December 9, 2020
The tool can accurately distinguish real mutations from sequencing mistakes to improve the early detection of cancer
DNA mutations in cancer cells are caused by different processes, each of which leaves a genetic fingerprint that can provide clues to how the cancer develops. Researchers have now applied this understanding to reduce errors when reading DNA, allowing them to accurately and efficiently detect the smallest traces of mutated cells in the blood.
In a recent publication in Science Advances, an OICR-supported research group outlines a new and improved statistical model to reduce error rates in DNA sequencing data. They demonstrate that their model, called Espresso, outperforms current error suppression methods.
“When we isolate, amplify and try to read the individual building blocks of DNA, we encounter a lot of errors,” says Dr. Sagi Abelson, OICR Investigator, Assistant Professor at the University of Toronto and first author of the publication. “This is a major obstacle. The high error background makes it difficult to pinpoint authentic rare mutations. This is what Espresso aims to solve.”
To build an effective error-suppressing statistical model, the group assessed the different types of errors in their relative genomic contexts across more than 1,000 sequencing samples. Their approach was based on assessing the genetic fingerprints within these samples and mapping them to the regions around the errors to understand if the error was a true mistake, or if it was an important mutation.
“The key advantage of our method is that it allows scientists to read DNA more accurately without the need to duplicate efforts using a set of independent control measurements to estimate error rates,” says Abelson. “This means that researchers can be more efficient with their time and resources. They can do more with less. We’re proud to have developed methods that can make research more practical and simple, but also more effective, efficient and accurate.”
This model is built on Abelson’s prior research published in Nature, which discovered early indicators of acute myeloid leukemia (AML) in the blood up to 10 years before symptoms surfaced. With Espresso, the research group was able to develop and test a new strategy to predict leukemia development, which could predict up to 30 per cent of AML cases years before clinical diagnosis with extremely high specificity. Importantly, this study demonstrated that the risk of developing AML can be measured by looking into only a small number of genomic bases, which suggests a more practical route to clinical testing and implementation.
“This work builds on our prior research, which has shown that we can detect AML earlier than thought possible,” says Dr. John Dick, Senior Scientist at the Princess Margaret Cancer Centre, Co-lead of OICR’s Acute Leukemia Translational Research Initiative and co-senior author of the study. “With these methods, we’ve now shown that we can focus in on specific areas of DNA to detect those early traces of AML with higher accuracy than ever before.”
“These methods are essential to advancing personalized cancer care in practice,” says Dr. Scott Bratman, Senior Scientist at the University Health Network’s Princess Margaret Cancer Centre and co-senior author of the study. “With these tools, we can enable clinicians to treat cancer more effectively, tailor treatment decisions and monitor minimal residual disease. We look forward to furthering our research for patients today and those who will develop cancer in the future.”
April 18, 2019
OICR is proud to welcome Dr. Sagi Abelson to its Computational Biology Program as a Principal Investigator. Here, Abelson discusses some of his past successes, including his recent leukemia research and his wide range of research interests.
How have you been involved with OICR in the past?
I came to Toronto and joined Dr. John Dick’s lab at the Princess Margaret Cancer Centre as a Postdoctoral Fellow, where I had the opportunity to work with OICR’s Genomics and Genome Sequence Informatics teams. I was investigating the differences between normal aging cells and the cells that lead to leukemia. To do that, we had to look into blood-derived DNA samples from many individuals that develop leukemia following blood collection and search for common genetic markers that indicated a high risk of developing leukemia. I worked closely with OICR teams to prepare and sequence these patient samples. We also collaborated to deploy specialized methodology that enabled us to accurately interpret the genomic data and to identify those harmful mutations.
What motivated you to become involved with that subject?
Far too many patients are diagnosed with leukemia when it is too late. This applies to many other cancers as well. If we can detect a disease earlier, we may benefit from a larger window of opportunity to prevent, manage, or treat the disease. There are many biological and computational challenges that need to be addressed in this area, including finding extremely small traces of a disease amidst a lot of noise in genomic data. I’m interested in the development and the optimization of methods and computational tools to find these first traces of a developing disease.
What will your future research focus on?
In the future I would like to expand my research program to other types of cancers. I truly believe that as a researcher I can achieve more by having a multidisciplinary team that address questions in other biological systems as well. In this era of big data, we are not the only ones realizing that multiple research skills are necessary to tackle the toughest problems. Research institutes and universities understand it as well and therefore introduced computational courses in their biology curricula. That said, conducting research is a team effort and collaboration is the key to approaching scientific problems in areas where you don’t have the expertise.
When approaching the end of your postdoctoral studies and deciding the next step in your career, what opportunities were you considering?
Well, I was looking for a combination of things. I was looking for a place that shares the same vision as I do, the same values of collaboration and translation and a place that has a high caliber of scientists. I believe in the things that OICR works on and how research is done here, so I think it’s a great fit.
What advice would you give to aspiring academics?
To do research well, you first need to love it. You need to be curious, know to identify the needs and ask the right question at the right time. Furthermore, you have to have persistence. You cannot give up in the pursuit of new knowledge.
July 10, 2018
Acute myeloid leukemia (AML) progresses quickly and requires treatment soon after diagnosis, but the disease begins long before becoming symptomatic. Early indicators of AML were thought to be indistinguishable from healthy aging. But now, an international group of researchers led in part by Dr. Sagi Abelson, a postdoctoral fellow in the lab of Dr. John Dick at the Princess Margaret Cancer Centre, has discovered distinctive traces of AML in patients up to 10 years before they were diagnosed with the disease.