Leukemia

University of Toronto-affiliated scientists have created a first-of-its-kind treatment for leukemia that has attracted potentially record-breaking funding and will start trials on cancer patients in Ontario.

The groundwork for the potential new drug was laid by Professor Cheryl Arrowsmith, chief scientist of the U of T-affiliated Structural Genomic Consortium (SGC), working with a team of scientists from the SGC’s chemical probe program, including biologist Dalia Barsyte and Masoud Vedadi, who is also an assistant professor in U of T's department of pharmacology and toxicology.

The SGC collaborated with the Ontario Institute for Cancer Research (OICR) drug discovery group, led by Rima Al-awar, an associate professor in the department of pharmacology and toxicology, in an open science collaboration that discovered the first inhibitor to a new drug target, WDR5.

Al-awar and her team at OICR further developed and tested the drug pre-clinically. After the success of these initial tests, the provincial cancer researchers attracted investment from Celgene Corporation that could potentially exceed US$1 billion – which would make it the largest transaction to date for a preclinical asset discovered in Canada.

This investment will allow for clinical trials based in Ontario, and will further research and development of the drug and other cancer research innovations developed in the province.

“The progress of this pre-clinical drug towards the clinic is an example of how OICR, working with its partners, is accelerating cancer research in Ontario and increasing investment so that new innovations can help patients as soon as possible,” says Laszlo Radvanyi, president and scientific director of OICR.

Scientists have long known that a protein called MLL-1 plays an important role in promoting the development of leukemia. It does this by binding with a partner protein called WDR5. This new therapy works by disrupting the interaction between these two proteins, which throws a wrench into the development of the cancer.

It was Arrowsmith and her ��οteam that first suggested targeting this protein combination as a way of fighting leukemia, and brought the idea to OICR cancer researchers. The two teams worked together to develop a “chemical probe” that could be used to test the anti-leukemia hypothesis. Under the SGC’s mandate of open science, the two teams shared their successful results openly with the world’s scientific community.

Other researchers then showed the probe could stop the growth of leukemia and other cancer cells.

“The development of this compound was a real community effort. When the team at SGC came to us with this idea, it was much easier to see a path forward because of our history of co-operation and the complementary skills of the two groups,” says Al-awar, director and senior principal investigator of OICR’s drug discovery program.

“Developing the inhibitor to a point where it can attract this type of investment demonstrates the power of Ontario’s research community and how we can maximize our strengths by working together.”

Arrowsmith, also a professor in the department of medical biophysics, agreed. “Collaboration is very important these days in cancer research, where is it difficult to find all the skills, knowledge and materials needed for a project in a single institution,” she says.

“However, our collaboration was facilitated by our open science sharing of the chemical probe – no need for contracts or negotiations over intellectual property. That helped our research to go faster, and facilitated the rapid sharing of the chemical probe with colleagues who could test it out in important cancer models.”

Arrowsmith says the drug should have potential for many different cancers and other diseases.

New genetic test predicts risk of leukemia relapse

noreen.rasbach — Thu, 13 Sep 2018 18:36:24 +0000

New genetic test predicts risk of leukemia relapse

noreen.rasbach Thu, 09/13/2018 - 14:36

Cutline

Histological view of leukemia cells in smear of a patient’s bone marrow (image via Wikimedia Commons)

Topic

A new test developed by Canadian and Korean scientists and physicians can tell which patients with acute myeloid leukemia (AML) are at risk of relapse – as early as three weeks after they receive treatment.

“In AML, it is very important to predict who is going to relapse,” says Dennis Kim, of the Princess Margaret Cancer Centre at the University Health Network (UHN). The associate professor in U of T’s department of medicine was a co-leader of the study.

“If we are able to identify someone who is at high risk of relapse then we can do therapeutic intervention earlier, which can improve their outcome in the long run.”

The first DNA-based test of its kind, it is administered to patients after they've received a standard course of treatment for AML: chemotherapy, followed by a bone marrow transplant.

“We can detect mutations in patients’ bone marrow cells three weeks after the transplant and based on that predict the likelihood of their relapse,” says Zhaolei Zhang, principal investigator in the University of Toronto’s Donnelly Centre for Cellular and Biomolecular Research and a professor in the departments of molecular genetics and computer science, who co-led the study.

