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Review
. 2016 Aug 9;7(32):52493-52516.
doi: 10.18632/oncotarget.9370.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Ekaterina A Kotelnikova  1   2   3 , Mikhail Pyatnitskiy  1   4   5 , Anna Paleeva  1 , Olga Kremenetskaya  1   6 , Dmitriy Vinogradov  1   2   7
Affiliations
Review

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Ekaterina A Kotelnikova et al. Oncotarget. .

Abstract

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

Keywords: next generation sequencing (NGS); pathways; personalized medicine; precision oncology; systems biology.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1. Generalized systems biology pipeline for the cancer-related NGS data processing
Here, solid blue and red lines correspond to DNA processing while dashed ones to RNA processing. Blue lines represent germline events, red ones - somatic. A. Sample preparation. Extraction of DNA and RNA from patient's tumor and normal tissue. B. Sequencing and Bioinformatics. Convert raw sequencing data into list of genetic variations. C. Functional annotation and Pharmacogenomics. D. Systems Biology and data integration. E. Clinical decision
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