Abstract
BackgroundVarious efforts to understand the relationship between biological information and disease have been done using many different types of highthroughput data such as genomics and metabolomics. However, information obtained from previous studies was not satisfactory, implying that new direction of studies is in need. Thus, we have tried profiling intracellular free amino acids in normal and cancerous cells to extract some information about such relationship by way of the change in IFAA levels in response to the treatment of three kinase inhibitors. We define two measures such as relative susceptibility (RS) and relative efficacy (RE) to numerically quantify susceptibility of cell line to treatment and efficacy of treatment on cell line, respectively.MethodsWe applied principal component analysis (PCA) to the intracellular free amino acids (IFAAs) of isogenic breast cells with oncogenic mutation in K-Ras or PI3K genes to investigate the change in IFAA levels in response to the treatment of three kinase inhibitors. Two-dimensional plot, which was graphically represented by using the first two principal components (PCs), enabled us to evaluate the treatment efficacy in cancerous cells in terms of the quantitative distance of two IFAA profiles from cancerous and normal cells with the same treatment condition.ResultsThe biggest change in metabolic states in K-Ras mutant cell was caused by REGO for both treatment time (RS=2.31 (24 h) and 1.64 (48 h)). Regarding RE, REGO was the most effective on K-Ras/PI3K mutant cell line for treatment time 24h (RE=1.28) while PI3K inhibitor had good effect on K-Ras mutant cell line for 48h (RE=1.1).ConclusionsNumerical study on the link between amino acid profile and cancer has been done in two different dimensions. We then summarized such link in terms of two new metrics such as RS and RE, which we first define in this work. Although our study based on those metrics seems to work, we think that the usefulness of the metrics in cancer study of this kind need to be further investigated.
Highlights
Various efforts to understand the relationship between biological information and disease have been done using many different types of highthroughput data such as genomics and metabolomics
The cells with knock-in mutation of K-Ras(G12V) and K-Ras(G12V)/PI3Ka(E545K) were named K-Ras and K-Ras/PI3K, respectively, compared to their wild type (WT) cells (Horizon Discovery, Cambridge, UK). These cells were cultured in DMEM:F12 (1:1) medium supplemented with 5% horse serum, 20 ng/ml epidermal growth factor (EGF), 10 μg/ml insulin, 0.5 μg/ml hydrocortisone, 0.1 μg/ml cholera toxin, 100 U/ml penicillin, and 100 μg/ml streptomycin
That is, compared to other inhibitors, REGO perturbs the metabolic status most in K-Ras mutant cell line and PI3K inhibitor produces the least perturbation in the same cell
Summary
Various efforts to understand the relationship between biological information and disease have been done using many different types of highthroughput data such as genomics and metabolomics. We have tried profiling intracellular free amino acids in normal and cancerous cells to extract some information about such relationship by way of the change in IFAA levels in response to the treatment of three kinase inhibitors. Cancer is recently viewed as a metabolic disease with altered metabolism, which drives many studies to analyze the change in the level of carbohydrate-, protein-, lipid-, or nucleic acid-based. Amino acids have been considered as potential disease biomarker because they serve as building blocks for protein synthesis and metabolic intermediators or regulators [6]. With plasma free amino acid (PFAA) profiles, some studies investigated the tumor-associated metabolism in cancer patients, being a potential biomarker of malignancy [7, 8]. It was mentioned that the metabolic pathways leading to cancers is yet completely understood, i.e, still remains elusive
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