Genetic variation is a key determinant that causes patients to respond differently to the same drug. On this account, generating a large genomic database to specifically interrogate the links between genetic sequence and drug response brings the promise to be effective in the discovery of new pathways of disease. This will allow researchers to uncover hidden patterns and unknown correlations, changing the way diseases are diagnosed and treated in each individual. Therefore, connecting the dots between big data and genetics holds a great potential of paving the way towards personalised medicine.
In recent years, the advances in genome sequencing have played an increasingly important role in identifying biomarkers to predict drug response to certain types of cancers. An example is the discovery of EGFR mutations as a predictor of responsiveness to gefitinib in patients with non-small-cell lung cancer. In two randomised phase II trials of gefitinib, subgroups of patients who are of Asian origin, female sex, adenocarcinoma, and non-smoking status demonstrated greater response rate to gefitinib. By sequencing and comparing mutations in the genome sequences of these patients, researchers were able to find the correlation between EFGR mutation and clinical response to gefitinib therapy.
Patients with mutations in the EFGR gene showed improved progression-free survival(PFS) when treated with gefitinib as compared to standard chemotherapy using carboplatin and paclitaxel. Identifying the biomarker from the DNA sequence of an individual alone is very challenging. However, the creation of large sets of genome sequences allowed the researchers to find commonly shared mutations in tumours of people with the same condition. The discovery of biomarker by leveraging big data also suggests new possibilities of the use of genomic data to improve diagnosis, treatment, and prevention of disease.
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Realising the potential benefits of genomic data in improving patient care outcomes, the National Health Service(NHS) is now rolling out a pioneering initiative to set up National Genomic Service with the aim to embed genome sequencing into routine hospital care. It involves building an integrated informatics system which combines genomic sequencing data and electronic health records to fully realise the potential of genomic medicine. Based on the genomic and clinical data available, doctors will be able to determine which drug will work better for different subtypes of patients within a given condition, tailoring therapy for each individual instead of adopting an empirically driven approach of trial-and-error. With this system, doctors can also make better risk prediction and focus on prevention before a disease develops. For example, if genetic testing results show that a patient is at increased risk of developing colon cancer, an alert in the electronic health records could notify the patient’s doctor to make an intervention at an earlier stage by suggesting a colonoscopy to be carried out more frequently.
As part of the Genomic Medicine Service, NHS has successfully harvested a massive amount of genomic data from a large number of patients with various clinical conditions. Despite the fact that genomic data can bring a plethora of benefits, inevitably it raises issues related to data privacy and confidentiality. The big question here is how to do so in a way that protects individual privacy, but still ensures that the data is of sufficient quality that the analytics are useful and meaningful. The current approach undertaken by the NHS to protect data privacy is de-identification, which means data from all personally identifiable information is removed to prevent a person’s identity from being connected with information.
Nevertheless, genomic data is unique and there is evidence to suggest that a sequence of 30 to 80 single nucleotide polymorphisms(SNPs) can identify an individual. In 2013, a Harvard professor managed to re-identify the names of more than 40% of a sample of anonymous participants in a DNA study. This highlights the risk of re-identification is still present even with the data protection technique. Breaching of highly sensitive genetic information will not only compromise the patients’ individual privacy but also the privacy of their relatives, and can open them up to subsequent fraud or discrimination. In my opinion, privacy safeguards on how genomic data should be managed, stored and shared should be heightened as the first priority on the agenda. Given that the implementation of genomic medicine is still at its infancy stage, we need to put more time and effort into ensuring the safety aspects are being taken care of.
Translating genomic advances into practice across the healthcare system to personalise care for patients requires a change in clinical care pathways. Doctors will need to know how to interpret genetic testing results and how to convey the information to their patients. This creates the need for a programme to educate doctors and other healthcare professionals on the use of genomic information into clinical practice. While I agree that it is important to upskill clinical staffs and ensure that they are up to speed with this evolving field, we must also not forget about preparing the future healthcare professionals to face the transformation ahead. Perhaps now is the time for us to start thinking about introducing the service in the curriculum and equipping the younger generation with knowledge and skills necessary to fully utilise these genomic technologies. This ensures that they can step into their working environment confidently without having to spend additional time to adapt to the change.
It seems plausible to me that genomic medicine will lead to a dramatic shift in the medical landscape over the next few years. It is crucial to realise that as much as we want to believe big data in genomic medicine will benefit the society in future, we also need to be realistic and manage our expectations about the risks it may pose. There is a need to lay out a framework in order to strike a fair balance between providing personalised care to patients and protecting patients’ privacy when incorporating genomic information into the electronic health records.
