Copyright© 2020 World Scientific Publishing Co Pte Ltd

  • LinkedIn Social Icon
  • Facebook Social Icon
  • Twitter Social Icon
  • Instagram Social Icon
  • AsiaBiotechLive Reporter

Discovery of new tumour genomic fingerprint may lead to more effective treatments for cancer

A team of researchers recently discovered a genomic fingerprint, the 500-gene-classifier, which can aid in subtyping of glioblastoma tumours prevalent in patients from Korea, China and the United States. This may lead to the development of more effective cancer treatments in the future.



SINGAPORE | 7 NOVEMBER 2019


A team of researchers from Duke-NUS Medical School, National Cancer Centre Singapore and the Institute of Cell and Molecular Biology A*STAR have discovered that each tumour has its genomic fingerprint. This means that each tumour is made up of a unique set of genes, which hence allows tumours to be classified into different subtypes.


For example, the prevalent cancerous brain tumour among adults is classified and termed as glioblastoma multiforme. It is life-threatening and has been observed that only approximately 20 per cent of patients live beyond five years after being diagnosed with this tumour.


The research team developed an AI-derived (making use of AI to run thousands of repeated simulations) genomic fingerprint known as the 500-gene-classifier, which effectively and efficiently recognises the molecular subtype of each glioblastoma patient tumour, or in simpler terms, the tumour’s genomic fingerprint.


The 500-gene-classifier enabled the research team to identify three main genomic fingerprints for different subtypes of glioblastoma tumours found among Koreans, Chinese and Caucasian patients. It has proven itself to be a dynamic tool in the subtyping of glioblastoma tumours among patients from these countries on several different genomic platforms. Based on experiments done on mice, it has also provided elementary information that allows researchers to conclude that tumours of different glioblastoma subtypes vary in response to radiation therapy and chemotherapy.


Compared to the original 840-gene-classifier founded in the year 2008, 500-gene-classsifier is much more effective in categorizing large groups of people. For example, 840-gene-classifer enabled researchers to identify four subtypes of glioblastoma based on a large group of Caucasian people from the United States, but was evidently less effective in helping researchers to identify different subtypes of glioblastoma when there was a larger Caucasian cohort from the United States. This, compared with the 500-gene-classifier which could help researchers subtype glioblastoma tumours prevalent in people from three different countries, definitely pales in comparison.


Throughout the past 20 years, genomic platforms have been evolving to become highly efficient at conducting genomic sequencing—they can now allow tens of thousands of genes in a cancer patient’s tumour sample to be studied within a single experiment. This huge number of genes is used to categorise each tumour into high-resolution molecular subtypes or the genomic footprint of the tumour.


“Data science research may not have the novelty appeal for many young researchers but the impact of such work for patient-driven clinical trials has far-reaching implications for future treatment and clinical care, and should not be overlooked,” said Assistant Professor Wan-Yee Teo, lead Principal Investigator for this study, Duke-NUS Medical School.


A national clinician-scientist and a paediatric neuro-oncologist trained in the US, Assistant Professor Wan-Yee Teo has been awarded the Research Fellowship Training Award, Transition Award, and Clinician-Scientist Award by the National Medical Research Council, Ministry of Health, Singapore. She is currently a physician with KK Women’s and Children’s Hospital while her laboratory unit of Pediatric Brain Tumor Research Office, SingHealth-Duke-NUS Academic Medical Center is competitively funded, and based at National Cancer Centre Singapore

“The large amount of genomic data should also be reviewed from time to time to align new clinical questions and revise older classifiers. This will benefit smaller and scattered international research groups that may be using these gene-classifiers on a variety of profiling platforms. It is also crucial that these updated gene-classifiers are also made available to all researchers,” added Assistant Professor Teo.


Assistant Professor Teo hopes to inspire more people from our younger generation to join the field of biomedical research in the area on data science. Her team of young researchers and her own research merges genomic methodologies and experiments done on mice to analyse the complicated regulation in the developmental biology of brain tumours.




This article was contributed by Ling Yi, an editorial intern at World Scientific Publishing Co. and a contributing writer for Asia-Pacific Biotech News. She is from Nanyang Girls' High School, has keen interest in learning more about life sciences and exploring literature, in both English and Chinese. She also enjoys studying different languages such as Japanese and Korean, and has a passion for dancing and reading.