Application of Biomarkers in personalized medicine

The concept of personalized medicine was first promoted with pharmacogenomics data on oncology trials which demonstrated subject’s unique genetic makeup (genome) influence on their response to medications (The Office of the Commissioner FDA, 2015). Treatment biomarkers comprise of genes or a DNA sequence or an RNA strand which expresses uniquely in every individual.

Understanding personalised medicine

National cancer institute defines personalized medicine as:

A form of medicine that uses information about a person’s genes, proteins and environment to prevent, diagnose and treat a disease.

There exists a problem with the current approach to clinical trials to validate drug (Schork, 2015). This approach, however, has challenges and issues. Considering there is evidence-based data on medicines being harmful to certain ethnic groups. A common example is Statins, which is used to lower cholesterol but benefits only 1 out of 50 people who regularly consume the medicine. Since every person has different physiology, biomarkers play a greater role in development of personalized medicine.

Need for personalized medicines

Personalised medicine used widely in oncology research, the advantage being precise, safe and efficacious dose selection and treatment regime management (Ong et al., 2012). Patients with melanoma showing a mutation in BRAF V600E gene exhibited risk of metastasis in 40-60% cases. Roche (drug manufacturer) registered BRAF V600E as a biomarker with FDA and presented Vemurafenib as a drug. Therefore, this drug causes tumor regression in melanoma patients exhibiting 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% compared to non-mutated BRAF V600E cases treated with vemurafenib (Ong et al., 2012).

Biomarkers for personalized medicines

Biomarkers play a key role in personalized medicines. However, predictive and prognostic biomarkers are the most commonly studied. Furthermore, they link the genetic makeup with the prediction of a person’s response to treatment (Schork, 2015). Again, treatment biomarkers are usually gene, a DNA sequence stretch or an RNA strand. Henceforth, they may express uniquely in every individual. Hence these biomarkers provide the genomic profile of the patient that help to develop precise treatment regime for patients. Thereby, exhibiting genetic variation amongst the patients. Conversely, diagnostic biomarkers (again, the genetic makeup of a patient) helps in early diagnosis or screening of condition (Matsui, 2013).

Personalized medicines for non-communicable diseases

With respect to personalized medicines for other non-communicable diseases, biomarkers have been important in the management of chronic conditions. The chronic conditions comprise of Diabetes, Stroke, Rheumatoid Arthritis, Parkinson’s diseases and many others (Ong et al., 2012). 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 55-75 years will experience stroke sometime during their life. Stroke is a syndrome of multiple clinical conditions, many of the stroke patients have past history of hypertension. Furthermore, epidemiological studies suggest that genetic risk factors are an important cause of ‘sporadic’ stroke, genetic predisposition like monozygotic twins, family history are linked to stroke.

Genome-wide association studies (GWAS) have identified the genetic linkage for stroke studies. Similarly, 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 to an increase in the risk of large artery stroke, by increasing atherogenesis. , Sodium valproate has an inhibitory action of HDAC9. In animal models, HDAC9 was used as a biomarker to study the inhibition of atherosclerosis using sodium valproate (Bellenguez et al., 2012). Interestingly sodium valproate therapy in humans with a mutation on HDAC9 has found to be lowering stroke and myocardial infarction rates compared to other people without HDAC9 mutation.

Case example

Another example is cystatin C antibodies, where plasma levels of cystatin C stronger predict chronic kidney disease.  In case of, Chronic kidney disease strongly associated with an increased risk for cardiovascular disease. Cystatin C is measured by immunoassays, using particles coated with cystatin-C specific antibodies, such that these antibodies show individual variation (Schork, 2015). These antibodies have been subjected to animal model studies, to be validated as biomarkers in prediction of renal dysfunction and cardiovascular disorders. However, a recently published systematic review of circulating predictive biomarker has been shown a correlation. Therefore between increased levels of cystatin C and initiation of preclinical renal dysfunction which in turn could be linked to the beginning of larger cardiovascular disorders (van Holten et al., 2013).

