Singapore, 11 June 2021 – A team of researchers from National Neuroscience Institute (NNI), National University of Singapore (NUS) and Duke-NUS Medical School (Duke-NUS) are studying new drug targets for the treatment of glioblastoma (GBM). The team received a grant under the Open Fund Large Collaborative Grant (MOH-000541), which is supported by the National Research Foundation Singapore and administered by the Singapore Ministry of Health’s National Medical Research Council.
Glioblastoma is a brutal form of cancer because it spreads quickly in the brain and is difficult to treat. Common symptoms include severe headaches, seizures, personality changes and confusion (impaired cognition) which can be distressing for patients and their family members.
“A glioblastoma diagnosis is particularly grim, because even with the current standard of care, recurrence is the rule rather than the exception. This generous funding gives us an opportunity to address the many challenges posed by this aggressive tumour, opening up the possibility of better diagnostics and treatment avenues directed at specific groups of patients,” explained Associate Professor Ang Beng Ti, Head and Senior Consultant, Department of Neurosurgery, NNI @ Singapore General Hospital campus and the project’s Principal Investigator.
NNI sees close to 100 new cases of glioblastoma every year. Although it is rare, the precision medicine technologies and processes the team is developing to tackle this tumour have the potential to be adapted for the diagnosis and management of other forms of cancer.
The team’s innovative use and development of technology has caught the attention of overseas researchers. Dr. Amy Heimberger, a Professor in the Department of Neurosurgery and the Scientific Director of the Lou and Jean Malnati Brain Tumor Institute at Northwestern University, is a leading researcher in glioblastoma and a key developer for the STAT3 inhibitor WP1066. She invited the Singapore team to join an international drug trial, termed GBM AGILE, to sub-type and stratify participants. This will be in conjunction with Moleculin Biotech, a clinical stage pharmaceutical company. Being actively involved in the research will also make it easier for NNI’s clinicians to include suitable Singapore patients in the international trial, giving them access to potential new treatments.
“We are excited that all these developments and advances provide a beacon of hope for patients suffering from GBM. We truly value the collaboration and synergy between NNI, NUS and Duke-NUS that brings together multi-disciplinary expertise to advance patient care,” says Professor Tan Eng King, Deputy Director (Academic Affairs) and Director of Research at NNI.
Details about the research
The multi-institution research team is fighting GBM on different fronts with four main themes:
Theme 1: Biology and identification of drug targets
- Associate Professor Ang Beng Ti, Head and Senior Consultant, Department of Neurosurgery, NNI @ SGH campus; Co-Principal Investigator, Neuro-oncology Programme, NNI; Associate Professor, Duke-NUS Medical School
- Adjunct Associate Professor Carol Tang, Co-Principal Investigator, Neuro-oncology Programme, NNI; Adjunct Associate Professor, School of Biological Sciences, Nanyang Technological University (NTU)
Understanding disease pathways starts at the laboratory bench with in-depth studies of tumours. To ensure high quality specimens, the team established a brain tumour tissue bank called Glioportal. Itcontains patients’ original tumours that are molecularly profiled, processed and banked immediately after surgery to ensure the tissue is in the best possible state for research. To date, 151 clinical specimens have been banked, all with patient consent. Tumour samples are de-identified, but patients can be re-identified and contacted if a suitable drug trial becomes available.
“With Glioportal, we are able to reform tumours in models that have the same molecular fingerprint as patients’ original tumours. The relative ‘purity’ of these models helps us advance our understanding of glioblastoma and identify and test potential drug targets, which in turn improves the accuracy of clinical trials in the later stage.” - Adj Assoc Prof Tang.
The team previously used the tumour samples to identify a signaling pathway that detects the activation of a protein called STAT3. The activation of this pathway in cells is linked with a higher likelihood of GBM recurring and spreading quickly.
Theme 2: Artificial intelligence (AI)-led drug discovery platform
Building upon findings from the first theme, the second theme has two projects that both use AI to accelerate drug discovery.
Principal Investigator: Professor Patrick Tan, Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School
The Duke-NUS team uses data-mining to identify genes linked to glioblastoma, what happens when they are turned on and off, and why this differs between patients. One key area of interest is how gene expression affects the spread and recurrence of tumours.
“One of the challenges of treating glioblastoma is the rapidity with which it spreads. Through data mining, we aim to better understand how cells at the edge of the tumour differ from those at the core, and how these cells transition, in order to discover effective new drug targets to stop the spread of GBM.” - Prof Patrick Tan
Principal Investigator: Adjunct Associate Professor Carol Tang
The NNI team is working with technology company BenevolentAI (BAI), to identify and prioritise drugs with the highest potential to de-activate the STAT3 pathway. BAI uses artificial intelligence and machine learning to filter through public and proprietary information to identify and prioritise potential drugs that might be effective on therapeutic targets within the STAT3 pathway.
“Developing a new drug and bringing it to the market takes years due to the amount of testing and regulatory approval required to ensure it is safe for humans. It is especially difficult for conditions that affect the brain because the drug must also be able to cross the blood-brain barrier. Using the AI tool to sieve through existing drugs that are already on the market to find and prioritise those with the highest potential for testing will help speed up the drug discovery process.” – Adj Assoc Prof Carol Tang.
Theme 3: Bioengineering
Principal Investigator: Assistant Professor Shao Huilin, Department of Biomedical Engineering, and Institute for Health Innovation & Technology (iHealthtech), National University of Singapore
The NUS team is developing a nanosensor platform that can be used in the clinical setting to detect a tumour’s complex molecular profile. The biomarkers are retrieved from patient blood samples which contain fragments (extracellular vesicles) with nucleic acids and proteins from the original tumour cells.
“The slightest difference within cell proteins can affect a tumour’s response to treatment. Therefore, tumours need to be sub-typed as accurately as possible to ensure patients receive the most appropriate treatment for their tumour. Using a minimally invasive blood test to detect these complex biomarkers solves the need to surgically remove the tumour for sub-typing. This is particularly important when tumours recur as it is usually not possible to remove the tumour due to the risk of brain damage.” – Asst Prof Shao Huilin
Theme 4: Adaptive clinical trials
- Associate Professor Ang Beng Ti
- Dr Lin Xuling, Senior Consultant, Department of Neurology, NNI
Clinical trials of drugs identified by AI technology in Theme 2 are expected to start in Asia in late 2022. Participants will be selected based on their tumour sub-type using the nanosensor platform developed in Theme 3. An algorithm, a set of instructions used by a computer to perform a task, developed by the Singapore team will sort the patients into different categories based on their sub-type and determine which clinical trials patients should participate in. The team will adopt an adaptive clinical trial concept which evaluates the effectiveness and side-effects of treatments at different stages throughout the trial, compared to the traditional method of analysing the results only at trial completion. This allows for the trial recruitment strategy to be adapted while in progress, such as stopping the trial for non-responders to minimise adverse effects. This concept has been well-demonstrated in breast cancer clinical trials.
“Knowing each participant’s tumour genetic and molecular profile when they enter the trial will make it easier to identify which treatments are effective for each sub-type. If successful, this trial will enable physicians to provide targeted therapy for their patients, thereby improving their survival while minimising unpleasant side-effects from ineffective treatments.” – Dr Lin Xuling