Revitalizing personalized medicine: respecting biomolecular complexities beyond gene expression

Abstract

Despite recent advancements in "omic" technologies, personalized medicine has not realized its fullest potential due to isolated and incomplete application of gene expression tools. In many instances, pharmacogenomics is being interchangeably used for personalized medicine, when actually it is one of the many facets of personalized medicine. Herein, we highlight key issues that are hampering the advancement of personalized medicine and highlight emerging predictive tools that can serve as a decision support mechanism for physicians to personalize treatments.

Figure 1

Manifestation of DNA sequence to molecular phenotypes and cellular responses. Each step in this process is confounded by several biochemical events that add dispersion and uncertainty to the subsequent steps. As such, it would be highly unlikely for there to exist a one-to-one relationship between a specific gene sequence and ultimate clinical outcome.

Figure 2

A hypothetical case for dispersion of biomolecular information from gene expression to molecular phenotype to cellular phenotype. For each specific gene variant (represented as gene score), there is a distribution of molecular phenotype among the patient population due to variations in random gene activation and repression, mRNA degradation, translational noise, alternate splicing, and protein degradation arising at the individual patient level. At the next level, for each value of molecular phenotype, there is a distribution of cellular response in the population due to protein phosphorylation, membrane drug efflux pumps, transportation limitations, and resistance mechanisms in apoptotic pathways. Eventually, two patients having the same gene variant might fall anywhere in the bivariate distribution in phenotype space. mRNA, messenger RNA.

Figure 3

Depiction of a single gene expression and regulation. Every step in this process is governed by stochastic biochemical events. The gene randomly transits between active and repressed promoter state and hence mRNA is produced in bursts. A fraction of mRNA is randomly degraded, and the rest is translated into protein. A fraction of protein also undergoes decay stochastically. Reprinted with permission from Macmillan Publishers: Nature Reviews Genetics. copyright 2005. mRNA, messenger RNA.

Figure 4

Different levels of variation observed during 6-MP treatment. As one moves from gene variant to clinical response, the downstream responses are dispersed for a given upstream genotypic/phenotypic variant. (a) For a few TPMT gene variants, several TPMT enzyme activities are observed on continuous scale in humans. (b) For a specific gene variant, there is a huge variation in TPMT activity and possible overlap with other gene variant. (c) Relationship between TPMT activity and 6-TGN concentration; for a given range of TPMT activity, a huge variation in 6-TGN concentration was observed. (d–f) Relationship between 6-TGN concentration and cellular response; for a given 6-TGN concentration, substantial dispersion in cellular responses were observed during 6-MP treatment. 6-MP, 6-mercaptopurine; 6-TGN, 6-thioguanine nucleotide; RBC, red blood cell; TPMT, thiopurine S-methyltransferase; WBC, white blood cell.

Figure 5

95% Confidence region for 6-TGN concentration predicted through nonparametric Bayesian population modeling approach. Black region: CR prediction based on genotypic information; gray: CR based on TPMT enzyme activity; red: CR based on 6-TGN measurement; solid dot: 6-TGN measurement. 6-TGN, 6-thioguanine nucleotide; CR, confidence region; RBCs, red blood cells; TPMT, thiopurine S-methyltransferase.