important to our understanding of cancer, and both areas of study need new computational methods and techniques. My group develops these methods and algorithms to better understand cancer progression to possibly come up with new targeted therapeutic strategies.

For example, some of my work focuses on identifying cancer-driving mutations – the changes in DNA that are at the root of cancers. Out of hundreds of point mutations, there are only a few that drive the disease. If we can find these mutations, we can discover new ways to predict the course of a patient’s disease, or new ways to treat the disease.

What excites you about your work?

I am excited by the beauty and complexity of biological systems. I am also excited by working with the dedicated, curious and smart people in our scientific community. My work isn’t just about making discoveries, it’s about designing methods to help other researchers to make their own discoveries.

What drew you to this field?

I grew up in Moscow and I was always interested in math and biology as a child. I was motivated to pursue science by my parents who are both scientists and the field of computational biology was a perfect combination of my two interests. Throughout my career, I met several other scientists who impressed me with their integrity, behavior and dedication to science. They inspired me to continue along this difficult but very gratifying path.

Why were you interested in coming to Canada? What’s next?

I love Canada, it feels like home. I’m now minutes away from Lake Ontario in a community of incredible scientists and clinicians. I feel like there are a lot of exciting opportunities here and I’m proud to be working in a high-caliber work environment. I appreciate the support from the government and I love the culture of collaboration. I’m excited to strengthen my collaborations with researchers at different departments of Queen’s University and across Ontario.

Read more about Dr. Anna Panchenko.