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

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

Dr. Lincoln Stein talks about the Pan-Cancer Project

An overview of the Pan-Cancer Project with Dr. Lincoln Stein.


Watch more Pan-Cancer Project videos


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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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January 10, 2020

New open-source software judges accuracy of algorithms that predict tumour evolution

Adriana Salcedo
Adriana Salcedo

OICR-led international research group develops new open-source software to determine the accuracy of computational methods that can map the genetic history of tumour cells.

A cancer patient’s tumour is often made up of many cells with different genetic traits that can evolve over time. Interest in tumour evolution has grown over the last decade, giving rise to several new computational tools and algorithms that can characterize genetic diversity within a tumour, and infer patterns in how tumours evolve. However, to date there has been no standard way to compare these tools and determine which are most accurate at deciphering these data.

The genetic differences between tumour cells can tell us a lot about a patient’s disease and how it evolves over time – Adriana Salcedo

In a study recently published in Nature Biotechnology, an OICR-led international research group released new open-source software that can be used to judge the accuracy of these novel algorithms.

Continue reading – New open-source software judges accuracy of algorithms that predict tumour evolution

November 21, 2019

Researchers teach hand-held DNA sequencing devices to read a new language

Paul Tang, Computational Biologist, and Philip Zuzarte, Scientific Associate pose for a photo at OICR headquarters.
Paul Tang, Computational Biologist, and Dr. Philip Zuzarte, Scientific Associate pose for a photo at OICR headquarters. Tang and Zuzarte were central to OICR’s contributions to the study.

International research group unlocks the promise of nanopore native RNA sequencing

Studying RNA may offer new answers to cancer – and the tools to read RNA directly are now in our hands.

An international research consortium, led in part by Dr. Jared Simpson at OICR, has developed new laboratory protocols and a suite of software tools that will allow the research community to exploit the promise of direct RNA sequencing.

Dr. Jared Simpson, OICR Investigator.

These techniques, published recently in Nature Methods, represent the first large-scale exploration of human RNA using nanopore sequencers – the advanced handheld sequencing devices that can read long strands of RNA.

“Unlike traditional sequencing devices that read copies of RNA strands that are cut into little pieces, nanopore sequencing allows us to study long strands of RNA directly without losing important information in the copying and cutting process,” says Paul Tang, Computational Biologist at OICR and co-first author of the publication. “Our methods combine the power of reading RNA directly with the power of long-read sequencing, enabling an entirely novel way to study cancer biology.”

In collaboration with researchers at Johns Hopkins University and the University of California Santa Cruz, Tang and Simpson developed the software methods that could decode the output data from a nanopore sequencer. Their methods used a machine learning technique, called a Hidden Markov Model, to determine the letters of code within an RNA strand.

“With these methods, we’ve shown that you can leverage nanopore RNA sequencing to gain a lot of valuable information that we couldn’t have otherwise,” Tang says. “We’re very happy to see this work published because we are enabling others to study a new aspect of cancer biology and we look forward to the research discoveries to come.”

These new methods have been integrated into Simpson’s already-popular nanopolish software suite which is routinely used by the nanopore community around the world.

Read more about Simpson’s work in our 2018-2019 Annual Report.

November 11, 2019

Solving Big Data problems

Dusan Andric talks about Overture and how its interchangeable tools can help scientists to “worry less, science more”
https://www.overture.bio/

October 9, 2019

Researchers discover a new cancer-driving mutation in the “dark matter” of the cancer genome

Change in just one letter of DNA code in a gene conserved through generations of evolution can cause multiple types of cancer

Toronto – (October 9, 2019) An Ontario-led research group has discovered a novel cancer-driving mutation in the vast non-coding regions of the human cancer genome, also known as the “dark matter” of human cancer DNA.

The mutation, as described in two related studies published in Nature on October 9, 2019, represents a new potential therapeutic target for several types of cancer including brain, liver and blood cancer. This target could be used to develop novel treatments for patients with these difficult-to-treat diseases.

“Non-coding DNA, which makes up 98 per cent of the genome, is notoriously difficult to study and is often overlooked since it does not code for proteins,” says Dr. Lincoln Stein, co-lead of the studies and Head of Adaptive Oncology at the Ontario Institute for Cancer Research (OICR). “By carefully analyzing these regions, we have discovered a change in one letter of the DNA code that can drive multiple types of cancer. In turn, we’ve found a new cancer mechanism that we can target to tackle the disease.”

Continue reading – Researchers discover a new cancer-driving mutation in the “dark matter” of the cancer genome

October 7, 2019

Inherited DNA provides blueprint for new advancements in prostate cancer

Kathleen Houlahan, first author of the study and a PhD candidate at OICR.

International study, led by researchers at OICR, takes a deep dive into how prostate cancer is inherited and points to new opportunities for improved screening, monitoring, treatment and prevention

Prostate cancer is one of the most common cancers in men, but remains one of the most difficult to prevent and a challenge to treat. Some DNA mutations that lead to prostate cancer are inherited yet some collect over a lifetime. Understanding how these mutations interact and contribute to the disease could help patients and their doctors better manage the disease.