The findings, published recently in the journal Blood, could help doctors improve patient outcomes by changing the treatment before cancer has returned in full force.

AML is the most common type of leukemia in adults, comprising about one-quarter of all cases. It affects the bone marrow, the spongy tissue inside the bone where all blood cells are made. The disease stems from an overproduction of immature blood cells that over time outgrow normal blood cells. It’s a type of cancer that starts suddenly and progresses quickly, requiring urgent treatment.Treatment involves chemotherapy to wipe out the diseased bone marrow, followed by a bone marrow transplant to reconstitute the patient’s blood with cells from a healthy donor.

While most patients go into remission after chemotherapy, about one-third will relapse three to six months after receiving the transplant.

Jae-Sook Ahn (Chonnam National University Hwasun Hospital, Republic of Korea and a visiting researcher in the Donnelly Centre), Dennis Kim (Princess Margaret Cancer Centre), Zhaolei Zhang (Donnelly Centre) and TaeHyung (Simon) Kim (Donnelly Centre)

Until now, there was no good way to detect the trace amounts of leukemia cells that resisted the treatment and drive relapse. By the time these cells are picked up by available methods, the cancer is usually already at an advanced stage.

Using new DNA sequencing technology called next generation sequencing, or NGS, the team was able to identify the treatment-resistant leukemia cells, or clones, even when they make up a tiny proportion of all cells in the bone marrow. The nature of mutations reveals further clues about how best to target the disease with drugs.

“With our method, not only can we say that this patient will relapse, but we can also say their relapsing clone contains certain mutations which can be a target for therapeutic compounds that can be used to treat the patient,” says Kim.

For the study, the researchers collected 529 bone marrow samples from 104 AML patients who underwent chemotherapy and bone marrow transplants. The samples were collected at different time points: at the time of diagnosis, during the chemotherapy-induced remission, and three weeks after the bone marrow transplant. A subset of patients also gave samples three, six and 12 months after the transplant. Some samples were also taken from bone marrow donors to rule out the possibility that the leukemia cells were introduced by the transplant.

The researchers then identified DNA mutations that were present at the time of diagnosis and looked for those same mutations at each sampling point. They found that while chemotherapy and bone marrow transplant eliminated most leukemia cells, leading to a reduction in mutation frequency, some initial mutations could still be detected three weeks after the transplant, indicating the presence of treatment-resistant cancer cells. As these same mutations expand in frequency upon relapse, the data suggest that the same cancerous cells that started the leukemia are also responsible for the disease comeback.

Data analysis required Zhang’s team to develop new computational tools to parse the leukemia-driving mutations from the sea of sequence data. This allowed them to identify low residual mutation frequency of 0.2 per cent to use as a surrogate marker for giving a personal chance of relapse.

“Patients who had a mutation burden greater than 0.2 per cent were four times more likely to relapse than patients who had a lower burden or no mutation burden,” says TaeHyung (Simon) Kim, a computer science graduate student in Zhang’s lab who analyzed the data along with Joon Ho Moon, from Kyungpook National University Hospital in Korea, and Jae-Sook Ahn, from Chonnam National University Hwasun Hospital in Korea, who is part of Zhang’s lab as visiting professor.

The researchers hope their DNA-based test will become routine for monitoring disease prognosis although they say this could take five to ten years. In some hospitals, DNA tests are becoming routine for diagnosis of AML, but not for predicting prognosis nor guiding treatment plan.

The research was done as a three-sided collaboration between U of T, UHN and the team led by Hyeoung-Joon Kim, president of the Korean Society of Hematology and a professor in the department of hematology-oncology at the Chonnam National University Hwasun Hospital in the Republic of Korea, who oversaw collection and sequencing of patient samples.

The study was supported by research grants from the Natural Science and Engineering Council of Canada, Leukemia and Lymphoma Society of Canada, Princess Margaret Foundation and National Research Foundation of Korea.

