2 Personal Patient Profiling by Use of Integrated Omics
In order to demonstrate the potential of personalized medicine, Chen et al.^19 started
a huge project and presented integrative personal omics profile from a single
individual. The performed analysis combined genomic, transcriptomic, proteomic,
metabolomic, and autoantibody profiles over a 14-month period. A key aspect of
this study (that was limited to only one person!) was its longitudinal nature with
involved sampling over the whole period. Additional complexity was added by two
viral infections that the patient contracted during the monitoring interval. This
protocol was performed on an unprecedented scale and, according to the authors,
“more than 3 billion measurements taken over 20 time points.” The summary of the
study is shown in Fig. 3. As expected, the disease risks can be assessed from the
patient’s genome sequence. One disease outbreak was diagnosed during the
study—the patient developed diabetes after one of viral infections. Although on
risk according to the patient’s genotype, other diseases such as hyper-
triglyceridemia were not diagnosed. The important role of proteomics (and
metabolomics) technology in this study was documented during and after second
viral infection (RSV, see Fig. 3 ). It was the onset of the evaluated glucose response
that was tightly associated with RSV infection and a particular subclinical response
2–3 weeks after. According to the authors, “this study demonstrates that a large
database with a time-dynamic profiles for more individuals that acquire infections
and other types of diseases will be extremely valuable in the early diagnostics,
monitoring and treatment of diseased states.” However, the authors failed to stress
the complexity of the study and necessity for further introduction of high-
throughput analytical methods and laboratory robotics combined with reliable
data analysis by the introduction of corresponding powerful computerization.
Accordingly, the echo in the scientific community was somewhat reserved.^20 This
reaction can be explained with the complexity of the acquired data (especially
proteomic and metabolomic ones), and difficulties of their analysis and inter-
pretation—a total number of 3731 proteins—and between 4000 and 6800 meta-
bolite peaks were detected and identified, and a total of 1020 metabolites were
tracked for both infections. Finally, glucose and glycated hemoglobin (HbA1c),
two already well-known biomarkers, were utilized for the final diagnosis of
patient’s outbreak of diabetes.
Personalized omics profiling, especially proteomics and phosphoproteomics
one, is also the topic in a recent study, again with a limited number (seven) of
patients; Pieborn et al.^21 used laser capture microdissection (LCM), coupled with
(^19) Chen et al. ( 2012 ), pp. 1293–1307.
(^20) Chan et al. ( 2012 ), p. A16530.
(^21) Pieborn et al. ( 2014 ), pp. 2846–2855.
The Role of Proteomics in Personalized Medicine 185