Personalised medicine: Application of Biomarkers


-By Dr. Akash G Prabhune


The concept of personalized medicine was first promoted when pharmacogenomics data on oncology trials, demonstrated that person’s unique genetic makeup (genome) influences his or her response to medications(“The future of personalized medicine,” n.d.). National cancer institute defines personalised medicine as “A form of medicine that uses information about a person’s genes, proteins, and environment to prevent, diagnose, and treat disease”(“NCI Dictionary of Cancer Terms,” n.d.). The problem with current approach of clinical trials to validate drug is, the method harvests a handful of measurements from thousands of people and replicates the results of same for entire population. This approach has inherent bias, when there is evidence based data on certain medicines being harmful to certain ethnic groups, the common example being of Statins, which are used to lower cholesterol levels benefits only 1 out of 50 people who regularly consume the medicine(Mukherjee and Topol, 2002). Personalised medicine is widely used in oncology research, the advantage being precise, safe and efficacious dose selection and treatment regime management(Ong et al., 2012a). The better example is of BRAF V600E for vemurafenib in melanoma. For the patients with melanoma a mutation on BRAF V600E gene exhibited risk of metastasis in 40-60% cases. Roche® the manufacturer of vemurafenib registered the BRAF V600E as a biomarker with FDA and presented vemurafenib as a drug which causes tumor regression in melanoma patients who exhibit mutation on BRAF V600E gene. The clinical trial demonstrated that the use of vemurafenib in mutated BRAF V600E cases decreases the tumor size by 84% vs 64% (95% CI: 0.26–0.55) compared to non-mutated BRAF V600E cases treated with vemurafenib(Ong et al., 2012a).

Biomarkers play a key role in personalized medicine; predictive and prognostic biomarkers are the most commonly studied. The predictive biomarkers are pre-treatment biomarkers that provide information on which patients are likely or unlikely to benefit from specific treatment. While a prognostic biomarker is the one which provide information about the long-term outcome of untreated patients or patients on standard treatment. Treatment biomarkers like the above-mentioned example help to develop precise treatment regime for patients exhibiting genetic variation. While Diagnostic biomarkers help to in early diagnosis or screening of condition(Matsui, 2013).Table 1. List the examples of biomarker which have been subjected to clinical trial to personalize either of the aspect of oncology management.

Table 1. Biomarkers in personalized medicine
Biomarker Condition Use in Clinical trial Reference
Her-2 Breast Cancer Her-2 is used as predictive biomarker in breast cancer wherein treating patients with trastuzumab blocks Her-2 (Matsui, 2013)
EGFR Non-small cell lung cancer NSCLC patients with EGFR-mutant tumors have shown better treatment results when treated with erlotinib & gefitinib (Cappuzzo et al., 2010)
PML/RARα Acute Myeloid Leukemia Prompt diagnosis is essential because of the high frequency of life-threatening disseminated intravascular coagulation, PML/RARα is a highly sensitive biomarker for early diagnosis (International Agency for Research on Cancer, 2008)
KRAS Colorectal Cancer The KRAS mutation in colorectal cancer selects against patients who will not benefit from anti-EGFR receptor therapy, namely cetuximab or panitumumab (Monzon et al., 2009)


We already know the importance of biomarkers in oncology, and when it comes to personalized medicine biomarkers have been found to be of importance in management of chronic conditions like diabetes, stroke, rheumatoid arthritis, Parkinson’s diseases and many others(Ong et al., 2012b). Stroke is one of the major causes of mortality and long term disability worldwide with cohort data suggesting that 1 in 5 women and 1 in 6 men aged 55 to 75 years will experience stroke sometime during their life(Seshadri et al., 2006). Stroke is syndrome of multiple clinical conditions, many of  the stroke patients have past history of hypertension(Qureshi et al., 2009). Epidemiological studys suggests that genetic risk factors are important cause of ‘sporadic’ stroke, genetic predisposition like monozygotic twins, family history are linked to stroke(Markus, 2012). Genome wide association studies (GWAS) are used to identify the genetic linkage for stroke studies. Wellcome Trust Case Control Consortium 2 ischemic stroke GWAS identified a novel association of gene HDAC9 which encodes proteins responsible for deacetylation of histones. A mutation on HDAC9 is found related with increase in risk of large artery stroke, by increasing atherogenesis. Sodium valproate has inhibitory action of HDAC9. In animal models HDAC9 was used as a biomarker to study the inhibition of atherosclerosis using sodium valproate(Consortium (ISGC) et al., 2012). Interestingly sodium valproate therapy in humans with a mutation on HDAC9 has found to be lowering stroke and myocardial infraction rates compared to other people without HDAC9 mutation(Olesen et al., 2011).

To infer, biomarkers and the extensive genomics data available today on different human population is a key to personalized medicine. Much of the work done today in cancer research is a step towards personalized and precise medicine. The barriers include, lack of validated biomarkers which could further propagate the research. Regulatory agencies and pharma companies are open to gather data on biomarkers for personalized medicine, yet emphasize the importance of standard clinical trials while approving new molecules. The other side of the coin is that as of today tailoring a treatment for an individual patient is a costly affair and hence restricted to cancer research. Pharmaceutical companies tend to focus on drugs that are likely to be used by millions of people as such drug drive up their share prices and profit margins. The process for sure is slow, but once we start validating more and more biomarkers for disease the things might shift more towards personalized approach.


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