In a study, published today in Nature Medicine, Kathleen Houlahan et al. take a deep dive into the inherited factors driving prostate cancer and how these factors affect the course of the disease at a cellular level.

“Prostate cancer is thought to be, in part, an inherited disease,” says Houlahan, first author of the study and a PhD candidate at OICR. “The DNA that a man is born with has an effect on whether he will develop prostate cancer and how aggressive the cancer will be. We set out to uncover how this happens.”

The study investigated the connection between inherited mutations – also known as germline mutations – and a range of important DNA-regulating processes, like DNA methylation.

The associations found in the study, Houlahan says, are a resource that can help bridge our gap in understanding between germline mutations and the mutations that men acquire over their lifetime that eventually lead to prostate cancer.

“When we understand how inherited mutations work, patients with these mutations can be screened and monitored more effectively to ensure the patient is receiving the most appropriate treatment and avoiding unnecessary side effects,” says Houlahan. “We’ve seen this work for patients with mutations in the BRCA genes, but we still need more personalized options for the many men who are living with prostate cancer.”

Since germline mutations can be inherited and are present in nearly all cells in a man’s body, this research demonstrates the possibility of using non-invasive blood-based tests, rather than invasive tumour biopsies, to monitor prostate cancers.

“We could use these findings to help identify a man’s risk of cancer and catch it earlier,” says Houlahan. “Detecting the disease earlier could significantly improve treatment success.”

Houlahan’s study was enabled by data from the Canadian Prostate Cancer Genome Network (CPC-GENE), which have previously been used to find a DNA signature of aggressive prostate cancers and link how a prostate tumour evolves with the severity of the tumour, amongst other significant advancements. CPC-GENE findings serve as a resource for future research and a scaffold on which diagnostic tests and new therapies can be built. 

This research was supported in part by OICR, Prostate Cancer Canada, the Terry Fox Research Institute, the Canadian Institutes for Health Research, the Canadian Cancer Society, the Movember Foundation and the National Cancer Institute.

September 18, 2019

When the tools you need don’t exist, create them

Dr. Ina Anreiter joins OICR as a Schmidt Fellow, bringing her background in behavioural genetics to bioinformatics

While writing her doctoral thesis, Dr. Ina Anreiter realized that there was a missing piece to her research. What she didn’t realize was that this missing piece would lead her into a prestigious postdoctoral fellowship in an entirely new scientific discipline. For decades, scientists have known that RNA – often referred to as DNA’s cousin – undergoes chemical modifications before running its course. These modifications, like RNA methylation, have an important effect in cancer cells, but without the tools to study RNA modifications, progress in this field had stalled for many years.

Recently, the study of these modifications – also known as the field of “epitranscriptomics” – has garnered new attention as the research community develops new methods to study RNA. These methods, Anreiter says, still rely on common chemistry lab techniques and cumbersome procedures that make studying RNA methylation difficult, especially in application to diseases like cancer.

“I found myself in need of a tool,” says Anreiter. “I needed a way to easily analyze RNA methylation across large datasets and found that nothing existed – well, nothing existed yet.”

From fruit flies to machine learning

Anreiter’s doctoral research focused on the behaviour of fruit flies, specifically how inherited characteristics and environmental factors influence their feeding patterns. While searching for a way to study RNA methylation, her background led her to a unique idea.

Anreiter knew of nanopore sequencing – a relatively new type of sequencing technology that could decode DNA and RNA as it passes through a tiny channel. By directly reading a strand of RNA, Anreiter says, nanopore sequencing has the potential to revolutionize how we study RNA modifications. To this day, however, there are no algorithms or tools that can accurately find RNA methylation patterns in the output data of a nanopore sequencer.

Anreiter had also heard of Dr. Jared Simpson’s breakthrough methods for detecting DNA methylation using nanopore sequencing. His computational methods allowed the nanopore community to sequence the entire – highly-methylated – human genome in 2017, and since, he has been working in part to study RNA modifications, like RNA methylation, using nanopore sequencing.

Anreiter pitched her idea to Simpson.

“RNA methylation occurs in normal fruit flies, but not in a certain type of mutant fly,” says Anreiter. “I had a crazy idea that we could sequence both of these types, and use the datasets to develop a machine learning algorithm that could find RNA methylation on its own.”

The potential of her idea would win her the prestigious Schmidt Science Fellowship and a $100,000 USD stipend to work with Simpson for a year.

From machine learning to cancer patients

Anreiter recently began her year-long postdoctoral fellowship in the Simpson Lab at OICR where she is working alongside a team of computational biologists to turn her idea into an algorithm. She is cross-appointed with the University of Toronto’s Department of Computer Science.

“At this point, we’re working on a preliminary dataset, but I’ve already learned so much. The team has been very welcoming and supportive and we’re working together to make better tools to understand diseases.”

The Schmidt Fellowship, which was co-founded by the former CEO of Google, is awarded to exceptional, early-career researchers making a “pivot” in their work.  Anreiter saw the fellowship as an opportunity to immerse herself in a completely new field.

“If we can develop this tool, it would allow us to study human diseases in a new way,” Anreiter says. “When we look at a problem in a new way, we don’t know what solutions we’ll find, but this angle could lead us to new cures.”

Read the UofT News release on Anreiter’s award.

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