March 19, 2019
Collaborative research group performs the most comprehensive analysis of curable prostate cancer to date, finds key connections between different data types
As cancer researchers delve deeper into different omics studies, and technologies enable their ability to do so, it is becoming increasingly important to understand how these areas of research are interconnected. Previous studies across multiple omes – such as the genome, proteome, transcriptome or epigenome – have led to important discoveries in colorectal cancer and ovarian cancer, but prostate cancer remains largely unresolved. Researchers from the Canadian Prostate Cancer Genome Network (CPC-GENE) set out to unravel some of these mysteries.
In the most recent CPC-GENE study, published today in Cancer Cell, the research group integrated multiple levels of omics analyses to better understand the biology of intermediate-risk prostate cancer – a type of cancer in which it is notoriously difficult to predict and treat accordingly. A better understanding of this disease could lead to improved tests that can determine which tumours are aggressive and require aggressive treatment, while helping spare those whose cancer will never become aggressive the negative side effects of treatment.
“We cannot overlook the important information that we gain from looking at the bigger picture,” says Julie Livingstone, bioinformatician at OICR and co-author of the study. “In this case, this means looking at prostate cancer from multiple angles – or multiple omes – to potentially find new markers of aggressive disease.”
The study explored 76 prostate cancer tumours and found new combinations of information that could act as a better predictor of a patient’s chance of relapse than any single piece of information alone. More specifically, they identified that the combination of protein and methylation data could, on average, predict the severity of a tumour better than looking at just the proteins – the proteome – or just the methylation patterns – the methylome – alone.
“Integrating datatypes is anything but straightforward, but it illuminates interesting aspects about prostate cancer that we haven’t seen before,” says Livingstone. “In the future, we intend to pursue our multi-omic investigation and translate this understanding into better tools to inform treatment selection for men with this disease.”
Find out more about research from the CPC-GENE project on OICR News.
January 14, 2019
Landmark pan-cancer study analyzes mutation signatures of low oxygen in more than 8,000 tumours
TORONTO (January 14, 2019) – Unlike healthy tissues, tumours thrive in low-oxygen environments, often acquiring the ability to resist treatment and spread to other sites in the body. Despite being a well-known cause of therapy resistance and metastasis, the impact of low oxygen, known as hypoxia, on tumour cells is poorly understood. As reported today in Nature Genetics, researchers have discovered molecular hallmarks of hypoxia in the first-ever pan-cancer analysis of low oxygen in human tumours, with a special focus on prostate cancer.
The study investigated more than 8,000 human tumours across 19 different cancer types, including prostate tumours from the Canadian Prostate Cancer Genome Network (CPC-GENE). The authors discovered common markers of hypoxia that could help predict cancer aggressiveness and inform treatment decisions.Continue reading – Researchers discover common markers of tumour hypoxia across 19 cancer types
September 20, 2018
Today, OICR’s Dr. Paul Boutros was named the 2018 winner of the Bernard and Francine Dorval Prize. The award is part of the Canadian Cancer Society’s Awards for Excellence in Cancer Research.
May 17, 2018
Dr. Michael Fraser, Director of the Prostate Program in the Computational Biology group at OICR, has been named a 2018 NextGen Star by the American Association for Cancer Research (AACR). Awarded to only eight researchers around the world, AACR’s NextGen Stars program recognizes outstanding early-career scientists who have made significant contributions to cancer research.
April 19, 2018
Largest-ever study of its kind uses a tumour’s past to accurately predict its future
Toronto (April 19, 2018) – Findings from Canadian Prostate Cancer Genome Network (CPC-GENE) researchers and their collaborators, published today in Cell, show that the aggressiveness of an individual prostate cancer can be accurately assessed by looking at how that tumour has evolved. This information can be used to determine what type and how much treatment should be given to each patient, or if any is needed at all.
The researchers analyzed the whole genome sequences of 293 localized prostate cancer tumours, linked to clinical outcome data. These were then further analyzed using machine learning, a type of statistical technique, to infer the evolutionary past of a tumour and to estimate its trajectory. They found that those tumours that had evolved to have multiple types of cancer cells, or subclones, were the most aggressive. Fifty-nine per cent of tumours in the study had this genetic diversity, with 61 per cent of those leading to relapse following standard therapy.
September 29, 2017
We are pleased to share the Ontario Institute for Cancer Research (OICR) Annual Report for 2016/17.
We are living in an era of unprecedented innovation in cancer research. Recent advances have helped us to better understand cancer and allowed for collaboration on a scale that was previously not possible. This work is happening now and it is happening right here in Ontario.
September 25, 2017
Since mitochondria are inherited maternally, it may strike some as an odd place to go looking for connections to prostate cancer. But recently an international research team explored that relationship by looking at how the small amount of DNA contained in mitochondria, a cellular structure, is involved in prostate cancer.
August 9, 2017
Prostate cancer researchers have mapped the impact of an acquired mutation that alters epigenetic identity, the make-up of DNA, in about 50 per cent of patient tumour samples. The discovery also identifies a new opportunity for targeted therapy.
January 10, 2017
Prostate cancer is the most common cancer in Canadian men, but there is still no one-size-fits-all strategy for treating the disease. Currently it is difficult to choose exactly the right type and amount of treatment for each individual because it is hard to accurately assess how aggressive the cancer is. Researchers are now a step closer to bringing a powerful new prognostic tool into clinical use.
January 9, 2017
A team of researchers and clinician-scientists from across Canada have discovered a signature of 41 mutations that are common in prostate cancer and will help to prevent patients with non-aggressive disease from being overtreated. Dr. Paul Boutros, a Principal Investigator in OICR’s Informatics and Bio-computing Program and Co-Lead of the Canadian Prostate Cancer Genome Network (CPC-GENE), answered a few questions about how the signature was developed and its potential impact on patients.
January 9, 2017
Findings published in renowned journal Nature
January 9, 2017 – TORONTO, ON – The Canadian Prostate Cancer Genome Network (CPC-GENE) has published findings from the world’s most comprehensive genetic analysis of prostate cancer tumours in the journal Nature. Led by Drs. Robert Bristow of the Princess Margaret Cancer Centre and Paul Boutros of the Ontario Institute for Cancer Research, CPC-GENE has uncovered the full set of mutations that can occur in the most common cancer in men. By fully cataloging these mutations, the CPC-GENE team was able to create a new signature that predicts at an early stage whether a prostate cancer tumour will become aggressive or not, allowing for personalized treatment.
October 19, 2016
Prostate cancer is the most common cancer in Canadian men and while it has been the focus of extensive research, an estimated 4,000 Canadians die of the disease each year. That is why six years ago Dr. Paul Boutros and Dr. Rob Bristow set out to sequence the normal and diseased tissue of 350 patients and learn from a clinical perspective how genomic information can be used to guide better treatment.