Machine-learning background helps PhD student at ��οdesign faster test for leukemia patients

ullahnor — Wed, 07 Dec 2016 17:02:09 +0000

Machine-learning background helps PhD student at ��οdesign faster test for leukemia patients

ullahnor Wed, 12/07/2016 - 12:02

Cutline

Researcher Stanley Ng holds up a cartridge used to measure the gene expression levels of cancer cells. A team led by Ng has developed the smarter, faster test (photo by Luke Ng)

Tyler Irving

Author legacy

Tyler Irving

Topic

Breaking Research

machine learning

IBBME

Faculty of Applied Science & Engineering

Leukemia

Subheadline

“This is why I switched fields and returned to academia: I wanted to contribute to the fight against cancer”

A new, rapid gene-expression test could help clinicians determine the best management for patients with acute myeloid leukemia (AML) by making it possible to accurately predict a patient’s response to chemotherapy within one to two days of diagnosis.

Although the standard treatment for AML is intensive chemotherapy, patients vary widely in their response. Currently, it is difficult to predict who will do well with chemotherapy, and who will not benefit and might do better with novel therapies offered by clinical trials.

“AML is an acute condition, and every day counts,” says researcher Stanley W. K. Ng. “For this reason, clinicians usually start treatment right away, without waiting for the test results.”

By contrast, Ng’s test can determine patient risk in just 24 to 48 hours.

Ng, a senior PhD candidate at the Institute of Biomaterials and Biomedical Engineering (IBBME), led the development of the test, which is described in a study published today in Nature.

The test is the result of collaboration between U of T’s Faculty of Applied Science & Engineering, the Faculty of Medicine, the Princess Margaret Cancer Centre in Toronto and leukemia clinics in France, Germany and the Netherlands.

Five years ago, Ng was working in various technical roles in telecommunications and the financial sector, but a major life event altered his path.

“When my uncle died of lymphoma, I saw how my family was affected, and I wanted to help,” he says. “I thought about my training in computer engineering and machine learning, and realized that it could be applied to biomedical research.”

Ng started learning all he could about cancer and stem cells and was accepted into Professor Peter Zandstra’s lab at IBBME. Zandstra, who is also executive director of Medicine by Design, a regenerative medicine and cell therapy hub at U of T, in turn introduced him to his collaborators from Princess Margaret, Dr. Jean Wang and John Dick, a professor of molecular genetics at U of T. They both conduct in-depth studies of leukemia stem cells (LSCs).

LSCs were first identified in AML in 1994 by Dick’s team, and are the cells that initiate and drive disease growth. Unlike the bulk of leukemia cells, LSCs are often resistant to standard chemotherapy and can cause disease recurrence in patients who appear to go into remission.

Dick and Wang used a disease model to produce a list of genes that are more strongly expressed in LSCs. Ng analyzed this data to identify 17 genes whose expression levels can be used to calculate a numerical risk score called LSC17 for each patient. By gathering gene expression and clinical data from more than 900 AML patients treated at Princess Margaret and across Europe, Ng was able to link LSC17 scores with patient outcomes.

Patients with low LSC17 scores responded well to standard chemotherapy and survived longer than those with high scores.

Genetic tests for AML patients are already carried out at clinics to estimate patient risk level, but the results typically take weeks to arrive.

“Right now in the clinic we don’t really have a rapid way to determine upfront who is at higher risk. Our new tool can do this quickly, allowing oncologists to direct those who are unlikely to benefit from standard treatment to clinical trials testing alternative therapies that may be of more benefit.” says Wang.

“This is a great example of how collaborating across disciplines can help develop new solutions to complex problems like AML treatment,” says Zandstra. “By applying machine learning techniques to data from both disease models and clinical outcomes, we were able to gain biologic insights that have a practical benefit for doctors and patients.”

The test is currently being validated at Princess Margaret, with plans to start a clinical trial next year. Ng continues to work closely with both clinicians and researchers to ensure proper translation of the LSC17 score from the lab to the clinic.

“The LSC17 score has the potential to change the way clinicians manage AML,” says Ng. “This is why I switched fields and returned to academia: I wanted to contribute to the fight against cancer.”

Leukemia

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New genetic test predicts risk of leukemia relapse

Machine-learning background helps PhD student at ��������οdesign faster test for leukemia patients

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Machine-learning background helps PhD student at ��οdesign faster test for leukemia patients