Personalised medicine for effective cancer treatment
- Drugs need to be specific for each patient’s genetic and immunological profile
- Side-effects of medication can be predicted by first testing them on stem cells
- The studies could lead to mapping of ethno-specific adverse effects of cancer drugs
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[MANILA] Two separate studies by Singapore-based researchers offer new insights into using personalised medicine to diagnose and treat cancer, and significantly reduce public health burden from the disease in developing countries.
Initial findings from a four-year study published online in January by Scientific Reports by researchers at the Institute of Bioengineering and Nanotechnology of the Agency for Science, Technology and Research (A*STAR), and the National Cancer Centre Singapore (NCCS) show that stem cells can predict possible side-effects of anti-cancer drugs. While a related research published February in Nature Communications by A*STAR’s Genome Institute of Singapore and NCCS characterises the genetic diversity of individual liver cancers through a study of intra-tumour heterogeneity in hepatocellular carcinoma (HCC), the most common type of liver cancer.
Both studies focused on customised treatment of patients. Pierce Chow, senior consultant surgeon at NCCS and Singapore General Hospital, says advances in personalised medicine can ease the public health burden, especially in countries where resources are limited.
“Non-personalised treatment wastes resources without benefitting the patient.”
Pierce Chow, NCCS
“It is specifically because of this great health burden that drugs must be specific for each patient’s genetic and immunological profile. Non-personalised treatment wastes resources without benefitting the patient,” Chow tells SciDev.Net.
According to WHO, cancer is the second leading cause of mortality worldwide, killing nearly nine million people in 2015. Around 70 per cent of cancer deaths are in developing countries where most patients have no access to medical services.
In 2015, only 35 per cent of low-income countries reported having pathology services available in the public sector.
Chow says the research was started in 2014 because of a shortage of drugs for liver cancer. Using next-generation DNA sequencing technology to study 66 liver tumour samples taken from nine patients, the researchers found a wide range of intra-tumoral genetic diversity in the samples. They also found that the original cancer cells had evolved to have diverse genomic makeup at different stages.
Chow and his team are now conducting tests that cover people from diverse ethnic and etiological backgrounds across the Asia-Pacific. They have enrolled 100 patients in Malaysia, Philippines, Singapore and Thailand to further their studies.
Min-Han Tan, principal research scientist at NCCS and leader of the other group, says their study could lead to the mapping of population-specific adverse effects of drugs for various ethnicities.
“As a medical oncologist, it is common to encounter patients experiencing side-effects to their chemotherapy drugs. Many of these side-effects are difficult to predict in advance, and I thought that a patient-specific, or bespoke approach with stem cells might allow us to manage this,” Tan says in an interview with SciDev.Net.Tan says the researchers will conduct further studies on the effect of drugs on different organs.
“The study has allowed us to figure out how a drug works from the way they react to the liver cells, so we can now introduce another agent to overcome the side-effect. The NCCS is planning formal clinical trials to look into this,” Tan says.
Leonila Dans, professor of clinical epidemiology, University of the Philippines’ College of Medicine, says the concept of personalised medicine is actually not new as medical schools teach that patient management needs to be individualised and that there’s “no cookbook prescriptions for all”.
But Dans says the research at A*STAR and NCCS can categorise patients into more specific groups for better prediction of individual response to medications which is way accurate than the current practice of average effect or patient’s probability of success with their medications.
This piece was produced by SciDev.Net’s Asia & Pacific desk.