List of biomarkers in use

The table below lists the examples of biomarkers subjected to clinical trial to personalize either of the aspects of oncology management.

Biomarker Condition Use in Clinical trial Reference
Her-2 Breast Cancer Her-2 is used as a predictive biomarker in breast cancer, thereby treating patients with trastuzumab blocks Her-2. (Matsui, 2013)
EGFR Non-small cell lung cancer NSCLC patients, however, with EGFR-mutant tumors have shown better treatment results when treated with erlotinib & gefitinib. (Cappuzzo et al., 2010)
PML/RARα Acute Myeloid Leukemia Furthermore, 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. (Schork, 2015)
KRAS Colorectal Cancer The KRAS mutation in colorectal cancer selects against patients who will not benefit from anti-EGFR receptor therapy, and hence namely cetuximab or panitumumab. (Monzon et al., 2009)

Future steps towards personalized medicine

Biomarkers and the extensive genomics data is available today on different human population and 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. On the other hand, regulatory agencies and pharma companies are open to gather data on biomarkers for personalized medicine. They, however, yet emphasize the importance of standard clinical trials while approving new molecules.

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. Therefore, the process for sure is slow, but once the process starts validating more and more biomarkers for the disease the things might shift more towards personalized approach.

References

  • Cappuzzo, F., Ciuleanu, T., …, G., SATURN investigators, 2010. Erlotinib as maintenance treatment in advanced non-small-cell lung cancer: a multicentre, randomised, placebo-controlled phase 3 study. Lancet Oncol. 11, 521–529. doi:10.1016/S1470-2045(10)70112-1.
  • The office of the Commissioner FDA, 2015. The Precision Medicine Initiative [WWW Document]. URL https://www.fda.gov/ScienceResearch/SpecialTopics/PrecisionMedicine/default.htm (accessed 9.13.17).
  • Bellenguez, C., Bevan, S.,…, Markus, H.S., 2012. Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke. Nat. Genet. 44, 328–333. doi:10.1038/ng.1081.
  • Matsui, S., 2013. Genomic Biomarkers for Personalized Medicine: Development and Validation in Clinical Studies [WWW Document]. Comput. Math. Methods Med. doi:10.1155/2013/865980.
  • Monzon, F.A., Ogino, S.,…, Nikiforova, M.N., 2009. The role of KRAS mutation testing in the management of patients with metastatic colorectal cancer. Arch. Pathol. Lab. Med. 133, 1600–1606. doi:10.1043/1543-2165-133.10.1600
  • Ong, F.S., Das, K.,…, Grody, W.W., 2012a. Personalized medicine and pharmacogenetic biomarkers: progress in molecular oncology testing. Expert Rev. Mol. Diagn. 12, 593–602. doi:10.1586/erm.12.59Schork, N.J., 2015. Personalized medicine: Time for one-person trials. Nat. News 520, 609. doi:10.1038/520609a.
  • Ong, F.S., Das, K.,…, Grody, W.W., 2012a. Personalized medicine and pharmacogenetic biomarkers: progress in molecular oncology testing. Expert Rev. Mol. Diagn. 12, 593–602. doi:10.1586/erm.12.59.
  • Schork, N.J., 2015. Personalized medicine: Time for one-person trials. Nat. News 520, 609. doi:10.1038/520609a.
  • Van Holten, T.C., Waanders, L.F., …, Roest, M., 2013. Circulating Biomarkers for Predicting Cardiovascular Disease Risk; a Systematic Review and Comprehensive Overview of Meta-Analyses. PLoS ONE 8. doi:10.1371/journal.pone.0062080.
Diksha Tomer

Diksha Tomer

Research Analyst at Project Guru
She is a bachelor in Botany honours from Miranda House (Delhi University) and is pursuing her master’s in Plant Biotechnology at TERI SAS. She is a recipient of Hemendra Kothari Scholarship and her interests lie in the field of Bioinformatics and Biotechnology.
Diksha Tomer

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